Feasibility Analysis of Battery Electric Heavy-Duty Trucks for Local Applications Based on Real Usage Profiles Using the Example of a Catering Lift Truck PDF Free Download
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Title Page
Feasibility Analysis of Battery Electric Heavy-Duty
Trucks for Local Applications Based on Real
Usage Profiles Using the Example of a Catering
Lift Truck
vorgelegt von
Lamyaa Wdaah, M.Sc.
ORCID: 0000-0002-1462-3212
an der Fakultät V – Verkehrs- und Maschinensysteme
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
- Dr.-Ing. -
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr.-Ing. Henning Meyer
Gutachter: Prof. Dr.-Ing. Steffen Müller
Gutachter: Prof. Dr.-Ing. Ralph Pütz
Tag der wissenschaftlichen Aussprache: 23.01.2024
Berlin 2024
Acknowledgements
I would like to express my gratitude to all the individuals who assisted and supported me during
the preparation of this thesis. A special acknowledgment goes to my doctoral supervisor, Prof.
Dr.-Ing. Müller, the head of the Chair for Automotive Engineering at the Technical University
of Berlin, under whom I worked as a research assistant. I am grateful for the opportunity to
delve into my dissertation topic within such a captivating field of research and for his willingness
to discuss the subject and provide valuable suggestions and advice throughout the process.
I extend my thanks to all the contributors involved in the research project, eLift, which forms
an integral part of my dissertation. In particular, I would like to acknowledge Mr. Schmälzle
from Doll Fahrzeugbau GmbH and Mr. Pitsch from LSG Sky Chefs for their invaluable
assistance in providing the necessary information and enabling the analyses conducted in this
study.
Furthermore, I am appreciative of the collaboration, professional interactions, and pleasant
working environment shared with my former colleagues. Additionally, I am grateful to my
student assistants for their diverse involvement in this research, as well as the students whose
bachelor's and master's theses I supervised, as they made significant contributions to the
overall outcome of this work.
I would like to express my heartfelt thanks to my parents for their unwavering support, without
whom I would not have reached this point. I am also grateful to my siblings and friends for their
continuous encouragement, which helped me overcome challenges along the way. Finally, I
want to convey my deepest appreciation to my husband, Mohammed, for his constant
presence, dedication, patience, and understanding over the years.
I extend my sincere gratitude to all of you!
Lamyaa Wdaah
V
Abstract
Electrification has emerged as the optimal approach for achieving higher energy efficiency and
reduced emissions in the realm of road vehicles. While electrification is gaining ground in the
passenger car and light-duty vehicle sectors, it exhibits a lower degree of maturity in the heavy-
duty truck domain. One of the key factors contributing to this situation is the substantial initial
investment costs resulting from higher energy consumption and more demanding applications,
leading to markedly different economic viability conditions for electric heavy-duty trucks.
Conversely, electric vehicles offer the advantage of significantly lower energy consumption
and reduced maintenance expenses. Consequently, the economic feasibility of employing
electric heavy-duty trucks hinges on whether the higher initial investment costs can be offset
by lower operating expenditures, necessitating a comprehensive analysis of operational
profiles and working conditions to ascertain the truck's realistic energy consumption for a given
application. This analysis enables the prediction of whether electrifying the specific heavy-duty
truck represents a viable option. Furthermore, it is crucial to underscore other favorable
aspects of electric trucks, such as emissions and noise reduction, as well as enhanced
efficiency.
To address this matter, this thesis presents a simulation-based approach to analyze and
evaluate the electrification potential of a special-purpose heavy-duty truck employed in a
specific context. The study investigates the competitiveness of an electric prototype truck,
developed within a research project, in comparison to conventional diesel catering lift trucks
utilized at airports. Real individual usage profiles form the basis for the assessment. Initially,
the relevant operating profiles and work conditions pertaining to the catering lift trucks at
Frankfurt Airport are determined. Subsequently, a simulation model is employed to calculate
the energy consumption for complete work cycles, encompassing driving and operation of the
lifting system, utilizing recorded operational data. Based on the simulation results, an efficiency
analysis is conducted for both the driving and lifting systems, as well as the overall vehicle.
This is followed by a total cost of ownership analysis, which considers all costs associated with
the acquisition, operation, and disposal of the trucks, aiming to determine the economic
potential and cost differentials throughout their service lifespan. The life cycle environmental
impact of the considered catering lift trucks is also evaluated, with a focus on the aspects that
distinguish the environmental balance between the two truck technologies. Additionally, noise
emissions from both trucks are measured and assessed during driving and lifting system
operation.
The findings of this study demonstrate that the electric truck exhibits significant advantages in
terms of efficiency improvement and consumption reduction compared to the conventional
diesel truck. The total cost of ownership analysis reveals that while the electric truck entails
VI
substantial incremental acquisition costs, it compensates for this with notable benefits in
operational expenses and residual value as compared to the diesel truck. However, the
economic operation and profitability of the electric truck are heavily contingent on the
acquisition costs and battery prices.
The comparison of the environmental impact between the two trucks indicates that, even when
utilizing the current German electricity mix, the electric truck demonstrates a superior
environmental footprint in comparison to the diesel truck. This advantage is further enhanced
when exclusively utilizing electricity from renewable energy sources. Lastly, the acoustic
measurements comparison demonstrates that the electric truck generates lower noise levels
than the diesel truck, both during driving and lifting system operation.
VII
Kurzfassung
Die Elektrifizierung hat sich als idealer Weg zu höherer Energieeffizienz und weniger
Emissionen für Straßenfahrzeuge erwiesen. Während sich die Elektrifizierung im Segment der
Personenkraftwagen und der leichten Nutzfahrzeuge bereits auszubreiten beginnt, zeigt sie
im Segment der schweren Lastkraftwagen (Lkw) einen geringeren Reifegrad. Einer der
wichtigsten Gründe dafür sind die hohen Anfangsinvestitionskosten aufgrund der höheren
Energieverbrauch und schwereren Anwendungen, die deutlich andere Rahmenbedingungen
für die Wirtschaftlichkeit elektrischer schweren Lkw bedeuten. Andererseits haben
Elektrofahrzeuge den Vorteil eines deutlich geringeren Energieverbrauchs und geringerer
Wartungskosten. Folglich könnte sich je nach Anwendung der Einsatz eines elektrischen
schweren Lkw wirtschaftlich lohnen, wenn die höheren Anfangsinvestitionskosten durch die
geringeren Betriebskosten kompensiert werden können. Dies erfordert eine gründliche
Analyse der Betriebsprofile und Arbeitsbedingungen, um den realistischen Energieverbrauch
des Lkw für den spezifischen Einsatz abzuleiten. Dementsprechend lässt sich prognostizieren,
ob die Elektrifizierung des betreffenden schweren Lkw eine tragfähige Option wäre. Darüber
hinaus gilt es, weitere positive Aspekte von elektrischem Lkw hervorzuheben, wie etwa die
Reduzierung von Emissionen und Lärm oder die Steigerung der Effizienz.
Vor diesem Hintergrund wird in dieser Dissertation ein simulationsbasierter Ansatz zur Analyse
und Bewertung des Elektrifizierungspotenzials eines schweren Lkw mit Sondereinsatz
vorgestellt. Die Wettbewerbsfähigkeit eines in einem Forschungsprojekt entwickelten Elektro-
Prototyps wird im Vergleich zu konventionellen dieselbetriebenen Catering-Hubfahrzeuge, die
auf Flughäfen eingesetzt werden, anhand realer individueller Nutzungsprofile untersucht.
Dazu werden zunächst die für die am Flughafen Frankfurt eingesetzten Catering-
Hubfahrzeuge relevanten Einsatzprofile und Arbeitsbedingungen ermittelt. Anhand eines
Simulationsmodells wird auf Basis der erfassten realen Betriebsdaten den Energieverbrauch
für komplette Arbeitszyklen inklusive Fahren und Bedienen des Hubsystems ermittelt.
Basierend auf den Simulationsergebnissen wird eine Effizienzanalyse individuell für die
Antriebs- und Hubsysteme sowie für das Gesamtfahrzeug durchgeführt. Anschließend wird
eine Gesamtbetriebskosten- (Total Cost of Ownership) Analyse durchgeführt, um das
wirtschaftliche Potenzial und die Kostenunterschiede zwischen den beiden Lkw über die
gesamte Lebensdauer zu ermitteln, wobei alle Kosten berücksichtigt werden, die mit der
Anschaffung, dem Betrieb und der Verwertung des Lkw verbunden sind. Auch die
Lebenszyklus-Umweltauswirkungen der betrachteten Catering-Hubfahrzeuge werden
bewertet, wobei der Schwerpunkt auf den Aspekten liegt, die einen Unterschied in der
Umweltbilanz zwischen den beiden Lkw-Technologien ausmachen. Zusätzlich werden die
VIII
Geräuschemissionen beider Lkw gemessen und bewertet sowohl für das Lkw-Vorbeifahren
als auch für den Betrieb des Hubsystems.
Die Arbeitsergebnisse zeigen, dass der Elektro-Lkw einen großen Vorteil hinsichtlich
Effizienzsteigerung und Verbrauchsreduzierung gegenüber dem konventionellen Diesel-Lkw
hat. Die Gesamtbetriebskosten-Analyse zeigt, dass der Elektro-Lkw zwar sehr hohe
inkrementelle Anschaffungskosten hat, aber gegenüber dem Diesel-Lkw deutliche Vorteile bei
den Betriebskosten sowie beim Restwert hat. Die Wirtschaftlichkeit und Rentabilität des
Elektro-Lkw hängen jedoch maßgeblich von den Anschaffungskosten und Batteriepreisen ab.
Der Vergleich der Umweltauswirkungen der beiden Lkw zeigt, dass der Elektro-Lkw auch bei
Verwendung des heutigen deutschen Strommixes eine bessere Umweltbilanz im Vergleich
zum Diesel-Lkw aufweist. Der Vorteil des Elektro-Lkw wird verstärkt, wenn ausschließlich
Strom aus erneuerbaren Energiequellen verwendet wird. Abschließend zeigen die
Vergleichsergebnisse der Akustikmessungen, dass der Elektro-Lkw sowohl beim Fahren als
auch beim Bedienen des Hubsystems weniger Lärm verursacht als der Diesel-Lkw.
IX
Table of Contents
Abstract ..................................................................................................................... V
Kurzfassung ............................................................................................................ VII
Table of Contents .................................................................................................... IX
List of Symbols ........................................................................................................ XI
List of Abbreviations .............................................................................................. XV
1 Introduction ........................................................................................................ 1
1.1 Motivation ............................................................................................................ 1
1.2 State of the Art ..................................................................................................... 3
1.3 Research Questions and Objectives ...................................................................18
1.4 Procedure of the Work ........................................................................................20
1.5 Outline of the Work .............................................................................................21
2 Reference Trucks and Usage Profile.............................................................. 24
2.1 Catering Lift Trucks .............................................................................................24
2.2 Operating Profile and Working Conditions ..........................................................27
2.3 Representative Work Cycles and Measurement Datasets ..................................28
3 Modeling and Simulation of the Catering Lift Truck ..................................... 38
3.1 Driving System Model .........................................................................................38
3.2 Lifting System Model ..........................................................................................50
4 Efficiency and Potential Analysis ................................................................... 61
4.1 Driving System ...................................................................................................61
4.2 Lifting System .....................................................................................................64
4.3 Entire Vehicle .....................................................................................................66
5 Economic Analysis Based on the Total Cost of Ownership (TCO) ............. 69
5.1 Calculation Method .............................................................................................69
5.2 Definition of the Operating Scenarios ..................................................................72
5.3 Important Influencing Factors on the TCO Analysis ............................................74
5.4 Cost Elements ....................................................................................................80
5.5 Comparison of the TCO of Conventional and Electrified Catering Lift Trucks ......84
5.6 Sensitivity Analysis .............................................................................................92
6 Ecological Analysis and Environmental Impact ........................................... 99
6.1 Emission Parameters ........................................................................................ 100
6.2 Results of the Life Cycle Analysis (LCA) ........................................................... 105
6.3 Sensitivity Analysis ........................................................................................... 107
7 Noise Emission Measurement ...................................................................... 109
X
7.1 Measurement and Assessment Parameters ..................................................... 109
7.2 Measurement Equipment and Logging Data ..................................................... 111
7.3 Measurement Concept...................................................................................... 111
7.4 Measurement Results ....................................................................................... 114
8 Possible Improvements for the Lifting System ........................................... 121
8.1 Reduce Pressure Losses .................................................................................. 121
8.2 Recovery of Potential Energy............................................................................ 123
9 Summary and Outlook ................................................................................... 126
9.1 Summary .......................................................................................................... 126
9.2 Outlook ............................................................................................................. 128
Appendix ............................................................................................................... 130
A – Technical Specifications of the Considered Catering Lift Trucks ............................... 130
B – Data Processing of the Driving Cycles ...................................................................... 132
C – Driving Cycles Recorded with the Diesel Truck ........................................................ 135
D – Impact of Payload Variation on Energy Efficiency Analysis ...................................... 137
E – Acoustic Analyzer Used for the Noise Emission Measurements ............................... 138
Bibliography .......................................................................................................... 139
List of Figures ....................................................................................................... 152
List of Tables ........................................................................................................ 155
Own Publications ................................................................................................. 157
Supervised Student Work .................................................................................... 158
XI
List of Symbols
Symbol
Description
Unit
vehicle cross-sectional area
m2
vehicle acquisition costs at the current year
€
pipeline cross-sectional area
m2
cylinder piston area on side
m2
cylinder piston area on side of stage 1 or 2 of the
telescopic cylinder
m2
cylinder piston area on side
m2
vehicle acceleration
m/s2
battery replacement cost in year
€
battery state of charge
%
available battery capacity at the start of the rout
kWh
total available capacity of the fully charged battery
kWh
energy or fuel consumption costs
€
total fuel consumption of the diesel truck
L
engine specific fuel consumption
g/kWh
coefficient of the viscous friction force
kg/s
Stribeck velocity
m/s
drag coefficient
-
pipeline inner diameter
m
input energy
kWh
total energy consumption of the diesel truck
kWh
total energy consumption of the electric truck
kWh
output energy
kWh
air resistance force
N
acceleration resistance force
N
translational acceleration resistance force
N
rotational acceleration resistance force
N
Coulomb friction force
N
speed-dependent damping force
N
gradient resistance force
N
XII
Symbol
Description
Unit
external load force on the cylinder piston
N
normal force of the cylinder piston
N
cylinder Coulomb friction force
N
rolling resistance force
N
stiction friction force
N
total driving resistance force
N
rolling resistance coefficient
-
gravitational acceleration
m/s2
diesel heating value
kWh/L
height of the hydraulic oil
m
vehicle insurance costs
€
infrastructure acquisition costs at the current year
€
annual interest rate
%
differential ratio
-
auxiliary gear ratio
-
transmission gear ratio
-
bulk modulus
GPa
exposure of A-weighted sound pressure level
dB
equivalent continuous A-weighted sound pressure level
dB
maximum A-weighted and F-weighted sound pressure
level
dB
sound exposure level
dB
equivalent continuous sound pressure level
dB
maximum sound pressure level
dB
equivalent continuous Z-weighted sound pressure level
dB
pipeline length
m
pump absorbed torque
Nm
vehicle maintenance costs
€
load mass
kg
cylinder piston mass
kg
cylinder piston mass of stage 1 or 2 of the telescopic
cylinder
kg
XIII
Symbol
Description
Unit
total vehicle mass
kg
motor rotational speed
1/min
total power demand of the diesel truck
kW
total power demand of the electric truck
kW
motor output power
kW
output power
kW
pressure in the pipeline
bar
pressure in the pipeline
bar
road angle of inclination
°
pressure losses due to the load-sensing function
bar
pressure losses in the pipelines
bar
pressure at the pump outlet
bar
pressure losses due to gravity
bar
volumetric flow rate in cylinder chamber
L/min
volumetric flow rate in cylinder chamber
L/min
total change of the volumetric flow rate in cylinder
chamber
L/min
total change of the volumetric flow rate in cylinder
chamber
L/min
volumetric flow rate difference due to volume change in
cylinder chamber
L/min
volumetric flow rate difference due to volume change in
cylinder chamber
L/min
vehicle residual value at the end of its service life
€
vehicle wheel radius
m
service life duration of the truck
yr
present value of the TOC
€
vehicle tax costs
€
motor output torque
Nm
vehicle wheel torque
Nm
future calculation year
-
time point (year) of calculation
-
year of the battery replacement
-
XIV
Symbol
Description
Unit
initial volume of cylinder chamber
m3
initial volume of cylinder chamber of stage 1 or 2 of the
telescopic cylinder
m3
initial volume of cylinder chamber
m3
volume of cylinder chamber
m3
volume of cylinder chamber
m3
vehicle driving speed
m/s
stroke of the cylinder piston
m
maximum stroke of the cylinder piston
m
velocity of the cylinder piston
m/s
acceleration of the cylinder piston
m/s2
Greek Symbol
Description
Unit
efficiency of the power electronics
%
efficiency of the differential
%
energy efficiency
%
energy efficiency of a new drive
%
energy efficiency of the reference drive
%
efficiency of the auxiliary gear
%
efficiency of the electric motor
%
efficiency of the transmission
%
efficiency comparison index
%
rotational mass addition factor
-
sliding friction coefficient
-
dynamic viscosity of the hydraulic oil
Ns/m2
stiction friction coefficient
-
motor output angular velocity
rad/s
vehicle wheel angular velocity
rad/s
air density
kg/m3
diesel density
kg/L
density of the hydraulic oil
kg/m3
XV
List of Abbreviations
ACEA
European Association of Automobile Manufacturers (Association des
Constructeurs Européens d'Automobiles)
BDEW
German Association of Energy and Water Industries (Bundesverband der
Energie- und Wasserwirtschaft)
BMDV
German Federal Ministry for Digital and Transport (Bundesministerium für
Digitales und Verkehr)
BMF
German Federal Ministry of Finance (Bundesministerium der Finanzen)
BMJ
German Federal Ministry of Justice (Bundesministerium der Justiz)
BMWK
German Federal Ministry for Economic Affairs and Climate Action
(Bundesministerium für Wirtschaft und Klimaschutz)
CO2
Carbon Dioxide
CO2-eq
Carbon Dioxide Equivalent
DoD
Depth of Discharge
EC
European Commission
EEG
Renewable Energy Act (Erneuerbare-Energien-Gesetz)
EU
European Union
UBA
Federal Environment Agency (Umweltbundesamt)
GHG
Greenhouse Gas
IINAS
International Institute for Sustainability Analyses and Strategies (Internationales
Institut für Nachhaltigkeitsanalysen und-strategien)
LCA
Life Cycle Analysis
PED
Primary Energy Demand
SOC
State of Charge
TCO
Total Cost of Ownership
TTW
Tank to Wheel
VAT
Value Added Tax
WTT
Well to Tank
WTW
Well to Wheels
1
1 Introduction
1.1 Motivation
The escalating pace of human activities is contributing to a surge in air pollution levels. The
increased concentration of greenhouse gases (GHGs) in the atmosphere, primarily resulting
from the combustion of fossil fuels, leads to the retention of a significant amount of the sun's
heat, contributing to global warming—a phenomenon with multifaceted humanitarian
consequences [UBA, 2022] [Rüdiger, 2018]. To address these challenges, the European
Union (EU) has established targets to mitigate carbon dioxide (CO2) emissions. Building upon
the principles of the Green Deal, presented by the European Commission (EC) in 2019, the
EU aims to achieve climate neutrality, meaning zero-net GHG emissions, by 2050. As an
interim milestone, the Green Deal sets a target of at least 55% reduction in GHG emissions by
2030 compared to 1990 [EC, 2019b]. Additionally, the Paris Agreement, ratified by numerous
countries including Germany, seeks to limit the global average temperature increase to well
below two degrees Celsius above pre-industrial levels [EC, 2016].
Within Europe, the road transport sector significantly contributes to GHG emissions and,
consequently, climate change. In 2019, the road transport sector accounted for approximately
one-fifth of the total EU GHG emissions, with heavy-duty vehicles, including trucks and buses,
contributing to around 27% of this figure [EP, 2019]. Notably, in Germany, the transport sector
ranks as the third-largest emitter of GHGs after the energy and industry sectors, with road
traffic constituting the primary source of emissions. The transport sector was responsible for
around 20% in 2019 and 19% in 2021 of Germany's total GHG emissions, with heavy vehicles,
including trucks and buses, accounting for 35% and 38% of this portion, respectively [BMUV,
2020] [BMUV, 2022].
Against this backdrop, mitigating GHG emissions in the transport sector assumes critical
significance in attaining local and global climate objectives. In Europe, the EU has established
CO2 emissions reduction targets for the transport sector, which have been extended to
encompass heavy-duty vehicles. According to Regulation (EU) 2019/1242, manufacturers of
heavy-duty trucks are mandated to reduce CO2 emissions from newly sold vehicles by 15% by
2025 and by 30% by 2030 compared to 2019/2020 levels [EU, 2019] [EC, 2019a]. Failure to
comply with these regulations will result in financial penalties for manufacturers [Rodríguez,
2019]. Similar regulations have also been adopted in the United States of America (USA)
through California's advanced clean truck regulations [CARB, 2021]. Consequently, truck
manufacturers are compelled to explore alternative driving options to meet these objectives
and avoid hefty fines.
2
Battery electric trucks are regarded as the most promising zero-emission truck technology that
has been explored in studies and research projects. These vehicles offer high efficiency,
leading to energy savings and reduced operating costs compared to conventional trucks.
Additionally, the elimination of internal combustion engines contributes to lower maintenance
expenses. Given the heavy-duty truck sector's substantial fuel consumption, there is a
favorable starting point for the adoption of electric vehicles. However, the initial development
and implementation of new technologies typically result in additional costs, making battery
electric heavy-duty trucks currently more expensive than their conventional counterparts,
primarily due to battery costs. This cost disparity discourages fleet operators, particularly those
for whom purchase costs constitute a significant portion of total cost of ownership (TCO), from
incorporating electric trucks into their fleets. To facilitate economic use, many governments
provide subsidies to make electric vehicles, including heavy-duty trucks, more affordable [IEA,
2019]. Once battery prices decline, battery electric heavy-duty trucks will become cost-
competitive or even cheaper than conventional trucks. At that stage, governments can
discontinue subsidies, and the widespread adoption of battery electric heavy-duty vehicles will
naturally take off [Basma et al., 2021] [Unterlohner, 2021] [Mareev, 2018].
Despite these challenges, there is considerable interest in the rapid proliferation of battery
electric trucks from governments, manufacturers, and users [IEA, 2020a]. Numerous
prototypes of battery electric heavy-duty trucks have already been developed, and their
applicability in commercial fleets is being examined in various projects. However, most studies
focus on long-distance applications, with limited coverage of distribution trucks and other local
uses.
Meanwhile, fleet operators are increasingly questioning the sustainable economic viability of
battery electric heavy-duty trucks in their specific applications, given rising cost pressures and
uncertainties surrounding these vehicles. These uncertainties pose significant barriers to the
market's rapid expansion and the decarbonization of the heavy truck sector. Therefore,
investigating the economically viable applications for battery electric heavy-duty trucks is
crucial, in addition to the support provided for their purchase.
Furthermore, despite the existing obstacles, other factors such as zero emissions in urban
areas, reduced noise pollution, and potential traffic diversion may incentivize commercial
customers to transition to battery electric heavy-duty trucks [IEA, 2020a].
In light of these considerations, conducting a comprehensive analysis of all relevant factors,
including usage profiles and contextual conditions, throughout the truck's lifespan is essential
to ascertain the advantages of a battery-powered electric truck in a specific application.
3
1.2 State of the Art
1.2.1 Electrification of Heavy-Duty Trucks
The emergence of the tightening emissions regulations, combined with the limited availability
of fossil fuel oil and high fuel costs, has increased the demand for alternative driving solutions.
Electric mobility is a smart solution to meet these challenges and achieve efficient and
environmentally friendly transportation, especially with the use of renewable energies. Electric
vehicles powered by electricity from renewable energy sources are almost emission-free and
produce no exhaust fumes. Moreover, the electrification of the powertrain yields a significant
increase in the efficiency of the vehicle and thus a lower primary energy consumption [Kampker
et al., 2013].
Promising zero-emission technologies for the electrification of heavy-duty trucks include those
that rely on clean electricity from renewable sources, either directly through electricity, such as
battery electric trucks and catenary electric trucks, or indirectly using electricity-based fuels,
such as fuel cell (hydrogen) electric trucks and e-fuel (electric fuel) trucks [Moultak et al., 2017]
[Mareev, 2018] [Unterlohner, 2021]. All these technologies have no exhaust emissions, and
this eliminates local CO2 and air pollutant emissions. However, each of these technologies has
its own technical, cost, and infrastructure requirements, and therefore appropriate applications.
The direct electrification technologies have an electric powertrain that offers advantages over
a conventional powertrain. For domestic applications, battery-powered electric trucks are most
appropriate as they are the most economical and easiest to provide charging infrastructure as
it is already fairly widely available [Mareev, 2018] [Unterlohner, 2021]. Although catenary
trucks technology has the advantage of high efficiency which is superior to that of battery
trucks, the implementation of the infrastructure in the roadway and the associated maintenance
require extensive work and high costs, which make it not suitable for local applications. They
are intended for applications with long distance transportation and large freight loads
[Siemens, 2012] [Moultak et al., 2017] [Wickert et al., 2018].
The indirect electrification technologies are characterized by a high energy density and
therefore provide a long driving range, but on the other hand they have the disadvantage of
very low efficiency, as they require a lot of electricity, which puts additional strain on the power
supply and are likely to cost more compared to direct electrification technologies [Unterlohner,
2021] [Ambel & Earl, 2017]. Although fuel cell trucks also have an electric powertrain and often
a traction battery to store braking energy, a great deal of energy is lost when hydrogen is
produced based on electricity and converted back into electricity in the vehicle [Göckeler et al.,
2020]. In e-fuel trucks, gaseous or liquid fuel produced on the basis of renewable electricity is
used to power a combustion engine, which has very low efficiency [Unterlohner, 2021].
4
In general, all electrified driving technologies for heavy-duty trucks are still in the early stages
of the technical and economic learning curve and are therefore not yet cost-effective when
compared to conventional diesel trucks. In the field of long-distance road transport, it is still
uncertain which technology will prevail. The transition to electric vehicles in these applications
still presents a particular challenge, as trucks that must cover long distances and at the same
time achieve high average speeds require high energy reserves, especially in light of the
current lack of charging stations on the highways. However, for urban and local applications,
battery powered electric trucks are suitable for everyday use and may be very attractive to
many customers as they are becoming increasingly commercially available. The electric motor
with the recuperation of braking energy enables the benefit of the frequent starting and
stopping phases in the driving profile to be utilized as there is already a significant amount of
braking energy that can be recovered and stored in the onboard storage. The stored energy
can both be used as a driving force as well as to deliver power to the auxiliary devices. It is
assumed in much of the literature that battery electric trucks will be the most technically and
overall cost-effective technology in the future as, given the technical and economic
developments, they are expected to be on par with diesel trucks in the near future [Unterlohner,
2021] [Göckeler et al., 2020] [Mareev, 2018].
1.2.2 Competitiveness of Battery Electric Heavy-Duty Trucks
1.2.2.1 Energy Efficiency and Consumption
Efficiency and final primary energy demand are of the most important parameters when
comparing competitive alternative vehicles to ensure the effective economic and
environmental operation of a vehicle. Direct electrification techniques are the most energy
efficient among all other technologies, and thus the least demanding of primary energy.
Therefore, battery electric vehicles have gained great popularity in the sectors of passenger
cars and light-duty vehicles, which has led to their widespread use in recent years. Likewise,
heavy-duty trucks are currently being converted to battery electric trucks and are expected to
increasingly penetrate the market in the coming years [Unterlohner, 2021] [Jöhrens et al.,
2021].
When analyzing the energy efficiency of a vehicle, in addition to the energy conversion
efficiency in the vehicle, which is called tank to wheel (TTW), the efficiency of energy
production and processing, which is called well to tank (WTT), must also be taken into account.
In the literature discussing the energy efficiency of battery electric heavy-duty trucks, it is often
compared to some other technologies with reference to diesel trucks. Battery electric trucks
cause the least losses both in the energy conversion in the vehicle and the provision of energy
5
and thus they have the highest overall efficiency from the well to wheels (WTW) among other
truck technologies [Mareev, 2018] [Göckeler et al., 2020].
Since the drive is the central consumer of energy in a vehicle, the efficiency of a vehicle mainly
depends on the efficiency of its drive. The higher efficiency of electric motors compared to
internal combustion engines, as well as the use of regenerative braking, are the main reasons
why electric vehicles are more efficient, whereby the efficiency of electrical motors ranges
around 90% depending on the design. By comparison, the efficiency of diesel engines is only
approximately 40% [Raiber et al., 2014]. This large variation in the efficiency of the drive
technologies leads to a big difference in energy consumption while another great advantage
of the electric motor is that it can be switched into alternator mode when braking and use the
braking energy to charge the battery. This recovery significantly reduces energy consumption
and thus enhances both the efficiency and range of the vehicle. This advantage is especially
evident in inner-city driving with many stops and low average speeds [Gräbener, 2017]. The
study by [Unterlohner, 2021] examines all technologies that can decarbonize Germany's long-
haul truck fleet based on the use of renewable electricity and concludes that the total efficiency
of the direct electrification technology is at least twice that of the renewable hydrogen
technology and more than three times the efficiency of liquid or gaseous e-fuels and diesel
technologies.
1.2.2.2 Economic Considerations
Cost criteria are some of the most important criteria when deciding to purchase a truck. In
addition to purchasing costs, operating costs also play an important role. Therefore, the truck's
economic viability should be evaluated based on a TCO analysis, which also allows for a
comparison of alternative investment options. In the car and light-duty vehicle segments, such
TCO tools may already be available as a service from the manufacturer or as a commercial
offer [Jerratsch, 2021], whereas they are currently not provided in the same format in the case
of heavy-duty vehicles, especially for battery electric trucks. However, some cost calculators,
e.g. the AFLEET tool (Alternative Fuel Life-Cycle Environmental and Economic
Transportation), can be used for comparative calculations of battery electric, diesel, and other
alternative drive technologies trucks [ANL, 2023]. On the other hand, investigating the TCO for
alternative vehicle technologies has recently become the subject of extensive research and
analysis and several studies have analyzed the TCO of heavy-duty trucks for different
applications, especially in the EU and the USA Alternative truck technologies are often
compared to conventional diesel trucks on the basis of their economic performance. However,
because inputs to the TCO analyses vary based on the application and where the truck is
operated, the results of the analyses are case-related and cannot be generalized.
6
The TCO analysis includes all costs incurred for the purchase of the truck, the costs for its use
over the entire ownership period, and its resale value. The capital value method is used to
calculate the present value of all costs incurred during the holding period. For this purpose, the
value of future payments is calculated in relation to the current point in time, assuming a
specified interest rate. This allows a comparison of the life cycle costs of two different vehicles
for the same holding period where the main result is the differences in the net present values
of the total costs of the two alternative vehicles [Mareev, 2018]. The TCO analysis will be
discussed in more detail in Chapter 5.
The comprehensive cost calculation is necessary to compare the economic evaluation of
battery electric trucks versus diesel trucks as these differ significantly in terms of their purchase
and operating costs. Although purchase prices are still high, electric trucks are generally more
energy efficient compared to internal combustion trucks, and therefore cheaper to operate. In
addition, electric trucks can also achieve savings in some other operating costs such as
maintenance costs and vehicle tax. On the other hand, there are also concerns about battery
service life and vehicle resale value and thus it is necessary to consider all of the TCO factors
that differ between battery electric trucks and diesel trucks by taking the framework conditions
into account. The factors that constitute the major cost differences between battery truck
technology and diesel truck technology are discussed individually below.
Vehicle battery
Battery electric trucks typically have higher purchase costs than diesel trucks, primarily due to
the cost of batteries. The battery accounts for the largest share of the surcharge for the electric
vehicle compared to a comparable vehicle with a combustion engine. The impact of other drive
components, such as the motor, inverter, and controller on the overall vehicle costs is not as
significant. The cost of the battery pack mainly depends on the unit cost (per kWh) and this
varies depending on the chemistry of the battery. As a result, the size and type of battery
determine its cost, and these are chosen based on the energy requirements of a particular
vehicle. Therefore, the battery pack costs for heavy-duty vehicles tend to be higher due to the
bigger sizes and different requirements and working conditions. In addition to the price of the
battery, battery life is especially important. If the battery has a short life, it may have to be
replaced during the life of the truck, which can significantly increase the life cycle costs.
Infrastructure
In the case of battery electric trucks, apart from the costs of purchasing, TCO calculations
should also take any necessary infrastructure costs into account. These may include, in
addition to the purchasing costs of the charging station, other costs such as those associated
with construction and installation, municipal permit costs, etc. In addition, annual maintenance
7
costs must also be considered. These are usually assumed as a percentage of the initial
investment costs. If the charging station is able to accommodate multiple vehicles, the costs
are divided between the number of vehicles [Den Boer et al., 2013] [Jöhrens et al., 2021].
Infrastructure requirements vary widely depending on the vehicle battery type and size, and
the duty cycle. For example, trucks that travel long distances and have larger payloads usually
contain large batteries, and therefore require higher capacity charging stations to
accommodate their operation cycles. Thus, charging stations have the potential to add
significant costs (up to 10–25%) to the total cost of battery electric trucks in terms of the cost
of ownership of the truck [Hall & Lutsey, 2019].
Energy cost
The fuel or energy costs of vehicles are due to their consumption and current and future fuel
or electricity prices. The vehicle consumption is usually calculated using a vehicle simulation
model and pre-defined operating scenarios whereby the fuel and electricity prices consist of
the costs for providing the fuel or electricity, which vary according to the supplier, in addition
to the added duties and taxes. There are significant differences in the prices of electrical
energy, depending on the supplier as well as the charging type and time. For example,
charging during the night is more beneficial, since electricity rates are cheaper. On the other
hand, future prices are subject to a great deal of uncertainty because they are based on
predictions. While various forecasts can be found in the literature, they all concur that
significant increases in diesel prices until 2030 can be expected due to higher taxes, fees, and
the CO2 offset tax imposed on diesel. In comparison, the forecasts also expect an increase in
electricity costs until 2030, although these are expected to be relatively moderate. Generally,
the costs of electricity from the grid have historically not been as volatile as those of diesel fuel
which is expected to increase the cost advantage of battery electric trucks.
Due to the driving efficiency, the energy costs of the electric battery truck are advantageous.
In comparison, fuel costs are the biggest cost factor for diesel trucks due to their lower
powertrain efficiency [Mareev, 2018].
Maintenance
Although the electric truck industry is still at an early stage of development and field experience
is limited so far, these trucks certainly have lower long-term maintenance costs compared to
diesel trucks. This is due to several reasons, including reduced brake wear resulting from
regenerative braking, engine oil elimination, and the simplicity of the electric powertrain
compared to that of an internal combustion engine. Battery electric trucks also require lower
maintenance costs compared to fuel cell trucks. Although there is considerable variation in the
literature regarding the estimation of maintenance costs for electric trucks, in general, the
8
assumed savings in maintenance costs can be assessed as being approximately one-third
compared to that of diesel trucks [Den Boer et al., 2013] [Kleiner & Friedrich, 2017].
Residual value
The residual value of a heavy-duty truck may play an important role in calculating the TCO,
especially for vehicles with a short ownership period by the first user. Evaluating the residual
value of a truck is based on several criteria such as, among others, the gross vehicle weight,
powertrain type, technology maturity, vehicle purchase price, total vehicle mileage, and
infrastructure availability [Kleiner & Friedrich, 2017]. In particular, the resale value of a battery
electric truck is subject to a great deal of uncertainty and cannot easily be predicted given the
early stage of the market as there is currently no used vehicle market for battery electric trucks.
The range of models available in the market are mostly prototypes, not serial products,
although this is likely to change soon due to the rapid development of battery technologies.
However, expectations about future prices in the used vehicle market cannot be quantified for
battery electric heavy-duty trucks from today's perspective. Furthermore, reducing the prices
of new vehicles through subsidies may have an impact on the used vehicle market in the future
[Jöhrens et al., 2021]. On the other hand, since the battery is the most valuable component of
any battery electric vehicle, battery aging and replacement are important factors in deriving the
resale value of a battery electric truck as the battery alone can provide a residual value to the
owner regardless of the residual value of the vehicle itself. At the end of its usual service life,
the used battery pack can be further used as a stationary energy storage system, for example
for energy generated by solar panels, or it can be recycled. The loss in the battery value is
usually determined by using the capacity loss due to the aging of the battery [Hill et al., 2019].
Payback period and cost parity
When factoring in all costs of ownership from the vehicle purchase, and infrastructure and
operating costs, in addition to the current taxes, fees, tolls, and subsidies, the payback period
for the battery electric truck can be calculated. The payback period reflects the minimum period
of ownership so that the additional purchase costs of the truck are offset by savings in
operating costs. Consequently, the payback period depends on the acquisition costs as well
as on the operating profiles. Applications in which the incremental cost is low and operating
period savings are high will have a short payback period, while applications with very high
incremental costs and modest operating period savings will have a long payback period that
may exceed the expected life of the vehicle. For example, a higher economic potential appears
in applications of long-haul trucks with higher annual mileage and fast charging possibilities
than in applications of regional distribution as providing the opportunity for fast charging for
long-haul trucks reduces the necessary battery capacities and hence the costs of purchasing.
On the other hand, long-distance use increases the profitability of the battery electric truck
9
compared to the conventional truck. In such applications, the savings in operating costs can
pay off an electric truck's higher initial purchase cost in just a few years. Once the payback
period is reached, the operational benefits will continue to accrue, and the battery electric truck
will have an economical advantage over a conventional truck [Göckeler et al., 2020].
At present, payback estimations cannot easily be made for battery electric heavy-duty trucks
as only a few heavy electric trucks have been in service for long enough to provide actual
operation and maintenance data. However, various studies have estimated payback periods
for battery electric heavy-duty trucks for a variety of applications. The currently estimated TCO
for battery electric heavy-duty trucks is higher than that of their conventional counterparts,
despite significant savings in operating costs. This is due to the significantly higher acquisition
costs, which are currently at least twice those of diesel trucks [Mareev, 2018]. However, the
cost difference is expected to diminish over the coming decades. The findings from several
studies focusing on heavy-duty trucks in Germany suggest that battery electric trucks may
become cost-competitive with diesel trucks between 2025 and 2030, both in distribution and
in long-haul applications. These forecasts are primarily based on assumptions concerning
lower battery costs and higher production numbers [Mareev, 2018] [Basma et al., 2021].
Ultimately, the cost-competitiveness of battery electric heavy-duty trucks will largely depend
on the development of certain factors in the coming decades, some of which are subject to
very high levels of uncertainty. The most important of these factors is the development of
production volume, battery prices, and energy prices, as well as the development of the
residual value of electric trucks. Battery cost developments are highly dependent on
technological developments and economies of scale. Vehicle battery prices in general are
currently witnessing a dynamic development that has paid off in lower vehicle costs in the
passenger car and light-duty vehicle sectors, and this is also expected to extend to the heavy-
duty truck segment soon. The faster the evolution of battery prices, the sooner the battery
electric heavy-duty trucks will become more profitable [Burke & Sinha, 2020].
Energy costs are also a component of the cost that is subject to future uncertainty. While there
are already some estimates of the evolution of diesel and electricity prices over the next
decade, they involve a very high level of uncertainty. Nevertheless, in most of the scenarios
examined in the literature, future expectations of electricity and diesel fuel prices enhance the
TCO parity for a battery-powered truck with that of its diesel-powered counterpart in the near
future.
In general, studies reviewed in this work, such as [Unterlohner, 2021], [Mareev, 2018] and
[Burke & Sinha, 2020], indicate that battery electric technology represents the most cost-
effective option in the long run compared to all other zero-emission technologies for heavy-
duty trucks.
10
1.2.2.3 Ecological Impact
The emissions caused by a vehicle are not only limited to the direct emissions that arise when
the vehicle is used but also extend to the indirect emissions that result from the manufacture
and recycling of the respective vehicle and the associated infrastructure as well as from the
production of fuel or electricity used by the vehicle. The issue of sustainability has recently
become very controversial for electric vehicles in particular. On the one hand, electric mobility
is seen as a key option for reducing emissions and achieving national climate targets in the
transport sector since electric vehicles have a decisive advantage over combustion engines
because they do not emit any local emissions and are therefore particularly suitable for use in
cities and densely populated areas, where emissions limits are often exceeded. On the other
hand, electric vehicles present a unique challenge as they use electricity grids to charge their
batteries and also cause emissions when electricity is generated from various power plants. In
addition, a variety of materials that have adverse environmental impacts are used in the
manufacture of battery cells. Therefore, the origin of the raw materials that are used and the
non-recyclability are among the critical points that make the sustainability of electric vehicles
questionable.
To assess the environmental impact of electric vehicles and compare them to conventional
vehicles, a full life cycle analysis (LCA) is usually performed for the vehicle in question and its
associated infrastructure. The LCA accounts for emissions associated with all phases of a
vehicle over its life cycle. This includes the manufacturing and operation phases as well as the
disposal phase at the end of the vehicle's service life [Nordelöf et al., 2014]. In general, the
analysis of the exact environmental impacts of battery electric vehicles is still a topic for
research and development. In the automotive sector, the results of several studies show that
the LCA of electric vehicles is much better than that of fossil-fueled vehicles, even in relatively
dirty power grids [Helmers et al., 2017] [Nordelöf et al., 2014]. However, although these
analyses can be complex and incomplete for heavy-duty trucks due to the lack of sufficient
data, some studies analyzed the environmental impact of battery electric heavy-duty trucks
and compared it with their diesel truck counterparts. On this basis, the environmental impact
of heavy battery electric trucks will be discussed below, according to the life phases of a truck.
Manufacturing phase
When analyzing emissions at the manufacturing stage of a vehicle, emissions associated with
the manufacture of all individual vehicle components are considered, including the engine or
motor, transmission, body, and batteries. Depending on the truck technology, the fabrication
of related infrastructure is also considered at this stage. The vehicle chassis generally accounts
for the largest share of total manufacturing GHG emissions. Since the same chassis is usually
used to build all vehicles under comparison regardless of their powertrain technology, when
11
excluding the battery, differences in vehicle powertrains to be compared are usually so small
that they are often neglected in the LCA. However, significantly high emissions originate from
battery production and, as a result, manufacturing phase emissions represent a large part of
the total vehicle life cycle emissions for a battery electric vehicle. Although the results of recent
studies that have investigated the effect of battery production on the total emissions of an
electric vehicle vary widely, some studies indicate that actual battery production accounts for
30–50% of GHG emissions [Wietschel et al., 2019a]. However, the GHG emissions that result
from the manufacture of vehicle batteries depend on several factors, and, as many studies
show, the largest share of emissions comes from the electricity used in the production process.
Using cleaner electricity can thus significantly reduce the emissions attributable to battery
manufacturing whereby factory efficiency also plays a role in this context as larger and more
efficient factories usually consume less electricity and therefore have lower emissions per kWh
of the batteries produced [Wietschel et al., 2019a]. The type of battery chemistry and the
materials used in manufacturing also make a difference, with some chemistry mixes containing
higher concentrations of energy-dense metals [Hall & Lutsey, 2018]. Since GHG emissions
are calculated per battery capacity, battery size is a relevant factor that influences the GHG
balance in electric vehicles. According to [Romare & Dahllöf, 2017] the GHG emissions are
relatively linear in relation to the battery size and thus, in general, battery production has more
emissions in heavy-duty trucks than in other vehicles since they also require larger battery
sizes. In addition, battery chemistry may also play a role as the use of certain materials in the
truck batteries may results in more emissions.
From these deliberations, it is evident that a battery electric truck has a larger environmental
burden in the manufacturing phase than a conventional truck. However, a significant
improvement is likely expected in the short term as a result of increased efficiency and the use
of low-carbon electricity in battery production.
Operation phase
This phase is generally characterized by emissions associated with fuel or electricity used to
operate the vehicle as well as emissions associated with vehicle maintenance. Among others,
this includes emissions associated with the production and transportation of fuel or electricity
as well as its end use.
A battery electric vehicle does not emit any pollutants while driving, as there is no onboard
source for emitting pollutants and, for this reason, it is also referred to as a zero-emission
vehicle. However, this is only true when viewed locally, i.e. in terms of the TTW emissions as
the generation of electricity used to charge a vehicle battery produces pollutant emissions, i.e.
WTT emissions. For diesel trucks, the largest percentage of emissions comes from the use of
fuel in the engine. In addition, diesel production also contributes a given percentage of
12
emissions and thus the complete upstream chains, i.e. WTW emissions, must be considered
in the analysis.
For electric trucks, the amount of GHG emissions associated with the operation phase will
depend on the emission characteristics of the power plants that provide the required electricity.
However, as a result of the higher efficiency of electric motors and the capability of generating
electricity from low-carbon sources, electric vehicles usually have lower emissions in the
operation phase compared to similar internal combustion engine vehicles. This also applies to
the heavy-duty truck category. Some studies have already shown that battery electric heavy-
duty trucks generally have lower operating emissions than those of conventional trucks as well
as trucks of other technologies such as fuel cell, e-fuel, or compressed natural gas trucks,
especially due to the increase in the share of renewable energies in the provision of electricity
[Earl et al., 2018] [Moultak et al., 2017]. There is also a great potential to reduce emissions
from operating a heavy battery electric truck to zero if it is only recharged using electricity from
renewable sources such as solar or wind energy, which would be essential to achieve long-
term climate goals.
For emissions associated with vehicle maintenance, there is not enough data to balance the
maintenance of a battery-powered electric truck. However, electric truck maintenance certainly
tends to be less energy-intensive and thus causes fewer pollutant emissions.
End-of-life phase
At the end of the vehicle's service life, emissions associated with the disposal or recycling of
the vehicle are also considered. In general, the disposal or recycling of a battery electric vehicle
would be similar to that of a conventional vehicle, except for batteries. At the end of their normal
service life, spent vehicle batteries may have a second life in other applications such as
stationary storage in the electricity grid, which can significantly improve the balance of battery
electric vehicle emissions [Hall & Lutsey, 2018]. However, the battery will ultimately eventually
be recycled.
The question concerning the impact of the carbon footprint of battery recycling on the total
ecological balance of battery electric vehicles has been raised in some literature and is also
still the subject of current research. Recycling the metals contained in the battery – the most
important material for recycling – is considered to have significant environmental benefits as
the recycling process can reduce the environmental impacts of the battery production stage by
using recycled materials instead of raw materials in the production of new batteries [Aichberger
& Jungmeier, 2020]. Generally, thanks to the development of battery recycling processes and
increased energy efficiency, the impact of battery disposal on the overall vehicle life cycle
emissions will be minimal [Helms et al., 2016].
13
Overall environmental impact
Although the battery electric truck has a larger environmental impact than conventional diesel
trucks in both the production and end-of-life phases because of its battery, it can gradually
compensate for this difference in the use phase by lower emissions during operation. Current
studies show that when considering the full life cycle analysis, battery electric heavy-duty
trucks actually outperform their diesel-powered counterparts and all other types of zero-
emission heavy-duty trucks, including fuel cell trucks, in terms of their environmental impact
and primary energy consumption, even when using the current electricity mix. This is mainly
due to the high efficiency of the powertrain of battery electric vehicles [Sen et al., 2017]
[Mareev, 2018] [Thinkstep & Prognos, 2017].
However, as in the case of the assessment of economic feasibility, the results of the
assessment of emissions also depend on several factors and hence the results should always
be evaluated in light of the assumptions made.
Different usage profiles for truck users lead to dramatically different results. For example, in
the case of long-haul trucks, the emissions from operation dominate to a large extent, so that
the emissions in the vehicle manufacture and disposal phases only play a minor role in the
total LCA. Additionally, the longer the battery service life, the greater the advantages of a
battery electric truck.
Ultimately, the overall balance of battery electric trucks is closely related to the energy mix of
electricity generation as emissions are significantly reduced if electricity is used that is
exclusively derived from renewable energy sources such as wind and solar energy. On the
other hand, additional emission reductions can be achieved to further improve the climate
balance of battery electric vehicles by increasing the overall energy efficiency of the vehicles
as well as by improving the carbon footprint of battery production and recycling [Wietschel et
al., 2019b] [Helms et al., 2016].
The infrastructure's share of the environmental impacts is also considered very low because
infrastructure usually has a long service life and is often used by several trucks.
1.2.2.4 Noise Emissions
The operation of heavy-duty trucks is associated with significant noise emissions which put a
strain on both workers and residents, especially in urban environments. Accordingly, reducing
noise emissions is also an important factor in the further development of these trucks.
However, the sources of noise associated with truck operations vary widely. While driving the
truck there is noise caused by the use of the engine and noise resulting from the wheels rolling
on the road. In conventional heavy trucks, engine noise usually dominates tire noise up to 50
14
km/h [Raiber et al., 2014]. Moreover, additional noise may be caused when the truck is
stationary by operating the engine at a standstill.
Electric vehicles are generally considered to be quieter compared to conventional vehicles.
Although no actual studies on noise emission of battery electric heavy-duty trucks are
available, low noise from electric trucks is reported in some literature as well as by certain
manufacturers. For example, Volvo advertises that its electric trucks, designed for local
deliveries, are less noisy compared to their diesel counterparts [Volvo, 2022]. Based on
predictions in some literature, the electrification of heavy-duty trucks can significantly reduce
noise emissions compared to conventional diesel trucks, particularly in urban areas because
an electric motor produces less noise than an internal combustion engine. This gives electric
trucks an advantage when driving at speeds under 50 km/h. In addition, electric trucks do not
generate any motor noise when they are stationary. In some cities, there are restrictions on
delivery times for trucks due to noise in certain areas such as residential and other noise-
sensitive areas and such restrictions could potentially be lifted for the quieter electric vehicles
in the future. Deliveries during the night and early morning hours can reduce congestion during
the day, which in turn will often result in lower delivery costs [Karle, 2017] [Gräbener, 2017]
[Raiber et al., 2014].
1.2.2.5 Further Aspects
Although electrification technologies have recently spread rapidly in the passenger cars and
light vehicles sectors, the classic internal combustion engines are still dominant in the heavy-
duty truck sector today due to the good energy storage capacity of the fuel that other
technologies still cannot surpass [ACEA, 2021] [Basma & Rodríguez, 2021].
Apart from the high investment costs, some other factors currently limit the use of battery
electric heavy-duty trucks whereby one of the biggest challenges for the widespread
implementation of this truck technology is the limited capacity of the battery and lower energy
density compared to conventional fuels. In addition, a portion of the battery's weight is not
useful, as 20% of the battery's capacity is generally left unused to maintain the life cycle.
Hence, for heavy-duty trucks, especially long-haul trucks, due to the high-power requirements
and long operation times, heavy and excessively expensive batteries will be required. This is
also undesirable since a large battery occupies a certain percentage of the vehicle's volume
and leads to an increase in the vehicle's weight, which contributes to increased driving power
requirements. In addition, the payload capacity could be compromised by the added battery
weight. However, vehicle battery technologies are subject to rapid development, as increasing
efforts and resources are constantly being put into their development whereby current
developments mainly focus on reducing the cost of production and increasing the storage
15
density by enhancing the efficiency of packing cells into smaller and lighter units [Thinkstep &
Prognos, 2017] [ Moultak et al., 2017] [Lienkamp, 2014].
Also, at present only relatively limited ranges can be achieved with battery electric heavy-duty
trucks compared to diesel trucks, due to the battery's weight and high costs [Lienkamp &
Homm, 2018]. However, the ranges of battery electric heavy-duty trucks currently available on
the market are sufficient to cover a large portion of the daily distances required in most
application, especially since electric motors are more efficient than conventional engines, and
hence batteries will not need the same energy density as diesel. For instance, a battery with
an energy density that is three times lower than that of diesel may be sufficient to achieve the
same driving range as the diesel equivalent [Earl et al., 2018] [IEA, 2019] [Den Boer et al.,
2013].
Access to charging points is another obstacle to electric mobility as the public charging
infrastructure is not sufficiently developed to date. On the other hand, the use of private
charging stations in warehouses also entails additional costs [IEA, 2021] [Hildermeier & Jahn,
2020].
However, these challenges are circumvented for trucks with special uses that are used in urban
and local application transport such as municipal service trucks, construction site trucks, and
airport trucks, as these applications involve relatively short daily trips and many start-stop
phases, potentiality with recuperation, wherefore smaller electric battery packs are required
[Moultak et al., 2017] [Smith et al., 2020]. It is also often possible to install charging stations at
work sites for these applications.
In general, the interest in battery electric trucks is gradually increasing and significant progress
has already been made in all aspects recently in parallel with an increase in the demand for
battery electric trucks and a larger number of models available on the market. Battery electric
trucks are expected to replace traditional diesel trucks in the coming decades, since many of
the single models available in the market today are expected to go into series production in
larger numbers. In addition, according to a statement by the European Association of
Automobile Manufacturers (Association des Constructeurs Européens d'Automobiles ACEA)
in December 2020, all new truck sales will be fossil-free by 2040. In parallel, large projects are
being undertaken globally to provide a suitable infrastructure for battery electric heavy-duty
trucks in all applications [IEA, 2021] [Basma & Rodríguez, 2021].
1.2.3 Calculation of Energy Consumption in Heavy-Duty Truck Applications
The comparison between alternative vehicles is usually based on determining the vehicle
energy demand, on the basis of which the emissions and economics of the vehicle in question
16
are then determined. The energy consumption of a vehicle for a given application depends on
the efficiency of its components, particularly the engine, whose efficiency usually varies at
different operating points depending on the speed and load applied. Therefore, there are
significant differences in determining the efficiency and consumption of trucks with different
applications. The consumption data provided by the manufacturers allows for an initial
comparison but does not reveal the real consumption associated with a particular application.
Simulation-based methods are particularly suitable for this purpose and are the easiest and
least expensive to implement. The main advantage of using a vehicle simulation model is its
reproducibility, that ensure the repetition of the same result with each new simulation with the
same initial data, which allows the evaluation of modifications or variable parameters more
easily, as well as the possibility of direct comparison and analysis of alternative vehicle
powertrain technologies in different application scenarios under various operating conditions.
This is helpful for making investment decisions on the one hand, and on the other hand, it
allows the identification of usage parameters that have an appropriate effect on energy
consumption, thereby allowing for the possibility of modification and improvement [Liebl et al.,
2014] [Haupt, 2013].
The vehicle model represents all the essential components that are relevant for the
consumption, however, when only energy demand is required to be determined, detailed
modeling of the relevant system components is usually not necessary, as the vehicle is
simulated based on the principle of cause and effect based on mathematical equations that
only describe the relationship between input and output variables without addressing the
internal mechanism of action. The required drive energy of the vehicle to be simulated is
determined by calculating the withdrawn energy from the system by driving resistance, which
is considered the same for all comparison vehicles, and losses along the drive train. For
vehicles with electric drives, the energy that is restored through recuperative braking is also
determined and supplied with energy again [Liebl et al., 2014]. This can be represented by
either forward simulation or backward simulation [Gräbener, 2017]. In the forward simulation,
the computation path corresponds to the causal chain and energy flows of the real vehicle,
where driving is simulated through a driver model in addition to the vehicle model in a closed
control loop. The driver model represents the controller that gives signals in order to regulate
the vehicle speed to comply with the pre-set driving cycle. This method is useful when there is
another purpose of the simulation in addition to determining the energy consumption, for
example, to optimize the regulation strategies to reduce the consumption in the vehicle.
While in the backward simulation, the calculations are done in the opposite direction to the
energy flows in the real vehicle, where the driving resistance affecting the vehicle is calculated
based on the specific driving cycle and thus the required engine power is determined in the
17
vehicle model, without the need to a driver model. In comparison to the forward simulations,
the backward simulations are simpler and require less computational effort.
The simulation model is simplified or detailed depending on the availability of the necessary
parameters and input data and the level of detail required. In some works, main vehicle
components are modeled in more detail instead of relying only on their efficiency
characteristics, which is often intended for other purposes in addition to calculating the energy
requirements of the vehicle. For example, in [Mareev, 2018], the simulation model determines
the aging of the battery and thus the life span based on the vehicle energy requirements. While
in other works, like [Sripad & Viswanathan, 2017] and [Link et al., 2021], the energy required
to overcome the driving resistances is calculated with a single simplified equation using an
overall vehicle efficiency, which represents the efficiency from the battery to the wheel.
However, the final energy consumption of a vehicle depends not only on the vehicle
parameters but also on the operating conditions. Therefore, in order to determine the energy
consumption of heavy-duty trucks of different drive train technologies, a specific operating
profile that adequately describes the user's application should also be defined. A
representative operating profile is typically represented by exemplary driving cycles of the daily
routes of the respective truck and related operating data such as payload and operating times.
Driving cycle data is ideally presented in time-based speed profiles corresponding to each
individual trip and used as input data in the energy consumption simulation model. However,
some works in the literature instead simulate the relevant roads using the GPS coordinates of
the particular road and possibly supplemented with road elevation information. Subsequently,
constant speed values are assumed based on the maximum speed allowed on the given road,
as in [Mareev, 2018], [Earl et al., 2018] and [Zhao et al., 2018], or cycle-specific average speed
values based on driving cycle databases are used, as in [Sripad & Viswanathan, 2017] and
[Link, et al., 2021]. Similarly, constant acceleration and deceleration values are also assumed,
representing the vehicle's acceleration or braking to keep with the set speed value. NREL
DriveCAT database [NREL, 2021] provides data of different real driving cycles, including
highway and urban driving, for heavy-duty vehicles.
However, assuming constant speeds may be feasible in long-haul applications, where the truck
travels at near constant speed on highways most of the time. But in urban applications or local
applications, as is the case in this work, this cannot represent the realistic consumption of the
truck, since here the truck goes through frequent phases of stopping and accelerating, which
is significantly reflected in the consumption of the truck, especially for electric trucks, where
there is the possibility of recovering braking energy. Therefore, the calculations in this work
are based on representative operating cycles that include real driving cycles for the typical
daily trips of the studied catering lift truck.
18
In the literature, simulation-based energy demand approaches have been used to evaluate the
deployment of battery-powered heavy-duty trucks versus their conventional diesel-powered
counterparts, mainly based on cost and environmental balance. However, most of the studies
focus on long-distance applications such as [Mareev, 2018] and [Burke & Sinha, 2020], while
very few of them cover urban and regional distribution applications such as [Link et al., 2021].
While the replacement of conventional trucks with battery-powered trucks has not been studied
for either local applications or special-purpose truck applications. The approach presented in
this work distinct from other works in that it deals with a specific, previously unaddressed
heavy-duty truck application, where the energy consumption of the catering lift truck is
determined using a simulation model based on real operational data, including recorded
dynamic speed data, as experienced by the user in daily operation of the truck.
On the other hand, when considering auxiliaries in calculating the energy requirements of
trucks, the focus in the literature is mostly on certain units such as heating and cooling in the
driver's cab, cooling in the freight container and driver assistance functions, where constant
consumption is usually assumed [Jerratsch, 2021]. However, these auxiliaries are considered
irrelevant in this work, since the truck is generally only driven for short periods of time, and the
container cooling is mostly not used in the truck under consideration because it mainly
transports hot food to the airplane and waste on the return. In contrast, in this work, the energy
demand for a different type of secondary system with special purposes, namely the hydraulic
container lifting system in the catering lift truck, is modeled and analyzed.
1.3 Research Questions and Objectives
Although there has been recent research on the replacement of traditional heavy-duty trucks
with battery-powered electric trucks, most studies focus on long-distance and some urban
applications. However, the findings from these studies cannot be directly applied to heavy-duty
trucks in local applications. The operating profiles of these trucks differ, especially when
considering trucks with additional systems for specialized uses beyond transportation. In such
cases, the trucks often operate at low speeds, make frequent stops with acceleration and
deceleration phases, and exhibit distinct energy consumption characteristics and associated
emissions. Additionally, these operating conditions present more opportunities for regenerative
braking energy. Unfortunately, these aspects have not been adequately addressed in
comparative analyses within the existing literature. Furthermore, the impact of power train
electrification on the energy consumption of specialized ancillary systems in heavy-duty trucks
has not been thoroughly explored. In addition, heavy-duty trucks are frequently used in close
proximity to people in local settings, necessitating a discussion not only on GHG emissions but
also on noise emissions generated by these trucks.
19
Considering the current state of knowledge, the following research questions arise, which this
study aims to address:
• What are the energy consumption characteristics of specialized heavy-duty trucks in
local applications?
• What is the economic feasibility of sustainable usage of battery electric trucks in
specific local applications, considering the high cost pressure for investments and the
lower savings associated with these applications?
• How does the real-world usage profile, as experienced by users in their daily lives,
influence truck energy consumption and the competitiveness of battery electric trucks?
• How does the electrification of the power train impact the energy consumption of
specialized ancillary systems in heavy-duty trucks?
• What are the expected reductions in GHG emissions and noise emissions resulting
from the utilization of electric drive systems in heavy-duty trucks for these applications?
In light of these considerations, this study analyzes and evaluates the competitiveness of an
electric prototype truck developed within the research project “eLift” (part of the E-PORT AN
project [E-PORT AN, 2015] [NOW, 2013]) compared to a conventional catering lift truck. The
analysis focuses on energy consumption, costs, and environmental impact over the estimated
service life of the truck, taking into account realistic individual usage profiles. These trucks are
employed at various airports worldwide for delivering catering and other supplies to airplanes.
However, this study specifically examines the catering lift truck used at Frankfurt Airport as a
case study.
The primary goals of this study, derived from the research questions, are as follows:
• Establish authentic operational profiles for the examined truck, considering the
pertinent contextual factors.
• Analyze the energy consumption patterns of the truck and delineate the distribution
between the driving system and the lifting system.
• Assess the effectiveness of electrification in enhancing the efficiency of the driving
system and the lifting system independently, as well as the overall vehicle efficiency.
• Showcase the benefits of recuperation in reducing energy consumption and increasing
energy efficiency.
• Evaluate the economic competitiveness of the electric truck compared to the
conventional diesel truck under various potential scenarios.
20
• Examine the potential environmental impacts of electrification in terms of GHG
emissions, primary energy demand (PED), and noise emissions in this particular
application.
1.4 Procedure of the Work
This thesis presents a comprehensive analysis approach to assess the competitiveness of an
electrified prototype truck compared to conventional catering lift trucks. The aim is to identify
potential advantages and disadvantages throughout the truck's service life. Figure 1-1
illustrates the basic steps of the approach.
Figure 1-1: Approach for evaluating the competitiveness of the electrified prototype truck
versus conventional catering lift trucks
Real Usage Profile and
Consumption
Evaluation Analysis
Analysis Parameters
Operating
Scenarios
Efficiency-
Analysis
TCO-Analysis
LCA-Analysis
Noise-Analysis
Energy-Consumption Simulation Model
Discussion of Potential Scenarios and Improvement
Possibilities
Input Data
Driving
Cycles
Lifting
Cycle
Noise
Measurement
Data Processing
Operating
Conditions
Vehicle
Parameters
Measurement Concept
21
The performance of both trucks is evaluated based on the actual usage profile of catering lift
trucks at Frankfurt Airport. This involves analyzing the operating conditions, determining
relevant work tasks and framework conditions, and recording speed profiles for the real driving
cycles of the truck, as well as hydraulic measurement data for the complete duty cycle of the
lifting system.
Using the collected operating information and measurement data, daily operating scenarios for
the truck are defined. A detailed simulation model is then employed to determine the realistic
daily energy consumption for both catering lift trucks considered in this work.
The first aspect of the evaluation focuses on analyzing and comparing the energy efficiency of
the trucks to demonstrate the efficiency gains achieved through electrification.
Considering the trade-off between increasing efficiency and ensuring profitability, the second
aspect of the evaluation involves analyzing the life cycle cost of an electric catering lift truck
and comparing it to its conventional counterpart. This analysis is based on the calculated
energy consumption results for the defined operating scenarios, taking into account all relevant
information about the truck's future operation under various possible scenarios.
Additionally, the evaluation analysis pays particular attention to assessing the environmental
impacts of the trucks. The third aspect of the evaluation examines the potential reduction in
GHG emissions and PED associated with the calculated energy consumption for the defined
operating scenarios. Furthermore, the analysis explores the potential reduction in noise
emissions based on dedicated noise measurements conducted specifically for this purpose.
The presented results aim to provide a valuable contribution to the ongoing transition in fleets
of heavy-duty trucks with local applications, as part of efforts to achieve the climate goals.
Based on the analyses conducted in this study, conclusions can be drawn regarding the
relationship between the truck's energy consumption, its economy, and the respective usage
profiles. This offers valuable insights for policymakers, corporate users, and fleet operators
operating in similar application areas. Moreover, the results offer an overview that facilitates
the identification of key factors influencing a vehicle's energy consumption, enabling the
assessment of the impact of usage modifications aimed at improving efficiency or reducing
costs.
1.5 Outline of the Work
This dissertation is divided into 9 chapters, which are briefly described below.
1. The first chapter introduces the work. In addition to the current state to relevant aspects
concerning battery electric heavy-duty trucks, the need for research and the objective of the
22
work are described. Then the procedure taken in this work to evaluate the feasibility of the
catering lift trucks under consideration is explained.
2. The second chapter provides an analysis of the usage profiles and framework conditions.
For this purpose, the trucks in question for the application and their specific purpose are
described first. The typical usage profiles and relevant framework conditions are then
described based on the operating data of catering lift trucks at Frankfurt Airport. This includes
a description of the basic workflow, typical work tasks, and operating times of the truck. In
order to obtain accurate operating data, a data logger is used to record the real driving profiles
of the trucks and representative work cycles are defined accordingly.
3. The third chapter describes the mathematical configuration of the numerical simulation
model developed to calculate the energy requirements for both driving and lifting. For this
purpose, the components of the catering lift truck are modeled that are involved in the
transmission of power for all related work tasks. The simulation model is applied to the
prototype truck as well as a conventional diesel truck as a reference for comparison. Finally,
the operating profiles described in the previous chapter are processed to determine the realistic
daily energy and fuel consumption using the available input data of the vehicles in question.
4. In the fourth chapter, the results of the simulation models are used to compare the efficiency
of the vehicles being investigated. The performance of the two catering lift trucks is evaluated
for the same work tasks under the same operating conditions and all related work functions
are analyzed with respect to energy consumption, work done, and losses generated. With the
help of simulation results, the possibility of energy saving during operation through
electrification is demonstrated. The amount of energy that can be saved in the electric truck by
braking energy recovery is also demonstrated.
5. In the fifth chapter, a TCO analysis of the respective trucks is performed. For this purpose,
operating scenarios are determined based on the previously described work cycles.
Furthermore, the development of cost parameters relevant to future operation is discussed.
The simulation results are used to calculate fuel and energy consumption costs. Accordingly,
all costs are recorded over the entire life cycle, and subsequently analyzed for both the
electrified and the reference catering lift trucks. Finally, cash values of the life cycle costs are
determined to compare and evaluate the economic feasibility of the alternative truck for the
determined scenarios. Sensitivity analyses are conducted to be able to examine the effect of
variable parameters.
6. In the sixth chapter, the impact of the GHGs and the PED over the life cycle of a catering lift
truck are analyzed and processes that represent differences between the two vehicles under
consideration are taken into account. This includes the manufacturing, operating, and end-of-
life phases. For this purpose, the definition of the analysis framework and the criteria included
23
in the analysis are first discussed and the emissions associated with the provision and
transportation of the fuel or electricity needed to operate the vehicles as well as the
manufacture and recycling of batteries are specified. In addition, the production of electricity
from 100% renewable energies is taken into account.
Consequently, the environmental impacts of the vehicles in question are determined whereby
the simulation results are used for the estimated diesel and electricity consumption. The trucks
are then compared according to the same operating scenarios that were defined for the TCO
analysis in Chapter 5. Finally, the influence of important factors is discussed in the sensitivity
analysis.
7. In the seventh chapter, an assessment of the noise emissions of the catering lift trucks under
consideration is made. For this purpose, acoustic standards and measurement methods are
first discussed. In addition, the measuring environment and the utilized measuring equipment
are presented. After that, the concept of acoustic measurement, which was developed and
implemented for noise measurement for the catering lift truck application, is explained. Finally,
both types of trucks are examined and compared for noise emissions when driving as well as
when operating the lifting system.
8. In the eighth chapter, improvement possibilities to increase energy efficiency of the lifting
system of the electric truck are discussed. The measures where there is the greatest energy-
saving potential are implemented in the simulation model and the results are compared with
those of the current system to determine the increase in energy efficiency.
9. The work closes with a summary and discussion of the results obtained for the examined
battery electric and the reference diesel catering lift trucks according to the defined operating
scenarios regarding their energy consumption and efficiencies, the TCO, the environmental
impact and noise emission. In addition, an outlook is provided for the connection of further
scientific work and the application in practice.
24
2 Reference Trucks and Usage Profile
In order to evaluate the battery electric prototype truck in comparison to the conventional diesel
truck, it is first necessary to describe the two vehicles that will be compared and to analyze
and define the work tasks and typical functions of such vehicles whereby the operating
conditions and important factors affecting the performance requirements are also presented.
Thereupon, representative work cycles are defined to be utilized next to determine the resulting
energy consumption using modeling and simulation. The simulation results are used later in
the implementation of the analyses that provide an objective comparison between the different
drive technologies to investigate the competitiveness of the electric catering lift truck compared
to the diesel truck.
2.1 Catering Lift Trucks
Catering lift trucks are specialized vehicles that are operated as a fleet at almost every airport
and are of tremendous importance when it comes to the speedy loading of airplanes. The
operators of such special-purpose vehicles at German airports are mainly catering companies.
The task of a catering lift truck is to provide the airplane on the ground with food and beverages,
as well as all other possible items needed on the plane such as newspapers and magazines,
blankets, pillows, headphones, toiletries, etc. [Klußmann & Malik, 2012]. One key challenge
that a catering truck has to overcome is the different heights of the airplanes it services that
need to be covered. As a result, catering lift trucks are available in different types and sizes
depending on the height the vehicle must cover and the permitted payloads. Hence, the choice
of vehicle depends on the type of airplane to be served [DOLL, 2022].
2.1.1 Reference Diesel Truck
A catering lift truck from DOLL Fahrzeugbau GmbH in size M, shown in Figure 2-1, is
considered a reference for this work. The truck is currently used at Frankfurt Airport by the
operator company LSG Sky Chefs. The detailed technical specifications of the diesel truck as
well as the electrified prototype truck are listed in Appendix A of this work. The vehicle has a
total permissible weight of 21 tons and a payload of 4.5 tons. The construction of this vehicle
consists of a commercial two-axle truck chassis, a driver's cab, and a lifting system. The vehicle
is driven by a six-cylinder diesel engine which achieves 142 kW at 2,000 rpm and 809 Nm at
1,500 rpm and meets the Euro 4 emission standard, as this emission class was still acceptable
at the time the truck was purchased and is not considered so low as it is today. The mechanical
energy is transmitted from the diesel engine to the rear wheels via a clutch, an automatic
transmission with five forward gears and one reverse gear, and a differential.
25
The lifting system consists of a subframe with a support system, single-stroke scissors, a
hydraulic system, and a catering container. The lifting system has the task of lifting the catering
container in the vertical direction to the required height. After parking next to the airplane, the
support legs extend to stabilize the vehicle after which the catering container rises with the
help of a double scissor lift until it is level with the height of the airplane door. For the loading
and unloading process, the catering container is equipped with a front platform, as shown in
Figure 2-1, that can be moved laterally to adjust the height of the container to align with that of
the door of the airplane. The front platform is also equipped with an extendible part, namely
the front platform tongue, which can be extended to dock the front platform onto the airplane
door, thereby creating a bridge to the inside of the airplane.
Figure 2-1: Reference diesel catering lift truck (Source: DOLL Fahrzeugbau GmbH)
All functions of the lifting system are driven hydraulically. The hydraulic power is provided to
the lifting system by an axial piston pump with constant displacement driven by the diesel
engine. The pump supplies the hydraulic circuit with oil from the tank. The distribution of the
oil flow to the individual cylinder actuators is adjusted to the respective operating function via
electrically controlled valves. An inlet flow regulator valve associated with every valve controls
a limited flow to prevent higher flow feed. The cylinder actuator converts hydraulic energy back
into mechanical energy and thus the respective function is performed. Since the functions of
the lifting system are operated separately, each function has its own hydraulic circuit that
transmits the hydraulic energy. For the main function of lifting/lowering the catering container,
two double-stage telescopic cylinders are arranged on the scissor lift with a stroke of 1,412
mm. This allows the catering container to be raised to 4.8 m from the chassis with a speed of
approximately 0.06 m/s. To ensure the lowering process even in emergency cases, a drain
valve is additionally arranged in front of the telescopic cylinder which can be operated
manually. This valve ensures a maximum lowering speed of 0.15 m/s in an emergency. For
each of the other functions, namely extending the support legs, shifting the front platform
laterally, and extending the front platform tongue, a double-acting cylinder is arranged. The
front platform can be displaced by a maximum of 1.46 m and the front platform tongue can be
Front platform
26
extended by a maximum of 0.6 m. The support legs extend until they rest on the ground, which
is a maximum distance of 0.38 m. Since the axial piston pump can always deliver a constant
oil volume, a load-sensing valve is used in the vicinity of the pump, so that the system pressure
is regulated depending on the function being operated.
The catering lift truck can not only be used on the airport apron but also on public roads.
However, during road travel, the lifting system cannot be operated, and the front platform
remains folded beside the driver's cab. The lifting system is thus only operated while
processing an airplane when the vehicle is stationary. While driving on the airport apron, the
average driving speed remains relatively low, and a constant driving speed is generally not
maintained. This type of driving profile fits perfectly into the application profile of electric
vehicles.
2.1.2 Electrified Prototype Truck
The battery electric prototype of the catering lift truck is shown in Figure 2-2. The basic
structure of the truck is almost identical to that of the conventional truck. In the developed
prototype vehicle, the classic diesel engine is replaced by two asynchronous motors, which
together have a maximum operating power of 138 kW and a maximum speed of 2,500 rpm.
The power supply is realized by four LiFePO4 batteries and the entire unit of the four batteries
has a capacity of 113 kWh. The power is transmitted to the motors via the power electronics.
In the same way as in the conventional vehicle, the energy of the motor is transferred
mechanically to the rear wheels using the same clutch, five-speed automatic transmission, and
differential.
Figure 2-2: Electrified catering lift truck (Source: DOLL Fahrzeugbau GmbH)
The function of lifting the catering container and extending the support legs remains hydraulic
and hence the hydraulic lifting system is retained although it is driven electrically by the main
electric motor. However, the functions of the front platform and the front platform tongue are
separated from the hydraulic lifting system. Each function is equipped with its own electric
27
drive, which is represented by an asynchronous three-phase geared motor and a spindle to
convert the rotational movement of the motor into translational movement. This reduces the
size and challenges of the hydraulic system.
However, the batteries cause an increase in weight and required installation space of the
electrified drive compared to the current conventional drives and while the permissible total
weight of 21 tons is maintained, the payload is reduced by 734 kg. The prototype truck can be
used on the street and in the operational area of the airport without restrictions.
2.2 Operating Profile and Working Conditions
The description of a realistic operating profile represents the basis for the evaluation analysis
of vehicles and hence representative operating profiles should be defined that come as close
as possible to the real conditions of use. The main task of the catering lift truck is the delivery
of articles from the catering kitchens to the airplane and back. On the ground, the catering
goods to be transported are prepared in the kitchen and placed in trolleys. A typical working
task begins when the catering lift truck is parked on a so-called forward ramp next to the kitchen
to be loaded with the freshly packed trolleys. Usually, the truck has to be on-site about an hour
before driving to the airport as the truck loading time is approximately one hour. Since the
kitchen is located outside the airport apron, the truck is first driven on the normal road from the
kitchen to the airport site. Before entering the airport, the truck must be stopped briefly and
checked at the checkpoint. The truck is then driven on the airport apron to the airplane to be
served and parked next to it. For loading and unloading the airplane, the four support legs are
first extended to keep the vehicle stable while delivering the catering. Then, the container is
lifted with the help of a double scissor lift to the level of the airplane door. Depending on the
relative position between the truck and the airplane, the front platform is moved sideways to
adjust it to the airplane door. Once this step is completed, the front platform tongue is extended
to dock the front platform to the airplane door. The airplane is then supplied with catering and
cleared from catering waste at the same time. The airplane handling time, including the loading
and unloading process, is usually approximately one hour. When the loading and unloading
processes are completed, the front platform tongue is retracted again, the front platform is
moved back, the container is lowered, and the support legs are retracted. The catering lift truck
subsequently leaves the airport apron and drives back to the kitchen and parks on the return
ramp so that the trolleys with the used materials and leftover food can be unloaded. It takes
approximately one hour for the truck to be unloaded.
Although the working tasks of the catering lift truck are clearly defined, the working conditions
are different as they are dependent on many factors, especially since the total distance traveled
on every route varies according to the parking position of the airplane for which the delivery is
28
intended. The velocity profiles of variant routes also differ relative to the driver behavior and
airport traffic conditions while the weight of the payload varies depending on the number and
type of trolleys and the number of articles that need to be transported. In addition, the functions
of the lifting system are also subject to different working conditions and as environmental
conditions affect the speed of the hydraulic actuators, the time taken to complete the same
function may vary every time. Furthermore, the lateral displacement of the front platform and
the extension of the front platform tongue also depend on the relative position between the
catering container and the airplane door. That is, the front platform and the frontal platform
tongue do not have to extend completely every time but should only move until they reach the
airplane door. Consequently, in this work, the operating profile and working conditions are
simplified by taking the typical tasks of the catering lift truck into account to determine ideal
work cycles that serve the comparison purpose.
2.3 Representative Work Cycles and Measurement Datasets
Based on the analysis of the operating profile in the previous section, the typical work cycle for
a catering lift truck was determined which comprises representative movement and load
profiles that come as close as possible to the actual conditions of real work. The defined typical
work cycle is outlined in Figure 2-3.
Figure 2-3: Typical work cycle of a catering lift truck
Driving System
Lifting System
Kitchen
Airplane
Driving to
▪Extending the support
legs
▪Lifting the container
▪
Shifting the front platform
▪Extending the front
platform tongue
Driving back
▪Retracting the front
platform tongue
▪Shifting back the front
platform
▪Lowering the container
▪Retracting the support
legs
29
In real operation, it is not scheduled how often or how many operating hours every truck should
be used per day. However, based on the experience of LSG Sky Chefs, a catering lift truck is
used three times a day on average. Accordingly, three work cycles per day are assumed for
each catering lift truck in this analysis whereby one work cycle takes an average of 4 hours.
Thus, a truck is in use for an average of 12 hours a day. It is further assumed that a truck is in
use 7 days a week. Regarding the loading volume, a further assumption is made here, namely
that the vehicle is not fully loaded when in use.
As shown in Figure 2-3, a total of five functions are activated in every work cycle of a catering
lift truck, namely the functions of driving, support legs, catering container, front platform, and
front platform tongue. The overall system of the catering lift truck can be divided into two areas,
namely the driving system, which represents the mechanical traction powertrain, and the lifting
system, which represents the hydraulic setup with the scissor lift. The driving system is
responsible for the driving function, while all other functions are related to the lifting system.
As the two systems are operated separately, the lifting system cannot be operated while
driving, and hence, for a detailed investigation, the two systems are analyzed individually in
the following. To obtain realistic work cycles, measurement data were recorded under typical
conditions for the trucks considered and are presented in the following sections.
2.3.1 Driving Cycles
In this work, the driving power requirements of the catering lift truck are determined using the
simulation model based on real measurement data. To this end, a set of measurement driving
cycles were carried out at Frankfurt Airport with both the reference diesel catering lift truck and
the electric prototype truck. These cycles are intended to reflect the real use of the truck as
realistically as possible. Therefore, the driving cycles recorded with the catering lift truck under
consideration are presented below.
2.3.1.1 Definition of Representative Driving Routes
In its normal operation, the catering lift truck drives along two key sections. In the first section,
the truck drives from the kitchen to the checkpoint of the airport apron. The kitchen is the station
where the truck is loaded with catering material and is located in the area allocated to the LSG
Sky Chefs company. On its way, the truck drives on an ordinary city road with a maximum
speed of 50 km/h. The truck is then parked at the checkpoint and checked for a few minutes.
Since the route of this section is short and the outward journey is the same as the return
journey, the traveling velocity was measured only once for this section. The second section is
the route from the checkpoint to the airplane parking position. The maximum speed allowed
30
on the airport apron is 30 km/h and driving there is usually characterized by multiple
consecutive stops because of the airport traffic. Since there are various ways to get to the
individual parking positions of the airplanes, this means that different routes can be taken
depending on the location of the airplane and the time of the day. For the measurements, three
driving routes were defined comprising different routes to various airplane positions. Figure 2-
4 shows the mapping of the defined driving routes using Google Earth.
Figure 2-4: Google Earth mapping of the measurement driving cycles
Driving cycle 1
Driving cycle 2
Driving cycle 3
31
The chosen routes fulfill the two general objectives: first to cover the majority of operating
routes that span the work envelope of catering lift trucks, and second to obtain different driving
profiles that cover a series of modal events such as various levels of accelerations and
decelerations, constant speed as well as speed-fluctuation driving, and idling periods.
In the first driving cycle, the truck drives from the kitchen passing through the checkpoint to
Gate C14, where airplanes are most frequently parked, and then the same way back to the
checkpoint. In the second driving cycle, the truck drives from the checkpoint to gate A62 and
then returns to the checkpoint using the same route. This driving cycle is significantly longer
than the first one because gate A62 is further away than gate C14. In the third driving cycle,
the truck drives from the checkpoint to position 296, which is the most distant parking position
for airplanes. Finally, the truck drives through the checkpoint and returns to the kitchen. The
return journey takes place on the southern side of the airport and this cycle is the longest
because it goes around the entire airport apron. Thus, the first and third cycles involve a
combination of driving on the airport apron and normal city driving, while the second cycle does
not include city driving. As Figure 2-4 shows, in the first and second cycles, the return journey
is the same as the outward journey, which is not the case in the third cycle.
2.3.1.2 Measured Driving Cycles
Velocity measurements for the three routes shown in Figure 2-4 were taken once for the diesel
truck and once for the electric prototype truck with the same driver. However, although both
trucks drove the same routes, their velocity profiles show considerable differences in terms of
the time and distance traveled. These differences are sometimes due to the driving style and
at other times they result from the recording time because measurement data is lost if the
sensor is turned on too late or turned off too early. These differences in the driving cycles can
lead to significant variations in fuel consumption and thus in the energy efficiency of the vehicle.
Therefore, the driving cycles used for both truck simulation models must be identical to ensure
a reasonable comparison. Therefore, only the driving cycles with the electric truck are
considered for the simulation of the power demand while driving, since they have fewer speed
fluctuations and appear more realistic than those of the diesel truck.
The measurement data was recorded with the data logger VBOX i3, which was directly
attached to the vehicle. Among other data, location (GPS data), speed, and acceleration are
recorded with a recording rate of 100 Hz. For all valid measurements, the local time is
determined and linked to the associated measured values. The recorded data was processed
by filtering to smooth it and correct various measurement errors before being used as input
data for the simulation models. A detailed description of the processing steps is given in
Appendix B of this work. However, it should be noted that the driving cycles were carried out
32
without payload and the process of airplane catering. The weather was stable and sunny, and
the roads were dry.
The driving cycles recorded with the electric truck are shown in Figure 2-5, while their
characteristics are presented in Table 2-1. The driving cycles recorded with the diesel truck
are presented in the Appendix C of this work, however they are not used for the simulation
models.
Figure 2-5: Velocity profiles of the driving cycles of the electric catering lift truck
0
5
10
15
20
25
30
35
40
45
0 100 200 300 400 500 600
Velocity [km/h]
Time [s]
Driving cycle 1
0
5
10
15
20
25
30
35
40
45
0 200 400 600 800 1000 1200
Velocity [km/h]
Time [s]
Driving cycle 2
0
5
10
15
20
25
30
35
40
45
0 400 800 1200 1600 2000 2400
Velocity [km/h]
Time [s]
Driving cycle 3
33
Table 2-1: Characteristics of the driving cycles of the electric catering lift truck
Route
Distance
[km]
Duration
[s]
Max. speed
[km/h]
Average
speed
[km/h]
Stop time
[s]
Driving cycle 1
3.70
661
39.72
21.67
46
Driving cycle 2
7.78
1,295
38.68
22.41
45
Driving cycle 3
15.68
2,527
41.81
26.64
409
As can be observed in Figure 2-5, the driving cycles are dominated by start-stop phases and
low velocities, which can especially be seen in the first and second cycles. However, compared
to the cycles recorded with the diesel truck, the maximum speeds are reasonable and there
are no cases in which the speed limit is extremely exceeded in all cycles of the electric vehicle.
There is even a moderate proportion of idle time, and the average speeds are also reasonable
in all cycles.
2.3.1.3 Analysis of the Driving Cycles
For further investigation, the driving cycles are segmented into four driving modes based on
the instantaneous values of speed and acceleration and these are defined as idle, cruise,
acceleration, and braking. Table 2-2 demonstrates the definitions of the driving modes. The
vehicle is in acceleration mode when the acceleration is above 0.5 m/s2 and in braking mode
when the acceleration is below –0.5 m/s2, while for any acceleration value between these two
values, the vehicle is assumed to be in cruise mode. Idle mode represents the vehicle when it
is not moving, and this is set when the velocity value is zero.
Table 2-2: Definition of the driving modes
Driving mode
Conditions
Idle
Cruise
and
Acceleration
and
Braking
and
where is velocity in m/s and is acceleration in m/s2
Figure 2-6 shows the time ratio of driving modes in the driving cycles considered. As shown in
the figure, all driving cycles involve substantial proportions of accelerations and decelerations,
and the cruise mode only makes up an average of one-third of the overall driving time. Larger
34
braking proportions present a greater opportunity for the electric truck to benefit from the
possibility of regenerative braking. The percentage of idling varies from one driving cycle to
another, as this depends on the nature of the traffic. The third cycle is the longest one and has
the largest proportion of idling time. The different effects of these modes on consumption and
energy efficiency are further discussed in Chapter 3.1.
Figure 2-6: Time distribution of the driving cycles according to the driving modes
2.3.2 Lifting Cycles
A work cycle of the lifting system can be subdivided into two sections, each involving the
operation of the four functions of the lifting system. The first section represents the process of
lifting the catering container and aligning it with the airplane door. Consequently, it begins with
the extension of the four support legs, followed by the lifting of the catering container, the
moving of the front platform sideways, and finally the extension of the front platform tongue.
7.0%
29.7%
35.5%
27.8%
Driving cycle 1
Idle Acceleration Cruise Braking
3.5%
35.5%
26.6%
34.4%
Driving cycle 2
Idle Acceleration Cruise Braking
16.2%
22.3%
37.8%
23.7%
Driving cycle 3
Idle Acceleration Cruise Braking
11.2%
27.2%
34.2%
27.4%
Total driving cycles
Idle Acceleration Cruise Braking
35
During the process of loading and unloading of the airplane catering, the vehicle is switched
off so that no energy is consumed and hence this process is not included in the work cycle of
the lifting system. The second section of the work cycle begins after the completion of the
loading of the given airplane's catering and comprises lowering the catering container and
retracting all extended parts. Hence, in this section the front platform tongue is retracted first,
after which the front platform is moved back, the catering container is lowered, and finally, the
support legs are retracted.
In the real operation of the lifting system, the catering container always has to reach its
maximum height to be parallel to the airplane door, which means that the telescopic cylinder
actuators have to be fully extended every time whereas the other functions are not always fully
actuated. The lateral displacement of the front platform and the extension of the front platform
tongue depend on the relative position between the container and the airplane door and thus
they are only actuated until the airplane door is reached. Similarly, the support legs are also
extended until they dock on the ground whereby their strokes may vary each time depending
on the evenness of the ground and on the weight of the vehicle. For simplicity, it is assumed
in the defined work cycle of the lifting system that all functions are fully actuated and reach
their maximum values.
The realistic energy consumption of the lifting system can be determined by simulating its
hydraulic circuit. For this purpose, hydraulic measurement data was recorded for the lifting
system by DOLL Fahrzeugbau GmbH, which will serve as the reference for the simulation
model. The measurement was carried out for a complete work cycle, as defined previously, for
the lifting system of the reference diesel truck. As the electric prototype truck was not yet in
existence at the time of the measurements, it was not possible to take measurements of its
lifting system.
In the measurement, all functions were operated until they reached their maximum and the
fluid pressure and volumetric flow rate at the outlet of the pump were recorded. Figure 2-7
shows the plots of the recorded measurement data. Table 2-3 provides a breakdown of the
defined work cycle based on measurement data after being analyzed and separated into
individual cycles.
Since all functions are operated to the end, the strokes of the hydraulic actuators are already
known. The duration of every individual operated function can be extracted from the
measurement dataset and taken as input for the simulation mode and based on these values,
the speed profiles of the hydraulic actuators can be generated while the data of the pressure
and volumetric flow rate are only used to validate the simulation model of the lifting system.
The idling represents the intervals between the operating functions, during which no action is
performed but the pump is running and works at the minimum pressure of the system. The
36
typical sequence of functions is fixed in every work cycle. However, the idling time between
the operating functions and thus the duration of the overall work cycle depends on the operator.
For simplicity, equal intervals between functions are assumed in the simulation model.
Figure 2-7: Hydraulic measurements of the lifting system (Source: DOLL Fahrzeugbau
GmbH)
Table 2-3: Characteristics of the defined work cycle of the lifting system based on
measurement data
Lifting Cycle
Lowering Cycle
Function
Stroke
[m]
Duration
[s]
Pressure
[bar]
Flow
[L/min]
Stroke
[m]
Duration
[s]
Pressure
[bar]
Flow
[L/min]
Support legs
0.38
10
110
30
0.38
10
170
16
Container
1.412
70
115/145*
28
1.412
50
27
-
Front platform
1.46
23
52
3
1.46
23
38
5
Front platform
tongue
0.6
6
51
8.5
0.6
6
67
5.5
Idling
-
24
27
-
-
-
-
-
* Cylinder first/second stage
The lifting/lowering of the container is the main function of the lifting system. The lifting process
requires high values of pressure and volumetric flow rate while the lowering process is carried
out by the container's own weight so that no additional pressure has to be applied by the pump.
Hence, the pump is running at the minimum pressure of the system during lowering. However,
it should be noted that the measurement for the container were carried out without any loads.
A higher pressure is therefore required when the container is loaded.
37
Figure 2-8 shows the proportional time distribution of the measured work cycle. The complete
duty cycle of the lifting system takes 222 seconds, including the lifting and lowering cycles. In
terms of time, the function of lifting and lowering the container accounts for the largest
proportion at 54.1%, followed by the function of the front platform at 20.7% whereas other
functions have relatively small proportions.
Figure 2-8: Time distribution according to the lifting functions
Since all functions were fully operated until their maximum values were reached during the
measurements, it is supposed that this is the maximum possible time that a work cycle of lifting
system can take. However, it should be noted that, in practice, the speed of movement of the
hydraulic fluid in the cylinder actuators can vary depending on its temperature, which can lead
to slight differences in the durations of the functions of the lifting system.
10.8%
9.0%
54.1%
20.7%
5.4%
Idling
Support legs
Container
Front platform
Front platform
tongue
38
3 Modeling and Simulation of the Catering Lift Truck
In this work, a simulation model was developed with MATLAB/Simulink to determine the energy
consumption and energy efficiency of the two trucks under consideration. The simulation
results are the basis for further analyses to show the competitiveness of the electrified truck in
all relevant aspects. The model is based on mapping all types of energy flows involved
(mechanical, electrical, and hydraulic) and significant energy losses in all relevant components
of the catering lift truck. For this purpose, the mechanics of the powertrain as well as the
hydraulic circuit of the lifting system are modeled with their respective functions. The input to
the model is the predefined work cycles of the catering lift truck that are presented in Chapter
2.3. In addition to the structure of the simulation model, a comprehensive tool was developed
to parameterize the individual models and to analyze the simulation results. Another tool
enables the analysis of the measurement data and the extraction of individual cycles from a
series of measurements. The simulation results were validated by comparison to the average
real diesel and electricity consumption of the two trucks.
The simulation model ensures an objective and reproducible comparison of different drive
concepts under identical boundary conditions. In addition, it offers the possibility of examining
the influence of significant variables on energy consumption and efficiency. However, given
the complexity of the system to be modeled, it is necessary to reduce the modeling effort
through appropriate simplification, as long as this is compatible with the model’s purpose while
maintaining the required accuracy of the results. In the following, the individual simulation
models of the driving and lifting systems are presented separately to consider the consumption
of the individual systems in more detail.
3.1 Driving System Model
The driving system model calculates the driving energy requirement of the catering lift truck
based on the measured driving cycles. The basic structure of the model for the two trucks
under consideration is shown in Figures 3-1 and 3-2. The model is based on backward
calculations, described in Chapter 1.2.3, and thus no driver model is required as mechanical
and electrical losses and losses inside the drive are represented in the efficiency of the
individual components. The input to the model is the speed and acceleration data of the
respective driving cycle. These are used to calculate the driving resistance force, which
opposes the movement of the vehicle, at any given point in time.
The model is the same for both trucks except for the drive model, which is represented by the
diesel engine model in the diesel truck and the corresponding electric drive model, including
the electric motor, the power electronics and the traction battery, in the electric truck.
39
Figure 3-2: Schematic structure of the simulation model of the electric truck driving system
Figure 3-1: Schematic structure of the simulation model of the diesel truck driving system
Driving Cycle
Vehicle
Wheels
Differential
Transmission
Diesel
Engine
Consumpti
on
Shifting Map
Consumption
Driving
Resistance
Acceleration
Resistance
Gradient
Resistance
Rolling
Resistance
Air
Resistance
Electric Drive
Motor/
Generator
Power
Electronics
Battery
Driving Cycle
Vehicle
Wheels
Differential
Transmission
Shifting Map
Consumption
Battery SOC
Driving
Resistance
Acceleration
Resistance
Gradient
Resistance
Rolling
Resistance
Air
Resistance
Recuperation
Power
40
The total driving resistance force is composed of the acceleration resistance force , the
gradient resistance force , the rolling resistance force and the air resistance force
[Mitschke & Wallentowitz, 2014] [Naunheimer & Bertsche, 2007]:
(1)
The acceleration resistance force is described in Newton’s second law of motion as the force
that has to be applied to a body to be accelerated. It comprises the inertia in the direction of
movement of the vehicle (translational part ), as well as the rotational resistance of the
rotating components (rotational part ) such as the motor shaft, transmission, differential
and vehicle wheels. Thus, the acceleration resistance force results in:
(2)
The translational part of the acceleration resistance force can be represented as:
(3)
where is the total vehicle mass and is the vehicle acceleration.
The rotational part of the acceleration resistance force is composed of the respective individual
inertia of the rotating components. Since these are usually difficult to determine, the rotational
acceleration resistance force is often described by a dimensionless rotational mass addition
factor
depending on the current gear ratio [Hoepke & Breuer, 2016]. Hence, the acceleration
resistance force can be simplified to:
(4)
The factor is given as a function of the transmission ratio in some literatures, such as [Mitschke
& Wallentowitz, 2014] and [Naunheimer & Bertsche, 2007].
The gradient resistance force is calculated by using the angle of inclination of the road and
the weight of the vehicle:
=
(5)
Where is the acceleration due to gravity. Since the catering lift truck only drives on flat
ground at the airport apron and the road leading to it, the gradient resistance is considered
irrelevant in this application and will therefore be neglected in the simulation.
The rolling resistance force represents the resistance forces that act on the wheels when rolling
on the road surface. It is an almost linear function of the wheel load that can be described with
the dimensionless rolling resistance coefficient [Mischke & Wallentowitz, 2014] [Naunheimer
& Bertsche, 2007]. Thus, the total rolling resistance force is calculated as:
41
=
cos
(6)
The angle of inclination is usually neglected for roads with uphill/downhill gradients below 10%.
The rolling resistance coefficient is mainly dependent on the tire conditions, road surface and
driving speed. However, in the lower speed range, it can be considered as a constant for
simplification. Typical values for the rolling resistance coefficient depending on the road
surface for speeds of less than 60 km/h can be found in [Naunheimer & Bertsche, 2007].
Air resistance force describes the resistance force of the ambient air that flows around the
moving vehicle and through it. Hence, the air resistance force is a result of the pressure
difference in the direction of flow, the friction on the surface of the vehicle, and the internal flow
resistance. It can be calculated as follows:
=
(7)
where is the vehicle driving speed, is the vehicle cross-sectional area, is the drag
coefficient, and is the air density. The values of the drag coefficient and cross-sectional area
for trucks can be derived from the literature.
Additional partial resistances can also occur due to the toe-in/toe-out or when cornering.
However, these are usually not considered because their effect is negligible.
The required torque at the vehicle wheels can then be calculated from the driving resistance
force using the dynamic wheel radius [ Breuer & Rohrbach-Kerl, 2015].
(8)
Similarly, the wheel’s angular velocity can be calculated from the vehicle velocity and the
dynamic wheel radius.
(9)
Since it is assumed that the speed and torque are the same on all wheels, the differential is
modeled by only taking the energy loss into account. The transmission is modeled with the
help of a shifting map and the gear step is selected depending on the speed of the transmission
output. The transmission ratio is then specified for the currently selected gear. Subsequently,
the required torque and speed at the engine, and , are determined based on the
differential ratio and the respective ratio of the current transmission gear taking the
efficiencies of the differential and the transmission into account.
(10)
(11)
42
The power requested by the engine can be given by:
(12)
In the overall vehicle simulation, modeling of the engine is of central importance, since it is
responsible for converting the primary energy and has the greatest energy loss in the vehicle.
However, the realizable level of detail of the modeling is determined by the availability of data
for the parameterization.
For the diesel truck, the diesel engine is modeled using its consumption map, which indicates
the specific fuel consumption of the engine at each operating point relative to the
engine speed. The total fuel demand for an input driving cycle can then be determined
as a function of the engine power, the specific fuel consumption, and the diesel density
over the time period to .
(13)
However, the purpose of the simulation model in this work is to consider the overall energy
balance of the whole vehicle to compare the two trucks being considered. Therefore, the total
power demand of the diesel truck is determined as the input power to the engine using
the consumption map of the engine:
(14)
where is the heating value of diesel. The total energy consumed can then be
determined in an integrative manner:
(15)
For the electrified catering lift truck, the efficiency map of the electric motor is used to determine
the motor efficiency at the respective operating point. The total power demand of the electric
truck is subsequently given by the motor power that is calculated above, the associated
electric motor efficiency , and the efficiency of the power electronics including the
battery :
(16)
Consequently, the total energy consumption of the electric truck can be similarly
determined by integration:
(17)
43
Since the simulation model focuses on modeling the energy consumption of the vehicle
components, the battery model does not represent the function of the traction battery, but only
calculates the state of charge of the battery. The current state of charge of the battery (SOC)
can be derived from the energy consumption, the initial capacity of the battery at the
start of the rout , and the total capacity available when the battery is fully charged
.
(18)
In addition, the applied output power is also determined. This is the power provided to the
wheels to drive the vehicle, i.e., the usable power without losses. From this, the output energy
is derived.
(19)
(20)
It should be noted that the model does not take into account the energy consumption of
auxiliary units such as the cooling system inside the container or the heating and cooling in the
driver’s cab, these functions are not operated continuously and therefore do not represent a
fixed part of the work cycle.
The model is parametrized according to the respective truck characteristics that are described
in Chapter 2.1 and Appendix A. A summary list of the parameters used in the simulation model
is provided in Table 3-1. Literature values are used for parameters for which data are not
available. Where the values used for rolling and drag resistance correspond to the average
values from [Mitschke & Wallentowitz, 2014] [Naunheimer & Bertsche, 2007]. While the values
for the efficiency of the differential and transmission were assumed based on the value ranges
given in [Naunheimer & Bertsche, 2007], [Lechner & Naunheimer, 1999] and [Jerratsch, 2021].
Due to the additional weight of the battery, the electric truck is slightly heavier than the diesel
truck, despite the omission of the diesel tank and the lightness of the electric motor compared
to the diesel engine.
The information on the payload is based on experience, as the weight of the catering cannot
be determined exactly. However, the truck travels always loaded with trolleys, which are either
packed with catering on the way to the airplane or with waste on the way back. The number
and weight of the trolleys are usually known, but the weight of the load inside cannot be
determined. Therefore, an average payload of 3,000 kg is considered for the simulation based
on estimations.
44
Table 3-1: Key parameters of the driving simulation model
Parameter
Diesel Truck
Electric Truck
Vehicle weight [kg]
16,174
17,234
Simulated pay load [kg]
3,000
3,000
Motor power [kW]
142
138
Motor torque [Nm]
809
780
Motor max. rotation speed [1/min]
2,000
2,500
Motor min. rotation speed [1/min]
800
–
Battery capacity [kWh]
–
113
Vehicle frontal area [m2]
9.3
Drag coefficient
0.53
Rolling resistance coefficient
0.01
Dynamic tire radius [m]
0. 5097
Differential ratio
9.49
Differential efficiency
98%
Transmission gear ratio
[3.49, 1.86, 1.41, 1.00, 0.75]
Transmission efficiency
92%
Rotational mass addition factor
[1.06, 1.04, 1.035, 1.03, 1.025, 1.02]
Figure 3-3 shows the time course of the required driving power of both trucks compared to the
vehicle speed for the driving cycles that are previously described in Chapter 2.3.1. Based on
the findings summarized in the graphs, it is evident that the electric truck has lower levels of
power demand across all driving cycles and the diesel truck requires almost twice the amount
of power needed by the electric truck to drive the same route. However, the difference depends
on the vehicle speed profile as the biggest differences occur at high accelerations. When
comparing at constant speeds, the difference decreases slightly with increasing speed and
increases slightly with lower speed, since the diesel engine is more efficient at high speeds,
whereas its efficiency decreases at low speeds. In contrast, the efficiency of the electric motor
hardly drops at lower speeds.
When considering the peak power, the diesel truck has a peak power of up to 400 kW, while
the peak power for the electric truck hardly exceeds 200 kW. Otherwise, the power required
for both vehicles is usually in the range of the nominal motor power.
The recovered braking power can be seen in the negative area which represents the
regenerated power supplied to the battery when the motor is operating in the generator mode.
45
Figure 3-3: Simulation results of the power demand of the driving model
-20
-10
0
10
20
30
40
50
-200
-100
0
100
200
300
400
500
0 100 200 300 400 500 600
Velocity [km/h]
Power [kW]
Time [s]
Driving cycle 1
Power of diesel truck Power of electric truck Velocity
-20
-10
0
10
20
30
40
50
-200
-100
0
100
200
300
400
500
0 200 400 600 800 1000 1200
Velocity [km/h]
Power [kW]
Time [s]
Driving cycle 2
Power of diesel truck Power of electric truck Velocity
-20
-10
0
10
20
30
40
50
-300
-200
-100
0
100
200
300
400
500
0 400 800 1200 1600 2000 2400
Velocity [km/h]
Power [kW]
Time [s]
Driving cycle 3
Power of diesel truck Power of electric truck Velocity
46
Table 3-2 compares the simulation results for the consumption of both trucks, which clearly
show the expected effect of electrification on consumption. It is noticeable that the electrified
truck requires significantly less energy than the diesel truck and, on average, approximately
two thirds of the energy consumption can be saved through electrification. As shown, the
energy savings in driving cycles 1 and 2 are almost the same, while the energy saving is slightly
lower in driving cycle 3. This could be attributed to the smaller proportions of acceleration and
braking maneuvers in addition to the higher average speed of this cycle, as can be seen in
Figure 2-5 and Table 2-1 in Chapter 2.3.1. In this cycle, there is thus less braking energy to
recover. Furthermore, the diesel engine operates with a relatively higher efficiency range at
high average speeds. The total absolute consumption difference at end of the three driving
cycles is 65.94%.
Table 3-2: Simulation results of the driving model
Route
Diesel truck
consumption
[kWh]*
Electric truck
consumption
[kWh]
Energy Saving
Driving cycle 1
11.63
3.82
67.15%
Driving cycle 2
25.03
8.15
67.44%
Driving cycle 3
42.93
15.14
64.73%
Total
79.59
27.11
65.94%
*The energy equivalence (calorific value) of 1 liter of diesel considered is 9.79 kWh/L
Figure 3-4 shows the average energy consumption per 100 km whereby all consumption
values are shown in liters of diesel for comparison. For the purpose of illustration, the
consumption values of the electric truck are also shown without recuperation. As shown, the
diesel truck has a significantly higher consumption rate in all driving cycles, which amounts to
almost three times that of the electrified truck. From the results, it is also evident how the
recuperation of braking energy significantly reduces the consumption of the electric truck.
However, the results also show that even when not considering the recuperation, there is a
considerable difference in consumption between the two trucks which is due to the different
characteristics of their driving technologies as the electric motor has a higher degree of
efficiency compared to the diesel engine. In addition, the diesel engine consumes a small
amount of fuel when idling even though the truck is not moving. Based on experience, it is
assumed that the diesel engine has a consumption of 2.6 L/h when idling. In comparison, the
electrified truck does not consume any energy when idling.
At the same time, Figure 3-4 shows the variation in consumption rate caused by the individual
driving cycles, which reflects the impact of driving modes on energy consumption. In driving
47
cycles 1 and 2, both trucks have higher consumption rates compared to driving cycle 3
because these driving cycles have higher acceleration maneuvers, which results in higher
consumption. On the other hand, the amount of recovered energy in cycles 1 and 2 should
also be higher because of the many abrupt stops. Therefore, it can be observed that the
influence of the increased proportion of acceleration on the consumption of the diesel truck is
greater than on the consumption of the electric truck. As for the third driving cycle, which has
the lowest acceleration proportion, and hence the lowest average power demand, both trucks
have the lowest consumption rate in this cycle and hence the proportion of recovered energy
is also not as high compared to other cycles.
In total, the simulated consumption rate based on the entire driving cycles is 29.93 liters per
100 km for the diesel truck, while it is 10.20 liters per 100 km for the electric truck with recovery
and 13.55 liters per 100 km without taking recovery into account.
Figure 3-4: Simulation results of the consumption rate of the driving system
For further examination and a more detailed comparison of the energy consumption of the two
trucks, the distribution of energy consumption according to driving modes, as defined in Table
2-2 in Chapter 2.3.1, is shown in Figure 3-5. Here, the energy consumption value is calculated
for each of the four driving modes for the total driving cycles. As shown, the bulk of the truck's
energy consumption is in the acceleration phase, which is consistent with the nature of the
truck's driving cycles. Almost half of the energy consumption in the acceleration and cruise
modes can be saved by using the electrified truck, while in idle and braking modes, the entire
energy consumption can be saved since the electric motor is turned off and therefore
consumes no energy. Furthermore, energy is recovered in the braking phases and added to
the system. This is represented by the negative energy value in Figure 3-5.
32.11 32.86
27.97 29.93
14.47 15.15
12.55 13.55
10.55 10.70 9.86 10.20
0
5
10
15
20
25
30
35
Cycle 1 Cycle 2 Cycle 3 Total
Consumption [L/100 km]
Diesel truck Electric truck without recuperation Electric truck with recuperation
48
Figure 3-5: Simulation results of energy consumption of the driving system according to the
driving modes
From the results, it can be deduced that the consumption values heavily depend on driving
behavior and the traffic environment as the speed profiles of different driving cycles vary
greatly, even for the same route. The results indicate that electrification is more beneficial in
driving cycles of high acceleration and deceleration frequencies, where the advantage of the
electric motor in energy saving and recuperation stands out.
It should be noted, however, that the simulation results only represent the consumption in the
vehicle (TTW) since the energy consumption for the generation and provision of electricity and
diesel fuel (WTT) is not considered in the simulation model but will be taken into account later
when discussing the total demand for primary energy in the life cycle analysis.
In addition to the energy consumption, the battery SOC of the electric truck is also simulated,
and the results are shown in Figure 3-6 for both with and without the recuperation. It is assumed
that the electric truck starts driving with a 90% battery charge since when the battery is fully
charged, the energy recovered during braking cannot be fed back into the battery. The truck
completes the three driving cycles in succession without being charged in between. As Figure
3-6 shows, the SOC level with recuperation is always above the SOC level without
recuperation. The curve of the SOC with recuperation clearly indicates that the discharging
process is repeatedly interrupted by short charging phases, which represent the energy
recovered during braking. This results in permanently lower SOC exhaustion and thus provides
a consumption advantage. At the end of the three driving cycles, the battery SOC is about
58.1%, while it is about 66% when considering recharging through recovery.
2.62
49.41
20.87
6.70
0.00
25.76
10.28
-8.94
-10
0
10
20
30
40
50
60
Idle Acceleration Cruise Braking
Energy [kWh]
Diesel truck Electric truck
49
Figure 3-6: Simulation results of the battery SOC of the driving system of the electric truck
0
10
20
30
40
50
85
86
87
88
89
90
91
0 100 200 300 400 500 600
Velocity [km/h]
SOC [%]
Time [s]
Driving cycle 1
SOC [%] SOC with recovery[%] Velocity
10
20
30
40
50
74
76
78
80
82
84
86
88
0 200 400 600 800 1000 1200
Velocity [km/h]
SOC [%]
Time [s]
Driving cycle 2
SOC [%] SOC with recovery[%] Velocity
0
10
20
30
40
50
54
58
62
66
70
74
78
82
0 400 800 1200 1600 2000 2400
Velocity [km/h]
SOC [%]
Time [s]
Driving cycle 3
SOC [%] SOC with recovery[%] Velocity
50
Table 3-3 presents the savings achieved in SOC as well as in energy consumption through
recuperation. At the end of the three driving cycles, 31.48% of the battery charge should have
been consumed but thanks to the recuperation, 7.63% was saved. The highest savings were
achieved in driving cycle 2 and the lowest savings in driving cycle 3. This is specifically to be
expected given the braking proportion on these cycles. The total average energy savings of all
driving cycles is 24.24%. The total saving percentage is considered reasonable and is
consistent with the expected range of values based on the literature. Similar values have
already been found for electric heavy vehicles in other studies such as [Lajunen, 2014] and
[Van Sterkenburg et al., 2011].
Table 3-3: Energy saving by recuperation in the electric truck
Route
SOC without
Recuperation
SOC with
Recuperation
SOC
Difference
Energy
Recovery
Driving cycle 1
4.63%
3.38%
1.25%
27.00%
Driving cycle 2
10.21%
7.21%
3.00%
29.38%
Driving cycle 3
16.64%
13.26%
3.38%
20.31%
Total
31.48%
23.85%
7.63%
24.24%
3.2 Lifting System Model
In the diesel truck, all functions of the lifting system are powered by the diesel engine. The
diesel engine drives an axial pump, which pumps oil from the tank into the hydraulic circuit of
the lifting system. Figure 3-7 shows the schematic representation of the basic lifting hydraulic
circuit. The oil flows from the pump through the control valves to the respective cylinder
actuator. The cylinder actuator converts the hydraulic energy back into mechanical energy as
the oil causes the piston rod to extend out of the cylinder tube and thus the respective function
is performed. The hydraulic circuit has a load-sensing control valve to keep the system
pressure relative to the operated function.
In the electrified truck, the functions of the container and the support legs remain hydraulically
operated in the same way. However, the hydraulic pump is powered by the vehicle’s electric
motor instead of the diesel engine while the functions of the platform and platform tongue are
performed electromechanically by their own associated electric drives. Therefore, these two
functions are excluded from the hydraulic circuit in the model of the electric truck lifting system
and are simulated in a separate model. Other than that, the model of the hydraulic circuit
remains the same for both trucks.
51
Figure 3-7: Simplified hydraulic circuit of the catering lift truck lifting system (Adapted from
the original hydraulic circuit diagram provided by DOLL Fahrzeugbau GmbH)
52
Accordingly, when modeling the lifting system, in addition to modeling the hydraulic circuit,
models of the vehicle engine as well as the connecting auxiliary gear also have to be included.
The structure of the simulation model of the lifting system of both trucks is shown in Figures 3-
8 and 3-9.
Figure 3-8: Schematic structure of the simulation model of the diesel truck lifting system
Figure 3-9: Schematic structure of the simulation model of the electric truck lifting system
The hydraulic model is simplified as much as possible so that all effects that are expected to
influence the behavior of the system in terms of energy consumption are included, whereby
the following components are considered: control valves, cylinder actuators, load sensing
valve, and pump. The pressure losses are also taken into account. Components that are not
relevant to the energy balance – such as the safety mechanisms – are not considered in the
model, since these are only triggered in emergency situations and not during the working
process. Since the functions of the lifting system are only operated separately, there is no
dependency between the individual actuators, and hence every function of the lifting system
will be simulated individually, and the successive individual performances of the actuators are
combined into a complete cycle.
53
Similar to the driving model, the purpose of the calculation in this model is to determine the
power balance and energy consumption of the lifting system. However, the calculation is based
on hydraulic data and not on motion data as in the drive system model. The input data to the
model represents the system settings data, which determines the opening degree of the
hydraulic control valves. Based on this, the volumetric flow distribution and the pressure build-
up are determined, which represent the power flows between the control valves and actuators.
The motion and associated speed profiles for the individual actuator cylinder are generated by
the difference between the incoming and outgoing hydraulic power flows on both sides of the
actuator. The main part of the model for the hydraulic circuit represents the valve-cylinder
combination. This is illustrated according to [Jelali & Kroll, 2003] [Will & Gebhardt, 2008] in
Figure 3-10 for the functions of the support legs, platform, and platform tongue, and in Figure
3-11 for the function of lifting/lowering the catering container.
Figure 3-10: Schematic representation of the combination of the 3-port/4-way directional
control valve and double-acting cylinder actuator with variable definitions (for functions of the
support legs, platform and platform tongue)
For the configuration of the 3/4 directional control valve and double-acting cylinder shown in
Figure 3-10, the inlet and outlet volumetric flow rates of the cylinder, and , are considered
to be the same as those of the valve and are therefore determined using the valve
characteristic map based on the pressure difference between the valve ports corresponding to
the flow direction. is determined based on the inflow pressure difference between the valve
ports S (pump-side port) and A/B. Since this pressure difference is constant due to the use of
a feed regulator in the valve, the value of is also constant and is specified based on the
54
valve setting data. is determined based on the return pressure difference between valve
ports A/B and T (tank-side port).
The cylinder actuator is modeled considering the dynamic behavior of the cylinder piston,
which governs the load motion [Jelali & Kroll, 2003] [Will & Gebhardt, 2008]. Hence, the
differential equation of motion of the cylinder piston can be formulated as follows:
(21)
where is the mass of the load to be moved, and
are the mass and acceleration of
the cylinder piston respectively, and are the pressures in the respective cylinder
chamber, which are equal to the pressures in the pipelines and ,and and are the
areas of the piston on sides and . denotes the force of the load to be moved. The term
describes the speed-dependent friction force in the hydraulic cylinder. It can be modeled
using the Tustin friction model, which is a form of the Stribeck curve [Beater, 1999] [Tang et
al., 2002]:
(22)
where
is the velocity of the cylinder piston that represents the integration of the acceleration
of the cylinder piston, is the Coulomb sliding friction force (kinetic friction), is the stiction
friction force (maximum static friction force), is the viscous friction force and is the Stribeck
velocity [Jelali & Kroll, 2003]. The stiction and sliding friction forces represent the frictional
resistance between the cylinder piston and the cylinder body and can be estimated with the
help of normal force and friction coefficients [Sommer et al., 2014]:
(23)
(24)
where is the normal force of the cylinder piston on the friction surface, which results from its
own weight. Reference values for the stiction and sliding friction coefficients and can be
found in the literature for a lubricated steel-steel pairing [Lackmann, 2007] [Gieck & Gieck,
2019]. The viscous friction force represents the damping resistance that arises because of the
movement of the piston in the oil:
(25)
The coefficient of that viscous friction force can be determined from experimental data [Jelali
& Kroll, 2003] or estimated based on the geometry of the cylinder piston for laminar flow in a
pipe with an annular cross-section [Gieck & Gieck, 2019]. In this work, it was estimated based
on the available data.
55
The total change of the volumetric flow rate in the cylinder chambers and can be
calculated from the balance of the volumetric flow rate in the respective cylinder chamber
(neglecting leakage flows):
(26)
(27)
where and are the volumetric flow rates in the cylinder throttle points that are considered
the same volumetric flow rates of the valve. and denote the volumetric flow rate
differences due to volume change in the respective cylinder chamber, and can be calculated
as follows:
(28)
(29)
The total change in the volumetric flow rate, in conjunction with the compressibility of the
hydraulic oil, determines the pressure build-up in the respective cylinder chamber volume.
(30)
(31)
Here, is the bulk modulus for oil and and are the volumes of the respective cylinder
chambers, which can be expressed as:
(32)
(33)
where and are respectively the current and maximum stroke of the cylinder piston
and and are the initial chamber volumes when the piston is fully extended or fully
retracted.
For the function of lifting/lowering the catering container, the two 2-stage telescopic cylinders
and the corresponding one-directional control valves are modeled as shown in Figure 3-11.
Since the oil flows in one direction through the control valve, there is only the inlet volumetric
flow rate and this is determined in the same way using the valve characteristic map and
setting data.
56
Figure 3-11: Schematic representation of the combination of the 2-port/1-way directional
control valve and single-acting telescopic cylinder actuator (for the function of lifting/lowering
the container)
The individual stages of the telescopic cylinder extend one after the other while hydraulic oil is
pumped to one side of the cylinder, so each stage can be modeled as a single-acting cylinder
[Findeisen, 2006] [Will & Gebhardt, 2008]. The model equations of the telescopic cylinder are
subsequently given as follows:
(34)
(35)
(36)
(37)
(38)
Here, the piston mass , piston area and initial chamber volume are taken into
account corresponding to the stage being extended, where subscripts 1 and 2 denote the
cylinder stages. Since the pistons of the telescopic cylinders move vertically, the friction force
includes only the viscous friction force , which is determined according to Equation (25),
while the other frictional forces in Equation (22) are not relevant here.
Pressure losses in the hydraulic circuit are also taken into account and encompass the
pressure losses due to the load-sensing function , the hydraulic resistances of the pipelines
, and the gravity . These pressure losses are added to the required pressure at the
57
respective cylinder actuator to determine the total system pressure at the pump outlet
:
(39)
For the load-sensing pressure loss, the pre-load pressure of the load-sensing valve is
considered, which can be taken from the valve data sheet. The pressure loss in the pipelines
is calculated for a laminar flow according to [Will & Gebhardt, 2008]:
(40)
where is the dynamic viscosity of the hydraulic oil. is the length, is the inner
diameter, and is the cross-sectional area of the pipeline respectively. The pressure loss
due to gravity represents the potential energy of the oil due to its elevation, so that it can be
calculated as:
(41)
where is the height to which the oil has to reach and is the density of the hydraulic oil.
In addition, in the functions of the support legs and the front platform tongue, a load-holding
valve is used to prevent the uncontrolled movement of the load. Therefore, the pre-load
pressure of the respective valve is also considered in these functions.
In the pump model, the hydraulic-mechanical map of the pump is used to determine the
absorbed torque based on the calculated system pressure. The power requested by the
motor can then be calculated based on the characteristics of the auxiliary gear
connecting the pump and the engine:
(42)
where denotes the fixed rotational speed of the engine, and and are the gear ratio
and efficiency of the connecting gear respectively.
The same models presented in Chapter 3.1 are used here to model the diesel engine and the
electric drive. The total power demand and the total consumed energy of the lifting system for
both trucks, as well as the consumed diesel fuel (for the diesel truck) and the SOC of the
battery (for the electric truck) can then be determined according to Equations (13) to (18). In
addition, the applied output power for the external load to be moved by the cylinder actuator
can be calculated as:
(43)
and the output energy can be then determined according to Equation (20).
58
The required data for the implementation of the model equations are taken from the data
sheets of the components involved. The required (but not specified) data can be estimated
from the given information, literature, or assumptions. The overall model is validated by
comparing the resulting simulated pressure and volumetric flow rate curves to those that are
measured whereby the measurement data presented in Figure 2-7 in Chapter 2.3.2 is
employed for this purpose.
Figure 3-12 shows the simulation results for the system pressure and output power of the lifting
system of both trucks for the reference lifting cycle presented in Table 2-3 in Chapter 2.3.2.
Figure 3-12: Simulation results of the power demand of the lifting model
The closeness of the simulation model to the real lifting system can be confirmed by the
correspondence of the simulation results to the measurement data as the pressure curve aligns
with the measurement data. However, the pressure for lifting the container is higher in the
simulation than in the measurement, because a payload of 3,000 kg is assumed in the
simulation, while the measurement was carried out without payload.
As shown in Figure 3-12, a clear difference can be observed in the power curves of both trucks
as the power required in the lifting system of the electric truck is always less than half that of
the diesel truck. Since the hydraulic functions of the container and support legs are identical
on both vehicles, the difference in their output power is related to the different truck drives. In
contrast, the functions of the platform and platform tongue of the electric vehicle are no longer
operated hydraulically but have their own electric drives, which is also reflected in their power
0
50
100
150
200
250
0
10
20
30
40
50
60
70
80
90
100
020 40 60 80 100 120 140 160 180 200 220
Pressure [bar]
Power [kW]
Time [s]
Power of diesel truck Power of electric truck Pressure
59
demand. It is also to be noted that – despite the low output power required – the power demand
of the support legs function is considered too high. However, this can be traced back to the
additional hydraulic power required for the sub-functions which results in energy losses in the
system. To some extent, this also applies to the front platform and platform tongue in the diesel
truck.
Table 3-4 summarizes the consumption results of the lifting system of both trucks and the
corresponding energy-saving potential. When looking at the individual functions of the lifting
system, it is evident that, overall, the differences are relatively significant, which reflects the
effect of the high efficiency of the electric motor on reducing consumption. This also applies to
the idle time, i.e. the time in which the engine is running without a function being operated.
Table 3-4: Simulation results of the lifting model
Function
Diesel truck
consumption
[Wh]*
Electric truck
consumption
[Wh]
Energy saving
Support legs
158.30
79.20
49.97%
Container
739.20
371.60
49.73%
Front platform
113.10
0.27
99.76%
Front Platform
tongue
40.30
0.27
99.32%
Idling
35.80
14.40
59.78%
Total
1,086.70
465.74
57.14%
*The considered energy equivalence (calorific value) of 1 liter of diesel is 9.79 kWh/L
Approximately 50% of the consumption can be saved in the functions of the container and
support legs whereas, for the front platform and front platform tongue functions, the savings
are much higher as they are close to 100%. This is due to the fact that, as already mentioned,
these functions involve significant pressure losses in the diesel truck, which is avoided in the
electric truck through the use of electromechanical actuators for these two functions. Moreover,
the poor efficiency of the diesel engine with lower power requirements leads to even greater
differences. However, these functions represent a small proportion of the energy requirement
of a complete work cycle and therefore have little effect on the overall saving potential while
the container function has the highest energy requirement, accounting for more than two thirds
of the total consumption in a complete work cycle. This function, therefore, has the greatest
influence on the total consumption efficiency and savings potential.
In general, the results show that the lifting system of the electric truck has a more efficient work
cycle compared to the diesel truck while maintaining a realistic operating performance. The
60
resulting total energy consumption for a full operating cycle is approximately 1.09 kWh for the
diesel truck and approximately 0.47 kWh for the electric truck which corresponds to a reduction
of 57.14% in the energy consumption of the electric truck compared to the diesel truck. This
means that in the diesel truck, more than twice the energy would have to be supplied to the
engine to achieve the same lifting work cycle.
Figure 3-13 shows the simulated battery SOC of the electric truck for a complete lifting work
cycle. At the end of the cycle, approximately 0.4% of the battery charge has been used up.
Figure 3-13: Simulation results of the battery SOC of the lifting system of the electric truck
89
89.2
89.4
89.6
89.8
90
020 40 60 80 100 120 140 160 180 200 220
SOC [%]
Time [s]
61
4 Efficiency and Potential Analysis
In the following, the energy efficiency of the catering lift trucks considered is evaluated using a
uniform procedure to provide an objective comparison and identify the energy-saving potential.
To this end, the energy efficiency and energy losses of the driving and lifting systems, as well
as the overall efficiency of the entire vehicle, are discussed. The analysis is based on the
simulation results for the energy consumption of both trucks that are presented in Chapters
3.1 and 3.2 according to the operating profile and working conditions defined in Chapter 2.2.
Energy efficiency generally describes the relationship between the benefits that are achieved
and the energy that is used. Considering an energy conversion process in a vehicle, energy
efficiency is defined as the ratio of the useful output energy to the supplied input energy
over a certain period of time [Liebl et al., 2014] [Pehnt, 2010]:
(44)
Energy loss is the part that is converted into a form of energy that cannot be used again in the
process and it represents the difference between the input energy and the output energy.
In addition, an energy efficiency comparison index is used to enable the comparison
between the different drive concepts of the two trucks being evaluated. This index describes
the percentage increase in the energy efficiency of a new drive to perform the same work
compared to the energy efficiency of the reference one and is defined as follows
[Fleczoreck, 2013] [Liebl et al., 2014]:
(45)
4.1 Driving System
Figure 4-1 shows the simulation results for both trucks of the TTW energy distribution when
driving. The bars shown represent the variation in useful energy that is released to the wheels
and the losses caused by the driving system for the different driving cycles. The recovered
braking energy in the electric truck is represented by the negative parts.
At first glance, it is easy to recognize that the diesel truck has a significantly higher proportion
of losses than the electrified truck. On the other hand, in the electric truck, in addition to greatly
reducing energy losses thanks to the high efficiency of the electric drive, the energy recovered
during braking may equal or even exceed the energy lost. Since both trucks drove the same
distances, the useful energy (or work performed) of the two trucks is almost the same and the
62
slight difference in the useful energy is due to the fact that the electrified truck is slightly heavier
because of the additional weight of the battery.
Figure 4-1: Simulation results of the energy distribution of the driving system
To further explain the big differences in energy use between diesel and electric trucks, the flow
diagrams in Figures 4-2 and 4-3 show the distribution of energy efficiencies and energy losses
while driving from the respective energy source in the vehicle to the wheels of both trucks. The
energy efficiency of the driving system is related to the efficiencies of the individual
components responsible for energy storage or conversion. In the case of the diesel truck, the
diesel engine has a very low efficiency for the provision of mechanical energy of around 36.1%.
Accordingly, most of the energy losses, approximately 63.9%, occur in the diesel engine while
a very small part of the energy, around 2.5%, is lost in the transmission and differential.
Consequently, only approximately 33.6% of the energy stored in diesel fuel is ultimately
provided to the wheels.
The driving system of the electrified truck is not only characterized by the higher efficiency of
the electric drive but also by the recovery and storage of braking energy in the battery. The
energy efficiency and loss analysis that is presented thereby represents the offsetting of
provided and circulated energy flows. When providing energy, approximately 10% of the
energy losses are caused by the power electronics including the battery and only 6% of the
energy losses occur at the electric motor while around 5.8% of the energy is lost in the
transmission and differential. Accordingly, approximately 78.2% of the energy supplied by the
battery is delivered to the wheels as useful energy.
-10
0
10
20
30
40
50
60
70
80
Diesel
Truck
Electric
Truck
Diesel
Truck
Electric
Truck
Diesel
Truck
Electric
Truck
Diesel
Truck
Electric
Truck
Cycle 1 Cycle 2 Cycle 3 Total
Energy [kWh]
Useful energy Energy losses Recuperation
63
Figure 4-2: Efficiency analysis of the driving system of the diesel truck
Figure 4-3: Efficiency analysis of the driving system of the electric truck
In comparison, the average energy losses for the diesel truck are approximately 66.4%
whereas for the electric truck they are around 21.8%. This means that nearly two-thirds of the
wasted energy has been saved. Moreover, roughly 24.8% of the supplied driving energy in the
electric truck goes back into the battery through recuperation. In accordance with the
assumptions in the simulation, the energy demand of the auxiliary units is low and is assumed
to be the same for both trucks and is therefore not taken into account here.
6%
4.2%
1.6%
78.2%
Battery & Power
Electronics η = 90%
Transmission
η = 95%
36.05 kWh
(three measured
driving cycles)
10%
Motor
η = 93.3%
Differential
η = 98%
1.6%
2.2%
2.8%
24.8%
0.6%
Differential
η = 98%
Power Electronics
η = 90%
Transmission
η = 95%
Generator
η = 92.7%
1.8%
0.7%
33.6%
63.9%
Motor
η = 36.1%
Transmission
η = 95%
Differential
η = 98%
79.59 kWh
(three measured
driving cycles)
64
4.2 Lifting System
The energy distribution of the lifting system based on the simulation results is illustrated in
Figure 4-4, whereby the useful energy delivered to the actuators and the energy losses for
each individual function is shown.
In general, high losses are evident in all functions of the lifting system which leads to a larger
energy demand although only a small amount of energy has to be delivered to the actuators.
As is evident from the figure, the amount of useful energy is mostly too small to be seen on the
bar chart. The function of lifting/lowering the container has the highest energy demand and
accordingly causes the largest part of the energy losses.
Figure 4-4: Simulation results of the energy distribution of the lifting system
The distribution of energy efficiencies and energy losses of the lifting system is shown,
analogously to the driving system, in the flow diagrams in Figures 4-5 and 4-6 for both trucks.
The lifting system of the diesel truck shows poor efficiencies in all functions. In the case of
small functions, in particular, very low efficiencies, i.e. close to zero, are evident due to the
enormous losses compared to the work done. These losses can be attributed to several causes
including the use of the oversized main drive, even for small functions. On the other hand, as
already mentioned, there are fixed losses in the main hydraulic circuit, regardless of which
function is currently in operation. This includes pressure drops due to load-sensing and pump
relief functions, as well as the hydraulic resistance specified in some components.
0
100
200
300
400
500
600
700
800
900
1,000
1,100
Diesel Truck
Electric Truck
Diesel Truck
Electric Truck
Diesel Truck
Electric Truck
Diesel Truck
Electric Truck
Diesel Truck
Electric Truck
Diesel Truck
Electric Truck
Support legs Container Front
platform
Front
platform
tongue
Idling Total
Energy [Wh]
Useful energy Energy losses
65
The load-sensing function regulates the system pressure in each section of the lifting work
cycle with the current actuator pressure plus the preset pressure difference, which corresponds
to the setting of the load-sensing valve. While the pump relief function provides the minimum
pressure required by the pump, often with axial piston pumps, to operate properly. This is
usually done by using a preload valve.
For small functions, the fixed losses in the hydraulic circuit are very high compared to the
energy required for the actual work. In addition, a large part of the losses in the function of the
support legs is due to the additional pressure required for the hydraulic load-holding
assignment.
For the electric truck, there are generally fewer losses in the lifting system. Since the functions
of the container and the support legs have the same hydraulic efficiencies as in the diesel
truck, the differences here are only related to the efficiency of the drive whereby a significant
improvement has been achieved in the efficiencies of the functions of the front platform and
front platform tongue thanks to the full electrification of these functions. As can be seen from
the efficiency distribution in Figure 4-6, the losses in the electric drive are small compared to
the losses in the hydraulic circuit, so the overall efficiency is still low for the electric truck lifting
system. In general, the lifting system of the electric truck has a total efficiency of 25.8%, while
that of the diesel truck has a total efficiency of 11.1%.
Figure 4-5: Efficiency analysis of the lifting system of the diesel truck
1,086.7 Wh
(one full
lifting cycle)
59.4%
1.2%
0.1%
Motor
η = 40.6%
16.2%
0.1%
0.5%
11.1%
Front platform
η = 0.26%
Container
η = 41.2%
Front platform tongue
η = 1.3%
0o.ö#-
28.3%
Hydraulic Circuit
(total η = 28.1%)
Gear
η = 97%
Support legs
η = 0.25%
66
Figure 4-6: Efficiency analysis of the lifting system of the electric truck
4.3 Entire Vehicle
After carrying out the efficiency analysis for each of the driving and lifting systems separately,
the efficiency analysis for the entire vehicle is conducted in the following. The analysis is
carried out here for the performance of three complete work cycles according to the work cycle
diagram of a catering lift truck previously defined in Figure 2-3 in Chapter 2.3, where each work
cycle consists of one of the three measured driving cycles presented in Chapter 2.3.1 and a
full lifting cycle as described in Chapter 2.3.2 and the corresponding simulation results from
Chapters 3.1 and 3.2 are used for this purpose. The overall energy balance of the work
processes for both trucks is shown in Figures 4-7 and 4-8. The total energy requirement for
the three simulated work cycles is 82.85 kWh for the diesel truck. For the electric truck, the
total energy requirement is supposed to be 37.45 kWh, but thanks to recuperation it is reduced
to 28.5 kWh which means that the electric truck only needs around 34% of the energy that the
diesel truck would need. Most of the energy flowing into the process, over 96%, is required for
the driving, and the remaining less than 4% is for the operation of the lifting system. On
average, the diesel truck delivers 32.7% of the total energy consumed in the process while, in
comparison, the electric truck delivers 76.2% on average, while 23.9% of the total energy
consumed in the electric truck is recovered through recuperation.
Motor
η = 89.9%
Gear
η = 97%
Hydraulic Circuit
(total η = 32.9%)
0.2%
32.3%
25.8%
Support legs
η = 0.25%
Container
η = 41.2%
465.74 Wh
(one full
lifting cycle)
Battery and Power
Electronics η = 90%
42.3%
72.1%
Front platform
Front platform
tongue
8.7/5.1%
39/12.7%
10%
52.7%
2.4%
9.1%
10%
Battery and Power
Electronics η = 90%
Motor
η = 90.3/94.3%
Gear
η = 52/85%
67
Figure 4-7: Vehicle efficiency analysis of the diesel truck
Figure 4-8: Vehicle efficiency analysis of the electric truck
The energy efficiency of the two trucks and the energy efficiency comparison index are
summarized in Table 4-1 for the driving and lifting systems as well as for the entire vehicle.
Both the driving system and the lifting system of the electric truck have better energy efficiency,
which is more than twice that of the diesel truck. As already mentioned, the lifting system has
very low energy efficiency in both trucks. However, it has no significant influence on the overall
efficiency of the vehicle, since its energy consumption only accounts for a small part of the
total consumption compared to the driving system. For the three complete work cycles, the
energy efficiency of the entire vehicle can be increased from 32.7% to 76.2% through
electrification and further to 100.1% through recuperation, i.e., three times the energy
efficiency of the diesel truck. The energy recovered is greater than the energy losses in the
electric truck, which explains why the overall efficiency of the electric truck is more than 100%
when calculated over the considered work cycles.
82.85 kWh
(3 complete
work cycles)
63.8%
Driving System
32.7%
32.3%
Lifting System
3.5%
0.4%
37.45 kWh
(3 complete
work cycles)
21%
Driving System
76.2%
75.27
Lifting System
2.77%
0.96%
23.9%
6.8%
68
The calculation of the energy efficiency comparison index shows that the percentage relative
increase in the energy efficiency of the catering lift truck through electrification is 133% and,
when taking recuperation into account, it increases to 206.1%.
Table 4-1: Comparison of the overall energy efficiency of the trucks for the three work cycles
Diesel truck
Electric truck
Δ
Driving system
33.6%
78.2%
132.7%
Lifting system
11.1%
25.8%
132.4%
Entire truck
32.7%
76.2%
133.0%
Entire truck
with
recuperation
-
100.1%
206.1%
However, it should be noted that the results may vary for different applications of the catering
lift truck due to several factors affecting the operating conditions, for example, the
environmental conditions. In addition, ancillary units absorb part of the engine power to perform
auxiliary functions such as cooling, steering, assistance systems, and provision of the onboard
power, which are not considered sufficiently in this analysis. The analysis in this work is carried
out assuming a specific and fixed payload value. However, the effect of payload variance on
the results of the efficiency analysis of the two trucks is discussed in Appendix D.
69
5 Economic Analysis Based on the Total Cost of
Ownership (TCO)
In addition to the basic technical feasibility, the cost balance for a catering lift truck plays a
major role as the acquisition and operating costs are important decision criteria when choosing
a truck, and the significantly higher acquisition costs of a battery-electric truck compared to a
conventional diesel truck represent the highest hurdle to purchasing at present. On the other
hand, although electric trucks can score with lower operating and maintenance costs, the
question arises as to whether this cost advantage of the electric truck is able to compensate
for its high acquisition costs. This is highly dependent on the usage profile and the service life
of the truck which, to some extent, correlates with the battery life.
A TCO analysis is thus necessary for a fair comparison based on real driving profiles. The
analysis aims to identify economic potential and quantify cost differences for the use of battery-
electric catering lift trucks compared to conventional diesel catering lift trucks under the same
boundary conditions. In this analysis, all of the costs associated with the acquisition and use
of the truck over its entire service life are taken into account as far as possible. In addition,
battery aging, replacement, and residual value are also considered. Possible uncertainties in
the development of influencing factors are depicted in the form of scenarios and sensitivity
analyses.
It should be noted, however, that the results of the TCO do not represent general statements
about the actual profitability of electric catering lift trucks, but rather provide important
information to the operators about the economic potential of these trucks with regard to the
application under consideration.
5.1 Calculation Method
The TCO analysis takes all relevant investment costs over the course of the vehicle ownership
period into account. These are usually divided into fixed costs and variable costs [Hilgers,
2016]. The fixed costs include the costs associated with the purchase of the truck,
infrastructure costs (if any), vehicle tax and insurance, as well as the value at the end of its
useful life (residual value). The variable costs include all costs that depend on the operation of
the vehicle, such as driver costs, fuel or electricity costs, maintenance and repair costs, and
tolls and vignettes. While the cost information can already be available at the time of the
analysis, it can also arise in the future and therefore needs to be predicted.
Both acquisition and operating costs play a major role when considering the TCO of a vehicle
and due to their influence on the total cost of the vehicle they thus determine the buyer's
willingness to purchase. In addition, when comparing the costs of electrified and conventional
70
vehicles, the policy of state financing of electromobility should be mentioned, as this makes
the economic advantage of electric vehicles even clearer. This also includes environmental
bonuses and tax exemptions for electric vehicles. Driver costs are not usually taken into
account when comparing the TCO for vehicles of the same type, as they are the same
regardless of the powertrain technology and the same applies to any other overhead costs that
are equal for all vehicles being compared.
The TCO analysis in this work is based on the calculation model VDMA-34160 (forecast model
for the life cycle costs of machines and systems). The model represents a generic TCO
calculation procedure that covers all areas of application and thereby ensures comparability.
Figure 5-1 gives an overview of the TCO analysis structure considered in this work in which
the life cycle of the catering lift truck is divided into three phases. In the acquisition phase, the
purchasing costs of the truck and the infrastructure are to be considered. The operation phase
forms the core of the TCO calculation and includes five cost elements while the consumption
costs represent all energy costs for operating the truck (driving and lifting). The costs of
maintenance, vehicle taxes, and vehicle insurance are taken into account as fixed costs that
are independent of mileage. In the recovery phase, the residual value of the truck at the end
of its service life is determined.
Even though they make up a large part of the total costs, the personnel and training costs are
not considered relevant elements in the analysis because these costs are assumed to be the
same for both trucks and thus have no influence on the cost comparison.
Figure 5-1: Composition of the TCO of the catering lift trucks
71
Since the costs arise at different points in the life cycle of the truck, all TCO elements are
calculated using the present value method whereby the present value references the value of
the future or past costs to the current point in time, assuming a certain interest or inflation rate
respectively [Wagner, 2011].
The present value of the TCO of the truck is calculated according to the formula:
= + +
+
-
(46)
where is the service lift duration and is the annual interest rate. and are
respectively the acquisition costs of the vehicle and the infrastructure for the current year (time
point of calculation) . The future costs over the entire service life of the vehicle are
represented in as the energy or fuel costs, as the maintenance costs, as the
vehicle tax costs and as the vehicle insurance costs, each in the future year respectively.
is the battery replacement cost in year and is the vehicle residual value at the
end of its service life.
Since the vehicle operating conditions are the basis of the analysis, the operating scenarios of
the catering lift truck are first defined according to the operating profile and work conditions
presented in Chapter 2.2. At the beginning of each scenario, a complete truck is purchased,
as well as an infrastructure in the case of the electric truck. It is assumed that the purchase
took place at the beginning of 2021. According to the operator information, the service life of a
conventional catering lift truck is 20 years, and it is assumed that the two types of trucks have
the same service life. The TCO analysis will thus extend to the end of 2040 and, accordingly,
the relevant cost elements are calculated over the entire life cycle of both truck types.
In the context of the analysis, the simulation results are used to determine the consumption
costs. While the technical specification and the operating conditions of the vehicle also provide
important data for the analysis, specific data and values are not available for some parameters
and these are therefore estimated based on information from the literature.
Based on the assumed battery lifetime in the respective scenario, a new battery may have to
be purchased during the scenario runtime, the residual value of which is determined at the end
of the scenario.
All estimated future cash flows are discounted to the year 2021 using a specified interest rate.
The cost parameters are determined first, and then the results of the TCO analysis of both
trucks are presented for individual scenarios. The cash values of both trucks are assessed and
compared with each other. Finally, the development of important cost parameters and their
influence on the TCO is examined in the sensitivity analysis. It should be noted, however, that
the TCO analysis presented in this work only relates to catering lift trucks that are used under
72
the operating conditions outlined here as the costs incurred by the different vehicle models
used under alternative operating conditions can vary widely. In addition, various additional
costs may arise over the entire service life of the trucks. The presented TCO analysis only
includes those costs that represent the most important cost components of the catering lift
truck, and the less significant costs are neglected, whereby these deviations limit the scope of
the analysis results.
5.2 Definition of the Operating Scenarios
To be able to calculate the costs over the life cycle of the catering lift trucks, a general daily
operating scenario for the trucks under consideration has to be defined. According to the
operator information, there is no precise operation plan and hence as soon as a truck is
available, it can be used whereby it is not regulated which truck has to be used or how many
hours a truck has to be in operation per day. Therefore, assumptions have to be made for the
definition of the daily operating scenarios.
To discuss and compare different options, two daily operating scenarios are defined that
represent two possible developments in energy consumption and investment costs for the
entire service life of the considered trucks. In scenario 1, three daily work cycles are assumed,
which represents the average daily work cycles of a catering lift truck whereas scenario 2
assumes five daily work cycles, which corresponds to the maximum daily work cycles of a
catering lift truck. Each work cycle consists of a driving cycle and a lifting cycle. The amount
of energy required to carry out a work cycle is therefore comprised of the energy required to
drive the truck and the energy required to operate the lifting system. The work cycles defined
in Chapters 2.3.1 and 2.3.2 and the corresponding consumption results presented in Chapters
3.1 and 3.2 are used for this purpose. For simplicity, it is assumed that both trucks perform the
same work cycles every day and that the operating conditions do not change over the life cycle
of the trucks. Only one week per year is excluded, which is intended for maintenance.
Since the cost of the battery is of central importance for the profitability of electric vehicles, the
probability of battery replacement is also discussed in the operating scenarios. As the service
life of the battery largely depends on the charging behavior [Doppelbauer, 2020], battery wear
and tear during routine operation should be taken into account. An important principle that
delays battery aging is that it should be charged regularly, and deep discharge should be
avoided as much as possible. This reduces the electrochemical load on the battery and
increases its service life. Battery discharging is described by the depth of discharge (DoD),
which represents the percentage of the battery that has been discharged relative to its total
capacity. Fast charging also plays a role in the aging of the battery, because charging with
high currents puts a special strain on the battery cells. If a battery system is not optimally
73
designed for this, rapid charging can lead to local overheating, which accelerates the
degeneration process of the battery.
In the electric truck, a lithium-ion battery with a total capacity of 113 kWh is used as the
accumulator. According to the battery manufacturer, the number of charging cycles over the
entire service life of the battery is 4,800 at a DoD of 80%. Assuming an average DoD of 80%
in the electric catering lift truck, 90.4 kWh is available for a fully charged battery each time.
According to the simulation results, the electric truck consumes 204,060 kWh in scenario 1
and 296,424 kWh in scenario 2 over its entire service life. This means that the battery needs
to be charged 2,257 times in scenario 1 and 3,279 times in scenario 2 which is significantly
lower than the available battery charging cycles. Accordingly, it is (theoretically speaking) not
necessary to change the battery during the service life of the truck in both scenarios. However,
while in scenario 1 it is assumed that the battery is charged during the longer night break with
a lower charging power to protect the battery, in scenario 2 on the other hand, it is assumed
that the battery is not being charged regularly but rather is exposed to poor charging conditions.
Subsequently, rapid charging and extreme charging, i.e., full charging (DoD = 100%) and deep
discharge (DoD = 0%) approaches are assumed to take place in scenario 2, which accelerates
the aging of the battery. Based on this, a battery replacement is assumed in the fifteenth year
of the truck service life in scenario 2.
The key data for the resulting daily operating scenarios are summarized in Table 5-1.
Table 5-1: Key data of the defined daily operating scenarios of the catering lift trucks
examined
Parameter
Scenario 1
Scenario 2
Work cycles
3
5
Traveled distance [km]
27.16
38.64
Operating hours [h]
1:26
2:06
Diesel truck consumption [kWh]
82.85
(8.46 liter diesel)
121.69
(12.43 liter diesel)
Electric truck consumption [kWh]
28.5
41.4
Battery replacement
-
Fifteenth year
74
5.3 Important Influencing Factors on the TCO Analysis
Since the TCO analysis extends over the entire service life of a catering lift truck, the total cost
is influenced by the variables that change over time. The analysis, therefore, focuses on the
period from 2021 to 2040 and depicts possible uncertainties in the development of influencing
factors during this period. The following factors are important for the cost development over
the calculation period: the economic interest and inflation rates and the price development of
diesel, electricity, and batteries. In the following, these factors are discussed, and assumptions
are made based on the relevant literature regarding their development for the time period
considered.
5.3.1 Interest and Inflation Rates
During the period of the vehicle service life, the costs are incurred at different times, and hence,
to make the costs as precisely comparable as possible, the different timescales of the costs
must be taken into account. According to the definition in the Gabler Business Lexicon, the
interest and inflation rates fulfill the time compensation function, with payments occurring at
different points in time being discounted or compounded to a reference point in time [Alisch et
al., 2004]. The time of commissioning of an investment property is normally chosen as the
reference point. By means of discounting, the values of future payments that are made after
the reference point are calculated back using the interest rate to a point in time that precedes
the payments. This means that discounting shows how much an amount of money was worth
at an earlier point in time. In contrast, all payments that occur before the reference point are
compounded using an inflation rate. The compounding thus determines how much an amount
of money will be worth at a later point in time [Wöhe et al., 2016].
The interest rate for companies is determined by the required minimum interest rate and the
entrepreneurial risk premium. The required minimum interest rate is derived from the base rate
which is the interest rate issued by the German Federal Ministry of Finance
(Bundesministerium der Finanzen BMF) for a risk-free return whereby the entrepreneurial risk
varies depending on the company. Since no specific data about the operating company of the
catering lift truck is available, a constant annual interest rate of 3% is assumed for all calculated
costs after the reference point. For costs prior to the calculation period, an average annual
inflation rate of 1.23% was assumed, which corresponds to the average inflation from 2018 to
2020 [Statista, 2022a].
75
5.3.2 Price Development of Diesel
The prices for conventional fuels in Germany mainly relate to the import of crude oil which is
significantly influenced by world market prices and exchange rates [Ederington et al., 2019].
Taxes and duties also play an important role in determining fuel prices. In Germany, energy
tax and value-added tax (VAT) as well as the CO2 tax make up a large proportion of the price
of petrol and diesel [Schlesinger et al., 2014] and, as a result, the price of diesel fuel in
Germany is subject to strong fluctuations [Statista, 2022b].
Since the beginning of 2021, CO2 emissions in individual sectors, including the energy sector,
have been subject to a CO2 price of €25 per ton, which is set to increase to €55 per ton by
2025 as was decided by the grand coalition in the Climate Protection Act 2019 [BReg, 2019].
Hence, many experts and research institutes expect a further surcharge on the fuel price for
the end customer in the coming years.
A reference forecast for the development of energy prices was presented in 2014 by the
consulting company Prognos AG as a likely development of the energy industry up to 2050.
The forecast assumes a relatively moderate increase in the diesel price since the proportion
of taxes and duties in the fuel price is assumed to be constant in real terms. Accordantly, a
linear development of fuel prices up to 2050 was assumed. Based on this forecast, the prices
for petrol and diesel fuel will be approximately 25% higher in 2030, and around 40% higher in
2050 in comparison to the prices from 2011 [Schlesinger et al., 2014].
A more recent study commissioned by the Federal Ministry for Economic Affairs and Climate
Action (Bundesministerium für Wirtschaft und Klimaschutz BMWK) is presented in 2020. In
this study, projections and impact assessments are made for the further development of the
energy system up to 2050. Based on the findings, it is assumed that after 2026 the CO2 tax
will rise sharply and therefore the price of fuel will increase even further [Kemmler et al., 2020].
Likewise, in the estimates according to other forecasts from the literature, such as [IEA, 2020b]
and [Capros et al., 2016] a significant increase in fuel prices is to be expected in the medium
term for all energy sources, among other things for reasons of climate protection. In summary,
it can be said that the anticipated CO2 tax will affect the price of diesel over the next 20 years,
despite the expected decline in demand for fossil fuel.
The assumptions for the development of fuel prices in this work are concluded from the
mentioned forecasts as well as further scientific publications. Figure 5-2 shows the
development of diesel prices, both with and without VAT, which is considered in the TCO
analysis. The diesel price is assumed to rise significantly by 2030 whereas, from 2030 onward,
it will only increase moderately with a constant increase per year. Since the areas of application
76
considered are commercial use of the catering lift truck, the value of VAT was excluded from
the diesel prices.
Figure 5-2: Estimated development of diesel prices in Germany over time (Source: own
assumptions based on literature review)
5.3.3 Price Development of Electricity
The electricity price in Germany is made up of three main components. The first component
comprises the costs for the generation and distribution of electricity which also includes the
provider's share of the profit, which can vary depending on the electricity provider. The second
cost component concerns the fees for the network use while the last component consists of
various surcharges and taxes. The main part is related to the surcharge for financing the
expansion of renewable energy. According to the German Association of Energy and Water
Management (Bundesverband der Energie- und Wasserwirtschaft BDEW), in 2020 surcharges
and taxes comprised 52% of the electricity price, whereas generation and distribution
contributed 23% and network fees added 25% to the overall costs [BDEW, 2021].
The Renewable Energy Act (Erneuerbare-Energien-Gesetz EEG) presented by the German
Federal Government, is intended to enable the long-term sustainable development of a secure,
economical, environmentally and climate-friendly energy supply. With this act, the Federal
Government has set itself the goal of increasing the proportion of electricity generated from
renewable energies to 65% by 2030. Furthermore, before the year 2050, all electricity that is
generated or consumed in Germany should be generated in a GHG-neutral manner [BMJ,
2020b]. Achieving these goals requires far-reaching changes in the infrastructure and the
expansion of the power grid for renewable energy generation. The investment costs are later
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
Diesel price [€/L]
With VAT Without VAT
77
passed on to the end consumer, which leads to an increase in the price of electricity. This is in
addition to the national pricing of CO2 emissions that was mentioned previously, which will also
be reflected in additional costs for end consumers.
The development of electricity prices is characterized by a continuous increase over the years
despite the low prices for primary energies and network charges. This is attributed to the
increasing EEG levy. Although a slight reduction in the EEG levy in autumn 2014 initially
lowered the electricity price in 2015, it subsequently rose again after that.
There are many forecasts and studies with different results on the price development of
electricity for the next 20 years. These can be traced back to different basic assumptions
related, on the one hand, to the expected development of the CO2 or the EEG levies, and on
the other hand, to the evolution of fossil fuel prices in the coming period. Moreover, other
studies take the effect of the growing electricity demand on the final electricity prices into
account.
To make assumptions about the electricity prices to be used in the TCO analysis, a survey
was made of the forecasts for the electricity price development that is available in the literature
whereby the assumptions of the reference forecast in [Schlesinger et al., 2014] were mainly
considered. Furthermore, the findings from a study provided by the German Energy Agency
GmbH, which discusses different scenarios for the transformation paths of the energy system
in Germany up to 2050 were also taken into account [Bründlinger et al., 2018].
In general, the studies indicate that electricity prices are expected to rise in the medium term
as a result of subsidizing green electricity in addition to the increase in fuel and CO2 taxes.
However, the question arises as to whether electricity prices will also continue to increase in
the long term or whether they could ultimately even fall as a result of the energy transition. The
estimates assume that electricity prices will fall when the nuclear phase-out is complete and
the electricity generation increases as a result of the expansion of renewable energies, which
means that most subsidies will end.
When it comes to electricity prices, however, a fundamental distinction must be made between
households and industry as industrial electricity prices are relevant for companies that
consume more than 100 MWh of electricity per year. Depending on the amount consumed,
commercial customers can receive lower tariffs due to numerous exceptions [BDEW, 2021]
[Schlesinger et al., 2014]. The electricity price development considered in this work for the
period up to 2040 is shown in Figure 5-3, which represents subjective assumptions based on
projections from the cited literature and further research.
For the catering companies that are the users of the catering lift trucks considered in this work,
there is the possibility of obtaining an industrial tariff as an electrical consumption of this
magnitude turns out to be realistic for a fleet of electric catering lift trucks. Based on the
78
simulation results, and as presented in Table 5-1, an average daily operating scenario requires
approximately 28.5 kWh of electrical energy, and hence using the truck on 358 days per year
results in about 10.2 MWh of energy consumption. According to the operator company, a fleet
of catering lift trucks comprises about 12 trucks which would require a total electricity
consumption of approximately 122.4 MWh per year. Therefore, for the purpose of the TCO
calculation, industrial electricity prices are taken into account.
Figure 5-3: Estimated development of electricity prices in Germany over time (Source: own
assumptions based on literature review)
5.3.4 Price Development of Battery
The economics of a battery electric truck, especially in the larger truck classes, mainly depends
on the cost of its battery and the further development of battery prices is therefore of central
importance for the profitability of these trucks. Looking at the prices on the international market,
it is noted that in recent years there has been a significant decline in battery prices. However,
future price developments are still subject to great uncertainty and numerous forecasts for
battery price development have been issued by various associations, research institutes, and
consulting firms. In some cases, although they deviate quite markedly from one another, the
common denominator of the forecasts is that they all assume that battery prices will continue
to fall in the near future.
In [Nykvist & Nilsson, 2015], a summary of future cost estimates is presented showing various
degrees of reductions in future prices of lithium-ion batteries for electric vehicles. In other
publications such as [Schmidt et al., 2017] and [Fluri, 2020], a larger number of battery
technologies for electric vehicles are analyzed and price developments are estimated based
on the market size. According to the latest forecasts by the research company BloombergNEF,
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Electricity price [€/kWh]
Industry price Household price
79
in the 2020 Battery Price Survey, average battery prices will be around 100 USD/kWh by 2023
and, from this point on, electric vehicles could be produced for the mass market at the same
price as vehicles with internal combustion engines [BloombergNEF, 2020].
However, the prices of lithium-ion batteries are dropping much faster than predicted in the
forecasts. On the one hand, this price drop is attributed to the increasing order size and
increasing electric vehicle sales and, on the other hand, to the introduction of new pack designs
and the increasing diversification in the chemicals used in battery manufacturing processes
[König et al., 2021] [Fries et al., 2017] [Goldie-Scot, 2019].
However, the development of lithium-ion batteries for heavy-duty vehicles has not yet made
great progress as heavy-duty vehicles require batteries of higher performance and longer
lifetimes than batteries for passenger cars. This is compounded by the small number of units
that the development costs have to be allocated to in this segment, which results in significantly
higher prices compared to batteries for passenger cars.
Therefore, the estimated battery prices in forecasts cannot directly be used for the application
under consideration. However, the general trend in battery price development is tracked to
create a conservative projection of the future cost development for the battery used in the
catering lift truck. The forecast shown in Figure 5-4 thus represents subjective assumptions
made on the basis of a comprehensive comparison of various cost forecasts from the literature,
whereby a decline of 50% by 2030, 60% by 2035, and 70% by 2040 relative to the current
value of the battery used in the catering lift truck is assumed.
Figure 5-4: Estimated development of battery prices in Germany over time (Source: own
assumptions based on literature review)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Relative Battery Price Developement
80
5.4 Cost Elements
In the following, the cost elements of the TCO analysis, which are illustrated in Figure 5-1, are
determined based on the previously defined operating scenarios for the catering lift trucks
under consideration whereby the procedure for deriving the cost elements is explained and the
underlying sources are named.
5.4.1 Vehicle Acquisition Costs
Fleet vehicles are usually leased or purchased, whereby leasing involves less effort to resell
and allows easier access to newer vehicles, making it particularly suitable for vehicles that
have a short service life. Vehicles that are to be used for a longer period are mainly purchased
and the purchase costs are therefore considered for the catering lift truck application. The
purchase cost plays a major role in the assessment of whether the use of an electric truck is
more efficient than a diesel truck since it accounts for the largest portion of the total costs. The
purchase costs for electric trucks are still very high and, in general, the expensive battery
makes the purchase price of electric trucks significantly higher than that of conventional trucks.
At present, the purchase costs of a battery powered heavy-duty truck are two to three times
that of an equivalent diesel-powered heavy-duty truck [Mareev, 20018] [Jöhrens et al., 2021].
The cost of purchasing a truck is made up of the basic costs for the vehicle body, the costs of
power train components and other costs depending on the drive technology, such as the
traction battery for electric trucks. In the case of a catering lift truck, the purchase cost is
composed of the costs of the basic truck including the power train, and the costs for additional
components making up the desired truck configuration, the most important of which is the
cooling in the container. Some ancillary service costs are incurred later such as the transport
cost to the user or approval for commercial road haulage.
According to the manufacturer and operator information, the conventional diesel catering lift
truck, with the characteristics listed in Table A-1 in Appendix A, was purchased in 2018 for
approximately €120,000 which corresponds to the price for a complete vehicle with a typical
configuration. In contrast, the cost of the newly developed electric catering lift truck, the
characteristics of which are also listed in Table A-1 in Appendix A, in the same configuration,
including the battery, amounted to €280,000 [Schmälzle, 2018]. These prices are considered
for the TCO analysis after being compounded to the year 2021, which is the reference time
point for the analysis. However, the truck delivery and registration costs are not taken into
account here as they did not fall within the project period.
According to the announcement by the German Federal Ministry for Digital and Transport
(Bundesministerium für Digitales und Verkehr BMDV), start-up financing was set to cover 80%
81
of the additional investment costs for a climate-friendly commercial vehicle compared to a
conventional diesel vehicle. In addition, the charging infrastructure required for operating
electric commercial vehicles is also funded to 80% of the eligible costs [BMDV, 2021].
Accordingly, the option of financing 80% of the additional costs of the electric catering lift truck
compared to the diesel truck will also be considered in the calculation.
5.4.2 Infrastructure Acquisition Costs
The switch to a new drive technology usually incurs additional costs for the infrastructure for
the energy supply of the respective vehicles. Therefore, in addition to the vehicle costs, the
costs for the acquisition of charging infrastructure for the electric catering lift truck are also
taken into account while there are no infrastructure costs for the diesel catering lift truck, as
there is always a diesel filling station available nearby. It is therefore assumed that the
infrastructure costs for the diesel filling station are already included in the fuel costs [Mareev,
2018].
The charging infrastructure costs are largely dependent on the specific conditions of the
application, such as the number of vehicles in the fleet, the operating profile, and the charging
concept. In addition to the purchase costs, there are other costs for the setup of the charging
station, including, for example, installation costs and costs of charging cables and assembly.
In addition, further costs for setting up a network connection may arise. These can also vary
greatly depending on the location and size of the electric vehicle fleet or the number of charging
stations [Hall & Lutsey, 2019].
Since there is no detailed information about the cost parameters of a charging station for the
electric catering truck, assumptions are made based on information from the operator. When
the electric prototype of the catering lift truck was purchased in 2018, a compatible charging
station of 80 kW was purchased for €25,000 [Schmälzle, 2018]. This price is considered for
the TCO analysis after being compounded to the year 2021 whereby it is assumed that the
setup costs are also included in the purchase price. The network connection costs are
irrelevant to the application while the option of funding with a financing rate of 80% for the
infrastructure is also considered. Since the service life of the charging stations usually exceeds
that of the vehicles, no replacement is considered in the analysis.
5.4.3 Fuel or Energy Costs
The fuel or energy costs are determined based on the consumption results of the simulation
model and the respective fuel or electricity prices whereby the cost development depends on
the operating scenarios defined in Chapter 5.2. First, the annual vehicle consumption is
82
calculated from the daily consumption given in Table 5-1 for the respective operating scenario.
As previously mentioned, the catering lift truck is expected to be operated every day, except
for just one week per year during which maintenance and repair work on the truck is carried
out. The consumption costs per year are thus calculated from the total annual consumption
and the corresponding predicted diesel or electricity price in the respective year. The assumed
diesel and electricity price forecasts presented in Chapters 5.3.2 and 5.3.3 are taken into
account here. Since the costs are incurred in different years, they are converted to their present
values using the interest rate. The current values are finally summed up for the years 2021 to
2040 to obtain the consumption costs over the entire service life and the costs are determined
separately for each of the operating scenarios considered.
5.4.4 Maintenance Costs
Electric vehicles have proven to be more advantageous than conventional vehicles in terms of
maintenance costs [Den Boer et al., 2013] [Mareev, 2018]. Since electric vehicles have a
simpler structure, the proportion of wearing parts they contain is greatly reduced compared to
that found in conventional vehicles. In addition, the wear and tear on brake systems are
reduced due to recuperation, thereby increasing the service life of the electric vehicle
components which are thus replaced less frequently than the parts in conventional vehicles.
In addition, some maintenance work that is required for vehicles with internal combustion
engines, such as oil changes and emissions tests, is no longer necessary with electric vehicles.
Therefore, determining the maintenance costs plays an important role in the TCO analysis. In
view of the early market stage and relatively small series production vehicles, there are no
comprehensive empirically proven findings regarding the maintenance of electric trucks.
Therefore, most of the values available in this regard are estimates. In some literature such as
the work of [Den Boer et al., 2013] and [Kleiner & Friedrich, 2017], it is estimated that the
maintenance costs for electric trucks are approximately one-third lower than for their internal
combustion engine counterparts. Consequently, this is considered in the context of the TCO
analysis in this work.
According to the operator, a fixed amount is paid per year for the maintenance and inspection
of a conventional catering lift truck, and every truck is checked once a year. As this is a special
vehicle, the inspection is carried out in two steps. In the first step, it is checked like a regular
truck according to technical regulations while in the second step, a more specific check is made
according to the special requirements of a catering lift truck. The entire maintenance and
inspection work takes one week and is carried out by a workshop at Frankfurt Airport. For the
electric truck, an overall reduction in maintenance costs of 33% compared to conventional
comparison vehicles is assumed. However, additional annual costs are assumed for the
83
maintenance of the charging infrastructure and, in this case, 2% of the acquisition costs of the
charging station are applied. The maintenance costs are considered to include all repair costs
to be incurred and are assumed to be constant in real terms over time.
5.4.5 Vehicle Tax Costs
In Germany, an annual motor vehicle tax is to be paid by the vehicle owner. This is determined
according to the Motor Vehicle Tax Act of the Federal Ministry of Justice (Bundesministerium
der Justiz BMJ) depending on the vehicle's gross weight and emission class [BMJ, 2020a].
Battery electric vehicles are tax-exempt for the first 10 years after registration and, after this,
the annual tax depends on the total weight of the vehicle. The annual tax for the catering lift
truck is calculated using the vehicle tax calculator of the BMF [BMF, 2017]. For a diesel truck
with a permissible total weight of 21 tons and emission class G1, the annual tax is €1,425
whereas, for an electric truck of the same weight, the annual tax is only €278. This applies
from the eleventh year of the service life of the electric truck.
5.4.6 Vehicle Insurance Costs
The insurance costs are only considered for the contribution to motor vehicle liability insurance
and thus optional comprehensive insurance is not taken into account for the calculation. Since
the insurance costs are generally independent of the drive train technology, they are set
equally for both conventional and electric trucks. The insurance costs for the catering lift truck
are calculated using the fleet premium calculator of an insurance broker portal [Georg Soller,
2021] whereby insurance premiums for trucks with an engine power of up to 149 kW are taken
into account. The insurance premiums are assumed to be constant in real terms over time for
the period of the TCO analysis.
5.4.7 Battery Replacement Costs
Assuming that lithium-ion batteries will remain dominant in the automotive sector in the future
[Thielmann et al., 2017], in the case of a battery replacement, it is assumed that a similar
battery will be purchased in the given year while taking the development of battery costs into
account. The service life of the battery used in the electric catering lift truck is defined according
to two different scenarios as already presented in Chapter 5.2. It is assumed that the battery
in scenario 1 does not have to be changed, while in scenario 2 it should be changed once in
the fifteenth year of the truck's service life, i.e. in 2035. The battery replacement costs are
determined based on the battery's current price and forecast assumptions of the battery price
development. The manufacturer indicates that the current costs for a LiFePO4 battery with a
84
total usable energy of 113 kWh amount to €110,000, which is to be understood as a pack price
that represents the end customer price. According to the assumed forecast in Chapter 5.3.4,
the price of the battery will tend to decrease by 60% of the current value in 2035 and the
estimated battery replacement cost is calculated accordingly, taking the interest rate into
account.
5.4.8 Residual Value
At the end of their service life, vehicles are typically either resold at residual value or scrapped.
The residual value of a vehicle depends on several factors, most notably the type of engine,
mileage, and how long the vehicle has been in use [Hacker et al., 2011]. Heavy-duty trucks
are often used for a long time, and thus the residual value at the end of their service life is
relatively low. Due to their early market stage, the residual value of electric heavy-duty trucks
in particular is subject to great uncertainty. Since no specific information is available on the
depreciation of the catering lift trucks, it is assumed that the trucks will no longer be usable at
the end of their long service life. Consequently, no potential vehicle residual value is included
in the TCO analysis.
In the case of the electric catering lift truck, however, the residual value of the batteries is
considered. It is assumed that the battery can be resold at its residual value at the end of the
service life of the truck, which is determined based on the remaining charging cycles and the
current price of the battery. In scenario 1, the battery still has more than half of its charging
cycles at the end of the truck service life whereas, in scenario 2, the replaced battery still has
more than two-thirds of its charging cycles. Taking into account the depreciation in value due
to the fact that the battery is no longer new, it is assumed that the battery still has a residual
value of half the price in scenario 1 and two-thirds of the price in scenario 2. The residual
battery value is calculated based on these assumptions taking into account the battery price
development in Chapter 5.3.4 and the interest rate. No residual value is taken into account for
the charging infrastructure in the TCO analysis.
5.5 Comparison of the TCO of Conventional and Electrified
Catering Lift Trucks
Table 5-2 summarizes the cost parameters of both catering lift trucks under consideration,
which have been calculated as explained in the Chapter 5.4. These are used to calculate and
compare the life cycle costs for the trucks for the period from 2021 until 2040 for the defined
operating scenarios in the Chapter 5.2. The calculation is carried out according to the Equation
(46) and taking into account the interest rate and the price development of the diesel, electricity
85
and battery presented in the Chapter 5.3. Potential cost differences between the two trucks
that arise on the basis of the TCO comparison are then discussed in order to understand when
a new technology can become cost-effective, while in the next section a more detailed
sensitivity analysis is presented comparing the influence of uncertainties in different TCO cost
parameters.
This TCO analysis attempts to account for all possible costs involved in using a catering truck,
gathered through inquiry to the truck manufacturer and truck operator and further research,
however additional costs could arise for certain individual applications of the truck.
It should also be noted that the costs considered here are based on current calculations,
however, all cost parameters are subject to change with time. Therefore, the analysis
represents the basis for an overall cost evaluation specific to the catering lift truck application
and reflects the current state of the truck electrification.
Table 5-2: Cost parameters of the TCO analysis
Parameter
Diesel Truck
Electric Truck
Source
Vehicle purchasing price
€124,483
€290,460
[Schmälzle, 2018]
Infrastructure purchasing price
–
€25,934
[Schmälzle, 2018]
Potential funding
–
€153,529
[BMDV, 2021]
Annual consumption: Scenario 1
3,030 L/a
10,203 kWh/a
Simulation results
Annual consumption: Scenario 2
4,450 L/a
14,820 kWh/a
Simulation results
Annual maintenance costs
€7,000/a
€5,209/a
(including
infrastructure)
LSG Sky Chefs,
[Den Boer et al., 2013],
[Kleiner & Friedrich, 2017]
and own assumptions
Annual vehicle tax costs
€1,425/a
€278/a (from
eleventh year)
[BMF, 2017]
Annual vehicle insurance costs
€1,121/a
€1,121/a
[Georg Soller, 2021]
Battery replacement costs
(scenario 2 in 2035)
–
€29,089
Truck manufacturer Terberg
and own assumptions
Residual value: Scenario 1
–
€9,410
Truck manufacturer Terberg
and own assumptions
Residual value: Scenario 2
–
€12,546
Truck manufacturer Terberg
and own assumptions
86
Figure 5-5 presents the results of the TCO analysis in which the vehicle total costs are broken
down into their components. It should be noted that all costs represent current values, which
means that inflation-adjusted or interest-adjusted values based on the reference year 2021 are
taken into account rather than nominal values.
Figure 5-5: Results of the TCO analysis for the defined operating scenarios
In general, the results of the TCO analysis for the catering lift truck in Figure 5-5 show high
additional acquisition costs, but on the other hand also show significant cost advantages for
the use of the battery-electric variants compared to the conventional diesel variants and a cost
difference in favor of the electric truck in the recovery.
Vehicle costs make up the largest proportion of the total costs. As an established technology,
the conventional diesel catering lift truck causes significantly lower vehicle costs, which
account for 34–37% of the total costs. In contrast, the vehicle costs of the electric truck are
more than double those of the diesel truck and they account for at least 60–67% of the total
costs. This is mainly due to the high costs of the large battery pack and, although the costs for
the charging infrastructure also represent an additional cost component for the electric truck,
these are low compared to the vehicle costs.
-€50,000
€50,000
€150,000
€250,000
€350,000
€450,000
€550,000
Diesel Truck Electric
Truck
Electric
Truck with
Funding
Diesel Truck Electric
Truck
Electric
Truck with
Funding
Scenario 1 Scenario 2
TCO [€]
Vehicle Infrastructure Fuel/Energy Maintenance
Vehicle Tax Vehicle Insurance Battery Replacement Risidual Value
87
The proportion of maintenance costs is also relatively high, and it makes up the second largest
share. Although the maintenance costs of the electric truck are lower than that of the diesel
truck, they still account for a large proportion of the total costs.
The fuel or energy costs account for the third-largest share of the total costs in both operating
scenarios whereas the vehicle tax and insurance costs only account for small proportions of
the total costs of both trucks. For the electric catering lift truck in particular, the tax costs have
very little impact on the overall costs which is due to the 10-year tax exemption for electric
vehicles and the very low taxation of electric vehicles in general, which makes a noticeable
difference compared to the tax costs for the diesel truck. In contrast, the insurance costs do
not cause any difference in the total costs as they are equal for both trucks.
In scenario 1, the battery has a long service life and therefore there are no additional costs for
changing the battery. This reduces the total costs in this scenario compared to scenario 2, in
which the costs for a battery replacement are added in the fifteenth year of the service life of
the electric truck.
The trucks have no residual value after the end of their service life because both scenarios are
based on the full useful life of a catering lift truck. For the electric vehicle, however, the battery
still has a residual value at the end of the truck's service life in both scenarios, which contributes
to reducing the difference in total cost between the two trucks.
In the following, the cost comparison is first made for the operation phase only to more clearly
illustrate the advantages of using the electric truck. After this, the acquisition and recovery
phases are also taken into account in the comparison, and, accordingly, a conclusion is drawn
concerning the range in which the acquisition costs of the electric truck should lie to be more
advantageous than conventional trucks.
Figure 5-6 shows the development of the payouts in the operation phase of both trucks for the
two different scenarios. These include energy costs, maintenance costs, vehicle taxes, vehicle
insurance costs, and battery replacement costs. Figure 5-7 portrays the development of the
present values of these payouts, which reflects the effect of the interest rate.
From the data presented, it is evident that the payout curves show slightly positive slopes due
to the increasing annual consumption costs of both diesel and electric trucks. This is due to
the expected increase in fuel and electricity prices until 2030. From the year 2030 onwards, it
is assumed that electricity prices will decrease while fuel prices continue increasing. The slight
increase in the costs of the electric truck from 2030 to 2040 is due to the tax costs after the
tenth year while the reason for the sudden increase in the operation costs of the electric truck
in 2035 is the cost of the replacement battery.
88
Figure 5-6: Development of the operation phase payouts over time
In contrast to the payout curves, the present value curves of both trucks show falling slopes
due to the effect of the interest rate whereby all payments are discounted to the reference time
for the purchase of the trucks. The further away from the reference time the payment is made,
the more it is discounted. The effect of interest rate is most clearly seen in the value of battery
replacement in the electric truck in scenario 2, as this represents the highest value over the
course of the operation phase.
The comparison shows that the electric catering lift truck is more advantageous than the diesel
truck in the operation phase under the examined conditions which means that these trucks can
already be operated economically today compared to the corresponding conventional trucks,
as there is a major advantage in terms of operating costs.
0
5,000
10,000
15,000
20,000
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
Costs [€]
Scenario 1
Diesel Truck Electric Truck
0
10,000
20,000
30,000
40,000
50,000
60,000
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
Costs [€]
Scenario 2
Diesel Truck Electric Truck
89
Figure 5-7: Development of the operation phase present values over time
However, despite these findings, the assessment of whether the transition to a battery-electric
catering lift truck is efficient greatly depends on the acquisition costs of the truck, since they
make up the largest proportion of the total costs. The acquisition costs of the electric catering
lift truck are currently too high and although the possibility of government funding or subsidies
for electric vehicles is often discussed, at the moment there are no concrete decisions in this
regard. To take the different options into account, the costs of the two trucks are compared for
various possible financing scenarios based on different assumptions regarding the acquisition
costs.
Basic TCO comparison without funding: The costs of the two trucks are initially compared
without taken any assumptions into account. The present values of the cost differences
between the two trucks are listed in Table 5-3 according to the phases of the TCO analysis.
0
5,000
10,000
15,000
20,000
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
Costs [€]
Scenario 1
Diesel Truck Electric Truck
0
10,000
20,000
30,000
40,000
50,000
60,000
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
Costs [€]
Scenario 2
Diesel Truck Electric Truck
90
Table 5-3: Basic comparison of the TCO of the two trucks
Scenario 1
Scenario 2
Acquisition Phase
€191,911
€191,911
Operation Phase
– €80,128
– €66,917
Recovery Phase
– €9,410 €
– €12,546
Total
€102,373
€112,448
The comparison shows that the acquisition costs of the electric truck are much higher than
those of the diesel truck resulting in a cost difference of €191,911 in the acquisition phase,
which means an increase of 154%. This leads to the fact that the acquisition of an electric
catering lift truck, including the charging infrastructure, is nearly 2.5 times more expensive than
a conventional diesel truck. On the other hand, based on the consumption scenarios and the
fuel and electricity prices assumed in the analysis, the electric truck achieves a cost advantage
in the operation phase of €80,128 or €66,917, depending on the operating scenario, over the
entire life cycle of the truck. This means that the operating costs of the electric truck are 29-
40% lower than those of the diesel truck. In scenario 2, there are additional costs for a
replacement battery for the electric truck, but these are more than compensated in the course
of use by significant savings in the operating costs, in particular the energy costs. The cost
difference in the recovery phase between the two trucks corresponds to the rest value of the
battery in the electric truck, which is calculated as indicated in Chapter 5.4.8. The batteries
used for the electric truck have a residual value of €9,410 scenario 1 or €12,546 scenario 2 at
the end of the truck's service life.
As a result, the incremental costs of the acquisition phase could not be offset by the cost
savings in the operation and recovery phases. The total additional costs of the electric truck
over the entire life cycle are €102,373 in scenario 1 and €112,448 in scenario 2. This makes
the TCO of the electric truck around 30% higher than that of the conventional diesel truck in
both scenarios. Given that the current prices for electric trucks are relatively high, the
electrification of the catering lift truck is currently not economically feasible without funding and
the purchase price would either have to drop sharply or be lowered through subsidies to give
the electric truck an advantage.
TCO comparison with funding: If the funding of 80% of the additional costs of the electric
catering lift truck and the corresponding charging infrastructure is calculated in the specific
case, this amounts to €153,529 which would have a very large impact on the total costs of the
life cycle. The present values of the cost differences, in this case, are listed in Table 5-4.
91
Table 5-4: Comparison of the TCO of the two trucks when government subsidies are
considered
Scenario 1
Scenario 2
Acquisition Phase
€38,382
€38,382
Operation Phase
– €80,128
– €66,917
Recovery Phase
– €9,410 €
– €12,546
Total
– €51,156
– €41,081
Under this condition, the additional acquisition costs of the electric truck, which are now only
€38,382, could be compensated by the cost savings in the operation phase. Moreover, the
electric truck would have an overall cost advantage of €51,156 in scenario 1 or €41,081 in
scenario 2 at the end of its service life. This makes the TCO of the electric truck 11–15% lower
than that of the conventional diesel truck and, in this case, the use of the electric truck would
be efficient for the end user.
TCO Comparison based on further assumptions regarding battery capacity and
charging station:
Since the catering lift truck is only used to deliver catering inside the airport and is not used for
long-distance transportation, it does not need a large amount of energy for daily operation, and
thus it does not expressly require a large battery. According to the operating scenarios defined
in Chapter 5.2 and based on the simulation results, the electric truck consumes a maximum of
41.4 kWh per day. Taking the decrease in capacity due to battery aging into account, 20% of
the total battery capacity is added to the calculated amounts of energy. This results in an
average required battery capacity of approximately 52 kWh which means that only half of the
battery currently used for the electric truck, which has a capacity of 113 kWh, is needed. This
saves half the price of the battery in the acquisition costs and also applies to the replacement
battery in scenario 2. At the same time, this improves the environmental balance of the electric
truck, as will be explained in the following chapter, and avoids increasing vehicle weight. In
addition, the public charging infrastructure can be used instead of a private charging station,
thereby saving on the purchase price. The present values of cost differences based on these
assumptions are listed in Table 5-5.
Under these assumptions, the difference in acquisition costs between the two trucks is reduced
to €108,922, making the acquisition of an electric catering lift truck about 1.9 times more
expensive than a conventional diesel truck. In addition, in scenario 2, the cost savings in the
92
operating phase increase by halving the size of the replacement battery, but on the other hand,
in both scenarios, the residual value of the battery in the recovery phase is also halved.
Table 5-5: Comparison of the TCO of the two trucks based on halving the battery capacity
and eliminating the charging station
Scenario 1
Scenario 2
Acquisition Phase
€108,922
€108,922
Operation Phase
– €80,128
– €81,462
Recovery Phase
– €4,705
– €6,273
Total
€24,089
€21,187
As a result, the total additional costs of the electric truck over the entire life cycle are reduced
to €24,089 in scenario 1 and €21,187 in scenario 2. This corresponds to a reduction of at least
76–81% of the total cost difference in the initial situation and makes the TCO of the electric
truck only around 6–7% higher than that of the conventional diesel truck.
However, it should be noted that the results of the TCO analysis only relate to the catering lift
truck models that are considered in this work and used under specified operating conditions
and the costs incurred by the various vehicle models can vary widely. The calculation of the
TCO only includes those costs that represent the most important cost components of a catering
lift truck, and the unimportant costs are neglected. The results of the analysis thus represent
the first indications for an economical approach of a predictive nature for the trucks examined
here.
5.6 Sensitivity Analysis
The TCO analysis is always fraught with uncertainties, as forecasts are made for the
parameters considered in the future. In order to map possible uncertainties, the influence of
parameters whose future development is subject to a high degree of uncertainty, and at the
same time are of high relevance for the TCO calculation, is examined in the form of sensitivity
analyses. For this purpose, a relative change of the respective cost parameter is assumed,
based on the initial situation. The cost parameters are varied independently of each other, i.e.,
while one parameter is being changed, the other parameters are kept constant. A combination
of the sensitivities is not taken into account here.
In the following, the future parameters of the fuel price, electricity price, and battery price are
considered particularly relevant for the economic comparison. In addition, however, the effect
of the variation in maintenance costs is examined within the framework of the sensitivity
93
analysis as they make up a large proportion of the total costs. A comprehensive sensitivity
analysis is then carried out to compare the impacts of all relevant cost parameters.
Diesel price variation
Since fuel costs make up a significant part of the total costs of the diesel truck, fuel prices are
expected to have a relevant influence on the results of the TCO analysis. The sensitivity
analysis shows how the increase and decrease in diesel prices by up to 50%, based on the
forecast for the diesel price development presented in Chapter 5.3.2, affects the results of the
TCO calculation. As Figure 5-8 shows, an increase in diesel prices leads to a significant
reduction in the TCO gap between the two trucks, whereby the more the diesel prices increase,
the more advantageous the electric truck becomes. This influence is greater in scenario 2
because the consumption is assumed to be higher.
Figure 5-8: Influence of diesel price variation on the TCO results
Electricity price variation
Analogous to the sensitivity analysis of the diesel price, the electricity prices are changed
relative to the initial situation, which is based on the forecast presented in Chapter
5.3.3, whereby it is investigated how the TCO present value of the electric vehicle changes
when the price of electricity is decreased or increased by up to 50%. As shown in Figure 5-9,
the TCO results also fluctuate with changes in the electricity price. Similar to the change in
diesel prices, the variation in electricity prices has a greater influence in scenario 2 due to the
higher consumption compared to scenario 1. However, the additional benefit that the electric
truck can achieve when decreasing the price of electricity is less than that observed when
increasing the price of diesel, given that consumption costs represent only a small portion of
the total costs over the service life of the electric truck.
250,000
300,000
350,000
400,000
450,000
500,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in diesel price
Scenario 1
Diesel Truck Electric Truck
250,000
300,000
350,000
400,000
450,000
500,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TOC [€]
Relative change in diesel price
Scenario 2
Diesel Truck Electric Truck
94
Figure 5-9: Influence of electricity price variation on the TCO results
Battery price variation
The comparison results in the previous section showed that the acquisition of the electric truck
is considerably more expensive compared to the diesel truck, whereby the bulk of the price
difference relates to the large battery pack. Therefore, assumptions in this regard have a major
impact on the total cost calculation.
In the context of the TCO analysis, when determining the acquisition costs, it was assumed
that the prices for batteries used in the electric catering lift truck have not changed in 2021
compared to 2018. This is based on the fact that the price development of batteries for heavy-
duty vehicles lags when compared to that of car batteries. However, advances in the
development of batteries for mobile applications can also result in greater economies of scale
in battery systems for heavy-duty vehicles in the coming years, and especially vehicles with
large batteries benefit from a favorable battery price trend. On the other hand, new
technologies in battery systems could also lead to higher costs in the heavy-duty vehicle
sector, and hence the uncertainty about the future development of battery prices for the electric
catering lift truck is examined in a sensitivity analysis. A decrease in the battery price of up to
70% as well as an increase of up to 30% is taken into account and the acquisition costs, battery
replacement costs, and residual values of the electric truck are subsequently adjusted
according to the variation in battery prices relative to the initial situation. As shown in Figure 5-
10, the variation in battery prices affects the TCO results more strongly than the previous
parameters, as a 70% reduction in battery prices would make the comparison results very
close to an overall cost parity for the electric variant with the reference diesel truck.
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in electricity price
Scenario 1
Diesel Truck Electric Truck
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in electricity price
Scenario 2
Diesel Truck Electric Truck
95
Figure 5-10: Influence of battery price variation on the TCO results
Maintenance costs variation
In contrast, the maintenance costs hardly change over time as do the prices for diesel,
electricity, and batteries. However, maintenance costs make up a large portion of the total truck
cost and thus the question arises as to what extent the high or low maintenance costs for the
catering lift truck play a role at all. Hence, maintenance costs represent an important focus in
this application and are therefore considered separately. As shown in Figures 5-11 and 5-12,
the relative variation in the maintenance costs of the respective trucks has a significant impact
on the TCO results whereby the TCO gap between the two trucks is reduced by increasing the
maintenance costs of the diesel truck or decreasing the maintenance costs of the electric
truck. However, in the case of the electric truck, the variation in maintenance costs of the
infrastructure is not considered here.
Figure 5-11: Influence of diesel truck maintenance costs variation on the TCO results
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
TCO [€]
Relative change in battery price
Scenario 1
Diesel Truck Electric Truck
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
TCO [€]
Relative change in battery price
Scenario 2
Diesel Truck Electric Truck
200,000
250,000
300,000
350,000
400,000
450,000
500,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
relative change in maintenance costs
of the diesel truck
Scenario 1
Diesel Truck Electric Truck
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in maitenance costs
of the diesel truck
Scenario 2
Diesel Truck Electric Truck
96
Figure 5-12: Influence of electric truck maintenance costs variation on the TCO results
Comparison of the impact of all cost parameters on the TCO results
A further analysis of all cost parameters involved in Figure 5-13 shows the relative change of
the respective parameter and the corresponding percentage change in the total cost difference
resulting from the TCO of the two trucks in relation to the initial situation. The results show that
the cost parameters battery price, diesel price, and vehicle maintenance have a large
influence, while all other cost parameters only have a small influence on the difference in TCO
of the two trucks.
Battery prices in particular have a decisive impact on the profitability of the electric truck, given
that battery costs make up a significant portion of the truck costs and thus a lower battery price
would both have a positive impact on the purchase price and replacement cost of the battery.
Therefore, the impact of a battery price change is greater in scenario 2, where the battery is
replaced once during the service life of the truck, than in scenario 1, where no battery change
is assumed. A 50% decrease in the battery price reduces the total cost difference between the
two trucks by 51% in scenario 1 and by 58% in scenario 2.
The maintenance costs of the diesel truck have the second largest impact on the cost
difference and hence with an increase of 50%, the total cost difference between the two trucks
decreases by 52% in scenario 1 and by 48% in scenario 2. The maintenance costs of the
diesel truck have a greater impact in scenario 1 since they make up a higher proportion of the
total costs compared to scenario 2. Although the effect of reducing the maintenance costs of
the electric truck is similar to the effect of increasing the maintenance costs of the diesel truck,
it is lower because the maintenance costs of the electric truck are lower than those of the diesel
truck.
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in maintenance costs
of the electic truck
Scenario 1
Diesel Truck Electric Truck
250,000
300,000
350,000
400,000
450,000
500,000
550,000
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
TCO [€]
Relative change in maintenance costs
of the electic truck
Scenario 2
Diesel Truck Electric Truck
97
Figure 5-13: Influence of parameter variations on the TCO results
The diesel price has the third largest impact on the total cost difference and a 50% increase in
the diesel prices leads to a decrease in the total cost difference by 33–43%. The higher the
consumption, the greater the impact of the diesel price. On the other hand, the optimistic
-60%
-40%
-20%
0%
20%
40%
60%
80%
-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%
Relative change in total cost difference
Relative change in cost parameters
Scenario 1
Interest rate Diesel price
Electricity price Battery price
Maintenance costs of diesel truck Maintenance costs of electric truck
Tax of diesel truck Tax of electric truck
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%
Relative change in TCO difference
Relative change in cost parameters
Scenario 2
Interest rate Diesel price
Electricity price Battery price
Maintenance costs of diesel truck Maintenance costs of electric truck
Tax of diesel truck Tax of electric truck
98
development in the electricity price did not lead to a significant economic advantage for the
electric truck as in this case, a reduction of 50% means a reduction in the total cost difference
of 17–22%. Accordingly, the electricity price only plays a minor role in terms of economic
balance in this application. However, it should be noted that the development of electricity and
fuel prices cannot be completely independent of an energy management perspective, and thus
an increase in diesel prices can correlate with an increase in electricity prices.
Since no significant changes in other cost parameters are expected, the battery price remains
the cost factor with the highest potential to influence the total cost difference. Therefore, the
development of battery prices in the coming years is an important key variable to achieve cost
parity. In addition, as discussed in the previous section, a decrease in the price of the battery
and thus the acquisition costs of the truck can also be achieved by choosing the appropriate
battery size for the application, which in turn leads to reducing the cost difference between the
two trucks.
However, it should be noted that all of the scenarios discussed in the sensitivity analyses do
not represent any inferences about the probability of the respective price or cost development,
but merely depict possible ranges and thus uncertainties.
99
6 Ecological Analysis and Environmental Impact
To compare the environmental impact of the catering lift trucks considered, an LCA is
performed, as explained in Chapter 1.2.2.3, for each of the respective trucks. However, the
assessment only takes into account aspects that make a difference between the two truck
technologies. In the manufacturing and end-of-life phases, it is assumed that the emissions
resulting from the manufacturing and disposal or recycling of the vehicles (excluding the
battery) are equal for both trucks, and therefore are not relevant for the comparison. According
to various studies, the manufacturing and disposal of vehicles only play a very subordinate role
in the overall balance [Amarakoon et al., 2013] [Helms et al., 2016] and therefore, only the
emissions associated with battery manufacturing and battery recycling are considered here as
the focus of the analysis lies on the emissions of the operation phase. An overall ecological
assessment is carried out here on the basis of the summation of the local and global emissions
resulting from the truck operation. Thus, the analysis does not represent a complete ecological
balance of the trucks but rather shows the differences between them in this regard.
The LCA is based on the determination of the GHG effect and PED, whereby the GHG
emissions are generally used as environmental indicators. The contribution of the substance
is given relative to the global warming potential (GWP) of CO2. Furthermore, the CO2
equivalent (CO2-eq) is usually used as a metric measure for various GHGs on the basis of their
global-warming potential, by converting amounts of other gases to the equivalent amount of
CO2 [Guinee, 2002]. The PED represents the total energy consumption including all losses
from the upstream chain. Due to the lack of sufficient data available in the literature, other
environmental impacts that can arise during the life cycle of a vehicle are not considered in the
assessment, although they are also important considerations for environmental pollution.
The LCA takes the environmental impacts associated with the fuel or electricity used in the
operation phase of the entire upstream chain (WTW) into account. This includes the production
and transmission of energy from the source to the vehicle (WTT), as well as the conversion of
energy into kinetic energy in the vehicle (TTW). The calculation of emissions associated with
the operation of vehicles is based on vehicle consumption as well as the CO2 emissions per
liter of fuel or per kWh of electricity whereas the primary energy consumption represents the
final energy consumption plus all losses from the upstream chain.
In the following, the parameters involved in the LCA are first discussed after which the climate
impact of the truck operation is determined, taking direct and indirect upstream chains into
account. For this purpose, the same diesel and electricity consumption values are used that
were already determined in the TCO analysis based on the operating scenarios defined in
Chapter 5.2. As a result, the LCA emission values are calculated and compared for the two
100
trucks. Finally, the influence of important factors, such as the diesel and electricity mix, is
shown in the sensitivity analysis.
6.1 Emission Parameters
In the following, the values considered for the provision and transport of diesel and electricity
needed to operate the trucks, as well as the manufacturing and recycling of the battery used
in the electric truck, are discussed.
6.1.1 Diesel Production
Diesel fuels have recently been developed with an increasing proportion of renewable
components, to improve the balance of GHGs in conventional vehicles. One option for
achieving this is to add biofuels or renewable fuels that come from non-fossil sources and the
proportion of biofuels in diesel consumed therefore plays an important role when determining
the GHG index.
The proportion of biofuels in Germany has remained almost constant at a maximum of 7% in
recent years [Müller-Lohse, 2021] [Mrani, 2021]. Based on the legal requirements from the
RED II renewable energy directive of the EC, at least 14% of the energy consumed in road
and rail transport has to come from renewable energy sources by 2030, which should lead to
a GHG saving of 65% compared to conventional fuels.
In this work, it is assumed that the proportion of biofuels in the diesel consumed by the
conventional catering lift truck is 7% until 2030 and 14% after 2030. The values considered for
the GHG emissions and the primary energy consumption associated with the provision of
diesel fuel are based on data from the guidelines of the BMDV [Schmied & Mottschall, 2014]
based on which the WTT emissions are 3.24 kg CO2-eq/L for conventional diesel fuel and 1.92
kg CO2-eq/L for biodiesel fuel. This includes the emissions that arise from the extraction of
crude oil and the raw materials used, the production of diesel fuel, and its distribution to the
filling stations.
The TTW emissions are those that result from the final utilization of the diesel fuel in the vehicle
when it is combusted in the engine. The complete combustion of one liter of diesel corresponds
to a production of 2.67 kg of CO2 while the combustion of biodiesel produces no emissions.
The estimated values for GHG emissions and primary energy consumption for the provision of
a liter of diesel fuel in the medium term are listed in Table 6-1.
101
Table 6-1: GHG and PED factors per liter of diesel (WTW)
Parameter
2021-2030
2031-2040
GHG [kg CO2-eq/L]
3.15
3.06
PED [MJ/L]
44.50
46.30
6.1.2 Electricity Production
Since the electrically powered vehicle does not cause any emissions when converting the
battery energy into kinetic drive energy, the emissions associated with its operation are only
due to the production of the energy that the vehicle consumes which depends heavily on the
power plant mix. Electricity generated with a high percentage of coal, for example, causes
much higher emissions than electricity largely generated from renewable sources. On the one
hand, there is a problem with determining the source of the consumed electricity, because the
electricity network is like a lake since several producers feed in electricity from one side and
electricity is drawn from the other side. As a result, it is not possible to identify the power plants
that produce electricity for the end consumer. On the other hand, it is difficult to determine the
losses in the electrical energy that is produced, especially for wind energy, for example, since
not everything that is produced is consumed. Therefore, when determining the emissions from
a power plant, the average annual emissions for the electricity mix are usually used. The
present value is often used for this purpose and remains constant for the future [Wietschel et
al., 2019b].
Compared to the EU, Germany currently has a relatively high CO2 electricity mix. However,
due to the increase in energy efficiency in Germany, GHG emissions from electricity generation
have decreased noticeably in recent years. According to numerous studies and forecasts, it is
also assumed that they will continue to decrease in the future. Such a reduction is essential to
realize the political scenarios in the context of the transition of the energy supply to renewable
energies to achieve the climate targets [Greiner & Hermann, 2016].
In this work, the average annual emissions for the electricity mix are used to calculate the GHG
emissions for the electricity consumed by the electric truck. The current values are based on
information from the Federal Environment Agency (Umweltbundesamt UBA), which publishes
an annual update on the development of the CO2 emission factor in the German electricity mix
[Icha et al., 2021]. Future values for emissions are based on a study presented by the
International Institute for Sustainability Analyses and Strategies (Internationales Institut für
Nachhaltigkeitsanalysen und-strategien IINAS), in which a projection for electricity generation
scenarios for 2030 to 2050 is calculated based on the target scenario of the German
102
government's NECP [Fritsche & Greß, 2020]. Here, it is assumed that GHG emissions from
electricity generation in Germany will fall linearly from the present value in 2021. The values
for the primary energy consumption are also based on [Fritsche & Greß, 2020].
Figure 6-1 shows the estimated development of the values for global emissions and primary
energy consumption from electricity generation in Germany for the period under review. These
values are assumed to represent emissions from the entire upstream chain, which include
emissions from the provision of primary energy sources and emissions from the power plants
themselves. In addition, losses for transmission and distribution lines in the power grid of 5%
as well as losses in the charging station of 10% are assumed [Doppelbauer, 2020] while the
operation of the electric truck is emission-free.
Figure 6-1: Development of GHG and PED factors per kWh electricity mix production
6.1.3 Battery Manufacturing and Recycling
In addition to the generation of energy needed to operate the vehicle, battery production can
have a significant impact on the environmental balance of electric vehicles. Therefore, in this
analysis, the effects of the manufacturing and recycling of the battery are also considered.
The production of lithium-ion battery packs for electric vehicles is one of the most controversial
aspects of global warming. In general, information in the literature on the CO2 emissions and
primary energy consumption associated with the manufacture of a kWh of lithium-ion battery
fluctuates greatly since most of the energy used in battery production is electricity. Therefore,
the environmental impact of battery manufacturing is highly dependent on the source of the
electricity mix as well as the demand for electricity. A meta-study by the Swedish environmental
research institute IVL presented in 2017, which is probably one of the best-known studies in
6.8
6.9
7.0
7.1
7.2
7.3
7.4
7.5
50
100
150
200
250
300
350
400
PED [MJ/kWh]
GHG [g CO2-eq/kWh]
GHG PED
103
this regard, estimated values for the emissions from the production of lithium-ion batteries in
the range of 150 to 200 kg CO2-eq/kWh [Romare & Dahllöf, 2017]. Two years later, in 2019,
an update of this study, with significantly lower results, was presented by the same institute,
indicating a range of 61 to 146 kg CO2-eq/kWh [Emilsson & Dahllöf, 2019]. One of the main
reasons for the lower results is the increase in the proportion of electricity from renewable
sources used in battery production to nearly 100%. However, according to the authors, this is
not the common scenario yet, although it likely will be in the future. In addition, larger
production plants and thus higher production efficiencies were also taken into account.
In a current meta-study in [Aichberger & Jungmeier, 2020] that analyzes information from
various literature sources on the life cycle assessment of lithium-ion batteries, an average
value of 120 kg CO2-eq/kWh is determined for the production of lithium-ion battery packs and
similar values can also be found in other studies such as [Peters et al., 2017]. This value is
therefore taken into account and is assumed to include the total emissions for battery
manufacturing, from the material production, through the manufacturing of the battery cells,
and up to the assembly of battery cells into packs. According to the same reference, a value
of 1,008 MJ/kWh is assumed for the primary energy consumption. On the other hand, there
are only very limited studies dealing with the end-of-life treatment of electric vehicle batteries.
Generally, these studies indicate that battery recycling can make a positive contribution to the
environmental balance. As the production of raw materials is responsible for almost half of the
GHG emissions from battery production, the substitution of raw materials with recycled
materials can reduce the environmental impact. In addition, the secondary use of vehicle
batteries, for example in stationary storage applications, could significantly improve the
balance of GHG emissions [Hall & Lutsey 2018].
Table 6-2 shows the estimated values considered for the manufacture and recycling of the
battery used for the electric catering lift truck.
Table 6-2: GHG and PED factors for battery manufacturing and battery recycling using the
current German electricity mix
Parameter
Battery
manufacturing
Battery
recycling
GHG [kg CO2-eq/kWh]
120
–20
PED [MJ/kWh]
1008
–
According to [Aichberger & Jungmeier, 2020], recycling can reduce battery life cycle emissions
by an average of approximately 20 kg CO2-eq/kWh. This value is considered in the calculations
in this work. Regarding the primary energy consumption, not enough information is available
in the literature and thus it is assumed that the energy saved is equal to the energy required in
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the recycling process. Therefore, there is no saving in the consumption of primary energy
through battery recycling.
6.1.4 Electricity Production from 100% Renewable Energy Sources
The share of renewable energies in electricity consumption is steadily growing and already
comprised 39.7% of total electricity generated in 2019, rose to 44% in 2022, and is expected
to increase further in the coming years [Destatis, 2023] [Rüger & Buchheim, 2021]. As a result,
the GHG emissions of today's electric vehicles will be lower than comparable vehicles with
internal combustion engines over the entire life cycle. Therefore, the scenario of electricity
production from 100% renewable sources is also discussed in this work. This scenario
assumes that the electricity consumed by the electric catering lift truck as well as the electricity
required for battery manufacture and recycling is completely derived from renewable sources.
Although such a scenario is not the norm today, it does appear feasible since the energy
transition away from fossil fuels to renewable sources is underway and is being promoted in
many areas. Consumers can choose whether their electricity tariff is based on the general
electricity mix or whether explicitly only green electricity from renewable sources is used.
The estimated values for GHG emissions and primary energy consumption considered in the
100% renewable electricity scenario are listed in Table 6-3.
Table 6-3: GHG and PED factors when using electricity 100% renewable energy
Parameter
Electricity
production
Battery
manufacturing
Battery
recycling
GHG [kg CO2-eq/kWh]
0.008
61
–10
PED [MJ/kWh]
3.67
505
–
The values for electricity production are based on the information from [Fritsche & Greß, 2020],
which provides data on cumulative energy consumption and associated GHG emissions for
Germany's electricity generation mix up to 2019 as well as projections up to 2050. The values
for electricity from onshore wind power plants are considered here, since wind power has the
largest share in renewable energy sources and are assumed to remain constant for the period
under review.
The values for battery manufacturing and recycling are based on [Emilsson & Dahllöf, 2019],
[Aichberger & Jungmeier, 2020], and further assumptions. The estimated values for the
production and recycling of lithium-ion batteries using the renewable electricity mix are taken
into account.
105
6.2 Results of the Life Cycle Analysis (LCA)
The ecological balance of both truck technologies considered is evaluated in terms of GHG
emissions and PED based on the approach discussed in the preceding sections and the results
are shown in Figures 6-2 and 6-3 respectively.
Figure 6-2: Comparison of the overall emission impact according to the defined operating
scenarios
Figure 6-3: Comparison of the overall PED according to the defined operating scenarios
-50
0
50
100
150
200
250
300
Diesel Truck Electric Truck
(electricity
mix)
Electric Truck
(renewable
electricity)
Diesel Truck Electric Truck
(electricity
mix)
Electric Truck
(renewable
electricity)
Scenario 1 Scenario 2
WTW GHG [t CO2-eq]
WTT TTW Battery manufacturing Battery recycling
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
Diesel Truck Electric Truck
(electricity
mix)
Electric Truck
(renewable
electricity)
Diesel Truck Electric Truck
(electricity
mix)
Electric Truck
(renewable
electricity)
Scenario 1 Scenario 2
WTW PED [GJ-eq]
WTT TTW Battery manufacturing Battery recycling
106
As shown in Figure 6-2, even when using today's German electricity mix, the electric truck has
a better ecological footprint in both operating scenarios compared to the diesel truck whereby
the advantage of the electric truck is further reinforced when electricity from exclusively
renewable energy sources is used. In the case of the electricity mix, the WTT emissions for
providing electricity are higher than the WTT emissions for providing diesel. However, on the
other hand, the electric truck does not cause any emissions when converting the electric
energy of the battery into kinetic drive energy, so its TTW emissions are zero while the
combustion of diesel fuel in the engine causes high emissions, so that its TTW emissions
represent the largest contribution to the overall balance of the diesel vehicle. Additional
emissions for the electric truck are caused by the battery although savings can be made by
recycling the battery.
Concerning the primary energy demand, it is evident from the results in Figure 6-3 that the
electric truck has a lower overall PED than the diesel truck. The TTW energy represents how
much final energy is used by the vehicle during operation whereas the WTT energy represents
how much energy is needed to produce that final energy. Thanks to the high efficiency of the
electric motor, the TTW energy of the electric truck is much lower than that of the diesel truck.
However, in the case of using an electricity mix, the electric truck has a higher WTT energy
than the diesel truck, which indicates that providing electricity requires more energy than
providing diesel. However, in the case of using renewable electricity, the required WTT energy
of the electric truck is significantly reduced, because the efficiency of renewable energy plants
is very high, namely close to 100%. The PED required for battery manufacturing accounts for
only a small proportion of the total energy requirement of the electric truck.
However, the environmental impacts of the trucks are highly relevant to the consumption and
the higher consumption in scenario 2 led to more advantages for the electric vehicle in the
operation phase compared to scenario 1. On the other hand, the additional emissions and
energy demand caused by the battery replacement in scenario 2 reduces the total advantage
of the electric vehicle compared to scenario 1 although the additional environmental impact
caused by the battery is offset by the savings in the operation phase in both scenarios.
The ecological advantage of the electric truck heavily depends on the various electricity plants.
As the results show, the option of using 100% renewable electricity helps to further improve
the environmental balance of the electric truck, and the assumed reduction in GHGs and the
PED are primarily due to the difference in electricity production. However, another difference
in the environmental considerations relates to battery manufacturing.
The overall ecological assessment shows that the electric catering lift truck is much more
climate-friendly than the diesel truck. With the current German electricity mix, it produces
around 62.9% less GHG emissions in scenario 1 and 61% less in scenario 2 than the diesel
107
truck. When using 100% renewable electricity, the savings in GHGs are much higher as the
electric truck would have 95.9% less GHG emissions in scenario 1 and 92.4% less in scenario
2. The emission reduction in the electricity used refers not only to the electricity used to operate
the truck, but also to the production of the battery, and in Scenario 2 this also includes the
replacement battery. These very large reductions, which amount to approximately two thirds
today, confirm that the use of electric catering lift trucks is an effective way to improve domestic
emissions at airports, with a possibility to further reduce emissions with the use of renewable
electricity.
The aggregated saving in PED over the entire truck service life is 41.7% in scenario 1 and
40.8% in scenario 2. By using 100% renewable electricity, the saving is increased to around
65.9% in scenario 1 and 65.5% in scenario 2.
6.3 Sensitivity Analysis
As the results in Chapter 6.2 have shown, it is primarily the electricity mix that determines to
what extent an electric truck is advantageous in terms of the CO2 balance compared to an
internal combustion engine. The results of the option of using 100% renewable electricity
showed a significant improvement in the ecological balance of the electric truck. However,
given the developments achieved in emissions from conventional internal combustion engine
drives, this advantage must be put into perspective and, therefore, the potential change in
diesel emissions should be considered, as this would also have an impact on the results. Also
in terms of battery manufacturing emissions, they represent a fair part of the total life cycle
emissions of the electric truck and their variation can thus play an important role. In addition,
consumption also plays an important role in demonstrating the benefit of the electric truck,
because the higher the consumption, the greater the savings that the electric truck will achieve
in terms of GHG emissions.
Therefore, in the following, the effect of the variation in emissions of electricity production,
diesel production, and battery manufacturing, as well as the variation in the consumption on
the results of LCA for GHG emissions will be examined in a sensitivity analysis. For this
purpose, the values of the respective parameters will be varied by up to 50% based on the
initial values that were considered in the calculation of the LCA. While one parameter is
changed, the others are held constant. Figure 6-4 shows the influence of the variation of the
respective parameters on the resulting percentage savings in GHG emissions. The results
show a very strong dependence on electricity production, while the other parameters have less
influence in both scenarios.
108
Figure 6-4: Influence of parameter variations on the LCA results
The total savings in GHG emissions can be increased to about 76–78% by reducing emissions
from the current electricity mix production by 50%. In contrast, the total saving in GHG
emissions drops to about 46–47% if a poorer electricity mix with 50% more emissions is used.
The variation effect is not significant when other parameters are concerned as the advantage
of the electric truck increases, but only slightly with decreased emissions from battery
manufacturing, or with increased emissions from diesel production. When the consumption of
the trucks increases, the advantage of the electric truck also increases.
40%
50%
60%
70%
80%
90%
-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%
Saving in GHG emissions
Relative change in emission parameters
Scenario 1
Electricity production Diesel production
Consumption Battery production
40%
50%
60%
70%
80%
90%
-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%
Saving in GHG emissions
Relative change in emission parameters
Scenario 2
Electricity production Diesel production
Consumption Battery production
109
7 Noise Emission Measurement
In addition to global GHG emissions, noise emissions are also of relevance for environmental
pollution and the reduction in noise emissions in the context of occupational safety and public
acceptance of flight operations is of increasing importance. In the speed range relevant for a
catering lift truck, up to 40km/h, the noise of the drive dominates over the rolling noise.
Therefore, a reduction in noise emissions is expected due to the relatively low noise emissions
of the electric drive compared to diesel engines. Moreover, electric motors are switched off
during standstill phases, which is especially useful because the truck often has to stop while
driving on the airport apron, for example, because it has to wait while an airplane is being
towed. This results in great potential for reducing noise pollution.
As part of this work, a concept for acoustic measurement was developed to enable an
assessment of the noise emissions of the catering lift trucks. For this purpose, acoustic
standards and measurement methods were initially discussed to derive a suitable concept for
the acoustic evaluation of the trucks under consideration. In addition, the measuring
environment and the measuring equipment utilized are presented, whereby the weather data
and conditions are also taken into account. Finally, the results of the measurement concept for
both trucks under consideration are presented to get an overview of which truck is acoustically
friendlier.
7.1 Measurement and Assessment Parameters
A sound signal measurement is based on changes in the air pressure caused by a sound
source. Therefore, in sound measurements, the sound pressure level is usually recorded and
described with a characteristic value as similar as possible to human perception whereby the
sensitivity of human hearing strongly depends on the frequency of the sound signal. The
human ear can generally perceive sound with a frequency range between 16 Hz and 20 kHz.
However, the sensitivity for low and high frequencies is lower than for medium frequencies. In
particular, the ear is most sensitive in the frequency range from 1 to 5 kHz [Henn et al., 2001].
In the evaluation of sound events, a frequency-dependent adjustment of sound pressure levels
is usually carried out in which the sound pressure level of the measured sound signal is
weighted using specific filters to be adapted to the human hearing perception. A, C, and Z
weighting filters are generally used here according to the IEC 61672-1 standard whereas the
B and D weighting filters are no longer relevant in recent applications. The A-weighting filter
approximates the frequency sensitivity of the human ear to normal ambient noise and hence it
is used in most practical applications. The signal is amplified in the range between 1 and 5
kHz, while in the low-frequency range, the signal effects are not included due to the strong
110
attenuation of sound pressure level values [Maute, 2006]. The much flatter C-weighting filter
is typical for measurements of high sound pressure levels and nowadays it is used in the sound
measurement of flight noises. The Z-weighting (zero) filter produces a flat frequency response
between 10 Hz and 20 kHz which basically has the same effect as if there would be no filter at
all, which means that there is almost no change in the original measured signal.
In addition to frequency weighting, time weighting is used to average fluctuating measured
sound pressure levels over time. Two different time weightings are described in the IEC 61672-
1 standard: fast time weighting (F) with an evaluation time of 125 ms, and slow time weighting
(S) with an evaluation time of 1 s. The F-weighting reacts more rapidly to a sudden change in
sound level and thus it is used in most situations when evaluating sound levels with rapid
successive fluctuations. The S-weighting on the other hand reacts slowly to level changes and
is thus more suitable for the evaluation of stationary, low-impulse sound events [Feldmann,
2018].
Depending on the purpose of the measurement, there are different options for specifying the
sound pressure level. The equivalent continuous sound pressure level is a widely used
parameter in measurements with sound levels that fluctuate over time. It represents the
average of the sound pressure levels within an observation period. In addition, the maximum
sound pressure level and the sound exposure level are also used whereby
denotes the highest sound pressure level that occurred during the measurement period. is
usually used to measure people's exposure to sound, where the total sound energy of a
measurement of any length is referenced to a 1 second period and specified as a level and
thus enables a direct comparison between sound measurements for different periods of time.
According to [Feldmann, 2018], it is used to assess individual sound events such as overflights
or trucks driving by.
For the evaluations in the context of this work, the A and Z frequency weighting filters as well
as F time weighting filter are relevant. The measurement parameters that are used for the
evaluation of the noise emissions of the catering lift trucks considered are shown in Table 7-1.
The Z-weighting is intentionally used in this work to represent sound level values over the
measurement period, because this corresponds approximately to the linearity and thus hardly
any deviations result, while A-weighted sound pressure level values are used to enable a direct
comparison between the two trucks. In addition, the sound pressure levels are represented
with the help of the third-octave spectrum which is an assessment based on frequency bands
in which the frequency range from 16 Hz and 20 kHz, which is audible to humans, is divided
into 32 third-octave bands that represent filters with relatively constant bandwidth.
111
Table 7-1: Parameters used for evaluating measured noise emissions
Parameter
Representation
The Z-weighted and F-weighted sound pressure level
The equivalent continuous (average) of the Z-weighted sound pressure
levels over the measurement period
The equivalent continuous (average) of the A-weighted sound pressure
levels over the measurement period
The maximum value of the A-weighted and F-weighted sound pressure
levels over the measurement period
The average of the A-weighted sound pressure levels related to 1
second
7.2 Measurement Equipment and Logging Data
The NTi Audio XL2 hand-held acoustic analyzer was used for all noise measurements in this
work. The analyzer has an omnidirectional free-field microphone of accuracy class 2, which is
particularly suitable for use in research and development purposes [NTi Audio, 2022]. The
microphone features a 1⁄4-inch condenser microphone capsule with permanent polarization
and the possible sound pressure level range is from 30 to 130 dB. Additional characteristics of
the microphone are shown in Appendix E.
According to the standard ISO 3746, windscreens for microphones must be used when
measuring outdoors [ISO, 2010b]. Therefore, an open-pored foam ball was placed on the
measurement microphone capsule, which reduces the interference from wind.
The measurement data obtained from the measurement device are represented in values of
the weighted sound pressure level with the frequency weightings A and Z and the time
weightings F. Values for the equivalent continuous, and the maximum and minimum sound
pressure levels are also provided, and the real-time information of the measurement is included
in the recorded measurement data. The signals were saved in 100 ms steps and the reading
and evaluation of the measurement data were subsequently carried out using MATLAB.
7.3 Measurement Concept
The measurement concept is divided into two different measurement setups, namely the
measurement setup for the truck drive-by and the measurement setup for the operation of the
lifting system. In the following, the concepts of the two measurement setups are first explained,
and then the results of both trucks are presented and compared separately for each
measurement setup.
112
7.3.1 Measurement Setup for the Drive-By
The concept of the drive-by measurement is based on the content of the standards ISO 362-1
and DIN 45642. The ISO 362-1 standard specifies a procedure for measuring the noise
emissions from road vehicles when accelerating under defined and repeatable conditions,
which allows different vehicles to be compared with one another. The sound pressure level is
measured exactly when accelerating to 50 km/h at full throttle [ISO, 2015]. The DIN 45642
standard defines procedures for determining the noise emissions from road traffic by
measuring individual event levels or the maximum drive-by levels. The noise emission is
described by specifying the equivalent continuous sound pressure level. Due to the movement
of the sound source, a larger safety distance is required for the drive-by measurement setup
[DIN, 2004].
Figure 7-1 shows the drive-by measurement setup. One microphone is used and is fixedly
positioned at a height of 1.2 m above the road surface and at a distance of 7.5 m from the
center of the acceleration path C-C. The reference axis of the microphone is aligned so that it
points perpendicular to the measuring surface. While the vehicle is approaching, i.e. up to line
A-A, which represents the beginning of the measuring section, the engine speed should be
75% of the driving speed of 50 km/h. After crossing line A-A, the vehicle must be accelerated
at full throttle until it has completely crossed line B-B. The accelerator pedal is then immediately
placed in the neutral position.
Figure 7-1: Concept of the drive-by noise measurement setup
According to the above standards, the interference by extraneous noises must be at least 10
dB lower than the maximum drive-by level. Alternatively, depending on the discrepancy
between the interfering sound and the sound event being measured, correction values are
specified to correct the results that are falsified by extraneous noise. In addition, the
113
measurement is only valid if the sound pressure level rises by at least 10 dB while driving by
and then drops again by 10 dB.
The measuring site is standardized in accordance with the standard ISO 10844, which
specifies requirements for test tracks used to measure noise emissions from road vehicles and
their tires. This states that acoustic measurements should be carried out at an ambient
temperature of 5 to 40 and preferably at wind speeds below 5 m/s but maximally below 10
m/s [ISO, 2014].
To obtain a sufficient range of data, three separate drive-by driving measurements were carried
out with the diesel and the electric trucks respectively and the measurements with the best
measurement data were used for the evaluation. The measuring point remained fixed in all
measurements. In accordance with the standards recommendations, the measurements were
carried out outdoors in an acoustic-free field and external noises as well as extreme ambient
conditions that have an unfavorable effect on the measurement, such as strong wind,
extremely high or low temperatures, or humidity, were avoided during the measurements.
7.3.2 Measurement Setup for the Lifting System
Since the lifting system is only operated when the truck is idling, the noise emissions of the
lifting system were measured using a measurement method for stationary noise sources based
on the standard ISO 3744 [ISO, 2010a]. However, according to this standard, the sound
pressure levels should be measured along a three-dimensional envelope that encloses the
sound source, which could not be implemented in this application and thus the procedure was
modified in relation to the work conditions.
As with the drive-by measurement setup, one measurement point with one microphone is used.
The microphone is likewise positioned 1.2 m above the road surface, with its reference axis
aligned perpendicular to the measuring surface. For stationary measurements, however, the
microphone can be placed significantly closer to the source of the sound, and it was therefore
placed at a distance of 1 m from the vehicle contour in accordance with the recommendations
of the ISO 3744 standard. The measuring point was chosen in such a way that the measuring
distances to both of the presumed sound sources, the truck drive and the hydraulic system,
were the same.
One measurement for lifting and one measurement for lowering was carried out with the diesel
truck and the electric truck respectively. The measurement duration for lifting includes raising
the container until the maximum height while the lowering measurement duration includes
lowering the container from the maximum lifting position back to its rest position. The
114
measurements were carried out at the same location and under the same measuring
conditions as the drive-by measurements.
7.4 Measurement Results
The measurements were carried out on October 26, 2017, on the site of LSG Sky Chefs at
Frankfurt Airport using the acoustic analyzer XL2, shown in Appendix E. The diesel truck was
first tested acoustically, both when driving by and when lifting/lowering the container. Then the
electric truck was tested, both when driving by and when lifting/lowering the container. The
results of the measurement campaigns are presented in the following sections. First, the
ambient noise measurement and the meteorological data collected during the measurements
are presented. After that, the results of both trucks obtained with the two measurement setups
are presented and compared.
7.4.1 Recording of Environmental Noise
Since the acoustic measurements are carried out outdoors, the ambient conditions at the time
of the measurements must be considered and thus the environment noise at the measurement
site was measured for 14 seconds. As shown in Figure 7-2, the acoustic values of the
environment are considered to be relatively high, with a maximum sound pressure level of 69.8
dB. As the measuring point was far away from the airport and there was neither aircraft noise
nor traffic noise, the wind is considered to be the main reason for these values. However, the
maximum sound pressure level was sufficiently below the measured sound events and
therefore no correction was needed for the measurement data.
Figure 7-2: Evaluation of the ambient noise
LAeq LAFmax LAE
63.30 69.80 74.60
0
10
20
30
40
50
60
70
80
Sound pressure level [dB (A)]
115
In addition, weather data were collected for the time of measurements to ensure that the
meteorological parameters are within the normative limits and that the acoustic measured
values are valid. For this purpose, the meteorological data were requested from the German
Weather Service, which has a measuring station at Frankfurt Airport. On the day when the
measurements were taken, the air temperature ranged between 6.4 °C and 16.7 °C and it was
a relatively sunny day with a rainfall of 0 L/mm2. However, for the wind speed, a maximum of
8.9 m/s was recorded. Although it is not below the preferable upper value, which is 5 m/s, it is
below the maximum acceptable value of 10m/s.
7.4.2 Noise Emission from Drive-By
Figure 7-3 shows the drive-by measurement results represented in the time course of the Z-
weighted and F-weighted sound pressure levels of both catering lift trucks. The comparison
shows a clear difference between the values of the electric truck and those of the diesel truck
where the acoustic event occurs, that is when the truck passes by the measurement point from
second 10 to second 18. Before this point, the values for the two trucks are similar and, at
some points, the sound pressure level of the electric truck is even higher than that of the diesel
truck. While this seems surprising at first, since low levels of noise emission are to be expected
with the electric motor over the entire measurement, the measurement data before second 10
represent the ambient noise. This is because the starting point of the truck is approximately 20
m away from the measurement microphone, and thus external sound sources can contribute
to the measurement.
Figure 7-3: Sound pressure level time course of the drive-by measurement
65
70
75
80
85
90
95
0 2 4 6 8 10 12 14 16 18
LZF
[dB (Z)]
Time [s]
Diesel truck Electric truck
116
Figure 7-4 provides a comparison of the A-weighted sound pressure level values of the two
trucks. As indicated, the values of the noise emission from the electric truck are less than that
from the diesel truck. The value, which represents an average value over the
measurement period, shows a difference in favor of the electric truck. Also, when looking at
the values of the maximum sound pressure level , there is a noticeable difference
between the maximum values recorded for the two trucks of 4.2 dB. What most clearly confirms
this is the value of , since, as already mentioned, it is used to compare measurements
regardless of their durations and it shows that people are more exposed to noise from diesel
trucks than from electric trucks. Hence, according to these results, the electric truck is
considered to have lower noise emissions compared to the diesel truck when driving.
Figure 7-4: Evaluation of the drive-by measurement
Furthermore, the measured drive-by noise is analyzed according to the frequency bands and
Figure 7-5 shows the third-octave spectrum of the sound pressure level (represented with )
for the drive-by measurement. As shown in the figure, the two trucks have relatively similar
spectrum courses since the sound pressure levels are high at low frequencies and decrease
with high frequencies. It is noticeable, however, that the sound pressure level of the electric
truck is generally lower than that of the diesel truck whereby the difference is particularly
evident in the frequency bands 160 to 315 Hz and 8,000 to 20,000 Hz. However, in the
frequency range below 20 Hz, the sound pressure level of the electric truck is a little higher
than that of the diesel truck, which is probably due to the contribution of the external ambient
sound sources before the drive-by event as explained previously.
LAeq LAFmax LAE
Diesel truck 72.70 83.80 85.40
Electric truck 69.50 79.60 82.10
0
10
20
30
40
50
60
70
80
90
Sound pressure level [dB (A)]
117
Figure 7-5: Third-octave spectrum of the drive-by measurement
7.4.3 Noise Emission from Lifting System
In this measurement setup, the noise emissions from the lifting system of both trucks were
measured while lifting and lowering the catering container, and Figures 7-6 and 7-7 show the
sound pressure level time course of the lifting and lowering measurements respectively. As is
evident from the two figures, the sound pressure level of the noise from the electric truck is
lower than that of the noise from the diesel truck in both measurements.
Figure 7-6: Sound pressure level time course of the lifting measurement
20
30
40
50
60
70
80
LZeq
[dB (Z)]
Frequency [Hz]
Diesel truck Electric truck
70
72
74
76
78
80
82
84
86
88
90
010 20 30 40 50
LZF
[dB (Z)]
Time [s]
Diesel truck Electric truck
118
Figure 7-7: Sound pressure level time course of the lowering measurement
However, since the time required to raise and lower the container depends on the hydraulic
conditions, the measurement duration cannot be specified, which led to differences in the
measurement durations between the two trucks, as can be seen in the two figures. The lifting
measurement took 52 seconds for the diesel truck and 50 seconds for the electric truck, while
the lowering measurement took 59 seconds for the diesel truck and 67 seconds for the electric
truck.
It is noted that at the end of the lifting measurement, when the container reaches its final
position on top, the sound pressure level of the diesel truck falls back to approximately the
level it started with at the beginning of the measurement whereas for the electric truck, the
measurement apparently ended before the container settled in its final position and therefore
the sound pressure level at the end of the measurement did not fall to the same level at the
beginning of the measurement. The opposite applies to the lowering measurement, in which
the measurement of the electric truck is continued until the container settles in its lower position
and the sound pressure level thus falls back to its initial level whereas, for the diesel truck, the
measurement seems to end a little earlier than that. This affects the calculation of the
comparative sound pressure level values shown in Figure 7-8.
However, as it turns out, the and values are inconsistent in the measurements, as in
the lifting measurement, these values are higher for the electric truck than for the diesel truck,
while for the lowering measurement the values are in favor of the electric truck, with a relatively
large difference to those of the diesel truck. This discrepancy can therefore be explained by
the different durations and endings of the measurements.
70
72
74
76
78
80
82
84
86
88
90
010 20 30 40 50 60
LZF
[dB (Z)]
Time [s]
Diesel truck Electric truck
119
Figure 7-8: Evaluation of the lifting system measurement
In comparison, for the diesel truck, a maximum sound pressure level of 87.2 dB was recorded
in the lifting measurement and 82.5 dB in the lowering measurement whereas, for the electric
truck, a maximum sound pressure level of 82.8 dB was recorded in the lifting measurement
and 79.8 dB in the lowering measurement. This results in a difference of 4.4 dB in the lifting
measurement and 2.7 dB in the lowering measurement, giving a clear indication that powering
the lifting system with an electric drive is more noise-friendly than with a diesel drive. This is
also confirmed by the clear difference in the course of sound pressure level over time of the
two trucks, shown Figures 7-6 and 7-7.
The lifting system generally shows higher levels than the drive-by measurements and there
are probably two reasons for this: firstly, because the measuring point is much closer to the
sound source, and secondly, in addition to the drive, the hydraulics also contribute to these
levels, which appears to be the dominant sound source. In addition, there is a safety feature
that beeps when the container is raised and lowered, which contributes to additional noise.
A comparison of the third-octave spectrum of both trucks is shown in Figures 7-9 and 7-10 for
the lifting and lowering measurements. As in the drive-by measurement, the sound pressure
levels in the measurements of the lifting system are higher with low frequencies and decrease
with high frequencies. The illustration shows hardly any differences in the lifting measurement,
as the sound pressure levels of the two trucks converge across the frequency spectrum and
the values for both trucks simply fluctuate up and down. However, a distinct difference between
the sound pressure levels of the two trucks can be seen in the lowering measurement and it is
clear that the values of the diesel truck are higher than those of the electric truck over most of
the frequency spectrum.
LAeq LAFmax LAE LAeq LAFmax LAE
Liftitng Lowering
Diesel truck 78.20 87.20 95.40 77.80 82.50 95.50
Electric truck 79.80 82.80 96.90 71.00 79.80 89.30
0
10
20
30
40
50
60
70
80
90
100
Sound pressure level [dB (A)]
120
Figure 7-9: Third-octave spectrum of the lifting measurement
Figure 7-10: Third-octave spectrum of the lowering measurement
20
30
40
50
60
70
80
90
LZeq
[dB (Z)]
Frequency [Hz]
Diesel truck Electric truck
20
30
40
50
60
70
80
90
LZeq
[dB (Z)]
Frequency [Hz]
Diesel truck Electric truck
121
8 Possible Improvements for the Lifting System
As the energy balance analysis in Chapter 4 showed, a lot of energy is lost in the hydraulic
circuit of the lifting system, which leads to a very low energy efficiency of the system. Therefore,
in this chapter, some improvement potentials in the lifting system and their impact on the
overall efficiency of the system are discussed.
8.1 Reduce Pressure Losses
In addition to energy losses due to component efficiencies, there are also pressure losses in
the hydraulic circuit, which can be minimized in order to increase the overall efficiency of the
lifting system. The most striking of these losses are the basic pressure losses due to the load
sensing and the pump relief functions, which cause a continuous drop in system pressure of
approximately 27 bar over the entire work cycle. This is shown in Figures 8-1 and 8-2 for both
trucks and is represented by the difference between the system pressure and the pressure
required for the function being operated. However, in the electric truck, this only applies to the
container and support legs functions as the platform and platform tongue functions are no
longer hydraulically driven. This pressure drop results in losses of approximately 30.4% in the
diesel truck and 25.6% in the electric truck of the total energy consumed for a complete lifting
cycle.
Therefore, reducing these pressure losses is the most appropriate measure to reduce energy
losses and thus increase the efficiency of the lifting system. This could be achieved for example
by selecting more appropriate components or even by optimizing setting of the relevant
components according to the hydraulic circuit design. Since the pump currently used is a fixed
displacement pump, these losses cannot be reduced to zero. However, by using a variable
displacement pump, for example, not only pressure losses could be avoided, but the flow rate
will be also adjusted to the pressure required by the consumer, avoiding too much oil being
delivered by the fixed displacement pump that is not used and has to be returned to the tank
again, which represents additional hydraulic losses in the system.
Using the simulation model, the effect of reducing pressure losses by up to 50% based on the
initial value on the efficiency of the lifting system is examined. The results are listed in Tables
8-1 and 8-2 for the two trucks. As the results show, the variation in the pressure losses has a
significant effect on the efficiency, as reducing the pressure losses by 50% increases the
efficiency of the lifting system by 17.88% for the diesel truck and 14.35% for the electric truck.
However, this is reflected in a relatively small increase in the overall vehicle efficiency, which
is almost the same for both trucks at about 0.6%, since, as explained in Chapter 4.3, the
122
consumption of the lifting system generally only represents a small proportion of the total
consumption of the truck compared to the consumption of the driving system.
Figure 8-1: Pressure losses in the lifting system of the diesel truck
Figure 8-2: Pressure losses in the lifting system of the electric truck
0
50
100
150
200
250
020 40 60 80 100 120 140 160 180 200 220
Pressure [bar]
Time [s]
System pressure Function pressure
0
50
100
150
200
250
020 40 60 80 100 120 140 160 180 200 220
Pressure [bar]
Time [s]
System pressure Function pressure
123
Table 8-1: Influence of variation in pressure losses on energy efficiency of the diesel truck
Pressure losses
[bar]
Lifting system
[%]
Lifting system
[%]
Entire truck
[%]
100%
11.08%
0.00%
0.00%
90%
11.42%
3.08%
0.12%
80%
11.79%
6.42%
0.24%
70%
12.19%
9.98%
0.37%
60%
12.61%
13.79%
0.49%
50%
13.06%
17.88%
0.61%
Table 8-2: Influence of variation in pressure losses on energy efficiency of the electric truck
Pressure losses
[bar]
Lifting system
[%]
Lifting system
[%]
Entire truck
[%]
100%
25.84%
0.00%
0.00%
90%
26.50%
2.50%
0.12%
80%
27.20%
5.22%
0.24%
70%
27.95%
8.10%
0.37%
60%
28.73%
11.13%
0.49%
50%
29.56%
14.35%
0.62%
8.2 Recovery of Potential Energy
The container lifting/lowering function provides a very good opportunity to recover potential
energy while lowering the container. As mentioned earlier, the container is lowered with the
help of its own weight only, however, the released energy in the process is currently being lost
and is therefore no longer available to the system. If this energy could be recovered, the energy
consumption would decrease, and the overall efficiency of the lifting system would increase
accordingly. Therefore, the potential of this possibility in the electric truck was investigated with
the help of the simulation model, and the results were evaluated and compared with those of
the current lifting system from the energy efficiency point of view. When lowering the container,
it is assumed that the potential energy of the container, resulting from its weight and the weight
of the payload, forces the hydraulic pump to rotate in the opposite direction, acting as a
hydraulic motor and converting the hydraulic energy into mechanical energy. This energy is
124
further converted into electrical energy by the electric drive, which in this case acts as a
generator, and fed to the battery via the AC/DC converter and stored there.
Figure 8-3 shows the energy distribution for the process of lifting/lowering the container with
potential energy recovery for different payloads. The positive segments in the diagram
represent the energy used for lifting the container. This includes the potential energy of the
load, which is the usable energy, and the energy lost in the system during the lifting process.
The negative segments represent the portion of the potential energy that can be recovered,
while the rest is lost again in the system during the lowering process due to the efficiency of
the components. It should be noted, however, that in the current lifting system, although no
supply pressure is required when lowering the container, the pump operates at the minimum
system pressure to keep the hydraulic circuit running, resulting in energy losses, which are
avoided in case of recovery.
Since the own weight of the container itself is large, lifting the container even when unloaded
consumes a significant amount of energy, while offering a significant potential energy that can
be recovered when lowering. The energy savings increase with increasing payload, because
the larger the lifted load, the more energy available for recovery when lowering, as well as the
higher the efficiency of the electric drive. Depending on the payload weight, 37.81–40.23% of
the available potential energy can be recovered, which corresponds to 11.18–14.14% of the
energy used for lifting the container.
Figure 8-3: Energy distribution of the lifting/lowering process
Table 8-3 shows the impact of potential energy recovery on increasing the energy efficiency
of the electric truck for different payloads. Considering a complete work cycle, by recovering
the potential energy when lowering the container, an increase in the energy efficiency of the
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000
Energy [kWh]
Payload [kg]
Potential energy Energy losses Recovered energy
125
lifting system of 21.48–22.53% can be achieved, which is reflected in an increase in the overall
vehicle energy efficiency of 0.83–0.92%.
However, higher potential energy recovery can be expected by increasing the efficiency of the
lifting system components. In addition, potential energy recovery can be combined with other
energy-saving measures to further increase the overall vehicle energy efficiency.
Table 8-3: Influence of potential energy recovery on energy efficiency of the electric truck
Payload [kg]
Lifting
system [%]
Lifting system
with recovery [%]
Lifting
system [%]
Entire truck
[%]
0
21.16%
25.70%
21.48%
0.83%
500
22.07%
26.84%
21.62%
0.85%
1,000
22.92%
27.91%
21.77%
0.86%
1,500
23.72%
28.92%
21.91%
0.87%
2,000
24.48%
29.87%
22.04%
0.88%
2,500
25.19%
30.77%
22.17%
0.89%
3,000
25.84%
31.62%
22.29%
0.90%
3,500
26.48%
32.42%
22.42%
0.91%
4,000
27.08%
33.18%
22.53%
0.92%
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9 Summary and Outlook
In the following, the results of this work are summarized. In addition, suggestions are made for
further research work to expand on some points that were not pursued further in this work,
either due to the focus on content or insufficient data that is currently available.
9.1 Summary
Battery electric trucks are considered to be one of the most promising zero-emission truck
technologies in heavy-duty truck applications due to their high efficiency and savings in energy
consumption compared to conventional trucks. However, the acquisition costs of battery
electric heavy-duty trucks are currently far higher than those of their conventional counterparts,
which represents a substantial barrier, particularly for users for whom the acquisition expenses
account for a sizable portion of the total cost of ownership. On the other hand, in addition to
the benefits of reducing emissions and noise pollution, electric trucks can achieve significant
savings in operating costs and maintenance costs. Since most of the researches dealing with
the evaluation of effectiveness of replacing traditional heavy-duty trucks with battery electric
trucks focuses on long-distance applications and some urban applications, there is a need to
research the use of battery electric trucks in local applications as well, especially those trucks
that have special-purpose ancillary systems.
In this thesis, an approach was presented to analyze and evaluate the competitiveness of a
prototype electric catering lift truck compared to a conventional diesel truck based on real
operational profile data of the catering lift trucks used at Frankfurt Airport. For this purpose,
driving cycles and operating data of the lifting system were recorded and used to determine
the realistic energy consumption of the trucks considered with the help of a simulation model.
Based on the simulation results, the energy efficiency and the energy losses of both trucks
were analyzed for the driving system and the lifting system individually as well as for the entire
vehicle.
The simulation results showed that the electric truck offers great advantages over the
conventional diesel truck in terms of increased efficiency and reduced consumption. Nearly
two-thirds of the energy consumed for driving and more than half of the energy consumed for
operating the lifting system can be saved with the electric truck. Considering a full work cycle,
including driving and operating the lifting system, the truck's overall efficiency has been
increased by about threefold through electrification.
A TCO analysis was performed to determine the economic potential and cost differences of
the two trucks over the entire service life of the truck. Since the assumed service life of the
catering lift truck is 20 years, the analysis was carried out from 2021, when the trucks and the
127
charging station for the electric truck are to be purchased, to the end of 2040. The analysis
was drawn up for two daily operating scenarios, while the possibility of replacing the battery of
the electric truck was also taken into account.
The results of the TCO analysis showed that although the electric truck has very high additional
acquisition costs, it has considerable advantages in operating costs and residual value
compared to the diesel truck. Without a subsidy, the incremental costs in the acquisition phase
cannot be offset by the cost savings in the operation and recovery phases, which makes
electrification of the catering lift truck economically unfeasible at present. However, if 80% of
the incremental costs of the electric truck and the associated charging infrastructure are
financed, its TCO will be lower than that of the diesel truck and in this case, using the electric
catering lift truck will be economically efficient for the operator.
The actual battery size required by the electric truck was also discussed based on the
simulation consumption results and the daily operating scenarios that are defined in this work.
Accordingly, if a scenario were considered in which only half the size of the battery currently
in use is used combined with forgoing the purchase of a charging station and using public
charging infrastructure instead, this would bring the TCO of the electric truck into a very close
range to that of the diesel truck.
The effect of variation in cost parameters on the total cost of ownership was shown in sensitivity
analyses in which the price of batteries, the price of diesel, and the costs of vehicle
maintenance have a significant impact, while other cost parameters only have a small impact.
Therefore, the expected decrease in the battery prices will certainly increase the economic
viability of the electric truck in the coming years.
A LCA was carried out to compare the environmental impact of the catering lift trucks studied
in this work in terms of the GHG emissions and the demand for primary energy. The analysis
considers aspects that make a difference to the environmental balance between the two truck
technologies at all phases of the truck's life cycle, including the manufacturing, operation, and
end-of-life phases. The possibility that the energy consumed by the electric truck and the
electricity required for the production and recycling of the battery come entirely from renewable
energy sources was also discussed.
The results of the LCA demonstrated that, the electric truck has a better environmental footprint
than the diesel truck even with today's German electricity mix. Although the battery causes
additional emissions, these can be offset by savings in operation emissions and emission
savings can also be achieved through battery recycling. The advantage of the electric truck is
enhanced when electricity exclusively from renewable energy sources is used, as this reduces
not only the GHGs and the PED associated with the production of electricity, but also those
associated with the manufacture of the battery. The sensitivity analysis of the emission
128
parameters showed that the advantage of the electric truck is heavily dependent on the
electricity mix. In addition, the higher the consumption, the larger the environmental benefits
of using the electric truck.
Furthermore, a concept was presented in this work that enables the measurement and
evaluation of the noise emissions of the considered catering lift trucks both when driving-by
and when operating the lifting system. The results of the noise measurements were presented
in sound pressure levels weighted with frequency weightings A and Z and time weighting F. In
addition, the third-octave spectrum of the sound pressure levels is also shown. In light of the
measurement results, it was concluded that the electric truck causes lower noise emissions
than the diesel truck when driving, as well as when operating the lifting system.
At the end of the work, the potential of some improvement measures for the lifting system and
their impact on the overall efficiency of the system was discussed. Minimizing basic pressure
losses and potential energy recovery when lowering the container in the electric truck were
considered, being the possibilities with the greatest potential for energy savings. The
respective measures were implemented in the simulation model and the results for increasing
energy efficiency were evaluated, which showed that these measures have a significant impact
on increasing the efficiency of the lifting system.
However, it should be noted that the results of this work are based on the defined operating
profile and work cycles that are presented here and, therefore, the values may vary for different
applications or different working conditions of the catering lift truck.
9.2 Outlook
The efficiency analysis of the catering lift truck presented in this work gives the possibility in
future developments to determine the potential for increasing the energy conversion efficiency
of the truck and thus reducing its consumption and associated environmental impacts.
Indeed, although the lifting system of the electric truck showed better efficiency than that of the
diesel truck, it is still very poor compared to the driving system, and therefore, it is necessary
to further investigate the possibility of increasing the efficiency of the lifting system. Even if the
lifting system only contributes a small proportion of the total consumption of the truck, given
the application as truck fleets that could be used at many airports around the world, any
improvement in efficiency can contribute to a significant reduction in energy consumption and
thus to an improvement in the environmental balance.
From a technical point of view, the application of the catering lift truck for local delivery is very
well suited to the use of electric trucks. However, the economic feasibility is the main challenge
due to the very high acquisition costs and low operating hours, and thus the savings in
129
operation costs are not enough to offset the initial cost difference. However, the acquisition
costs considered in the analysis in this work refer to the price of the prototype truck and not to
the price of a series truck, and no consideration is given to possible reductions in future truck
costs. Therefore, in future work, the effect of economies of scale on the purchase price of the
catering lift truck – and thus on the results of the TCO analysis – should be investigated.
At the same time, the high acquisition costs of the electric catering lift truck are mainly due to
the costs of the large battery, which may actually not be required with this capacity. Therefore,
given the very high cost of truck batteries at present, dimensioning the battery capacity
required in this application, by taking possible operating scenarios into account, is essential.
In addition, the selection of a suitable charging station, as well as the option of using public
charging stations for a fleet of electric trucks rather than a single truck, should also be
considered more extensively.
Finally, assumptions about some cost parameters such as maintenance costs have so far been
based on estimates, since empirical values for heavy electric trucks are still lacking in the early
stages of market availability. Since maintenance costs also make up a large proportion of the
TCO, there is a need for further research in this regard.
The approach developed in this thesis can also be applied to many other similar applications,
for example, trucks on construction sites like dump trucks or crane trucks, loaders for large
individual goods, garbage trucks and others, where the results of this work provide the basis
for a comparative view.
130
Appendix
A – Technical Specifications of the Considered Catering Lift Trucks
Table A-1 shows the technical data of the catering lift trucks considered in this work based on
the available truck datasheets and product brochures. The truck model is based on the truck
chassis of the Terberg YT182 for the diesel truck and the Terberg YT202-EV for the electric
truck.
Table A-1: Technical specifications of the catering lift truck X-Cat M from DOLL (Source:
DOLL Fahrzeugbau GmbH)
Vehicle Data
Diesel Truck
Electric Truck
Weight
Vehicle gross weight
21,000 kg
21,000 kg
Vehicle curb weight
16,174 kg
17,234 kg
Payload
4,500 kg
3,766 kg
Drive Motor
Type
Cummins QSB6.7-190
(Euro IV)
Siemens ELFA Drive
Power
142 kW
138 kW (188 PS)
at 2000-2500 1/min
Torque
809 Nm
780 Nm
at 0-1800 1/min
Max. rotation speed
2000 1/m
2500 1/min
Min. rotation speed
800 1/m
-
Battery System
Type
-
LFMP
(Lithium-Ion
Magnesium
Phosphate)
Capacity
-
113 kWh (184 Ah)
Nominal voltage
-
614 V
Charging capacity
-
40-80 kW
Charging port
-
400 V DC
Charging time
-
Ca. 2.5 h
Charge cycles
-
2800 at 100% DoD
4800 at 80% DoD
6700 at 70% DoD
10000 at 50% DoD
131
Tires
Wheel size
11R22.5 (6 wheels)
Axle ratio
11.98:1
Transmission
Type
Allison 3000 Automatic Manual
gearbox
Gear ratio
Ratio
3.487
1.864
1.409
1.00
0.75
5.027
Speed
8
19
25
35
47
backward
132
B – Data Processing of the Driving Cycles
This chapter describes the procedure used for processing the recorded measurement driving
data, which is derived on the basis of the procedure presented in [Duran & Earleywine, 2012]
and in proportion to the nature of the recorded data. The data processing is carried in three
steps as follows:
1) Removing of zero-velocity drifts:
Zero-speed drifts occur when the GPS data logger is turned on while the vehicle is stopped,
as the GPS data logger records very small speed values due to the frequent interruptions and
reconnections between the GPS signal receiver and the available satellites [Duran &
Earleywine, 2012] [Gräbener, 2017]. This leads to falsifications in the distance calculations
based on the integration of the speed data and associated time data, as well as in the energy
consumption calculations, particularly for the electric truck, since it consumes no energy while
stationary. Therefore, the fluctuations in speed data recorded during these periods are
mitigated by low speed removal, whereby all speed values below a specified lower limit are set
to zero. Examining the measurement data revealed that the speed values recorded when
stationary are often less than 0.75 km/h and therefore this is considered the lower speed limit.
Additionally, any speed point that falls between zero points is also set to zero. An example of
the effect of this filtering step is shown in the Figure B-1.
Figure B-1: Filtering zero-speed drifts
2) Modifying of Signal Gaps
Measurement gaps that occur when there are temporarily not enough GPS satellites available,
such as when driving through a tunnel, have been corrected. First, the GPS signal data is
checked for the intervals where there is no satellite signal, and then values for the
corresponding speed points are interpolated based on the previous and next signal points.
-2
0
2
4
6
8
10
12
0 5 10 15 20 25 30 35 40 45 50 55 60
Speed [km/h]
Time [s]
Unfiltered data Filtered data
133
This also removes the abrupt zero speed values caused by temporary GPS signal interruptions
where the measured speed drops to zero for only a few milliseconds and then returns again to
the actual value. Figure B-2 shows an example of a corrected speed data gap.
Figure B-2: Filtering signal gaps
3) Denoising and Smoothing of the Signal
For denoising and smoothing the measured data, a moving-average filter is used. The effect
of this filter is shown in Figure B-3.
Figure B-3: Signal smoothing
Finally, the measurement data is further processed so that all data points recorded in one
second are represented by the mean value. This has two effects, first, reducing the intensity
of the measurement data and thus reducing the computational effort in the simulation model,
and second, removing the remaining fluctuations from previous filtering steps. Figure B-4
shows how data processing removes most of the noise and errors from the measured data
while preserving the profile of the original signal.
-5
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
Speed [km/h]
Time [s]
Unfiltered data Filtered data
22
24
26
28
30
32
34
010 20 30 40 50 60 70 80 90 100
Speed [km/h]
Time [s]
Unfiltered data Filtered data
134
Figure B-4: Signal processing of the driving cycle 1
0
5
10
15
20
25
30
35
40
45
0 100 200 300 400 500 600
Speed [km/h]
Time [s]
Original signal
0
5
10
15
20
25
30
35
40
45
0 100 200 300 400 500 600
Speed [km/h]
Time [s]
Final processed signal
135
C – Driving Cycles Recorded with the Diesel Truck
Figure C-1 shows the velocity profiles of the driving cycles recorded for the diesel truck
according to the driving routes defined in Chapter 2.3.1.1, the characteristics of which are
presented in Table C-1.
Figure C-1: Velocity profiles of the driving cycles of the diesel catering lift truck
0
10
20
30
40
50
60
70
80
90
100
0 100 200 300 400 500 600
Velocity [km/h]
Time [s]
Driving cycle 1
0
20
40
60
80
100
120
0 200 400 600 800 1000 1200
Velocity [km/h]
Time [s]
Driving cycle 2
0
10
20
30
40
50
60
0 300 600 900 1200 1500 1800
Velocity [km/h]
Time [s]
Driving cycle 3
136
Table C-1: Characteristics of the driving cycles of the diesel catering lift truck
Route
Distance
[km]
Duration
[s]
Max. speed
[km/h]
Average
speed
[km/h]
Stop time
[s]
Driving cycle 1
5.06
665
90.74
28.91
35
Driving cycle 2
9.11
1,344
109.71
24.72
18
Driving cycle 3
14.56
1,849
48.84
28.78
29
As can be seen in Figure C-1, high speeds of up to 110 km/h were measured several times,
which unreasonably exceed the maximum speed of 30 km/h on the airport apron. This is
especially noticeable in the first and second cycles. The measured values at these measuring
points are therefore considered to be measurement errors. Nevertheless, the average speed
remains relatively low, owing to the frequent stops in the speed profile.
137
D – Impact of Payload Variation on Energy Efficiency Analysis
Tables D-1 and D-2 indicate the impact of variation in payload weight on the efficiency of the
catering lift trucks under consideration.
Table D-1: Efficiency analysis of the diesel truck according to payload weight
Payload [kg]
Driving system
[%]
Lifting system
[%]
Entire truck
[%]
0
32.76%
8.84%
31.85%
500
32.92%
9.27%
32.02%
1,000
33.08%
9.68%
32.17%
1,500
33.22%
10.06%
32.32%
2,000
33.36%
10.42%
32.46%
2,500
33.49%
10.76%
32.60%
3,000
33.62%
11.08%
32.73%
3,500
33.74%
11.39%
32.85%
4,000
33.85%
11.68%
32.97%
Table D-2: Efficiency analysis of the electric truck according to payload weight
Payload [kg]
Driving system
[%]
Lifting system
[%]
Entire truck
[%]
0
103.07%
21.16%
99.24%
500
103.23%
22.07%
99.40%
1,000
103.39%
22.92%
99.56%
1,500
103.54%
23.72%
99.71%
2,000
103.68%
24.48%
99.85%
2,500
103.82%
25.19%
99.99%
3,000
103.96%
25.84%
100.13%
3,500
104.09%
26.48%
100.26%
4,500
104.21%
27.08%
100.38%
138
E – Acoustic Analyzer Used for the Noise Emission Measurements
Specifications
Frequency range
5 to 20 kHz
Sound pressure level range (SPL)
30 to 130 dB
Sensitivity
26.4 mV/Pa
Typical self-noise
21 dB
Temperature range
0 to +40
Temperature coefficient
< ±0.02 dB/
Influence of air pressure
-0.04 dB/kPa
Influence of air humidity (non-
condensing)
< ±0.4 dB
Figure E-1: XL2 acoustic analyzer [NTi Audio]
139
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List of Figures
Figure 1-1: Approach for evaluating the competitiveness of the electrified prototype truck versus
conventional catering lift trucks ............................................................................................................. 20
Figure 2-1: Reference diesel catering lift truck (Source: DOLL Fahrzeugbau GmbH).......................... 25
Figure 2-2: Electrified catering lift truck (Source: DOLL Fahrzeugbau GmbH) ..................................... 26
Figure 2-3: Typical work cycle of a catering lift truck ............................................................................ 28
Figure 2-4: Google Earth mapping of the measurement driving cycles ................................................ 30
Figure 2-5: Velocity profiles of the driving cycles of the electric catering lift truck ................................ 32
Figure 2-6: Time distribution of the driving cycles according to the driving modes ............................... 34
Figure 2-7: Hydraulic measurements of the lifting system (Source: DOLL Fahrzeugbau GmbH) ........ 36
Figure 2-8: Time distribution according to the lifting functions .............................................................. 37
Figure 3-1: Schematic structure of the simulation model of the diesel truck driving system ................ 39
Figure 3-2: Schematic structure of the simulation model of the electric truck driving system .............. 39
Figure 3-3: Simulation results of the power demand of the driving model ............................................ 45
Figure 3-4: Simulation results of the consumption rate of the driving system ....................................... 47
Figure 3-5: Simulation results of energy consumption of the driving system according to the driving
modes .................................................................................................................................................... 48
Figure 3-6: Simulation results of the battery SOC of the driving system of the electric truck ............... 49
Figure 3-7: Simplified hydraulic circuit of the catering lift truck lifting system (Adapted from the original
hydraulic circuit diagram provided by DOLL Fahrzeugbau GmbH) ...................................................... 51
Figure 3-8: Schematic structure of the simulation model of the diesel truck lifting system ................... 52
Figure 3-9: Schematic structure of the simulation model of the electric truck lifting system ................. 52
Figure 3-10: Schematic representation of the combination of the 3-port/4-way directional control valve
and double-acting cylinder actuator with variable definitions (for functions of the support legs, platform
and platform tongue) ............................................................................................................................. 53
Figure 3-11: Schematic representation of the combination of the 2-port/1-way directional control valve
and single-acting telescopic cylinder actuator (for the function of lifting/lowering the container) ......... 56
153
Figure 3-12: Simulation results of the power demand of the lifting model ............................................ 58
Figure 3-13: Simulation results of the battery SOC of the lifting system of the electric truck ............... 60
Figure 4-1: Simulation results of the energy distribution of the driving system ..................................... 62
Figure 4-2: Efficiency analysis of the driving system of the diesel truck ............................................... 63
Figure 4-3: Efficiency analysis of the driving system of the electric truck ............................................. 63
Figure 4-4: Simulation results of the energy distribution of the lifting system ....................................... 64
Figure 4-5: Efficiency analysis of the lifting system of the diesel truck ................................................. 65
Figure 4-6: Efficiency analysis of the lifting system of the electric truck ............................................... 66
Figure 4-7: Vehicle efficiency analysis of the diesel truck ..................................................................... 67
Figure 4-8: Vehicle efficiency analysis of the electric truck ................................................................... 67
Figure 5-1: Composition of the TCO of the catering lift trucks .............................................................. 70
Figure 5-2: Estimated development of diesel prices in Germany over time (Source: own assumptions
based on literature review) .................................................................................................................... 76
Figure 5-3: Estimated development of electricity prices in Germany over time (Source: own assumptions
based on literature review) .................................................................................................................... 78
Figure 5-4: Estimated development of battery prices in Germany over time (Source: own assumptions
based on literature review) .................................................................................................................... 79
Figure 5-5: Results of the TCO analysis for the defined operating scenarios ....................................... 86
Figure 5-6: Development of the operation phase payouts over time .................................................... 88
Figure 5-7: Development of the operation phase present values over time ......................................... 89
Figure 5-8: Influence of diesel price variation on the TCO results ........................................................ 93
Figure 5-9: Influence of electricity price variation on the TCO results................................................... 94
Figure 5-10: Influence of battery price variation on the TCO results..................................................... 95
Figure 5-11: Influence of diesel truck maintenance costs variation on the TCO results ....................... 95
Figure 5-12: Influence of electric truck maintenance costs variation on the TCO results ..................... 96
Figure 5-13: Influence of parameter variations on the TCO results ...................................................... 97
154
Figure 6-1: Development of GHG and PED factors per kWh electricity mix production ..................... 102
Figure 6-2: Comparison of the overall emission impact according to the defined operating scenarios
............................................................................................................................................................. 105
Figure 6-3: Comparison of the overall PED according to the defined operating scenarios ................ 105
Figure 6-4: Influence of parameter variations on the LCA results ....................................................... 108
Figure 7-1: Concept of the drive-by noise measurement setup .......................................................... 112
Figure 7-2: Evaluation of the ambient noise ........................................................................................ 114
Figure 7-3: Sound pressure level time course of the drive-by measurement ..................................... 115
Figure 7-4: Evaluation of the drive-by measurement .......................................................................... 116
Figure 7-5: Third-octave spectrum of the drive-by measurement ....................................................... 117
Figure 7-6: Sound pressure level time course of the lifting measurement .......................................... 117
Figure 7-7: Sound pressure level time course of the lowering measurement ..................................... 118
Figure 7-8: Evaluation of the lifting system measurement .................................................................. 119
Figure 7-9: Third-octave spectrum of the lifting measurement ............................................................ 120
Figure 7-10: Third-octave spectrum of the lowering measurement ..................................................... 120
Figure 8-1: Pressure losses in the lifting system of the diesel truck ................................................... 122
Figure 8-2: Pressure losses in the lifting system of the electric truck ................................................. 122
Figure 8-3: Energy distribution of the lifting/lowering process ............................................................. 124
Figure B-1: Filtering zero-speed drifts ................................................................................................. 132
Figure B-2: Filtering signal gaps .......................................................................................................... 133
Figure B-3: Signal smoothing .............................................................................................................. 133
Figure B-4: Signal processing of the driving cycle 1 ........................................................................... 134
Figure C-1: Velocity profiles of the driving cycles of the diesel catering lift truck ................................ 135
Figure E-1: XL2 acoustic analyzer [NTi Audio] .................................................................................... 138
155
List of Tables
Table 2-1: Characteristics of the driving cycles of the electric catering lift truck ................................... 33
Table 2-2: Definition of the driving modes ............................................................................................. 33
Table 2-3: Characteristics of the defined work cycle of the lifting system based on measurement data
............................................................................................................................................................... 36
Table 3-1: Key parameters of the driving simulation model .................................................................. 44
Table 3-2: Simulation results of the driving model ................................................................................ 46
Table 3-3: Energy saving by recuperation in the electric truck ............................................................. 50
Table 3-4: Simulation results of the lifting model ................................................................................... 59
Table 4-1: Comparison of the overall energy efficiency of the trucks for the three work cycles ........... 68
Table 5-1: Key data of the defined daily operating scenarios of the catering lift trucks examined ....... 73
Table 5-2: Cost parameters of the TCO analysis .................................................................................. 85
Table 5-3: Basic comparison of the TCO of the two trucks ................................................................... 90
Table 5-4: Comparison of the TCO of the two trucks when government subsidies are considered ..... 91
Table 5-5: Comparison of the TCO of the two trucks based on halving the battery capacity and
eliminating the charging station ............................................................................................................. 92
Table 6-1: GHG and PED factors per liter of diesel (WTW) ................................................................ 101
Table 6-2: GHG and PED factors for battery manufacturing and battery recycling using the current
German electricity mix ......................................................................................................................... 103
Table 6-3: GHG and PED factors when using 100% renewable energy............................................. 104
Table 7-1: Parameters used for evaluating measured noise emissions ............................................. 111
Table 8-1: Influence of variation in pressure losses on energy efficiency of the diesel truck ............. 123
Table 8-2: Influence of variation in pressure losses on energy efficiency of the electric truck ........... 123
Table 8-3: Influence of potential energy recovery on energy efficiency of the electric truck .............. 125
Table A-1: Technical specifications of the catering lift truck X-Cat M from DOLL (Source: DOLL
Fahrzeugbau GmbH) ........................................................................................................................... 130
156
Table C-1: Characteristics of the driving cycles of the diesel catering lift truck .................................. 136
Table D-1: Efficiency analysis of the diesel truck according to payload weight .................................. 137
Table D-2: Efficiency analysis of the electric truck according to payload weight ................................ 137
157
Own Publications
Partial results of this dissertation have already been published in some various publications
have emerged from this work, which are listed below:
Wdaah, L.; Müller, S., (2016). “Efficiency Analysis of an Electrification Concept for a Catering
Truck”, 2016 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC
Asia-Pacific), Busan, South Korea, pp. 837-842.
Wdaah, L.; Müller, S., (2017). “Catering Truck of the Future — Efficiency Increase by full
Electrification”, 2017 2nd IEEE International Conference on Intelligent Transportation
Engineering (ICITE), Singapore, pp. 328-334.
Wdaah, L.; Müller, S., (2024). “Recovery of Potential Energy in the Lifting System of a Battery
Electric Catering Lift Truck”, submitted to: International Journal of Powertrains.
Wdaah, L.; Müller, S., (2018). “eLift-Cateringhubwagen der Zukunft”, Endbericht der TU
Berlin, Berlin.
158
Supervised Student Work
The following student theses were written as part of this work and supervised by me in the
context of my work as an assistant researcher in the Department of Automotive Engineering
at the Technical University of Berlin. Some of the topics are partial aspects of this dissertation
and have therefore been included but not referred to as references.
Bachelor theses and student research projects:
• Ghazal Safarini, Mohamed (2017): Potenziale zur Minderung des Energieverbrauchs
eines Catering-Fahrzeugs
• Hussain, Suleman (2018): Entwicklung und Durchführung eines Messkonzepts zur
Ermittlung von Lärmemission eines Catering-Fahrzeugs
• Nguyen, Phuong Anh (2015): Lebenszyklusanalyse als Entscheidungshilfe bei
Elektrifizierung eines Catering-Hubwagens
• Polat, Kemal (2018): Alternative Antriebskonzepte für ein Catering Hubfahrzeug -
Wirtschaftlichkeit und Leistungsvergleich
• Stulberg, Alex (2018): Potential zur Energierückgewinnung in einem elektrifizierten
Catering Hubwagen.
Master theses:
• Al-Sewari, Mohammed (2014): Design and Analysis of Linear Electrical Drives for a
Lift-Vehicle
• Bogdan, Niko (2016): Potential zur Elektrifizierung eines Sondernutzfahrzeuges
• Fang, Zhenjie (2106): Entwicklung eines Energierückgewinnungssystems für einen
Catering-Hubwagen
• Li, Wanjun (2106): Entwicklung und Anwendung einer Methodik zur Effizienzbewertung
eines Hubfahrzeugs
