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USE OF ELECTRIC TRAILERS IN HEAVY DUTY TRANSPORT LOGISTICS

Lappeenranta–Lahti University of Technology LUT

Master’s thesis

Master’s programme in Electric Transportation Systems

2025

Dinuka Praveen Weerasinghe

Examiners: Professor Lassi Aarniovuori

Professor Pia Lindh

ABSTRACT

Lappeenranta–Lahti University of Technology LUT

LUT School of Engineering Sciences

Electrical Engineering

Dinuka Praveen Weerasinghe

Use of Electric Trailers in Heavy Duty Transport Logistics

Master’s thesis

2025

77 pages, 20 figures, 13 tables

Examiner(s): Professor Lassi Aarniovuori and Professor Pia Lindh

Keywords: E-trailers, Heavy-duty transportation, High-capacity truck, Fuel efficiency.

This thesis investigates the potential of electric axle-assisted trailers (E-trailers) to reduce

energy consumption and CO₂ emissions in Finnish long-haul freight operations. The study

focuses on a 1,444 km round-trip route between Kuusamo and Kotka, comparing the

performance of a conventional diesel-powered Scania R540 6×4 truck with E-trailer-

supported variants (battery capacities ranging from 20–50 kWh) and a fully battery-electric

Scania 6×4 truck (BEV) equipped with a 728 kWh energy storage system.

The analysis employs secondary data sourced from validated engineering literature and

transport simulation models. Key performance indicators include fuel consumption (L/100

km), electric energy consumption (kWh/km), and lifecycle CO₂ emissions. Results show that

E-trailer configurations achieve up to 8.9% fuel savings and proportional CO₂ reductions

compared to the diesel baseline. While the BEV eliminates tailpipe emissions entirely, it

requires significantly higher energy input (2.99 kWh/km under a 76-tonne payload)

compared to lighter-duty BEVs (1.78 kWh/km under 40-tonnes), limiting its range and

operational viability in current infrastructure contexts.

These results emphasize the viability of electrified trailers as transitional solutions for

decarbonizing long-haul freight in the Nordic region, particularly where infrastructure for

full electric trucks remains under development. The study underscores the importance of

using aggregated simulation data for evidence-based comparisons of advanced powertrain

systems.

ACKNOWLEDGEMENTS

First, I would like to express my profound appreciation to my thesis examiners, Associate

Professor Lassi Aarniovuori and Associate Professor Pia Lindh, whose expert guidance,

constructive feedback, and unwavering support have profoundly influenced the quality of

my thesis.

I am also immensely thankful to my professors and mentors at LUT University, whose

dedication and encouragement have continuously fostered my growth in innovation,

critical thinking, and academic development.

I am sincerely appreciative of my family and friends, whose unwavering support and belief

in me provided the strength I needed during the most challenging phases of this journey.

Finally, I wish to thank everyone who contributed, directly or indirectly, to help me

achieve this significant achievement.

SYMBOLS AND ABBREVIATIONS

Roman characters

Q heat generation per cell W

Rinternal internal resistance in cell Ω

T temperature ºC, K

U terminal voltage V

I current A

m mass kg,t

Fc fuel consumption per 100km L/100km

P power W

Hu fuel specific calorific valvue MJ/kg

Pregen regenerative braking power W

EOCV open circuit voltage V

D total distance travelled km

EFdiesel diesel combustion emission factor

Greek characters

ηe Efficiency of engine

ρdiesel Density of diesel fuel kg/L

Abbreviations

BEV Battery Electric Vehicle

EM Electric Machine

ICE Internal Combustion Engine

SOC State of Charge

NMC Nickel Manganese Cobalt

GCW Gross Combination Weight

HDV Heavy Duty Vehicle

HCV High-Capacity Vehicle

GTW Gross Train Weight

GHG Green House Gas

BET Battery Electric Trucks

EU European Union

BESS Battery Energy Storage System

TCO Total Cost of Ownership

BMS Battery Management System

MCL Motor Control Logic

PDC Predictive Drivetrain Control

PMSP Permanent Magnet Synchronous Motor

GCL Gear Control Logic


 
Table of contents 
 
Abstract 
Acknowledgements 
Symbols and abbreviations 
 
Introduction .................................................................................................................. 12 
1 Background of Heavy-Duty Transportation (HDT) ............................................ 12 
2 Role of Heavy-Duty Transportation on Environment ......................................... 15 
3 Green Logistics and Energy Concepts ................................................................. 19 
3.1 Green Logistics Concept .................................................................................. 19 
3.2 Net Zero Emission Concept ............................................................................. 20 
4 Electrification in Heavy-Duty Transportation ..................................................... 22 
Electrified Trailer Systems and Energy Storage Technologies .................................... 25 
1 Trailer types, Sizes and Weight Regulations ....................................................... 25 
2 Trailer Axle Configurations and Integration of E-Trailers .................................. 28 
3 Electric Trailers Electric Trailer Concepts and Industrial developments ............ 32 
4 Battery Systems and Energy Storage for Electric Trailers .................................. 34 
5 Market Analysis ................................................................................................... 38 
5.1 Patent study ...................................................................................................... 46 
Comparative  Analysis  of  Fuel  Consumption  and  CO₂  Emissions  in  Nordic  Freight 
Operations from Kuusamo to Keltakallio ............................................................................ 51 
1 Vehicle Configuration and Technical Specifications .......................................... 51 
1.1 E – Axle Configurations .................................................................................. 52 
2 Vehicle Performance Analysis ............................................................................. 53 
3 Battery Electric Truck .......................................................................................... 56 
3.1 Commercial BEV References .......................................................................... 57 
4 Overview of Transport Route and Logistics ........................................................ 58 
5 Fuel consumption for a Diesel truck with E-axle ................................................ 59 
6 CO2 Emission for a Diesel truck with E-axle ...................................................... 61 
7 Battery Electric Truck without E-axle ................................................................. 62 


 
7.1 Scenario 1 – 40t GCW, Energy Consumption: 1.78 kWh/km ......................... 64 
7.2 Scenario 2 – 40t GCW, Energy Consumption: 1.78 kWh/km ......................... 65 
7.3 Scenario 3 – 76t GCW, Energy Consumption: 2.99 kWh/km ......................... 65 
7.4 Scenario 4 – 64t GCW, Energy Consumption: 1.611 kWh/km ....................... 67 
7.5 Scenario 5 – 64t GCW, Energy Consumption: 1.611 kWh/km ....................... 68 
7.6 Scenario 6 – 42t GCW, Energy Consumption: 1.32 kWh/km ......................... 69 
7.7 Scenario 7 – 42t GCW, Energy Consumption: 1.32 kWh/km ......................... 70 
Discussion..................................................................................................................... 72 
Conclusion .................................................................................................................... 74 
References ............................................................................................................................ 75 
 
   

10 
 
Figures 
 
Figure 1. Authorized Combinations in Finland (Larsson, L., & Pettersson, E., 2022) ....... 14 
Figure 2. Average Vehicle Emissions Rates (Kusoglu, 2024) ............................................ 15 
Figure 3. Mileage and Fuel Consumption for Heavy Duty Transports (Kusoglu, 2024) .... 16 
Figure 4. CO2 Emission per kilometer in HDV (McKinnon et al.,2015) ............................ 18 
Figure 5. Fuel & Energy Consumption Diesel and battery powered tractor trailer (Basma, 
Saboori and Rodríguez, 2021) ............................................................................................. 23 
Figure 6. Trailer Configurations and steerable axles (Larson & Pettersson, 2022) ............. 26 
Figure 7. Axles Configurations of Heavy-Duty Vehicles (Volvo Group, 2023) ................. 29 
Figure 8:Trailer Dynamics E-Axle and Chasi (Trailer Dynamics, 2024) ............................ 39 
Figure 9. ZF E-Trailer (ZF, 2024) ....................................................................................... 40 
Figure 10. Range Energy E-Trailer Electrical Design (Range Energy,2023) ..................... 42 
Figure 11. Randon semi-trailer with e-Sys auxiliary electric system (Randon Companies, 
2024) .................................................................................................................................... 42 
Figure 12. Design overview of VAK’s E-trailer system (VAK, 2024) ............................... 43 
Figure 13. Einride’s intelligent electric semi-trailer platform (Einride, 2024) .................... 44 
Figure 14. Scania’s solar-assisted trailer body for hybrid applications (Scania, 2023) ....... 45 
Figure 15. Randon’s solar-integrated trailer with rooftop photovoltaic panels (Randon, 
2022) .................................................................................................................................... 46 
Figure 16. Fuel Consumption for GTW 76t (Rahkola, 2025) ............................................. 54 
Figure 17. CO2 Emission for Loaded 76t HDV (Rahkola, 2025) ....................................... 55 
Figure 18. Kuusamo to Kotka Route with Most available (higher than 300kW) Charging 
Stations ................................................................................................................................. 58 
Figure 19. Fuel Consumption with and without E-Axle on Kuusamo-Kotka route ............ 60 
Figure 20. CO2 Emission on Kuusamo-Kotka route with and without E-Axle .................. 62 
 
 
 
 
 

11 
 
Tables 
 
Table 1. Diesel and Battery electric Truck Maintenance Cost (Basma, Saboori and 
Rodríguez, 2021) .................................................................................................................. 24 
Table 2. Trailer combinations permitted in Finland ............................................................ 27 
Table 3. Maximum Weight on an Axle Configuration ( Tieliikennelaki, 122 §, liite 6.6.) . 32 
Table 4. Li-ion Batteries used in HDV (Sekar, 2021) ......................................................... 36 
Table 5. Range Energy E-Trailer Specifications ................................................................. 41 
Table 6. Scenario 1: 40t GCW with 3 stops ........................................................................ 64 
Table 7. Scenario 2: 40t GCW with 2 stops ........................................................................ 65 
Table 8. Scenario 3: 76t GCW with 3 stops ........................................................................ 66 
Table 9. Scenario 4: 64t GCW with 3 stops ........................................................................ 67 
Table 10. Scenario 5: 64t GCW with 2 stops ...................................................................... 68 
Table 11. Scenario 6: 42t GCW with 2 stops ...................................................................... 69 
Table 12. Scenario 7: 42t GCW with 1 Stops ...................................................................... 70 
Table 13. Summary of All BEV truck scenarios ................................................................. 71 
 
 
  

1 Introduction

The urgent need to decarbonize the transport sector has placed growing pressure on freight

logistics to adopt cleaner, more energy-efficient technologies. Among the most critical

segments is heavy-duty transportation (HDT), which, despite representing a relatively small

portion of total vehicle fleets, accounts for a disproportionately high share of greenhouse gas

emissions.

This thesis explores the role of electric axle-assisted trailers (E-trailers) as a transitional

solution in reducing fuel consumption and CO₂ emissions in Nordic long-haul freight

operations. With advancements in electrification technologies and evolving regulatory

landscapes across Europe, now is a pivotal time to investigate practical, scalable alternatives

to traditional diesel-based freight. The research focuses on comparative performance

analysis between conventional diesel trucks, E-trailer-supported hybrids, and full battery-

electric vehicles (BEVs) operating along a typical Finnish transport corridor.

1.1 Background of Heavy-Duty Transportation (HDT)

This section outlines the technical definitions, classification, and regulatory framework of

heavy-duty transportation (HDT), with a specific focus on its role in Finland’s logistics and

economic landscape.

Heavy-duty transportation (HDT) is integral to global logistics, encompassing the movement

of goods across industrial, agricultural, energy, and construction sectors. Vehicles in this

category either surpass a total length of 25.25 meters or exceed a gross vehicle weight of 64

tonnes. such as semi-trucks and tractor-trailers, are typically classified under Class 7 and 8

in the U.S., with a Gross Combination Weight (GCW) exceeding 16,000 kg, and under

categories N2 and N3 in the EU (Regulation No 2018/858), with payload capacities over 7.5

tons. (European Parliament and Council, 2018)

The road transport sector in Finland is highly developed and plays a crucial role in the

nation's infrastructure, economy, and daily life. It supports a wide range of businesses,

industries, and individual needs. Finland boasts an extensive and well-maintained road

network spanning approximately 450,000 kilometers.

The transport industry comprises a diverse range of operators from small independent

owner-operators to large international corporations. Trucks dominate this sector, accounting

for around 90% of all goods transported. Road transport is the most practical, efficient, and

widely used method of moving goods across the country, especially given Finland’s

geographically dispersed industries and settlements (Logistiikan Maailma, 2023). Due to the

consistently high demand for transport services, the sector continues to evolve and expand.

It not only drives economic activity but also significantly contributes to employment and

revenue generation within Finland.

The Finnish market for truck manufacturing, logistics, and equipment production is highly

competitive, with several key players driving the sector. One of the leading logistics

companies is Posti Group, which offers a comprehensive range of delivery services. It

handles a significant portion of parcel deliveries across Finland, utilizing various types of

vehicle fleets to meet diverse logistical needs.

In Finland, trailer units equipped with electric drivelines are regulated identically to

conventional trailer configurations. As per the 2019 legislative directive, the permissible

maximum vehicle combination length is defined as 34.5 meters across most of the national

road network, excluding specific exceptions pertaining to urban zones, bridge structures, and

infrastructure with restricted load-bearing capacity. Since 2013, statutory limits of 76-tonnes

gross vehicle weight and 4.4 meters overall height have been enforced.

Finland currently authorizes eleven heavy duty transport (HDT) configurations, as

delineated in Figure 1 (Larsson, L., & Pettersson, E., 2022).

Figure 1. Authorized Combinations in Finland (Larsson, L., & Pettersson, E., 2022)

In the truck manufacturing segment, the primary competitors operating in Finland are Volvo,

Scania, and Mercedes-Benz. These manufacturers supply a broad selection of heavy-duty

vehicles tailored for different transportation requirements.

According to statistics, a total of 3,444 new trucks were registered in Finland in 2024

(Helsinki Times, 2025).

1.2 Role of Heavy-Duty Transportation on Environment

This section discusses the environmental impact of heavy-duty transport (HDT), focusing

on emissions, energy consumption, noise, vibrations, and congestion. The goal is to highlight

the disproportionate environmental burden caused by HDT relative to its share of the overall

vehicle fleet.

Despite comprising a small fraction of the total vehicle fleet, HDTs contribute

disproportionately to transportation-related emissions. For instance, in the European Union,

HDVs are responsible for over 25% of greenhouse gas emissions from road transport and

more than 6% of total EU GHG emissions (Kusoglu, 2024). As shown in Figure 2, the

average emissions from HDVs in U.S. significantly exceed those from passenger vehicles.

Figure 2. Average Vehicle Emissions Rates (Kusoglu, 2024)

Logistics operations generate a wide range of externalities, including air pollution,

greenhouse gas emissions, noise, and vibration. Considering growing concerns about climate

change, recognized as one of the most critical environmental challenges, understanding the

environmental impact of logistics has become increasingly important.

According to McKinnon et al. (2015) explain, emissions from road transport are closely tied

to the type of fuel used. Diesel, the most widely used fuel in logistics today, produces

harmful pollutants such as carbon monoxide, nitrogen oxides, and hydrocarbons due to

incomplete combustion in engines. Similarly, it points out that these emissions can

contribute to pollution on local, regional, and global scales. Additionally, fine particulate

matter from vehicle emissions poses health risks, particularly respiratory issues, highlighting

the environmental and public health impacts of road transport. (McKinnon et al, 2015).

Diesel engines in HDTs exhibit thermal efficiencies between 35% and 42%, constrained by

thermodynamic limits and mechanical losses. Fuel consumption typically ranges from 30 to

50 liters per 100 km, translating to energy inputs of approximately 1,200–2,000 MJ per 100

km, considering the lower heating value (LHV) of diesel (~42.5 MJ/kg) and diesel density

(~0.832 kg/L). Figure 3 presents the historical development of fuel consumption and mileage

for both light-duty and heavy-duty vehicles from 1950 to 2020.

Figure 3. Mileage and Fuel Consumption for Heavy Duty Transports

(Kusoglu, 2024)

The figure shows that while fuel economy in light-duty vehicles has improved significantly

due to emissions regulations and engine optimization, HDVs have seen comparatively

modest gains. HDT fuel consumption has remained high due to increased freight demand,

rising vehicle weight, and limited drivetrain innovation prior to the past decade.

1.2.1 CO₂ Emission Quantification

The carbon dioxide emissions per kilometer for a diesel HDT can be calculated as:

    

 (1)

Where Fc is Fuel consumption (L/km), diesel is diesel density (0.832 kg/L), 

is an

emission factor (3.16 kg CO₂/kg fuel).

Heavy-duty vehicles also generate vibrations that can damage roadside buildings and

infrastructure. These vibrations may lead to both architectural damage (e.g., surface cracks)

and structural damage (e.g., risks of collapse or subsidence). McKinnon et al. (2015)

attribute such vibrations to fluctuations in wheel contact loads, which can be reduced by

maintaining smoother road surfaces. (McKinnon et al., 2015)

Noise pollution is another significant concern, with road freight transport producing constant

and often more intense noise than other modes of transport. This can negatively affect

individuals by causing sleep disturbances, annoyance, and difficulty concentrating. Research

has identified three main sources of road transport noise: propulsion systems, aerodynamic

effects, and tire-road contact. To address this, Regulation 540/2014 introduced new noise

limits, phased in starting from 2016, with additional requirements set for 2022 and 2026

(Academy of European Law, 2023).

CO₂ emissions from heavy-duty vehicles are strongly influenced by operating speed. At

lower speeds, engines tend to operate less efficiently due to frequent idling, gear shifts, and

stop-and-go motion. At higher speeds, aerodynamic drag becomes the dominant factor

contributing to increased fuel consumption.

Figure 4 illustrates the relationship between vehicle speed and CO₂ emissions per kilometer,

showing that emissions are minimized at moderate cruising speeds (typically between 60–

80 km/h), while both lower and higher speeds result in elevated emissions due to

inefficiencies in engine load and aerodynamic losses, respectively.

Figure 4. CO2 Emission per kilometer in HDV (McKinnon et al.,2015)

As shown in Figure 4, maintaining moderate speeds not only enhances fuel efficiency but

also contributes significantly to emissions reduction targets.

Congestion further exacerbates the environmental impact of logistics operations. As noted

by McKinnon et al. (2015), congestion leads to time delays, higher operational costs, and

increased fuel consumption thereby intensifying air pollution and greenhouse gas emissions.

Congestion occurs when demand exceeds the transport system's capacity, forcing vehicles

to travel at slower speeds, which significantly raises CO₂ emissions per kilometer.

For a truck consuming 40 L/100 km, over an annual distance of 120,000 km, the total CO2

emissions amount to approximately 128,640 kg of CO2. This level of emissions is

comparable to that of 20 to 25 passenger vehicles annually, highlighting the significant

environmental impact of heavy-duty vehicles in terms of greenhouse gas emissions.

1.3 Green Logistics and Energy Concepts

This section presents key environmental strategies shaping modern logistics, with a focus on

the concepts of green logistics and net-zero emissions. As freight transport continues to

grow, so does its environmental impact prompting a shift toward more sustainable practices

and energy-efficient systems.

The logistics industry is experiencing rapid growth due to its critical role in supply chains.

However, this expansion is closely associated with increased pollution, greenhouse gas

emissions, and significant resource consumption. As environmental awareness rises,

organizations are increasingly prioritizing not only cost reduction and profit maximization

but also the implementation of environmentally responsible practices. Consequently, there

is a growing emphasis on developing "green" logistics solutions aimed at promoting

sustainability within the sector.

1.3.1 Green Logistics Concept

According to Rodrigue, Slack, and Comtois (2001), the concept of green logistics was first

introduced in 1972 as a model aimed at increasing productivity while reducing the

environmental impact of logistics operations. This approach integrates ecological goals into

company strategies and values. Similarly, McKinnon et al. (2015), in their book Green

Logistics: Improving the Environmental Sustainability of Logistics, describe green logistics

or sustainable logistics as a set of practices related to the flow of goods, services, and

information throughout the supply chain. The main objective is to ensure efficient operations

with minimal environmental impact, promoting sustainability at all stages.

Key strategies include:

• Load Consolidation and Route Optimization: Reducing empty runs and

optimizing delivery routes.

• Adoption of Low-Emission Vehicles: Transitioning to electric or hybrid vehicles.

• Warehouse Energy Efficiency: Implementing energy-saving measures in storage

facilities.

• Reverse Logistics: Managing returns and recycling processes efficiently.

These pillars create a comprehensive framework that enables companies to support

economic and social progress while promoting sustainability.

It has been identified two main drivers behind the adoption of green logistics have been

identified: environmental concerns and market demand. Many companies promote eco-

friendly logistics to enhance their public image, as sustainably produced goods are

increasingly popular among consumers. Customers increasingly prefer companies that

prioritize sustainability. Consequently, businesses that ignore environmental issues may face

reduced demand, while those embracing sustainable logistics may gain a competitive

advantage.

1.3.2 Net Zero Emission Concept

Achieving near-zero greenhouse gas (GHG) emissions, followed by the removal of

remaining emissions through natural processes such as absorption by forests and oceans, is

a key global objective. According to the (UNEP, 2022), although the growth of GHG

emissions has slowed in the past decade compared to the previous one, the absolute level of

emissions remains at a record high. From 2000 to 2009, emissions grew by 2.6% annually,

while from 2010 to 2019, the growth slowed to 1.1% per year Despite this slowdown, even

small increases in methane, CO₂, and other harmful gases are affecting the planet,

highlighting the urgent need to reduce or eliminate these emissions to restore ecological

balance.

UNEP (2022) reported that, as of September 23, 2022, 88 countries representing

approximately 79% of global GHG emissions have committed to net-zero pledges. These

commitments are formalized through legislation, national policy documents, or high-level

governmental declarations, signaling a global move toward carbon neutrality.

The net-zero concept involves balancing the amount of emitted greenhouse gases with

equivalent removals from the atmosphere, thereby stabilizing the climate. Countries such as

Switzerland and members of the European Union (EU) have reaffirmed this goal under the

Paris Agreement. While transitioning to a climate-neutral society poses significant

challenges, it also presents an opportunity to create a more sustainable future.

The EU has committed to achieving climate neutrality by 2050, making it a central goal of

the European Green Deal. Reaching this target requires full participation from all economic

and social sectors. It also demands substantial investments in innovation, technology,

infrastructure, and research, supported by strong governmental policy and regulation. EU

Member States are expected to develop long-term national strategies aligned with EU

climate objectives and their Paris Agreement commitments.

To close the emissions gap and meet the agreement’s targets, systemic changes across

sectors are necessary by 2030. These include transformations in housing, transportation,

food production, service provision, and supply chain management. Key measures involve

phasing out fossil fuels, electrifying vehicle fleets, halting deforestation, and improving

building efficiency. While the pace and specifics of implementation may vary by country,

global and cross-sectoral action must begin simultaneously (UNEP, 2022).

The transportation sector is the second-largest source of global energy-related CO₂

emissions, accounting for 25% of the total (International Energy Agency, 2021). UNEP’s

Emissions Gap Report (2022) emphasizes the need for a shift to low-emission transport

modes, rapid adoption of zero-emission vehicles, and preparation for decarbonizing shipping

and aviation.

1.4 Electrification in Heavy-Duty Transportation

This section explores the transition toward battery-electric technologies in heavy-duty

freight vehicles (HDVs) as a core strategy to decarbonize the logistics sector. It highlights

performance benchmarks, policy drivers, cost competitiveness, and infrastructure challenges

associated with electrifying HDT fleets.

Electrification in heavy-duty transportation is rapidly gaining momentum as a critical

strategy to reduce greenhouse gas (GHG) emissions from the freight sector. Heavy-duty

vehicles (HDVs) are responsible for a disproportionate share of emissions while they make

up less than 2% of vehicles on the road in Europe, they account for nearly 25% of road

transport emissions (McKinsey & Company, 2023). Transitioning from diesel to battery-

electric powertrains can cut lifecycle GHG emissions by up to 63%, and when powered by

renewable energy, reductions can reach as high as 92% (McKinsey & Company, 2023).

Complementary advancements in conventional fuel efficiency technologies, such as

aerodynamic improvements and hybrid systems, further amplify decarbonization efforts,

with potential fuel-use reductions of 39–50% by 2030 for diesel tractor-trailers (ICCT, 2015;

ACEEE, 2015).

Stringent mandates are accelerating electrification and efficiency improvements. The

European Union requires all new freight trucks to be zero-emission by 2035, while the U.S.

Environmental Protection Agency (EPA) has proposed Phase 3 standards (2027–2032) to

reduce HDV emissions through a mix of electrification and advanced diesel technologies

(McKinsey & Company, 2023). These policies align with projections from the International

Council on Clean Transportation (ICCT), which estimates that 30–50% of new HDV sales

in the U.S. and Europe could be zero-emission vehicles (ZEVs) by 2030 (Analysis Group,

2023). California’s Advanced Clean Trucks rule and similar state-level policies further

reinforce this trajectory. Figure 5 illustrates the difference in fuel and energy consumption

between diesel and battery-electric tractor-trailers.

Figure 5. Fuel & Energy Consumption Diesel and battery powered tractor trailer (Basma,

Saboori and Rodríguez, 2021)

Modern lithium-ion batteries now enable battery-electric trucks (BETs) to achieve ranges

exceeding 500 km for long-haul tractor-trailers with a battery capacity of around 1,000 kWh,

and future advancements in energy density and road-load technologies could reduce the

required battery size to 700 kWh for the same range (ICCT, 2021). EU weight tolerances

allow an additional 2 tons for zero-emission trucks, mitigating payload penalties, which

currently stand at about 11% compared to diesel equivalents. With anticipated battery

improvements, this penalty could be eliminated by 2030 (ICCT, 2021). Megawatt charging

systems (MCS) and ultra-fast chargers are critical for minimizing downtime, but installation

costs range from €150,000–300,000 per site and deployment timelines can exceed 18 months

(ICCT, 2021). Efficient thermal management, such as advanced heat pumps, limits range

loss to 9% or less in extreme temperatures, ensuring reliable operation across climates

(ICCT, 2021). Table 1 illustrates the difference in fuel and energy consumption between

diesel and battery-electric tractor-trailers.

Table 1. Diesel and Battery electric Truck Maintenance Cost (Basma, Saboori and

Rodríguez, 2021)

Item

Diesel Truck

Battery Electric Truck

Cost in €/100 km

Lubricants, Oils

0.75

AdBlue refilling

0.55

Repair and preventive maintenance

8.04

Tires: front and driven axis

2.47

Tires: Trailers

2.73

Total

18.5

13.24

The total cost of ownership (TCO) for BETs is projected to reach parity with diesel trucks

in Europe by 2025–2026, driven by lower energy costs (€0.12–0.25/kWh versus €1.20–

1.50/liter for diesel) and 30% lower maintenance costs (ICCT, 2021). Government

incentives, such as €50,000 purchase subsidies and 75% road toll discounts, further narrow

the TCO gap (ICCT, 2021). However, batteries account for 40–50% of BET upfront costs,

posing barriers for small fleets, though advancements like NMC battery chemistry and

lightweight materials are expected to reduce costs by 2030 (ICCT, 2021). Electrifying HDVs

could increase electricity demand by 10–15% by 2035 in the U.S., necessitating grid

upgrades and smart charging solutions (Analysis Group, 2023).

To accelerate progress, it is recommended to prioritize high-power charging corridors,

streamline permitting for MCS sites, promote policies that incentivize both electrification

and conventional efficiency upgrades, and encourage logistics innovations to maximize load

efficiency and reduce empty miles. The electrification of heavy-duty transportation is

advancing rapidly, with battery-electric tractor-trailers now capable of 500 km ranges and

payload penalties set to diminish through technological advancements. Parallel

improvements in diesel efficiency remain critical for near-term emissions reductions. By

2030, a hybrid approach combining BETs, efficiency retrofits, and operational optimizations

could reduce sector-wide emissions by 50–60%, positioning the freight industry for a

sustainable future (ICCT, 2021).

2 Electrified Trailer Systems and Energy Storage Technologies

This chapter presents the technical structure and operating principles of electrified trailer

systems and their associated energy storage technologies. It begins by outlining trailer

classifications, regulations, and axle configurations relevant to the Nordic freight industry.

It then introduces the concept of electric trailers (E-trailers), recent industrial developments,

and the integration of electric drive systems in trailers. The final sections examine battery

systems used in E-trailers and provide a market landscape analysis, including a patent study

reflecting innovation trends in this sector.

2.1 Trailer types, Sizes and Weight Regulations

The foundation of heavy-duty vehicle electrification lies not only in powertrain technologies

but also in the structural and regulatory characteristics of trailers themselves. This section

provides an overview of the trailer types, legal size and weight limits, and combination

configurations used in Finland and Sweden.

In heavy-duty freight transport, the trailer is a crucial component of the vehicle system,

affecting aerodynamics drag, energy efficiency, and regulatory compliance. Trailers are

designed in various configurations, optimized for different cargo types and logistics needs.

From an engineering perspective, understanding trailer dimensions, weight distributions, and

load regulations is essential for integrating electrified systems

Finland and Sweden have both advanced regulatory frameworks supporting the deployment

of longer and heavier vehicles under High-Capacity Transport (HCT) programs. While their

goals are aligned emphasizing freight efficiency, emissions reduction, and road safety their

regulatory approaches differ significantly. Sweden applies a structured road classification

system known as Bärighetsklasser (BK), comprising four classes (BK1–BK4) that define

permissible gross vehicle weights based on axle spacing and road capacity. In contrast,

Finland does not use a formal road classification scheme. Instead, it enforces nationwide

maximum mass and dimension limits, with specific local restrictions applied through bridge

load ratings or signage (Trafikverket, 2022a).

Finland has taken a notably progressive stance on vehicle dimensions. In 2013, the legal

gross vehicle weight was raised from 60 to 76 tonnes, and the height limit was increased to

4.4 meters. By 2019, Finland became the first country in Europe to permit vehicle

combinations up to 34.5 meters in length across most of its road network (Tieliikennelaki,

122 §, liite 6.6). Sweden, while allowing longer combinations through permits and pilot

programs, retains a standard maximum length of 25.25 meters, with BK4 permitting up to

74-tonnes gross weight under specific conditions. Sweden’s system ensures that road usage

aligns with infrastructure capacity through controlled BK class expansion; by 2022, 26% of

the state road network was classified for BK4, with a goal of 48% by 2025 (Trafikverket,

2022a).

In terms of infrastructure compatibility, both countries face challenges. Finland reports

approximately 390 bridges with weight restrictions and 50 junctions that are not suitable for

the longest vehicle combinations. Finland began issuing these permits in 2013, leading to

measurable operational savings up to 20% in sectors such as forestry, container, and food

logistics. Sweden also supports HCT trials but employs a more conservative rollout,

emphasizing regulatory control and infrastructure adaptation.

Various trailer configurations using two trailer types enable vehicle lengths from 25.25 to

34.5 meters. Some of these setups, shown in Figure 6, include steerable axles (marked in

red) to improve manoeuvrability in longer combinations (Larsson & Pettersson, 2022).

Figure 6. Trailer Configurations and steerable axles (Larson & Pettersson, 2022)

According to section 151 (§ 151) of Finnish Road Traffic Act, Uusi tieliikennelaki ja

rekkojen käyttösäännöt, 11 types of trailer combinations are explicitly permitted to be

connected to a truck (kuorma-auto) in Finland.

Table 2. Trailer combinations permitted in Finland

Combination image

Trailer Type

Nickname

Max

Weight

(t)

Max

Lengt

h (m)

Puoliperävaunu /

Semi-trailer

Puolikkaat /

"Semi"

Keskiakseliperävaunu

/ Central axle trailer

Keskari /

"Center axle"

20.75

Varsinainen perävaunu

/ Full trailer

Täysperä /

"Full trailer"

18.75

Apuvaunu +

puoliperävaunu / Dolly

+ Semi-trailer

A-dolly /

"Modular"

34.50

Puoliperävaunu+puoli

perävaunu / Semi +

Semi

B-linkki / "B-

link"

34.50

Puoliperävaunu+

keskiakseliperävaunu /

Semi + Central axle

Vasikka /

"Calf combo"

34.50

Puoliperävaunu +

varsinainen perävaunu

/ Semi + Full trailer

A-tupla

(TPV) / "A-

duo"

34.50

Puoliperävaunu+apuva

unu + puoliperävaunu /

Semi + Dolly + Semi

A-tupla

(dolly) / "A-

duo (dolly)"

34.50

Apuvaunu+puoliperäv

aunu+ puoliperävaunu

/ Dolly + Semi + Semi

AB-duo /

"AB-duo"

34.50

Varsinainen perävaunu

+ puoliperävaunu / Full

+ Semi

AB-duo /

"AB-duo"

34.50

Puoliperävaunu+puoli

perävaunu +

puoliperävaunu /

Triple Semi

B-trio / "B-

triple"

34.50

This modular design capability enables Finland to test and adopt new trailer technologies

including electric axles and on-board energy storage systems without requiring large-scale

changes to the legal framework.

2.2 Trailer Axle Configurations and Integration of E-Trailers

This section examines the various axle configurations used in heavy-duty vehicles and

trailers, highlighting their operational roles, integration potential with electrified systems,

and legal implications. A special focus is placed on electric axles (e-axles) and how they

support hybridization of trailers in high-capacity freight applications.

Trucks are designed to accommodate a wide range of cargo types and driving conditions,

necessitating various axle configurations tailored to specific operational needs, as illustrated

in Table 2. These configurations incorporate both driven & non-driven axles, depending on

factors such as load requirements, terrain, and desired fuel efficiency. To standardize axle

setups across the industry, a numerical designation system is commonly employed. This

system represents the total number of wheels on a vehicle, including both the truck and

trailer, followed by the number of wheels driven. In this context, twin wheels on either side

of an axle are considered a single wheel.

Each axle configuration is engineered to balance key performance factors, including traction,

payload capacity, maneuverability, and fuel consumption. Therefore, selecting the correct

axle layout is essential to ensure the vehicle is suitable for its intended application and

capable of operating safely and efficiently under the given load and road conditions.

A widely used example is the 4x2 configuration, which denotes a vehicle with four wheels,

two of which are driven. This format allows for quick assessment of a truck's driving

capabilities and typical usage scenarios. Below is an overview of the most common axle

configurations and their functional characteristics (Volvo Group, 2023). Figure 7 illustrates

the most common heavy-duty axle setups.

Figure 7. Axles Configurations of Heavy-Duty Vehicles (Volvo Group, 2023)

• 4x2: Four wheels with two driven. Common for lighter-duty trucks, offering

simplicity and good fuel economy.

• 4x4: All four wheels are driven, ideal for off-road or poor road conditions due to

enhanced traction.

• 6x2: Six wheels with two driven. Balances load capacity and efficiency, typically

used in long-haul applications.

• 6x4: Six wheels with four driven. Provides improved traction, suited for heavier

loads and uneven terrain.

• 6x6: All six wheels are driven, maximizing traction and vehicle stability under

extreme or off-road conditions.

• 8x2: Eight wheels with two driven. Designed for carrying very heavy loads on well-

paved roads.

• 8x4: Eight wheels with four driven. Offers strong load-bearing capacity and

improved traction, used in demanding and mixed terrain conditions.

Tandem Axle

A tandem axle configuration features two axles positioned closely together, most commonly

found on semi-trailers (Volvo Group, 2023). This setup facilitates even load distribution

across a broader section of the trailer frame, allowing for higher payload capacity. The close

spacing of the axles improves vehicle stability at high speeds and under highway conditions,

while also offering better manoeuvrability. Additionally, tandem axles can mitigate the

effects of tire damage by redistributing load to the remaining tires. Newer systems often

include adjustable components like air-slide suspension, which enables repositioning of the

axle group to optimize weight distribution. However, tandem systems may result in

increased tire wear if the load is unevenly distributed and generally require a higher upfront

investment.

Tag Axle

A tag axle is a non-driven axle positioned behind the main driven axle. Its function is to

support additional load and promote balanced weight distribution along the chassis.

Depending on operational needs, tag axles can be liftable to reduce tire wear and fuel

consumption when not in use, or steerable to enhance manoeuvrability. They are commonly

used in high-capacity transport vehicles to improve vehicle handling and stability.

Pusher Axle

Unlike the tag axle, the pusher axle is located in front of the driven axle and serves to increase

the vehicle’s load-bearing capacity without being directly powered. Its forward position

contributes to a more balanced load distribution, especially toward the front of the chassis,

and improves vehicle dynamics and steering stability. Pusher axles are especially beneficial

for vehicles with uneven loading or variable weight distributions.

Lift Axle

A lift axle can be raised or lowered manually or automatically depending on the operational

requirement. This configuration is widely used in heavy-duty trucks and trailers for

improved load flexibility and fuel efficiency. When not needed, lifting the axle reduces

rolling resistance, decreases tire wear, and improves manoeuvrability by allowing tighter

turning radii. Lift axles are environmentally advantageous as they extend tire life and reduce

material waste.

Steerable Axle

Steerable axles are designed to pivot independently from the primary steering mechanism,

enabling better navigation in confined or complex driving environments. These axles

enhance handling precision, especially during tight turns, and reduce tire wear by ensuring

better alignment during motion. Additionally, steerable axles contribute to improved load

distribution and reduced mechanical stress on the vehicle’s fixed axles.

Electric Axle

An electric axle, or e-axle, integrates an electric motor, power electronics, and a transmission

system into a single compact unit that provides direct drive to the axle. This configuration

significantly reduces the weight and size of conventional drivetrains and enhances energy

efficiency, making it well-suited for heavy-duty applications such as trucks and electrified

trailers (Volvo Group, 2023). The compact design also allows for increased battery

installation capacity, which can extend the vehicle's driving range. Despite its size, the e-

axle delivers power comparable to traditional systems and supports full traction

functionality.

Rear Suspension

Rear suspension systems in trailers are generally categorized into single-axle and bogie

configurations. A single-axle suspension contains one axle, while a bogie suspension

includes two or more axles mounted together. In bogie systems, the axles may include

combinations of driven and non-driven axles, such as a configuration with two driven axles

or one driven axle paired with a pusher or tag axle. Some non-driven axles in bogie setups

may also be lift axles, offering further adaptability. The distance between these axles varies

across models, affecting load dynamics and turning behaviour.

The legal maximum weight for truck and trailer combinations not only depends on the type

of combination but is also strictly defined by the total number of axles. These weight

regulations are intended to ensure road safety, protect infrastructure from excessive wear,

and maintain the operational efficiency of transport vehicles.

For a combination consisting of a truck and a centre-axle trailer, the maximum permitted

gross weight is 50 tonnes. This configuration is commonly used for regional distribution and

medium-distance transport, offering a balance between manoeuvrability and load capacity

within legal weight constraints. Table 3 summarizes legal weight thresholds based on axle

count.

Table 3. Maximum Weight on an Axle Configuration ( Tieliikennelaki, 122 §, liite 6.6.)

Type

Combination

Max Weight

A truck + center-axle trailer

a truck and a trailer or a truck and multiple trailers

b.1

4-axles

b.2

5-axles

b.3

6-axles

b.4

7-axles

b.5

8-axles

b.6

At least 8-axles, if at least 65% of the mass of the trailer or of the total mass of

the trailers is applied to axles fitted with twin wheels

b.7

9-axles

b.8

At least 9-axles, if at least 65% of the mass of the trailer or of the total mass of

the trailers is applied to axles fitted with twin wheels

b.9

10-axles

b.10

At least 11-axles

These regulatory thresholds ensure safe distribution of weight across infrastructure, preserve

road surface longevity, and provide design targets for engineers.

2.3 Electric Trailers Electric Trailer Concepts and Industrial developments

Electric trailers (or e-trailers) represent a paradigm shift in trailer functionality from passive

cargo carriers to active propulsion systems. These trailers integrate electric drive

components such as e-axles, battery packs, and control electronics to assist the tractor in

propulsion and braking.

The concept of a driven axle on a trailer is not new. As early as the 1960s, the Finnish

manufacturer Sisu developed a hydraulically powered axle system known as Nemo (short

for Nestemoottori, or "fluid motor"). The system was highly praised for its exceptional

traction performance. At the time, vehicle combinations equipped with the Nemo system

had a theoretical climbing gradient of 25% gradient, assuming a friction coefficient of 0.4

(Jaakko Kinnunen, 2023)

In tipper truck and timber truck operations, E-axles offer similar benefits: improved traction

and hill-climbing ability. Additionally, regenerated energy can power auxiliaries like bucket

tipping. It's even technically feasible to recuperate energy from the downward movement of

the bucket, although this can also be done hydraulically.

For long-haul trucks, the main advantage lies in downsizing the engine. Since these trucks

operate mostly on flat roads and carry full loads, an E-axle can assist during acceleration and

hill climbs, allowing a smaller engine to handle cruising more efficiently. E-axles can also

power systems like refrigeration units, a concept already demonstrated by SAF-Holland

(Jaakko Kinnunen, 2023).

A key consideration across all vehicle types is the need for in-motion charging, especially

for operations with minimal downtime, where vehicles stop only briefly for driver changes

or refueling.

Previous research on electric trailers in heavy-duty transport (HDT) has largely concentrated

on the technical aspects of integrating electric axles (E-axles) into vehicle architecture, with

limited exploration of how these systems perform at the fleet or system level across various

operational use cases. Most studies emphasize the mechanical design, powertrain

configuration, and component efficiency, rather than evaluating how E-axles impact real-

world transport performance under different driving conditions.

For instance, (Jost Auf der Stroth & Sellgren, 2024) explored E-axles from a vehicle

manufacturer’s perspective, analyzing their structural integration and design trade-offs

within the vehicle chassis. While technically detailed, the study did not address broader

implications such as energy consumption patterns, total cost of ownership, or logistical

performance.

2.4 Battery Systems and Energy Storage for Electric Trailers

The integration of electric powertrains into heavy-duty trailers relies fundamentally on

advanced battery energy storage systems (BESS), which serve as the backbone for

propulsion, regenerative braking, and auxiliary power management under demanding

operational conditions.

Battery Energy Storage Systems (BESS) are the critical subsystems enabling electrification

in heavy-duty trailers (HDTs), functioning as the primary energy reservoir for traction,

regenerative breaking, and auxiliary operations. Unlike conventional automotive

applications, HDTs impose significantly higher electrical and mechanical demands,

including prolonged duty cycles, high mass payloads, and extreme temperature exposures.

Therefore, battery system design for such platforms must consider not only volumetric and

gravimetric energy densities but also system-level integration aspects such as modularity,

thermal behavior, electrical safety, lifecycle durability, and recyclability.

The battery systems used in electric trailers face significantly more demanding requirements

compared to those in light-duty vehicles. These systems must deliver high energy density to

maximize driving range while staying within legal axle load limits. Additionally, they

require a long cycle life to ensure a low total cost of ownership (TCO) over extended

operational periods. Given the harsh operating environments, robust thermal performance is

essential across a wide temperature range, typically from –30°C to 60°C. Moreover, high

power density is crucial to provide the necessary torque support during acceleration phases

and while navigating steep gradients.

2.4.1 Overview of Battery Chemistries

The deployment of battery electric vehicles (BEVs), particularly heavy-duty vehicles, hinges

critically on the choice of battery technology. Among rechargeable battery systems, lithium-

ion (Li-ion) batteries have emerged as the dominant technology due to their high energy

density. In freight vehicles, the energy density of the battery plays a pivotal role, as it directly

influences the vehicle's payload capacity and range. However, factors such as battery

durability, which is strongly dependent on the cathode and anode materials, electrolyte

composition, separator, and manufacturing processes, are also crucial considerations in

battery selection. The cathode chemistry is often the most critical design parameter to

optimize both energy density and durability (Boukhalfa & Ravichandran, 2020).

Lithium Nickel Manganese Cobalt Oxide (NMC)

NMC is the most common cathode chemistry, accounting for over 28% of global EV sales

and projected to rise to 63% by 2027 (Boukhalfa & Ravichandran, 2020). It typically has an

energy density of 250 Wh/kg, which could increase to 300 Wh/kg in the coming years.

Nickel-rich variants like NMC-532, NMC-622, and NMC-811 provide higher energy density

and reduced reliance on cobalt but may negatively affect cycle life. NMC batteries also show

strong cycle life performance, exceeding 2,000 cycles with 80% capacity retention (Jost Auf

der Stroth & Sellgren, 2024). Despite the high cost of cobalt, NMC batteries cost around 70–

90 $/kWh (Wentker et al., 2019), and the pack-to-cell cost ratio is between 2.4 and 2.6

(Wentker et al., 2019). Major manufacturers like Daimler, MAN, and Volvo use NMC in

their heavy-duty electric vehicles.

Lithium Nickel Cobalt Aluminium Oxide (NCA)

NCA offers similar energy densities as NMC, typically above 200 Wh/kg, with expectations

to reach 300 Wh/kg. However, NCA is slightly costlier than NMC, with costs ranging from

70–80 $/kWh (Wentker et al., 2019). Tesla is the only major manufacturer using NCA cells,

although it has not confirmed whether it will continue using NCA for its upcoming Tesla

Semi or switch to NMC cells (Tesla, 2020).

Lithium Iron Phosphate (LFP)

LFP batteries offer lower energy densities compared to NMC and NCA but excel in cycle

life, exceeding 2,500 cycles, and have higher charge/discharge rates (30% better than NMC

and NCA). LFP batteries are cost-effective, as they do not contain cobalt, leading to lower

costs around 60 $/kWh at the cell level (Manthey, 2020). The gravimetric cell-to-pack ratio

(GCTPR) for LFP is 80–90%, significantly higher than the 55–65% ratio for NMC and NCA

While LFP batteries are not as energy-dense as their nickel-based counterparts, recent

developments from companies like CATL and Guoxuan have achieved energy densities up

to 260 Wh/kg (Manthey, 2020). LFP technology is increasingly adopted in European heavy-

duty vehicles by manufacturers such as VDL and DAF (Kane, 2020; DAF, 2021).

The three most prominent Li-ion cathode chemistries used in HDT applications can be

simplify as this. Table 4 provides a comparative overview of the most used lithium-ion

battery chemistries in heavy-duty electric vehicles NMC, NCA, and LFP.

Table 4. Li-ion Batteries used in HDV (Sekar, 2021)

NMC

NCA

LFP

Energy density cell (Wh/kg)

150-220

200-250

120-160

GCTPR%

55-65

80-90

Cycle life ( cyles at 80% capacity retention)

1000-2000

1000-1500

2500-5000

Thermal Stability

Medium

High

Cell cost ($/kWh)

70-90

70-80

65-80

Solid-State Batteries (SSBs)

Solid-State Batteries (SSBs) represent the next-generation technology for heavy-duty

vehicle (HDV) applications, offering significant advantages over conventional lithium-ion

batteries. Projected to deliver energy densities between 350–500 Wh/kg, SSBs could enable

longer ranges for HDVs without increasing weight or size. They also promise a much longer

cycle life, with projections exceeding 2,500 cycles at 80% capacity retention, outpacing

traditional lithium-ion chemistries. Additionally, SSBs offer very high safety due to their

solid-state electrolytes, which are less prone to thermal runaway compared to liquid

electrolytes in current battery technologies. However, SSBs are still in the development

phase and have not yet been commercialized for HDVs. Despite this, their potential to

revolutionize battery performance in heavy-duty applications is substantial, positioning them

as a key technology for the future of electric freight transport.

2.4.2 Thermal Management

Thermal management is essential for ensuring the optimal performance, safety, and

longevity of batteries, particularly lithium-ion batteries used in electric vehicles. Batteries

must operate within specific temperature ranges, typically between 15°C and 35°C for

optimal performance. Elevated temperatures accelerate aging processes, reducing the

battery’s lifespan, while exceeding thermal limits may trigger thermal runaway, an

uncontrollable exothermic reaction with potentially catastrophic consequences. On the other

hand, extremely low temperatures can impair battery performance and may cause complete

failure. To mitigate these risks, two primary cooling systems are used: liquid cooling and air

cooling. Liquid cooling is more efficient, requiring less energy to achieve the same cooling

effect as air cooling, which can consume 2 to 3 times more energy. Additionally, fin cooling

systems can add approximately 40% extra weight, reducing their efficiency. Liquid cooling

also offers a more compact design compared to air cooling systems. A Battery Management

System (BMS) is employed to monitor and regulate temperature, voltage, and current at both

the cell and pack levels, ensuring that the cells' state of charge is balanced and the vehicle

operates optimally (Sekar, 2021). Furthermore, high C-rates or events like regenerative

braking can significantly increase cell temperatures, further affecting battery performance

and lifespan.

The heat generation in a battery cell can be quantified by,

 I2     󰇛  󰇜 (2)

Where Q is Heat generated per cell (W), I is Current flowing through the cell (A), Rinternal is

Internal resistance of the cell (Ω), EOCV is Open-circuit voltage (V), U is Terminal voltage

of the cell (V)

This equation represents the heat produced due to both the internal resistance and the

difference between the open-circuit voltage and the terminal voltage, with both factors

contributing to the overall heat generation, especially during charge and discharge cycles.

2.4.3 Regenerative Braking

Electric trailers are increasingly integrated into freight transport systems, offering energy

efficiency and enhanced control through regenerative braking and intelligent traction support

systems. These functionalities not only reduce energy consumption and mechanical wear but

also significantly enhance vehicle stability, especially in dynamic or low-traction

environments.

Regenerative braking allows electric trailers to convert kinetic energy generated during

deceleration into electrical energy, which is then stored in onboard batteries. This process

reduces dependence on mechanical brakes, thereby minimizing brake wear and extending

energy range.

The regenerative braking power can be estimated using:

regen  

 ( 3)

Where: η is system efficiency, typically 0.75–0.85 (Zemanek et al., 2022),  is mass of the

trailer (kg),  is velocity of the vehicle (m/s).

This equation estimates the instantaneous power available for recovery during breaking.

Real-world measurements show regenerative systems can recover 10–15% of total

propulsion energy in urban and mixed driving conditions (Zemanek et al., 2022)

2.5 Market Analysis

When analysing the emerging E-trailer industrial developments, it is essential to provide a

structured overview of the key market participants and the range of vehicle configurations

currently available. This involves identifying the specific trailer models offered by major

manufacturers and highlighting the claimed technical and operational advantages associated

with each product. A comparative assessment of these offerings enables a clearer

understanding of the competitive landscape, allowing for the identification of distinguishing

features such as propulsion system design, energy efficiency, regenerative capabilities, and

integration with tractor units. These differentiators are critical in evaluating the market

positioning of each company and understanding the potential influence of design innovation

on the future development and adoption of E-trailers within the heavy-duty transport sector.

Trailer Dynamics

On November 21, 2022, Trailer Dynamics introduced a novel semi-trailer equipped with an

electric driveline, marking a significant advancement in E-trailer technology. According to

the company, this innovation is expected to reduce CO₂ emissions by approximately 20% to

40%, while also enabling a range extension of up to 650 kilometers, facilitated by onboard

battery support (Trailer Dynamics, 2024b). The trailer is designed to be compatible with all

standard truck configurations, offering seamless integration into existing fleets.

Figure 8:Trailer Dynamics E-Axle and Chasi (Trailer Dynamics, 2024)

The system replaces one of the conventional free-rolling axles with a powered electric axle,

enhancing traction and energy efficiency. The trailer utilizes modular lithium iron phosphate

(LiFePO₄) battery technology, selected for its stability and suitability in heavy-duty freight

applications. Additionally, the trailer incorporates real-time control mechanisms at the

kingpin, ensuring precise force transfer and safe operation of the electrified axle.

To further optimize performance, the system includes Predictive Drivetrain Control (PDC),

which enables intelligent energy management based on route and load conditions (Trailer

Dynamics, 2024a). The E-trailer also features a fully electric refrigeration unit, contributing

to noise reduction and improved environmental sustainability. An integrated control system

with active ride control and ultra-fast data processing enhances safety and operational

responsiveness (Jost Auf der Stroth & Sellgren, 2024).

In a major commercial move, DB Schenker placed an order for 2,000 E-trailers from Trailer

Dynamics in 2022, with deployment across the European road network scheduled

throughout 2024 (DB Schenker, 2024). This large-scale adoption highlights growing market

confidence in electric trailer technologies and their role in advancing decarbonized freight

transport.

On September 20, 2023, ZF unveiled its latest trailer electrification system, targeting

significant improvements in fuel efficiency and emission reduction. The solution is designed

to deliver a 16% reduction in both fuel consumption and CO₂ emissions under standard

operating conditions. For configurations equipped with the plug-in hybrid variant, the

emission reduction potential increases to up to 40% (ZF, 2024).

Figure 9. ZF E-Trailer (ZF, 2024)

The system features two electric axles coupled with a modular battery architecture, enabling

enhanced energy recuperation and traction support. This setup not only improves vehicle

dynamics but also contributes to extending the operational range of electrified heavy-duty

trucks. The trailer utilizes regenerative braking to harvest kinetic energy, which is then stored

and reused to assist propulsion, particularly in demanding driving scenarios.

ZF’s approach reflects a strategic step toward hybridizing the trailer segment within heavy

transport, combining modular electrification with plug-in capabilities to reduce

environmental impact while maintaining operational flexibility.

Range

On May 8, 2023, Range Energy conducted a test of its advanced electric semi-trailer, which

is engineered to reduce diesel consumption by approximately 30% to 40% under typical

operating conditions (Range Energy, 2024). The trailer is equipped with a 200 kWh battery

system, capable of supporting both DC fast charging, achieving a full charge in

approximately 45 minutes and AC charging, which requires around 10.5 hours. The technical

specifications of Range Energy's electric trailer system are summarized in Table 5,

highlighting key performance metrics such as battery capacity, power output, fuel savings

potential, and charging capabilities.

Table 5. Range Energy E-Trailer Specifications

Component / Feature

Specification

Standard Battery

192 kWh, 600 V

Extended Range Battery (Optional)

288 kWh, 600 V

eAxle Output

250 kW peak power, 200 kW continuous

power

Full-Assist Fuel Savings

35% to 45%*

Mild Hybrid Range

1,000+ miles

DC Charging (Option 1)

60 kW / 2.0 hours / 100A, 480 VAC 3-

phase

DC Charging (Option 2)

170 kW / 0.8 hours / 200A, 480 VAC 3-

phase

Standard Battery Warranty

5 years, or 120,000 miles

eAxle Warranty

2 years, or 200,000 miles

The design features a standardized interface, ensuring compatibility with a wide range of

tractor units. The trailer operates via an electric drive system integrated with embedded

sensors, including a smart kingpin that detects the vertical load transferred from the trailer

to the tractor. This load data is used to dynamically control the electric axle, providing

propulsion support particularly during acceleration phases, thereby reducing the engine’s

workload. The powertrain architecture as in Figure 10 is designed with modularity in mind,

allowing for customizable and scalable battery configurations up to 800 volts, tailored to

meet varying operational demands (Range Energy, 2023).

Figure 10. Range Energy E-Trailer Electrical Design (Range Energy,2023)

Additionally, the system incorporates regenerative braking, which captures kinetic energy

during deceleration and feeds it back into the battery, further improving overall energy

efficiency.

Random

On October 14, 2019, Randon Companies introduced a semi-trailer equipped with an electric

auxiliary traction system, representing a key advancement in hybrid trailer technology. The

system, branded as e-Sys, is designed to achieve up to a 25% reduction in fuel consumption,

although the effectiveness is highly dependent on specific driving conditions (Randon

Companies, 2024). Figure 11 illustrates the external structure of the Randon E-trailer

platform.

Figure 11. Randon semi-trailer with e-Sys auxiliary electric system (Randon Companies,

2024)

The trailer assists the towing vehicle particularly during uphill driving and acceleration,

while also enhancing energy efficiency during downhill motion through regenerative

braking. In such cases, the electric motor functions as a generator, converting kinetic energy

into electrical energy, which is then stored in the trailer’s onboard battery system.

The e-Sys driveline includes a WEG electric motor integrated into the axle, an electronic

control unit (ECU), a power switch, and a modular battery pack. The entire system is

governed by an intelligent control algorithm, which monitors operational parameters and

usage patterns to dynamically optimize energy deployment and recovery. This real-time

energy management enhances the trailer’s trailer’s traction performance and energy

utilization under variable load and terrain conditions.

VAK

On May 25, 2023, VAK announced the launch of a new eco-friendly and energy-efficient

electric trailer (E-trailer) designed to enhance the sustainability of heavy-duty transport. The

trailer was officially introduced on Finnish roads in May 2023, with Ahola Transport

becoming the first fleet operator to adopt the technology in June 2023 (VAK, 2024).

The E-trailer utilizes regenerative energy, harvested during both coasting and braking, to

provide propulsion support during vehicle start-up, acceleration, and uphill driving.

Performance testing revealed a 20% improvement in acceleration from 0 to 70 km/h, and a

10% increase in uphill driving speed, demonstrating the trailer's contribution to drivability

and load management. Figure 12 shows the E-trailer system layout and powertrain

integration developed by VAK.

Figure 12. Design overview of VAK’s E-trailer system (VAK, 2024)

In terms of fuel efficiency, initial trials recorded a 5–10% reduction in diesel consumption,

depending on the route profile and operating conditions. The electric assistance system helps

alleviate engine load during high-demand scenarios, contributing to overall energy savings.

The company has announced plans to deploy 5 to 10 additional pilot units during 2024, with

full-scale serial production scheduled to begin in 2025. These developments position VAK

as a key player in the Scandinavian E-trailer market, with a focus on optimizing performance

under Nordic logistics conditions.

Ein ride

On May 23, 2022, Einride unveiled its electric semi-trailer, which the company described as

“the most intelligent trailer to hit the road” (Einride, 2024). The trailer integrates a suite of

cameras and sensors, enabling real-time monitoring for enhanced cargo security and theft

prevention. Figure 13 illustrates the external design and integrated components of the Einride

E-trailer.

Figure 13. Einride’s intelligent electric semi-trailer platform (Einride, 2024)

A defining feature of the trailer is its connectivity to Einride Saga, an intelligent cloud-based

operating system that provides end-to-end visibility, facilitates cargo space optimization, and

supports predictive maintenance. Through real-time data analytics, Saga enhances

operational efficiency and asset utilization across freight networks.

From a performance standpoint, the trailer boasts a fossil-free operating range of up to 650

kilometers, contributing significantly to zero-emission logistics goals. It also supports rapid

charging, achieving a full battery charge in approximately 30 minutes, further improving

vehicle turnaround and uptime.

Einride’s approach positions the trailer as a convergence of electrification and digital

intelligence, marking a significant step toward fully integrated, autonomous-ready freight

systems.

In parallel with advancements in electric drivelines, several manufacturers have begun

integrating solar energy systems into their trailer designs to further improve sustainability.

Scania and Randon are notable among the early adopters in this area.

Figure 14. Scania’s solar-assisted trailer body for hybrid applications (Scania, 2023)

On August 31, 2023, Scania introduced a solar-powered trailer concept equipped with solar

panels mounted along the trailer’s sides. This system, shown in Figure 14, is designed to be

paired with a hybrid-electric truck and incorporates an energy storage capacity of 200 kWh,

which is directly connected to the solar array. The energy harvested from the solar cells is

intended to support propulsion and auxiliary systems, thereby reducing overall fuel

dependency (Scania, 2023).

Similarly, on November 8, 2022, Randon released a demonstration video highlighting their

solar-integrated trailer, featuring photovoltaic panels installed on the trailer roof, as depicted

in Figure 15.

Figure 15. Randon’s solar-integrated trailer with rooftop photovoltaic panels (Randon,

2022)

The system can generate up to 15 kW of power, which can be used to support both propulsion

assistance and onboard electrical systems, thereby contributing to reduced emissions and

operational costs (Randon, 2022).

These developments signal an emerging trend in the trailer industry toward energy-

autonomous vehicles, leveraging solar energy as a complementary solution to electric

drivetrains.

2.5.1 Patent study

As part of a comprehensive market and technological analysis of the electric trailer (E-

trailer) sector, a review of recent patents offers insight into cutting-edge innovations,

commercial trends, and engineering challenges addressed by leading manufacturers. These

patents cover a broad spectrum of developments from intelligent driveline control and

regenerative braking to energy storage optimization and autonomous trailer operation.

Selected patents are summarized below to highlight the technological directions and

priorities within the industry.

Patent Number: CN109435699A

Keywords: Electric, Semitrailer

Published on March 8, 2019, this patent introduces an intelligent electric semi-trailer

incorporating a frame, a driven tandem axle, multiple battery packs, and an electronically

controlled steering system. The steering mechanism is independently connected to both the

power unit and the batteries, allowing for precise control of each component. A central

control device manages motor behaviour in real-time, improving safety during downhill

travel, enhancing fuel efficiency, optimizing performance, and minimizing environmental

impact. This design represents one of the earlier innovations in integrating intelligent

driveline control in E-trailers (Jost Auf der Stroth & Sellgren, 2024).

Patent Number: CN116265270A

Keywords: Trailer, ZF, Driven

Filed by ZF and published on June 20, 2023, this patent outlines a system for controlling a

liftable electric drive axle in a trailer using pneumatic bellows. The mechanism adjusts the

axle's position by inflating the lifting bellows while simultaneously decreasing pressure in

the load-bearing bellows. Positioned either ahead of or behind the primary axle, the liftable

axle configuration improves turning radius, reduces tire wear, and enhances traction. This

technology contributes to more adaptive and efficient trailer dynamics under varying

operational conditions (ZF, 2023).

Patent Number: DE102022123162B3

Keywords: Trailer, Trailer Dynamics

Published on August 31, 2023, this patent by Trailer Dynamics focuses on enhancing battery

integration in electric trailers. The design features a battery box mounted to the chassis and

side beams, mechanically isolated from other structural elements to reduce the impact of

dynamic loads. This isolation improves the stability and longevity of the battery system by

minimizing vibration and mechanical stress. The innovation contributes to safer, more

durable electric trailer configurations by addressing one of the critical challenges in high-

capacity battery deployment in freight vehicles (Trailer Dynamics, 2023).

Patent Number: FR2980438A1

Keywords: Semi-trailer, Propulsion, Electric, Self-propelling

Published on March 29, 2013, this patent presents a semi-trailer capable of self-propelled

movement without a connected truck, ideal for use during loading or yard manoeuvring. The

design includes a propulsion device located on a rear axle and a front steering axle, allowing

the trailer to operate independently. This system enhances usability and efficiency by

enabling the driver to focus on manoeuvring while propulsion and steering are automatically

managed (Jost Auf der Stroth & Sellgren, 2024).

Patent Number: WO2023072496A1

Keywords: ZF, Electric, Semi-trailer

Published on May 4, 2023, this ZF patent focuses on increasing energy storage capacity in

electric semi-trailers. The trailer is designed to support or entirely substitute for the truck’s

propulsion, reducing dependency on high-power charging by enabling low-power

recharging during rest breaks. With a payload capacity between 15 and 18 tons, this system

provides operational flexibility and promotes energy autonomy in long-haul freight

operations (ZF, 2023).

Patent Number: US11453292B2

Keywords: Electric, Semi-trailer

Published on September 27, 2022, this patent introduces an electric motor system that

functions as an electromotive brake, eliminating the need for mechanical braking

components in the trailer. This innovation reduces overall trailer weight, frees up internal

space, and simplifies maintenance, leading to lower operational costs and increased energy

efficiency in electric trailer configurations (Jost Auf der Stroth & Sellgren, 2024).

Patent Number: US2015060160A1

Keywords: Electric, Semi-trailer

Published on March 5, 2015, this patent describes a semi-trailer equipped with an electric

driveline and an interface that enables coupling with a truck’s electric drive system. This

connection facilitates synchronized propulsion between the trailer and the towing vehicle,

improving power distribution, vehicle handling, and overall energy efficiency during

transport (Jost Auf der Stroth & Sellgren, 2024).

Patent Number: US11884124B2

Keywords: E-axle, Drivetrain

Published on January 30, 2024, this patent introduces an e-axle system integrated into the

trailer chassis with the ability to interpret route data to assess terrain type downhill, uphill,

or flat. Based on the terrain, the system adjusts the axle's position and engagement: lifting it

to reduce drag on descents or lowering it to boost traction and energy regeneration during

climbs. This enables adaptive performance and efficient drivetrain behaviour under real-

world conditions (Jost Auf der Stroth & Sellgren, 2024).

Patent Number: US2020238990A1

Keywords: Electric, Semi-trailer

Published on July 30, 2020, this patent focuses on a smart kingpin system integrated into an

electric semi-trailer. A sensor located at the kingpin detects force transfer between the trailer

and the tractor. This data informs a control system that manages one or more electric motors

responsible for trailer propulsion. The result is an intelligent, load-responsive driveline that

supports vehicle stability and efficiency during transport (Jost Auf der Stroth & Sellgren,

2024).

Patent Number: WO2023110358A1

Keywords: Trailer, ZF, Driven

Published on June 22, 2023, this patent from ZF presents a hybrid braking system for electric

semi-trailers that integrates both electric and friction braking technologies. During normal

operation, braking is handled by the electric system, while friction brakes engage under

higher demand conditions. The system calculates deceleration to coordinate braking between

driven and non-driven axles, ensuring balanced performance and safety under varying loads

and road conditions (ZF, 2023).

3 Comparative Analysis of Fuel Consumption and CO₂ Emissions

in Nordic Freight Operations from Kuusamo to Keltakallio

This chapter presents a case study assessing fuel consumption and carbon dioxide (CO₂)

emissions for heavy-duty freight transport along the Kuusamo to Kotka route. The analysis

is based on technical data and simulation results published in existing literature and

combined with original route-specific calculations performed as part of this thesis.

3.1 Vehicle Configuration and Technical Specifications

This analysis is based on performance of N3-class 6×4 tractor units, specifically designed

for heavy-duty long-haul freight transport with gross vehicle weights exceeding 12,000 kg,

in accordance with EU Regulation (EU) No 2018/858. The 6×4 drivetrain configuration is

particularly well-suited for high-payload and traction-intensive applications when paired

with semi-trailers. The simulation approach is adapted from (Kinnunen, 2023), who

developed a validated model for analysing energy efficiency in E-axle-equipped heavy-duty

trucks. The modelling framework was implemented using MATLAB Simulink (R2023a),

incorporating Simscape Driveline for dynamic powertrain behaviour and Simulink libraries

for control logic. Complementary subsystem-level simulations were performed in Siemens

Amesim (2021.1),

The (Kinnunen, 2023) model includes a driver behaviour module, a longitudinal vehicle

dynamics block, and detailed powertrain elements such as a 14-speed transmission, an

internal combustion engine with a peak efficiency of 46%, and a BorgWarner Platform 240

E-axle. The E-axle is powered by modular battery packs (20, 35, or 50 kWh at 840 V) and

controlled via a Motor Control Logic (MCL) system, which manages torque assist and

regenerative braking. Gear selection is governed by the Gearbox Control Logic (GCL).

Fuel consumption in the simulation environment is computed by the generic engine block

and expressed in units of kilograms per second (kg/s). Engine efficiency is derived

dynamically from simulated engine power output and fuel consumption rate using the

thermodynamic relation described by Koch et al. (2018, p. 528).



 ( 4)

where ηe is engine efficiency, Peff is engine power [W], B is fuel consumption [g/s], Hu is

fuel specific calorific value [MJ/kg]

The simulation in this study is based on a Scania R540 6×4 long-haul tractor unit, modelled

to capture realistic mechanical and environmental dynamics. The drivetrain is assigned an

efficiency of 0.90 to account for mechanical losses, with rotational inertias defined as

0.81 kg·m² between the clutch and transmission, and 2.44 kg·m² between the transmission

and final drive. Each 315/70R22.5 tire has a rolling radius of 0.492 m and a rotational inertia

of 14.9 kg·m². The model evaluates both loaded (76,000 kg gross combination mass) and

unloaded (24,000 kg) scenarios. Rolling resistance coefficients were set at 0.014 (loaded)

and 0.006 (unloaded), while aerodynamic drag was calculated using a drag coefficient of

1.055 and a frontal area of 9.96 m², resulting in a combined of 10.51 m². The simulation uses

a standard gravitational constant of 9.81 m/s² and an air density of 1.292 kg/m³ to reflect

average ambient conditions, ensuring accurate modelling of road load forces. The final drive

ratio is specified as 2.85:1 for the given engine configuration.

3.1.1 E – Axle Configurations

The E-axle system modelled in (Kinnunen, 2023), as illustrated is based on BorgWarner’s

Platform 240, a liquid-cooled permanent magnet synchronous motor (PMSM) tailored for

heavy-duty applications. It delivers a continuous torque output of 500 Nm at a nominal

voltage of 700 V and operates through a fixed gear ratio of 12:1, enabling motor speeds up

to approximately 5,500 rpm. The motor achieves a high efficiency of 97% and includes an

over-torque capability for 30 seconds with a required 60-second cooldown period.

The energy storage system comprises modular battery packs with capacities of 20, 35, and

50 kWh, each operating at 840 V and featuring an internal resistance of 0.11 Ω. A 350-kW

inverter, rated at 700 V and operating with 98% efficiency, manages the bidirectional power

flow between the battery and the traction motor, supporting both propulsion and regenerative

braking

3.2 Vehicle Performance Analysis

A comparative analysis was performed on the Scania R540 6×4 truck using both ICE-only

and E-axle-assisted configurations, based on simulations by Kinnunen (2023). The

evaluation focused on a 338 km loaded long-haul transport scenario at a reference speed of

82 km/h.

In the baseline ICE-only case, the truck consumed 199.7 liters of diesel, equating to

59.08 L/100 km. When a 20-kWh battery-powered E-axle was integrated, fuel consumption

decreased to 188.1 liters (55.65 L/100 km), marking a 5.8% reduction. Increasing the battery

capacity to 35 kWh further reduced fuel usage to 183.8 liters (54.38 L/100 km), an 8.0%

decrease, while the 50-kWh configuration achieved the best result at 182.0 liters

(53.85 L/100 km), representing an 8.9% reduction overall. This improvement corresponds to

a 20.5% reduction in energy demand from the ICE due to electric traction support and

regenerative breaking.

In the unloaded condition over the same 338 km route, the ICE-only truck consumed

118.4 liters (35.04 L/100 km). The E-axle system with a 20-kWh battery reduced this to

32.99 L/100 km, while 35 kWh and 50 kWh batteries further improved fuel efficiency to

32.23 L/100 km and 31.91 L/100 km, respectively. These results demonstrate that even

modest electrification through an E-axle significantly improves fuel economy for both

loaded and unloaded operations.

calculated using the relative difference formula:

  󰇛󰇜

  ( 5)

Where, ΔFC is Percentage reduction in fuel consumption (%), FC100, ICE is Fuel consumption

per 100 km for the internal combustion engine vehicle (L/100 km), FC100, eAxle is Fuel

consumption per 100 km for the vehicle using an electric axle (L/100 km).

These calculations follow established methodologies used in energy and transport

engineering, including those defined in VECTO (Vehicle Energy Consumption Calculation

Tool) and validated by sources such as Koch et al. (2018) and regulatory fuel economy test

standards.

To validate the simulation outcomes of this study, comparative reference data was obtained

from Rahkola (2025), who conducted VECTO-based simulations for 76-ton Extra Heavy

Combination (EHC) lorries operating under a long-haul driving cycle with a reference

payload of 42,000 kg. As shown in Figure 16, the study focused on a box-body truck and

trailer configuration representative of high-capacity transport standards.

Figure 16. Fuel Consumption for GTW 76t (Rahkola, 2025)

The simulated fuel consumption for this setup was 54.1 L/100 km, aligning closely with

measured values observed under real-world loaded conditions. For the unloaded

configuration, fuel consumption was reported at 35.7 L/100 km.

In terms of emissions, Figure 17 illustrates that CO₂ output for the 76-tonne GVW truck was

measured at 2,690.4 g/km. This aligns with the European Union’s standard diesel emission

factor of 2.68 kg CO₂ per liter, as established by the European Environment Agency (2019).

These validation results support the accuracy and credibility of the simulation-based findings

in this thesis, confirming consistency with both regulatory benchmarks and empirical

performance metrics for heavy-duty transport vehicles.

Figure 17. CO2 Emission for Loaded 76t HDV (Rahkola, 2025)

3.3 Battery Electric Truck

In addition to hybridized solutions such as E-axle-assisted configurations, fully battery

electric trucks (BETs) have emerged as a promising alternative for decarbonizing long-haul

freight transport, particularly in regions with developed charging infrastructure and strict

emissions targets.

The study by Pihlatie et al. (2023) utilized the Smart eFleet simulation platform, developed

by VTT, to evaluate the energy consumption of battery-electric heavy-duty trucks under

Nordic transport conditions. Originally validated using empirical data from electric urban

buses (Anttila et al., 2019), Smart eFleet can replicate longitudinal vehicle dynamics by

dividing real-world transport routes into computational segments. These segments

incorporate geographically specific factors such as topography, road curvature, speed limits,

traffic signals, and distance profiles, which are derived from publicly accessible databases.

The platform also includes a traffic modeling module and uses a Proportional-Integral (PI)

control system to simulate realistic driving behaviors. It accounts for vehicle constraints

including motor torque limits and acceleration performance, making it a robust tool for

assessing electric vehicle powertrains, especially when empirical velocity profiles are

unavailable.

Within the Smart eFleet simulation framework, Pihlatie et al. (2023) analyzed modular zero-

emission powertrains under two long-distance freight scenarios involving battery-electric

heavy-duty trucks. The first scenario assessed a 750 km route using a 6×4 configuration truck

with a gross vehicle weight (GVW) of 40-tonnes, including a 24-tonne payload. This

configuration resulted in an average energy consumption of 1.83 kWh/km. The second

scenario focused on a 520 km return journey between Vuosaari Port in Helsinki and

Jyväskylä, examining energy performance under varying load conditions.

According to Pihlatie et al. (2023), the energy consumption was 1.85 kWh/km for the

northbound leg and 1.78 kWh/km for the southbound return, highlighting improved

efficiency on the return trip likely due to favorable topography or reduced traffic.

In contrast, Battery SoC Level for Route Jyväskylä – Vuosaari 76t demonstrates that under

heavier operating conditions a GVW of 76-tonnes with a 50-tonne payload, consumption

increased significantly to 3.21 kWh/km northbound and 2.99 kWh/km southbound.

To maintain operational feasibility, a 1 MW rapid charging session lasting 45 minutes was

incorporated at Vuosaari Port, aligning with the EU Regulation (EC) No 561/2006, which

mandates a minimum 45-minute rest break for truck drivers after every 4.5 hours of

continuous driving. This synchronization between rest periods and charging windows makes

electric long-haul freight logistics more viable, especially in the Nordic region.

Overall, the study underscores the substantial impact of vehicle mass, elevation changes, and

road conditions on BEV energy efficiency and demonstrates the value of simulation-based

planning in designing real-world electrified freight solutions.

3.3.1 Commercial BEV References

Also, Scania’s electric long-haul tractor is configured with a 6x4 wheel arrangement (A/B)

and features an Electric Propulsion System (EM C3–6) and will be available in 2025. This

drivetrain incorporates a six-speed transmission paired with a continuous power output of

450 kW (610 hp), optimized for high gross train weight (GTW) operation (Scania, 2024).

The system is supported by an installed battery capacity of 728 kWh, utilizing 83% State-

of-Charge (SoC) window, depending on the operational use case average energy

consumption 1.61kWh/km. At 64-tonnes GTW, the vehicle achieves a maximum possible

range of 375 km under fully loaded conditions. At GTW 42t vehicle achieve maximum

possible range of 550km. Energy replenishment is conducted via a CCS2 DC fast-charging

interface capable of 375 kW (500 A). Under optimal conditions, the battery can be fully

charged within 110 minutes, assuming a full 728 kWh capacity and the 83% usable SoC

window.

3.4 Overview of Transport Route and Logistics

This case study presents a secondary data-based study investigates the energy efficiency and

carbon emissions of heavy-duty freight transport across a 1,444-kilometre round-trip route

between Kuusamo and Keltakallio (Kotka) in Finland. Three vehicle configurations were

evaluated: a conventional internal combustion engine (ICE) truck, the same vehicle

retrofitted with an electric axle (E-axle) powered by modular batteries (20, 35, and 50 kWh),

and a fully battery-electric vehicle (BEV) truck, operating on a real-world logistics route in

Finland. The objective is to evaluate the effectiveness of E-Trailer integration in reducing

the environmental impact of diesel freight transport and to benchmark these results against

a fully electric truck solution.

The Figure 18 illustrates that selected route connects Kuusamo, a town in northeast Finland,

with Keltakallio, a site in southeast Finland where a battery factory is planned with including

the locations of available DC fast-charging stations rated over 300 kW, which are essential

for the feasibility of battery-electric long-haul operations

Figure 18. Kuusamo to Kotka Route with Most available (higher than 300kW) Charging

Stations

3.5 Fuel consumption for a Diesel truck with E-axle

This subsection quantifies total fuel use for a long-haul logistics corridor between Kuusamo

and Keltakallio (Kotka), employing secondary data from the validated simulation work of

Kinnunen (2023). This case study evaluates the energy consumption and CO₂ emissions of

a heavy-duty long-haul vehicle over a real-world logistics route in Finland, using secondary

data from previously validated simulation models. The selected route connects Kuusamo

(NE Finland) with Keltakallio, Kotka (SE Finland), covering a one-way distance of 722 km,

and a round-trip distance of 1,444 km.

The total fuel consumption was computed using the standard equation:

 

   ( 6)

Where, FCtotal is Total fuel consumption (L), FC100 is Fuel consumption per 100 km

(L/100 km), D is Total distance travelled (km)

as recommended in the VECTO methodology (European Commission, 2021)

The vehicle studied is a Scania R540 6×4 diesel truck operating at full gross combination

mass of 76 tonnes, without any E-axle assistance. According to Kinnunen (2023) the vehicle

achieves an average fuel consumption of 59.08 L/100 km in full loaded and 35.04 L/100km

for unloaded, based on simulation and validated energy models.

For the full round-trip between Kuusamo and Kotka, covering 1,444 km, the Scania R540

6×4 diesel truck without E-axle assistance consumed a total of 680 liters of fuel. This value

is the sum of 427 liters used during the fully loaded outbound journey (722 km) and 253

liters consumed on the unloaded return leg over the same distance.

In the E-axle-assisted configuration with a 20-kWh battery, the route between Kuusamo and

Kotka. For the fully loaded outbound journey (722 km), the vehicle consumed 402 liters of

diesel, based on an average fuel consumption of 55.64 L/100 km. On the unloaded return

trip, the fuel consumption was further reduced to 238 liters, using a specific consumption

rate of 32.99 L/100 km. The total round-trip fuel consumption in this hybrid scenario was

therefore 640 liters, representing a reduction of approximately 5.8% compared to the 680

liters consumed in the ICE-only baseline configuration.

With the integration of an E-axle powered by a 35-kWh battery, On the fully loaded

outbound journey, the truck consumed 393 liters of diesel, based on a fuel consumption rate

of 54.38 L/100 km. During the unloaded return leg, fuel usage was reduced to 233 liters,

corresponding to an average consumption of 32.23 L/100 km. The total round-trip fuel

consumption in this configuration was 625 liters,

In the configuration equipped with a 50 kWh E-axle battery, For the fully loaded outbound

leg, the truck consumed 389 liters of diesel, based on a specific consumption rate of 53.85

L/100 km. On the unloaded return journey, the fuel consumption further decreased to 230

liters, with a rate of 31.91 L/100 km. The total round-trip fuel consumption was thus 619

liters, A summary of the comparative fuel consumption results for all configurations is

presented in Figure 19.

Figure 19. Fuel Consumption with and without E-Axle on Kuusamo-Kotka route

These findings confirm that even modest E-axle battery capacities can deliver material fuel-

economy gains on Nordic long-haul operations, offering a pragmatic interim pathway toward

lower-emission freight while full battery-electric solutions mature.

678 640 625 619

427 402 393 389

253 238 233 230

100

200

300

400

500

600

700

800

ICE-Only E-axel 20kWh E-axel 35kWh E-axel 50kWh

FUEL CONSUMTION(LIERS)

VEHICLE CONFIGURATION

Fuel Consumption ( Kuusamo-Kotka Route)

Total Fuel Consumtion Loaded (76t) Kuusamo-Kotka Unloaded (24t) Kotka-Kuusamo

3.6 CO2 Emission for a Diesel truck with E-axle

To comprehensively assess the environmental implications of integrating electric axles into

diesel-powered heavy-duty vehicles, this section presents a CO₂ emissions analysis based on

fuel consumption data across the Kuusamo–Kotka logistics route.

To quantify the environmental impact of diesel consumption, the following standard

equation was used to calculate CO₂ emissions:

2totalliters diesel ( 7)

Where CO₂total is Total CO₂ emissions [kg], FCliter is Fuel consumption [L], EFdiesel is

2.68 kg CO₂/liter (EU standard emission factor for diesel combustion).

In the ICE-only case, the Scania R540 6×4 truck emitted 1,821 kg of CO₂ over the full round-

trip (1,444 km). This includes 1,143 kg during the loaded outbound journey and 678 kg on

the unloaded return leg, representing the highest emissions baseline.

With the integration of a 20 kWh E-axle, CO₂ emissions were reduced to 1,715 kg, a 5.8%

reduction from the ICE baseline. The loaded leg produced 1,077 kg and the unloaded leg

638 kg. Using a 35-kWh battery, emissions fell to 1,676 kg. The truck emitted 1,052 kg on

the loaded trip and 624 kg on the return, achieving strong efficiency with moderate battery

capacity. The 50 kWh E-axle setup yielded the lowest emissions at 1,659 kg of CO₂. The

breakdown includes 1,042 kg during the loaded leg and 617 kg for the return trip, though

gains beyond 35 kWh were incremental. Figure 20 presents a comparative summary of CO₂

emissions across all evaluated configurations for the Kuusamo–Kotka route.

Figure 20. CO2 Emission on Kuusamo-Kotka route with and without E-Axle

As shown in Figure 20, the implementation of an E-axle leads to a noticeable reduction in

CO₂ emissions across all battery capacities, with the most significant drop occurring between

the baseline and the 35-kWh configuration.

3.7 Battery Electric Truck without E-axle

The Scania battery-electric truck with a 6×4 axle configuration and a maximum battery

capacity of 728 kWh currently represents one of the highest-capacity electric trucks

commercially available. When utilizing an 83% State of Charge (SoC) window, the usable

energy amounts to approximately 604 kWh.

According to the study Zero-Emission Truck Powertrains for Regional and Long-Haul

Missions by Pihlatie et al. (2023), Based on empirical energy consumption rates derived

from real-world Nordic conditions, the vehicle demonstrates varying range performance

depending on the gross total weight (GTW).

For Kuusama to Kotka route in a fully loaded configuration with 76-tonnes GTW, where the

average energy demand is 2.99 kWh/km at 80km/h average speed, the maximum achievable

range is approximately 202 km. In contrast, under a lighter 40-tonne GTW configuration,

1143 1077 1052 1042

678 638 624 617

200

400

600

800

1000

1200

1400

ICE- Only E-axel 20kWh W-axel 35kWh E-axel 50kWh

CO2 EMISSION (KG)

VEHICLE CONFIGURATION

Co2Emission ( Kuusamo - Kotka Route)

Loaded Kuusamo-Kotka Unloaded Kotka-Kuusamo

with a reduced energy consumption of 1.78 kWh/km, the truck can travel approximately

339.5 km on the same SoC window.

According to Scania’s own data fully loaded configuration GTW with 64t can drive

maximum 375 km and GTW 42t configuration can drive 550 km by single charge.

In Finland, 350 kW CCS2 DC fast chargers are widely used for heavy-duty electric vehicle

applications. When charging from 0% to 83% SoC using a 350 kW CCS2 charger, the total

charging time is 104 minutes. In a more typical fast-charging scenario lasting 45 minutes,

the charger can deliver around 262.5 kWh of energy equivalent to 43.5% of the total battery

capacity.

With a 300-kW charger, the energy delivered in a typical 45-minute fast-charging scenario

would be approximately 225 kWh, which is equivalent to about 37.5% of the total battery

capacity. When charging from 0% to 83% State of Charge (SoC), the estimated charging

time using a 300-kW charger would be around 100 minutes, or roughly 1 hour and 40

minutes. This is a slight increase in time compared to a 350-kW charger, highlighting the

impact of charger power on the overall charging duration.

To assess the practical feasibility of battery-electric freight transport along the Kuusamo–

Kotka corridor, this study considers the availability of high-power DC fast charging

infrastructure.

The route includes several key locations equipped with CCS2 chargers rated at 300 kW or

higher. These locations-Kuusamo, Suomussalmi, Kontiomäki, Kajaani, Iisalmi, Lapinlahti,

Siilinjärvi, Kuopio, Varkaus, Joroinen, Juva, Nuutilamäentie, Mikkeli, Kouvola, and Kotka

and illustrate in Figure 23. These locations are strategically distributed along the 722 km

route, enabling efficient recharging during mandatory driver rest periods, in line with

Regulation (EC) No 561/2006. To evaluate energy consumption and State of Charge (SoC)

performance under realistic driving conditions, seven simulation scenarios were developed

for different gross combination weights (GCWs): 40t, 42t, 64t, and 76t.

3.7.1 Scenario 1 – 40t GCW, Energy Consumption: 1.78 kWh/km

This scenario demonstrates a feasible long-haul journey using a 728 kWh Scania BEV truck

at 40 tonnes GCW. With energy consumption at 1.78 kWh/km, each segment ends with a

manageable state of charge (SoC), and 45-minute charging intervals replenish sufficient

energy (~262.5 kWh).

The route successfully aligns with EU driver rest regulations, supporting full completion of

the 722 km one-way trip with four charging sessions and no operational SoC risk.

The Table 6 illustrates the segment-wise energy consumption, travel time, and SoC levels of

a 40-tonne GCW Scania BEV truck with three fast-charging stops in Kajaani, Kuopio, and

Mikkeli each lasting 45 minutes the vehicle maintains a safe SoC margin, ranging from

12.5% to 22.8%.

Table 6. Scenario 1: 40t GCW with 3 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

437.9

22.8%

58.9%

Kajaani →

Kuopio

179

135

318.6

15.1%

51.2%

Kuopio →

Mikkeli

158

119

281.2

12.5%

48.5%

Mikkeli →

Kotka

166

125

295.5

7.9%

—

The data in Table 6 highlights that with three strategically placed fast-charging stops, the

truck maintains a safe operational margin throughout the journey. The final SoC upon arrival

in Kotka is 7.9%, indicating efficient energy use and effective trip planning.

3.7.2 Scenario 2 – 40t GCW, Energy Consumption: 1.78 kWh/km

An alternative to Scenario 1, this version includes longer travel legs between charging

stations, which results in tighter SoC margins reaching as low as 0.5% before charging.

The Table 7 shows a 40-tonne GCW Scania BEV truck completing the Kuusamo–Kotka

route with two 45-minute charging stops. The SoC drops to 0.5% before Joroinen and 0.0%

at arrival, indicating a high-risk, low-margin energy scenario.

Table 7. Scenario 2: 40t GCW with 2 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

437.9

22.8%

58.9%

Kajaani →

Joroinen

239

180

425.4

0.5%

36.5%

Joroinen →

Kotka

238

179

423.6

0.0%

—

On the 722 km Kuusamo–Kotka route, Scenario 1, with four fast-charging stops (≥300 kW)

in Kajaani, Kuopio, Mikkeli, and Kotka, maintained safe SoC levels (7.9%–22.8%) and

required 135 minutes of total charging, aligning with EU driver rest regulations. Scenario 2

included only two charging stops, resulting in critically low SoC (down to 0.0%). This

analysis confirms that Scenario 1 is the more viable option under current Finnish charging

infrastructure.

3.7.3 Scenario 3 – 76t GCW, Energy Consumption: 2.99 kWh/km

This scenario reveals the current limitations of battery-electric trucks under maximum

payload conditions. With energy usage reaching 735.5 kWh in the first 246 km leg alone,

the available 604 kWh is insufficient to reach even the first destination without depleting the

battery.

Despite consistent 45-minute recharging intervals, the truck never recovers adequate SoC

for subsequent legs. Therefore, full electrification at 76 tonnes GTW is not currently feasible

for extended routes using present battery technology.

Table 8. Scenario 3: 76t GCW with 3 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

735.5

0.0%

36.1%

Kajaani →

Kuopio

179

135

535.2

0.0%

36.1%

Kuopio →

Mikkeli

158

119

472.4

0.0%

36.1%

Mikkeli →

Kotka

166

125

496.3

0.0%

—

The Table 08 presents the segment-wise performance of a 76-tonne GCW Scania BEV truck

along the Kuusamo–Kotka route. Despite three 45-minute charging stops, the truck reaches

0% SoC at the end of each segment, indicating that current battery and charging

infrastructure are insufficient for fully loaded long-haul operations at this weight.

This analysis highlights a fundamental constraint of current battery technology and charger

power in Nordic long-haul applications at the 76-tonne weight class. Until higher-energy

batteries (> 1 MWh usable) or en-route megawatt-level charging become common place,

fully electric operation at maximum GCW is not yet logistically feasible on extended Finnish

routes.

3.7.4 Scenario 4 – 64t GCW, Energy Consumption: 1.611 kWh/km

At 64 tonnes GTW, this configuration aligns with Scania’s internal data and offers a

balanced compromise between payload capacity and energy efficiency. The truck retains

between 25%–28% SoC at the end of each leg and consistently benefits from 45-minute

recharging sessions.

The Table 08 shows the energy consumption and SoC progression of a 64-tonne GCW

Scania BEV truck on the Kuusamo–Kotka route. With three 45-minute fast-charging stops,

the truck maintains a stable SoC margin between 25% and 28%, demonstrating that this

configuration is well-suited for current long-haul operations under existing infrastructure.

Table 9. Scenario 4: 64t GCW with 3 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

396.3

28.6%

64.6%

Kajaani →

Kuopio

179

135

288.4

25.0%

61.1%

Kuopio →

Mikkeli

158

119

254.5

26.1%

62.2%

Mikkeli →

Kotka

166

125

267.4

25.4%

—

The route’s alignment with mandatory rest breaks (per EU Regulation EC No 561/2006) also

supports operational practicality. Scenario 4 thus presents a realistic and low-risk option for

near-term electrified long-haul freight in Finland’s logistics ecosystem.

3.7.5 Scenario 5 – 64t GCW, Energy Consumption: 1.611 kWh/km

The Table 10 outlines the performance of a 64-tonne GCW Scania BEV truck on the

Kuusamo–Kotka route with two 45-minute charging stops. The SoC drops to 11.7% before

the final leg and reaches 0.0% upon arrival in Kotka, despite identical energy consumption

of 1.611 kWh/km, the reduced number of charging intervals results in a significantly lower

State of Charge (SoC) before the final leg.

Table 10. Scenario 5: 64t GCW with 2 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

396.3

28.6%

64.6%

Kajaani →

Joroinen

239

180

385.0

11.7%

47.8%

Joroinen →

Kotka

238

179

383.4

0.0%

—

In comparison, Scenario 4, which included three charging stops, maintained State of Charge

(SoC) margins between 25% and 28% throughout the Kuusamo–Kotka route, ensuring a

safer and more predictable operation.

By contrast, Scenario 5, which relied on only two charging stops, resulted in critically low

SoC levels, dropping to 11.7% before the final leg and reaching 0.0% upon arrival in Kotka.

While Scenario 5 may be technically feasible under ideal conditions, the absence of an

energy buffer introduces significant operational risk and limits the system’s flexibility to

accommodate delays, detours, or adverse weather conditions.

Therefore, Scenario 4 emerges as the preferred strategy for current long-haul operations,

offering greater resilience, improved reliability, and better alignment with the capabilities of

existing charging infrastructure.

3.7.6 Scenario 6 – 42t GCW, Energy Consumption: 1.32 kWh/km

This scenario represents on Table 11 the most energy-efficient and operationally robust

configuration tested. With only modest energy demands (270–262 kWh per leg), the truck

retains nearly 46% SoC throughout the trip.

Charging needs are minimal, and two sessions suffice for a complete 722 km trip. It

highlights that at moderate payload levels, BEV trucks can operate efficiently with minimal

disruption, making this scenario ideal for scalable zero-emission logistics.

Table 11. Scenario 6: 42t GCW with 2 stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Kajaani

246

185

270.4

45.9%

81.9%

Kajaani →

Joroinen

239

180

262.7

45.8%

81.9%

Joroinen →

Kotka

238

179

261.6

46.0%

—

This configuration not only ensures energy security but also aligns well with EU-mandated

rest breaks, making it highly practical for real-world logistics. The minimal charging

requirement combined with consistently high SoC margins demonstrates the potential of

BEV trucks to support scalable, zero-emission freight transport when operating under

moderate payload conditions.

3.7.7 Scenario 7 – 42t GCW, Energy Consumption: 1.32 kWh/km

Using fewer, longer route legs (362 km and 360 km), this configuration still operates

effectively within SoC constraints due to low energy consumption. SoC levels remain above

10% after the final leg, confirming high operational confidence even with reduced charging

frequency.

This route layout maximizes range utilization and minimizes dwell time, reinforcing the 42t

GTW configuration as the most suitable for commercial battery-electric freight transport in

current Nordic infrastructure.

Table 12. Scenario 7: 42t GCW with 1 Stops

From → To

Distance

(km)

Time (min)

Energy Used

(kWh)

Arrival SoC

(%)

Charging

Time (min)

SoC After

Charge (%)

Kuusamo →

Lapinlahti

362

272

397.8

28.4%

64.4%

Lapinlahti

→ Kotka

360

270

395.6

10.1%

—

Scenarios 6 and 7 analyse a 42-tonne GCW Scania BEV truck with energy consumption of

1.32 kWh/km (according to Scania own data), representing the most efficient configuration

tested. Scenario 6, with three shorter segments and two 45-minute charging stops, maintains

a high and steady SoC of ~46% throughout. Scenario 7 uses fewer, longer legs, ending with

10.1% SoC still within safe limits while minimizing total charging time.

Both scenarios demonstrate excellent energy efficiency, but Scenario 6 offers more

consistent SoC margins, whereas Scenario 7 optimizes range utilization and charging

frequency.

Table 13 provides a comparative overview of seven long-haul battery electric vehicle (BEV)

truck scenarios modelled along the 722 km Kuusamo–Kotka corridor.

Table 13. Summary of All BEV truck scenarios

Scenario

GCW

(t)

Energy

Consumption

(kWh/km)

Total Energy

Consumption

(kWh)

Chaging

Stops

Charging

Time (min)

Total Travel

Time

Min

Soc

(%)

1.78

1333.2

135

11h 39min

7.9

1.78

1287

10h 34min

2.99

2239.4

135

11h 39min

1.611

1206.6

135

11h 39min

1.611

1164.7

10h 34min

1.32

794.7

10h 34min

45.8

1.32

793.4

9h 47min

10.1

Each scenario considers a different gross combination weight (GCW) ranging from 40 to 76

tonnes, using realistic energy consumption values derived from validated studies (e.g.,

Pihlatie et al., 2023; Scania, 2024). The table highlights critical performance parameters

including total energy consumption, number of charging stops, total charging and travel time

(expressed in hours and minutes), state-of-charge (SoC) safety margins, and operational

viability.

Scenarios with moderate payloads (42–64t) demonstrate feasible and efficient operation

under current charging infrastructure (≥300 kW DC CCS2 stations), while the 76t

configuration fails to sustain viable SoC levels. Notably, Scenario 6 (42t, two charging stops)

and Scenario 4 (64t, three stops) offer optimal performance with safe SoC buffers and

minimal logistical compromise. In contrast, scenarios with fewer charging intervals

(Scenarios 2 and 5) show elevated risk due to SoC depletion upon arrival. This analysis

supports route planning and fleet electrification decisions by identifying configurations best

suited for immediate implementation within the existing Nordic infrastructure.

4 Discussion

This study systematically evaluated the fuel consumption, CO₂ emissions, and operational

feasibility of heavy-duty long-haul transport in Nordic conditions using three configurations:

a conventional diesel truck, a diesel truck with an electric axle (E-axle), and a fully battery

electric truck (BET). The investigation was grounded in secondary data from validated

sources, including simulation-based studies (Kinnunen, 2023; Pihlatie et al., 2023), VECTO

modelling parameters, and manufacturer technical data from Scania.

The integration of E-axles demonstrated measurable fuel savings and CO₂ reductions. Across

the full 1,444 km round-trip between Kuusamo and Kotka, diesel consumption decreased by

up to 8.9% with a 50 kWh E-axle battery, compared to the internal combustion engine (ICE)-

only baseline. Corresponding CO₂ emissions were reduced from 1,822 kg to 1,659 kg. These

benefits stem from E-axle regenerative braking and torque assistance during traction-

intensive phases, especially on gradients and acceleration cycles. However, marginal gains

above 35 kWh indicate diminishing returns, suggesting an optimal battery sizing strategy

should balance weight, cost, and energy efficiency.

The performance of the Scania 728 kWh battery electric truck was assessed through multiple

load-based transport scenarios along the Kuusamo–Kotka corridor. At 40-tonnes gross

vehicle weight (GVW), energy consumption averaged 1.78 kWh/km, allowing the truck to

complete the route with planned fast-charging stops aligned with EU-regulated driver rest

periods (Regulation EC No. 561/2006). In contrast, under 76 t GVW with an energy demand

of 2.99 kWh/km, the vehicle could not complete even the first 246 km leg on a single charge,

exposing critical limitations for current BEV technologies in ultra-heavy, long-haul freight.

Scenarios at intermediate weights (e.g., 64 t and 42 t) proved operationally feasible. The 64 t

configuration required multiple charging intervals but aligned with existing Finnish CCS2

350 kW charging infrastructure. The 42 t configuration, consuming only 1.32 kWh/km,

exhibited surplus SoC throughout the route, confirming its suitability for uninterrupted

regional transport.

Real-world deployment of BEVs in Nordic logistics requires harmonizing vehicle mass,

charging time, route topography, and regulatory breaks. The alignment of fast-charging

durations with statutory driver rest periods (e.g., 45 minutes every 4.5 hours) provides an

operational window for BEV energy replenishment without additional downtime. However,

battery degradation, temperature impacts, and charging infrastructure availability remain

constraints requiring further study.

5 Conclusion

This research demonstrates that the integration of E-axles into diesel heavy-duty trucks can

yield fuel savings of up to 8.9% and proportionally reduce CO₂ emissions without

compromising operational logistics. The marginal benefit curve suggests an optimal E-axle

battery size above 35 kWh under Nordic long-haul conditions.

For fully electric heavy-duty trucks, feasibility strongly depends on vehicle gross weight and

route energy intensity. While 42 to 64 t configurations are achievable with current 728 kWh

battery packs, 76 t operations exceed current technological limitations without opportunity

charging or battery swaps. The importance of charging infrastructure density, power

capacity, and integration with regulatory rest requirements was also validated.

Future research should explore real-world driving data integration with dynamic route

simulation, lifecycle cost assessment of hybrid vs. full-electric systems, and policy

incentives needed to scale BEV deployment in heavy freight sectors. Additionally, e-trailer

configurations with E-axles and intelligent energy-sharing algorithms could provide a

scalable bridge technology for sustainable logistics in Nordic and pan-European corridors.

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