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Decarbonising
Europe’s Trucks
How to Minimise
Cost Uncertainty

Case-Specific Policy Analysis

Decarbonising
Europe’s Trucks
How to Minimise
Cost Uncertainty

Case-Specific Policy Analysis

The International Transport ForumThe International Transport Forum is an intergovernmental organisation with 64 member countries. It acts
as a think tank for transport policy and organises the Annual Summit of transport ministers. ITF is the only
global body that covers all transport modes. The ITF is politically autonomous and administratively
integrated with the OECD.
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understanding of the role of transport in economic growth, environmental sustainability and social
inclusion and to raise the public profile of transport policy.
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Case-Specific Policy Analysis Reports
The ITF’s Case-Specific Policy Analysis series presents topical studies on specific issues carried out by the
ITF in agreement with local institutions. Any findings, interpretations and conclusions expressed herein arethose of the authors and do not necessarily reflect the views of the International Transport Forum or theOECD. Neither the OECD, ITF nor the authors guarantee the accuracy of any data or other information
contained in this publication and accept no responsibility whatsoever for any consequence of their use.
This work is published under the responsibility of the Secretary-General of the ITF. This document, as wellas any data and map included herein, are without prejudice to the status of or sovereignty over any
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Cite this work as: ITF (2022), “Decarbonising Europe’s Trucks: How to Minimise Cost Uncertainty”,
International Transport Forum Policy Papers, No. 107, OECD Publishing, Paris.

Acknowledgements
The author of this report is Matteo Craglia of the International Transport Forum (ITF). The report is basedon original research and exchanges that took place during meetings held as part of the ITF DecarbonisingTransport: Driving Implementation Actions project.
The author would like to thank the following individuals for their contributions and insightful comments
during the review process:Patrik Akerman (Siemens), Berger Anders (Volvo Group), Stefan Bakker (Institute for Transport Policy
Analysis, Netherlands), Hussein Basma (International Council on Clean Transportation, ICCT), Pierpaolo
Cazzola (UC Davis), Elizabeth Connelly (International Energy Agency, IEA), Bert D’Hooghe (Volvo Group),
Shobhan Dhir (IEA), Henrik Engdahl (Volvo Group), Eric Jeanniere (DGITM, France), Andreas Kopf (ITF), Sita
Holtslag (CALSTART), Martin Koning (IFSTTAR-SPLOTT), Marc Lejeune (Volvo Group), William Lilley(Aramco), Steffen Link (Fraunhofer ISI), Luis Martinez (ITF), Eamonn Mulholland (ICCT), Arne Nabo (VTI
Sweden), Leonardo Paoli (IEA), Andrea Papu Carrone (ITF), Patrick Plotz (Fraunhofer ISI), John Pritchard(ITF), Sacha Scheffer (MINIENW, Netherlands), Christian Schiller (Bosch), Agnieska Sulowska (Ministry of
Climate and Environment, Poland), Elisabeth Windisch (ITF) and Seiar Zia (NHTSA, USA).
Thanks also go to Hilary Gaboriau (ITF) for her insightful comments in the copy-editing process and forco-ordinating the publication of the report.
The “Decarbonising Transport: Driving Implementation Actions” project is part of the ITF’s wider
Decarbonising Transport (DT) initiative. The DT initiative is a key instrument to help governments andindustry translate climate ambitions into actions. It currently unites more than 70 governments,organisations, institutions, foundations and companies. More information on the DT Initiative is available
at https://www.itf-oecd.org/decarbonising-transport. Funding for this work was kindly provided by theEuropean Union’s Horizon 2020 research and innovation programme under grant agreement
No. 101021600.

TABLE OF CONTENTS

Table of contents
Abbreviations and acronyms ................................................................................................................ 6
Glossary ............................................................................................................................................... 7

Executive summary .............................................................................................................................. 8
In brief: Six recommendations for adopting zero-emission vehicles .................................................. 11
Zero-emission trucks: The road to carbon dioxide reduction ............................................................ 15
The advantages of zero-emission vehicles..................................................................................... 15
Potential challenges for zero-emission vehicles ............................................................................ 16
The role of technology neutrality in policy making ........................................................................ 17
Comparing truck technologies in an uncertain future ....................................................................... 19
Estimating the total cost of ownership .......................................................................................... 20
Quantifying uncertainty ................................................................................................................. 20
The impact of uncertainty on total cost of ownership................................................................... 25
The impact of uncertainty on technologies’ cost-competitiveness ............................................... 26
Policy measures can pave the way for zero-emission vehicles ...................................................... 30
Conclusions........................................................................................................................................ 32
References ......................................................................................................................................... 33
Annex A. Methodology ...................................................................................................................... 38
Annex B. Equations and descriptions ................................................................................................. 51
FiguresFigure 1. Uncertainty ranges of model inputs .................................................................................... 24
Figure 2. Total cost of ownership with uncertainty: Three European vehicle groups using four
different powertrains ......................................................................................................................... 25
Figure 3. Potential sales shares of lowest total-cost-of-ownership technology .................................. 27
Figure 4. Year in which BEVs and FCEVs reach total cost of ownership parity with ICEVs ................... 28
Figure 5. Potential sales shares of electric road systems vehicles with uncertainty ............................ 29
Figure 6. The impact of policy measures on adoption of BEVs ........................................................... 31
Figure 7. Vehicle purchase cost, engine power and average range requirements .............................. 39
Figure 8. Annual mileage by vehicle group ......................................................................................... 40
Figure 9. Electricity costs for battery electric vehicles and electric road system vehicles ................... 45
Figure 10. The effects of utilisation on electric road systems and high-power charging costs ............ 47

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TABLE OF CONTENTS

TablesTable 1. Definitions of different powertrains...................................................................................... 15

Table 2. European vehicle types as defined by European Union heavy-duty vehicles CO2 emission
regulation 2017/2400 ........................................................................................................................ 19
Table 3. A summary of sources of uncertainty ................................................................................... 21
Table 4. Energy efficiency (in 2020) by powertrain, drive cycle allocation and vehicle group ............. 39
Table 5. Electric road system infrastructure cost uncertainty bounds ................................................ 47

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ABBREVIATIONS AND ACRONYMS

Abbreviations and acronyms

BEV

Battery electric vehicle

ERS

Electric road system

ERSVElectric road system vehicle

FCEV

Fuel cell electric vehicle

GHG

Greenhouse Gas

HDV

Heavy-duty vehicle

ICE

Internal combustion engine

ICEVInternal combustion engine vehiclekg

Kilogram

kW

Kilowatt

kWe

Kilowatt-electric

kWhKilowatt hour

MW

Megawatt

MWh

Megawatt hour

TCO

Total cost of ownership

ZEV

Zero-emission vehicle

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GLOSSARY

Glossary
Overhead catenary electric road system

Infrastructure enabling vehicles to draw electricity from cables
installed over a road. A pantograph is used to connect the vehicle tothe cables (in a similar fashion to electric trains).

Pantograph

A mechanical linkage used to connect a vehicle to overhead catenary
cables. Electrical contact between the pantograph and the wires
allows the vehicle to draw electricity from the cables whilst driving.

Powertrain

The source of power used to provide motion to a vehicle. This
includes the motor, transmission and fuel/energy storage system.

Total cost of ownership (TCO)

The sum of all expenditure over the ownership period of a vehicle
including: purchase costs, residual value at end of life, fuel/energy
costs and operation and maintenanceZero-emission vehicle (ZEV)

A vehicle producing no tailpipe emissions at the point of use.
Examples include battery electric vehicles and fuel cell electricvehicles.

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EXECUTIVE SUMMARY

Executive summary
What we did
This report investigates the feasibility of decarbonising heavy-duty trucks in Europe with zero-emission
powertrain technologies. It compares three technological approaches: battery electric vehicles, electricroad systems and hydrogen fuel cell vehicles. Currently, uncertainty regarding their relative merits
hampers decision-making about which technologies to pursue and postpones decarbonisation.
This report investigates the financial viability of the three different technologies and compares it to thatof conventional diesel vehicles by examining their total cost of ownership. The analysis accounts for thesignificant uncertainty associated with each technology for the first time by exploring 1 000 unique
scenarios for each powertrain technology and across nine different vehicle size segments in Europe. The
scenarios range from conservative to ambitious in their assumptions about the future of each powertraintechnology to explore a broad spectrum of possible solutions. By explicitly quantifying the uncertainty
associated with each technology and exploring a range of vehicle size segments, the analysis offers agreater understanding of the potential of zero-emission powertrain technologies.

What we foundThis analysis finds that zero-emission vehicles should generally become cost-competitive with dieselpropelled trucks between 2030 and 2040 across all vehicle sizes. When exactly zero-emission vehicles will
become cost-competitive with traditional trucks varies with vehicle size: the smallest vehicle categories
could reach parity on total cost of ownership with diesel vehicles in 2022. Larger road freight vehicles are
more likely to be cost-competitive around 2035.
The total cost of ownership of trucks varies significantly between the three powertrain technologies
examined. Battery electric vehicles (BEVs) and electric road system vehicles (ERSVs) – which use catenaries
installed above roads to supply vehicles with electricity – have the potential to be the most costcompetitive technologies in Europe due to their energy efficiency and low operational costs, which offset
upfront purchase costs.
How battery electric trucks and ERSVs will compare in terms of cost is not yet clear, as their total cost ofownership is comparable in most scenarios. BEVs will likely cost more to purchase and face greater
operational constraints than vehicles using an electric road system, which could impede their rapid
adoption. Conversely, it is unclear how quickly electric road systems might be deployed, and they would
face significant upfront construction costs, which would need government support.
A clearer understanding of the deployment timelines for the required infrastructure – charging stations
for battery electric vehicles, overhead contact lines for electric road systems – is needed to allow industry
and policy makers to prioritise and understand how these technologies may complement each other.

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EXECUTIVE SUMMARY

Based on the scenarios explored, hydrogen fuel cell electric vehicles (FCEVs) are less competitive than theother two zero-emission technologies. FCEVs are cost competitive in only a small number of marginal casesthat assume ambitiously low hydrogen fuel costs and very conservative assumptions for BEVs. Thissuggests that FCEVs might play a niche role in the future fleet of heavy-duty road vehicles, which in turnraises doubts about whether large-scale hydrogen refuelling infrastructure would be sufficiently utilised.
At least for Europe, the results of the analysis thus call into question whether policies should necessarily
remain technology-neutral regarding the mass-market adoption of hydrogen as a fuel for trucks. That said,
it is possible that these findings apply to the European truck market only. FCEVs might offer adecarbonisation solution in other regions where road freight covers longer distances.
Progress in improving energy efficiency reduces future uncertainty about vehicles’ total cost of ownership,
as low energy efficiency increases owners’ exposure to fluctuating energy costs. Promoting energyefficient powertrain options and other efficiency-enhancing solutions for vehicles, such as improved
aerodynamics, will reduce the effect of rising energy prices and increase the probability of lowering
emissions.
A number of barriers could delay the adoption of zero-emission vehicles. Truck operators may haveinsufficient capital for the required investments. The supporting infrastructure required may not (yet) be
deployed. Behavioural factors such as a hesitancy to switch to new technologies requiring changes inoperations as well as imperfect knowledge and foresight may also hamper adoption. Policies targetingsuch barriers are essential to strengthen the market’s confidence in the most promising vehicle
technologies.

What we recommend
Ensure that policies to promote direct electrification of trucks remain technology-neutral
Both battery-electric vehicles and electric road system vehicles can be cost-effective replacements fordiesel trucks. Battery electric trucks are well placed to be adopted in the short term in certain market
segments. However, it is unclear whether electric road systems could play a complementary role helping
to decarbonise the most challenging road freight applications. Policy towards the electrification of trucks
should therefore avoid closing the door to either and both technologies should be actively considered inpolicy discussions about future infrastructure deployment. Short-term technology-neutral policies include
strengthening the capacity of the electricity grid along main roads so it can feed charging stations forbattery-electric vehicles as well as electric road systems along those routes with potentially sufficientdemand.
Launch targeted studies and pilot projects to assess the merits of electric road systems for roadfreight decarbonisation
Given the uncertainties about the relative merits of battery electric trucks and electric road systems, only
further detailed assessments will clarify which technology is financially more viable and how they mightcomplement each other to deliver the largest reductions in carbon dioxide (CO2) emissions. Pilot projects
to test overhead catenary electric road systems and high-power charging systems for battery electricvehicles will be invaluable to better understand the real-world infrastructure needs, operational costs andrealistic timelines for rolling out such infrastructure.

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EXECUTIVE SUMMARY

Further investigate decarbonisation technologies for particularly challenging road freight applications
Electrifying heavy-duty road freight with battery electric trucks or electric road systems may be challenging
for certain niche use cases. Further investigations are needed to better understand how othertechnologies could provide a complementary role to electrification in decarbonising such road freight
applications. It is possible that hydrogen and other propulsion technologies not included in this analysis
may be necessary to reduce the carbon intensity of trucks in a limited number of road freight sectors, even
if they are unlikely to be cost-competitive for mass-market applications.
Introduce policies that help zero-emission vehicles become cost-competitive sooner
Accelerating the adoption of zero-emission vehicles requires targeted policy support. The high upfront
purchase costs of zero-emissions vehicles present a barrier to large-scale adoption, particularly for small
trucking companies. Differentiated purchase subsidies and low-interest loans for the purchase of zeroemission vehicles, together with road pricing and carbon taxation, would make them cost competitive withdiesel trucks before 2030 and help accelerate the decarbonisation of the road freight sector. Such policies
can be designed to be time-limited, revenue-neutral and would reduce the range of uncertainty on the
adoption of zero-emission vehicles.
Accelerate the deployment of zero-emission vehicle infrastructure
The adoption of zero-emission vehicles will not be possible without enabling infrastructure. Policy makers
should set clear and ambitious targets for its deployment. They should provide targeted financial support
and accelerate procedures for planning permission where possible. In doing so, they can create market
confidence and help reduce uncertainty. Any policy actions should prioritise those infrastructure solutions
most likely to be highly utilised and cost competitive in the long term. Low-risk opportunities include
speeding up the construction of depot charging infrastructure and reinforcing electricity grids along main
roads and in areas of high future demand.
Strengthen regulations that make trucks more energy-efficient
Many ways exist to improve the energy efficiency of trucks, including aerodynamic improvements andvehicle weight reduction. Promoting energy-efficiency improvements (e.g. by strengthening CO2 emissions
standards) protects against rising energy costs and reduces uncertainty regarding the total cost ofownership of vehicles. Efficiency improvements can also help to accelerate the viability of zero-emission
vehicles by increasing the vehicle range.

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IN BRIEF: SIX RECOMMENDATIONS FOR ADOPTING ZERO-EMISSION VEHICLESIn brief: Six recommendations for adopting
zero-emission vehicles
This analysis of zero-emission heavy-duty road freight vehicles shows that battery electric vehicles andelectric road system vehicles are the most cost-efficient and effective technologies for reducing carbon
dioxide (CO2) emissions in the European heavy-duty road freight sector. Policy efforts should focus on
enabling their implementation and success.
The rapid decarbonisation of the heavy-duty road freight sector is essential to meeting the goals of the
Glasgow Climate Pact adopted at the 2021 UN Climate Change Conference (COP26). Active policy
interventions by governments to help promote zero-emission road freight are essential in light of the
speed of the required CO2 emissions reductions. Given this urgency, policy makers do not have the luxuryof pursuing all technological avenues equally. They must lay the regulatory foundation to support thetechnologies with the most promise.
A common principle in technology policy is that of “technology neutrality” – that is, setting a policy goal,such as emissions reduction, without being prescriptive about the technology used to attain the goal. The
advantage of technology neutrality is that it allows market forces to meet policy goals in a cost-effective
way and avoids closing the door to future technologies. Successful examples enacted in a number ofcountries include CO2 emissions standards for new vehicles, which set a target emissions intensity that
manufacturers need to meet regardless of the technology used.
The challenge to technology neutrality comes with infrastructure policies. Adopting new powertraintechnologies in heavy-duty road freight requires the construction of supporting infrastructure and willneed government financial support to overcome market risk. Since governments cannot fundinfrastructure for all technologies equally, there inevitably has to be a prioritisation, in which strict
technology neutrality is no longer maintained. This prioritisation must focus on the technologies with thegreatest potential for rapid emissions reductions and cost-competitiveness.
1. Ensure that policies to promote direct electrification of trucks remaintechnology-neutral

The cost-competitiveness of new technologies is impacted by economies of scale and the level of utilisationof recharging and refuelling infrastructure. Technologies must be able to gain a sufficient share of themarket to lower vehicle production costs and intensively use an infrastructure network. This analysis findsthat both BEVs and ERSVs could be cost-competitive with conventional internal combustion engine
vehicles (ICEVs) in Europe, suggesting their potential to gain a large market share with the appropriate
policy support.
BEVs, in particular, seem well-placed to enter the heavy-duty vehicle (HDV) market in the short term.
However, there remains uncertainty about a complementary need for electric road systems (ERS) in thelonger term. Until there is greater clarity on the relative roles of BEVs and ERSVs, policies should remaintechnology neutral, promoting both where possible, and avoid closing the door on either. Policies mayachieve that end by, for example, strengthening the electricity grid’s capacity along main roads withpotentially high electricity demand since this can benefit all zero-emission vehicle (ZEV) technologies. Both

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IN BRIEF: SIX RECOMMENDATIONS FOR ADOPTING ZERO-EMISSION VEHICLES

technologies should be actively considered in policy discussions about future infrastructure deployment
for zero-emission vehicles.
2. Launch targeted studies and pilot projects to assess the merits of electric roadsystems for road freight decarbonisation

The total cost of ownership of BEV and ERSV technologies remain similar in most scenarios. However, there
remains uncertainty about ERS design and business models, which could alter their financial feasibility.
Additionally, the primary actors differ between the two technologies. Governments will need to be
primarily responsible for the construction of ERS and provide financial support for the high upfront
infrastructure costs and long payback periods needed for ERSVs to be competitive. Conversely, theadoption of BEVs can be more incremental and shouldered by the private sector to a greater extent, withcomplementary support from governments to accelerate the transition.
Further detailed assessments are needed to understand which technology can offer the most significant
financial and CO2 emissions savings, in addition to energy and resource efficiency benefits. It is possiblethat BEVs may impose greater technical or operational constraints on truck operators that could limit theiradoption under real-world operations. Conversely, ERS would likely take significant time to build and would
have to be highly utilised upon completion. Pilot tests of overhead catenary electric road systems and highpower BEV chargers are needed, to better understand the real-world infrastructure costs, operational
characteristics and time scales involved in their adoption. Both Germany and the United Kingdom haverecently announced plans to construct sections of ERS above motorways for trials and comparisons withother heavy-duty road freight technologies (BMVI, 2021; UK DfT, 2021).
3. Further investigate decarbonisation technologies for particularly challenging
road freight applications

This analysis finds that hydrogen fuel cell electric vehicles (FCEVs) are unable to compete with other vehicle
technologies extensively. It is possible that FCEVs may be used in niche applications; however, the small
scale of the potential market means that achieving significant economies of scale in production volumes
will be a challenge. Furthermore, the required network of hydrogen refuelling infrastructure would haveto be government-financed at significant costs. If the infrastructure network is not sufficiently utilised due
to a small share of FCEVs in circulation, governments risk creating stranded assets or locking inrequirements for long-term public subsidies. Similar findings pointing to the marginal role of FCEVs havebeen highlighted by the Traton group of European truck manufacturers (Gründler and Kammel, 2021),
among others (Plötz, 2022).
It is possible that these findings are specific to the European truck market and that FCEVs could offer asolution in other regions with longer distance range requirements. However, for Europe, the analysis calls
into question whether hydrogen refuelling infrastructure should benefit from a similar level of government
support as direct electrification technologies, which are better-placed to enter the mass market.
This does not mean there are not useful applications for FCEVs. A greater understanding of particularly
challenging road freight applications would be beneficial to contextualise the role of alternative lowemission road freight technologies. Assessments need to consider the infrastructure requirements in suchniche use-cases and its potential utilisation.

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IN BRIEF: SIX RECOMMENDATIONS FOR ADOPTING ZERO-EMISSION VEHICLES

4. Introduce policies that help zero-emission trucks become cost-competitive
sooner

The analysis in this report finds that the total cost of ownership of zero-emission vehicles could becompetitive with conventional diesel vehicles. The smallest vehicle segments can reach parity in ownershipcosts before 2030 and larger vehicle segments will follow shortly thereafter. However, every conventional
diesel vehicle sold in the meantime will lock in additional emissions. Governments can help to accelerate
their replacement with ZEVs by introducing dedicated policy support that kick-starts the economies ofscale that will lower ZEV purchase costs.
Electric vehicles’ future cost-competitiveness with diesel remains uncertain. This is due to a range offactors, including the costs of diesel, electricity and batteries. The high upfront purchase costs of many
ZEV solutions can present particular barriers to adoption. Possible measures to overcome these challenges
include differentiated purchase incentives and low-interest-rate loans to reduce vehicle financing costs.
Additional policy measures that could significantly improve the total cost of ownership of ZEVs comparedwith conventional vehicles include differentiated road pricing and carbon and fuel taxation. For example,
this report finds that applying a carbon price of EUR 100 per tonne of CO2 to the trucking sector would
allow electric vehicles to be cost-competitive before 2030.
5. Accelerate the deployment of zero-emission vehicle infrastructure

Some of the most important measures for the successful adoption of ZEVs involve stimulating theconstruction of charging and refuelling infrastructure, including necessary electricity grid upgrades. There
are important differences between the three ZEV technologies explored in this report in terms of thebarriers to infrastructure roll-out.
For BEVs, there is already a substantial roll-out of charging infrastructure for passenger cars that truck
operators and manufacturers may be able to leverage. Recent announcements by truck manufacturers
highlight that the private sector is already willing to act. An example is the 2021 joint venture between
European manufacturers Daimler Trucks, Volvo Group and the Traton Group. It aims to deploy at least
1 700 high-power charging points by 2026 with a total investment of EUR 500 million (Daimler Trucks,2021). However, more action will be necessary. The deployment of ZEV infrastructure will take time,requiring energy production, transmission and distribution infrastructure. It is therefore imperative to
begin as soon as possible.
This report finds that the majority of energy needs for BEV trucks could be supplied with depot charging,where truck operators may have greater agency to install their own charging infrastructure and ensurehigh utilisation. However, small operators may not have the financial means to install chargers, particularly
if grid infrastructure needs to be strengthened and costs are passed on to truck operators. Governments
can provide support through targeted loans for ZEV infrastructure, potentially differentiated by the size ofthe trucking company, to help smaller, more capital-constrained companies.
Governments can also help to accelerate the roll-out of grid infrastructure by helping to reduce permitting
timescales and potentially offering support to reduce market risk, given the uncertain speed of adoptionof ZEVs, which has an important impact on the utilisation of infrastructure and its financial viability. Policies
improving grid infrastructure along roads can also be considered to be technology neutral since they canhelp facilitate BEVs, ERSVs and, potentially, hydrogen refuelling infrastructure involving electrolysis nearroads. Depot charging can also be useful for all three ZEV technologies making them low-regret avenues
to pursue.

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6. Strengthen regulations that make trucks more energy-efficient

Energy price shocks in 2022 significantly increased the cost of road freight, with diesel prices rising by 40%
in July compared with the previous year (UK BEIS, 2022). The worse the energy efficiency of a vehicle, themore energy costs raise vehicle ownership costs. Energy-efficiency improvements can therefore help
protect the sector from future high energy prices and give truck operators greater confidence in the costof their operations.
Strengthening CO2 emissions standards of new vehicles can help to promote the adoption of more energyefficient vehicles. Aerodynamic improvements and light-weighting can already help to reduce theoperating costs of existing vehicles; strengthening emissions standards will accelerate their adoption.
Stringent regulations can also help promote new energy-efficient powertrains such as battery electricvehicles, which can further improve fleet energy efficiency and ensure the sector’s resilience.
A call out to stakeholders

The findings of this report, particularly regarding the role of FCEVs, may run counter to current consensus.
The European Commission’s Alternative Fuels Infrastructure Regulation (European Commission, 2021a),for example, sets infrastructure provisions for both BEV and FCEV trucks (with little consideration of ERS).
The analysis in this report aims to be transparent and examine a broad range of possible futures. The ITF
invites an open dialogue building on additional evidence to further refine assessments of different
technology pathways in order to minimise uncertainty in the decarbonisation of the heavy-duty roadfreight sector.

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ZERO-EMISSION TRUCKS: THE ROAD TO CARBON DIOXIDE REDUCTION

Zero-emission trucks: The road tocarbon dioxide reduction
Globally, 68% of surface freight (road, rail and inland waterways) is carried by road vehicles. These vehicles
account for 73% of freight transport greenhouse gas (GHG) emissions. Demand for road freight is expected
to more than double by 2050. Without further policy action, this will lead to higher levels of GHG emissions(ITF, 2021b).
Several actions can reduce emissions from the road freight sector. Aerodynamic design, eco-driving andincreasing average vehicle loads through improved logistics can improve vehicle energy efficiency andachieve short-term gains (ITF, 2018). Longer-term solutions to reduce truck emissions include reducing
demand, removing the most polluting vehicles from roads and improving diesel engine efficiencies.
However, these actions alone will not suffice to effectively tackle the dangers of climate change and meetgovernment aspirations to keep global temperature rises in line with the Paris Agreement (IEA, 2021b).

The advantages of zero-emission vehicles
One of the decarbonisation solutions with the most significant potential to reduce road freight emissions
is to adopt zero-emission vehicle (ZEV) powertrains. The most prominent ZEV powertrains considered
today include battery electric vehicles (BEV), electric road system vehicles (ERSV) with overhead catenary
pantograph and hydrogen fuel cell electric vehicles (FCEV).
Table 1. Definitions of different powertrains
Powertrain type

Description

Internal combustion engine vehicle (ICEV)A conventional diesel engine vehicle which currently dominates the heavy-duty
vehicle market globally.

Battery electric vehicle (BEV)A vehicle using a fully electrified powertrain and a large battery.

Electric road system vehicle (ERSV)

A battery electric vehicle using an overhead catenary electric road system with apantograph. ERSVs have smaller battery requirements than BEVs.

Fuel cell electric vehicle (FCEV)

A vehicle using hydrogen in a fuel cell to power an electrified powertrain.

Zero-emission vehicles produce no GHG emissions during vehicle use and can significantly reduce airpollutant emissions. There are emissions produced during the manufacture of zero-emission vehicles andto produce the energy used to power vehicles. However, these emissions can be further reduced by using
low-carbon forms of energy, such as renewable electricity.
Approximately 60% of the electricity produced globally is made using fossil fuels (IEA, 2021c). Despite this,direct electrification solutions such as BEVs and ERSVs already produce approximately 40% fewer GHGs
than conventional diesel ICEVs over their lifecycle, including emissions from vehicle production and use

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ZERO-EMISSION TRUCKS: THE ROAD TO CARBON DIOXIDE REDUCTION

and based on the global average carbon intensity of electricity (ITF, 2021a). BEV and ERSV lifecycle
emissions are even lower in regions with lower-carbon electricity and stated ambitions to increase theshare of renewable electricity, as is the case in Europe and the United States. Similarly, hydrogen FCEVs
could significantly reduce GHGs when using hydrogen produced via low-carbon production routes such as
electrolysis with renewable electricity. However, almost all hydrogen production today is still made using
fossil fuels (IEA, 2019).
Zero-emission vehicles may also improve countries' energy security by reducing dependence on foreign
fossil fuels and potentially opening up to a broader range of countries for energy imports. Electricity andhydrogen can be produced using several technologies and in a broad range of environments, potentiallyoffering greater resilience to global energy markets and geopolitical crises than the continued dependence
on fossil fuels.
Despite these significant opportunities, there remain a number of important barriers to the adoption of
zero-emission vehicles.

Potential challenges for zero-emission vehicles
New, low-carbon vehicle technologies need to be financially competitive with competing technologies tobe adopted into the mass market. ZEVs have considerably higher upfront purchase costs than incumbent
technologies, which may hinder their adoption by small companies with limited capital. Crucial
mechanisms enabling cost reductions include increases in economies of scale and learning-by-doing
effects.
A number of potential barriers to adopting ZEVs relate to the operational requirements and possible
logistical changes that new technologies may entail. The carrying capacity of both BEVs and FCEVs may behindered by the weight and volume of their powertrains (e.g. batteries in BEVs and hydrogen storage tanks
in FCEVs). Battery charging times may be an additional barrier for BEVs. It is expected that BEVs will
primarily rely on overnight depot charging (Borlaug et al., 2021), but any additional electricity needed tomeet range requirements would likely need high-power charging. Power ratings of one megawatt,currently under discussion (ITF, 2020), could allow 30- to 45-minute charging times and fit within regulated
driver rest periods. However, this may require some adaptation to daily operations and a significant anduncertain deployment of new charging and electricity grid infrastructure in the coming years. Furthermore,high-power charging is likely to be more expensive than depot charging and its costs are dependent on theutilisation of the infrastructure.
An electric road system (ERS) could overcome many of the battery weight, range and charging limitationsof BEVs. Vehicles operating on an ERS charge while connected to overhead wires, therefore requiring
smaller batteries. Barriers to an ERS relate primarily to the high upfront costs associated with the
deployment of the overhead wires, which must be constructed before pantograph vehicles can be
adopted. The uncertain timescales associated with the construction of an ERS and subsequent uptake ofpantograph vehicles pose significant financial risks to investors due to the unclear utilisation of the asset.
This also leads to uncertainty in the fees that users would need to be charged to pay back infrastructure
investments.
Additional barriers to ERS relate to ensuring common international standards, allowing for interoperability
for trucks travelling across borders (ITF, 2020). Finally, there is uncertainty about how the adoption of BEVs
might impact ERS utilisation, both positively and negatively. Other electric road system concepts, including
conductive transmission from electric rails on the road surface and inductive transmission from the road,

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are also under consideration but are currently at a lower level of technological maturity compared withoverhead catenary solutions (Ainalis, Thorne and Cebon, 2020) and are not considered in this report. The
numerous barriers to ERS mean strong concerted action is needed from a broad range of stakeholders
including vehicle manufacturers, governments, electricity transmission and distribution system operators,
road network managers, logistic fleet owners, logistic service providers and users.
Battery swapping stations, which physically replace a truck’s discharged battery with a fully charged
replacement, could be a solution to managing battery size and avoiding some long-distance range
limitations. Large-scale pilot tests of battery swapping solutions for heavy-duty vehicles are currently
underway and showing promise in the People’s Republic of China (Liu and Danilovic, 2021). Businessmodels associated with battery swapping include offering battery rental services (also known as “batteriesas-a-service”). These could help to reduce upfront purchase costs of electric trucks and shift costs tooperational expenditure, which is already comparatively low for electric vehicles. Battery swapping canalso avoid high-power charging of batteries since they can be removed from a vehicle and charged at alower power over longer time periods. This could reduce charging costs and place less stress on batteries,
which can improve the longevity of batteries by approximately 20% (Liu and Danilovic, 2021). Current
barriers include the standardisation of batteries to allow swapping between different truck manufacturers,
safety issues and untested (at scale) business models ensuring high infrastructure utilisation.
Range limitations could also be overcome with hydrogen-powered FCEVs since refuelling times forhydrogen vehicles are expected to be faster than BEV recharging times. However, hydrogen truck concepts
using liquefied hydrogen refuelling will require preconditioning that may take longer than simply refuellingdiesel (ITF, 2020). Similarly to high-power BEV charging and an ERS, the cost of hydrogen refuelling
infrastructure is highly dependent on its utilisation, with uncertainty about how quickly the market will
adopt FCEVs. However, the lack of existing low-carbon hydrogen production and widespread hydrogen
refuelling and distribution infrastructure means that FCEVs face significant barriers to scaling up anddelivering GHG emission benefits in the short term. Finally, the energy efficiencies of FCEVs and hydrogenproduction from electrolysis are relatively low compared to powering BEVs or ERSVs with renewableelectricity. This means that a significantly greater quantity of electricity generation will be needed to fuel
FCEVs with hydrogen made from renewable electricity than BEVs, implying higher energy costs (ITF,
2021a).
A number of other vehicle technologies could also play a role in decarbonising heavy-duty road freight,
particularly in applications where ZEVs might not be feasible. These include hybrid powertrain solutions
and low-carbon fuels used in internal combustion engines, such as biofuels, hydrogen and diesel
electrofuels. These technologies are not considered in this report but will be considered in future work.
Battery swapping solutions are also not explicitly considered in the analysis.

The role of technology neutrality in policy making
There are many zero-emission technologies and all require appropriate infrastructure to accelerate theiradoption. However, policy makers and industry experts remain uncertain about the long-term potential of
new technologies, as each has advantages and disadvantages, and their adoption so far has been limited.
This uncertainty is potentially compounded for international transport if countries adopt diverging
technology pathways. This creates hesitancy to invest for fear of stranded assets.
Technology neutrality is the principle that policy makers do not cater policy to favour specific technologies
but create opportunities for innovation to meet society’s needs. Policies should target a desired outcome
and remain neutral to the technologies used to achieve that goal. In doing so, policy makers avoid picking
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winners and closing the door to specific technological pathways and instead leave industry and market
forces to choose how to meet targets in an efficient, cost-optimal way. Technologies develop quickly, as
does the understanding of their relative pros and cons. Policies that are agnostic about technologies canbe resilient to technological change.
However, there are two fundamental limitations to technology-neutral policies:
The first is in an uncertain technological context, where the financial risks are too large for market forcesto act alone in adopting new technologies. This is particularly the case when significant infrastructure
investments are required. In such cases, a government may be called on to provide support in the form of
infrastructure investment. Government budgets are limited, meaning that only a selection of the mostpromising options can be funded, which can “lock-in” specific technology options.
The second limitation to technology neutrality is in a time-limited context, where market forces alone will
struggle to adopt technologies at the speed necessary to meet targets. Meeting aggressive GHG emission
reduction targets by 2050 will require additional policy intervention, and remaining agnostic about
technologies risks postponing action on decarbonisation. The implications of choosing a technology thathelps to reach climate targets but that, in the long term, is not “optimal” may be less severe than notchoosing any technology at all, given the looming impacts that climate change will impose upon the global
economy.
Technologies develop quickly, and governments are rightfully wary about committing to specific optionsin an uncertain technological environment. However, are technologies likely to develop quickly enough tomake an appreciable difference to the choice of preferred technology, and is it worth postponing
decisions? This report aims to shed light on potential technological pathways to help prioritise policy
actions and unlock action to promote ZEVs.
The total cost of ownership (TCO) is one of the most important metrics influencing trucking companies’
purchase decisions and the adoption of new technologies in the commercial vehicle sector. The TCO is thesum of all expenses over the ownership period of a vehicle and includes vehicle purchase costs, financing,residual value and the cost of operation. This analysis investigates the TCO of the three most prominentzero-emission vehicle technologies for the heavy-duty vehicle sector and compares them to conventional
diesel vehicles. The novelty of this analysis is the assessment of multiple vehicle technologies across a wide
range of vehicle classes, range requirements and possible futures. Particular emphasis is placed on theuncertainty associated with each technology to answer the following questions:18Which technologies are most likely to reach TCO parity with conventional diesel vehicles?

When might TCO parity be reached?

How does TCO parity differ between vehicle classes and market segments?

What degree of certainty can be placed in the results? How might further actions by government
or industry reduce uncertainty?

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Comparing truck technologies in an
uncertain future
Global analyses suggest that zero-emission vehicles will likely play a pivotal role in meeting ambitious
climate targets (IEA, 2021b), including in heavy-duty road freight. Recent initiatives highlight that action
stimulating their adoption is already underway. In 2021, several countries signed a memorandum of
understanding pledging to enable 100% of zero-emission medium- and heavy-duty truck sales by 2040
(CALSTART, 2021). Additionally, a number of private-sector companies have announced the initialcommercial production of ZEV trucks and the building of charging infrastructure for trucks (Daimler Trucks,
2021; Basma and Rodriguez, 2021; Scania, 2021; Volvo, 2022).
Table 2. European vehicle types as defined by European Union heavy-duty vehicles
CO2 emission regulation 2017/2400
Vehicle group

Axle type

ChassisGross weight (inmetric tonnes)

Sales share (2019)

Included inanalysis1

4x2

Rigid/Tractor

7.5-10

1.63%

Yes

2

4x2

Rigid/Tractor>10-12

4.17%

Yes

3

4x2

Rigid/Tractor>12-16

4.22%

Yes

4

4x2

Rigid

>16

10.10%

Yes

5

4x2

Tractor>16

52.25%

Yes

6

4x4

Rigid

7.5-16

1.21%

No

7

4x4

Rigid

>16

1.02%

No

8

4x4

Tractor

>16

0.84%

No

9

6x2

Rigid

All weights

13.77%

Yes

10

6x2

TractorAll weights

2.86%

Yes

11

6x4

Rigid

All weights

1.93%

Yes

12

6x4

TractorAll weights

0.57%

Yes

13

6x6

Rigid

All weights

0.53%

No

14

6x6

TractorAll weights

0.02%

No

15

8x2

Rigid

All weights

0.59%

No

16

8x4

Rigid

All weights

3.85%

No

17

8x6, 8x8

Rigid

All weights

0.43%

No

Sources: Vehicle groups: European Commission (2017); heavy-duty vehicle sales shares: Mulholland et al. (2022).

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Heavy-duty vehicles come in a range of sizes to suit a wide spectrum of applications, from urban deliveries
to long-haul regional and international travel. The differences between truck types and regions will likely
affect the ease of transition to zero-emission powertrain technologies in each market segment. For
example, it may be more challenging to electrify particularly heavy vehicles with high-energy requirements
or vehicles with long-distance range requirements, than smaller vehicles. To account for these differences,
the analysis in this report covers a broad range of heavy-duty road freight vehicles with a gross weight
above 7.5 metric tonnes in groups defined by EU CO2 emissions regulations, shown in Table 2. EU
regulations define vehicle groups according to their gross vehicle weight, chassis type (rigid or tractortrailer) and axle configuration. This analysis only studies vehicles included in EU HDV certification
regulation 2017/2400 (European Commission, 2017), as limited data is available for other vehicle groups.
Vehicle group 16, which is included in EU regulation 2017/2400, is also omitted due to a lack of data. The
vehicle groups included in the analysis accounted for 91.5% of heavy-duty vehicle sales in 2019.

Estimating the total cost of ownershipA growing number of studies have attempted to predict the TCO of trucks with various powertrains. Eachstudy typically creates a future scenario for the TCO of each vehicle powertrain, using several assumptions
about the costs of different vehicle components and vehicle performance attributes such as fuel efficiency.
Typically, a single class of truck is evaluated across a number of technologies and a number of scenarios.
This report explores the TCO of a broad range of heavy-duty vehicle classes in different market segments
in Europe and across a wide spectrum of uncertainty.
The TCO of a new vehicle is estimated by adding the capital expenditure (CAPEX) into the purchase of thevehicle with all operational expenditure (OPEX) over a seven-year vehicle ownership period (broadly, theaverage lifetime of European road tractors estimated from OECD (2022) statistics) and subtracting theresidual value at the end of ownership (equation 1 in Annex B). The CAPEX of the vehicle is calculated as
the sum of vehicle glider and vehicle component costs such as batteries, power electronics, motors, fuelcells and pantograph. The OPEX of using a vehicle each year is calculated as the sum of energy, operation,
maintenance and vehicle financing costs. Other costs such as driver wages, which are common to all
powertrains, are omitted, as are road tolls such as the Eurovignette directive and additional policy support
measures in the base results. Energy costs include infrastructure costs associated with electric charging,
ERS construction and hydrogen refuelling.

Quantifying uncertainty
Developing a scenario for the TCO of a vehicle technology requires making several assumptions about
different variables, including the costs of different vehicle components and how they evolve over time.
The choices in defining these variables can greatly impact the results, increasing the risk of unintentional
bias. While this risk of bias can never be mitigated entirely, it can be significantly reduced by exploring awide range of possibilities.
Table 3 presents the primary sources of uncertainty considered in this report. For each source of
uncertainty, an upper and lower bound is chosen to develop a range of possible future values of eachvariable based on available academic literature and industry reports. Full details of the uncertainties foreach variable are outlined in Annex A.

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Table 3. A summary of sources of uncertainty
Source of uncertainty

Uncertainty
type

Lower boundUpper boundA

Possible cost reductions inelectricity production due to theincreasing penetration of lowcost renewable electricity

Time varying
range~2% price reduction per year
from EUR 0.125/kWh in 2020
(EUROSTAT, 2022).

Fixed at EUR 0.125/kWh(EUROSTAT, 2022).

B

Variance in future diesel fuelprices due to market fluctuations

Fixed range
across all
years

Lowest average price between
2010-2019 excluding VAT andother recoverable taxes:
EUR 0.95/Litre(OECD, 2020)

Highest average price between
2010-2019 excluding VAT andother recoverable taxes:
EUR 1.50/Litre(OECD, 2020)

C

Energy-efficiency improvements
to all powertrain types frombetter aerodynamics, lightweighting, reduced friction anddrivetrain improvements

Time varying
range

Energy-efficiency
improvements of 35% in 2030
and levelling out at a 40%reduction in 2050 with respect
to 2020 levels.

Energy-efficiency improvementsof 10% by 2030 and levelling out
at 20% by 2050 with respect to2020 levels.

D

The costs of electric drivetrain
components including electricmotors, converters, on-board
chargers, thermal management
and high-voltage systems. These
could reduce over time due toeconomies of scale and technicalimprovements.

Time varying
range

Costs fall from around
EUR 170/kW to EUR 110/kW
with growing production(based partly on Ricardo and
ICCT (2021)).

Costs fall from around
EUR 340/kW to EUR 220/kW
with growing production (based
partly on Ricardo and ICCT(2021)).

E

The cost of hydrogen fuel cells
for heavy-duty vehicle use, which
could reduce over time due toeconomies of scale and technicalimprovements. These cost morethan those used for passenger
vehicles due to longer life
requirements and duty cycles.

Time varying
range

Costs fall from USD 323/kW to
USD 60/kW by 2050 (based on
US DOE (2021b)).

Costs fall from USD 323/kW to
USD 176/kW by 2050 (based on
US DOE (2021b) without
economies of scale.

F

Hydrogen fuel cell energyefficiency improvements

Time varying
range

Fuel cell efficiencies rise from
45% in 2020 to 50% in 2030
and 55% in 2050 (based on US
DOE (2019, 2021a)).

Fuel cell efficiencies rise from
50% in 2020 to 62% in 2030 and
66% in 2050 (based on US DOE(2019, 2021a)).

GThe cost of on-board hydrogenfuel tanks could reduce overtime due to economies of scale
and technical improvements.

Time varying
range

Costs fall from around
EUR 1 000/kg in 2020 to
EUR 526/kg in 2030 reaching
EUR 260/kg by 2050 withgrowing production (based
partly on Ricardo and ICCT(2021)).

Costs fall from around
EUR 1 600/kg in 2020 to
EUR 1 000/kg in 2030 reaching
EUR 700/kg by 2050 withgrowing production (based partly
on Ricardo and ICCT (2021)).

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H

Hydrogen fuel costs at the pump.
This cost comprises refuelling
infrastructure costs andhydrogen production,
purification, transmission anddistribution costs, which could
reduce over time with increasing
scale of low-carbon production.

Time varying
range

Hydrogen costs at the pumpfall from EUR 9.50/kgH2 in2020 (H2live, 2022) to
EUR 8.5/kgH2 in 2050
assuming low station
utilisation (Rose, 2020).

Hydrogen costs at the pump fallfrom EUR 9.50/kgH2 in 2020
(H2live, 2022) to EUR 1.5/kgH2 in
2050 assuming high station
utilisation and likely subsidies.

I

The average global cost ofbatteries over time across all
applications, including passenger
cars and stationary storage(These have historically dropped
over time due to economies ofscale and technicalimprovements.)

Time varying
range

Average global lithium ion
battery pack prices fall from
EUR 122/kWh in 2020 toEUR 70/kWh by 2030 and
EUR 50/kWh by 2050 (basedon BNEF (2021a)).

Average global lithium ion
battery pack prices rise from
EUR 122/kWh in 2020 to
EUR 150/kWh in 2025 (based on
BNEF (2021a) and Kim (2022))
then fall to EUR 100/kWh by
2050.

J

Additional costs between
batteries used for heavy-duty
vehicles (HDVs) compared to theaverage across all applications(Batteries currently cost
significantly more for HDV
applications than for passenger
vehicles, principally due tosmaller production volumes andhigher durability requirements.)

Time varying
range

HDV battery packs cost 46%
more than average passenger
car batteries in 2020, then fallin cost to 20% more in 2030
and arrive at parity withaverage passenger car
batteries by 2035 withgrowing production (based
partly on Ricardo and ICCT(2021)).

HDV battery packs cost 146%
more than average passenger car
batteries in 2020, falling in cost
to 66% more in 2030 and arriveat parity by 2050 with growing
production (based partly on
Ricardo and ICCT (2021)).

K

Energy density improvements tovehicle lithium-ion batteries

Time varying
range

Energy densities are
140 Wh/kg in 2020,
220 Wh/kg in 2030 and
250 Wh/kg in 2050 (based
partly on Schmuch et al.
(2018) and BNEF (2021b)).

Energy densities are 200 Wh/kg
in 2020, 260 Wh/kg in 2030 and
400 Wh/kg in 2050 (based partly
on Schmuch et al. (2018) andBNEF (2021b)).

L

The average power rating of
charging stations available on
main roads for high-power
charging

Time varying
range

Average HDV charger station
power evolves from 20 kW in
2020, to 50 kW by 2025,
100 kW by 2030 and 1 MW by
2050.

Average HDV charger station
power evolves from 500 kW by2025 and 1 MW by 2030.

M

The cost of high-power charging
infrastructure as a function of
the rated power

Range as afunction of
chargerpower

Charging costs based on
CAPEX of charging station:
EUR 1 000 for 20 kW,
EUR 50 000 for 100 kW,
EUR 170 000 for 350 kW and
EUR 300 000 for 1 MW.

Charging costs based on CAPEX
of charging station: EUR 3 000
for 20 kW, EUR 70 000 for
100 kW, EUR 240 000 for 350 kW
and EUR 700 000 for 1 MW.

N

The cost of electric road systemand depot charging
infrastructure, estimated basedon the costs needed to pay backinfrastructure, operation and

Fixed range
across allyears

EUR 0.05/kWh based on
construction costs of
EUR 1.1 million/lane-km(Wietschel et al., 2019) and
70% utilisation.

EUR 0.19/kWh based on
construction costs of
EUR 1.65 million/lane-km(Movares, 2020) and 32%
utilisation.

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maintenance costs (assumed tobe constant over time).

O

The costs of pantograph systems
for vehicles using an overheadcatenary electric road system

Time varying
range

Assumed to cost EUR 18 000
per vehicle in early-stage
deployment by 2030 (basedon Ainalis, Thorne and Cebon(2020)) dropping to
EUR 10 000 by 2050 (based on
IEA (2017)).

Assumed to cost EUR 28 000 per
vehicle in early-stage
deployment by 2030 (based on
Kühnel, Hacker and Görz (2018))
dropping to EUR 12 000 by 2050
(based on Ainalis, Thorne andCebon (2020)).

P

The residual value of vehicles
and batteries, as a percentage ofinitial capital expenditure

Fixed range
across all
years

Residual value 20% of initial
CAPEX for vehicle, 5% forbattery.

Residual value 35% of initial
CAPEX for vehicle, 20% forbattery.

Note: Full details of assumptions available in Annex A. Lower and upper bounds limit the range of uncertainty
considered for each variable. The letters in column 1 of this table correspond to those in Figure 1.

Each variable is used in a numerical model to estimate the TCO of each powertrain and how it changes
over time until 2050. Importantly, this analysis takes a possibilistic rather than a probabilistic approach.
The aim is to explore a range of possible scenarios without necessarily attributing specific probabilities totheir occurrence. For example, this analysis cannot predict the probability of a certain technology reaching
cost parity with conventional diesel vehicles in a given year. Instead, it can suggest that a certain
percentage of the 1 000 scenarios explored between conservative and optimistic bounds reach cost parity
in a given year. The GHG emissions produced by trucks are not considered here and will be the subject of
future research. Figure 1 presents the main variables explored in this analysis with estimates of theiruncertainty.

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Figure 1. Uncertainty ranges of model inputs

Note: A. Electricity costs reductions; B. Diesel fuel price; C. Energy-efficiency improvements to all powertrain types;
D. Electric powertrain costs (motors, inverters, thermal management); E. Fuel cell cost; F. Fuel cell efficiency;
G. Hydrogen fuel tank cost; H. Hydrogen fuel costs at the refuelling station pump; I. Average global Lithium-ion
battery pack costs; J. Additional costs of heavy-duty vehicle battery packs per kWh compared with passenger car
batteries; K. Battery pack energy density; L. Average power capacity of charging stations available on main roads
for high-power charging; M. Cost of charging needed to pay back charger infrastructure deployment as a functionof charger power capacity; N. Electric road system and depot charging infrastructure costs; O. Range of vehicle
pantograph system costs; P. Residual value of vehicles and batteries after seven years of use. Full details for all
assumptions are available in Annex A. “Model year” refers to the year a vehicle is sold, rather than the years it isused. “w.r.t”: with respect to.

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The impact of uncertainty on total cost of ownership
In this analysis, the TCO is estimated over time for trucks in each vehicle group and powertrain combinationand for different annual mileage and range requirements within each group. Full details of the assumptions
included in the model are available in Annex A.
In 2020 (the base year in this analysis), a newly purchased conventional ICE diesel vehicle remained themost cost-competitive technology option, with the lowest TCO over a seven-year lifetime and uncertainty
driven particularly by diesel fuel prices and annual mileage. However, with expected technologicalimprovements and cost reductions in various components from economies of scale, the TCO of ZEV canreduce over time and overlap with conventional diesel vehicles. Figure 2 shows the range of TCOs for
European vehicle groups 4, 5 and 9, which are the groups with the highest sales shares (10%, 52% and 14%
of sales respectively, see Table 2).
Figure 2. Total cost of ownership with uncertainty: Three
European vehicle groups using four different powertrains

Notes: Range of total cost of ownership (TCO) for vehicle group 4 (rigid 4x2 vehicles), group 5 (4x2 tractors) andgroup 9 (rigid 6x2 vehicles), based on uncertainty in input variables and between varying range requirements. TCOs
for electric road system vehicles (ERSV) and fuel cell electric vehicles (FCEV) are not plotted before 2030 as massmarket uptake is unlikely given the lack of enabling infrastructure. BEV= Battery Electric Vehicle, ICEV= Internal
Combustion Engine Vehicle. The black line in each boxplot denotes the median scenario, box edges denote the 25thand 75th percentiles and horizontal black lines are 5th and 95th percentiles of the multiple scenarios explored. Vehicle
groups are those defined by the European Commission (2017).

BEVs have relatively high upfront purchase costs and low operational costs compared with ICEVs. The highcapital costs are principally due to the cost of a large battery. BEVs have low operational costs because
they are far more energy-efficient than other powertrain types and can use relatively cheap energy whenusing low-power overnight charging. They also have lower maintenance costs due to reduced mechanical

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complexity and are therefore likely to have a higher residual value (Hunter et al., 2021), particularly given
similar trends in passenger vehicle segments (Guo and Zhou, 2019).
Two factors could make the TCO of BEVs cost-competitive with ICEVs. First, purchase prices could be
reduced with economies of scale in battery and powertrain component costs. Second, further
improvements in energy efficiency and battery energy density could increase vehicle maximum range
capabilities and allow BEVs to avoid high-power charging on main roads. These trends mean that the low
operational costs over the lifetime of BEVs could more than compensate for the higher initial investment.
ERSVs can have a similar TCO to BEVs. ERSVs could have smaller battery requirements, which reduce
upfront purchase costs (explained further in Annex A). They could possibly face slightly higher energy costs
if their primary source of electricity is from overhead catenary wires rather than cheaper, low-power
overnight charging. However, this depends partly on the costs of any potential grid upgrades, and thedegree to which they are passed on to consumers. Additionally, for countries with a high share of solar
electricity, overnight electricity delivered through low-power depot chargers is not necessarily cheaper
than day-time electricity delivered through high-power charging or an ERS.
FCEVs also have high upfront purchase costs due to the fuel cell system and hydrogen storage tanks. The
TCO of FCEVs is dominated by the cost of hydrogen fuel, particularly in the early years. Future hydrogenfuel costs are particularly uncertain, leading to a wide range of possible ownership expenses. The
uncertainty in hydrogen costs substantially affects the future TCO of FCEVs because FCEVs are less energyefficient than BEVs. In general, the ownership costs of vehicles with energy-efficient powertrains are more
resilient against energy price fluctuations.

The impact of uncertainty on technologies’ cost-competitivenessSome factors influencing the range of uncertainty of different powertrain technologies are unique to thetechnology (e.g. uncertainty in hydrogen costs only affects FCEVs), while others affect a number oftechnologies (e.g. electric drivetrain costs affect all ZEVs). To understand which new powertraintechnology has the lowest TCO each year, this analysis compares 1 000 unique scenarios exploring the full
spectrum of uncertainty in input variables across technologies, vehicle groups and daily mileage
requirements. The lowest cost powertrain technology in each year is selected for each vehicle group anddaily mileage requirement in each scenario.
The uncertainty explored in this analysis includes both favourable and conservative scenarios for eachparticular technology. The exact date that a technology reaches cost parity with ICEVs is based on theunique combination of input assumptions in each scenario and its probability of occurrence is inherently
subjective. The aim of this analysis is to highlight the range of years in which each technology may (or maynot) reach cost parity with ICEVs across a large number of unique scenarios in an attempt to reduce thelevel of subjectivity.
Figure 3 consolidates all scenarios across all HDV vehicle groups and excludes electric road systems (which
are included in the following section) to show the share of the HDV market that each technology could
theoretically attain by having the lowest TCO in each year. This omits considerations about model
availability and the infrastructure to support the adoption of new technologies, both of which will preventthe adoption of ZEVs initially. However, it paints a picture of the potential cost-competitiveness of eachtechnology in the future.

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Figure 3. Potential sales shares of lowest total-cost-of-ownership technology accounting for uncertainty(excluding ERSVs)

Notes: Black line denotes the median scenario, shading denotes 50th, 75th and 95th confidence intervals of themultiple scenarios explored. Electric road systems are excluded from this analysis.

Figure 3 shows that BEVs are most likely to reach TCO parity with ICEVs between 2020 and 2040. In the
median scenario, 80% of vehicle sales could be cost competitive with ICEVs by 2037 without policy
intervention. While the majority of scenarios explored point to BEVs rapidly becoming cost-competitive,
some scenarios are driven by more conservative input variable combinations and see a more gradualuptake in BEVs. These include scenarios with low diesel prices, high battery prices and slow reductions inelectricity costs. Furthermore, there may remain a share of approximately 10% of vehicles for which diesel
ICEVs remain the most cost-competitive into the future without any additional policy support.
The results suggest that, for the vehicle categories investigated, FCEVs are unlikely to be able to compete
economically with BEVs or ICEVs. This is primarily due to their high upfront purchase costs (which aresimilar to those for BEVs) combined with relatively high energy costs. In a number of edge cases, withhydrogen fuel costs below EUR 2.5/kgH2 and conservative scenarios for other technologies, FCEVs could
be adopted in certain segments. However, in 90% of the scenarios explored FCEVs do not attain more than
10% market share before 2050. This analysis investigates the mass market vehicle categories used in roadfreight. It is possible that FCEVs could be the preferred technology in relatively niche use-cases notconsidered in this report (e.g. heavy-duty 70-tonne applications or vehicles used in construction).
Figure 4A shows the year of TCO parity between BEVs and ICEVs by vehicle group and daily mileage
percentile. It highlights that BEVs in vehicle groups 1, 2 and 3 are likely to reach TCO parity with ICEVs first.
These vehicles have relatively low range requirements, leading to manageable battery costs and a limited
need for high-power charging. This means they are likely to be cost-competitive with ICEVs in the shortterm. Larger vehicle groups without excessively high range requirements are likely to follow.
Vehicles with higher daily mileage tend to have an earlier TCO parity year than vehicles driven shortdistances, despite the larger battery requirements. This is because OPEX account for a relatively high shareof the TCO, meaning the low energy costs of BEVs compared with ICEVs can more than offset the cost ofadditional battery capacity. The exception to this rule is for vehicles with particularly large daily mileage
requirements such as vehicle groups 5 and 9 driven over 1 000 km per day. In these cases, more expensive
high-power charging and opportunity costs increase the TCO. The majority of BEV trucks are likely to becost-competitive with ICEVs before 2040. Some truck segments with low average daily mileage have muchgreater uncertainty in reaching TCO parity with ICEVs and, in some scenarios, are never cost-competitive
with ICEVs before 2050.

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COMPARING TRUCK TECHNOLOGIES IN AN UNCERTAIN FUTURE

Figure 4. Year in which BEVs and FCEVs reach total cost of ownership parity with ICEVs
by vehicle group and daily mileage percentile

Notes: TCO parity year is the year that the TCO of BEVs or FCEVs is equal to or less than that of ICEVs. Boxplotsshow the range of uncertainty from input variables in Figure 1. The black line in each boxplot denotes the medianscenario, box edges denote the 25th and 75th percentiles and horizontal black lines are 5th and 95th percentiles ofthe multiple scenarios explored. Daily mileage percentiles show the different daily mileage within each vehicle
group (further details are available in Annex 1: Methodology). For example, 50% of group 1 vehicles travel lessthan 148 km per day on average and all of group 1 vehicles travel less than 418 km per day on average. Vehicle
groups are those defined by the European Commission (2017).

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COMPARING TRUCK TECHNOLOGIES IN AN UNCERTAIN FUTURE

Figure 4B shows the year of TCO parity between FCEVs and ICEVs by vehicle group. It shows that FCEVs
can be cost-competitive with ICEVs under certain scenarios for vehicles with high daily mileage
requirements where, similarly to BEVs, the lower operational costs can present an advantage comparedwith ICEVs. For vehicles without high daily mileage, this operational cost advantage is insufficient to offset
higher purchase costs. This highlights the importance of considering different vehicle use cases and range
requirements when comparing HDV technologies.
A comparison of figures 4A and 4B highlights that BEVs are likely to reach TCO parity earlier than FCEVs inmost of the applications explored in this analysis. In a limited number of scenarios, FCEVs could competewith BEVs in groups 5 and 9 travelling over 1 000 km per day. However, the lack of cost-competitivenessacross the majority of the European market means achieving the economies of scale in vehicle production
necessary to bring down vehicle purchase prices and ensure high utilisation of refuelling infrastructure is
likely to remain a challenge for these use cases.
Figure 5 shows the share of the heavy-duty vehicle market for which ERSVs could be the lowest TCO
technology, assuming that ERS infrastructure is available (the deployment of ERS infrastructure is not
explicitly investigated in this analysis). ERSVs could be adopted progressively with the construction of ERSs
but would be unlikely to reach mass-market adoption before the mid-2030s. For example, Ainalis, Thorne
and Cebon (2020) have estimated the construction of a complete ERS to take approximately eight years.
In the majority of the scenarios explored, ERSVs are more cost-competitive than BEVs when initiallyintroduced in the 2030s. Furthermore, ERSVs have the advantage of lower upfront vehicle purchase costs
than large-battery BEVs. The lower initial cost may encourage truck operators’ adoption of ERSVs andstimulate the technology’s more rapid introduction into the vehicle fleet, resulting in rapid CO2 emissions
reductions (although this is not modelled in this analysis). However, the utilisation of ERS infrastructure isa major source of uncertainty.
Figure 5. Potential sales shares of electric road systems vehicles with uncertainty

Notes: Black line denotes the median scenario, shading denotes 50th, 75th and 95th confidence
intervals of the multiple scenarios explored. ERSV are only introduced into the mass market in 2030
post-construction of overhead lines on main roads.

The ERS design explored in this analysis is prone to risks. The ERSVs have a small battery to cover the
distance between depot and motorway and then use overhead wires for the majority of electricity
consumption. If too many trucks choose to source their electricity from cheap depot charging, theutilisation of overhead catenary wires could drop and lead to higher ERS charging costs. The rise in cost, inturn, could produce a vicious cycle of low ERS utilisation and stranded assets.

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COMPARING TRUCK TECHNOLOGIES IN AN UNCERTAIN FUTURE

However, alternative configurations may be possible. For example, if ERS were used to charge ERSVs’
batteries (rather than just maintain the state-of-charge), the higher flow of energy could cut the cost ofusing the overhead catenary equipment per unit of energy. These “charging corridors” could be a costcompetitive substitute for high-power stationary charging. This and other plausible variations make it
uncertain whether BEVs or ERSVs will dominate the market. Further research and pilot tests, such as thoseunder consideration in Germany and the UK (BMVI, 2021; UK DfT, 2021), may help to clarify additionalcosts or barriers to either solution.
One significant variable determining the cost of an ERS is the assumed payback period, here assumed to
be 35 years, broadly similar to other long-lasting government-funded infrastructure. Halving this payback
period would decrease the potential market share of ERSVs to roughly 40% of the market. This highlights
the importance of financing and suggests significant government support would likely be needed for it tobe viable.
This analysis tracks the lowest-cost powertrain over time as soon as it becomes cost-competitive on a TCO
basis. However, it cannot account for all real-life situations. Imperfect knowledge, behavioural factors,
additional costs or incentives, or a lack of enabling infrastructure deployment or production capacity of
new vehicle powertrain technologies may mean that the real-world adoption of ZEVs into the vehicle fleet
will take longer than projected.
Given the urgent need to counter climate change, the opportunities for lower-cost logistics and reduced
energy demand for fossil fuels, further policy measures could help strengthen the case for adopting ZEVs
and are examined in the following section.

Policy measures can pave the way for zero-emission vehicles
Since some ZEV technologies have the potential to be cost-competitive with conventional ICEVs in the near
future, introducing regulations to stimulate their adoption is likely better than introducing financial
incentives. However, financial incentives and other economic measures could encourage industry todevelop effective ZEV technologies more quickly, therefore making them cost-competitive with ICEVs
earlier and accelerating GHG emissions reduction. Economic measures can also help to address equity
challenges in the uptake of new technologies.
Figure 6 shows the effects of three example policy measures to further incentivise the adoption of BEVs
with respect to baseline scenarios, excluding FCEVs and ERSVs. In the baseline, the median scenario
suggests that BEV trucks could be cost-competitive with ICEVs in 50% of annual European truck sales by2035. (BEV costs would be marginally higher than ICEVs in the remaining 50% of the market, which includes
particularly challenging road freight applications.) Vehicle financing costs are higher for ZEVs thanconventional diesel trucks, given their higher upfront purchase costs. Offering government-backed loans
with a 0%-interest rate to truck operators purchasing a BEV (rather than the assumed baseline case of 3%interest) could accelerate the adoption of BEVs in all vehicle groups by approximately six years comparedwith the baseline.
Similar results could be achieved by offering purchase incentives of approximately EUR 20 000 per vehicle
on new purchases of BEVs (assumed to be fully passed on to consumers). These policy measures could
allow even the most challenging vehicle groups to be cost-competitive with ICEVs by the mid-2030s.
Introducing a carbon tax of EUR 100/tonne of CO2 on diesel fuel, equivalent to approximately
EUR 0.25/litre of diesel, would have an even greater effect, allowing BEVs to be cost-competitive in morethan half of heavy-duty truck sales by 2028.
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COMPARING TRUCK TECHNOLOGIES IN AN UNCERTAIN FUTURE

Notably, all three policy measures would shift the range of uncertainty on the TCO of trucks to earlier
years. Their introduction could unlock edge-case possibilities of TCO parity between BEVs and ICEVs inmost vehicle segments in the mid-2020s. Furthermore, combining policy measures could be revenueneutral: for example, the revenues from carbon taxes could be used to fund low-interest-rate loans orpurchase subsidies.
These policy measures should be time-limited and would only be necessary to accelerate the deployment
of ZEVs until economies of scale, efficiency improvements and battery cost reductions allow ZEVs tocompete with ICEVs without further policy support, which baseline scenarios suggest is possible.
Figure 6. The impact of policy measures on adoption of BEVs (excluding ERSVs and FCEVs)

Notes: Black line denotes the median scenario, shading denotes 50th, 75th and 95th confidence intervals of themultiple scenarios explored. Baseline results are the same as those presented in Figure 3 and exclude ERS. Zeropercent interest loans set financing costs of BEVs to zero assuming 0% interest rates rather than the default 3%.
Purchase subsidy includes a EUR 20 000 incentive for BEVs compared with ICEVs. Carbon tax includes anadditional cost to diesel fuel from a EUR 100/tonne CO2 price.

Some of the most important measures needed for the adoption of ZEVs involve stimulating theconstruction of charging and refuelling infrastructure, including necessary electricity grid upgrades.
Without the necessary infrastructure, the adoption of ZEVs will not be possible. The timescales and market
dynamics of the adoption of ZEV recharging and refuelling infrastructure are not explicitly modelled in thisanalysis. Nonetheless, there are important differences in the barriers to infrastructure roll-out between
the three ZEV technologies explored in this report.
For BEVs, there is already a substantial roll-out of charging infrastructure for passenger cars that truck
operators and manufacturers may be able to leverage. Deploying charging infrastructure can beincremental due to the relatively low cost of individual charging stations. Additionally, chargers can beinstalled on private property with relative ease, giving truck operators greater power of adopting BEVs into
their fleet. Conversely, infrastructure for ERSVs and FCEVs is much more likely to require concerted
government support since both require larger initial investments, with greater associated risk, to ensure asufficiently large infrastructure network for the purchase of vehicles to make sense. An underexplored
topic is whether such strong policy initiatives could help to roll out the required infrastructure for ZEVs
faster than the individual adoption of BEV depot chargers by small trucking companies.

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31CONCLUSIONS

Conclusions
Zero-emission vehicles are set to be cost-competitive with conventional ICEVs before 2040 without policy
support. They will be competitive in all vehicle size segments, ranging from the smallest 7.5-tonne rigid
trucks to the largest 40-tonne tractor-trailers. However, the year that ZEVs become cost-competitive with
ICEVs varies by size segment and daily mileage needs. The smallest vehicles, with high daily mileage and a
reliance on depot charging, already had the potential in 2022 to reach TCO parity with diesel vehicles in
the range of futures considered in this analysis. Larger vehicle size segments, which typically travel longer
distances, are likely to be cost-competitive with ICEVs by around 2037 without policy support.
BEVs and ERSVs have the greatest promise to be cost-competitive with conventional diesel ICEVs in theEuropean vehicle segments explored in this analysis. This is predominantly due to their higher energy
efficiencies, which keep operational costs low and offset higher upfront purchase costs. Further detailed
assessments of the relative merits of BEVs and ERSVs are needed to understand which technology canoffer the most significant financial and CO2 emissions savings, while accounting for real-world constraints.
Technology-neutral policies that avoid closing the door to either technology are warranted until it is clearthat policy objectives (e.g. GHG emission reductions) could be better achieved by one over the other.
In the wide range of uncertainty explored in this analysis, hydrogen FCEVs are not able to compete
significantly with other vehicle technologies. FCEVs would only be cost competitive in a small number ofedge cases, with highly ambitious hydrogen fuel costs below EUR 2.5/kgH2 (at the pump) and conservative
scenarios for other technologies. This lack of cost-competitiveness across the majority of the European
market means that achieving the necessary economies of scale in vehicle production to bring down vehicle
purchase prices and ensure high utilisation of refuelling infrastructure is likely to remain a challenge for
FCEVs.
Policy measures are essential to help accelerate the adoption of ZEVs. Effective measures include purchasesubsidies, carbon taxation and low-interest-rate loans to reduce vehicle financing costs associated withvehicle purchase. These policies would help shift the range of uncertainty on the adoption of ZEVs, kickstarting economies of scale of production and opening possibilities for ZEVs to be competitive with diesel
vehicles before 2030.

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REFERENCES

References
Abid, H. et al. (2021), Report on energy efficiency potentials in the transport sector and conclusions fromthe developed scenarios, sEEnergies, Horizon 2020, https://vbn.aau.dk/ws/portalfiles/portal/411666417/
846463_sEEnergies_D2.3_EE_potentials_transport_scenarios.pdf.
Ainalis, D. T., C. Thorne and D. Cebon (2020), “White Paper: Decarbonising the UK ’s Long-Haul Road
Freight at Minimum Economic Cost”, The Centre for Sustainable Road Freight, https://www.csrf.ac.uk/
2020/07/white-paper-long-haul-freight-electrification/ (accessed 5 July 2022).
Basma, H. and F. Rodriguez (2021), Race to Zero: How manufacturers are positioned for zero-emissioncommercial trucks and buses in Europe, International Council on Clean Transportation, Washington, D.C.,
https://theicct.org/wp-content/uploads/2021/12/race-to-zero-ze-hdv-eu-dec21-1.pdf.
Basma, H., A. Saboori and F. Rodríguez (2021), “Total Cost of Ownership for Tractor-Trailers in Europe:
Battery Electric Versus Diesel”, ICCT White Paper, International Council on Clean Transportation,
https://theicct.org/wp-content/uploads/2021/11/tco-bets-europe-1-nov21.pdf.
BMVI (2021), “BMVI bringt Innovationscluster für klimafreundliche Lkw-Antriebstechnologien auf den
Weg” (“BMVI launches innovation clusters for climate-friendly truck drive technologies”), Press release,https://www.bmvi.de/SharedDocs/DE/Pressemitteilungen/2021/104-scheuer-innovationsclusterstrassennutzverkehr.html (accessed 5 July 2022).
BNEF (2021a), “Battery Pack Prices Fall to an Average of $132/kWh, But Rising Commodity Prices Start toBite”, BloombergNEF, https://about.bnef.com/blog/battery-pack-prices-fall-to-an-average-of-132-kwhbut-rising-commodity-prices-start-to-bite/ (accessed 5 July 2022).
BNEF (2021b), “Electric Vehicle Outlook”, BloombergNEF, https://about.bnef.com/electric-vehicleoutlook/ (accessed 5 July 2022).
Borjesson, M., M. Johansson and P. Kågeson (2021), “The Economics of Electric Roads”, Transportation
Research Part C: Emerging Technologies, Vol. 125, https://doi.org/10.1016/j.trc.2021.102990.
Borlaug, B. et al. (2021), “Heavy-duty truck electrification and the impacts of depot charging onelectricity distribution systems”, Nature Energy, Springer US, Vol. 6/6, pp. 673-682, doi: 10.1038/s41560021-00855-0.
Bureau of Labor Statistics (2022), Occupational Outlook Handbook: Heavy and Tractor-trailer Truck
Drivers, U.S. Department of Labor, https://www.bls.gov/ooh/transportation-and-material-moving/heavyand-tractor-trailer-truck-drivers.htm (accessed 5 July 2022).
Burges, K. and S. Kippelt (2021), “Grid-related challenges of high-power and megawatt charging stations
for battery-electric long-haul trucks”, study on behalf of Transport and Environment,
https://www.transportenvironment.org/wp-content/uploads/2022/01/2022_01_TE_grid_integration_
long_haul_truck_charging_study_final.pdf.

DECARBONISING EUROPE’S TRUCKS: HOW TO MINIMISE COST UNCERTAINTY © OECD/ITF 2022

33

REFERENCESCALSTART (2021), Global memorandum of understanding on zero-emission medium- and heavy-duty
vehicles, Global Commercial Vehicles Drive to Zero campaign, CALSTART,
https://globaldrivetozero.org/mou-nations/ (accessed 5 July 2022).
Daimler Trucks (2021), “Daimler Truck, the Traton Group and Volvo Group sign joint venture agreement
for European high-performance charging network”, Press release, 16 December
https://media.daimlertruck.com/marsMediaSite/en/instance/ko.xhtml?oid=51853937 (accessed
5 July 2022).
EEA (2021), “Monitoring of CO2 emissions from heavy-duty vehicles”, dataset, European Environment
Agency, https://www.eea.europa.eu/data-and-maps/data/co2-emission-hdv (accessed 5 July 2022).
EU (2019), “Regulation (EU) 2019/1242 of the European Parliament and of the Council of 20 June 2019setting CO2 emission performance standards for new heavy-duty vehicles and amending Regulations (EC)No 595/2009 and (EU) 2018/956 of the European Parliament and of the Council and Council Directive
96/53/EC”, Official Journal of the European Union, L 198/202, https://eur-lex.europa.eu/eli/reg/2019/
1242/oj (accessed 21 July 2022).
European Commission (2021a), “Proposal for a regulation of the European Parliament and of the Council
on the deployment of alternative fuels infrastructure, and repealing Directive 2014/94/EU of theEuropean Parliament and of the Council”, European Commission, Brussels, https://eur-lex.europa.eu/
legal-content/EN/TXT/?uri=CELEX%3A52021PC0559 (accessed 21 July 2022).
European Commission (2021b), “Vehicle Energy Consumption calculation Tool – VECTO” (webpage),
Documentation, European Commission, Brussels, https://ec.europa.eu/clima/eu-action/transportemissions/road-transport-reducing-co2-emissions-vehicles/vehicle-energy-consumption-calculation-toolvecto_en (accessed 21 July 2022).
European Commission (2017), “Commission Regulation (EU) 2017/2400 of 12 December 2017
implementing Regulation (EC) No 595/2009 of the European Parliament and of the Council as regards thedetermination of the CO2 emissions and fuel consumption of heavy-duty vehicles and amending
Directive 2007/46/EC of the European Parliament and of the Council and Commission Regulation (EU) No
582/2011”, Official Journal of the European Union, L 349/1, https://eur-lex.europa.eu/legal-content/EN/
TXT/PDF/?uri=CELEX:32017R2400 (accessed 21 July 2022).
EUROSTAT (2022), “Electricity prices for non-household consumers - bi-annual data”, EUROSTAT Data
Browser (dataset), https://ec.europa.eu/eurostat/databrowser/view/NRG_PC_205__custom_
1930213/default/table?lang=en (accessed 21 July 2022).
Gaete-Morales, C. et al. (2021), “An open tool for creating battery-electric vehicle time series fromempirical data, emobpy”, Scientific Data, Vol. 8/152, pp. 1-18, https://doi.org/10.1038/s41597-02100932-9.
Gründler, M. and A. Kammel (2021), Why the future of trucks is electric. Traton, https://traton.com/
en/newsroom/current-topics/furture-transport-electric-truck.html (accessed 21 July 2022).
Guo, Z. and Y. Zhou (2019), “Residual value analysis of plug-in vehicles in the United States”, Energy
Policy, Vol. 125, pp. 445-455, https://doi.org/10.1016/j.enpol.2018.10.023.
H2live (n.d.), H2live (website), https://h2.live/en/ (accessed 21 July 2022).
Hacker, F. (2020), “Getting zero emission trucks on the road: From regional to long-haul”, Öko-Institute.V., Berlin, https://www.oeko.de/fileadmin/oekodoc/TE-ZE-Truck-WS-Brussels.pdf (accessed
21 July 2022).
34

DECARBONISING EUROPE’S TRUCKS: HOW TO MINIMISE COST UNCERTAINTY © OECD/ITF 2022

REFERENCES

Hunter, C. et al. (2021), Spatial and Temporal Analysis of the Total Cost of Ownership for Class 8 Tractors
and Class 4 Parcel Delivery Trucks, NREL/TP-5400-71796, National Renewable Energy Laboratory, Golden,
Colorado, https://www.nrel.gov/docs/fy21osti/71796.pdf.
IEA (2021a), Global Hydrogen Review 2021, OECD Publishing, Paris, https://doi.org/10.1787/39351842en.
IEA (2021b), Net Zero by 2050: A Roadmap for the Global Energy Sector, OECD Publishing, Paris,
https://doi.org/10.1787/c8328405-en.
IEA (2021c), World Electricity Generation Mix by Fuel, 1971-2019 (chart), International Energy Agency,
Paris, https://www.iea.org/data-and-statistics/charts/world-electricity-generation-mix-by-fuel-19712019 (accessed 21 July 2022).
IEA (2019), The Future of Hydrogen: Seizing Today’s Opportunities, OECD, Paris,
https://doi.org/10.1787/1e0514c4-en.
IEA (2017), The Future of Trucks: Implications for Energy and the Environment, International Energy
Agency, Paris, https://doi.org/10.1787/9789264279452-en.
ITF (2021a), “Cleaner Vehicles: Achieving a Resilient Technology Transition”, International Transport
Forum Policy Papers, No. 90, OECD Publishing, Paris, https://doi.org/10.1787/08cb5e7e-en.
ITF (2021b), ITF Transport Outlook 2021, OECD Publishing, Paris, https://doi.org/10.1787/16826a30-en.
ITF (2020), “Regulations and Standards for Clean Trucks and Buses: On the Right Track?”, International
Transport Forum Policy Papers, No. 77, OECD Publishing, Paris, https://doi.org/10.1787/ed7ef18b-en.
ITF (2018), “Towards Road Freight Decarbonisation: Trends, Measures and Policies”, International
Transport Forum Policy Papers, No. 64, OECD Publishing, https://doi.org/10.1787/3dc0b429-en.
Kim, H. (2022), “Korean EV Battery Prices May Jump 40% in Coming Years, SNE Says”, Bloomberg,
https://www.bloomberg.com/news/articles/2022-04-13/korean-ev-battery-prices-may-jump-40-incoming-years-sne-says (accessed 5 July 2022).
Kühnel, S., F. Hacker and W. Görz (2018), Oberleitungs-Lkw im Kontext weiterer Antriebs- undEnergieversorgungsoptionen für den Straßengüterfernverkehr (Trolley Trucks in Context: Further Drive
and Power-supply Options), Oeko-Institut e.V., Berlin, pp. 139, https://www.oeko.de/fileadmin/oekodoc/
StratON-O-Lkw-Technologievergleich-2018.pdf.
Liu, J. and M. Danilovic (2021), Electrification of the Transportation System in China: Exploring battery
Swapping for Heavy Trucks in China 1.0., Report No. 2021-2, Swedish National Road and Transport
Research Institute, https://hh.se/download/18.7b11fe917c2ac07303bf9d3/1632994342544/SwedenChina%20Bridge%20-%20Exploring%20Battery%20Swapping%20for%20Heavy%20Trucks%20in%
20China%201.0%5B68%5D.pdf.
Lutsey, N. (2008), Assessment of Out-of-State Heavy-Duty Truck Activity Trends In California, Contract
No. 04-328, California Air Resources Board, Sacramento, California, https://ww2.arb.ca.gov/sites/
default/files/classic/research/apr/past/04-328.pdf.
Mareev, I., J. Becker and D. Sauer (2018), “Battery dimensioning and life cycle costs analysis for a heavyduty truck considering the requirements of long-haul transportation”, Energies, Vol. 11/1,
https://doi.org/10.3390/en11010055.

DECARBONISING EUROPE’S TRUCKS: HOW TO MINIMISE COST UNCERTAINTY © OECD/ITF 2022

35

REFERENCES

Mercedes-Benz (2021), “Portfolio Truck Classic: EURO III, EURO V and EEV”, Mercedes-Benz Powertrain,
Stuttgart, https://www.mercedes-benz.com/content/dam/brandhub/vehicles/aggregates/brochures/
MB_Powertrain_Truck_Classic_2021.pdf.
Metrolinx (2018), Regional Express Rail Program Hydrail Feasibility Study Report, CPG-PGM-RPT-245,
https://www.metrolinx.com/en/news/announcements/hydrail-resources/CPG-PGM-RPT-245_Hydrail
FeasibilityReport_R1.pdf.
Movares (2020), “Verkenning Electric Road Systems”, Utrecht, https://open.overheid.nl/repository/ronl850ffb2c-44a7-4122-9173-0c04c4e64bf6/1/pdf/bijlage-2-rapport-movares-electric-road-systems.pdf.
Mulholland, E. et al. (2022), “The CO2 standards required for trucks and buses for Europe to meet its
climate targets”, ICCT White Paper, International Council on Clean Transportation, Washington, DC,
https://theicct.org/wp-content/uploads/2022/03/hdv-co2standards-recs-wp-mar22.pdf.
OECD (2022), “Detailed annual transport statistics: Road-Equipment” (dataset: Transport – Data from
ITF/Eurostat/UNECE questionnaire – Detailed annual transport statistics – Road-Equipment), OECD.Stat,
http://dotstat.oecd.org/?lang=en (accessed on 16 November 2021).
OECD (2020), Consumption Tax Trends, OECD Publishing, Paris, https://www.oecd-ilibrary.org/taxation/
consumption-tax-trends_19990979.
Plötz, P. (2022), “Hydrogen technology is unlikely to play a major role in sustainable road transport”,
Nature Electronics, Vol 5, pp. 8-10, https://doi.org/10.1038/s41928-021-00706-6.
Ricardo and ICCT (2021), E-Truck Virtual Teardown Study: Final Report, Ricardo, Inc., Van Buren
Township, https://theicct.org/wp-content/uploads/2022/01/Final-Report-eTruck-Virtual-TeardownPublic-Version.pdf.
Rose, P. (2020), Modeling a potential hydrogen refueling station network for fuel cell heavy-duty vehicles
in Germany in 2050, Karlsruher Instituts für Technologie, Karlsruhe,
https://publikationen.bibliothek.kit.edu/1000119521 (accessed 21 July 2022).
Scania (2021), Scania’s commitment to electrification – our initiatives so far, https://www.scania.com/
group/en/home/newsroom/news/2021/Scanias_commitment_to_electrification_our_initiatives_so_far.
html (accessed 21 July 2022).
Schmuch, R. et al. (2018), “Performance and cost of materials for lithium-based rechargeable automotive
batteries”, Nature Energy, Vol. 3/4, pp. 267-278, https://doi.org/10.1038/s41560-018-0107-2.
Siemens (2020), “A new era of sustainable road freight transport”, Whitepaper, Siemens AG, Munich,Washington, DC, https://new.siemens.com/global/en/products/energy/mediumvoltage/solutions/emobility/emobility-latest-technologies.html (accessed 21 July 2022).
TuttoTrasporti (2022) Listini Nuovo, https://www.tuttotrasporti.it/listino.html (accessed 26 July 2022).
UK BEIS (2022), “Weekly road fuel prices”, National statistics, United Kingdom Department for Business,
Energy and Industrial Strategy, https://www.gov.uk/government/statistics/weekly-road-fuel-prices(accessed 26 July 2022).
UK DfT (2022), AADF Data - major and minor roads (downloadable database), United Kingdom
Department for Transport, https://roadtraffic.dft.gov.uk/downloads (accessed 21 July 2022).
UK DfT (2021), Zero emission road freight trials funding winners, webpage, UK Department for Transport,
https://www.gov.uk/government/publications/zero-emission-road-freight-trials-funding-winners(accessed 5 July 2022).
36

DECARBONISING EUROPE’S TRUCKS: HOW TO MINIMISE COST UNCERTAINTY © OECD/ITF 2022

REFERENCES

UK DfT (2020), Road Traffic Estimates: Great Britain 2019, Statistical Release, United Kingdom
Department for Transport, https://assets.publishing.service.gov.uk/government/uploads/system/
uploads/attachment_data/file/916749/road-traffic-estimates-in-great-britain-2019.pdf.
US DOE (2019), “DOE Advanced Truck Technologies: Subsection of the Electrified Powertrain Roadmap –Technical Targetsfor Hydrogen-fueled Long-haul Tractor-trailer Trucks”, DOE Hydrogen Program Record#19006, United States Department of Energy, Washington, DC,
https://www.hydrogen.energy.gov/pdfs/19006_hydrogen_class8_long_haul_truck_targets.pdf.
US DOE (2021a), “DOE Hydrogen Program Record #2100”, United States Department of Energy,
Washington, DC, https://www.hydrogen.energy.gov/pdfs/21001-durability-adjusted-fcs-cost.pdf.
US DOE (2021b), “Fuel Cell Technologies Overview”, Presentation by Dimitrios Papageorgopoulos at the2021 Annual Merit Review and Peer Evaluation Meeting, United States Department of Energy,
Washington, DC,
https://www.hydrogen.energy.gov/pdfs/review21/plenary8_papageorgopoulos_2021_o.pdf.
Volvo (2022), “Sales start for Volvo’s heavy-duty electric trucks”, Press release,https://www.volvotrucks.com/en-en/news-stories/press-releases/2022/may/sales-start-for-volvosheavy-duty-electric-trucks.html (accessed 21 July 2022).
Wentzel, H. (2020), “Battery electric and plug-in hybrid vehicles”, Presentation at the Interational
Transport Forum’s workshop “ITF Mapping standards for low- and zero-emission electric heavy-duty
vehicles” held 17-18 February 2020 in Paris, https://www.itf-oecd.org/sites/default/files/docs/batteryelectric-plug-in_hybrid-vehicles-wentzel.pdf .
Wietschel, M. et al. (2019), “Electric Trolley Trucks - A Techno-economic Assessment for Germany”,
World Electric Vehicle Journal, Vol. 10/4, pp. 86, https://doi.org/10.3390/wevj10040086.
Yang, C. and J. Ogden (2007), “Determining the lowest-cost hydrogen delivery mode”, International
Journal of Hydrogen Energy, Vol. 32/2, pp. 268-286, https://doi.org/10.1016/j.ijhydene.2006.05.009.
Zhou, Y. and S. Searle (2022), “Cost of Renewable Hydrogen Produced Onsite at Hydrogen Refueling
Stations in Europe”, ICCT White Paper, International Council on Clean Transportation, Washington, DC,
https://theicct.org/wp-content/uploads/2022/02/fuels-eu-cost-renew-H-produced-onsite-H-refuelingstations-europe-feb22.pdf.

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ANNEX A. METHODOLOGY

Annex A. Methodology
This section includes details about the modelling and input assumptions. The analysis draws from recent
reports published by Ainalis, Thorne and Cebon (2020), Basma, Saboori and Rodríguez (2021), Hunter etal. (2021) and Ricardo and ICCT (2021) and builds on them with additional data sources. The novelty of thepresent report is that it examines how the uncertainty in underlying variables affects the total cost ofownership (TCO) and rates of adoption of zero-emission vehicles (ZEV) in multiple market segments andacross three ZEV technologies: battery electric vehicles (BEV), electric road system vehicles (ERSV) and fuelcell electric vehicles (FCEV).

Vehicle energy efficiency
The fuel efficiency of each vehicle group and powertrain configuration is estimated over a range of speeds
and accelerations, known as a drivecycle, to approximate conditions during real-world driving. Each vehicle
is simulated over three drivecycles (urban delivery, rural delivery and long-haul delivery) used for fueleconomy testing regulations in the European Union (as part of the VECTO modelling suite). Each simulation
is used to determine road load forces (aerodynamic, inertia, gravitational and friction forces) for eachvehicle and powertrain type over each drivecycle with a methodology that is similar to that of GaeteMorales et al. (2021). For each European vehicle group, frontal area, aerodynamic drag coefficient, friction
coefficient, vehicle mass and payload are sourced from default VECTO model inputs (European
Commission, 2021b). Adding an additional 1 000 kg of weight (for example from a particularly large
battery) to a group 5 vehicle adds approximately 2% to its energy use per kilometre. Road loads areconverted into vehicle energy requirements using average powertrain energy efficiencies for diesel
vehicles, BEVs and FCEVs and are calibrated with VECTO outputs for conventional diesel vehicles for eachdrivecycle (EEA, 2021). VECTO results for ZEV powertrain vehicles are not yet available so could not be
used for calibration. BEVs are assumed to have a powertrain energy efficiency of 90%. The electric charging
efficiency of BEVs and ERSVs is assumed to be 95% (Burges and Kippelt, 2021). ERSVs are assumed to havethe same energy efficiency as BEVs.
The energy efficiency of FCEVs are calculated by dividing the energy efficiency of an equivalent BEV by
average efficiency of a fuel cell. Given their limited deployment to date, there remains significant
uncertainty about expected improvements in fuel cell energy efficiencies. Upper and lower bounds arechosen to account for this uncertainty and shown in Figure 1F. US DOE (2021a) estimates peak (rated)
efficiencies of fuel cells to be 54% in 2020 with targets to reach 68% in 2030 and an “ultimate” target of
72% peak efficiency (US DOE, 2019). Average fuel cell efficiencies during use are lower than these peakefficiencies and are assumed to be between 45-50% in 2020, rising to between 50%-62% in 2030 and
55-66% efficient in 2050.
The average energy efficiency in typical operations for each truck type and powertrain is included inTable 4. These are estimated by weighting the fuel consumption over each drivecycle using the shares.
Future incremental energy-efficiency improvements to all trucks from improved aerodynamics,
powertrain efficiencies and light-weighting from the base year are estimated between a lower bound withenergy-efficiency improvements of 35% in 2030 and levelling out at a 40% reduction in 2050 with respect
to 2020 levels. An upper bound assumes efficiency improvements of 10% by 2030 and 20% by 2050.

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Table 4. Energy efficiency (in 2020) by powertrain, drive cycle allocation and vehicle group
Energy Efficiency (MJ/km)

Drive cycle allocation

Vehicle weight (kg)

Vehicle
Group

BEV

ERSV

FCEV

ICEV

RD

LH

UD

GVW

ICEV
powertrain

Max
battery

1

1.75

1.75

3.82

6.45

40%

0%

60%

8 800

1 189

1 949

2

1.89

1.89

4.08

6.51

50%

0%

50%

22 500

1 453

2 213

3

2.21

2.21

4.84

7.89

50%

0%

50%

15 500

1 525

2 285

4

3.13

3.13

6.29

7.74

68%

27%

5%

19 000

1 654

2 414

5

5.26

5.26

10.34

12.07

12%

88%

0%

40 000

2 246

3 006

9

4.12

4.12

8.18

9.65

43%

57%

0%

27 000

2 142

2 902

10

5.36

5.36

10.54

12.3

10%

90%

0%

40 000

2 426

3 186

11

3.58

3.58

7.33

8.92

100%

0%

0%

30 000

2 468

3 228

12

5.28

5.28

11

13.57

100%

0%

0%

40 000

2 562

3 322

Notes: BEV: battery electric vehicle; ERSV: electric road system vehicle; FCEV: fuel cell electric vehicle; ICEV:internal combustion engine vehicle (diesel); RD: regional delivery; LH: long haul; UD: urban delivery; GVW: grossvehicle weight. Vehicle groups are those defined by the European Commission (2017).

The fuel efficiency of FCEVs degrades substantially over the course of their life due to catalyst poisoning.
In this analysis, fuel economy degradation in line with DOE future targets of 20% reduction in fuel economy
after 25 000 hours of operation is assumed (US DOE, 2021b) and estimated using equation 2 in Annex B.
Fuel cell replacements are not considered since the use of a FCEV for approximately 10 hours per day,
270 days per year over a seven-year lifetime is below the US DOE target lifetime of 25 000 hours.

Vehicle technical characteristics, annual mileage and range
requirements
This section includes details of vehicle technical specifications and annual mileage and range requirements.
Figure 7. Vehicle purchase cost, engine power and average range requirements

Notes: A: Vehicle retail price estimated using data from TuttoTrasporti (2022); B: Rated diesel engine power,sourced from EEA (2021); C: Range requirements are calculated based on annual mileage in the first year of use.
Vehicle groups are those defined by the European Commission (2017).

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ANNEX A. METHODOLOGYEngine PowerMedian engine power for each vehicle group is shown in Figure B and is sourced from vehicle type approvaltest data (EEA, 2021). Findings from vehicle simulations performed by Hunter et al. (2021) suggest thatdifferences in engine power requirements between ZEV powertrains are relatively minor. For simplicity,
power requirements for BEV, ERSV and FCEV powertrains are therefore assumed to be similar to those of
conventional diesel vehicles in each vehicle group.
Annual mileage and range requirements

Important factors affecting the TCO of a vehicle are the assumed annual mileage and range requirements.
Trucks differ in their operational requirements and environments, with different annual mileages basedon the application. Furthermore, the average annual mileage of trucks decreases as they age, with vehicles
typically being driven the most when they are new. To account for this diversity, each vehicle group isevaluated over a wide range of annual mileage distributions. Annual mileage requirements for each vehicle
group are estimated using publicly available information on used vehicle sales from www.Truck1.eu,
www.Gratka.pl and www.Mascus.co.uk. Knowing the vehicle cumulative mileage and age of different typesof vehicles, a cubic function was fitted to the data using quantile regression to estimate how cumulative
mileage grows with the vehicle age across each percentile and vehicle group. By taking the derivative of
the fitted cubic functions, a quadratic function representing annual mileage as a function of vehicle age
could be estimated for each vehicle group and percentile (plotted in Figure C).
Figure 8. Annual mileage by vehicle group

Notes: Figure shows the median annual mileage of each vehicle group (solid line); shaded areas are between the
25th and 75th percentiles. Vehicle groups are those defined by the European Commission (2017).

Finding publicly available information on vehicle range requirements for different trucks is challenging. In
this analysis, range requirements are estimated based on the first year of use (which is typically where
maximum annual mileage occurs). The annual mileage distributions from the first year of use (Figure ) are
divided by annual days of operation to approximate vehicle daily use. Data from Scania for European 4x2
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group 5 tractors during their first year of use (Wentzel, 2020) is compared with second-hand vehicle sales
data and alignment is achieved when assuming the annual usage of European trucks to be approximately
270 days/year. This is similar to survey data in the United States, which found the median annual usage oftrucks to be approximately 260 days/year (Lutsey, 2008). Range requirement distributions are thenestimated for each vehicle group assuming the same number of days of use and are shown in Figure C.
Battery size, hydrogen storage requirements and gross vehicle weight limits

The mass of trucks on the road is regulated to ensure safe operating conditions. Regulations include
specific weight limits – for both the vehicle and its payload – based on the number of axles. BEVs with longdistance range requirements may need a large and heavy battery, limiting maximum payload capabilities
and negatively affecting operational feasibility. This is less likely for FCEVs and ERSVs, which are expected
to need smaller battery sizes than BEVs. The European Commission recognised the risk of operational
limitations to ZEVs from heavy batteries and recently passed regulations allowing ZEVs to operate with anadditional two tonnes of weight (EU, 2019). This analysis assumes that the payload capabilities of trucks
must remain unchanged from conventional diesel vehicles. Therefore, the weight of BEV batteries andpowertrain must not exceed the sum of the weight of a conventional diesel powertrain and the additional
two-tonne regulatory allowance. The weight of conventional diesel and BEV powertrains in each vehicle
group is estimated using industry and literature data (Mareev, Becker and Sauer, 2018; Ricardo and ICCT,2021; Mercedes-Benz, 2021) and is shown in Table 4.
A key factor influencing the weight of batteries is their energy density (the amount of energy per kg ofbattery). Battery energy density differs by battery chemistry and is continuing to improve over time, with
better technologies and cell production methods, which are driving significant reductions in the weight of
BEV batteries per kWh. A range of energy densities are used in the analysis to account for the uncertainty
in battery chemistries and future improvements, shown in Figure 1K. Energy densities in year 2020 are
assumed to be between 140 Wh/kg and 200 Wh/kg (Basma, Saboori and Rodríguez, 2021; BNEF, 2021b).
Average pack energy density has doubled over the past decade (BNEF, 2021b). Energy densities are
assumed to continue at a similar rate reaching between 220 Wh/kg and 260 Wh/kg by 2030. Energy
densities in 2050 are highly uncertain, partly due to the unknown adoption of advanced technologies suchas novel solid-state batteries (Schmuch et al., 2018), and are assumed to range between 250 Wh/kg and
400 Wh/kg.
It is assumed that BEVs are fitted with sufficient battery capacity to satisfy daily range requirements (asshown in Figure C). In this analysis, different range requirements for each vehicle group are investigated.
For example, 50% of vehicle group 5 tractor-trailers are driven less than 435 km per day on average; it istherefore assumed that a BEV with 435 km-range capabilities could satisfy the requirements of half thevehicle group 5 tractors without any charging en-route. Similarly, approximately 5% of group 5 tractors
travel over 650 km on an average day, meaning a particularly large range would be needed for thesevehicles to avoid stopping to recharge during the day. The 50th, 75th, 95th and 100th percentiles of average
range requirements are investigated for all vehicle groups.
The effective battery capacity required (in kWh) is calculated by multiplying the daily range requirementsof each truck by its energy efficiency. An additional 20% of battery capacity is added to this effective
battery size to account for oversizing to ensure battery longevity over the vehicle's life. However, if thesize of the battery means the vehicle mass overshoots the maximum permissible vehicle weight, or an
arbitrary limit of 1 MWh, then battery capacity is limited to the size needed to meet weight regulations. In
these cases, trucks are likely to need to use high-power charging throughout the day to satisfy energy
requirements. It is assumed that once the vehicle’s battery charge is depleted, any additional electricity

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ANNEX A. METHODOLOGY

would be delivered by high-power charging (for example, if a vehicle requires 1 000 kWh to meet daily
range requirements but has a maximum (effective) battery size of 700 kWh, then an additional 300 kWhwould need to be sourced from a high-power charger). High-power charging is assumed to cost more thandefault charging at a depot, explained in further detail below. As battery-energy density improves overtime, a lower share of high-power charging is required.
FCEVs are assumed to make use of a 70-kWh battery for all vehicle types. ERSVs are assumed to havesufficient battery capacity to have 150 km of range, which, according to Abid et al. (2021), would be
sufficient to cover the distances required between main ERS corridors on motorways and finaldestinations.

Vehicle purchase cost
Vehicle purchase costs (CAPEX) for conventional vehicles are shown in Figure A. The purchase costs for
ZEVs, and how they evolve in the future, are calculated based on cost estimates of batteries, vehicle glider(chassis), powertrain and subcomponents for each vehicle group, explained in greater detail in thefollowing sections.
Battery costs

Battery cost reductions are assumed to be exogenous to the uptake of BEV HDVs, given the far larger
market developing for passenger cars than HDVs. BNEF (2021a) estimate that average sales-weighted costsof lithium-ion battery packs globally in the year 2020 were EUR 122/kWh. Large battery cost reductions in
the past decade have been primarily driven by economies of scale. There remain many opportunities tofurther reduce the costs of batteries with further increases in the scale of production, advanced
manufacturing techniques and improvements in battery chemistries. The lower bound of future average
battery prices chosen in this analysis sees cost reductions with average prices reaching EUR 70/kWh by
2030, in line with publicly stated ambitions by Ford and Renault (BNEF, 2021a) and reaching EUR 50/kWhby 2050. This is a relatively moderate decline compared to average annual reductions of 18.5% between
2013 and 2021. However, it is uncertain whether the learning rates observed historically will continue inthe future. The share of raw material costs in the price of batteries is increasing, reaching approximately
80% in 2020 (BNEF, 2021a). Furthermore, high demand with relatively constrained supply in the short termmeans achieving similar rates of cost reductions may be challenging. The upper bound sees battery packcosts increase to reach EUR 150/kWh by 2025, similar to recent industry announcements pointing to shortterm cost increases until new raw material production sources are operational (Kim, 2022). Long-term
battery pack costs in the upper bound are conservatively assumed to reach EUR 100/kWh by 2050. The
range of uncertainty in the upper and lower bounds is shown in Figure 1-I.
Batteries for truck manufacturers have historically been more expensive than passenger car batteries,
principally due to the limited market and volumes. This cost difference, or “mark-up”, is likely to decreaseover time with increases in the scale of production of BEV trucks. In this analysis, upper and lower bounds
for the cost difference between HDV battery prices and global Li-ion battery prices are derived from
Ricardo and ICCT (2021) for the 2020-2030 timeframe and further estimated until 2050, shown in Figure
1J. Therefore, the cost of batteries in ZEV trucks is calculated by multiplying global average battery packprices in each year (Euro/kWh) by an HDV mark-up factor and the size of batteries (kWh) in each vehicle.

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Fuel cell and hydrogen storage tank costs

Commercial FCEVs are currently produced at a small scale of approximately 3 000 per year (IEA, 2021a).
Only a fraction are likely to be HDVs, likely for pilot projects and early-stage trials. This makes it challenging
to understand the current and future costs of fuel cell powertrains for HDVs. There remains a significant
degree of uncertainty in base year costs, how costs may reduce with increasing scale of production(learning curves) and future production levels.
The US DOE (2021b) estimates the average HDV fuel cell power system cost in 2020 to be approximately
EUR 283/kWe when produced at 1 000 units per year. This cost includes adjustments to reflect a
25 000-hour lifetime via measures such as stack oversizing (US DOE, 2021b). The same US DOE study also
includes an ambitious pathway to lower FCEV costs to approximately EUR 142/kWe by 2025 andEUR 70/kWe by 2030, assuming a production ramp-up to 100 000 units per year by 2030 and an ultimate
goal of EUR 53/kWe (here assumed to be by 2050). The present analysis uses the US DOE scenario as thelower bound of HDV FCEV costs since most cost reductions are driven by the uncertain and relatively rapid
increase in production volumes. The upper bound assumes fuel cell costs decrease in line with US DOE
technical improvements in components but without the cost reductions associated with increasing
production scale, dropping from EUR 283/kW in 2020 to EUR 208/kW in 2025, EUR 154/kW in 2030 and
EUR 123/kW in 2050. It should be noted that US DOE fuel cell cost estimates are considerably lower thanvalues quoted in a recent review by Ricardo and ICCT (2021), which are based in part on Ballard supplied
costs for the Hydrail project (Metrolinx, 2018). The range of uncertainty for fuel cell system costs ispresented in Figure 1E.
FCEV vehicles are assumed to have hydrogen storage tanks tailored to meet their range requirements(similarly to battery requirements for BEVs). The size of the hydrogen storage tank needed for each truck
type is estimated by multiplying the fuel efficiency by range requirements of each truck type. Hydrogenstorage system costs (expressed in Euro/kg) are sourced from (Ricardo and ICCT, 2021), extended to 2050
and presented in Figure 1G.
Electric road system vehicle pantograph costs

Pantograph costs are assumed to range between EUR 18 000 and EUR 28 000 in early-stage deployment(pre-2030) and are assumed to drop to between EUR 10 000 and EUR 12 000 in 2050 due to market scaleup based on estimates by the IEA (2017), Kühnel, Hacker and Görz (2018) and Ainalis, Thorne and Cebon(2020), as shown in Figure 1-O.
Powertrain costs

Ranges on the costs of electric drivetrain components are sourced from Ricardo and ICCT (2021). All ZEVtechnologies (BEV, ERSV and FCEV) are assumed to share similar electric powertrain components such as
electric motors, inverters, high-voltage systems and on-board charging (excluding batteries, pantographs
and fuel cells). This is a simplification, as there are likely to be differences in a number of components suchas thermal management and high-voltage distribution systems. However, these are considered to beminor (<10% of costs) and are omitted to simplify the model. Upper and lower bound estimates of total
electric drivetrain costs are shown in Figure 1D. The cost of a diesel engine is estimated according toequation 3 (in Annex B) used in Hunter et al. (2021) and assumed to be constant over time and applicable
to all vehicle groups. Additional costs associated with meeting increasingly stringent air pollutant emissions
regulations are not accounted for.

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ANNEX A. METHODOLOGY

Glider costs

The cost of the vehicle excluding powertrain (also known as the vehicle glider) is estimated from median
ICEV purchase costs (see Figure , left) for each vehicle group, minus the cost of the diesel powertrain
calculated according to equation 3, and is assumed to be constant over time.

Residual value
The residual value of diesel trucks drops rapidly after first purchase, with upper and lower bounds of 35%
and 20% of initial purchase cost after seven years respectively (calculated based on the interquartile rangeof the vehicle purchase costs shown in Figure A and second-hand vehicle sales data detailed in Annex B),
shown in Figure 1P.
The residual value of ZEVs is highly uncertain. It is possible that the lower operation and maintenancerequirements of ZEVs, due to their reduced mechanical complexity, could extend their maximum vehicle
lifetime and increase residual value. The residual value of battery packs is influenced by battery
degradation and the number of battery charging cycles over its lifetime. Cycle lifetimes are increasing overtime and could further improve residual values. Conversely, the residual value of new technologies could
be lower than that of conventional vehicles, at least initially for first-generation trucks, due to immaturesecond-hand markets and unknown expectations about vehicle lifetimes. Neither of these effects is
explicitly modelled but would be a valuable addition in future work.
ZEVs are assumed to show similar market dynamics to diesel vehicles with residual values after seven years
ranging between 20-35% of the sum of vehicle glider, powertrain, fuel cell, hydrogen fuel tank andpantograph costs and with respect to the cost of a new vehicle in the year that vehicle ownership ends
(for example the residual value of a model year 2020 vehicle reaching end of ownership in 2027 would be
between 20-35% of the cost of a model year 2027 vehicle). Vehicle battery packs are assumed to have
between 5% and 20% residual value after seven years (again with respect to a new equivalent batterypack).

Vehicle operational costs
Vehicle operational costs (OPEX) are a major factor in the total cost of ownership and include energy costs(diesel fuel, hydrogen and/or electricity) and operation and maintenance costs.
Diesel costs

Estimates for future uncertainty of diesel prices are based on historical variance over the past decade inEurope. As users of fuel for commercial operations, road freight companies are typically able to receive arebate on value added tax (VAT) paid on fuel and electricity. Fuel prices and excise duties are sourced from
OECD (2020), average diesel fuel prices in Europe between 2010 and 2019 varied between EUR 0.95/Litreand EUR 1.50/Litre excluding VAT and other recoverable taxes. These are used as upper and lower bounds
to assess in the future.
Electricity costs

Base electricity price data for non-household consumers in Europe, excluding VAT and other recoverable
taxes and levies are sourced from (EUROSTAT, 2022) (Band IC: 500 MWh-2 000 MWh). There is significant

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ANNEX A. METHODOLOGY

variance in electricity costs between countries, for simplicity this analysis assumes an average electricity
cost of EUR 0.125/kWh in line with EU averages in 2020.
Future electricity costs are uncertain. The increasing adoption of renewable electricity generation is likely
to reduce costs on average, although a number of other factors may limit this effect. A range of futurebase electricity costs are examined in this analysis to account for this uncertainty and are shown in Figure
1A. In the lower bound, electricity costs are assumed to decrease by 2% every year from 2020 values
leading to a decrease of approximately 50% by 2050. In the upper bound, electricity costs are assumed
unchanged from the base year.
The cost paid by an electric truck (BEV or ERSV) per unit of electricity is estimated as the sum of baseelectricity costs and an additional cost needed to pay off infrastructure investments such as chargers,
overhead catenary wires and grid connection costs, shown in Figure 9. Infrastructure costs associated withcharging are expected to be dominated by the cost of chargers. A study by Burges and Kippelt (2021)
estimated that grid connection and other infrastructure costs are expected to account for just 10% of
charging infrastructure costs. Depot charging costs are particularly dependent on assumptions of therequired payback periods and the assumed utilisation. Basma, Saboori and Rodríguez (2021) estimate
depot charging costs to be approximately EUR 0.03/kWh for a 100 kW charger and assuming eight hoursof use per day. Burges and Kippelt (2021) estimate just under EUR 0.05/kWh for a similar concept including
grid infrastructure costs. In this analysis, a range of depot charging costs between EUR 0.03-0.06/kWh is
used to account for uncertainty in operations and infrastructure costs. When added to base electricity
costs (EUR 0.125/kWh in 2020), these give the total cost per unit of electricity for a truck using depot
charging. The costs associated with high-power charging on motorways and ERS are more uncertain andare explained in the following sections.
Figure 9. Electricity costs for battery electric vehicles and electric road system vehicles

BEV high-power charging costs and opportunity costs

BEVs are assumed to use depot charging by default due to its lower cost and the fact that vehicles can be
charged overnight when not in use. However, when vehicles have limited battery capacity due to grossweight limits (as explained above) a share of the electricity consumption will have to be sourced from highpower charging (see equation 6 in Annex B).

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ANNEX A. METHODOLOGY

High-power charging infrastructure for trucks would be necessary for vehicles to recharge in a service
station on a main road in a reasonable time without incurring significant additional logistical costs. Thereare currently proposals for standards enabling HDV chargers with power ratings around one megawatt.
However, the installation of the required infrastructure will not be immediate. There is currently limited
charging infrastructure dedicated to BEV trucks available on main roads and its future availability is
uncertain. To account for this uncertainty, a conservative lower bound scenario sees the average power
rating of high-power chargers on main roads rise from 20 kW in 2020, to 50 kW by 2025, 100 kW by 2030
and 1 000 kW by 2050. A more ambitious upper bound scenario sees average power ratings hit 500 kW by2025 and 1 000 kW by 2030. This range of uncertainty is shown in Figure 1L.
The cost of charging infrastructure and its use are also uncertain. The cost of chargers are assumed to vary
between EUR 1 000 to EUR 3 000 for a 20 kW charging station, EUR 50 000 to EUR 70 000 for a 100 kW,
EUR 170 000 to EUR 240 000 for a 350 kW charging station and EUR 300 000 to EUR 700 000 for a onemegawatt charging station based on literature estimates (Basma, Saboori and Rodríguez, 2021; Burges andKippelt, 2021). These costs are included in charging costs per kWh and are estimated according toequation 5 for both upper and lower bounds and shown in Figure 1M. High power charging costs are highly
dependent upon the number of hours each charger is used each day, as shown in Figure 9 right, whichshows the cost of a 1 MW charger as a function of the hours of use per day. High-power public chargers
are assumed to be used between three and six hours per day to account for uncertainty in their utilisation.
The cost per kWh of high-power charging is therefore a function of the average rated power in any given
year of the charger (Figure 1L) and the associated cost per kWh accounting for its utilisation (Figure 1M).
Furthermore, additional time spent charging is likely to incur significant opportunity costs upon truck
operators if it increases journey times. This can be partly mitigated by charging during mandatory drive
rest periods (45 minutes every 4.5 hours of driving in Europe). Median truck driver wages in 2020 in the
United States were approximately EUR 19/hour and are assumed to be similar in Europe (Bureau of Labor
Statistics, 2020). Median TCO of large BEVs is roughly EUR 520 000 over seven years for an estimated
2 700 hours per year, meaning earnings have to be at least EUR 52/hour simply to cover driver wages andtruck lifetime costs. Charging opportunity costs are therefore set at EUR 60/hour after the first 45 minutesof driver rest period. This is converted into a cost per kWh in each year by dividing by the rated power ofchargers (Figure 1L). For a 20 kW charger, this equates to a cost of EUR 3/kWh, which is likely financially
unfeasible for most operations. However, opportunity costs drop to EUR 0.06/kWh when using a highly
utilised one-megawatt charger. The total cost per kWh associated with the use of high-power charging inany given year is therefore the sum of base electricity costs, high-power charging costs and opportunity
costs as shown in Figure 9.
ERS usage costs

The adoption of an ERS would require building overhead lines on main arterial roads. Electrifying thebusiest motorways would cover the majority of HDV vehicle travel. In the United Kingdom, for example,
7 230 km (4 519 miles) of motorways and main roads (approximately 2.4% of the total national road
network) carried 68% of national HDV road traffic in 2019 (UK DfT, 2020). Similar estimates for Germany
show 60% of HDV tonne kilometres are carried on the most intensively used 3 966 km of motorways(Wietschel et al., 2019). There are economies of scale in the size and spread of the network of ERS up to a
threshold, above which further expansion of the network yields marginally lower benefits due to lower
traffic (Borjesson, Johansson and Kågeson, 2021).
Literature estimates of the cost of an ERS range between EUR 1.1-1.65 million per kilometre and direction(Wietschel et al., 2019; Ainalis, Thorne and Cebon, 2020; Hacker, 2020; Movares, 2020). This is a significant

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ANNEX A. METHODOLOGY

investment but, in the context of road infrastructure, is not extreme. Hacker (2020) put the cost of covering
4 000 km of motorways in Germany with overhead ERS at approximately EUR 12.2 billion, equivalent toapproximately 18% of truck toll revenues over a 15-year period of construction. ERS infrastructure costs
can be reduced by installing an intermittent overhead line system, avoiding particularly challenging andexpensive stretches of road (e.g. tunnels, bridges), this would require pantograph systems that are
retractable, such as those developed by Siemens (2020).
Table 5. Electric road system infrastructure cost uncertainty bounds

Lower

Infrastructure costs(per lane-km)
1.1 million

Upper1.65 million

Bound

Share of trucks using ERS

Usage cost per km

Cost per kWh*70%

EUR 0.08

EUR 0.05

32%

EUR 0.28

EUR 0.19

Note: Assuming mean 3 400 vehicles/day in a single direction and average truck energy efficiency of
1.5 kWh/km and a 35-year payback period.
*Cost per kWh purely to payback infrastructure costs and excluding base electricity costs.

The present analysis aims to account for the uncertainty in ERS construction costs as well as accounting
for uncertainty in the utilisation of the ERS infrastructure, as shown in Table 5. The financial feasibility ofan ERS is influenced by the daily flow rates of trucks on the road and the share of those trucks using the
ERS. If ERS-capable trucks entered the mass market in 2030 and reached 100% of new vehicle sales by
2040, the average utilisation of an ERS built in 2030 over a 35-year asset life, accounting for the turnoverof the vehicle fleet, would be approximately 70% if all trucks used the ERS. If instead ERS-capable trucks
only reached 50% market share by 2045, then the average utilisation would be just 32%.
Figure 10. The effects of utilisation on electric road systems and high-power charging costs

Notes: ERS: electric road system; HDV: heavy-duty vehicle; MW: megawatt.
A: Range shows how ERS usage costs per km change based on vehicle flow rates (needed to payback
infrastructure investments and excluding electricity costs). The lower bound of the range assumes ERS
infrastructure costs to be EUR 1.1 million/lane-km with high utilisation of 70% of trucks using the ERS over a
35 year timeframe. The upper bound assumes EUR 1.65 million/lane-km and low utilisation of 32%. Heavy-duty
vehicle traffic flow (per direction) histograms on motorways sourced from (UK DfT, 2022).
B: High-power charging costs, a one-megawatt charger is assumed to cost EUR 300 000 lower bound andEUR 700 000 upper bound.

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ANNEX A. METHODOLOGY

The cost per kilometre for each truck needed to ensure payback of the construction of an ERS is calculatedusing equation 7 in Annex B and shown in Figure . Figure includes a distribution of annual average daily
flow rates of heavy-duty freight vehicles on all sections of motorways in Great Britain (weighted by theirlength) to illustrate typical traffic volumes on roads where an ERS might be built (UK DfT, 2022). Also shownis the same distribution omitting rigid 4x2 vehicles, in case either their smaller size means that apantograph is not appropriate or a large battery BEV solution is preferable.
Average costs for ERS payback and use are between EUR 0.08 per vehicle km and EUR 0.28 per vehicle km
assuming flow rates of 3 400 vehicles in a single direction/day (the mean excluding 4x2 rigid trucks). These
costs are assumed to be transferred to truck operators as an additional cost per kWh of electricity
consumed when using the ERS and are estimated by dividing costs per kilometre by the average energy
efficiency of a BEV truck fleet (~1.5 kWh/km). Upper and lower bounds for ERS costs, accounting for
uncertainty in infrastructure construction costs and its utilisation are therefore EUR 0.19/kWh and
EUR 0.05/kWh respectively (excluding base electricity cost). These are assumed to be constant over timeand are shown in Figure 1N. It is assumed that ERSVs use depot charging to supply the energy needed tocover the distance to and from an ERS on main roads (here, assumed to be 150 km). All further electricity
needed to meet average daily range requirements is assumed to come from overhead catenary wires. For
example, if a vehicle has a 400-km range requirement, energy needs for 150 km are priced at low depot
charging costs and the remaining 250 km of energy needs are priced at higher ERS costs.
Hydrogen costs

This analysis uses a broad range of uncertainty for hydrogen price projections. Specific detail on hydrogenproduction routes are not considered for simplicity. Hydrogen prices in the base year are estimated at
EUR 9.50/kg based on current average prices available at the pump in Europe (H2live, 2022). Hydrogen iscurrently mainly produced by steam methane reformation (IEA, 2019). Moving to low-carbon hydrogenproduction would increase the cost in the short term. Hydrogen is also typically dispensed at 350 bar;refuelling stations for heavy-duty vehicles would likely need to operate at 700 bar or with liquid hydrogen.
They would also require larger hydrogen storage capacities than are currently available (Rose, 2020). These
are all factors that would increase costs in the short term. Conversely, the increasing adoption of heavyduty hydrogen vehicles and higher utilisation of refuelling infrastructure would reduce hydrogen costsat the pump in the longer term. Relatively expensive tube trailers would likely be used for hydrogen
distribution initially with low demand for hydrogen. As demand for hydrogen refuelling grows, liquidhydrogen trucks and pipelines may become more feasible with lower associated costs (Yang andOgden, 2007).
A cost optimisation model developed by Rose (2020) for HDV hydrogen refuelling stations in Germany
estimated hydrogen costs of between EUR 5.50-6.50/kg assuming highly utilised infrastructure in 2050, an
FCEV share in the total truck fleet above 60%, reductions in costs from grid balancing and with hydrogenproduced via electrolysis and electricity at EUR 0.10/kWh (lower than the base year assumed for electricity
in this analysis), broadly similar to past literature estimates. The analysis by (Rose, 2020) also highlightsthat the cost of hydrogen at the pump would increase significantly with lower FCEV shares in the total
vehicle fleet since a minimum number of hydrogen refuelling stations would be required in the motorway
network. With a 40% FCEV share of the vehicle fleet, the hydrogen cost would be approximately
EUR 8.1/kg. The upper bound used in this analysis assumes hydrogen prices will reduce from EUR 9.5/kg
in 2020 to reach EUR 8.1/kg in 2050. In an ambitious lower bound, assuming very low hydrogen production
costs and high utilisation, hydrogen costs at the pump are assumed to reach EUR 1.5/kg by 2050, similar
to optimistic estimates by Zhou and Searle (2022) and likely requiring significant government subsidies.

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ANNEX A. METHODOLOGY

Operation and maintenance costs and vehicle financing costs

Hunter et al. (2021) conducted a comprehensive review of truck operation and maintenance (O&M) costs
between different powertrain types and categories of vehicles. They highlighted that the reduced
mechanical complexity of BEVs leads to O&M costs that are approximately a third lower than conventional
diesel equivalents. Informed by Hunter et al. (2021), estimates for American class 4, class 8 and buses,
O&M costs in this analysis are assumed to be EUR 0.74/km for diesel and FCEV vehicles and EUR 0.47/km
for BEVs. ERS vehicles are assumed to have the same O&M costs as BEVs since O&M costs associated withthe upkeep of overhead catenary wires and high power charging systems are included in electricity costs
for each type of vehicle. O&M costs are assumed constant over time and between vehicle types for
simplicity.
Purchasing a truck is likely to have additional financing costs that must be paid off over the lifetime of thevehicle. These are estimated according to equation 4 in Annex B. All TCO estimates assume a 10% discountrate and are quoted in 2020 prices.

Uncertainty allocation
Uncertainty is allocated to a number of input variables and its impact on TCO is estimated by randomly
sampling input values for each variable over a number of iterations (Montecarlo methods). This
uncertainty is implemented in one of two ways:
1. For variables with uncertainty that are assumed to be constant over time, the variable in each
iteration (i) is calculated using the following formula:𝑥𝑖 = 𝑥𝑙𝑏 + 𝑈𝑖 (𝑥𝑢𝑏 − 𝑥𝑙𝑏 )2. For variables with uncertainty that change over time, the variable in each iteration (i) is calculatedusing the following formula:𝑥𝑖,𝑦𝑟 = 𝑥𝑙𝑏,𝑦𝑟 + 𝑈𝑖 (𝑥𝑢𝑏,𝑦𝑟 − 𝑥𝑙𝑏,𝑦𝑟 )Where Ui is a randomly sampled value from a uniform distribution between 0 and 1 for each iteration andeach variable with uncertainty. Subscripts ub and lb denote upper and lower bounds respectively.
Limitations

Like all models, there are a number of limitations and caveats. This analysis does not explore the following:
1. This analysis assumes battery and hydrogen storage capacity can be modular and designed tomeet specific range requirements. This assumes that vehicle fleets can be used relatively flexibly,with short-range applications serviced by vehicles with relatively small batteries or hydrogen fueltanks, and longer-distance applications serviced by appropriately specified vehicles.
2. This analysis only considers low-power depot charging and en-route public fast charging. It does
not consider warehouse charging taking place during deliveries where vehicles could potentiallyavoid any opportunity costs. These could help to reduce BEV battery size requirements.

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ANNEX A. METHODOLOGY

3. This analysis accounts for battery weight limitations to avoid reduced payload and arbitrarily setsa maximum possible battery size of one megawatt hour. However, it does not consider volume
constraints to batteries or hydrogen fuel tanks, or limitations to their installation on vehicles.
4. This analysis assumes all input variables in Figure 1 are independent. This is a simplification. The
most significant limitation this raises is that electricity costs and hydrogen costs are considered
independent from each other when in reality, low (green) hydrogen prices are likely to requirecheap electricity to be feasible.
5. This analysis does not explicitly account for vehicle production and manufacturing costs and how
they might be reduced with economies of scale.
6. This analysis also does not account for possible revenues or discounted electricity costs from BEVs
providing grid flexibility services such as demand management and vehicle-to-grid charging. It also
does not account for wider system costs such as changes in the electricity price due to additional
peak demand produced by HDV charging.
7. This analysis explores a wide range of vehicle groups and different range requirements and annual
mileage within each group. However, it does not consider additional detail on specific use cases
or machinery operated by vehicles e.g. refrigeration, crane or lift equipment.
8. It is assumed that the market shares of different truck categories and their operational
requirements remain constant over time.

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ANNEX B. EQUATIONS AND DESCRIPTIONS

Annex B. Equations and descriptions#
1

Equation

Description
𝑇𝐶𝑂𝑀𝑌,𝑃 = 𝐶𝐴𝑃𝐸𝑋𝑌𝑟=𝑀𝑌,𝑃

The TCO of a vehicle with powertrain P produced inmodel year MY and with lifetime L.

−𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑌𝑟=𝐿,𝑃
𝑀𝑌+𝐿+ ∑ 𝑂𝑃𝐸𝑋𝑀𝑌,𝑌𝑟,𝑃
𝑌𝑟=𝑀𝑌

2

Efficiency𝑌𝑟 = Efficiency𝐴𝑔𝑒=0 ×

1
1 − (𝐴𝑔𝑒 ×

0.2)
25000/2700

Fuel efficiency (MJ/km) degradation of FCEVs due tocatalyst poisoning is estimated in line with DOE futuretargets of 20% reduction in fuel economy after
25 000 hours of operation (US DOE, 2021b) andassuming 2 700 hours of vehicle use per year.

3

Engine cost (USD) = 7617 + 15.1*EnginePowerkW+0.1*
EnginePowerkW^2

The cost of a diesel engine is estimated according to theequation used in Hunter et al. (2021).

4

PMT(Int, AL, CAPEX) = (Int x CAPEX(1-Int)^AL)/(1-(1+Int)^AL)

Payment of a loan. “Int” is the Interest rate, “CAPEX” isthe capital expenditure, “AL” is the asset life.

5

High power charging cost

High power charging costs. “Int” is the Interest rate,
assumed to be 3%; “CAPEX” is the cost per charging
point (here assumed to be EUR 300 000 lower bound,
EUR 1M upper bound); “O&M” are annual operationaland maintenance costs, expressed as a share of CAPEX
and here assumed to be 4%; “AL” is the asset life,
assumed to be five years; “Daily Hours” is the number ofhours per day that the charger is in use; “Power rating”
is assumed to be 1 000 kW.

O&M ∗ CAPEX − PMT(Int, AL, CAPEX)EUR/kW =Daily Hours ∗ 365 ∗ Power Rating

6

If ERange requirement <= Max effective battery capacity, then thehigh power charging share is equal to zero.
If ERange requirement > Max effective battery capacity, then thehigh power charging share is:
Share % =7

The share of BEV's electricity consumption from highpower chargers (rather than depot charging). Where
ERange requirement is the energy required to meet a truck’s
daily range requirements.

ERange requirement−1
Max effective battery capacity

ERS cost per kilometre:O&M ∗ CAPEX − PMT(Int, AL, CAPEX)EUR/km =Daily Flow ∗ Utilisation ∗ 365

The cost of using a catenary electric road system on
motorways and high-traffic main roads. “Int” is theInterest rate, assumed to be 3%; “CAPEX” is the cost per
km of building the ERS (here assumed to be EUR 1M/km
lower bound, EUR 3M/km upper bound); “O&M” areannual operational and maintenance costs, expressed asa share of CAPEX and here assumed to be 2%; “AL” is theasset life, assumed to be 35 years; “Daily Flow” is thenumber of heavy-duty trucks that flow over each km of
motorway per day; “Utilisation” is the share of trucks
that use the ERS infrastructure.

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Decarbonising Europe’s Trucks
How to Minimise Cost Uncertainty

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This project has received funding from theEuropean Union’s Horizon 2020 research andinnovation programme under grant agreement
No. 101021600.

Published: 09/2022 | Photo credit: s_oleg/Shutterstock

Trucks account for one-fifth of transport sector emissions in Europe.
To decarbonise, heavy-duty road freight must switch to zero-emission
vehicles quickly. This report examines whether battery electric vehicles,
electric road systems and hydrogen fuel cell vehicles could competewith diesel-driven vehicles. It looks at the total cost of ownership
across nine different vehicle-size segments in Europe. The report gives
six recommendations to accelerate the transition to zero-emission
trucks, including the provision of necessary infrastructure.