Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Energy | Volume 9 | August 2024 | 1032–1039
1032
nature energy
Analysis
https://doi.org/10.1038/s41560-024-01531-9
Rapidly declining costs of truck batteries
and fuel cells enable large-scale road freight
electrification
Steffen Link
  1,2
, Annegret Stephan
  1, Daniel Speth
  1 & Patrick Plötz
  1
Low-carbon road freight transport is pivotal in mitigating global warming.
Nonetheless, electrifying heavy-duty vehicles poses a tremendous
challenge due to high technical requirements and cost competitiveness.
Data on future truck costs are scarce and uncertain, complicating
assessments of the future role of zero-emission truck (ZET) technologies.
Here we derive most likely cost developments for price setting ZET
components by meta forecasting from more than 200 original sources.
We find that costs are primed to decline much faster than expected, with
significant differences between scientific and near-market estimates.
Specifically, battery system costs could drop by 64% to 75% and fall below
€150 kWh−1 by no later than 2035, whereas fuel cell system costs may exhibit
even higher cost reductions but are unlikely to reach €100 kW−1 before the
early 2040s. This fast cost decline supports an optimistic view on the ZET
market diffusion and has substantial implications for future energy and
transport systems.
The fast electrification of heavy road freight transport is pivotal in
limiting global warming in line with the Paris Climate Agreement1–3.
This follows since heavy-duty vehicles (HDVs) contribute a noteworthy
proportion of annual greenhouse gas emissions despite a low share
in the vehicle stock4. Whereas the European Union has agreed on
ambitious tail-pipe emissions reduction targets for newly sold HDVs
of −43% by 2030, −65% by 2035 and −90% by 2040 (compared with
2019/2020)5, California has effectively imposed the phase out of con-
ventional combustion trucks by 20366, with other US states expected
to follow. Similarly, China is anticipated to tighten its tail-pipe emis-
sions reduction targets soon to comply with its near-zero emissions
target by 20607,8. These ambitions require the fast deployment of
zero-emission trucks (ZETs), where demand from and, hence, afford-
ability for operators are key. However, high acquisition costs are cur-
rently hampering fast ZET market diffusion9–13. This culminates in
an active and cross-national debate between industry, politics and
academia about different measures and technological pathways of
how to decarbonize HDVs1,14–19, particularly about the respective roles
of battery-electric trucks (BETs) and fuel cell trucks (FCETs) in future
ZET fleets.
Although many studies have explored cost-reduction potentials
using qualitative or quantitative methods20,21, such as literature-based
projections, expert elicitation, detailed cost breakdowns or learning
and experience curves, results are limited to the respective application
category and system configuration22,23. For example, studies empha-
sizing private passenger car electrification have shown that costs for
key components such as batteries are expected to fall substantially
and quickly24–26, with increasing evidence that battery-electric vehi-
cles will constitute the primary technology1,27. However, electrifying
heavy commercial trucks, such as US Class 7/8 or European N2/N3,
still poses a tremendous challenge, particularly due to altered require-
ments limiting the transferability of passenger car findings. This can
be ascribed to inherently versatile operating characteristics from
urban delivery to international long haul, distinct utilization sched-
ules, versatile value-adding duties from cargo transport to ancillary
power- or energy-intensive services, greater lifetime mileage even
Received: 9 October 2023
Accepted: 17 April 2024
Published online: 14 May 2024
 Check for updates
1Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany. 2Karlsruhe Institute of Technology (KIT), Institute of Electrical
Engineering (ETI), Karlsruhe, Germany.
 e-mail: steffen.link@isi.fraunhofer.de

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Analysis
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settings, data originality, forecast method and source category. Addi-
tionally, we used three robust approaches to strengthen our results by
reducing potential outliers’ contribution (Methods).
Rapidly declining battery system and fuel cell
system costs
Figure 1 illustrates that battery system costs, broken down by source
categories, may decline by 64% to 75% until 2050. We observe rapid and
consistent cost reductions per annum (p.a.), with similar patterns for
all source categories. We find cost reductions (CRs) of around 5% p.a.
(scientific and others) to 6.5% p.a. (near market) until 2030 and 3.3–4.5%
p.a. over an extended 2020–2050 period. Notably, near-market esti-
mates (blue) are more optimistic, less heterogeneous and more stable
compared with the other categories. This consolidates into expected
cost estimates, where near-market estimates project a decrease from
around €2020275 kWh−1 in 2020 to €2020140 kWh−1 by 2030 and around
€202070 kWh−1 by 2050. In contrast, scientific estimates (green) indicate
a drop from roughly €2020310 kWh−1 in 2020 to €2020180 kWh−1 by 2030
and around €2020100 kWh−1 by 2050. Other estimates (purple) also pro-
ject more conservative progress, cutting €2020200 kWh−1 by 2030 and
approximating €2020115 kWh−1 by 2050. The cross-category projection
(black) closely aligns with scientific projections.
Figure 2 illustrates that FC system costs, broken down by source
categories, may decline by 65% to 85% until 2050. Notably, our obser-
vations unveil significant heterogeneity among these categories.
Near-market estimates (blue) initiate at approximately €2020540 kW−1 in
2020, undercut the €2020100 kW−1 threshold by 2045 and attain around
€202085 kW−1 by 2050. This equals CRs of around 9% p.a. until 2030 and
around 6% p.a. over 2020–2050. Conversely, scientific estimates (green)
initiate at approximately €2020 kW−1 in 2020 and fall below €2020100 kW−1
in the late 2030s, ultimately reaching around €202080 kW−1 by 2050 and
CRs of around 3.5% p.a. over 2020–2050. Other estimates (purple) are
centred between near-market and scientific estimates without reaching
sub-€2020100 kW−1 levels.
Cost overview for five major ZET components
Table 1 shows the derived heavy ZET component costs (mean ± two
standard errors). Unlike for battery and FC costs, we find less substan-
tial CR potentials for the adjacent ZET components using the same
method, albeit with smaller samples and less detail (Supplementary
Figs. 14–17). Precisely, costs for electric motors probably fall from
beyond 1 million km, high reliability and longevity and an even more
pronounced cost sensitivity13,16,18,28. Hence, accurate and comprehen-
sive data on current and projected ZET acquisition costs are essential to
assess the future roles of these technologies. However, data are scarce
and heterogeneous, whereas a holistic overview assessing multiple
components for heavy ZETs within a consistent scope and compara-
tive method is missing. Thus, we address the following research ques-
tion: what are the most likely future cost developments of central ZET
components until 2050?
This paper analyses projected costs for five crucial BET and/or FCET
components based on an extensive literature record: whereas we find lim-
ited cost reductions for the three adjacent components (that is, electric
traction motors, power electronics and high-voltage system (PE&HV)
components and hydrogen tanks), costs for battery and fuel cell (FC) sys-
tems are primed to decline much faster than expected and due in course.
Despite inevitable uncertainty, a rapid ZET market diffusion associated
with ambitious learning rates (LRs) at the required breakthrough costs
seems within reach soon. Yet, prospects for BETs as primary technology
seem more favourable at higher confidence, with faster availability and
achieving cost effectiveness as of today, thus supporting an optimistic
view on the fast decarbonization of road freight transport.
Meta forecast using regression analysis
Meta forecasting involves synthesizing existing projections from litera-
ture encompassing historical trends, future expectations and different
methods to yield novel ones. Pooling individual forecasts through this
well-established approach29,30 enhances accuracy, often outperforming
individual forecasts31.
We identified relevant literature via distinct search strings using
the Scopus and Google Scholar databases. Following Nykvist and Nils-
son25, we considered three different source categories: near market
(that is, market outlooks from renowned analysts and consultancies and
industry announcements), scientific (that is, peer-reviewed papers) and
others (that is, non-peer-reviewed academic publications and reports).
All cost values were harmonized to represent system-level original
equipment manufacturer (OEM) purchase prices for the respective ZET
component, including profit mark-ups (+35%) and specified in 2020
euros (€2020). Component-specific cost developments were calculated
by regression of log harmonized cost data and log time data using either
weighted least squares (WLS) or ordinary least squares (OLS). Herein we
controlled for several auxiliary variables such as release dates, scenario
0
200
400
Battery system costs (€2020 kWh–1)
600
800
1,000
1,200
1,400
1,600
1,800
Harmonized data points
All, N = 1,104, R2 = 0.49
Near-market, N = 310, R2 = 0.69
Scientific, N = 339, R2 = 0.51
Others, N = 455, R2 = 0.4
2010
2015
2020
2025
2030
2035
2040
2045
2050
Time in years
2020
2025
2030
Year
2035
2040
2045
2050
0
100
200
Battery system costs
 (€2020 kWh
−1)
300
Fig. 1 | Projections for heavy ZET battery system costs. System-level costs per
kWh of total gross battery capacity. These include, among others, battery and
thermal management systems, cell modules, housing, connectors, wiring and
assembly. Black circle markers represent the original harmonized data. Solid
lines represent the regression results (log–log, WLS, mean values), with the
shaded areas (that is, error bars) in the zoom-level plot (period 2020–2050) as
95% confidence intervals (mean ± 2 s.e.m.). The source category is colour coded:
near market in blue (analysts, consultancies, industry announcements), scientific
in green (peer-reviewed papers) and others in purple (non-peer-reviewed
academic publications). Total results (that is, cross-category) in black. The total
sample covers N = 1,104 data points from 200 unique studies. Cross-category R²
is 0.49. The figure legend states the number of data points (N) and the R² values
per source category. Additional information is available in Supplementary
Tables 1 and 4.

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Analysis
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around €202042 kW−1 by 2020 to €202030 kW−1 by 2050 (−1.2% p.a.). For
hydrogen tanks, we find CRs of around 2.6–2.9% p.a. over 2020–2050.
This translates into decreasing system costs from around €202017 kWh−1
(liquid-LH2) and €202024 kWh−1 (compressed-CH2) by 2020 to around
€20207 kWh−1 (LH2) and €202011 kWh−1 (CH2) by 2050. Last, we derive stable
system costs of around €202050 kW−1 for PE&HV components.
Different cost expectations for batteries and
fuel cells
Our meta forecasts project a rapid cost decline for both batteries and
FCs, while we disclose a contrasting dynamic that highlights the complex
interplay between scientific projections and tangible market realities
for emerging technologies at different stages of commercial maturity.
Our analysis of battery cost predictions unveils that near-market
estimates are remarkably stable over different release dates. These
projections are prone to only minor downward adjustments, as indi-
cated by the difference between OLS and WLS results (Supplementary
Figs. 5 and 6 and Supplementary Table 2), and are consistently more
optimistic than those from scientific literature (p < 0.05, two-tailed
t-test; Supplementary Table 4). Conversely, scientific cost estimates
published in 2010–2023 faced substantial downward adjustments.
Hence, battery costs have experienced a more rapid decline than ini-
tially expected, at least in the scientific community. This echoes Nykvist
and Nilsson25, who found similar divergences for industry vs market
leaders vs peer-reviewed literature estimates and supports conclu-
sions from Frith et al.32, who emphasize substantial gaps between
academic and industry perspectives. One explanation might be that
near-market sources may have more practical in-depth knowledge
about technologies, manufacturing or cost-saving measures and better
access to industry insights such as market trends, partnerships, supply
chain dynamics or confidential pricing data. In contrast, parts of the
scientific literature may be classified as theoretical estimates or may
be affected by citation patterns or time-delaying review processes,
leading to the self-confirmation of outdated values and assumptions.
However, FC system costs exhibit an inverse trend. Scientific
estimates show higher stability and are consistently more optimistic
(p < 0.05, two-tailed t-test; Supplementary Table 5) than near-market
estimates.
Harmonized data points
All, N = 424, R2 = 0.33
Near-market, N = 96, R2 = 0.42
Scientific, N = 64, R2 = 0.26
Others, N = 264, R2 = 0.37
0
200
400
Fuel cell system costs (€2020 kW–1)
600
800
1,000
1,200
1,400
1,600
1,800
2010
2015
2020
2025
2030
2035
2040
2045
2050
Time in years
0
100
200
Fuel cell system costs
 (€2020 kWh
−1)
300
400
2020
2025
2030
2035
Year
2040
2045
2050
Fig. 2 | Projections for heavy ZET fuel cell system costs. System-level costs
per kW of rated power. These include, among others, control systems, thermal
management, housing, hydrogen supply except for storage, air intake, including
compressor and humidifier and assembly. Black circle markers represent the
original harmonized data. Solid lines represent the regression results (log–log,
WLS, mean values), with the shaded areas (that is, error bars) in the zoom-level
plot (period 2020–2050) as 95% confidence intervals (mean ± 2 s.e.m.). The
source category is colour coded: near market (blue: analysts, consultancies,
industry announcements), scientific (green: peer-reviewed papers) and others
(purple: non-peer-reviewed academic publications). Total results (that is,
cross-category) in black. The total sample size covers N = 424 data points from 83
unique studies. Cross-category R² is 0.33. The figure legend states the number of
data points (N) and the R² values per source category. Additional information is
available in Supplementary Tables 1 and 4.
Table 1 | Specific system-level component costs in €2020 for five major ZET components: batteries, fuel cells, electric
traction motors, PE&HV components, hydrogen storage tanks
Component
Source category
2020
2030
2040
2050
Number of observations
Battery in €2020 kWh−1
Near market
275 ± 9.5
141 ± 6.4
94 ± 6.1
70 ± 5.6
310
Scientific
310 ± 15
178 ± 8.9
127 ± 8.9
100 ± 8.7
339
Others
316 ± 16
193 ± 9.0
143 ± 8.9
116 ± 8.9
455
All
300 ± 8.1
174 ± 5.0
126 ± 5.0
99 ± 4.9
1,104
Fuel cell in €2020 kW−1
Near market
538 ± 75
216 ± 26
125 ± 24
84 ± 21
96
Scientific
219 ± 39
132 ± 20
97 ± 20
78 ± 20
64
Others
415 ± 39
219 ± 16
149 ± 15
113 ± 14
264
All
392 ± 30
204 ± 12
138 ± 12
104 ± 11
424
Electric motor in €2020 kW−1
All
42 ± 5.3
35 ± 3.3
32 ± 4.3
29 ± 5.2
147
PE&HV in €2020 kW−1
All
48 ± 6.5
48 ± 5.2
49 ± 7.8
49 ± 10
101
CH2 tank in €2020 kWh−1
All
24 ± 2.1
16 ± 1.0
13 ± 1.1
11 ± 1.2
213
LH2 tank in €2020 kWh−1
All
17 ± 1.4
11 ± 0.6
8.5 ± 0.7
7.1 ± 0.8
213
Results are given as rounded mean value ± 2 standard errors (95% confidence intervals) based on log–log regression (WLS) for 2020, 2030, 2040 and 2050. All costs relate to either rated/
continous power (kW) or gross capacity (in kWh). Values greater than ten as integers. Additional information is available in Supplementary Table 1 and Supplementary Figs. 14–17.

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One explanation for these different dynamics might be the techno-
logical maturity of both technologies and the related uncertainty about
future pathways. The lower spread and consistency across all source
categories for battery system costs may indicate higher confidence
and technological readiness, as BETs are increasingly projected to
inherit a pivotal role in future road freight transport1—at least on short
to medium distances—and have been announced in the portfolios of
all major truck OEMs27,33. In contrast, the future role of FCETs and their
development from laboratory to market is still more uncertain, result-
ing in larger data spread and potentially conservative projections from
near-market sources.
Required volumes, learning rates and
breakthrough costs
Whereas cumulative production volumes may be the best-performing
predictor of theoretical technology cost compared with others22,34,
findings are hard to generalize into policy decisions and anticipated
timescales. In parallel, the underlying data and thus observed cost
estimates implicitly assume a certain relevance of battery technology
and fuel cells for future road freight transport or even other sectors.
This relevance may be expressed as growing cumulative investments,
economies of scale, supply chain improvements, spill-over effects and
material or production improvements that would not occur without
this relevance. Hence, we compare our cost estimates against the poten-
tial S-shaped market diffusion of ZETs in North America, Europe and
China, totalling around 2.5–3 million trucks produced p.a. (Methods
and Supplementary Figs. 12 and 13).
To achieve battery system costs of approximately €2020150 kWh−1
as indicated by our regression to be achievable between 2028 (near
market) and 2032 (scientific), cumulative production volumes must
range from 1,300 GWh (near market) to 5,200 GWh (scientific). This
yields short-term compound annual growth rates (CAGRs) of 39–49%.
Those volumes may be feasible in the early 2030s if BETs take large mar-
ket shares fast, given their head start in the early 2020s. Correspond-
ing LRs would be around 16% (scientific) to 19% (near market). Falling
below €2020100 kWh−1, as indicated by our regression to be achievable
between 2039 (near market) and 2049 (scientific), would require up to
11,000 GWh (near market) or even 68,000 GWh (scientific), with the
former being probably feasible within the late 2030s given that BETs
comprise substantial market shares and long-term CAGRs of 25–29%.
Considering breakthrough levels, Teichert et al.35 find system costs
of €2020120–200 kWh−1 to reach cost parity with current diesel trucks if
fast charging becomes available. This confirms Nykvist and Olsson19,
who state around €2020200 kWh−1 as the upper threshold. Basma et al.36
specify system costs of around €2020100 kWh−1 as viable tipping point for
European BETs to become cost effective, even without policy support
through purchase incentives, adjusted road toll schemes or CO2 pric-
ing. For the United States, Phadke et al.37 quantify system costs below
US$135 kWh−1, confirming Sripad and Viswanathan38, who quantified
the Tesla Semi case to be economically viable well below US$150 kWh−1.
Similarly, to attain fuel cell system costs of approximately €2020150
kW−1, as our regression suggests between 2027 (scientific) and 2035
(near market), cumulative production volumes span from 135,000
units (scientific) to 1.4 million units (near market). The latter appears
attainable, considering the broader availability of FCET models antici-
pated to emerge in the late 2020s1, with short-term CAGRs of 35–46%.
Corresponding LRs would be around 14% (scientific) to 26% (near mar-
ket). Falling below €2020100 kW−1, as indicated by our regression to be
achievable between 2040 (scientific) and 2045 (near market), would
then demand cumulative volumes from 2.3 million (scientific) to 6.8
million units (near market) and long-term CAGRs of 26–29%.
Our ambitious LRs align with other technologies, with Nykvist
and Nilsson25 suggesting conceivable LRs for batteries of 12–14%, and
Schmidt et al.22 finding LRs of 9–18% for electrical energy storage
technologies. Similarly, historical growth rates for wind and solar
capacity were at least 15% and often 39–50% (ref. 39), whereas gen-
eral technology adoption growth rates were often below 13–14% but
selectively exceeded 30–40% (ref. 40). For BEVs, sales growth ranged
around 25–55% (Supplementary Table 7), with Norway achieving nearly
90% BEV sales within 13 years (CAGR2010–2023 = 41.4%), compared with
Europe’s 25% share (CAGR2010–2023 = 28.1%).
Discussion
Over-extrapolation and theoretical assessments may result in deceptive
conclusions of future performance and relevance, which is critical for
public and private funding and, hence, policy decisions32. Therefore,
we contrast our findings to target or floor costs—typically defined
by raw material and production costs—to avoid excessively low-cost
estimates22,34,41.
Recalibrating our battery system costs to the cell level (Sup-
plementary Table 6), we arrive at around €202090 kWh−1 by 2030 or
€202070 kWh−1 by 2050. Several facts underpin the feasibility of the
derived costs despite potential non-negligible disruptions caused
by raw material shortages, supply chain disruptions, higher inflation
levels, increased energy costs or raw material shortages42–44, whereof
the latter’s effect would depend on the specific chemistry. Calculated
system- and cell-level costs do not fall below original scientific or
near-market estimates and announced industry values, as could have
happened by the regression. For example, the European Battery Stra-
tegic Research and Innovation Agenda (SRIA)45 targets system costs
of around €202075 kWh−1 by 2025 and even below by 2030 and beyond.
Plus, Tesla confirmed cell-level cost targets for its 4,680 cylindrical
cells of around US$70 kWh−1 at their third quarter Earning Call in 2022—
even before US incentive programmes such as the Inflation Reduction
Act. The projected ready-to-drive prime costs of the first generation
500-mile Tesla Semi truck, equipped with these cells and suspected
800–900 kWh, stand at roughly US$200,000 in 202346, indicating
associated battery system costs below US$150 kWh−1. However, we
also address potential strategies for gaining initial market shares by
internally subsidizing battery packs.
For fuel cell system costs, the US Department of Energy ultimately
targets HDV FC system costs of around US$201680 kW−1 by 2030 and
US$201660 kW−1 by 205047, with both targets adopted by numerous
studies. Similarly, the European hydrogen SRIA48 targets system costs
of below €100 kW−1 by 2030. This indicates a notable level of ambition
or required federal support but might also mean that our projections
(specified as OEM purchase price) may be quite optimistic and close
to anticipated floor and target costs.
Our cost-centred meta forecast focuses on most likely develop-
ments and consistent comparisons, potentially omitting scenario
dependencies and technical particularities/trends.
Underlying scenarios and assumptions, such as different mitiga-
tion pathways, can impact production volumes and, thus, component
cost developments. However, insufficient standardized scenario clas-
sifications and missing congruent information impede robust and
consistent cross-study assessments. Further regressions indicate that
optimistic cost scenarios imply additional cost benefits for batteries
and FCs (Supplementary Tables 4 and 5).
The current effects of technical designs and material choices on
costs and performance are well documented24,49, with pre-series and
commercial components disclosing real-world pricing or facilitat-
ing cost versus performance trade-off analyses35 and product tear
downs50,51. However, exact technical designs, materials and properties
of next-generation components until 2050 are theoretical, remain
highly uncertain and are only selectively available. This impedes com-
prehensive techno-economic assessments for single technologies
across 2020–2050.
Whereas cost reductions and increased technical performance
are anticipated for batteries52,53 and FCs47,48, our stable results for
PE&HV and minor savings for electric motors indicate that technical

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advancements involving new components and trends may offset cost
reductions. This may involve advancements54,55 to increase efficiency
and thus driving range per charge, such as transitioning to higher
voltage, new motor topologies and materials or bidirectional system
architectures to facilitate Vehicle-to-Grid (V2G) applications.
Concerning data limitations and potential bias, we state that our
data sample is very recent, with most sources being published between
2018 and 2022 (Supplementary Fig. 1). Whereas we consider our find-
ings regarding battery system costs as highly robust, we acknowledge
uncertainty for FC system costs.
The battery dataset, comprising N = 200 sources, displays a bal-
anced representation across all categories of sources, encompassing
various methods and scenarios employed therein. Consequently,
we have not identified either substantial bias originating from the
data sample or issues from the data harmonization (Supplementary
Figs. 2–4 and 11 and Supplementary Table 4).
The FC data sample consists N = 83 sources and exhibits an imbal-
ance towards other sources, particularly limited upon near-market
estimates, limiting the significance of cross-category outcomes. Our
findings indicate that methods and scenario considerations exert more
pronounced effects on the results. Furthermore, the impact of data
harmonization is more accentuated, with harmonized values being
lower than the original ones, indicating cost developments are too low
(15–25%) (Supplementary Figs. 2–4 and 11 and Supplementary Table 5).
Three approaches using two techniques, namely robust norms and
outlier removal, strengthened the validity of our original regression
results. Precisely, HuberT regression, RANSAC regression and WLS
regression with only the central 50% or 80% of observations within
five-year time windows yield future costs close to or within our origi-
nal prediction errors (Supplementary Figs. 7–10 and Supplementary
Table 3).
For both battery and fuel cell data, and similar to Schmidt et al.22, we
find that more recent sources embed faster and larger cost-reduction
potentials, expressed by the difference between OLS and WLS results
(Supplementary Figs. 5 and 6 and Supplementary Table 2).
Total Cost of Ownership (TCO) benefits against diesel trucks (DTs)
typically constitute the key ZET criterion for fleet operators9–13, with
other factors being also relevant. Using a recent TCO framework56 along
with our cost projections, we find that BETs may realize cost benefits
versus DTs as of today. In contrast, FCETs may struggle to reach TCO
parity throughout the 2030s because green hydrogen prices remain
probably too high. Herein the share of acquisition costs substantially
rises for ZETs compared with current DTs, whereas energy storage size,
energy prices and mileage are the most sensitive parameters (Methods
and Supplementary Figs. 18 and 19).
Alongside economic considerations, technical capabilities such
as feasible range, realizable payload, reliability, ageing behaviour
and recharging/refuelling times are further influencing factors on
truck purchase decisions. Several studies indicate that current and
announced ZETs are already close to become technically competitive
with DTs19,35,49,57. Finally, infrastructure availability and user acceptance
will be decisive9–13.
Conclusion
This article presents a systematic overview of cost estimates for five
major ZET components with meta forecasting and regression analysis.
We draw four conclusions from this analysis.
First, we show that ZET component costs are likely to decline
substantially and in due course. Precisely, future battery system costs
are more robust, likely to fall below €2020150 kWh−1 by no later than
2035, and to approach or even cross €2020100 kWh−1 upon 2050, with
the former corresponding to typical expected breakthrough levels.
FC system costs are likely to reach around €2020150 kW−1 in the late
2030s and to approach €2020100 kW−1 at best in the late 2040s, with
lower values close to target and floor cost values warranting careful
consideration. We emphasize that calculated LRs, cost reductions and
growth rates are challenging to reach, but similar ranges have been
witnessed for (energy) technologies in the past22,39,40, with similar to
higher scales for FC values.
Second, we find that cost predictions differ systematically between
different source categories, potentially depending on technological
maturity. Whereas near-market sources turn to be the most stable
source for batteries, their predictions are more optimistic than those
from scientific sources. However, the opposite is true for fuel cells,
with the scientific sources being more optimistic and often close to
floor or target costs.
Third, these substantial and fast cost-reduction potentials sup-
port an optimistic outlook for both technologies. This indicates rapid
ZET market deployments that will substantially impact transporta-
tion and energy sector players, such as value chain reconfigurations,
establishing national and international hydrogen ecosystems and
electricity infrastructure expansions from transformers to distribution
and transmission grids.
Finally, we highlight the competition among ZET technologies,
raising questions about market leadership and whether we need dif-
ferent technologies. All anticipated cost reductions rely on successful
transitions to low-carbon road freight transport. This entails building
large-scale production facilities supported by policy measures in key
markets like North America, Europe and China, particularly in the
early market phases. These measures may include purchase subsidies,
infrastructure development, ZET mandates and carbon pricing. This
policy support may phase out later when the technology has matured
and costs have decreased. Our TCO indicates that BETs may constitute
the most cost-effective pathway in reaching TCO parity with less policy
support needed, in contrast to FCETs, which might require more policy
support throughout the 2030s.
Methods
Literature identification
We identified the relevant academic literature via distinct search
strings using the Scopus and Google Scholar databases and comple-
mented the identified studies with commercial market outlooks from
renowned analysts and consultancies and industry announcements.
Table 2 presents the search strategy, which has been used on article
titles, abstracts and keywords published between 2010 and May 2023.
Available literature has then been analysed and filtered based on the
abstracts. The final sample for batteries covered NB = 200 sources and
DPB = 1,104 data points. The final sample for fuel cells covered NFC = 83
sources and DPFC = 424 data points.
Literature parameters
We collected available auxiliary data from each source to either harmo-
nize the data or integrate those parameters as control variables in the
regression analysis to compare and validate the results: (1) value type,
differentiating between cost or price, with the latter typically includ-
ing additional overheads, mark-ups, indirect costs or supplier profits.
However, we denote that both terms are also used synonymously.
(2) Application type, differentiating between automotive- or
HDV-certified values, as altered requirements and scales also lead to
different configurations, designs and costs. (3) Currency and (4) refer-
ence year information were collected to ensure accurate contextualiza-
tion and temporal accuracy. (5) Scenario information was collected,
differentiating between low (mass-market), high (niche market) and
medium. (6) Forecast method, differentiating between literature-based
projections, expert elicitation, detailed bottom-up cost modelling
or learning and experience curves. (7) Data originality, differenti-
ating between original or adopted values. (8) Integration level, dif-
ferentiating between cell- or system-level values for batteries and
stack- or system-level values for fuel cells. This raw data are available
for download, yet proprietary is anonymized.

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Analysis
https://doi.org/10.1038/s41560-024-01531-9
Literature classification
In addition to the parameters stated above, all sources have been clas-
sified into three categories to further differentiate results (similar to
Nykvist and Nilsson25). Accordingly, we differentiated between scien-
tific (literature) covering peer-reviewed papers and PhD theses. Others
involved non-peer-reviewed academic publications such as conference
articles, reports, working or white papers and book contributions.
Near market involved market outlooks from renowned analysts or
consultancies and industry announcements.
Data harmonization
Data harmonization, based on the upper parameters, ensured that
all data points were specified as system-level OEM purchase prices in
€2020 for the respective component. Initially, this involved (3) currency
conversions based on the respective historical exchange rate (subject
to the reference year or release date) and (4) inflation adjustment to
2020 levels (annual mean over all member states of the European Union
(EU-27) issued by the European Commission and downloadable via
Eurostat). Potential inhomogeneities caused by (1) cost or price type,
(2) application type or (8) integration level are harmonized based on
studies that explicitly state multiple values differentiated according
to (1), (2), (8). Hence, all cost values were topped with a 35% surcharge
(median value from battery data as no temporal trend could be iden-
tified) to derive OEM purchase prices. Scaling automotive-certified
components to HDV-certified ones, data showed a temporal trend
for batteries but not for fuel cells. Hence, we used a decreasing scal-
ing factor for batteries from around 80% in 2020 to 50% in 2030 and
20% in 2050, indicating higher integration, potential enhancements
through on-purpose designs and usable synergies. For fuel cells, all
automotive-type values were topped with a 100% surcharge (median
value). For cell-to-system or stack-to-system scaling, data showed a
clear temporal trend for both batteries and fuel cells. Therefore, we
used a decreasing scaling for batteries from around 40% in 2020 to
30% in 2030 and 20% in 2050. In contrast, we used an increasing scal-
ing factor for fuel cells from 60% in 2020 to 90% in 2030 and 125% in
2050, meaning that the cost share of the actual stacks on total system
costs was expected to decrease. Supplementary Figs. 2–4 provide more
details. These harmonized data are available for download.
Regression analysis and control variables
Results were calculated by regression using Python statsmodels. Spe-
cifically, we used the log harmonized cost data and log time data to
approximate the typical learning curve shape and controlled for several
auxiliary variables stated above. Weighting (WLS) was performed by
source age using the following exponential function for smoothing,
as proposed by refs. 58,59:
wi = 0.8
2023−yi with i ∈{study 1, … study n}
and yi as the respective study year
(1)
Robust approaches
We used two robust techniques, namely robust norms and outlier
removal, in three approaches to exclude outliers and noise, thus
increasing the accuracy and robustness of our original regression
results. First, we performed HuberT regression analyses that are less
sensitive to outliers by minimizing a combined loss function of squared
errors for small residuals and absolute errors for larger residuals but
still using the full data sample. Second, we filtered the data by labelling
the central 50% (that is, values within the lower and upper quartile) and
80% (that is, values within the 10% and 90% quantile) of observations
as inliers and others as outliers. We then performed a WLS regression
with inliers only. Third, we performed a RANSAC (Random Sample Con-
sensus) regression that iteratively selects random data subsets to fit a
regression model, identifies inliers based on a predetermined thresh-
old (that is, median absolute deviation), refits the model using these
inliers and selects the best model (that is, regression coefficients) based
on the minimum absolute error. Supplementary Figs. 7–10 provide
further details and Supplementary Table 3 provides model comparison.
TCO framework
We adopted the total cost of ownership (TCO) framework (that is,
calculation and parameter assumptions, excluding component costs)
from Noll et al.56 to calculate TCO per kilometre (€ km−1) over the whole
vehicle service life. This includes capital expenditures such as truck pur-
chase (Supplementary Note 1) and resale and operating expenditures,
such as energy costs, road tolls, maintenance and service. The capital
recovery factor discounts future payments using a specific discount
rate. We excluded any subsidy or purchase price premiums for ZETs,
averaged all parameters at a European level and tested our results
against various energy prices and annual vehicle mileages. Supplemen-
tary Figs. 18 and 19 and Supplementary Table 8 provide more details.
Cumulative volumes and learning rates
We used S-shaped diffusion curves (sigmoid functions) to obtain poten-
tial BET/FCET shares per year, using the following function:
yt =
y0 × S
y0 + (S −y0) × e−kSt
(2)
where yt (%) is the annual BET/FCET share in a particular year, S (%) is
the total annual market capacity (= 100%), y0 (%) is the initial share for
the starting year and k is the growth rate. This share is then multiplied
by the combined North American, European and greater China HDV
production volume of around 2.5 million–3 million units per year to
derive annual volumes. Following the announced or expected ZET sales
shares for those regions, we assume most new trucks will have zero
emissions by 2040 to 2050 (y2040 > 90% and y2050 = 100%) in Europe and
North America, whereas greater China will reach this threshold later
(y2060 = 100%). This leads to annual growth rates between around 25%
(China) and 40% (Europe and North America). We assume the same
isolated market diffusion for both technologies—BET and FCET—to
guarantee comparability, and we ignore any other ZET technology. The
average BET battery capacity was assumed to increase from 300 kWh
in 2018 to 500 kWh in 2025 and 600 kWh from 2030 onwards. The
cumulative volume (in GWh) is calculated by multiplying the battery
capacity and annual volumes. Initial sales/production data on BETs
and FCETs were then matched to respective reference years
( y2018, … , y2022), which allowed calculation of learning rates by regres-
sion of log cost data and log cumulative volumes. Supplementary
Figs. 12 and 13 provide more details.
Data availability
All fuel cell and battery data presented in this study (that is, raw and
harmonized datasets) are publicly available and can be found in the
attached Supplementary Information. However, for raw data files, pro-
prietary data from commercial sources, such as purchased market stud-
ies, cannot be published and corresponding records are anonymized.
Data for other components are available upon request. Source data
are provided with this paper.
Table 2 | Search strategy applied in the databases Scopus
and Google Scholar
Batteries
Fuel cells
Keywords
‘batter*’ AND (‘cost’ OR
‘price’) AND (‘truck*’ OR
‘heavy-duty’)
‘fuel cell*’ AND (‘cost’ OR
‘price’) AND (‘truck*’ OR
‘heavy-duty’)
Field of search
Article title, abstract, keywords
Period
2010–May 2023

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1038
Analysis
https://doi.org/10.1038/s41560-024-01531-9
Code availability
The Python model (OLS and WLS regression, robust regression and
visualization for Figs. 1 and 2) is available in the attached Supplemen-
tary Information. Reproduced figures or regression results based
on the raw data files may not involve any proprietary data leading to
changes compared with the original results and plots in this paper.
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Acknowledgements
We gratefully acknowledge funding from the EU STORM project
(grant number 101006700: P.P., D.S., S.L.) by the European Union’s
Horizon 2020 research and innovation programme, the HoLa project
(FKZ 03EMF0404A: P.P., D.S., S.L.) by the German Federal Ministry for
Digital and Transport and the BEMA project (FKZ 03XP0272B: S.L.,
A.S.) by the German Federal Ministry for Education and Research.
We thank M. Wietschel and H. Döscher for their comments and
discussions and S. Lange (all Fraunhofer ISI) for English editing.
Author contributions
S.L. conceived and designed the study in consultation with P.P. and
A.S. S.L. collected the data, implemented the model and created the
visualizations. S.L. wrote the original manuscript with contributions
from all co-authors. In particular, P.P. and A.S. contributed to the
discussion, interpretation of findings, recommendations and policy
implications. D.S. edited the paper and contributed to the data
collection. P.P. supervised the study.
Funding
Open access funding provided by Fraunhofer-Gesellschaft zur
Förderung der angewandten Forschung e.V.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains
supplementary material available at
https://doi.org/10.1038/s41560-024-01531-9.
Correspondence and requests for materials should be addressed to
Steffen Link.
Peer review information Nature Energy thanks Heikki Liimatainen,
Mehdi Jahangir Samet and the other, anonymous, reviewer(s) for their
contribution to the peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to
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Open Access This article is licensed under a Creative Commons
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© The Author(s) 2024, corrected publication 2024

    Policy Brief:

Answer:
**Declining costs imply fast market uptake of zero‑emission trucks**

**Policy Problem**Reducing greenhouse gas emissions from road transport requires a fast transition to zero-emission trucks. However, policymakers face uncertainty about the speed of this transition and which truck technologies merit support. Adequate timing of carbon dioxide reduction regulation, infrastructure roll-outs, grid expansions and the level and duration of potential subsidies, require thorough knowledge of the expected transition speed. Carbon dioxide reduction targets should be ambitious, but still achievable for manufacturers and infrastructure providers, yet not intimidate fleet operators and preclude profitability to ensure robust demand. Electricity grid upgrades and charging infrastructure deployment as well as electrifying the product portfolio and establishing new supply chains often require significant time; however, technological uncertainty should not delay action and investment decisions in new technologies.  To accelerate the transition while limiting public spending, policymakers need to prioritize financial support for the most promising  technologies.

**Scientific Research Findings**We find that costs for zero-emission truck components will decline faster than expected, projecting battery costs to cut €200 kWh–1 soon and approach €100 kWh–1 towards 2050. In contrast, fuel cell costs are likely to reach €150 kW–1 in the late 2030s, albeit with greater uncertainty due to lower commercial maturity and doubts about realizing floor costs. The analysis stresses the challenge of achieving ambitious learning rates and cost reductions while highlighting the historical precedent for such advancements. Battery‑electric trucks are very likely the most cost‑effective pathway for achieving cost parity with diesel trucks. They may also require less financial incentives than fuel cell trucks, which are likely to struggle from hydrogen scarcity. Thus, prospects for battery‑electric trucks as the leading future truck technology seem more favourable at higher confidence, backed by faster availability and higher profitability. Additionally, other studies indicate that their technical competitiveness with diesel trucks is close for most cases  of logistics.

**Scientific Research Study Methods**This study derives future cost developments for price‑setting components of zero‑emission trucks, centred on batteries and fuel cells, by meta‑forecasting from more than 200 sources. Different cost expectations related to the literature sources, from near‑market to scientific, were analysed and discussed with respect to consistency and level of ambition. Regression‑based cost projections are compared with implied market evolution, including cumulative volumes and time scales, as well as growth rates and learning rates. Necessary actions to realize these cost reductions in practice are discussed. Additional statistical techniques are used to validate the robustness of the results. Feasibility is assessed by considering component‑specific floor costs and historical advances of other technologies. The cost developments are finally compared with breakthrough costs from other studies and incorporated into a total cost of ownership framework to assess the cost‑effectiveness of battery‑electric trucks and fuel cell trucks versus diesel trucks for 2020, 2030 and 2040.

**Policy Implications**• The costs of zero‑emission truck technologies are expected to decline substantially until 2030, implying a need for establishing large‑scale production facilities to boost their market diffusion.
• Battery‑electric trucks can be expected to reach cost parity in due course requiring only short‑term policy incentives, with lower public spending than for fuel cell trucks.
• Support for battery‑electric trucks as the most likely primary technology is a no‑regret policy. This will guide the market and reduce uncertainties, which is enessential for fast transition.
• Electricity grids and charging infrastructure must be prepared soon to avoid delays in the deployment of zero‑emission trucks, which requires immediate investment.