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

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 10 | April 2025 | 526–534
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nature energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Feasibility of meeting future battery demand
via domestic cell production in Europe

Steffen Link
  1,2
, Lara Schneider
  1, Annegret Stephan
  1,
Lukas Weymann
  1 & Patrick Plötz
  1
Batteries are critical to mitigate global warming, with battery electric
vehicles as the backbone of low-carbon transport and the main driver
of advances and demand for battery technology. However, the future
demand and production of batteries remain uncertain, while the
ambition to strengthen national capabilities and self-sufficiency is gaining
momentum. In this study, leveraging probabilistic modelling, we assessed
Europe’s capability to meet its future demand for high-energy batteries
via domestic cell production. We found that demand in Europe is likely to
exceed 1.0 TWh yr−1 by 2030 and thereby outpace domestic production,
with production required to grow at highly ambitious growth rates of
31–68% yr−1. European production is very likely to cover at least 50–60% of
the domestic demand by 2030, while 90% self-sufficiency seems feasible but
far from certain. Thus, domestic production shortfalls are more likely than
not. To support Europe’s battery prospects, stakeholders must accelerate
the materialization of production capacities and reckon with demand
growth post-2030, with reliable industrial policies supporting Europe’s
competitiveness.
Batteries are of critical importance for the rapid reduction of green-
house gas (GHG) emissions to mitigate global warming and meet the
1.5 °C target of the Paris Agreement by enabling the widespread use of
renewable electricity1,2. This requires transformative changes in the
transportation sector2–5 as transport accounts for approximately 20%
or 7 GtCO2-equivalent (7 GtCO2e) of global annual GHG emissions6, with
European transport emitting ~800 MtCO2e (ref. 7).
While some studies have emphasized the difficulties involved
in decarbonizing transport4,8, there is robust evidence that battery
electric vehicles (BEVs) will form the backbone of future low-carbon
road transport2,5. Accordingly, BEVs prevail in the future portfolios of
car manufacturers9,10 and several European countries will enforce 100%
zero-emission vehicle (ZEV) sales for cars by at least 20359–11, banning
large-scale sales of conventional vehicles as sufficient quantities of
sustainable fuels are unlikely11. Other key markets, such as the United
States and China, have also set ambitious ZEV targets from the 2030s9,11.
Regarding battery demand, electrified transport is widely
recognized as the key driver12 and catalyst for battery advances13.
However, any projection of battery demand is highly uncertain and
scenario-dependent, generally based on concealed models and sub-
ject to unclear assumptions. Hence, global demand projections for
2030 cover a wide spectrum, typically 3–6 TWh yr−1 and up to almost
9 TWh yr−1, with European projections at around 0.7–1.4 TWh yr−1 (for
an overview, see Supplementary Table 2).
Regarding battery production, electrification requires indus-
trial transformation and the establishment of new battery ecosys-
tems alongside the entire value chain from raw material extraction
to end-of-life, and including concepts of circularity, such as second
use or recycling14,15. In addition, the COVID-19 pandemic and other
geopolitical tensions have created awareness of vulnerable economic
dependencies and spurred the development of modern concepts such
as technological sovereignty16,17 and resilience14,15. In response, the
European Union (EU) recently finalized the Net-Zero Industry Act18,
intending to ensure ample capacity for strategic net-zero technologies
by 2030, including a target to satisfy at least 90% of its battery demand
from domestic cell production18. Global battery production capacity is
Received: 20 May 2024
Accepted: 24 January 2025
Published online: 6 March 2025
 Check for updates
1Fraunhofer Institute for Systems and Innovation Research ISI, Karlsruhe, Germany. 2Institute of Electrical Engineering (ETI), Karlsruhe Institute of
Technology (KIT), Karlsruhe, Germany.
 e-mail: steffen.link@isi.fraunhofer.de

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Analysis
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in Europe, which theoretically add up to 2.55 TWh yr−1 by 2030. We
imputed an S-shaped production ramp-up for each announcement to
derive its effective, materialized annual capacity using a logistic growth
model (LGM) and probabilistic parameterization of influencing factors
(that is, utilization, scrap, delays and materialization probabilities).
These characteristics result from two typical ramp-up phases following
an earlier planning period: time-to-market and time-to-volume30,31,27.
The time-to-market stage typically comprises pre-series and pilot
production and ends with the start of production and is characterized
by low utilization yet high scrap30. The time-to-volume stage repre-
sents the progression towards established processes with lower scrap,
higher utilization and high output30,27. Finally, we determined the
effective European production capacity per year by accumulating the
individual announcements (Methods).
Second, we considered high-energy batteries for BEVs to be the
main demand driver with lithium-ion batteries as the prevalent cur-
rent technology. We collected historical automotive data, such as
total sales and segment splits, as well as historical BEV-specific data,
such as model-specific sales, installed battery capacities, segment
splits and energy consumption. We combined these data with pro-
jections of future European battery demand based on probabilistic
parameterization of future BEV shares, segment-specific battery
capacities, segment splits, total sales and additional demand from
other mobile or stationary applications. To project BEV shares, we fit-
ted an LGM to historical BEV shares and potential future shares. Here,
we assumed the potential BEV share by 2035 to follow the announced
ZEV targets, and further varied the inflection point to refine the
S-curve and capture an extended feasibility space for battery demand
(Methods).
European demand and production approach
1 TWh by 2030
Figure 1 shows European battery demand and its domestic produc-
tion capacity up to 2035. Fuelled by substantial BEV diffusion up to
2035, European battery demand is likely to surpass 1.0 TWh yr−1 by
2030 (in 69% of all scenarios). The interquartile range (IQR) in 2030 is
0.97–1.2 TWh yr−1. Some high-demand scenarios may exceed the 1 TWh
threshold as early as 2026 and even approach 1.6 TWh yr−1 by 2030,
with the top 10% exceeding 1.30 TWh yr−1. High demand may emerge
from the superposition of high total sales, high BEV shares, higher seg-
ment shares for larger vehicles, larger battery capacities per segment
expected to approach 7.0 TWh yr−1 by 2030, with European capacity at
around 0.8–1.6 TWh yr−1 (for an overview, see Supplementary Table 1).
However, it is unclear what proportion of the announced capacities will
materialize or whether production facilities can expand fast enough
to meet growing demand.
Hence, in this study, we addressed the following research ques-
tion: how likely is it that Europe can cover its future battery demand
via domestic production? Leveraging probabilistic modelling, we
found that European demand is likely to exceed 1.0 TWh yr−1 by 2030
and thereby outpace domestic production, with production capacity
required to grow at almost exceptionally high growth rates (31–68% yr−1)
over the long term with the latter’s momentum increasing after 2025.
However, it is very likely that Europe can cover at least 50–60% of its
demand via domestic production by 2030. Even 90% self-sufficiency
seems within reach, yet far from certain, so there is an increased risk
of domestic production shortfalls. In contrast, if lower production
capacity materializes and domestic production remains limited, this
will likely pose high economic risks for Europe and imply less European
battery sovereignty and setbacks for rapid climate change mitigation.
Modelling future battery demand and production
Probabilistic modelling is developing as an accurate and flexible tool
to assess the feasibility of future climate change mitigation pathways19
and modelling technology diffusion20. We adopted this approach to
independently project future battery demand and domestic produc-
tion in Europe and to evaluate Europe’s pathway towards battery
self-sufficiency via feasibility spaces and probabilistic statements.
Accordingly, we extended the recent advances by Odenweller et al.21,
which were based on Roger’s concept of technology adoption22 and
typical S-shaped diffusion curves23.
First, we estimated domestic production using existing and
announced cell production facilities and their stated capacities. There
are different motivations for publicly announced production facilities
and taking them at face value can easily lead to the overestimation of
actual capacity and high ad hoc availability. However, uncertainties
in multiyear projects are inevitable as they invariably involve delays
related to construction, permits or equipment readiness, cancella-
tions or postponements, step-by-step expansion plans, evolutionary
advances in production technologies or quality issues15,24–29. Therefore,
announced capacities usually decrease to an effective, materialized
output. In our model we used N = 144 facilities between 2020 and 2030
2,000
1,750
1,500
1,250
1,000
750
500
250
0
Battery capacity (GWh yr–1)
2025
2030
2035
Year
Probability density
for year 2025
Probability density
for year 2030
Efective European battery production
Total European battery demand
Fig. 1 | European battery demand and its domestic production capacity up to
2035. Probabilistic feasibility space for different scenarios (N = 1,000) of total
battery demand and battery production capacity up to 2035 for Europe. The
shaded areas show all potential model results, the dashed lines indicate the 5th
and 95th percentiles, and the solid line indicates the median. Probability density
plots for 2025 and 2030 are shown on the right. Reference values for production:
Around 150 GWh yr−1 of stated capacity was available at the beginning of 2024.

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and increased demand by other applications, and vice versa for low
demand. Low-demand scenarios barely fall below 0.85 TWh yr−1 by 2030
(5th percentile). Concerning achievable BEV sales shares (Extended
Data Fig. 1), the feasibility space is deemed to narrow towards 2035
due to the European ZEV regulations. Accordingly, we will most likely
observe around 30% BEV sales by 2025 (median, IQR = 25–34%) and
70% by 2030 (median, IQR = 65–80%).
In contrast to the demand scenarios, the feasibility spaces
for domestic production have more distinct peaks (Fig. 1). Domestic pro-
duction is unlikely to surpass 1.0 TWh yr−1 by 2030 (39%) and the IQR is
0.93–1.04 TWh yr−1. A few high-production scenarios reach 1.2 TWh yr−1,
with the top 5% exceeding 1.10 TWh yr−1. Low-production scenarios
barely fall below 0.86 TWh yr−1 (5th percentile). Low production
capacity may emerge from the superposition of a low materialization
rate, low utilization, extended delays and high scrap, and vice versa
for high production.
The resulting average growth rates for demand (Extended Data
Table 1) are ambitious at 31–43% yr−1, but they have been witnessed
for other technologies. Historically, wind and solar capacity growth
rates have been at least 15% yr−1 and often between 39 and 50% yr−1
(ref. 21). General technology adoption growth rates have typically
been below 13–14% yr−1, but occasionally have exceeded 30–40% yr−1
(ref. 32). In contrast, the calculated growth rates for production are
more ambitious, ranging from 55 to 68% yr−1, and close to the levels
witnessed historically in times of emergency when unconventional
rates of growth were observed, as indicated by Odenweller et al.21.
Accordingly, if such exceptional growth rates were not realized, we
would observe substantially lower European production capacity by
2030, affirming that materialized capacity expands more slowly than
announcements might suggest.
Self-sufficiency of 90% in 2030 is feasible but far
from certain
Figure 2 shows the individual scenarios as trajectories (left) and density
plots with cumulative density (right) for production versus demand
for 2025 and 2030. It is very likely that domestic production can meet
at least 60% of demand in 2025 (90.1%) and 2030 (99.5%), although the
results for 2025 reveal a slightly elevated risk of short-term domes-
tic production shortfalls. For 2030, the 90% self-sufficiency target
seems feasible as this corresponds to the mean and median value
(IQR = 80–100%), but is far from certain as nearly half of the scenarios
(49%) do not reach the 90% self-sufficiency target. Production capacity
exceeds domestic demand by more than 10% in a minor share of sce-
narios (11%).
Our results are stable, even if we replace the LGM with Gompertz
or Bass diffusion models to capture asymmetric growth (Extended
Data Fig. 3a,b). The results from the Gompertz model show narrower
feasibility spaces at lower growth rates (20–27% yr−1 for demand and
31–48% yr−1 for production, Extended Data Table 1) and an aggravated
shortfall of domestic production capacity, particularly around 2025.
Moreover, if we limited the growth rates (15–39% yr−1) for the total pro-
duction capacity using the Gompertz model (Extended Data Fig. 3c),
we would observe ~0.89 TWh yr−1 by 2030 (IQR = 0.84–0.96 TWh yr−1)
and exceeding 1 TWh would be more unlikely (15%). Accordingly, the
90% self-sufficiency target would be even more at risk (25%), while at
least 50% self-sufficiency would still be very likely (98.1%, median = 83%,
IQR = 75-90%).
Growing engagement of European companies
Beyond mere domestic production capacity and self-sufficiency, the
company’s origin is relevant in the context of accessibility and techno
logy sovereignty. While the corporate landscape was nearly 100% Asian
in the early 2020s, the share of European companies is projected to
increase substantially. In 2025, around two-thirds of the materialized
production capacity is likely to result from Asian-affiliated compa-
nies and more than one-third from European companies (Extended
Data Fig. 2). By 2030, European companies are projected to hold
the largest share (45–55%), while the share of Asian companies
is expected to decline (40–50%) with US companies anticipated to
capture modest shares (3–8%).
Europe’s position on battery raw materials is
improving
The complexity of battery value chains13,33 implies that production
capacity should be assessed alongside raw material sourcing, where
domestic availability is a decisive factor for Europe and also for other
countries34,35. Expressing the European battery demand in terms of
required raw material quantities reveals that the cumulative demand
for key materials, namely, nickel, cobalt, graphite, lithium and man-
ganese, is projected to increase substantially by 2035, with expected
9-fold (cobalt) and 12–15-fold (nickel, manganese, graphite and lithium)
increases relative to the quantities required in 2025 (Table 1). While
Europe will rely on raw material imports until 2030–2035, three factors
indicate a strengthening position. First, and in relation to expected
demand, substantial domestic reserves of manganese and natural
graphite are available, with possibly lower prospects for lithium and
nickel, but primary cobalt is scarce. Second, existing self-sufficiency
assessments (Table 1, rightmost column) indicate progress in building
2,000
0.030
0.025
0.020
0.015
0.010
0.005
0
2025
2030
1,750
1,500
1,250
1,000
750
500
250
250
500
750
1,000
1,250
1,500
1,750
2,000
40
60
80
100
120
140
0
0.2
0.4
0.6
0.8
1.0
0
0
Battery demand (GWh yr–1)
Battery production (GWh yr–1)
Coverage of total demand by domestic production (%)
Probability density
Cumulative density function
90% European
self-suficiency
2025
2030
Fig. 2 | Comparison of anticipated European battery demand and supply in
2025 and 2030. Left: comparison of results of different scenarios (N = 1,000)
for domestic production capacity versus demand in Europe. The trajectories
for 2025–2030 are included as grey lines. The black line corresponds to supply
matching demand (100%). Right: feasibility space (N = 1,000) for the relative
coverage of total European demand. Probability density (histograms, left y axis)
and cumulative density (right y axis) are shown for 2025 and 2030. The black
dashed line marks the 90% self-sufficiency level.

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European value chains, however, ramp-ups must be extremely quick.
While cobalt and nickel imports (all grades) are likely to remain neces-
sary for domestic processing, it is likely that major shares of lithium
and most of the manganese can be sourced and refined domestically.
Natural graphite (all grades) is likely to require both local sourcing and
refining as well as imports. However, global supply diversification is
anticipated to also lower general dependency risks36,37. Third, emphasiz-
ing the circular economy and recycling, as proposed in the EU’s Critical
Raw Materials Act38 or incentivized by the US Inflation Reduction
Act35, is likely to reduce dependency and further improve sustainability
within a comprehensive battery ecosystem13,39–41, also securing mate-
rial availability even beyond 205042. Yet, current estimates of battery
returns36,43,44 strongly indicate that recycling or second use/repurposing
of batteries will be less relevant in the early 2030s, but will sharply grow
in importance thereafter, in particular, unlocking potential for nickel37,43
and cobalt41. In addition, battery technologies such as sodium-ion
batteries are an intended alternative to lithium-ion batteries; these
are currently at the beginning of large-scale commercialization24,45.
Uncertainties and policy implications
Our results indicate that the course has been set for transitioning from
battery research and development (R&D) to production and establish-
ing value chains in Europe. While substantial capacity additions can be
expected until 2030, reaching the adopted EU 90% self-sufficiency tar-
get by 2030 is highly challenging as production capacity would require
a highly ambitious growth rate. Nonetheless, our results imply that the
domestic production of batteries is unlikely to be a bottleneck for BEV
market diffusion in Europe and will conform to the transport-specific
emission budgets required to comply with the 1.5 °C global warming
target (that is, ~70% ZEV sales by 2030 and 100% by no later than 20335).
However, our analysis has limitations related to probabilistic
parameter choice and parameter assumptions, independent model-
ling with missing feedback mechanisms, data availability or neglected
vehicle trading. Nevertheless, our individual projections align with
other studies (Supplementary Tables 1 and 2), and furthermore, we
have demonstrated robustness by varying the diffusion models and
certain parameters.
First, parameters were sampled from independent programme
evaluation review technique (PERT) distributions (Methods). Specific
minimum, most likely and maximum values were primarily based on
literature values, but some were in-house assumptions and projections
based on recent developments. While this implies representative fea-
sibility spaces per parameter, real-world distributions are unknown,
parameter dependencies were disregarded and potential trend breaks
may have been missed, such as declining popularity in sport utility
vehicles (SUVs) or shrinking battery sizes. Future studies could further
decompose parameters such as demand from other applications.
Second, capturing dependencies and feedback mechanisms
between supply and demand might constrain the joint feasibility space
as substantially decoupled scenarios, such as high production with low
demand or vice versa, may be deemed improbable. Similarly, demand
scenarios where BEV ranges remain low but their sales shares rise high
may be considered improbable, given that perceived range anxiety
remains a significant barrier for many potential buyers46,47. Future
studies could use other approaches, such as system dynamics.
Third, production capacities were derived from individual
announcements (bottom-up, cut-off January 2024) rather than
model-based or demand-driven outlooks (top-down), confining our
dataset and results. On the one hand, we emphasize that the lack
of standardization makes it difficult to distinguish what is or is not
accounted for in the announced capacity. If capacity utilization were
included, we would obtain higher production capacities by 2030
(IQR = 1.15–1.30 TWh yr−1) with unrestricted growth rates and an
almost parallel trend slightly below demand with limited growth rates
(Extended Data Fig. 4a–c). On the other hand, as there are no explicit
announcements beyond 2030, supply curves flatten towards 2030
even though our model accounts for delayed realization, potentially
leading to underestimated production capacities between 2030 and
2035. Of course, additional capacities may emerge after 2030. However,
no announcements have been made so far, even though there are only
5 years left before this date and not every factory is likely to expand at
short notice.
Fourth, we linked domestic battery demand to domestic sales,
assuming that European sales equal production figures and neglecting
any foreign trade, as with the batteries themselves. While total Euro-
pean vehicle sales and production figures are a good match (±10%),
exports dominated imports in the trade balances from 2015 to 2022,
with clear distinctions between imported (smaller and volume-type)
and exported (larger and more premium-type) vehicle segments.
In our analysis, we disregarded upstream and downstream parts
of the value chain that might constrain Europe’s prospects too. Most
notably, capacity for the refinement of raw materials as well as produc-
ers of further processed cathode and anode active materials (CAMs
and AAMs) and cell components will be required to supply factories
domestically. In this respect, there have been several announcements
in Europe, with more progress for CAM processing27 than for AAMs48.
Nevertheless, most of the announced battery cell plants are likely to
have secured long-term supply contracts, mitigating the risk of material
Table 1 | Overview of European demand, production, reserve (primary) and resource (primary) data for five critical battery
raw materials
Material (unit)
Annual values (kt yr−1)
Cumulative values (kt)
Potential European
self-sufficiency by
2030 (%)
Current European
production
Demand by
2025
Demand by
2035
Demand
by 2025
Demand by
2035
Current European
reserves
European
resources
Li (kt LCE
content)
P: <1
R: NA
30 ± 6
170 ± 10
100 ± 10
1,230 ± 130
60–470
6,700–7,000
P: 25–60
R: 27–85
Ni (kt Ni
content)
P: ~50
R: ~70
180 ± 30
820 ± 90
510 ± 50
6,170 ± 700
~5,200
~12,300
P: 20–23
R: 20–28
Co (kt Co
content)
P: ~50
R: <10
40 ± 8
120 ± 20
130 ± 10
1,150 ± 150
90–340
520
P: 1–20
R: 20–37
Mn (kt Mn
content)
P: <7
R: NA
50 ± 10
320 ± 40
150 ± 10
2,140 ± 260
~2,000
~2,000
P: 45–100
R: 28–100
Gr (kt refined
graphite)
P: <15
R: NA
260 ± 50
1,520 ± 120
730 ± 80
10,360 ± 1,070
2,000–7,500
~30,900
P: <5
R: 21–26
All values are rounded and specified in kilotonnes. Demand values (refined and battery-grade quality) are specified as mean ± standard deviation. The ranges for potential European
self-sufficiency for each material by 2030 (without recycling) are based on literature data; reference details and original values are provided in Supplementary Tables 22 and 23. Calculated
demand values and data for 2030 are provided in Supplementary Tables 25 and 26 and visualized in Supplementary Figs. 2 and 3. P, primary; R, refined, battery quality; Li, lithium; LCE, lithium
carbonate equivalent; Ni, nickel; Co, cobalt; Mn, manganese; Gr, natural graphite; NA, not available.

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shortages. Finally, the current shortage of skilled workers may continue
to outpace the build-up in Europe15,24,25. This shortage affects scientific
and industrial R&D at all technology readiness levels, as well as direct
and indirect jobs in the sector, from factory build-up to cell manufac-
turing and system-level integration. Future studies could adapt our
probabilistic modelling to integrate these aspects.
Focusing on the implications for decision-makers in policy and
industry, Europe is striving for a competitive and sustainable battery eco-
system with concrete agendas and roadmaps49, including the Net-Zero
Industry Act18. This includes launching several initiatives and public–
private partnerships, such as Batteries Europe and the Batteries Europe
Partnership Association, or funding under the framework of the Impor-
tant Projects of Common European Interest to align industry and policy
perspectives on cross-cutting issues such as scale-up, sustainability,
recycling and digitalization50. In addition, there are special trade and
cooperation agreements, such as the EU–UK Battery Rules of Origin.
Although there is likely no single policy strategy to boost battery
ecosystems, we highlight the role of industrial policies in balancing
trade protectionism and global competitiveness. While the current
EU battery policy has a supply-side emphasis with many initiatives
focusing on R&D50, recent initiatives such as the Strategic Technologies
for Europe platform51 or the Temporary Crisis and Transition Frame-
work52 have an explicit focus on industrial development, competitive-
ness and sovereignty. Recent examples, such as the Northvolt battery
factory in Germany53, indicate that such industrial policies might be
effective in spurring domestic projects (irrespective of an individual
company’s future development). The global race might call for poli-
cies that create attractive, predictable home markets33 and reduce the
risks for industry players, such as public–private risk–reward–sharing
instruments, while purely inward-looking policies might be rather
detrimental17,54. Regarding prioritization, we emphasize that establish-
ing fully scaled and sustainable value chains simultaneously presents
enormous challenges and the inherent risk of impeding fast competi-
tiveness. China, for instance, started building internationally competi-
tive battery value chains years ago, leads current battery R&D, employs
an extensive specialized workforce, and is now progressing towards
more circular, safer and more sustainable batteries50. Finally, we high-
light the importance of using net materialized production capacities
as a basis for projections rather than announced capacities, which fail
to consider scrap rates, new technology developments, non-optimal
capacity utilization or delays related to construction and permits.
For industry players, our results indicate substantial potential for
domestic added value, tailoring batteries to Europe’s needs, as well as
localizing raw material production and battery recycling. Moreover,
our results call for further investments in local battery production to
avoid shortages in domestic production, while keeping track of inter-
national developments.
Conclusions
In this study, we used probabilistic modelling of S-shaped produc-
tion ramp-up and technology diffusion based on the latest empirical
data to project future battery demand and domestic production in
Europe. This allowed us to evaluate Europe’s prospects towards battery
self-sufficiency via feasibility spaces and probabilities in a consistent,
transparent and thorough manner. Based on our results, we can draw
four main conclusions.
First, we have shown that European demand is likely to experience
ambitious growth to at least 1.0 TWh yr−1 by 2030. In contrast, domes-
tic production capacities are more likely to fall behind terawatt hour
scales by 2030, with momentum increasing after 2025 and production
capacities required to grow at unconventional growth rates.
Second, the prospects for a European footprint along the battery
value chain are remarkable. On the one hand, covering at least 50–60%
of its demand via domestic production by 2030 is very likely and the
contribution from European-affiliated companies is projected to grow
substantially, while potential to improve the raw material situation in
Europe also exists. On the other hand, 90% self-sufficiency by 2030
seems feasible but far from certain. We thus emphasize an increased
likelihood of limited European competitiveness and domestic produc-
tion shortfalls, particularly before 2030.
Third, the short-term volatility of battery demand, mainly caused
by uncertain BEV popularity and availability, should not unsettle invest-
ment decisions and detract from long-cycle commitments based on
GHG mitigation, manufacturer announcements, and ZEV targets in
Europe and automotive core markets worldwide. Note that inter-
national ZEV targets will also restrict European export capacity for
non-BEVs in the future.
Fourth, the global battery manufacturing race, complex value
chains, raw material dependencies and time-consuming ramp-ups
stress the urgency of immediate action and reliable policies to lower
risks and ensure certain predictability. Without these, Europe’s need
to import batteries is likely to persist, or even increase, thus keeping
Europe dependent on imports in a key technology for current and
future sustainable transport and energy.
Methods
General approach
We used probabilistic modelling of S-shaped production ramp-up and
technology diffusion based on the latest empirical data to project future
battery demand and domestic production in Europe, covering the EU,
the European Free Trade Association (EFTA) and the United Kingdom.
We also translated this battery demand into the corresponding amounts
of required critical raw materials. This probabilistic approach captures
the nonlinear propagation and simultaneous interaction of major
parameters with inevitable uncertainties, in contrast to deterministic
models that rely on single estimate inputs. However, the goodness of
the projections still depends on the quality of the input data and chosen
model parameterization. A Monte Carlo simulation (N = 1,000) then
allows the construction of feasibility spaces and the classification of
findings by probability. The model was implemented in Python. Model
components and procedures are illustrated in Supplementary Fig. 1.
We performed all calculations on a standard Lenovo notebook with an
i7-8565U @1.8 GHz processor and 16 GB RAM (random access memory).
Parameter documentation
We provide comprehensive data tables for each modelling parameter,
including further information and data sources, in the Supplementary
Information (production parameters in Supplementary Tables 3–7,
demand parameters in Supplementary Tables 8–18 and raw material
parameters in Supplementary Tables 19–23).
Data on announced battery production capacities
We collected data on existing and announced battery production
facilities from the Fraunhofer Institute for Systems and Innovation
(ISI) database, which lists 144 European projects for the period 2020–
2030. The database includes details of development status/risk level
(secure, likely or insecure), technology characteristics, company infor-
mation and, most importantly, annual production capacities for the
announced projects, totalling 2.55 TWh yr−1 by 2030. The dataset for
this study, as of January 2024, is available at Zenodo via https://doi.org/
10.5281/zenodo.14505410 (ref. 55), while the latest aggregated version
can be accessed at https://metamarketmonitoring.de/en/.
Historical automotive data
We used data published by the European Automobile Manufacturers’
Association56 and JATO Dynamics57 for general automotive market
data. These data comprise European sales until 2023 and segment-
specific shares (first half of 2022) covering the EU, EFTA and the
United Kingdom. We differentiated between the AB segment (mini
and small vehicles), the CM segment (compact cars and multipurpose

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vehicles), the DEF segment (large and premium-type vehicles) and the
SUV segment. The SUV system is split into small and medium (SM) and
large (L) vehicles.
Historical BEV-specific data
We obtained BEV-specific data (see Link et al.58), comprising sales data
and comprehensive information on installed batteries until 2022,
from the Fraunhofer ISI xEV battery database. Based on these data,
we calculated sales-weighted average battery capacities per segment
and BEV-specific segment splits covering the EU, EFTA and the UK. The
gross battery capacities were derived by assuming an average share
of usable energy of 93% (ref. 59). The segment-specific BEV energy
consumption was calculated on the basis of European CO2 certification
data, as prescribed under Regulation (EU) 2019/631 and provided by
the European Environment Agency60. We evaluated all vehicles with
specific CO2 emissions of 0 gCO2e km−1 (by the World Harmonized
Light Vehicles Test Procedure). These data are available up to 2022 and
cover the EU, the United Kingdom (until 2019), Iceland (from 2018) and
Norway (from 2019). We assumed energy consumption improvements
of at least 5% until 2030–2035 due to increased energy efficiency or
road load reductions. The latter mainly concern lightweight materi-
als and decreasing battery weight61–63 as there is increasing evidence
that higher specific energy levels and enhanced system integration
overcompensate increasing battery sizes.
Modelling the effective, materialized domestic production
capacity
First, we used the announced annual production capacities (in GWh yr−1)
as the base matrix, where each project corresponds to a row and each
column to a year from 2020 to 2030. We extrapolated 2030 values to
2035. The year in which the first batteries were announced marks the
start-up year.
Second, we used matrix multiplication to add four influencing
factors via probabilistic parameterization: (1) capacity utilization,
where we used the overall equipment effectiveness as proxy, (2) scrap
rates, (3) project delays or postponements and (4) materialization
categories. This results in the probable production capacity. Spe-
cifically, modelling the evolution of overall equipment effectiveness
(OEE) is announcement-specific, starting with an initial OEE (oee0) in
the first year. We then assumed a gradual increase to the target OEE
(oeeT) over several years (TOEE) using linear interpolation. All three
values were drawn from PERT distributions (Supplementary Table 4).
Accordingly, the OEE matrix contains the evolution of OEE for each
project from its start-up year until 2035. Similarly, modelling the evo-
lution of the scrap rate is announcement-specific, starting with high
initial scrap rates (sr0) in the first year. We then assumed a gradual
decrease to the target scrap rate (srT) over several years (TSR) using
linear interpolation. Again, all three values were drawn from PERT
distributions (Supplementary Table 5). Accordingly, the scrap matrix
contains the evolution of scrap rate for each project from its start-up
year until 2035. Modelling potential delays or postponements (tDelay)
is announcement-specific, whereas values depend on the risk level
(secure, likely or insecure) and were drawn from PERT distributions
(Supplementary Table 7). Finally, modelling the probability of mate-
rialization (pMat) is announcement-specific, whereas values depend
on the risk level (secure, likely or insecure) and were drawn from PERT
distributions (Supplementary Table 6).
Third, we fitted each announcement and its probable produc-
tion capacity using an LGM. This smooths the data and captures the
S-curve-shaped production ramp-up. Curve fitting was conducted
using the Python SciPy software package64. We determined the
materialized European production capacity per year by accumulating
the individual fitted announcements, where we again used curve
fitting to determine certain indicators, such as total growth rate and
inflection point.
Logistic growth model
The empirically observed S-shape over time emerges because of expo-
nential growth/progress in the initial phase, which slows over time and
gradually approaches an asymptotic maximum. The accompanying
standard logistic growth model is defined as follows:
C (t) =
CMax
1 + e−k(t−tinfl)
(1)
where C(t) is the output over time t, CMax is the asymptote, k is the growth
constant, tinfl is the inflection point and e is Euler’s number.
Other diffusion models
Other common diffusion models, such as the Gompertz (equation (2))
or Bass diffusion (equation (3)) models, allowed us to capture asym-
metric growth and compare the results with those of the symmetric
LGM model. Both models are defined as follows:
C (t) = CMax × e−e−k(t−tinfl)
(2)
C (t) = CMax ×
1 −e−(p+q)t
1 +
q
p × e−(p+q)t
(3)
where q is the coefficient of imitation and p is the the coefficient
of innovation.
Battery demand from BEVs
The European battery demand from BEVs results from the probabil-
istic parameterization of future BEV sales shares, battery capacities
per segment, BEV-specific segment shares and total vehicle sales.
Future parameters were calculated until 2035. The interim values were
calculated via linear interpolation using the future target and historical
values as references.
Future battery capacities result from segment-specific target
ranges for 2030–2035. Targets were inspired by the European Council for
Automotive R&D (EUCAR)65, which specifies 400 km for average
short-range vehicles and 600 km for long-range vehicles. Concrete
range values were drawn from segment-specific PERT distributions. We
then calculated the gross battery capacity by using segment-specific
energy consumption and accounting for the average share of usable
energy. The segment-specific BEV energy consumption was drawn
from PERT distributions based on historical values60 and potential
efficiency improvements.
Total vehicle sales were projected on the basis of the correlation of
gross domestic product (GDP) and automotive sales, with sales histori-
cally anticipating GDP growth. Specifically, we followed GDP expecta-
tions from the International Monetary Fund (IMF) and Organisation for
Economic Co-operation and Development (OECD) for the rest of the
2020s, and matched historical year-on-year sales growth rates with his-
torical GDP growths to determine their relation. Concrete sales growth
values were then drawn from PERT distributions using this relation.
For segment-specific BEV sales shares, we assumed a uniform
diffusion of BEVs across all segments until 2035, as well as the con-
tinuation of ongoing trends such as the SUV boom. More precisely,
we initiated the BEV-specific split for each segment in 2022 and gradu-
ally aligned this share with the projected total market segment split
until 2035. For the latter, we used the historical evolution of segment
shares (compound annual growth rate for 2011–2022) and the total
market segment shares by 2023 as references. Concrete growth rate
values were drawn from PERT distributions. Finally, the total share of all
segments was normalized to 100%.
For BEV sales shares, we fitted an LGM to historical BEV sales
data (2015–2023) and potential future sales. Specifically, we added a
potential inflection point and BEV sales shares by 2035 using proba-
bilistic parameterization. This inflection point affects the shape of the

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BEV market diffusion and is intended to approximate various strate-
gies from BEV boosts to delays. For 2035 and beyond, we followed
the announced emission regulations of most European countries,
meaning that (nearly) all new cars must be ZEVs (that is, locally
emission-free vehicles with 0 gCO2e km−1 exhaust emissions). We
defined 90% BEV share as the lower limit and 100% as the upper limit.
Final curve fitting was achieved using the Python SciPy package64, and
the final asymptote (CMax), growth rate (k) and actual inflection point
(tinfl) were calculated.
The final European battery demand from BEVs was then calculated
by multiplying the battery capacities per segment, segment-specific
sales shares, BEV sales shares and total vehicle sales.
Total battery demand
The final European battery demand from BEVs was then scaled up by
the demand from other first-life applications to derive the total bat-
tery demand. This includes other automotive applications, such as
plug-in hybrids, electrified commercial vehicles (trucks and buses) and
light-electric vehicles. In addition, stationary systems, from home to
industrial-scale buffer storages, are likely to evolve as the second largest
market if electrification and the integration of renewables are pushed
globally and locally, especially in Europe. Concrete scaling values were
drawn from PERT distributions.
Raw materials
The quantities of required raw materials were calculated using three
average cathode stoichiometries (that is, medium-nickel, high-nickel
and iron- or manganese-based materials according to Maisel et al.66)
and the accompanying specific material demand (in g Wh−1). Potential
cathode chemistry market shares for 2035 were drawn from PERT
distributions and normalized, with values between 2024 and 2034
being derived via linear interpolation. All modelling parameters and
detailed references are provided in Supplementary Figs. 2 and 3 and
Supplementary Tables 19–26. Please note that the model therefore
estimates the amount of raw material input required to produce the
battery cells. The model does not, however, take into the consideration
the fact that the actual ramp-up and construction of European mining
capacity will require additional time.
PERT distribution
The PERT distribution is frequently used in risk analysis and corres
ponds to a re-parameterized/transformed beta distribution. The PERT
probability distribution function is defined as:
f (x, xmin, xml, xmax) = (xmax −xmin)
1
B (α, β)
x α−1(1 −x)
β−1 + xmin
(4)
where x is the realized value, xmin is the minimum, xml is the most likely
value and xmax is the maximum. The beta function Β and shape para
meters α and β are defined as:
B (α, β) = ∫
1
0
t α−1(1 −t)
β−1dt
α = 1 + λ × xml −xmin
xmax −xmin
, λ = 4
β = 1 + λ × xmax −xml
xmax −xmin
, λ = 4
(5)
Data availability
All data are available in Supplementary Tables 3–18 and within the
Python model. Data can be accessed at Zenodo via https://doi.org/
10.5281/zenodo.14505410 (ref. 55). Source data are provided with this
paper.
Code availability
The Python model code can be accessed at Zenodo via https://doi.org/
10.5281/zenodo.14505410 (ref. 55).
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Acknowledgements
We gratefully acknowledge funding from the Ariadne 2 (FKZ
03SFK5D0-2, S.L. and P.P.) and BEMA (FKZ 03XP0272B, L.S., L.W.
and A.S.) projects by the German Federal Ministry of Education and
Research, KAMO—High-Performance Center Profilregion, funded by
the Fraunhofer Gesellschaft (S.L. and P.P.), and a strategic internal
research project (FKZ 4009286, A.S.) funded by Fraunhofer ISI. We thank
T. Wicke for providing data and also for his comments, as well as
G. Bowman-Köhler and L. Antill-Blum (all Fraunhofer ISI) for English editing.
Author contributions
S.L. and L.S. conceived and designed the study in consultation
with P.P. S.L. and L.S. collected the data, implemented the model
and created the visualizations. S.L. wrote the original paper
with contributions and revisions from all authors. P.P., A.S. and
L.W. contributed to the discussion, interpretation of findings,
recommendations and policy implications. P.P. supervised the study.
FundingInformation
Open access funding provided by Fraunhofer-Gesellschaft zur
Förderung der angewandten Forschung e.V.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41560-025-01722-y.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41560-025-01722-y.
Correspondence and requests for materials should be addressed to
Steffen Link.
Peer review information Nature Energy thanks the anonymous
reviewers for their contribution to the peer review of this work.
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© The Author(s) 2025

Nature Energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Extended Data Fig. 1 | Probabilistic feasibility space (N = 1,000) for BEV sales shares until 2035. Logistic growth model (LGM). Historical values are in black (until
2023) and projections are in green. Shaded areas mark all model results, while the dashed lines indicate the 5% and 95% percentiles, and the solid line indicates the
median. Probability density (PD) plots for 2025, 2030, and 2035.

Nature Energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Extended Data Fig. 2 | Probabilistic feasibility space (N = 1,000) for production capacities differentiated by the company’s country affiliation (location of
headquarter (HQ)). Covering Europe (left), Asia (middle), and North America (mainly the US, right), visualized as density plots. Logistic growth model (LGM).
Probability densities for 2025 (teal) and 2030 (purple).

Nature Energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Extended Data Fig. 3 | Sensitivity with different diffusion models. Gompertz
diffusion model (Upper-A), Bass diffusion model (Middle-B), and Gompertz
diffusion model with limited growth rate (Lower-C) - N = 1,000. (1): BEV sales
shares until 2035. Historic values are in black (until 2023) and projections are in
green. Shaded areas mark all model results, while the dashed lines indicate the
5% and 95% percentiles, and the solid line indicates the median. (2): BEV battery
demand (blue) versus battery production capacity (red) until 2035. Shaded
areas mark all the potential model results, while the dashed lines indicate the
5% and 95% percentiles, and the solid line indicates the median. (3) Comparison
for domestic production capacity (x-axis) versus demand (y-axis) in GWh yr−1
for 2025 (teal triangle) and 2030 (purple diamond), including trajectories. (4)
Feasibility space for relative coverage of total European demand for 2025 (teal)
and 2030 (purple), including the 90% self-sufficiency level (black dashed line).
The results for 2025 (teal) and 2030 (purple) are shown as histograms (left y-axis,
no ticks) and cumulative density (right y-axis).

Nature Energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Extended Data Fig. 4 | Sensitivity without utilisation constraint. Logistic
growth model (Upper-A), Gompertz diffusion model (Middle-B), and Gompertz
diffusion model with limited growth rate (Lower-C) - N = 1,000. (1): BEV battery
demand (blue) versus battery production capacity (red) until 2035. Shaded
areas mark all the potential model results, while the dashed lines indicate the
5% and 95% percentiles, and the solid line indicates the median. (2) Comparison
for domestic production capacity (x-axis) versus demand (y-axis) in GWh yr−1
for 2025 (teal triangle) and 2030 (purple diamond), including trajectories. (3)
Feasibility space for relative coverage of total European demand for 2025 (teal)
and 2030 (purple), including the 90% self-sufficiency level (black dashed line).
The results for 2025 (teal) and 2030 (purple) are shown as histograms (left y-axis,
no ticks) and cumulative density (right y-axis).

Nature Energy
Analysis
https://doi.org/10.1038/s41560-025-01722-y
Extended Data Table 1 | Statistical figures for calculated growth rates and inflection points for demand and production via
the Logistic Growth Model (LGM) and Gompertz (GOM)
Parameter
Target
Model
Median
IQR
5% percentile
95% percentile
Growth Rate
(% per year)
Demand
LGM
36.2%
10.3%
(43.0%-32.8%)
31.3%
52.9%
GOM
22.1%
2.6%
(23.6%-21.1%)
20.5%
26.5%
Production
LGM
61.8%
5.8%
(64.7%-58.9%)
54.9%
68.0%
GOM
37.3%
5.0%
(39.8%-34.7%)
30.8%
43.0%
Inflection
Point
(year)

Demand
LGM
2027.3
2.0
(2028.4-2026.4)
2025.6
2029.0
GOM
2025.4
0.5
(2025.6-2025.1)
2024.6
2025.8
Production
LGM
2027.6
0.5
(2027.8-2027.3)
2027.1
2028.2
GOM
2026.4
0.4
(2026.6-2026.2)
2027.1
2026.0
This involves the median (first column), the interquartile range (IQR, second columns), as well as the 5% (third column) and 95% percentiles (fourth column). Growth rates as percentage
values. Inflection points in years.

    Scientific Research Findings:

Answer: We find that European battery cell demand will likely surpass 1.0 TWh per year by 2030, whereas domestic production capacity is expected to fall short, creating a risk of supply constraints. Although Europe can be expected to meet at least 50–60% of its demand through domestic production by 2030, achieving the EU’s 90% self‑sufficiency target is feasible but uncertain, as nearly half of our modelled scenarios fail to meet this target. If Europe wants more independence from battery cell imports, our findings highlight the urgency of accelerating production capacity expansion, scaling up a battery supply chain, and implementing strong industrial policies to support competitiveness and supply sovereignty. Our approach is broadly applicable to regions aiming for battery self‑sufficiency and should be examined with interacting factors such as policy support and supply chain resilience. However, our analysis does not account for disruptive market shifts, policy reversals, or unexpected technological breakthroughs, which could substantially alter production and demand trajectories.