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US industrial policy may reduce electric vehicle battery supply chain vulnerabilities and influence technology choice

Nature Energy volume 9, pages 1561–1570 (2024)Cite this article

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An Author Correction to this article was published on 19 May 2025

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Abstract

We analyse US Inflation Reduction Act (IRA) incentives for electric vehicle battery technology and supply chain decisions. We find that the total value of available credits exceeds estimated battery production costs, but qualifying for all available credits is difficult. IRA cell and module credits alone bring estimated US battery production costs in line with China. In contrast, IRA material extraction and processing credits are modest. IRA’s end-user purchase credits are restricted to electric vehicles whose battery supply chains exclude foreign entities of concern, including China. This incentivizes diversification of the entire supply chain, but leasing avoids these restrictions. Lithium iron phosphate batteries have potential to more easily reduce supply chain vulnerabilities and qualify for incentives, but they have smaller total available incentives than nickel/cobalt-based batteries. Overall, the IRA primarily incentivizes downstream battery manufacturing diversification, whereas upstream supply implications depend on automaker responses to foreign entities of concern and leasing rules.

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Fig. 1: The geographical distribution of national production for each electric vehicle battery supply chain stage.
Fig. 2: Comparison of critical minerals in a 70-kWh battery to meet requirements for the 30D critical mineral credit of the Inflation Reduction Act.
Fig. 3: Current 2024 modelled production costs for the LFP, NMC and NCA battery chemistries, in comparison to the relative value of each IRA credit.
Fig. 4: The IRA appears to spur the development of the EV battery supply chain in North America but mostly in the battery and vehicle manufacturing segments.

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Data availability

The source and processed data generated in this study are attached as Supplementary Data. The model used in this study (BatPaC)41 is publicly available via the Argonne National Lab. We only use publicly available production and critical mineral price data for the production analysis, primarily from the BatPaC model and Trost and Dunn23. Estimates of production costs in China are estimated from Krishna42. Data used to generate supply chain data in Fig. 1 are from publicly available US Geological Survey60, Sun et al.58, Endo et al.59 and International Energy Agency31 datasets and a Frost and Sullivan report61 that is not in the public domain. Additionally, we have created an interactive tool to allow users to adjust model assumptions around mineral prices and see how they affect key results in Figs. 2 and 3 at https://acheng98.shinyapps.io/IRAMineralPriceEffectsSim/.

Code availability

The code that is used for this analysis and an interactive tool is freely available in the repository linked to this paper (https://doi.org/10.5281/zenodo.11182063). Additionally, the scripts used to automate some of the modelling are provided as a Supplementary Code zip file and in the repository.

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Acknowledgements

This research was supported by the 2021 Carnegie Mellon University College of Engineering Moonshot Award for ‘Engineering Competitiveness: Critical Technologies, Supply Chains, and Infrastructure’ (A.L.C., E.R.H.F.) the National Science Foundation award number 2241237 (A.L.C., E.R.H.F., J.J.M.) and the National Science Foundation Graduate Research Fellowship under grant number DGE2140739 (A.L.C.). We would like to acknowledge J. P. Helveston, W. Cohen, V. Karplus and K. MacMahon for feedback that helped us improve initial drafts of this article; J. Axsen, D. Gohlke, A. Jenn, D. Mackenzie, A. Mohan, M. Ziegler, J. Jenkins, L. Reynolds, J. P. MacDuffie, S. Helper, A. Reamer, K. Bhuwalka, M. Davidson, H. Khan, J. Dunn, M. Mauter and C. Scown for their feedback and perspectives at various conferences and presentations; and the Fuchs, Karplus (LEO) and Michalek (VEG) research groups for their feedback throughout the development of this work. We would also like to acknowledge the use of large language models to edit and refine some of the text and code in this work.

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Authors and Affiliations

  1. Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

    Anthony L. Cheng, Erica R. H. Fuchs & Jeremy J. Michalek

  2. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Jeremy J. Michalek

  3. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Jeremy J. Michalek

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  1. Anthony L. Cheng
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  2. Erica R. H. Fuchs
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Contributions

A.L.C.: conceptualization, methodology, software, formal analysis, writing–original draft, writing–review and editing, visualization. E.R.H.F.: conceptualization, methodology, writing–review and editing, visualization, supervision, funding acquisition. J.J.M.: conceptualization, methodology, writing–review and editing, visualization, supervision.

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Correspondence to Jeremy J. Michalek.

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Supplementary Information

Supplementary Notes 1–4, Tables 1–12 and Figs. 1 and 2.

Supplementary Data 1

Supplementary Data 0–9 and a cover sheet describing the contents of the file to process data to be used in and received from the model (BatPaC, developed by the Argonne National Lab).

Supplementary Code 1

The Microsoft Office scripts used in conjunction with the BatPaC model to make analysis easier.

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Cheng, A.L., Fuchs, E.R.H. & Michalek, J.J. US industrial policy may reduce electric vehicle battery supply chain vulnerabilities and influence technology choice. Nat Energy 9, 1561–1570 (2024). https://doi.org/10.1038/s41560-024-01649-w

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