The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning only buys you so much: Practical limits on battery price reduction
Open accessAbstract
Abstract Wide deployment of electric vehicles (EVs) would greatly facilitate global de-carbonization, but achieving the emission targets depends on future battery prices. Conventional learning curves for manufacturing costs, used in many battery projections, unrealistically predict battery prices will fall below $100/kWh by 2030, pushing EVs to hit price parity with internal combustion engine vehicles (ICEVs) in the absence of incentives. However, in reality, essential materials costs set practical lower bounds on battery prices. Our 2-stage learning curve model projects the active material costs and NMC-based Lithium-ion battery pack price with mineral and material costs as the respective price floors. The improved model predicts nickel-manganese-cobalt (NMC) battery prices will fall only to about $124/kWh by 2030 – much cheaper than today, but still too expensive to truly compete with ICEVs, due primarily to the high prices of cobalt, nickel, and lithium. Our results suggest that stabilizing raw materials prices and/or stimulating R&D activities on alternative battery chemistries will be important to achieve environmentally sustainable EV-based ground transportation at an attractive price.
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Cites
Rapidly falling costs of battery packs for electric vehicles
Nykvist, B., & Nilsson, M. (2015) · Nature Climate Change · doi:10.1038/nclimate2564 · Cited by 1,819
Cited by
Battery cost forecasting: a review of methods and results with an outlook to 2050
Mauler, L., Duffner, F., Zeier, W. G., & Leker, J. (2021) · Energy & Environmental Science · doi:10.1039/d1ee01530c · Cited by 499
Yao, A., Benson, S. M., & Chueh, W. C. (2025) · Nature Energy · doi:10.1038/s41560-024-01701-9 · Cited by 448
End-of-life or second-life options for retired electric vehicle batteries
Zhu, J., Mathews, I., Ren, D., Li, W., Cogswell, D., Xing, B., Sedlatschek, T., Kantareddy, S. N. R., Yi, M., Gao, T., et al. (2021) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2021.100537 · Cited by 306
Cobalt‐Free High‐Capacity Ni‐Rich Layered Li[Ni0.9Mn0.1]O2 Cathode
Aishova, A., Park, G., Yoon, C. S., & Sun, Y. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903179 · Cited by 269
Hsieh, I.-Y. L., Pan, M. S., & Green, W. H. (2020) · Energy Policy · doi:10.1016/j.enpol.2020.111654 · Cited by 94
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Metadata
- Authors
- Hsieh, I.-Y. L., Pan, M. S., Chiang, Y.-M., & Green, W. H.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 239
- Pages
- 218-224
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2914168703
- Cited by
- 186
- References
- 1
- FWCI
- 8.11
- Type
- journal article
- Language
- en
- Open access
- green
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- Hsieh, I.-Y. L., Pan, M. S., Chiang, Y.-M., & Green, W. H. (2019). Learning only buys you so much: Practical limits on battery price reduction. Applied Energy. https://doi.org/10.1016/j.apenergy.2019.01.138
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.