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Energy and cost analysis of automotive batteries based on learning curve and configuration design

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  • Yang, Chen
  • Liu, Ding-Fei
  • Liu, Si-Meng

Abstract

The vast adoption of electric vehicles (EVs) depends on cost reduction and energy-density improvement of automotive batteries. The learning curve model is capable of quantitatively predicting the cost reduction through the installed battery capacity. The precision of predicted battery cost can be boosted and verified by the refined learning curve model that considers the comprehensive effects of economic scale and technological innovations. The refined model suggests a more rapid battery cost decline compared with single factor models and previous literatures. It is noted that battery cost in China will fall below 100 $ kWh−1 by 2024, which would make battery powered EV cost competitive with gasoline powered vehicle. Especially, lithium iron phosphate (LFP) batteries will fall faster with a forecast of 80 $ kWh−1 by 2023, closely approaching to the actual market price of 88 $ kWh−1 in 2023. These findings also validate the significance of technological innovations on cost reduction. The predicted battery energy density is confirmed by the state-of-the-art battery technologies, such as tab-free cell configuration and cell-to-pack pattern design. Finally, based on the forecasted cost and energy density, this work depicts the quantitative relationship of EV driving range and cost in current and advanced scenarios.

Suggested Citation

  • Yang, Chen & Liu, Ding-Fei & Liu, Si-Meng, 2025. "Energy and cost analysis of automotive batteries based on learning curve and configuration design," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s036054422502290x
    DOI: 10.1016/j.energy.2025.136648
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    References listed on IDEAS

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