IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v328y2025ics036054422502290x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422502290X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.136648?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:328:y:2025:i:c:s036054422502290x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.