IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v320y2025ics0360544225009703.html
   My bibliography  Save this article

Fault cause inferences of onboard lithium-ion battery thermal runaway using convolutional neural network

Author

Listed:
  • Shuhui, Wang
  • Zhenpo, Wang
  • Zhaosheng, Zhang
  • Ximing, Cheng

Abstract

Lithium battery thermal runaway fires are the most common and alarming type of electric vehicle accident. While pre-accident lithium battery fault diagnosis is well-studied, post-accident analysis for identifying causes and determining liability remains limited. This paper categorizes such accidents into "latent" and "sudden" failures, introducing a novel feature indicator and a convolutional neural network (CNN) for automatic classification. The method explores data characteristics linked to different accident causes and their correlation with thermal runaway mechanisms. Based on this, a data-driven framework is proposed for identifying causes and determining liability, aiding on-site investigations. The study analyzes 41 electric vehicles with actual thermal runaway incidents, achieving 100 % precision and 75 % recall, validating the approach's effectiveness. Compared to existing research, this work enables more precise cause identification through classification based on diverse, coupled features rather than broad assumptions. The data-driven, principled framework also offers generalizability, extending its applicability to accident analyses beyond the current dataset.

Suggested Citation

  • Shuhui, Wang & Zhenpo, Wang & Zhaosheng, Zhang & Ximing, Cheng, 2025. "Fault cause inferences of onboard lithium-ion battery thermal runaway using convolutional neural network," Energy, Elsevier, vol. 320(C).
    • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009703
    DOI: 10.1016/j.energy.2025.135328
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225009703
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135328?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:320:y:2025:i:c:s0360544225009703. 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.