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

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  • 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
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    References listed on IDEAS

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