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Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Author

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  • Zhao, Hongqian
  • Chen, Zheng
  • Shu, Xing
  • Xiao, Renxin
  • Shen, Jiangwei
  • Liu, Yu
  • Liu, Yonggang

Abstract

Online diagnosis of abnormal temperature is vital to ensure the reliability and operation safety of lithium-ion batteries, and this study develops a hybrid neural network and fault threshold optimization algorithm for their online surface temperature prediction and abnormal diagnosis. To be specific, a hybrid neural network incorporating convolutional neural network and long short-term memory neural network is firstly employed to predict the battery temperature, and a residual monitor is designed to track the deviation between the measure and the prediction. Then, the acquired residual is compared with the fault threshold to diagnose whether the battery temperature is abnormal. Moreover, to improve the correctness and reliability of fault diagnosis, a fault threshold optimization algorithm based on the receiver operating characteristic curve is defined to automatically find the optimal fault threshold. The accuracy and reliability of temperature prediction is verified under various aged state and temperatures, as well as different battery types. The abnormal experiment validation on three datasets reveals that the proposed method can diagnose and unwind temperature fault warning timely and reliably. Additionally, the average execution time of each prediction and diagnosis is less than 3.5Â ms, manifesting the real-time application capability of the proposed method.

Suggested Citation

  • Zhao, Hongqian & Chen, Zheng & Shu, Xing & Xiao, Renxin & Shen, Jiangwei & Liu, Yu & Liu, Yonggang, 2024. "Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007123
    DOI: 10.1016/j.ress.2023.109798
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