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An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making

Author

Listed:
  • Yao, Lei
  • Dai, Huilin
  • Xiao, Yanqiu
  • Zhao, Changsheng
  • Fei, Zhigen
  • Cui, Guangzhen
  • Zhang, Longhai

Abstract

Multiple lithium-ion battery cells and multi-contact connection methods increase the chances of connection failures in power battery packs, posing a significant threat to the operational safety of electric vehicles. To this end, the study proposes an intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making. The method uses Pearson correlation coefficients (PCC), Spearman correlation coefficients (SCC), and Kendall correlation coefficients (KCC) to simultaneously quantify the voltage synchronization between different cells in the battery pack, which is used to shield against the voltage measurement noise. The images of the correlation series transformed by the Gramian angular field are used as the input values of the convolutional neural network to classify the fault states by combining the adaptive weights of the three correlation series images on the fault levels. The experimental data shows that the accuracy of the method is 97.75 %, which is an improvement of 4.56 %, 5.98 %, and 2.39 % over the fault diagnosis accuracy using only PCC, SCC, and KCC, respectively, and effectively avoids misdiagnosis and omission due to the limitation of a single measurement. The proposed battery pack connection fault diagnosis method is robust and reliable, and has great practical application value.

Suggested Citation

  • Yao, Lei & Dai, Huilin & Xiao, Yanqiu & Zhao, Changsheng & Fei, Zhigen & Cui, Guangzhen & Zhang, Longhai, 2024. "An intelligent diagnosis method for battery pack connection faults based on multiple correlation analysis and adaptive fusion decision-making," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224023478
    DOI: 10.1016/j.energy.2024.132573
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    References listed on IDEAS

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    Cited by:

    1. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Xu, Shaochun & Bai, Miao, 2025. "Detection of connection faults and estimation of contact resistance in lithium-ion battery packs with canonical variable analysis and local Mahalanobis distance," Energy, Elsevier, vol. 318(C).
    2. Liu, Wei & Teh, Jiashen, 2025. "Remaining useful life prediction of lithium-ion batteries based on an incremental internal resistance aging model and a gated recurrent unit neural network," Energy, Elsevier, vol. 333(C).
    3. Yang, Qifan & Yu, Zhiguo & Liu, Yiqing & Kang, Yongzhe, 2025. "High-reliability multi-fault diagnosis of lithium-ion batteries based on low-redundancy cross-measurement and affine transformation," Energy, Elsevier, vol. 318(C).

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