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Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification

Author

Listed:
  • Liang Zhang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Qizhi Wu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Longfei Wang

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Ling Lyu

    (School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China)

  • Linru Jiang

    (China Electric Power Research Institute Limited, Haidian District, Beijing 100192, China)

  • Yu Shi

    (Changchun Electric Power Exploration & Design Institute, Changchun 130000, China)

Abstract

Battery cell voltage is an important evaluation index for electric vehicle condition estimation and one of the main monitoring parameters of the battery management system, and accurate voltage prediction is crucial for electric vehicle battery failure warning. Therefore, this paper proposes a novel hybrid gated recurrent unit and long short-term memory (GRU-LSTM) neural network to predict electric vehicle lithium-ion battery cell voltage. Firstly, Pearson coefficient correlation analysis is carried out to determine the input parameters of the neural network by analyzing the influence factors of the voltage parameters, and the hyperparameters of the neural network are determined through cross-validation to construct the lithium-ion battery single-unit voltage prediction model based on GRU-LSTM. Secondly, the voltage prediction accuracy and robustness of the GRU-LSTM model are verified by training the historical data of real vehicles in spring, summer, fall, and winter, combined with four different error indicators. Finally, the feasibility of the proposed method is verified by designing hierarchical warning rules based on the prediction data to realize the accurate warning of multiple voltage anomalies.

Suggested Citation

  • Liang Zhang & Qizhi Wu & Longfei Wang & Ling Lyu & Linru Jiang & Yu Shi, 2025. "Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification," Energies, MDPI, vol. 18(6), pages 1-25, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1315-:d:1607327
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    References listed on IDEAS

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    2. Wang, Jianfeng & Zuo, Zhiwen & Wei, Yili & Jia, Yongkai & Chen, Bowei & Li, Yuhan & Yang, Na, 2024. "State of charge estimation of lithium-ion battery based on GA-LSTM and improved IAKF," Applied Energy, Elsevier, vol. 368(C).
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    4. Chen, Liping & Xie, Siqiang & Lopes, António M. & Li, Huafeng & Bao, Xinyuan & Zhang, Chaolong & Li, Penghua, 2024. "A new SOH estimation method for Lithium-ion batteries based on model-data-fusion," Energy, Elsevier, vol. 286(C).
    5. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
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