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Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data

Author

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  • Zhifu Wang

    (School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
    School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Wei Luo

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
    These authors contributed equally to this work and should be considered co-first authors.)

  • Song Xu

    (School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Yuan Yan

    (School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Limin Huang

    (School of Mechanical Engineering, ChengDu University, Chengdu 610106, China)

  • Jingkai Wang

    (School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Wenmei Hao

    (School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Zhongyi Yang

    (School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China)

Abstract

Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.

Suggested Citation

  • Zhifu Wang & Wei Luo & Song Xu & Yuan Yan & Limin Huang & Jingkai Wang & Wenmei Hao & Zhongyi Yang, 2023. "Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1120-:d:1027756
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    References listed on IDEAS

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    1. Jiang, Lulu & Deng, Zhongwei & Tang, Xiaolin & Hu, Lin & Lin, Xianke & Hu, Xiaosong, 2021. "Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data," Energy, Elsevier, vol. 234(C).
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    Cited by:

    1. Hegazy Rezk & Mohammad Ali Abdelkareem & Samah Ibrahim Alshathri & Enas Taha Sayed & Mohamad Ramadan & Abdul Ghani Olabi, 2023. "Fuel Economy Energy Management of Electric Vehicles Using Harris Hawks Optimization," Sustainability, MDPI, vol. 15(16), pages 1-15, August.
    2. Seydali Ferahtia & Hegazy Rezk & Rania M. Ghoniem & Ahmed Fathy & Reem Alkanhel & Mohamed M. Ghonem, 2023. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

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