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Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation

Author

Listed:
  • Jinrui Nan

    (National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Shenzhen Automotive Research Institute (SZART), Beijing Institute of Technology, Beijing 100081, China)

  • Bo Deng

    (National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Shenzhen Automotive Research Institute (SZART), Beijing Institute of Technology, Beijing 100081, China)

  • Wanke Cao

    (National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Shenzhen Automotive Research Institute (SZART), Beijing Institute of Technology, Beijing 100081, China)

  • Jianjun Hu

    (China North Vehicle Research Institute, Beijing 100072, China)

  • Yuhua Chang

    (Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, 02-524 Warsaw, Poland)

  • Yili Cai

    (National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Zhiwei Zhong

    (National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning.

Suggested Citation

  • Jinrui Nan & Bo Deng & Wanke Cao & Jianjun Hu & Yuhua Chang & Yili Cai & Zhiwei Zhong, 2022. "Big Data-Based Early Fault Warning of Batteries Combining Short-Text Mining and Grey Correlation," Energies, MDPI, vol. 15(15), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5333-:d:869456
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    References listed on IDEAS

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    1. Sun, Zhenyu & Han, Yang & Wang, Zhenpo & Chen, Yong & Liu, Peng & Qin, Zian & Zhang, Zhaosheng & Wu, Zhiqiang & Song, Chunbao, 2022. "Detection of voltage fault in the battery system of electric vehicles using statistical analysis," Applied Energy, Elsevier, vol. 307(C).
    2. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).
    3. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    4. Kapucu, Ceyhun & Cubukcu, Mete, 2021. "A supervised ensemble learning method for fault diagnosis in photovoltaic strings," Energy, Elsevier, vol. 227(C).
    5. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    6. Yang, Ruixin & Xiong, Rui & Ma, Suxiao & Lin, Xinfan, 2020. "Characterization of external short circuit faults in electric vehicle Li-ion battery packs and prediction using artificial neural networks," Applied Energy, Elsevier, vol. 260(C).
    7. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
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