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Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data

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  • Jiang, Lulu
  • Deng, Zhongwei
  • Tang, Xiaolin
  • Hu, Lin
  • Lin, Xianke
  • Hu, Xiaosong

Abstract

Battery fault diagnosis is essential to ensure the safe and reliable operation of electric vehicles. Early detection of battery faults can reduce battery incidents and property losses. However, early warning of battery thermal runaway is still a challenging task. This paper proposes a novel data-driven method for lithium-ion battery pack fault diagnosis and thermal runaway warning based on state representation methodology. The normalized battery voltages are used to achieve accurate identification of battery early faults. The proposed method calculates the real-time state of each cell to characterize the internal characteristics of the battery cell, and the state changes are recorded to achieve battery fault diagnosis. The fault detection time is compared with the alarm time of real vehicles to verify the effectiveness of the proposed method. The real-world operation data of four electric vehicles with different specifications are used to verify the feasibility, robustness, and reliability of the proposed method. The results show that the method can achieve not only the accurate identification of the faulty cells and accurate determination of the voltage fault type but also the early detection of faults and early warning of thermal runaway.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221015140
    DOI: 10.1016/j.energy.2021.121266
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    7. Chang, Chun & Wang, Qiyue & Jiang, Jiuchun & Jiang, Yan & Wu, Tiezhou, 2023. "Voltage fault diagnosis of a power battery based on wavelet time-frequency diagram," Energy, Elsevier, vol. 278(PB).
    8. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
    9. Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
    10. Chen, Mingyi & Yu, Yue & Ouyang, Dongxu & Weng, Jingwen & Zhao, Luyao & Wang, Jian & Chen, Yin, 2024. "Research progress of enhancing battery safety with phase change materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    11. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    12. Li, Chao & Zeng, Kaidi & Li, Guanzheng & Chen, Peiyu & Li, Bin, 2023. "A novel fault diagnosis method for battery energy storage station based on differential current," Applied Energy, Elsevier, vol. 352(C).
    13. Wang, Shuhui & Wang, Zhenpo & Cheng, Ximing & Zhang, Zhaosheng, 2023. "A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model," Energy, Elsevier, vol. 281(C).
    14. Li, Jiangkuan & Lin, Meng & Wang, Bo & Tian, Ruifeng & Tan, Sichao & Li, Yankai & Chen, Junjie, 2024. "Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants," Energy, Elsevier, vol. 290(C).
    15. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    16. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Electric vehicle powertrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 238(PC).
    17. Shen, Dongxu & Lyu, Chao & Yang, Dazhi & Hinds, Gareth & Wang, Lixin, 2023. "Connection fault diagnosis for lithium-ion battery packs in electric vehicles based on mechanical vibration signals and broad belief network," Energy, Elsevier, vol. 274(C).
    18. Hong, Jichao & Liang, Fengwei & Chen, Yingjie & Wang, Facheng & Zhang, Xinyang & Li, Kerui & Zhang, Huaqin & Yang, Jingsong & Zhang, Chi & Yang, Haixu & Ma, Shikun & Yang, Qianqian, 2024. "A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles," Energy, Elsevier, vol. 299(C).

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