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A novel semi-supervised fault detection and isolation method for battery system of electric vehicles

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  • Yang, Jiong
  • Cheng, Fanyong
  • Liu, Zhi
  • Duodu, Maxwell Mensah
  • Zhang, Mingyan

Abstract

The detection and isolation of early and minor faults in vehicle battery systems is vital to safe driving and improving power utilization. This paper proposes a data-driven model to achieve accurate, early, and economical fault detection and isolation. The model is based on kernel principal component analysis (KPCA), which maps complex nonlinear data from the input space into a high-dimensional feature space to gain a detection model with good performance. To overcome the difficulty of hyperparameter selection, KPCA is trained using Bayesian Optimization (BO) iterations with a small amount of labeled data and a large amount of unlabeled data. This step can obtain the optimal hyperparameter to greatly improve the model fault detection capability, which is beneficial for detecting both early faults and minor faults. In addition, a unified contribution graph based on the partial differentiation of KPCA was adopted to build a reasonable isolation scheme. The semi-supervised model of KPCA based on Bayesian Optimization and contribution graph is developed to reveal the relationship between fault and variable. Finally, the proposed method is fully tested on four fault datasets and the results prove the excellent detection capability in the early stage of faults compared with other methods and the accurate fault isolation capability from the occurrence to the end of the fault.

Suggested Citation

  • Yang, Jiong & Cheng, Fanyong & Liu, Zhi & Duodu, Maxwell Mensah & Zhang, Mingyan, 2023. "A novel semi-supervised fault detection and isolation method for battery system of electric vehicles," Applied Energy, Elsevier, vol. 349(C).
  • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923010140
    DOI: 10.1016/j.apenergy.2023.121650
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    References listed on IDEAS

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    1. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    2. Peng Liu & Zhenyu Sun & Zhenpo Wang & Jin Zhang, 2018. "Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, January.
    3. 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).
    4. Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
    5. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    6. Yang, Bo & Zhu, Tianjiao & Zhang, Xiaoshun & Wang, Jingbo & Shu, Hongchun & Li, Shengnan & He, Tingyi & Yang, Lei & Yu, Tao, 2020. "Design and implementation of Battery/SMES hybrid energy storage systems used in electric vehicles: A nonlinear robust fractional-order control approach," Energy, Elsevier, vol. 191(C).
    7. Jinhwan Park & Donghyeon Yoo & Jaemin Moon & Janghyeok Yoon & Jungtae Park & Seungae Lee & Doohee Lee & Changwan Kim, 2021. "Reliability-Based Robust Design Optimization of Lithium-Ion Battery Cells for Maximizing the Energy Density by Increasing Reliability and Robustness," Energies, MDPI, vol. 14(19), pages 1-13, September.
    8. Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
    9. 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.
    10. 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).
    11. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    12. 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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