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Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles

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  • Xiong, Rui
  • Sun, Wanzhou
  • Yu, Quanqing
  • Sun, Fengchun

Abstract

Due to the limited capacity and voltage of single battery cell, the battery system for electric vehicles often consists of hundreds or thousands of single cells in series and parallel connection. The inconsistency of individual cell in capacity, voltage, internal resistance, etc., and their coupling effects with aging make the battery system fail frequently, which brings great challenges to the safe and reliable operation of the battery system. This paper discusses the research progress of battery system faults and diagnosis from sensors, battery and components, and actuators: (1) the causes and influences of sensor fault, actuator fault, internal/external short circuit fault, overcharge/over-discharge fault, connection fault, inconsistency, insulation fault, thermal management system fault are analyzed; (2) the fault diagnosis methods and their application characteristics in up-to-date battery system fault research are discussed, and the research trends of battery system fault diagnosis are sorted out; (3) Further, the future challenges and potential research directions of battery system fault diagnosis driven by new technologies such as big data are discussed. Finally, the summarization of whole paper is presented.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920313301
    DOI: 10.1016/j.apenergy.2020.115855
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    1. Changwen Zheng & Yunlong Ge & Ziqiang Chen & Deyang Huang & Jian Liu & Shiyao Zhou, 2017. "Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter," Energies, MDPI, vol. 10(11), pages 1-14, November.
    2. Jalkanen, K. & Karppinen, J. & Skogström, L. & Laurila, T. & Nisula, M. & Vuorilehto, K., 2015. "Cycle aging of commercial NMC/graphite pouch cells at different temperatures," Applied Energy, Elsevier, vol. 154(C), pages 160-172.
    3. Shulan Kong & Mehrdad Saif & Guozeng Cui, 2018. "Estimation and Fault Diagnosis of Lithium-Ion Batteries: A Fractional-Order System Approach," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, October.
    4. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    5. 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.
    6. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    7. Feng, Xuning & Weng, Caihao & Ouyang, Minggao & Sun, Jing, 2016. "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Elsevier, vol. 161(C), pages 168-180.
    8. 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.
    9. 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).
    10. 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.
    11. Zhentong Liu & Hongwen He, 2015. "Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles," Energies, MDPI, vol. 8(7), pages 1-19, June.
    12. Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Fang Zhou & Cheng Chang & Da Wang, 2017. "Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering," Energies, MDPI, vol. 10(5), pages 1-13, May.
    13. Sheng Yang & Wenwei Wang & Cheng Lin & Weixiang Shen & Yiding Li, 2019. "Investigation of Internal Short Circuits of Lithium-Ion Batteries under Mechanical Abusive Conditions," Energies, MDPI, vol. 12(10), pages 1-16, May.
    14. 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.
    15. Zhao, Rui & Liu, Jie & Gu, Junjie, 2015. "The effects of electrode thickness on the electrochemical and thermal characteristics of lithium ion battery," Applied Energy, Elsevier, vol. 139(C), pages 220-229.
    16. Zhao, Rui & Liu, Jie & Gu, Junjie, 2016. "Simulation and experimental study on lithium ion battery short circuit," Applied Energy, Elsevier, vol. 173(C), pages 29-39.
    17. Zheng, Changwen & Chen, Ziqiang & Huang, Deyang, 2020. "Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter," Energy, Elsevier, vol. 191(C).
    18. Taesic Kim & Darshan Makwana & Amit Adhikaree & Jitendra Shamjibhai Vagdoda & Young Lee, 2018. "Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems," Energies, MDPI, vol. 11(1), pages 1-15, January.
    19. Wang, Zhenpo & Hong, Jichao & Liu, Peng & Zhang, Lei, 2017. "Voltage fault diagnosis and prognosis of battery systems based on entropy and Z-score for electric vehicles," Applied Energy, Elsevier, vol. 196(C), pages 289-302.
    20. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    21. Guo, Shanshan & Xiong, Rui & Wang, Kan & Sun, Fengchun, 2018. "A novel echelon internal heating strategy of cold batteries for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 256-263.
    22. Wang, Limei & Cheng, Yong & Zhao, Xiuliang, 2015. "Influence of connecting plate resistance upon LiFePO4 battery performance," Applied Energy, Elsevier, vol. 147(C), pages 353-360.
    23. 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.
    24. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    25. 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.
    26. Zhao, Xin & Doering, Otto C. & Tyner, Wallace E., 2015. "The economic competitiveness and emissions of battery electric vehicles in China," Applied Energy, Elsevier, vol. 156(C), pages 666-675.
    27. Liu, Lishuo & Feng, Xuning & Zhang, Mingxuan & Lu, Languang & Han, Xuebing & He, Xiangming & Ouyang, Minggao, 2020. "Comparative study on substitute triggering approaches for internal short circuit in lithium-ion batteries," Applied Energy, Elsevier, vol. 259(C).
    28. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
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