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Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis

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  • Ma, Mina
  • Wang, Yu
  • Duan, Qiangling
  • Wu, Tangqin
  • Sun, Jinhua
  • Wang, Qingsong

Abstract

Inter-cell virtual connection is likely to occur in the process of electric vehicles driving, which could cause fire or explosion accident. This paper presents a connecting fault detection method of lithium-ion power batteries in series. The cross-voltage test is adopted to distinguish contact resistance increases and internal resistance increases fault. The battery voltage and negative surface temperature are collected by battery test system and auxiliary channels equipment. The experimental battery based on first-order resistance and capacitance (RC) equivalent circuit and MATLAB/Simulink platform is simulated. The mean square error which indicates the difference between experiment and simulation is employed to describe voltage state of the cell. If the abnormal voltage exists, it is concluded that a fault occurs through analyzing the voltage abnormal coefficients based on modified Z-score, which is considered as a second-degree fault. The temperature rise rate is regarded as a secondary parameter to determine whether the second-degree fault deteriorates into a first-degree fault. According to different levels of failure, battery management system reminds users to take appropriate measures in practice. This work can provide an effective method of detecting lithium-ion power batteries connecting failure in series.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:164:y:2018:i:c:p:745-756
    DOI: 10.1016/j.energy.2018.09.047
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    4. Gil-Sayas, Susana & Komnos, Dimitrios & Lodi, Chiara & Currò, Davide & Serra, Simone & Broatch, Alberto & Fontaras, Georgios, 2022. "Analysing the potential of a simulation-based method for the assessment of CO2 savings from eco-innovative technologies in light-duty vehicles," Energy, Elsevier, vol. 245(C).
    5. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
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    17. Zhu, Xiaoqing & Wang, Zhenpo & Wang, Yituo & Wang, Hsin & Wang, Cong & Tong, Lei & Yi, Mi, 2019. "Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O2 cathode for electric vehicles: Thermal runaway features and safety management method," Energy, Elsevier, vol. 169(C), pages 868-880.
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    19. Xiang Wang & Jianjun He & Fuxin Huang & Zhenjie Liu & Aibin Deng & Rihui Long, 2024. "A Novel Voltage-Abnormal Cell Detection Method for Lithium-Ion Battery Mass Production Based on Data-Driven Model with Multi-Source Time Series Data," Energies, MDPI, vol. 17(14), pages 1-14, July.
    20. 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).
    21. Seunghwan Jung & Minseok Kim & Eunkyeong Kim & Baekcheon Kim & Jinyong Kim & Kyeong-Hee Cho & Hyang-A Park & Sungshin Kim, 2024. "The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance," Energies, MDPI, vol. 17(2), pages 1-23, January.
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    25. 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).

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