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Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter

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  • Zheng, Changwen
  • Chen, Ziqiang
  • Huang, Deyang

Abstract

Fault diagnosis is very critical for battery management systems. This paper proposes a fault diagnosis method for voltage sensor and current sensor in Lithium-ion battery pack system using hybrid system modeling and unscented particle filter. Stochastic hybrid automata model the battery pack system as a hybrid system to process simultaneously the continuous variables including state of charge and voltages, and discrete dynamics including faulty modes and normal modes. The unscented particle filter algorithm, which is responsible for the computation of the hybrid system states or modes, is also used to estimate both discrete states and continuous states and output diagnosis results. By using a serial-parallel configuration battery pack, the experimental validation is conducted in different fault scenarios of voltage sensor and current sensor. The results indicate that the method proposed in this paper not only has effective state tracking ability but also achieves accurate diagnosis to the Lithium-ion battery system sensor faults.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321991
    DOI: 10.1016/j.energy.2019.116504
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    References listed on IDEAS

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    1. Q. Zhang, 1999. "Optimal Filtering of Discrete-Time Hybrid Systems," Journal of Optimization Theory and Applications, Springer, vol. 100(1), pages 123-144, January.
    2. 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.
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    Cited by:

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    9. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
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    12. Quanqing Yu & Changjiang Wan & Junfu Li & Rui Xiong & Zeyu Chen, 2021. "A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles," Energies, MDPI, vol. 14(4), pages 1-15, February.
    13. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    14. 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).
    15. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    16. Shen, Dongxu & Yang, Dazhi & Lyu, Chao & Ma, Jingyan & Hinds, Gareth & Sun, Qingmin & Du, Limei & Wang, Lixin, 2024. "Multi-sensor multi-mode fault diagnosis for lithium-ion battery packs with time series and discriminative features," Energy, Elsevier, vol. 290(C).
    17. Ganesh Mayilsamy & Kumarasamy Palanimuthu & Raghul Venkateswaran & Ruban Periyanayagam Antonysamy & Seong Ryong Lee & Dongran Song & Young Hoon Joo, 2023. "A Review of State Estimation Techniques for Grid-Connected PMSG-Based Wind Turbine Systems," Energies, MDPI, vol. 16(2), pages 1-27, January.
    18. 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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