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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. 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.
    2. Q. Zhang, 1999. "Optimal Filtering of Discrete-Time Hybrid Systems," Journal of Optimization Theory and Applications, Springer, vol. 100(1), pages 123-144, January.
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    4. 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.
    5. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    6. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
    7. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).
    8. 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).
    9. Xie, Jiale & Xu, Jingfan & Wei, Zhongbao & Li, Xiaoyu, 2023. "Fault isolating and grading for li-ion battery packs based on pseudo images and convolutional neural network," Energy, Elsevier, vol. 263(PD).
    10. Joelton Deonei Gotz & João Eustáquio Machado Neto & José Rodolfo Galvão & Taysa Millena Banik Marques & Hugo Valadares Siqueira & Emilson Ribeiro Viana & Manoel H. N. Marinho & Mohamed A. Mohamed & Ad, 2023. "Studying Abuse Testing on Lithium-Ion Battery Packaging for Energy Storage Systems," Sustainability, MDPI, vol. 15(15), pages 1-18, July.
    11. 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).
    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. 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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