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A state-space-based prognostics model for lithium-ion battery degradation

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  • Xu, Xin
  • Chen, Nan

Abstract

This paper proposes to analyze the degradation of lithium-ion batteries with the sequentially observed discharging profiles. A general state-space model is developed in which the observation model is used to approximate the discharging profile of each cycle, the corresponding parameter vector is treated as the hidden state, and the state-transition model is used to track the evolution of the parameter vector as the battery ages. The EM and EKF algorithms are adopted to estimate and update the model parameters and states jointly. Based on this model, we construct prediction on the end of discharge times for unobserved cycles and the remaining useful cycles before the battery failure. The effectiveness of the proposed model is demonstrated using a real lithium-ion battery degradation data set.

Suggested Citation

  • Xu, Xin & Chen, Nan, 2017. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 47-57.
    • Handle: RePEc:eee:reensy:v:159:y:2017:i:c:p:47-57
    DOI: 10.1016/j.ress.2016.10.026
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    References listed on IDEAS

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    Citations

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    1. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2024. "Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning," Applied Energy, Elsevier, vol. 358(C).
    2. Chen, Dinghong & Zhang, Weige & Zhang, Caiping & Sun, Bingxiang & Cong, XinWei & Wei, Shaoyuan & Jiang, Jiuchun, 2022. "A novel deep learning-based life prediction method for lithium-ion batteries with strong generalization capability under multiple cycle profiles," Applied Energy, Elsevier, vol. 327(C).
    3. Mishra, Madhav & Martinsson, Jesper & Rantatalo, Matti & Goebel, Kai, 2018. "Bayesian hierarchical model-based prognostics for lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 25-35.
    4. Tao, Tao & Zio, Enrico & Zhao, Wei, 2018. "A novel support vector regression method for online reliability prediction under multi-state varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 35-49.
    5. Gandoman, Foad H. & Ahmadi, Abdollah & Bossche, Peter Van den & Van Mierlo, Joeri & Omar, Noshin & Nezhad, Ali Esmaeel & Mavalizadeh, Hani & Mayet, Clément, 2019. "Status and future perspectives of reliability assessment for electric vehicles," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 1-16.
    6. Chenqiang Luo & Zhendong Zhang & Dongdong Qiao & Xin Lai & Yongying Li & Shunli Wang, 2022. "Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML," Energies, MDPI, vol. 15(13), pages 1-15, June.
    7. Hajiha, Mohammadmahdi & Liu, Xiao & Lee, Young M. & Ramin, Moghaddass, 2022. "A physics-regularized data-driven approach for health prognostics of complex engineered systems with dependent health states," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. 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).
    9. Lin, Chun Pang & Ling, Man Ho & Cabrera, Javier & Yang, Fangfang & Yu, Denis Yau Wai & Tsui, Kwok Leung, 2021. "Prognostics for lithium-ion batteries using a two-phase gamma degradation process model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Zhang, Yong & Tu, Lei & Xue, Zhiwei & Li, Sai & Tian, Lulu & Zheng, Xiujuan, 2022. "Weight optimized unscented Kalman filter for degradation trend prediction of lithium-ion battery with error compensation strategy," Energy, Elsevier, vol. 251(C).
    11. Liu, Xinyang & Zheng, Zhuoyuan & Büyüktahtakın, İ. Esra & Zhou, Zhi & Wang, Pingfeng, 2021. "Battery asset management with cycle life prognosis," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
    13. Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    14. Downey, Austin & Lui, Yu-Hui & Hu, Chao & Laflamme, Simon & Hu, Shan, 2019. "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 1-12.

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