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State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles

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  • Li, Guanzheng
  • Li, Bin
  • Li, Chao
  • Wang, Shuai

Abstract

Lithium-ion batteries are playing an increasingly important role in industrial applications such as electrical vehicles and energy storage systems. Their working performance and operation safety are significantly impacted by state of health (SOH), which will decrease after cycles of charging and discharging. This paper has proposed a novel two-stage SOH estimation method that can realize SOH estimation flexibly, rapidly and robustly. In the first stage, eight typical 300-s voltage profiles are used for describing the whole charging process and multiple aging features are extracted. Then, a novel stacking ensemble model with five base models is proposed. In the second stage, a Shapley additive explanation approach is introduced to obtain the contributions of features and understand why a prediction is made, thus reducing the concern of applying black-box model. The performance of the proposed model is verified using two different battery degradation datasets and the results show that the accuracy of the proposed model is better than conventional machine learning models including lightGBM, XGBoost, RF, SVR, and GPR. In addition, with various forms of noise interference, the proposed stacking model is proved to be more robust than conventional machine learning models.

Suggested Citation

  • Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pe:s0360544222029504
    DOI: 10.1016/j.energy.2022.126064
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
    2. Gu, Xin & Li, Jinglun & Zhu, Yuhao & Wang, Yue & Mao, Ziheng & Shang, Yunlong, 2023. "A quick and intelligent screening method for large-scale retired batteries based on cloud-edge collaborative architecture," Energy, Elsevier, vol. 285(C).
    3. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
    4. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
    5. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
    6. Mona Faraji Niri & Koorosh Aslansefat & Sajedeh Haghi & Mojgan Hashemian & Rüdiger Daub & James Marco, 2023. "A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation," Energies, MDPI, vol. 16(17), pages 1-38, September.

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