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State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression

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  • Jin, Haiyan
  • Cui, Ningmin
  • Cai, Lei
  • Meng, Jinhao
  • Li, Junxin
  • Peng, Jichang
  • Zhao, Xinchao

Abstract

State-of-Health (SOH) estimation is crucial for the safety and reliability of battery-based applications. Data-driven methods have shown their promising potential in battery SOH estimation, yet creating a high-performance model with a compact structure is still a grand challenge. This paper focuses on constructing the elastic feature to formulate auto-configurable Gaussian Process Regression (GPR) to address this issue. To eliminate the impacts of the kernels on GPR, an evolutionary framework is designed to organize the kernel configuration. Meanwhile, a hierarchical feature construction strategy reduces the complexity of the extracted feature according to the geometry of the charging curve. Experiments on three battery datasets demonstrate the effectiveness of the proposed method, demonstrating the practical value of the proposed method for the battery management system (BMS) to construct feature more feasible, and to provide the optimal kernel configuration automatically.

Suggested Citation

  • Jin, Haiyan & Cui, Ningmin & Cai, Lei & Meng, Jinhao & Li, Junxin & Peng, Jichang & Zhao, Xinchao, 2023. "State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222023854
    DOI: 10.1016/j.energy.2022.125503
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    References listed on IDEAS

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    Cited by:

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    2. Lu, Zhenfeng & Fei, Zicheng & Wang, Benfei & Yang, Fangfang, 2024. "A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve," Energy, Elsevier, vol. 288(C).
    3. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
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    8. Lin, Mingqiang & Wu, Jian & Meng, Jinhao & Wang, Wei & Wu, Ji, 2023. "State of health estimation with attentional long short-term memory network for lithium-ion batteries," Energy, Elsevier, vol. 268(C).

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