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An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

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  • Jiang, Bo
  • Zhu, Yuli
  • Zhu, Jiangong
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Capacity estimation is essential for battery health management during the whole lifecycle. The data-driven technique has shown advanced performance in battery capacity estimation. However, the strict limitations on application scenarios and the long duration for feature determination are still the bottlenecks of existing data-driven estimation methods. This study proposes a data-driven capacity estimation method only using 10-min relaxation voltage data, which is adaptable to the high state of charge (SOC) range. The experiments of commercial batteries are designed to investigate the coupling relationship between relaxation voltage, battery aging, and charging cut-off SOC. Further, a novel dual Gaussian process regression (GPR) framework is put forward, in which one GPR is responsible for the battery open-circuit voltage (OCV) estimation using the sequential relaxation voltage feature, and another GPR estimates battery capacity with the corresponding relaxation voltage feature and the estimated OCV. Quantitative experimental results demonstrate that the proposed approach can achieve accurate OCV estimation with extremely sparse voltage data. When SOC is larger than 90%, the capacity estimation achieves a mean absolute error of 2.493% over the battery lifecycle, showing a noticeable improvement over the traditional estimation method.

Suggested Citation

  • Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222026883
    DOI: 10.1016/j.energy.2022.125802
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    References listed on IDEAS

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    Cited by:

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    2. Xie, Yanxin & Wang, Shunli & Zhang, Gexiang & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2023. "Optimized multi-hidden layer long short-term memory modeling and suboptimal fading extended Kalman filtering strategies for the synthetic state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 336(C).
    3. Wei, Gang & Huang, Ranjun & Zhang, Guangxu & Jiang, Bo & Zhu, Jiangong & Guo, Yangyang & Han, Guangshuai & Wei, Xuezhe & Dai, Haifeng, 2023. "A comprehensive insight into the thermal runaway issues in the view of lithium-ion battery intrinsic safety performance and venting gas explosion hazards," Applied Energy, Elsevier, vol. 349(C).
    4. 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).
    5. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    6. 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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