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State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression

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  • Zhou, Yong
  • Dong, Guangzhong
  • Tan, Qianqian
  • Han, Xueyuan
  • Chen, Chunlin
  • Wei, Jingwen

Abstract

Due to lithium-ion batteries’ complex behaviors, accurate estimation of state-of-health is still a critical challenge in battery systems’ prognosis and health management. Most existing efforts in battery health prognosis focus on feature engineering using low-frequency sampled time-domain response. These efforts may not completely reflect the battery health status in automotive applications due to information missing in the high or medium frequency range. This paper proposes a data-driven state-of-health estimation method using high and medium frequency range impedance spectroscopy data. First, battery health indicators are extracted from electrochemical impedance spectroscopy data. It is found that the Nyquist diagram shows semicircle characteristics at high and medium frequency ranges. The center and radius of this circle show high dependence on battery health. Then, a recurrent Gaussian process regression with a one-step delay feedback loop is designed to provide a smooth and accurate battery state-of-health estimate. Finally, the proposed health indicators and state-of-health estimators are validated using experimental data on different cells. The results demonstrate the high accuracy and robustness of the proposed health indicators and state-of-health estimator, suggesting a 1.12% estimation error. This study shows the prospect of health prognosis using robust geometric impedance spectrum indicators in energy storage systems.

Suggested Citation

  • Zhou, Yong & Dong, Guangzhong & Tan, Qianqian & Han, Xueyuan & Chen, Chunlin & Wei, Jingwen, 2023. "State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222023969
    DOI: 10.1016/j.energy.2022.125514
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    References listed on IDEAS

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    Cited by:

    1. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    2. 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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