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Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries

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  • Li, Junfu
  • Wang, Lixin
  • Lyu, Chao
  • Wang, Dafang
  • Pecht, Michael

Abstract

Due to the inevitable degradation for lithium-ion batteries, the accuracy of state of charge (SOC) estimation decreases along with battery aging. How to ensure the accuracy of battery SOC estimation during a battery’s lifetime at different operating conditions leaves a big challenge. This work develops a parameter updating method of a simplified first principles-thermal coupling model that has good parameter identifiability and applicability for different operating conditions to ensure the accuracy of the model-based SOC estimation. Because updating all the model parameters offline is time-consuming, this work first conducts a model parameter sensitivity analysis and determines which parameters need to be accurately updated according to their sensitivities. Offline prediction methods are then developed to update the sensitive parameters according to their degradation laws. SOC validations show that the offline prediction methods can ensure the short-term accuracy, but the accuracy will decrease gradually along with the increase of the offline prediction errors of the capacity-related parameters. To address this problem, a multi-time-scale updating method combining the offline prediction and the capacity parameter online estimation is developed. Essential validations are provided to assess the SOC estimation accuracy using different parameter updating methods.

Suggested Citation

  • Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
    • Handle: RePEc:eee:appene:v:256:y:2019:i:c:s0306261919316113
    DOI: 10.1016/j.apenergy.2019.113924
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    References listed on IDEAS

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    Cited by:

    1. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    2. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    3. Lin, Wei-Jen & Chen, Kuo-Ching, 2022. "Evolution of parameters in the Doyle-Fuller-Newman model of cycling lithium ion batteries by multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).

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