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Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization

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  • Wang, Bing-Chuan
  • He, Yan-Bo
  • Liu, Jiao
  • Luo, Biao

Abstract

Lithium-ion batteries encompass a comprehensive set of parameters crucial for constructing an efficient battery management system. Utilizing parameter identification assisted by the pseudo-two-dimensional (P2D) model is far more cost-effective than employing direct measurement methods. Nonetheless, the time-consuming simulations associated with the P2D model can significantly hamper the efficiency of a parameter identification algorithm. This situation would be even worse when encountering inappropriate parameter vectors, which can cause the P2D model to fail to converge, consequently leading to further computational time consumption. To address these two issues, this paper proposes a classification model-assisted Bayesian optimization (CMABO) framework for parameter identification of lithium-ion batteries. In CMABO, Bayesian optimization is employed to search for optimal parameters. Its inherent capability to leverage the complete information conveyed by historical data renders Bayesian optimization sample-efficient, thereby enhancing the efficiency of the identification process. Additionally, a classification model is established to discern parameter vectors that could lead to unsuccessful simulations of the P2D model. This additional step of classification enhances the efficiency even further. CMABO is the first attempt to consider the failed simulations of an electrochemical model when identifying parameters. Simulations and experiments show that it is more accurate and efficient than some electrochemical model-based methods including genetic algorithm (GA), particle swarm optimization (PSO), and SA-TLBO. Besides, among different acquisition functions for Bayesian optimization, the lower confidence bound reveals the best performance.

Suggested Citation

  • Wang, Bing-Chuan & He, Yan-Bo & Liu, Jiao & Luo, Biao, 2024. "Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization," Energy, Elsevier, vol. 288(C).
    • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s036054422303061x
    DOI: 10.1016/j.energy.2023.129667
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    References listed on IDEAS

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    1. Juliane Müller & Marcus Day, 2019. "Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 689-702, October.
    2. Solai, Elie & Guadagnini, Maxime & Beaugendre, Héloïse & Daccord, Rémi & Congedo, Pietro, 2022. "Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack," Energy, Elsevier, vol. 249(C).
    3. Román-Ramírez, L.A. & Marco, J., 2022. "Design of experiments applied to lithium-ion batteries: A literature review," Applied Energy, Elsevier, vol. 320(C).
    4. Kim, Minho & Chun, Huiyong & Kim, Jungsoo & Kim, Kwangrae & Yu, Jungwook & Kim, Taegyun & Han, Soohee, 2019. "Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search," Applied Energy, Elsevier, vol. 254(C).
    5. Zheng, Yuejiu & Cui, Yifan & Han, Xuebing & Ouyang, Minggao, 2021. "A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method," Energy, Elsevier, vol. 237(C).
    6. He, Xitian & Sun, Bingxiang & Zhang, Weige & Fan, Xinyuan & Su, Xiaojia & Ruan, Haijun, 2022. "Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery," Energy, Elsevier, vol. 244(PB).
    7. Guo, Ruohan & Shen, Weixiang, 2022. "A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles," Energy, Elsevier, vol. 254(PA).
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    1. Tian, Aina & Dong, Kailang & Yang, Xiao-Guang & Wang, Yuqin & He, Luyao & Gao, Yang & Jiang, Jiuchun, 2025. "Physics-based parameter identification of an electrochemical model for lithium-ion batteries with two-population optimization method," Applied Energy, Elsevier, vol. 378(PA).

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