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Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm

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  • Tang, Ruoli
  • Zhang, Shangyu
  • Zhang, Shihan
  • Zhang, Yan
  • Lai, Jingang

Abstract

In the operational control of renewable energy system, the efficient parameter identification for lithium battery is of great importance. In this study, the parameter identification of lithium battery is modelled as a large-scale global optimization problem with thousands of dimensionalities. In addition, the developed identification model is proved to be a partial-separable problem by comprehensively analysing its variable-coupling relationships, and the detailed proof is also provided. In order to overcome the high-dimensional characteristic of the developed model, a novel algorithm namely incomplete multi-context cooperatively coevolving PSO (IMCCPSO) is developed, in which some efficient algorithmic mechanisms are proposed: On one hand, the non-separable variables are grouped together with each of the separable-variable components, and the context vectors for separable and non-separable variables are discriminatively reconstituted for balancing the local and global exploration; On the other hand, the coevolving efficiency index is proposed for selecting the group-size values and coevolving rules dynamically and adaptively. Experimental results show that the developed methodology can effectively identify the parameters of the evaluated lithium battery bank under typical load profiles, and the developed IMCCPSO also outperforms the compared state-of-the-art algorithms on identification accuracy and robustness.

Suggested Citation

  • Tang, Ruoli & Zhang, Shangyu & Zhang, Shihan & Zhang, Yan & Lai, Jingang, 2023. "Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm," Energy, Elsevier, vol. 263(PB).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pb:s0360544222026482
    DOI: 10.1016/j.energy.2022.125762
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    References listed on IDEAS

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    1. Tang, Ruoli & An, Qing & Xu, Fan & Zhang, Xiaodi & Li, Xin & Lai, Jingang & Dong, Zhengcheng, 2020. "Optimal operation of hybrid energy system for intelligent ship: An ultrahigh-dimensional model and control method," Energy, Elsevier, vol. 211(C).
    2. Aktaş, Ahmet & Kırçiçek, Yağmur, 2020. "A novel optimal energy management strategy for offshore wind/marine current/battery/ultracapacitor hybrid renewable energy system," Energy, Elsevier, vol. 199(C).
    3. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    4. Kuo Yang & Yugui Tang & Zhen Zhang, 2021. "Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter," Energies, MDPI, vol. 14(4), pages 1-15, February.
    5. Xiao Yang & Long Chen & Xing Xu & Wei Wang & Qiling Xu & Yuzhen Lin & Zhiguang Zhou, 2017. "Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization," Energies, MDPI, vol. 10(11), pages 1-16, November.
    6. Tang, Ruoli & Li, Xin & Lai, Jingang, 2018. "A novel optimal energy-management strategy for a maritime hybrid energy system based on large-scale global optimization," Applied Energy, Elsevier, vol. 228(C), pages 254-264.
    7. 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).
    8. Tang, Ruoli & Wu, Zhou & Li, Xin, 2018. "Optimal operation of photovoltaic/battery/diesel/cold-ironing hybrid energy system for maritime application," Energy, Elsevier, vol. 162(C), pages 697-714.
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    Cited by:

    1. An, Qing & Peng, Jian, 2023. "Parameter identification of lithium battery pack based on novel cooperatively coevolving differential evolution algorithm," Renewable Energy, Elsevier, vol. 216(C).
    2. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).

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