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Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery

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  • He, Xitian
  • Sun, Bingxiang
  • Zhang, Weige
  • Fan, Xinyuan
  • Su, Xiaojia
  • Ruan, Haijun

Abstract

An equivalent circuit model is developed for the virtual battery, which plays a key role in the application of the virtual battery. In this paper, a multi-time scale variable-order equivalent circuit model is proposed. The short and long time scale polarization characteristics are simulated respectively by the fractional-order model and the integer-order RC model, and a first-order RC model is used as the transition model between different time scales. Moreover, the time range for short-time scale model parameter identification is determined based on the electrochemical impedance spectroscopy acquired by the wavelet analysis. The evolution rule of short and long time scales model parameters under different initial polarization conditions and current rate is revealed. The model order changes in order of fractional order value, third-order and second-order. The voltage simulation result of the proposed model exhibits good agreement with current profile experiment, where the root mean square error (RMSE) is 2.838 mV. Under the power profile, compared to the conventional RC model, the RMSE of voltage and current simulation can be reduced by 79.65% and 79.27%.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544221033338
    DOI: 10.1016/j.energy.2021.123084
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    Cited by:

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    4. Torregrosa, Antonio José & Broatch, Alberto & Olmeda, Pablo & Agizza, Luca, 2023. "A generalized equivalent circuit model for lithium-iron phosphate batteries," Energy, Elsevier, vol. 284(C).
    5. Chen, Lin & Yu, Wentao & Cheng, Guoyang & Wang, Jierui, 2023. "State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter," Energy, Elsevier, vol. 271(C).
    6. 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).
    7. Park, Shina & Song, Youngbin & Kim, Sang Woo, 2024. "Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval," Energy, Elsevier, vol. 290(C).
    8. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).
    9. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).

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