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Optimization of battery charging strategy based on nonlinear model predictive control

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  • Wang, Yujie
  • Zhou, Caijie
  • Chen, Zonghai

Abstract

With the increased applications of lithium-ion batteries in energy storage systems and electric vehicles, there is a growing demand for battery energy storage systems and management systems. Considering that the temperature especially internal temperature significantly can affect the performance and safety of the battery, a triple-objective optimization charging method which can reduce the cell charging time, energy loss, and internal temperature rise is proposed based on a thermoelectric coupling model in this paper. Specifically, a thermoelectric coupling model suitable for a wide temperature range from −5 °C to 45 °C is formulated. On this basis, a nonlinear model predictive control framework is proposed to obtain the real-time charging current by solving the nonlinear optimization problems. The impacts of the objective function weights and internal temperature thresholds on the charging result are discussed through experiments, and another multi-stage constant current charging method is conducted as a comparison. Results show that the nonlinear model predictive control can achieve a good balance between three objectives while satisfying constraints. Compared with the traditional multistage constant current charging method, the proposed strategy can reduce energy loss by 150 J and temperature rise by 1–2 °C in similar charging time.

Suggested Citation

  • Wang, Yujie & Zhou, Caijie & Chen, Zonghai, 2022. "Optimization of battery charging strategy based on nonlinear model predictive control," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031261
    DOI: 10.1016/j.energy.2021.122877
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    References listed on IDEAS

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    Cited by:

    1. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    2. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
    3. Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
    4. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
    5. Olis, Walker & Rosewater, David & Nguyen, Tu & Byrne, Raymond H., 2023. "Impact of heating and cooling loads on battery energy storage system sizing in extreme cold climates," Energy, Elsevier, vol. 278(PB).
    6. Liu, Haoran & Yu, Jiaqi & Wang, Ruzhu, 2022. "Model predictive control of portable electronic devices under skin temperature constraints," Energy, Elsevier, vol. 260(C).

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