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Multi-Objective Optimal Charging Method for Lithium-Ion Batteries

Author

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  • Xiaogang Wu

    (College of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China
    State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China)

  • Wenwen Shi

    (College of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China
    State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Jiuyu Du

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

Abstract

In order to optimize the charging of lithium-ion batteries, a multi-stage charging method that considers the charging time and energy loss as optimization targets has been proposed in this paper. First, a dynamic model based on a first-order circuit has been established, and the model parameters have been identified. Second, on the basis of the established model, we treat the objective function of the optimization problem as a weighted sum of charging time and energy loss. Finally, a dynamic programming algorithm (DP) has been used to calculate the charging current of the objective function. Simulation and experimental results show that the proposed charging method could effectively reduce the charging time and decrease the energy loss, compared with the constant-current constant-voltage charging method, under the premise of exerting little influence on the attenuation of battery capacity.

Suggested Citation

  • Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1271-:d:109928
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    References listed on IDEAS

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    Cited by:

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    3. Ran Li & Xue Wei & Hui Sun & Hao Sun & Xiaoyu Zhang, 2022. "Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model," Energies, MDPI, vol. 15(19), pages 1-19, September.
    4. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    5. Ji, Guansong & He, Lianfang & Wu, Tianyuan & Cui, Guanglei, 2025. "The design of fast charging strategy for lithium-ion batteries and intelligent application: A comprehensive review," Applied Energy, Elsevier, vol. 377(PC).

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