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Research on Optimal Charging of Power Lithium-Ion Batteries in Wide Temperature Range Based on Variable Weighting Factors

Author

Listed:
  • Boshi Wang

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Haitao Min

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Weiyi Sun

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

  • Yuanbin Yu

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China)

Abstract

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

Suggested Citation

  • Boshi Wang & Haitao Min & Weiyi Sun & Yuanbin Yu, 2021. "Research on Optimal Charging of Power Lithium-Ion Batteries in Wide Temperature Range Based on Variable Weighting Factors," Energies, MDPI, vol. 14(6), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1776-:d:522702
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    References listed on IDEAS

    as
    1. Zhang, Caiping & Jiang, Jiuchun & Gao, Yang & Zhang, Weige & Liu, Qiujiang & Hu, Xiaosong, 2017. "Charging optimization in lithium-ion batteries based on temperature rise and charge time," Applied Energy, Elsevier, vol. 194(C), pages 569-577.
    2. Guangwei Chen & Zhitao Liu & Hongye Su, 2020. "An Optimal Fast-Charging Strategy for Lithium-Ion Batteries via an Electrochemical–Thermal Model with Intercalation-Induced Stresses and Film Growth," Energies, MDPI, vol. 13(9), pages 1-16, May.
    3. Yongpeng Shen & Zhendong He & Dongqi Liu & Binjie Xu, 2016. "Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model," Energies, MDPI, vol. 9(2), pages 1-18, February.
    4. Haitao Min & Weiyi Sun & Xinyong Li & Dongni Guo & Yuanbin Yu & Tao Zhu & Zhongmin Zhao, 2017. "Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization," Energies, MDPI, vol. 10(5), pages 1-15, May.
    5. Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
    6. Abdel Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Mantels, Bart & Mulder, Grietus & Van den Bossche, Peter & Van Mierlo, Joeri, 2015. "Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process," Applied Energy, Elsevier, vol. 152(C), pages 143-155.
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    Cited by:

    1. Emilia M. Szumska & Rafał Jurecki, 2022. "The Analysis of Energy Recovered during the Braking of an Electric Vehicle in Different Driving Conditions," Energies, MDPI, vol. 15(24), pages 1-16, December.

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