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Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy

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  • Li, Yunjian
  • Li, Kuining
  • Xie, Yi
  • Liu, Jiangyan
  • Fu, Chunyun
  • Liu, Bin

Abstract

This paper proposes an adaptive multistage constant current–constant voltage (MCCCV) strategy for charging electric vehicles in different situations. First, a high-fidelity thermoelectric-aging coupling model based on a resistor–capacitor pair electrical model, a thermal network model, and a semiempirical aging model is constructed. Second, an adaptive MCCCV charging strategy involving optimization of the charging current using particle swarm optimization is developed. It can satisfy the preference of users for reducing the charging time or the battery degradation. Finally, three charging strategies based on the Pareto boundary curve of the battery charging time–state of health are developed: a fast-charging strategy for motorway driving, a minimum-aging charging strategy for family use, and a balanced charging strategy for daily use. Additionally, according to the Pareto boundary, the effects of key factors on the optimization of the charging strategy are analyzed and compared. The results show that the balanced charging strategy is 3.60% better than the 0.5C constant current–constant voltage (CCCV) charging strategy recommended by the battery manufacturer with regard to aging loss. Moreover, the charging time is reduced by 37%. Compared with the traditional CCCV charging strategy, the proposed adaptive MCCCV charging strategy has good application prospects with regard to both the charging time and the battery degradation.

Suggested Citation

  • Li, Yunjian & Li, Kuining & Xie, Yi & Liu, Jiangyan & Fu, Chunyun & Liu, Bin, 2020. "Optimized charging of lithium-ion battery for electric vehicles: Adaptive multistage constant current–constant voltage charging strategy," Renewable Energy, Elsevier, vol. 146(C), pages 2688-2699.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2688-2699
    DOI: 10.1016/j.renene.2019.08.077
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    References listed on IDEAS

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    Cited by:

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    3. Jiang, Li & Li, Yong & Ma, Jianmin & Cao, Yijia & Huang, Chun & Xu, Yong & Chen, Hong & Huang, Yuduo, 2020. "Hybrid charging strategy with adaptive current control of lithium-ion battery for electric vehicles," Renewable Energy, Elsevier, vol. 160(C), pages 1385-1395.
    4. Chen, Wanying & Gong, Yeming & Chen, Qi & Wang, Hongwei, 2024. "Does battery management matter? Performance evaluation and operating policies in a self-climbing robotic warehouse," European Journal of Operational Research, Elsevier, vol. 312(1), pages 164-181.
    5. Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    6. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    7. Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    8. Ruifeng Shi & Jie Zhang & Hao Su & Zihang Liang & Kwang Y. Lee, 2020. "An Economic Penalty Scheme for Optimal Parking Lot Utilization with EV Charging Requirements," Energies, MDPI, vol. 13(22), pages 1-21, November.
    9. Themistoklis Stamadianos & Nikolaos A. Kyriakakis & Magdalene Marinaki & Yannis Marinakis, 2023. "Routing Problems with Electric and Autonomous Vehicles: Review and Potential for Future Research," SN Operations Research Forum, Springer, vol. 4(2), pages 1-34, June.
    10. Peter Makeen & Hani A. Ghali & Saim Memon, 2022. "Theoretical and Experimental Analysis of a New Intelligent Charging Controller for Off-Board Electric Vehicles Using PV Standalone System Represented by a Small-Scale Lithium-Ion Battery," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    11. Yi, Feng & E, Jiaqiang & Zhang, Bin & Zuo, Hongyan & Wei, Kexiang & Chen, Jingwei & Zhu, Hong & Zhu, Hao & Deng, Yuanwang, 2022. "Effects analysis on heat dissipation characteristics of lithium-ion battery thermal management system under the synergism of phase change material and liquid cooling method," Renewable Energy, Elsevier, vol. 181(C), pages 472-489.
    12. Makeen, Peter & Ghali, Hani A. & Memon, Saim & Duan, Fang, 2022. "Impacts of electric vehicle fast charging under dynamic temperature and humidity: Experimental and theoretically validated model analyses," Energy, Elsevier, vol. 261(PB).
    13. Daniels, Rojo Kurian & Langeh, Harsh & Kumar, Vikas & Chouhan, Satyendra Singh & Prabhakar, Aneesh, 2024. "Faulty cell prediction accuracy comparison of machine learning algorithms using temperature sensor placement optimization approach in immersion cooled Li-ion battery modules," Applied Energy, Elsevier, vol. 367(C).

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