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A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time

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  • Fan, Zhaohui
  • Fu, Yijie
  • Liang, Hong
  • Gao, Renjing
  • Liu, Shutian

Abstract

The contradiction between fast charging and battery lifetime has become one of the main obstacles for the development of electric vehicles. The large currents of fast charging protocols will bring about a high temperature rise of battery, which can be controlled by the liquid-cooled battery thermal management system. However, the temperature difference of the battery module is unavoidable due to the temperature gradient effect of liquid cooling. In this study, considering temperature gradient effect of liquid cooling, a charging optimization strategy at a battery module level is proposed to balance the charging time and temperature difference. Genetic algorithm is utilized to determine the specific charging protocol by evaluating the fitness function. Compared with the multistage constant current-constant voltage charging strategy, the temperature difference of the battery module using the optimal charging strategy reduces by 37.9%. Besides, the charging time of the optimal charging strategy decreases by 11.9% than that of the constant current-constant voltage charging strategy. The experimental results demonstrate that the optimal charging strategy can weigh the charging time and temperature difference of the battery module. In addition, the different charging protocols can be determined by adjusting the weighting coefficients of battery temperature difference and charging time.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032170
    DOI: 10.1016/j.energy.2022.126331
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    6. 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).

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