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Charging optimization in lithium-ion batteries based on temperature rise and charge time

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  • Zhang, Caiping
  • Jiang, Jiuchun
  • Gao, Yang
  • Zhang, Weige
  • Liu, Qiujiang
  • Hu, Xiaosong

Abstract

Lithium-ion battery fast charging issues have become a main bottleneck of large-scale deployment of electric vehicles. This paper develops a polarization based charging time and temperature rise optimization strategy for lithium-ion batteries. An enhanced thermal behavior model is introduced to improve the solution accuracy at high charging current, in which the relationship between polarization voltage and charge current is addressed. Genetic algorithm (GA) is employed to search for the optimal charging current trajectories. The effects of weighting coefficients of charging time and temperature rise on battery charging performance are discussed. The charging time of the optimized charging protocol is reduced by 50%, and the associated temperature rise is almost identical, compared to 1/3C constant current-constant voltage (CC-CV) charging. Aging experiments demonstrate that the proposed charging method has a similar capacity retention ratio to that of 0.5CC-CV charging after 700 cycles, thereby accomplishing a good balance between charging speed and lifetime.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:569-577
    DOI: 10.1016/j.apenergy.2016.10.059
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    References listed on IDEAS

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    1. Jingyu Yan & Guoqing Xu & Huihuan Qian & Yangsheng Xu & Zhibin Song, 2011. "Model Predictive Control-Based Fast Charging for Vehicular Batteries," Energies, MDPI, vol. 4(8), pages 1-19, August.
    2. Xiong, Rui & Sun, Fengchun & Chen, Zheng & He, Hongwen, 2014. "A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 463-476.
    3. Shuo Zhang & Chengning Zhang & Rui Xiong & Wei Zhou, 2014. "Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles," Energies, MDPI, vol. 7(10), pages 1-15, October.
    4. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
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