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Optimization of multi-stage constant current charging pattern based on Taguchi method for Li-Ion battery

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  • Jiang, Li
  • Li, Yong
  • Huang, Yuduo
  • Yu, Jiaqi
  • Qiao, Xuebo
  • Wang, Yixiao
  • Huang, Chun
  • Cao, Yijia

Abstract

Due to the complexity of characteristics, the charging performance of Li-ion batteries needs to be further improved. In this paper, Taguchi method is employed to search an optimal charging pattern for 5-stage constant-current charging strategy. The charging capacity, efficiency and time are analyzed as quality functions simultaneously, and the influences of charging currents at each stage on each quality function are revealed. This universal method provides a reasonable basis for the selection of the optimal currents. By reasonably updating the currents at each stage, a broader range is searched to make experimental results more representative. Compared with the constant current- constant voltage method, the obtained charging pattern improves the charging efficiency by 0.6–0.9%, and the temperature rise of the battery is reduced by about 2 °C. Compared with the charging pattern obtained by optimizing the charging time and capacity, the charging pattern obtained in this paper improved the charging efficiency by 2.8%, the temperature rise is reduced by 9.3 °C, and the charging capacity is basically the same.

Suggested Citation

  • Jiang, Li & Li, Yong & Huang, Yuduo & Yu, Jiaqi & Qiao, Xuebo & Wang, Yixiao & Huang, Chun & Cao, Yijia, 2020. "Optimization of multi-stage constant current charging pattern based on Taguchi method for Li-Ion battery," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318355
    DOI: 10.1016/j.apenergy.2019.114148
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    References listed on IDEAS

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    Cited by:

    1. Pablo Carrasco Ortega & Pablo Durán Gómez & Julio César Mérida Sánchez & Fernando Echevarría Camarero & Ángel Á. Pardiñas, 2023. "Battery Energy Storage Systems for the New Electricity Market Landscape: Modeling, State Diagnostics, Management, and Viability—A Review," Energies, MDPI, vol. 16(17), pages 1-51, August.
    2. Syed Naeem Haider & Qianchuan Zhao & Xueliang Li, 2020. "Cluster-Based Prediction for Batteries in Data Centers," Energies, MDPI, vol. 13(5), pages 1-17, March.
    3. Liu, Yongjie & Huang, Zhiwu & He, Liang & Pan, Jianping & Li, Heng & Peng, Jun, 2023. "Temperature-aware charging strategy for lithium-ion batteries with adaptive current sequences in cold environments," Applied Energy, Elsevier, vol. 352(C).
    4. Román-Ramírez, L.A. & Marco, J., 2022. "Design of experiments applied to lithium-ion batteries: A literature review," Applied Energy, Elsevier, vol. 320(C).
    5. Osmani, Khaled & Haddad, Ahmad & Lemenand, Thierry & Castanier, Bruno & Ramadan, Mohamad, 2021. "An investigation on maximum power extraction algorithms from PV systems with corresponding DC-DC converters," Energy, Elsevier, vol. 224(C).
    6. Ouyang, Quan & Fang, Ruyi & Xu, Guotuan & Liu, Yonggang, 2022. "User-involved charging control for lithium-ion batteries with economic cost optimization," Applied Energy, Elsevier, vol. 314(C).

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