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Prognostics of the state of health for lithium-ion battery packs in energy storage applications

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  • Chang, Chun
  • Wu, Yutong
  • Jiang, Jiuchun
  • Jiang, Yan
  • Tian, Aina
  • Li, Taiyu
  • Gao, Yang

Abstract

The prognostics of the state of health (SOH) for lithium-ion battery packs in the long-time scale is critical for the safe and efficient operation of battery packs. In this paper, based on two available energy-based battery pack SOH definition considering both the aging and the consistency deterioration of battery cells, the prognostics algorithm of SOH is developed. The proposed method integrates the parameter estimation of battery cells, the parameter prognostics of battery cells, and the prognostics of battery pack SOH. The proposed method is verified by a cycle life test of a battery pack with 16 series connected LiFePO4 cells. The prognostics errors for the two SOH indexes are within 2.5% and 1.5%, respectively. The proposed method not only reflects the overall aging of battery cells, but also reflects the utilization efficiency decrease caused by the consistency deterioration of battery cells. Therefore, the proposed could synthetically and accurately evaluate and predict the SOH of lithium-ion battery packs, and could provide helpful equilibrium and maintenance information to decision makers.

Suggested Citation

  • Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221024373
    DOI: 10.1016/j.energy.2021.122189
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    6. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    7. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    8. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
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