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State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method

Author

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  • Cui, Yingzhi
  • Zuo, Pengjian
  • Du, Chunyu
  • Gao, Yunzhi
  • Yang, Jie
  • Cheng, Xinqun
  • Ma, Yulin
  • Yin, Geping

Abstract

Impedance and open circuit voltage (OCV) parameter identification is the key technology for state of health (SOH) diagnosis of lithium ion battery (LIB) in an equivalent circuit model (ECM). The current identification methods of impedance and OCV parameter are time consuming, destructive, non-real-time and costly. It is usually difficult to identify each component from the overall impedance parameter using aforesaid impedance identification methods, which severely affects the identification precision of impedance parameter. Furthermore, fast OCV identification is another difficult issue to be resolved. In this paper, a new real-time and nondestructive method is developed to identify dynamic impedance parameter for SOH diagnosis ECM (SDEM) of LIB. This method can identify ohmic impedance and charge transfer impedance from internal impedance and realize the transformation of Warburg diffusion impedance from frequency domain to time domain. Fast determination method of OCV is proposed based on the short-time and low current pulse to realize real-time measurement and identification of the OCV. Dynamic update of the all parameters is conducted based on least squares method (LSM). SDEM with new developed impedance and OCV parameter identification method is validated with high accuracy.

Suggested Citation

  • Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:647-656
    DOI: 10.1016/j.energy.2017.12.033
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    9. Shen, Jiangwei & Ma, Wensai & Shu, Xing & Shen, Shiquan & Chen, Zheng & Liu, Yonggang, 2023. "Accurate state of health estimation for lithium-ion batteries under random charging scenarios," Energy, Elsevier, vol. 279(C).
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    11. Qiaohua Fang & Xuezhe Wei & Tianyi Lu & Haifeng Dai & Jiangong Zhu, 2019. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model," Energies, MDPI, vol. 12(7), pages 1-18, April.
    12. Damoon Soudbakhsh & Mehdi Gilaki & William Lynch & Peilin Zhang & Taeyoung Choi & Elham Sahraei, 2020. "Electrical Response of Mechanically Damaged Lithium-Ion Batteries," Energies, MDPI, vol. 13(17), pages 1-15, August.
    13. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
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    15. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).

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