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Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope

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Listed:
  • Xiong, Wei
  • Xie, Fang
  • Xu, Gang
  • Li, Yumei
  • Li, Ben
  • Mo, Yimin
  • Ma, Fei
  • Wei, Keke

Abstract

As electric vehicles become more common there is increasing concern regarding electric vehicles battery disposal. Considering resource savings and environmental friendliness, reuse rather than disposal of retired batteries is considered to be the most suitable solution. For safely and efficiently reusing retired batteries, a precise state of charge estimation over a wide temperature range and battery degradation scope is crucial. The primary content of this work is described as follows. (1) To obtain accurate closed-loop state estimation, a model that is dependent upon the temperature and aging state of the retired battery is developed. (2) Noise can affect the accuracy of model parameter identification and state of charge estimation, regardless of whether current or voltage measurements are corrupted. As a result of the state of charge estimation error, the reliability of reusing retired batteries will be greatly reduced. To solve this problem and improve the state of charge estimation precision, we propose a co-estimation method employing recursive restricted total least squares for parameter identification and adaptive H-infinity filters for state estimation. (3) In the range of 0 °C–45 °C, the proposed method is completely validated within the state of health range of 80%–50%. Specifically, a comparison is carried out between the proposed method and the H-infinity filter to demonstrate its superiority. In addition, the reliability of the co-estimation method is evaluated in different practical application scenarios. The results show that the proposed co-estimation method is highly robust, and the error in the state of charge estimation is restricted to 1% over a wide temperature range and battery degradation range.

Suggested Citation

  • Xiong, Wei & Xie, Fang & Xu, Gang & Li, Yumei & Li, Ben & Mo, Yimin & Ma, Fei & Wei, Keke, 2023. "Co-estimation of the model parameter and state of charge for retired lithium-ion batteries over a wide temperature range and battery degradation scope," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123011928
    DOI: 10.1016/j.renene.2023.119277
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    References listed on IDEAS

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    Cited by:

    1. Xiong, Xin & Wang, Yujie & Jiang, Cong & Zhang, Xingchen & Xiang, Haoxiang & Chen, Zonghai, 2024. "End-to-end deep learning powered battery state of health estimation considering multi-neighboring incomplete charging data," Energy, Elsevier, vol. 292(C).
    2. Fahmy, Hend M. & Alqahtani, Ayedh H. & Hasanien, Hany M., 2024. "Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm," Energy, Elsevier, vol. 294(C).

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