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Remaining useful life prediction of lithium battery based on capacity regeneration point detection

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  • Ma, Qiuhui
  • Zheng, Ying
  • Yang, Weidong
  • Zhang, Yong
  • Zhang, Hong

Abstract

Lithium batteries have been widely used in various electronic devices, and the accurate prediction of its remaining useful life (RUL) can prevent the occurrence of sudden equipment failure. Battery capacity is a commonly used indicator to represent the health status of lithium batteries. However, the capacity regeneration is usually unavoidable due to the impact of battery “rest time” between two cycles, which leads to inaccurate prediction of the RUL. To solve this problem, this paper combines the particle filter (PF) and Mann-Whitney U test (PF-U) to detect the capacity regeneration point (CRP). In this light, the autoregressive (AR) model and PF algorithm are adopted for RUL prediction. The predicted capacity through AR model is used to update the degradation model parameters of PF algorithm, and the validation of our approach is verified through the lithium battery dataset of NASA. In comparison, our proposed method exhibits the highest precision and provides a platform to detect the points with capacity regeneration, and further reduce the RUL prediction error.

Suggested Citation

  • Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s036054422101481x
    DOI: 10.1016/j.energy.2021.121233
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    2. Lyu, Zhiqiang & Wang, Geng & Gao, Renjing, 2022. "Synchronous state of health estimation and remaining useful lifetime prediction of Li-Ion battery through optimized relevance vector machine framework," Energy, Elsevier, vol. 251(C).
    3. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    4. Yongsheng Shi & Tailin Li & Leicheng Wang & Hongzhou Lu & Yujun Hu & Beichen He & Xinran Zhai, 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory," Energies, MDPI, vol. 16(16), pages 1-16, August.
    5. Wang, Shunli & Fan, Yongcun & Jin, Siyu & Takyi-Aninakwa, Paul & Fernandez, Carlos, 2023. "Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    7. Gong, Dongliang & Gao, Ying & Kou, Yalin & Wang, Yurang, 2022. "State of health estimation for lithium-ion battery based on energy features," Energy, Elsevier, vol. 257(C).
    8. Huang, Yaodi & Zhang, Pengcheng & Lu, Jiahuan & Xiong, Rui & Cai, Zhongmin, 2024. "A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon," Applied Energy, Elsevier, vol. 360(C).
    9. Meng, Huixing & Geng, Mengyao & Xing, Jinduo & Zio, Enrico, 2022. "A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena," Energy, Elsevier, vol. 261(PB).
    10. Pang, Hui & Chen, Kaiqiang & Geng, Yuanfei & Wu, Longxing & Wang, Fengbin & Liu, Jiahao, 2024. "Accurate capacity and remaining useful life prediction of lithium-ion batteries based on improved particle swarm optimization and particle filter," Energy, Elsevier, vol. 293(C).
    11. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).

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