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A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries

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  • Yang, Fangfang
  • Song, Xiangbao
  • Dong, Guangzhong
  • Tsui, Kwok-Leung

Abstract

Coulombic efficiency, as an important battery parameter, is highly related to the loss of lithium inventory, which is the dominant aging factor for lithium-ion batteries. In this paper, a semi-empirical model is derived from this relationship to capture the capacity degradation of lithium-ion batteries. The coulombic efficiency-based model effectively captures the convex degradation trend of lithium iron phosphate batteries and presents better fitting performance than the existing square-root-of-time model. To evaluate the proposed model, a battery cycle life experiment was designed, in which the subjects were continuously cycled under a federal urban driving schedule to simulate real-life battery usage. To perform online battery health estimation and prognostics, a particle filtering framework incorporating the proposed model was constructed to update the model parameters regularly with periodically measured data. Remaining useful life of the battery was then predicted by extrapolating the models with renewed parameters. The experimental results indicated that the proposed prognostic method can provide higher prediction accuracy than the existing square-root-of-time model.

Suggested Citation

  • Yang, Fangfang & Song, Xiangbao & Dong, Guangzhong & Tsui, Kwok-Leung, 2019. "A coulombic efficiency-based model for prognostics and health estimation of lithium-ion batteries," Energy, Elsevier, vol. 171(C), pages 1173-1182.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:1173-1182
    DOI: 10.1016/j.energy.2019.01.083
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    References listed on IDEAS

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    Cited by:

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    7. Lai, Xin & Zhou, Long & Zhu, Zhiwei & Zheng, Yuejiu & Sun, Tao & Shen, Kai, 2023. "Experimental investigation on the characteristics of coulombic efficiency of lithium-ion batteries considering different influencing factors," Energy, Elsevier, vol. 274(C).
    8. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    9. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    10. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
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    12. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
    13. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    14. Lin, Chun Pang & Ling, Man Ho & Cabrera, Javier & Yang, Fangfang & Yu, Denis Yau Wai & Tsui, Kwok Leung, 2021. "Prognostics for lithium-ion batteries using a two-phase gamma degradation process model," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    15. Wu, Jiafeng & Li, Lin & Yin, Zichao & Li, Zhe & Wang, Tong & Tan, Yunfeng & Tan, Dapeng, 2024. "Mass transfer mechanism of multiphase shear flows and interphase optimization solving method," Energy, Elsevier, vol. 292(C).
    16. Meng, Fanbing & Yang, Fangfang & Yang, Jun & Xie, Min, 2023. "A power model considering initial battery state for remaining useful life prediction of lithium-ion batteries," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    17. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    18. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
    19. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    20. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).

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