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Differential current in constant-voltage charging mode: A novel tool for state-of-health and state-of-charge estimation of lithium-ion batteries

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  • Ko, Chi-Jyun
  • Chen, Kuo-Ching
  • Su, Ting-Wei

Abstract

Compared with extensive discussions on constant current (CC) charging for lithium-ion batteries, the probe into constant voltage (CV) charging is currently insufficient. Analogous to the differential analysis of the voltage curve in CC charging, this paper introduces the differential current curve (dQ/dI curve) in CV charging and uses it as the feature to identify battery states. The differential curve is qualitatively interpreted with an equivalent circuit model, and the relationship between the dQ/dI value and the battery states is fitted with the Gaussian process regression (GPR) model. With a total of 4836 sets of CV charging data, an excellent state of health (SOH) estimation with a mean absolute error (MAE) of 0.18 % can be achieved by using the first 190 data points of the dQ/dI curve as the model input. For state of charge (SOC) estimation, a brand-new experimental work is introduced by adjusting the cutoff voltage of the previous CC charging to generate a benchmark database illustrating the relation between the SOC value at the initial CV charging and the subsequent current curve. Through GPR, the SOC prediction MAE is reduced to about 0.88 %, which confirms that the dQ/dI curve is a promising candidate for battery state estimation.

Suggested Citation

  • Ko, Chi-Jyun & Chen, Kuo-Ching & Su, Ting-Wei, 2024. "Differential current in constant-voltage charging mode: A novel tool for state-of-health and state-of-charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 288(C).
  • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223032206
    DOI: 10.1016/j.energy.2023.129826
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    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Fan, Guodong & Zhang, Xi, 2023. "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network," Applied Energy, Elsevier, vol. 330(PA).
    3. Hu, Xiaosong & Feng, Fei & Liu, Kailong & Zhang, Lei & Xie, Jiale & Liu, Bo, 2019. "State estimation for advanced battery management: Key challenges and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    4. Jun Liu & Zhenan Bao & Yi Cui & Eric J. Dufek & John B. Goodenough & Peter Khalifah & Qiuyan Li & Bor Yann Liaw & Ping Liu & Arumugam Manthiram & Y. Shirley Meng & Venkat R. Subramanian & Michael F. T, 2019. "Pathways for practical high-energy long-cycling lithium metal batteries," Nature Energy, Nature, vol. 4(3), pages 180-186, March.
    5. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    6. Wang, Limei & Pan, Chaofeng & Liu, Liang & Cheng, Yong & Zhao, Xiuliang, 2016. "On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis," Applied Energy, Elsevier, vol. 168(C), pages 465-472.
    7. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    8. Yang, Jufeng & Xia, Bing & Huang, Wenxin & Fu, Yuhong & Mi, Chris, 2018. "Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis," Applied Energy, Elsevier, vol. 212(C), pages 1589-1600.
    9. Zheng, Fangdan & Xing, Yinjiao & Jiang, Jiuchun & Sun, Bingxiang & Kim, Jonghoon & Pecht, Michael, 2016. "Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 183(C), pages 513-525.
    10. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    11. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    12. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    13. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    14. Zheng, Linfeng & Zhu, Jianguo & Lu, Dylan Dah-Chuan & Wang, Guoxiu & He, Tingting, 2018. "Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries," Energy, Elsevier, vol. 150(C), pages 759-769.
    15. Lin, Wei-Jen & Chen, Kuo-Ching, 2022. "Evolution of parameters in the Doyle-Fuller-Newman model of cycling lithium ion batteries by multi-objective optimization," Applied Energy, Elsevier, vol. 314(C).
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