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Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition

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  • Jiang, Bo
  • Dai, Haifeng
  • Wei, Xuezhe

Abstract

Incremental capacity analysis (ICA) has been widely employed to investigate the degradation mechanism and perform the capacity estimation of lithium-ion batteries. However, the traditional capacity estimation based on ICA is limited by the computational efficiency and charging condition. In this paper, a rapid acquisition method of the incremental capacity (IC) curve is established, then an adaptive capacity estimation framework based on ICA considering the charging condition is proposed. Aiming at improving the computational efficiency, the Kalman filter is employed to acquire the smooth IC curves expeditiously, which requires small data length to handle the IC value. A considerable number of battery standard and non-standard charging experiments are designed and conducted. The influence of battery aging status, charging initial state of charge (SOC), and temperature on IC curves is revealed. Three features of the IC curve during standard charging are selected to investigate the relationship between battery capacity and the height of features. Furthermore, an adaptive correction method for capacity estimation considering the charging initial SOC is established, and the validation results show the effectiveness of the proposed correction method, which provides high accuracy and robustness.

Suggested Citation

  • Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920305869
    DOI: 10.1016/j.apenergy.2020.115074
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    1. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    2. Feng, Fei & Hu, Xiaosong & Hu, Lin & Hu, Fengling & Li, Yang & Zhang, Lei, 2019. "Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 102-113.
    3. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    4. Hu, Chao & Jain, Gaurav & Zhang, Puqiang & Schmidt, Craig & Gomadam, Parthasarathy & Gorka, Tom, 2014. "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Elsevier, vol. 129(C), pages 49-55.
    5. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    6. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    7. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    8. 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.
    9. Deng, Zhongwei & Yang, Lin & Cai, Yishan & Deng, Hao & Sun, Liu, 2016. "Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery," Energy, Elsevier, vol. 112(C), pages 469-480.
    10. Li, Xue & Jiang, Jiuchun & Wang, Le Yi & Chen, Dafen & Zhang, Yanru & Zhang, Caiping, 2016. "A capacity model based on charging process for state of health estimation of lithium ion batteries," Applied Energy, Elsevier, vol. 177(C), pages 537-543.
    11. 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.
    12. Weng, Caihao & Feng, Xuning & Sun, Jing & Peng, Huei, 2016. "State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking," Applied Energy, Elsevier, vol. 180(C), pages 360-368.
    13. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe & Xu, Tianjiao, 2019. "Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Ma, Zeyu & Yang, Ruixin & Wang, Zhenpo, 2019. "A novel data-model fusion state-of-health estimation approach for lithium-ion batteries," Applied Energy, Elsevier, vol. 237(C), pages 836-847.
    15. Xuning Feng & Caihao Weng & Xiangming He & Li Wang & Dongsheng Ren & Languang Lu & Xuebing Han & Minggao Ouyang, 2018. "Incremental Capacity Analysis on Commercial Lithium-Ion Batteries using Support Vector Regression: A Parametric Study," Energies, MDPI, vol. 11(9), pages 1-21, September.
    16. 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.
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