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Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model

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  • Chu, Zhengyu
  • Feng, Xuning
  • Lu, Languang
  • Li, Jianqiu
  • Han, Xuebing
  • Ouyang, Minggao

Abstract

Fast charging is critical for the application of lithium-ion batteries in electric vehicles. Conventional fast charging algorithms may shorten the cycle life of lithium-ion batteries and induce safety problems, such as internal short circuit caused by lithium deposition at the negative electrode. In this paper, a novel, non-destructive model-based fast charging algorithm is proposed. The fast charging algorithm is composed of two closed loops. The first loop includes an anode over-potential observer that can observe the status of lithium deposition online, whereas the second loop includes a feedback structure that can modify the current based on the observed status of lithium deposition. The charging algorithm enhances the charging current to maintain the observed anode over-potential near the preset threshold potential. Therefore, the fast charging algorithm can decrease the charging time while protecting the health of the battery. The fast charging algorithm is validated on a commercial large-format nickel cobalt manganese/graphite cell. The results showed that 96.8% of the battery capacity can be charged within 52min. The post-mortem observation of the surface of the negative electrode and degradation tests revealed that the fast charging algorithm proposed here protected the battery from lithium deposition.

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  • Chu, Zhengyu & Feng, Xuning & Lu, Languang & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2017. "Non-destructive fast charging algorithm of lithium-ion batteries based on the control-oriented electrochemical model," Applied Energy, Elsevier, vol. 204(C), pages 1240-1250.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:1240-1250
    DOI: 10.1016/j.apenergy.2017.03.111
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    References listed on IDEAS

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    2. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Jiang, Benben & Berliner, Marc D. & Lai, Kun & Asinger, Patrick A. & Zhao, Hongbo & Herring, Patrick K. & Bazant, Martin Z. & Braatz, Richard D., 2022. "Fast charging design for Lithium-ion batteries via Bayesian optimization," Applied Energy, Elsevier, vol. 307(C).
    4. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.
    5. Li, Junqiu & Sun, Danni & Jin, Xin & Shi, Wentong & Sun, Chao, 2019. "Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation," Applied Energy, Elsevier, vol. 254(C).
    6. Zou, Changfu & Hu, Xiaosong & Wei, Zhongbao & Tang, Xiaolin, 2017. "Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control," Energy, Elsevier, vol. 141(C), pages 250-259.
    7. Hong Zhao & Li Wang & Zonghai Chen & Xiangming He, 2019. "Challenges of Fast Charging for Electric Vehicles and the Role of Red Phosphorous as Anode Material: Review," Energies, MDPI, vol. 12(20), pages 1-23, October.
    8. Yang, Xiaofeng & He, Hongwen & Wei, Zhongbao & Wang, Rui & Xu, Ke & Zhang, Dong, 2023. "Enabling Safety-Enhanced fast charging of electric vehicles via soft actor Critic-Lagrange DRL algorithm in a Cyber-Physical system," Applied Energy, Elsevier, vol. 329(C).
    9. Lai, Xin & Huang, Yunfeng & Deng, Cong & Gu, Huanghui & Han, Xuebing & Zheng, Yuejiu & Ouyang, Minggao, 2021. "Sorting, regrouping, and echelon utilization of the large-scale retired lithium batteries: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    10. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    11. Sattarzadeh, Sara & Padisala, Shanthan K. & Shi, Ying & Mishra, Partha Pratim & Smith, Kandler & Dey, Satadru, 2023. "Feedback-based fault-tolerant and health-adaptive optimal charging of batteries," Applied Energy, Elsevier, vol. 343(C).

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