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Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control

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  • Yin, Yilin
  • Choe, Song-Yul

Abstract

Previous research has shown that a drastic reduction of charging time compared to constant current constant voltage (CC/CV) charging protocol is possible while suppressing degradation causes by combining constant charging and pulse discharging current. However, effects of temperature variation on side reaction and lithium plating and effects of the frequency of pulse current on lithium stripping have not been considered. In fact, charging currents and operating temperatures dominantly affect the cycle life of battery, where both side reaction and lithium plating are competing. In this paper, reduced order electrochemical life models are developed and validated against experimental data collected at different currents and temperatures and then used to find out optimal temperatures at given charging currents with respect to degradation rates. Thereafter, an optimal charging current at different SOCs has been found using nonlinear model predictive control and then the optimal temperature is determined from the relationship obtained by the models, which reduces side reaction rate and lithium plating rate. In addition, pulse discharging current is added to promote the lithium stripping that recovers ions out of the plated lithium, where the amplitude and frequency of the pulse current are optimized using the models. Finally, the proposed charging method and the 1 C CC/CV charging method were tested in Battery-In-the-Loop using a large format pouch type lithium ion battery to compare each other, where 61% and 39% of charging time is reduced up to 80% and 100% SOC, respectively, while the capacity fade is comparable.

Suggested Citation

  • Yin, Yilin & Choe, Song-Yul, 2020. "Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control," Applied Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920307443
    DOI: 10.1016/j.apenergy.2020.115232
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    References listed on IDEAS

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    1. Guan-Jhu Chen & Yi-Hua Liu & Yu-Shan Cheng & Hung-Yu Pai, 2021. "A Novel Optimal Charging Algorithm for Lithium-Ion Batteries Based on Model Predictive Control," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Wang, Yujie & Zhou, Caijie & Chen, Zonghai, 2022. "Optimization of battery charging strategy based on nonlinear model predictive control," Energy, Elsevier, vol. 241(C).
    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. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
    5. Ouyang, Quan & Fang, Ruyi & Xu, Guotuan & Liu, Yonggang, 2022. "User-involved charging control for lithium-ion batteries with economic cost optimization," Applied Energy, Elsevier, vol. 314(C).
    6. Song, Minseok & Choe, Song-Yul, 2022. "Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries," Applied Energy, Elsevier, vol. 305(C).
    7. 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).

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