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A framework for charging strategy optimization using a physics-based battery model

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Abstract

This paper uses a physics-based battery model to develop a generic framework to solve optimal charging strategies. The study will also provide insight into the interplay between optimized charging strategies and the battery internal electrochemical kinetics. With a physics-based battery model, a multi-objective optimal control problem is proposed to investigate the charging strategies that optimally trade off the temperature rise, charging time, and loss. First, a fast-charging strategy (minimum time) with the sole purpose of reducing charging time is presented and experimentally validated. The fast-charging strategy can significantly reduce the charging time but causes a high-temperature rise and charging loss. Next, the interplays between temperature rise and charging time, charging loss, and charging time are investigated, respectively. It is found that, in order to reduce the battery temperature during charging, high-current charging at the initial stage should be avoided. Finally, a balanced charging strategy, which considers temperature rise, charging time, and charging loss simultaneously, is developed and analyzed. Experimental results show that the balanced charging strategy has a similar temperature rise as 4C CC/CV charging, but the charging time is reduced by 24.8% and the charging loss is reduced by 56.4%.

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Acknowledgements

This research was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grant Program (RGPIN-2018-05471).

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Authors and Affiliations

  1. Department of Automotive, Mechanical and Manufacturing Engineering, University of Ontario Institute of Technology, Oshawa, ON, L1G 0C5, Canada

    Xianke Lin & Siyang Wang

  2. Department of Mechanical Engineering, University of Michigan, Dearborn, MI, 48128, USA

    Youngki Kim

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  1. Xianke Lin
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  2. Siyang Wang
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Correspondence to Xianke Lin.

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Lin, X., Wang, S. & Kim, Y. A framework for charging strategy optimization using a physics-based battery model. J Appl Electrochem 49, 779–793 (2019). https://doi.org/10.1007/s10800-019-01322-1

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