Health-aware Fast-charging Control of Lithium-Ion Battery Based on Reinforcement Learning

Yikun Yang, Jingwen Wei, Chunlin Chen
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Abstract

Lithium-ion battery stands out from many kinds of energy storage devices due to its promising characteristics and has become the energy supply unit of Electric Vehicles (EVs). However, the charging speed of lithium-ion batteries limits the sustainable range of EVs, resulting in "range anxiety" for drivers and necessitating a faster charging method. To fulfill the safety and economic requirements of battery operations, the temperature and degradation of batteries must be taken into account during fast charging control. Consequently, a reinforcement-learning-based health-aware fast charging control scheme is proposed in this paper. First, a coupling model that considers aging and thermal dynamics simultaneously is established to capture battery behaviors. Then, the health-aware fast-charging problem is formulated as a comprise between charging time and battery degradation. After that, a reinforcement learning control scheme is proposed to find the optimal fast charging solution, where the influence of battery parameter drift on the charging strategy is considered. Finally, simulations are performed to validate the effectiveness and superiority of the proposed method. Compared with the commonly used constant current and constant voltage charging strategy, the proposed charging strategy can improve safety and prolong battery lifetime by sacrificing some charging speed.
基于强化学习的锂离子电池健康感知快速充电控制
锂离子电池以其优异的性能从众多储能装置中脱颖而出,成为电动汽车的能源供应单元。然而,锂离子电池的充电速度限制了电动汽车的可持续续航里程,导致司机产生“里程焦虑”,需要一种更快的充电方式。为了满足电池运行的安全性和经济性要求,在快速充电控制中必须考虑电池的温度和退化问题。因此,本文提出了一种基于强化学习的健康感知快速充电控制方案。首先,建立了一个同时考虑老化和热动力学的耦合模型来捕捉电池的行为。然后,将健康感知快速充电问题表述为充电时间和电池退化的综合问题。在此基础上,考虑了电池参数漂移对充电策略的影响,提出了一种寻找最优快速充电方案的强化学习控制方案。最后通过仿真验证了所提方法的有效性和优越性。与常用的恒流恒压充电策略相比,本文提出的充电策略在牺牲一定的充电速度的前提下,提高了安全性,延长了电池寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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