Adaptive Model-Based Reinforcement Learning for Fast-Charging Optimization of Lithium-Ion Batteries

Abstract

Fast charging problem of lithium-ion batteries with minimum-charging time while limiting battery degradation, has been receiving increasing attention and is a critical challenge to battery community. Difficulties in this optimization lie in that: (i) The parameter space of charging strategies is high dimensional while the budget of the experimental cost is often limited; (ii) The evaluation of charging strategies' performance is expensive, and (iii) the degradation process of battery is strongly nonlinear and multiple degradation mechanisms occur simultaneously leading to difficulties for establishing accurate first-principles models. Current methods to address these difficulties are mainly electrochemical model-based optimization and grid search, which are rarely adaptive to battery degradation and/or are of low sample efficiency. In this work, we propose an adaptive model-based reinforcement learning (RL) approach for fast charging optimization while limiting battery degradation, in which a probabilistic surrogate model of differential Gaussian process (GP) is adopted to adaptively describe the degradation of cells. The effectiveness of the proposed approach is demonstrated on PETLION, a high-performance PET-based battery simulator. The results show that (i) compared with the model-free RL method, the proposed adaptive GP-based RL approach possesses superior charging performance and high sample efficiency, and (ii) the proposed method performs well in the handling of degradation constraints on voltage and temperature for dynamically aging batteries with its adaptability to the variations of environment.