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

The fast charging problem of lithium-ion batteries with minimum charging time while limiting battery degradation is receiving increasing attention and is a critical challenge to battery community. Difficulties in this optimization lie in that: 1) the parameter space of charging strategies is high dimensional, while the budget of the experimental cost is often limited; 2) the evaluation of charging strategies' performance is expensive; and 3) the degradation process of the battery is strongly nonlinear, and multiple degradation mechanisms occur simultaneously leading to difficulties for establishing accurate first-principle 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 article, 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 porous-electrode-theory-based battery simulator. The results show that 1) compared with the model-free RL method, the proposed adaptive GP-based RL approach possesses superior charging performance and high sample efficiency and 2) 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.