Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization With Recurrent Neural Network

Abstract

Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batteries or even hazardous accidents. To optimize fast-charging strategies under various constraints, particularly safety limits, we propose a novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent neural network (BRNN) as the surrogate model, given its capability in handling sequential data and providing uncertainty quantifications for the output. In addition, a combined acquisition function of expected improvement (EI) and upper confidence bound (UCB) is developed to better balance the exploitation and exploration. The effectiveness of the proposed approach is demonstrated on the PETLION, a porous electrode theory-based battery simulator. Our method is also compared with the state-of-the-art BO methods that use Gaussian process (GP) and non-recurrent network as surrogate models. The results verify the superior performance of the proposed fast charging approaches, which mainly results from that: 1) the BRNN-based surrogate model provides a more precise prediction of battery lifetime than that based on GP or non-recurrent network; and 2) the combined acquisition function outperforms traditional EI or UCB criteria in exploring the optimal charging protocol that maintains the longest battery lifetime. Note to Practitioners—This study is motivated by the need to develop fast-charging strategies for batteries to reduce the charging time while maintaining the safety and longevity of batteries. Traditional methods to optimize battery fast-charging protocols require either solving complex battery models or conducting tremendous repetitive and costly cycling experiments. In addition, the majority of works in this direction mainly focus on minimizing the charging time without considering the battery degradation caused by the designed charging protocol. To address these issues, we present a data-driven optimization technique to rapidly discover the optimal fast-charging strategies that not only decrease the charging time but also slow down the battery degradation. Our method is efficient and can find the optimal solution within tens of iterations (or repetitive experiments), thus greatly reducing the cost and efforts in optimizing battery charging protocols.