Optimal Charging of Lithium-Ion Battery Using Distributionally Robust Model Predictive Control With Wasserstein Metric

Developing a fast and safe charging strategy has been one of the key breakthrough points in lithium battery development owing to its range anxiety and long charging time. The majority of current model-based charging strategies are developed for deterministic systems. Real battery dynamics are, however, affected by model mismatches and process uncertainties, which may lead to constraint violations and even premature aging. This article proposes a fast charging scheme based on distributionally robust model predictive control (DRMPC) against uncertainty. Specifically, a coupled electrothermal-aging model is first introduced to describe the battery behavior, and electrothermal parameters of the adopted model are identified online based on the recursive least-squares algorithm. Subsequently, an online DRMPC-based charging framework is proposed, utilizing the Wasserstein ball centered on the empirical distribution to characterize uncertainty. Finally, the proposed algorithm is compared to model predictive control and constant current-constant voltage algorithms, and its effectiveness is validated on a real battery simulator. Results show that the proposed algorithm can handle the uncertainty effectively while satisfying the constraints, and significantly improve the charging speed.