Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery's performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80% nickel, 10% manganese, and 10% cobalt, and a lithium metal foil anode - a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4% over the nominal charge rate, demonstrating the potential of the developed approach.