Fast Charging of Li-ion Batteries via Learning and Optimization

Complex electrochemical processes of Li-ion batteries result in nonlinear and high-dimensional dynamics. With the increased presence in critical applications, there is a demand for advanced fast-charging strategies to reduce the charging time while maximizing the battery's lifespan. Fast charging is limited by several factors, such as elevated temperature, since they accelerate electrochemical aging and, in turn, result in increased lithium plating, higher mechanical stresses, and an increased growth rate of the solid-electrolyte interface layer. Here, we propose an aggressive but efficient charging strategy using an adaptive control strategy that learns the closed-loop system's Jacobian from input/output data and optimizes the response based on the learned dynamics. To avoid subjecting the cell to accelerated aging, we optimize the electrical current for minimum battery charge time while respecting constraints such as maximum cell temperature and voltage. The battery data was generated using the Doyle-Fuller-Newman (P2D) model with a thermal model to characterize the cell's thermal effects. Our optimized charging strategy is comprised of a hybrid (mixed continuous-discrete) solution that fully charges a 5Ah 21700 NMC-811 cylindrical cell, 66% faster than the recommended 0.3C constant-current constant-voltage strategy while respecting safety constraints, including a maximum voltage of 4.2V and a maximum temperature of 57°C.