Deep Learning-Based Predictive Control for the Optimal Charging of a Lithium-Ion Battery with Electrochemical Dynamics

The fast charging of a lithium-ion battery is a complex task, which needs to be addressed by a proper control methodology to find the highest charging current while guaranteeing safety. Among the different approaches, model predictive control appears particularly suitable due to its ability in dealing with nonlinear systems and constraints. However, its use in a realistic scenario is limited due to the high computational burden required by the online solution of an optimal control problem. To overcome this issue, we consider a neural network-based algorithm, which can reduce the online computational effort by approximating the solution of the model predictive control. Such a deep learning-based approach is here applied for the first time to the real-time management of a lithium-ion cell, described by an electrochemical model with thermal dynamics. The results highlight the effectiveness of the proposed methodology in terms of computational burden reduction.