  In the realm of battery charging, several complex aspects demand meticulous
attention, including thermal management, capacity degradation, and the need for
rapid charging while maintaining safety and battery lifespan. By employing the
imitation learning paradigm, this manuscript introduces an innovative solution
to confront the inherent challenges often associated with conventional
predictive control strategies for constrained battery charging. A significant
contribution of this study lies in the adaptation of the Dataset Aggregation
(DAGGER) algorithm to address scenarios where battery parameters are uncertain,
and internal states are unobservable. Results drawn from a practical battery
simulator that incorporates an electrochemical model highlight substantial
improvements in battery charging performance, particularly in meeting all
safety constraints and outperforming traditional strategies in computational
processing.
