  One of the most crucial challenges faced by the Li-ion battery community
concerns the search for the minimum time charging without irreversibly damaging
the cells. This can fall into solving large-scale nonlinear optimal control
problems according to a battery model. Within this context, several model-based
techniques have been proposed in the literature.However, the effectiveness of
such strategies is significantly limited by model complexity and uncertainty.
Additionally, it is difficult to track parameters related to aging and re-tune
the model-based control policy. With the aim of overcoming these limitations,
in this paper we propose a fast-charging strategy subject to safety constraints
which relies on a model-free reinforcement learning framework. In particular,
we focus on the policy gradient-based actor-critic algorithm, i.e., deep
deterministic policy gradient (DDPG), in order to deal with continuous sets of
actions and sets. The validity of the proposal is assessed in simulation when a
reduced electrochemical model is considered as the real plant. Finally, the
online adaptability of the proposed strategy in response to variations of the
environment parameters is highlighted with consideration of state reduction.
