  A reinforcement learning-based optimal charging strategy is proposed for
Li-ion batteries to extend the battery life and to ensure the end-user
convenience. Unlike most previous studies that do not reflect real-world
scenario well, in this work, end users can set the charge time flexibly
according to their own situation rather than reducing the charge time as much
as possible; this is possible by using soft actor-critic (SAC), which is one of
the state-of-the-art reinforcement learning algorithms. In this way, the
battery is more likely to extend its life without disturbing the end-users. The
amount of aging is calculated quantitatively based on an accurate
electrochemical battery model, which is directly minimized in the optimization
procedure with SAC. SAC can deal with not only the flexible charge time but
also varying parameters of the battery model caused by aging once the offline
learning is completed, which is not the case for the previous studies; in the
previous studies, time-consuming optimization has to be implemented for each
battery model with a certain set of parameter values. The validation results
show that the proposed method can both extend the battery life effectively and
ensure the end-user convenience.
