IElectric vehicle battery charging with safe-RL

Resumen

To become the standard power supply for electric vehicles
(EVs), Li-ion batteries need balanced current profiles in order to avoid
undesirable electrochemical reactions and excessive charging times. In
this work, we propose a safe exploration deep reinforcement learning
(SDRL) approach in order to determine optimal charging profiles under
variable operating conditions. One of the main advantages of reinforce-
ment learning (RL) techniques is that they can learn from interaction
with the real or simulated system while incorporating the nonlinear-
ity and uncertainty derived from fluctuating environmental conditions.
However, since RL techniques have to explore undesirable states before
obtaining an optimal policy, no safety guarantees are provided. The pro-
posed approach aims at maintaining zero constraint violations through-
out the learning process by incorporating a safety layer that corrects the
action if a constraint is likely to be violated. Tests performed on the
equivalent circuit of a li-ion battery under variability conditions show
early results where SDRL is able to find safe policies while considering
a trade-off between the charging speed and the battery lifespan.