Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery
Fast-Charging Protocols
Abstract
Optimizing charging protocols is critical for reducing battery charging
time and decelerating battery degradation in applications such as
electric vehicles. Recently, reinforcement learning (RL) methods have
been adopted for such purposes. However, RL-based methods may not ensure
system (safety) constraints, which can cause irreversible damages to
batteries and reduce their lifetime. To this end, this work proposes an
adaptive and safe RL framework to optimize fast charging strategies
while respecting safety constraints with a high probability. In our
method, any unsafe action that the RL agent decides will be projected
into a safety region by solving a constrained optimization problem. The
safety region is constructed using adaptive Gaussian process (GP)
models, consisting of static and dynamic GPs, that learn from online
experience to adaptively account for any changes in battery dynamics.
Simulation results show that our method can charge the batteries rapidly
with constraint satisfaction under varying operating conditions.