Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols

TL;DR

This paper introduces an adaptive safe reinforcement learning framework for optimizing fast charging protocols of batteries, ensuring safety constraints are met while reducing charging time and adapting to changing battery dynamics.

Contribution

It presents a novel safe RL method that projects unsafe actions into safety regions using adaptive Gaussian process models, improving safety and efficiency in battery charging.

Findings

Rapid battery charging with safety constraints satisfied.

Adaptive Gaussian processes effectively model battery dynamics.

Method adapts to varying operating conditions.

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.