Safe Reinforcement Learning for Energy Management of Electrified Vehicle With Novel Physics-Informed Exploration Strategy

This article introduces a novel physics-informed exploration (PIE) strategy for a deep reinforcement learning (DRL)-based energy management system (EMS), specifically targeting the challenge of dealing with constrained action sets. RL-based controllers for electrified vehicle EMS have faced obstacles stemming from the selection of infeasible actions, obstructing their practical deployment. The absence of a mechanism for assessing control action …