Review on Reinforcement Learning-Based Energy Management Strategies for Hybrid Electric Vehicles

Abstract

Hybrid Electric Vehicles (HEVs) achieve better fuel economy than conventional vehicles by employing two different power sources: a mechanical engine and an electrical motor. These power sources have conventionally been controlled by a rule-based algorithm or optimization-based control. Besides these conventional approaches, reinforcement learning-based control algorithms have actively been studied recently. Reinforcement learning, which is one of three machine learning paradigms, has the capability of determining optimal control actions to maximize a vehicle’s fuel economy without the vehicle model nor a priori driving route information. To provide a useful reference to researchers interested in this technology, this article reviews reinforcement learning-based energy management strategies for HEVs with their advantages and disadvantages.