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The growing adoption of hybrid electric vehicles (HEVs) presents atransformative opportunity for revolutionizing transportation energy systems.The shift towards electrifying transportation aims to curb environmentalconcerns related to fossil fuel consumption. This necessitates efficient energymanagement systems (EMS) to optimize energy efficiency. The evolution of EMSfrom HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotalshift. For HEVs, EMS now confronts the intricate energy cooperationrequirements of CHEVs, necessitating advanced algorithms for routeoptimization, charging coordination, and load distribution. Challenges persistin both domains, including optimal energy utilization for HEVs, and cooperativeeco-driving control (CED) for CHEVs across diverse vehicle types. Reinforcementlearning (RL) stands out as a promising tool for addressing these challenges.Specifically, within the realm of CHEVs, the application of multi-agentreinforcement learning (MARL) emerges as a powerful approach for effectivelytackling the intricacies of CED control. Despite extensive research, fewreviews span from individual vehicles to multi-vehicle scenarios. This reviewbridges the gap, highlighting challenges, advancements, and potentialcontributions of RL-based solutions for future sustainable transportationsystems.

Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning