The growing adoption of hybrid electric vehicles (HEVs) presents a transformative opportunity for revolutionizing transportation energy systems. The shift towards electrifying transportation aims to curb environmental concerns related to fossil fuel consumption. This necessitates efficient energy management systems (EMS) to optimize energy efficiency. The evolution of EMS from HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotal shift. For HEVs, EMS now confronts the intricate energy cooperation requirements of CHEVs, necessitating advanced algorithms for route optimization, charging coordination, and load distribution. Challenges persist in both domains, including optimal energy utilization for HEVs, and cooperative eco-driving control (CED) for CHEVs across diverse vehicle types. Reinforcement learning (RL) stands out as a promising tool for addressing these challenges at hand. Specifically, within the realm of CHEVs, the application of multi-agent reinforcement learning (MARL) emerges as a powerful approach for effectively tackling the intricacies of CED control. Despite extensive research, few reviews span from individual vehicles to multi-vehicle scenarios. This review bridges the gap, highlighting challenges, advancements, and potential contributions of RL-based solutions for future sustainable transportation systems.

翻译：混合动力电动汽车（HEVs）的日益普及为交通运输能源系统的变革提供了契机。交通电气化旨在减少与化石燃料消耗相关的环境问题，因此需要高效的能量管理系统（EMS）来优化能源效率。EMS从HEVs向联网混合动力电动汽车（CHEVs）的演进标志着一个关键转变。对于HEVs，EMS目前面临CHEVs复杂的能量协同需求，需要先进的算法来实现路线优化、充电协调和负载分配。两个领域仍存在挑战，包括HEVs的最优能量利用，以及针对不同车辆类型的CHEVs协同生态驾驶控制（CED）。强化学习（RL）成为解决这些问题的有效工具。具体而言，在CHEVs领域，多智能体强化学习（MARL）的应用为有效应对CED控制的复杂性提供了强大方法。尽管已有大量研究，但鲜有综述从单一车辆扩展到多车辆场景。本综述弥补了这一空白，重点阐述了基于RL的解决方案在应对挑战、取得进展及为未来可持续交通系统做出贡献方面的潜力。