  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.
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.
