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The article provided an overview of recent advancements in utilizing reinforcement learning to manage the energy of Connected Hybrid Electric Vehicles (#CHEVs). It first emphasized the increasing popularity of Hybrid Electric Vehicles (#HEVs), which presents an opportunity for transforming transportation energy systems to reduce environmental issues associated with fossil fuel consumption. Efficient Energy Management Systems (EMS) are required to optimize energy efficiency. The article discussed the evolution of EMS from HEVs to CHEVs, highlighting the complex energy collaboration requirements of CHEVs, necessitating advanced algorithms for route optimization, charging coordination, and load allocation. Despite extensive research, few reviews cover the transition from single-vehicle to multi-vehicle scenarios. This paper aimed to fill this gap, highlighting the potential of RL in addressing these challenges, particularly the application of Multi-Agent Reinforcement Learning (MARL) in dealing with the complexity of CED control. The article also discussed current challenges and potential solutions, emphasizing the importance of interdisciplinary collaboration in developing RL-based EMS solutions to shape future environmentally friendly transportation systems.   Find out more: https://lnkd.in/gwMkpCZt #IJAMM #openaccess #reinforcement learning

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

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

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