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

• URL: http://arxiv.org/abs/2308.14602v2
• Date: Sat, 23 Dec 2023 19:21:13 GMT
• Title: Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning
• Authors: Min Hua, Bin Shuai, Quan Zhou, Jinhai Wang, Yinglong He, Hongming Xu
• Abstract summary: The shift towards electrifying transportation aims to curb environmental concerns related to fossil fuel consumption. The evolution of energy management systems (EMS) from HEVs to connected hybrid electric vehicles (CHEVs) represent a pivotal shift. This review bridges the gap, highlighting challenges, advancements, and potential contributions of RL-based solutions for future sustainable transportation systems.
• Score: 6.851787321368938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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