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

Abstract

Review Recent Progress in Energy Management of Connected Hybrid Electric Vehicles Using Reinforcement Learning Min Hua 1, Bin Shuai 1,2, ∗ , Quan Zhou 1, Jinhai Wang 1, Yinglong He 3, and Hongming Xu 1 1 School of Engineering, University of Birmingham, Birmingham B152TT, UK 2 School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China 3 School of Mechanical Engineering Sciences, University of Surrey, Guildford, GU27XH, UK * Correspondence: shuaib@mail.tsinghua.edu.cn Received: 29 August 2023 Accepted: 11 December 2023 Published: 19 December 2023 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.