Integrated Velocity Optimization and Energy Management Strategy for Hybrid Electric Vehicle Platoon: A Multiagent Reinforcement Learning Approach

Coordinating a platoon of connected hybrid electric vehicles (HEVs) poses challenges due to the intricacy of their powertrains and the diverse driving scenarios encountered. The existing mainstream framework uses a hierarchical control scheme, simplifying the unified optimization problem into two separate series control processes: the powertrain level and the vehicle level. However, this approach overlooks the inherent interdependence between the vehicle and powertrain systems, which can hinder effective optimization and collaboration in terms of energy management across multiple vehicles. To address this problem, a multiagent reinforcement learning (RL)-based energy control framework is proposed, aiming to unleash the energy-saving potential through an integrated collaborative optimization of velocity optimization and energy management strategy for the HEV platoon. The proposed strategy constructs a joint-goals value function based on Markov games for HEV platooning and utilizes long short-term memory networks to capture temporal associations of the platoon dynamics. In addition, an asynchronous RL method is introduced for knowledge sharing among HEVs in the platoon. The simulation results demonstrate that the proposed approach effectively improves driving behavior and powertrain energy efficiency through multivehicle coordination. Compared to the rule-based baseline, the fuel consumption of the platoon is reduced by 19.2% through the coordination of connected HEVs.