Energy Management for Fuel Cell Electric Vehicle Based on Multi-agent Reinforcement Learning

The coordinated optimization of adaptive cruise control (ACC) and energy management strategy (EMS) is promising for improving the performance of fuel cell electric vehicle (FCEV). This paper proposes an ensemble learning-based multi-agent proximal policy optimization (EL-MAPPO) strategy to address the complex multi-objective optimization problem in car-following scenarios. In specific, multi-agent deep reinforcement learning (RL) is constructed for ACC and EMS respectively to optimize the vehicle speed and power distribution, while EL is devised to coordinate the interactions among multiple agents.