An Intelligent Power and Energy Management System for Fuel Cell/Battery Hybrid Electric Vehicle using Reinforcement Learning

Hybrid electric vehicles powered by fuel cells and batteries have attracted significant attention as they have the potential to eliminate emissions from the transport sector. However, fuel cells and batteries have several operational challenges, which require a power and energy management system (PEMS) to achieve optimal performance. Most of the existing PEMS methods are based on either predefined rules or prediction that are not adaptive to real-time driving conditions and may give solutions that are far from the actual optimal solution for a new drive cycle. Therefore, in this paper, an intelligent PEMS using reinforcement learning is presented, that can autonomously learn the optimal policy in real time through interaction with the onboard hybrid power system. This PEMS is implemented and tested on the simulation model of the onboard hybrid power system. The propulsion load is represented by the new European drive cycle. The results indicate that the PEMS algorithm is able to improve the lifetime of batteries and efficiency of the power system through minimizing the variation of the state of charge of battery.