Energy management control strategy for plug-in fuel cell electric vehicle based on reinforcement learning algorithm

Abstract

To cope with the increasingly stringent emission regulations, major automobile manufacturers have been focusing on the development of new energy vehicles. Fuel-cell vehicles with advantages of zero emission, high efficiency, diversification of fuel sources, and renewable energy have been the focus of international automotive giants and Chinese automotive enterprises. Establishing a reasonable energy management strategy, effectively controlling the vehicle working mode, and reasonably using battery energy for hybrid fuel-cell vehicles are core technologies in domestic and foreign automobile enterprises and research institutes. To improve the equilibrium between fuel-cell hydrogen consumption and battery consumption and realize the optimal energy distribution between fuel-cell systems and batteries for plug-in fuel-cell electric vehicles (PFCEVs), considering vehicles as the environment and vehicle control as an agent, an energy management strategy for the PFCEV based on reinforcement learning algorithm was proposed in this paper. This strategy considered the immediate return and future cumulative discounted returns of a fuel-cell vehicle's real-time energy allocation. The vehicle simulation model was built by Matlab/Simulink to carry out the simulation test for the proposed strategy. Compared with the rule-based strategy, the battery can store a certain amount of electricity, and the integrated energy consumption of the vehicle was notably reduced under different mileages. The energy consumption in 100 km was reduced by 8.84%, 29.5%, and 38.6% under 100, 200, and 300 km mileages, respectively. The hardware-in-loop-test was performed on the D2P development platform, and the final energy consumption of the vehicle was reduced by 20.8% under urban dynamometer driving schedule driving cycle. The hardware-in loop-test results are consistent with the simulation findings, indicating the effectiveness and feasibility of the proposed energy management strategy.

Keywords

• fuel-cell vehicle
• reinforcement learning
• energy management
• q_learning algorithm
• control strategy