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A Reinforcement Learning Based Dynamic Power Management for Fuel Cell Hybrid Electric Vehicle

With the rapid development of electric vehicle and hybrid energy vehicle, the balance between driving performance, driving distance, and energy storage has become extremely important. If too much power than driver's demand is provide to the moving vehicle, the limited stored energy will decrease rapidly so that the energy might not be enough to reach the planned travel distance. However, if the energy/power supplied to the vehicle is limited, the performance of the moving vehicle will be degraded and it could also affect the driving experience. In this study, a reinforcement learning approach is utilized for the dynamic energy management of fuel cell and battery hybrid electric vehicles. In comparing with an existing fuzzy logic based approach, our proposed method performs better in maintaining the stability of battery SoC and in consuming less fuel cell.