Reinforcement Learning Based Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

With increasingly serious environmental pollution and the energy crisis, fuel cell hybrid electric vehicles have been considered as an ideal alternative to traditional hybrid electric vehicles. Nevertheless, the total costs of fuel cell systems are still too high, thus limiting the further development of fuel cell hybrid electric vehicles. This paper presents an energy management strategy (EMS) based on deep reinforcement learning for the energy management of fuel cell hybrid electric vehicles. The energy management model of a fuel cell hybrid electric bus and its main components are established. Considering the power response characteristics of the fuel cell system, the power change rate of the fuel cell system is reasonably limited and introduced as action variables into the network of Double Deep Q-Learning (DDQL), and a novel DDQL-based EMS is developed for the fuel cell hybrid electric bus. Subsequently, a comparative test is conducted with the DP-based and the Rule-based EMS to analyze the performance of the DDQL-based EMS. The results indicate that the proposed EMS achieves good fuel economy performance, with an improvement of 15.4% compared to the Rule-based EMS under the training scenarios. In terms of generalization performance, the proposed EMS also achieves good fuel economy performance, which improves by 13.3% compared to the Rule-based energy management strategy under the testing scenario.