基于强化学习和路况信息的燃料电池汽车能量管理策略

Energy Management Strategy of Fuel Cell Vehicles Based on Reinforcement Learning and Traffic Information

为提升整车经济性和耐久性,提出了一种基于强化学习和路况信息的燃料电池汽车能量管理策略。首先,根据关键部件参数搭建了动力系统模型,并根据城市道路工况特征在VISSIM软件中搭建交通模型并提取了车辆行驶数据及路况数据。其次,将路况数据作为输入,利用长短期记忆神经网络对车速进行预测。最后,基于强化学习算法,将预测车速、加速度以及动力电池荷电状态作为输入,燃料电池系统功率作为输出进行能量管理策略的设计。仿真结果表明,所提策略的百公里氢耗量与动态规划策略相比仅相差1.27%,且燃料电池系统的平均功率波动降低了5.01%,因此可有效提升整车的经济性和耐久性。

In order to improve the fuel economy and durability of fuel cell vehicles(FCVs),an energy management strategy(EMS)for FCVs based on reinforcement learning(RL)and traffic information was proposed.First,a powertrain model was built based on parameters of key components.Then,based on the characteristics of urban road conditions,a traffic model was built in VISSIM and the vehicle driving data and traffic data were extracted.The traffic data was then used as input to predict velocity by using the long short-term memory neural network.Finally,based on the RL algorithm and using the predicted velocity,acceleration,and the state of charge of battery as inputs,the fuel cell system power was used as the output to design the EMS.The simulation results show that the hydrogen consumption per hundred kilometers of the proposed strategy is only 1.27%different from that of the dynamic programming strategy,and fuel cell system average power fluctuation is reduced by 5.01%,effectively improving the fuel economy and durability of the vehicles.