The fuel cell vehicle is an ideal new energy vehicle development direction, and its energy management strategy is one of the core technologies to ensure the safe and efficient operation of the vehicle. We proposed a novel reinforcement learning-based energy management method for the fuel cell/lithium battery hybrid system in this paper. In order to improve the reliability of the EMS, the real-time driving profile classification and velocity prediction method based on data driven and statistical analysis is proposed to forecast vehicle velocity in the near future. Then a reinforcement learning method is designed to realize the real-time power allocation. The reward value function which comprehensively considers the system safety, economics and fuel cell durability is creatively put forward. The double Q-learning strategy is applied to update the Q value function. In addition, the real-time reference path of power allocation is designed by taking battery state-of-charge as an indicator. A new dynamic test profile is conducted to verify the proposed method. The multiple groups of comparative simulation experiments show that the proposed EMS can effectively reduce the life decay rate of fuel cell, but also improves fuel economics by up to 6% compared with other commonly used methods.

燃料电池汽车是理想的新能源汽车发展方向，其能源管理策略是保障汽车安全高效运行的核心技术之一。我们在本文中提出了一种新的基于强化学习的燃料电池/锂电池混合系统能量管理方法。为了提高EMS的可靠性，提出了基于数据驱动和统计分析的实时行驶轨迹分类和速度预测方法来预测近期的车辆速度。然后设计了一种强化学习方法来实现实时的功率分配。创造性地提出了综合考虑系统安全性、经济性和燃料电池耐久性的奖励价值函数。应用双 Q 学习策略来更新 Q 值函数。此外，以电池充电状态为指标，设计了功率分配的实时参考路径。进行了新的动态测试配置文件以验证所提出的方法。多组对比仿真实验表明，所提出的EMS可以有效降低燃料电池的寿命衰减率，与其他常用方法相比，燃料经济性提高了6%。