A reinforcement learning-based energy management strategy for fuel cell hybrid vehicle considering real-time velocity prediction

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 crea-tively 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.

Automated summary

This paper proposes a reinforcement learning-based energy management method for the fuel cell/lithium battery hybrid system. By using real-time driving profile classification and velocity prediction, the reliability of the energy management system is improved. The real-time power allocation is achieved through a reward value function and double Q-learning strategy, which comprehensively considers system safety, economics, and fuel cell durability. Simulation experiments show that this method can effectively reduce the life decay rate of the fuel cell and improve fuel economics by up to 6%.