Deep reinforcement learning based energy management strategy for fuel cell/battery/supercapacitor powered electric vehicle

Abstract

Vehicles using a single fuel cell as a power source often have problems such as slow response and inability to recover braking energy. Therefore, the current automobile market is mainly dominated by fuel cell hybrid vehicles. In this study, the fuel cell hybrid commercial vehicle is taken as the research object, and a fuel cell/battery/supercapacitor energy topology is proposed, and an energy management strategy based on a double-delay deep deterministic policy gradient is designed for this topological structure. This strategy takes fuel cell hydrogen consumption, fuel cell life loss, and battery life loss as the optimization goals, in which supercapacitors play the role of coordinating the power output of the fuel cell and the battery, providing more optimization ranges for the optimization of fuel cells and batteries. Compared with the deep deterministic policy gradient strategy (DDPG) and the nonlinear programming algorithm strategy, this strategy has reduced hydrogen consumption level, fuel cell loss level, and battery loss level, which greatly improves the economy and service life of the power system. The proposed EMS is based on the TD3 algorithm in deep reinforcement learning, and simultaneously optimizes a number of indicators, which is beneficial to prolong the service life of the power system.