Deep reinforcement learning based energymanagement strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle

The main contribution of this study is to integrate energy source aging and running costs into the deep reinforcement learning (DRL) based EMS of fuel cell hybrid electric vehicles (FCHEV). For the FCHEV, a multi-objective energy management strategy (EMS) based on twin delayed deep deterministic policy gradient (TD3) is proposed, which aims to simultaneously reduce energy source degradation and lower running costs. To achieve this, the paper innovatively designs the reward function and it's comparative approach. Additionally, it verifies the superiority of the proposed EMS over other EMS based on continuous action space algorithm, including previous action guided deep deterministic policy gradient (PA-DDPG) and soft actor-critic (SAC). Lastly, the agent's action output is changed from fuel cell (FC) current to FC power ratio, and a comparative analysis on results generated by different action outputs is conducted. Simulation results show that the proposed EMS can reduce the running costs while extending the lifespan of battery and FC efficiently. This work holds significant practical significance in the energy distribution of automobiles.