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

Yin Huang, Zehao Kang, Xuping Mao, Haoqin Hu, Jiaqi Tan and Dongji Xuan

Energy, 2023, vol. 283, issue C

Abstract: 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.

Keywords: Fuel cell hybrid electric vehicle; Energy management strategy; Deep reinforcement learning; Continuous action space; Energy source aging; Running costs (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025719

DOI: 10.1016/j.energy.2023.129177

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