Title: Deep reinforcement learning-based energy management strategy for hybrid electric vehicles

Authors: Shiyi Zhang; Jiaxin Chen; Bangbei Tang; Xiaolin Tang

Addresses: School of Automotive Engineering, Chongqing Jiaotong University Chongqing, 400074, China ' School of Automotive Engineering, Chongqing University, Chongqing, 400044, China ' School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, 400044, China ' School of Automotive Engineering, Chongqing University, Chongqing, 400044, China

Abstract: In recent years, with the development of new energy vehicle industry, the development potential of hybrid electric vehicles (HEVs) is increasing. As one of the key technologies, energy management strategy (EMS) has always been a hot research area for HEVs. This paper proposed a deep Q-network (DQN) based EMS for a parallel HEV. Simulation results after training show that, compared with the EMS based on dynamic programming (DP), the DQN-based EMS can achieve 8.38% of the fuel consumption gap while the calculation time is only 12.5%. By the computational advantage of neural network, the average output time of an action in each state is 1ms, which has the potential for real-time applications. Since the final EMS is parameterised and fitted by deep neural networks (DNNs) of deep learning, it is necessary to find further methods for the actual experimental scheme instead of simulation in the future.

Keywords: HEV; hybrid electric vehicle; energy management strategy; deep reinforcement learning.

DOI: 10.1504/IJVP.2022.119433

International Journal of Vehicle Performance, 2022 Vol.8 No.1, pp.31 - 45

Received: 23 Jul 2020
Accepted: 29 Oct 2020
Published online: 05 Dec 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article