A deep reinforcement learning approach to energy management control with connected information for hybrid electric vehicles

Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hiera...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence Vol. 123; p. 106239
Main Authors: Mei, Peng, Karimi, Hamid Reza, Xie, Hehui, Chen, Fei, Huang, Cong, Yang, Shichun
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.08.2023
Subjects:
Connected vehicle
Deep reinforcement learning algorithm
Energy management strategy
Hybrid electric vehicles
Connected vehicle
Deep reinforcement learning algorithm
Energy management strategy
Hybrid electric vehicles
ISSN:0952-1976, 1873-6769
Online Access:Get full text
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Summary:Considering the importance of the energy management strategy for hybrid electric vehicles, this paper is aiming at addressing the energy optimization control issue using reinforcement learning algorithms. Firstly, this paper establishes a hybrid electric vehicle power system model. Secondly, a hierarchical energy optimization control architecture based on networked information is designed, and a traffic signal timing model is used for vehicle target speed range planning in the upper system. More specifically, the optimal vehicle speed is optimized by a model predictive control algorithm. Thirdly, a mathematical model of vehicle speed variation in connected and unconnected states is established to analyze the effect of vehicle speed planning on fuel economy. Finally, three learning-based energy optimization control strategies, namely Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) algorithms, are designed under the hierarchical energy optimization control architecture. It is shown that the Q-learning algorithm is able to optimize energy control; however, the agent will meet the ”dimension disaster” once it faces a high-dimensional state space issue. Then, a DQN control strategy is introduced to address the problem. Due to the limitation of the discrete output of DQN, the DDPG algorithm is put forward to achieve continuous action control. In the simulation, the superiority of the DDPG algorithm over Q-learning and DQN algorithms in hybrid electric vehicles is illustrated in terms of its robustness and faster convergence for better energy management purposes.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2023.106239