TY - GEN
T1 - Reinforcement Learning-Based Energy Management Control Strategy of Hybrid Electric Vehicles
AU - Chen, Fei
AU - Mei, Peng
AU - Xie, Hehui
AU - Yang, Shichun
AU - Xu, Bin
AU - Huang, Cong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This article is aimed at developing a control strategy based on the Q-learning algorithm for HEVs. The Q-learning algorithm deals with high-dimensional state space problems, and the agent will have a 'dimension disaster' problem during the training process. Then a control strategy based on the Deep Q Network (DQN) algorithm is introduced. Since DQN can only output discrete actions, in order to achieve continuous action control, an optimized control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. Simulation results show that compared with Q-learning and DQN algorithms, the DDPG algorithm converges faster, and the training process is more robust. Besides, the energy optimization control strategy based on the DDPG algorithm can better control the energy of HEVs.
AB - This article is aimed at developing a control strategy based on the Q-learning algorithm for HEVs. The Q-learning algorithm deals with high-dimensional state space problems, and the agent will have a 'dimension disaster' problem during the training process. Then a control strategy based on the Deep Q Network (DQN) algorithm is introduced. Since DQN can only output discrete actions, in order to achieve continuous action control, an optimized control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. Simulation results show that compared with Q-learning and DQN algorithms, the DDPG algorithm converges faster, and the training process is more robust. Besides, the energy optimization control strategy based on the DDPG algorithm can better control the energy of HEVs.
KW - Deep Deterministic Policy Gradient algorithm
KW - Deep Q Network algorithm
KW - Hybrid electric vehicles
KW - Q-learning algorithm
KW - energy management strategy
UR - https://www.scopus.com/pages/publications/85132552112
U2 - 10.1109/ICCAR55106.2022.9782662
DO - 10.1109/ICCAR55106.2022.9782662
M3 - 会议稿件
AN - SCOPUS:85132552112
T3 - 2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022
SP - 248
EP - 252
BT - 2022 8th International Conference on Control, Automation and Robotics, ICCAR 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Conference on Control, Automation and Robotics, ICCAR 2022
Y2 - 8 April 2022 through 10 April 2022
ER -