@inproceedings{89b9ea326d854d72b43e87e3499f20b1,
title = "Energy Management Strategy Based on an Improved TD3 Reinforcement Algorithm with Novel Experience Replay",
abstract = "The energy management strategy (EMS) plays an important part in the systematic control of hybrid electric vehicles (HEVs). In recent years, the EMS based on deep reinforcement learning (DRL) receives more attention. This paper proposes an EMS based on TD3 deep reinforcement learning algorithm with novel experience replay. The experience replay is introduced to select samples via an evaluation network aiming to improve the learning ability and the convergence speed. The results show that compared with the traditional TD3-based EMS, the proposed EMS reduces the training time by 8.73% and improves the fuel economy by 2.14%.",
keywords = "Deep reinforcement learning, Energy management strategy, Hybrid electric bus, TD3",
author = "Zegong Niu and Ruchen Huang and Hongwen He and Zhiqiang Zhou and Qicong Su",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE Vehicle Power and Propulsion Conference, VPPC 2023 ; Conference date: 24-10-2023 Through 27-10-2023",
year = "2023",
doi = "10.1109/VPPC60535.2023.10403251",
language = "English",
series = "2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE Vehicle Power and Propulsion Conference, VPPC 2023 - Proceedings",
address = "United States",
}