@inproceedings{40b4ab3de5f54020aba4572d3fa2ab3f,
title = "A multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling",
abstract = "The multiple electric vehicles (EVs) charging scheduling problem is a challenging issue in the context of sustainable urban mobility. To address this complex problem, a mixed reward multi-agent deep deterministic policy gradient (MRMADDPG)- based method is proposed in this paper. The proposed MR-MADDPG provides a framework for multiple EVs to collaboratively and adaptively make charging decisions in a shared charging infrastructure. By learning and adapting from interactions with the charging environment, the MR-MADDPG empowers every EV within the fleet to make real-time charging decisions based on its local observation and eventually gets relatively low charging costs. This research contributes to the advancement of sustainable urban transportation by harnessing the capabilities of MRMADDPG, promoting efficient energy use and reducing charging cost of EV.",
keywords = "Charging scheduling, EVs, MR-MADDPG, real-time electricity price",
author = "Kuan Li and Ying Zhang and Tiantian Zhang and Junyi Xiao and Jun Zhou and Chenglie Du",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE. All rights reserved.; 2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023 ; Conference date: 10-11-2023 Through 12-11-2023",
year = "2024",
doi = "10.1117/12.3023877",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Wei Shangguan and Jianqing Wu",
booktitle = "Third International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023",
}
