A multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling

Kuan Li; Ying Zhang; Tiantian Zhang; Junyi Xiao; Jun Zhou; Chenglie Du

doi:10.1117/12.3023877

9 April 2024 A multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling

Kuan Li, Ying Zhang, Tiantian Zhang, Junyi Xiao, Jun Zhou, Chenglie Du

Proceedings Volume 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023); 129890G (2024) https://doi.org/10.1117/12.3023877
Event: Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 2023, Xi'an, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kuan Li, Ying Zhang, Tiantian Zhang, Junyi Xiao, Jun Zhou, and Chenglie Du "A multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling", Proc. SPIE 12989, Third International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2023), 129890G (9 April 2024); https://doi.org/10.1117/12.3023877

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