Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging Stations

Lo Pang Yun Ting, Chi Chun Lin, Shih Hsun Lin, Yu Lin Chu, Kun Ta Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As global interest shifts toward sustainable transportation with the proliferation of electric vehicles (EVs), the demand for an efficient, real-time, and robust charging infrastructure becomes increasingly pronounced. This paper introduces an approach to address the imbalance between the surging EV demand and the existing charging infrastructure: the concept of Mobile Charging Stations (MCSs). The research develops an algorithm for the dynamic placement of MCSs to significantly reduce the waiting time for EV owners. The core of this research is the Two-stage Placement and Management with Multi-Agent Reinforcement Learning (2PM-MARL) for a dynamic balancing of charging demand and supply. The complexity of the problem is elaborated by showing the NP-hard nature of the MCS placement issue through a relation to the Uncapacitated Facility Location Problem (UFLP), underscoring the computational challenges and emphasizing the need for intelligent real-time solutions. Our framework is validated through comprehensive experiments using real-world charging session data. The results exhibit significant reductions in the waiting time, suggesting the potential practicality and efficiency of our proposed model.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings
EditorsDe-Nian Yang, Xing Xie, Vincent S. Tseng, Jian Pei, Jen-Wei Huang, Jerry Chun-Wei Lin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages284-296
Number of pages13
ISBN (Print)9789819722648
DOIs
Publication statusPublished - 2024
Event28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024 - Taipei, Taiwan
Duration: 2024 May 72024 May 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14649 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024
Country/TerritoryTaiwan
CityTaipei
Period24-05-0724-05-10

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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