Effective Charging Planning Based on Deep Reinforcement Learning for Electric Vehicles

Electric vehicles (EVs) are viewed as an attractive option to reduce carbon emission and fuel consumption, but the popularization of EVs has been hindered by the cruising range limitation and the inconvenient charging process. In public charging stations, EVs usually spend a lot of time on queuing especially during peak hours of charging. Therefore, building an effective charging planning system has become a crucial task to reduce the total charging time for EVs. In this paper, the authors first introduce EVs charging scheduling problem and prove the NP-hardness of the problem. Then, they formalize the scheduling problem of EV charging as a Markov Decision Process and propose deep reinforcement learning algorithms to address it. The objective of the proposed algorithms is to minimize the total charging time of EVs and maximal reduction in the origin-destination distance. Finally, the authors experiment on real-world data and compare with two baseline algorithms to demonstrate the effectiveness of their approach. It shows that the proposed algorithms can significantly reduce the charging time of EVs compared to EST and NNCR algorithms.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2020.3002271
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Zhang, Cong
  • Liu, Yuanan
  • Wu, Fan
  • Tang, Bihua
  • Fan, Wenhao
• Publication Date: 2021-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 542-554
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 22
  • Issue Number: 1
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Electric vehicle charging; Electric vehicles; Energy consumption; Machine learning; Markov chains; Mathematical models; Scheduling
• Geographic Terms: Beijing (China)
• Subject Areas: Energy; Highways; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01766336
• Record Type: Publication
• Files: TLIB, TRIS
• Created Date: Feb 28 2021 4:52PM