6G Based Intelligent Charging Management for Autonomous Electric Vehicles

Recently, significant advances have been made in the autonomous driving field, with vehicles capable of traveling vast areas independently. Meanwhile, the operators face several uncertainties such as volatility in charging demand, intrinsic intermittency of green energy supply, etc....Accordingly, this study proposes an integrated green transportation system based on 6G Internet of Things (IoT) and big data technology, aiming at integrating state grid, electric vehicles and renewable energy to address these concerns. Furthermore, this study analyzes the performance of the proposed system using both actual and simulation data. The numerical results justify that the suggested techniques greatly enhance the charging index of electric vehicles compared to the benchmark method.

• Record URL:
  • https://doi.org/10.1109/TITS.2022.3177586
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Hong, Tao
  • Cao, Jihan
  • Fang, Chaoqun
  • Li, Da
• Publication Date: 2023-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 7574-7585
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 7
  • 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: Autonomous vehicles; Cellular networks; Electric vehicle charging; Integrated systems; Machine learning; Schedules
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01898384
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 7 2023 2:24PM