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6G Based Intelligent Charging Management for Autonomous Electric Vehicles

  • Tao Hong*
  • , Jihan Cao
  • , Chaoqun Fang
  • , Da Li
  • *Corresponding author for this work
  • Yunnan Innovation Institute (BUAA)
  • Beihang University
  • Ltd.

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)7574-7585
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number7
DOIs
StatePublished - 1 Jul 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Green renewable energy
  • charging schedule
  • electric vehicles
  • machine learning
  • multi-objective optimization
  • power prediction

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