EV charging behavior analysis using hybrid intelligence for 5G smart grid

Yi Shen, Wei Fang, Feng Ye, Michel Kadoch

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


With the development of the Internet of Things (IoT) and the widespread use of electric vehicles (EV), vehicle-to-grid (V2G) has sparked considerable discussion as an energy-management technology. Due to the inherently high maneuverability of EVs, V2G systems must provide on-demand service for EVs. Therefore, in this work, we propose a hybrid computing architecture based on fog and cloud with applications in 5G-based V2G networks. This architecture allows the bi-directional flow of power and information between schedulable EVs and smart grids (SGs) to improve the quality of service and cost-effectiveness of energy service providers. However, it is very important to select an EV suitable for scheduling. In order to improve the efficiency of scheduling, we first need to determine define categories of target EV users. We found that grouping on the basis of EV charging behavior is one effective method to identify target EVs. Therefore, we propose a hybrid artificial intelligence classification method based on the charging behavior profile of EVs. Through this classification method, target EVs can be accurately identified. The results of cross-validation experiments and performance evaluations suggest that this method is effective.

Original languageEnglish (US)
Article number80
JournalElectronics (Switzerland)
Issue number1
StatePublished - Jan 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


  • 5G
  • Cloud computing
  • Fog computing
  • Hybrid intelligence
  • Machine learning
  • Smart grid
  • V2G


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