EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Yi Shen; Wei Fang; Feng Ye; Michel Kadoch

doi:10.3390/electronics9010080

journal article Open Access Jan 01, 2020

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

Yi Shen

Wei Fang

Feng Ye

Michel Kadoch

Electronics Vol. 9 No. 1 pp. 80 · MDPI AG

View at Publisher Save 10.3390/electronics9010080

Abstract

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.

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Details

Published: Jan 01, 2020
Vol/Issue: 9(1)
Pages: 80
License: View

Authors

Y

Yi Shen

Rutgers Business School, Rutgers University, Newark, NJ 07102, USA

W

Wei Fang

Rutgers Law School, Rutgers University, Newark, NJ 07012, USA

F

Feng Ye

School of Engineering, University of Dayton, Dayton, OH 45469, USA

M

Michel Kadoch

Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada

Cite This Article

Yi Shen, Wei Fang, Feng Ye, et al. (2020). EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid. Electronics, 9(1), 80. https://doi.org/10.3390/electronics9010080

EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid

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