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Open AccessFeature PaperArticle

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

by Yi Shen 1,*, Wei Fang 2, Feng Ye 3 and Michel Kadoch 4
1
Rutgers Business School, Rutgers University, Newark, NJ 07102, USA
2
Rutgers Law School, Rutgers University, Newark, NJ 07012, USA
3
School of Engineering, University of Dayton, Dayton, OH 45469, USA
4
Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(1), 80; https://doi.org/10.3390/electronics9010080
Received: 21 November 2019 / Revised: 15 December 2019 / Accepted: 20 December 2019 / Published: 1 January 2020
(This article belongs to the Special Issue Recent Advances in Mobile Ad Hoc Networks)
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. View Full-Text
Keywords: smart grid; 5G; V2G; cloud computing; fog computing; machine learning; hybrid intelligence smart grid; 5G; V2G; cloud computing; fog computing; machine learning; hybrid intelligence
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Shen, Y.; Fang, W.; Ye, F.; Kadoch, M. EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid. Electronics 2020, 9, 80.

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