A showcase of ÉTS researchers’ publications and other contributions

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


Downloads per month over past year

Shen, Yi, Fang, Wei, Ye, Feng and Kadoch, Michel. 2020. « EV charging behavior analysis using hybrid intelligence for 5G smart grid ». Electronics, vol. 9, nº 1.
Compte des citations dans Scopus : 21.

[thumbnail of Kadoch M 2020 20042.pdf]
Kadoch M 2020 20042.pdf - Published Version
Use licence: Creative Commons CC BY.

Download (5MB) | Preview


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.

Item Type: Peer reviewed article published in a journal
Kadoch, Michel
Affiliation: Génie électrique
Date Deposited: 22 Jan 2020 18:48
Last Modified: 13 Oct 2020 14:56

Actions (login required)

View Item View Item