ScienceDirect Available online at

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ScienceDirect
Available online at 
www.sciencedirect.com
Procedia Computer Science 141 (2018) 127–134
1877-0509 
© 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (
https://creativecommons.org/licenses/by-nc-nd/4.0/
)
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018.
10.1016/j.procs.2018.10.158
10.1016/j.procs.2018.10.158
© 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (
https://creativecommons.org/licenses/by-nc-nd/4.0/
)
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018.
1877-0509
 
Available online at 
www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2018) 000–000 
 
www.elsevier.com/locate/procedia 
 
1877-0509 © 2018 The Authors. Published by Elsevier Ltd. 
This is an open access article under the CC BY-NC-ND license (
http://creativecommons.org/licenses/by-nc-nd/3.0/
).
Selection and peer-review under responsibility of the scientific committee of EUSPN 2018
The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks 
(EUSPN 2018) 
A Prediction Model of Electric Vehicle Charging Requests 
A. Nait-Sidi-Moh
1
*, A. Ruzmetov
2
, M. Bakhouya
3
, Y. Naitmalek
3
, J. Gaber
2
1 
University of Picardie Jules Verne-INSSET-LTI, Saint - Quentin, France 
2
 University of Technology of Belfort-Montbéliard Belfort, France
3 
International University of Rabat, FIL, LERMA Lab, Technopolis Sala el Jadida, Morocco 
Abstract 
In the last decade, many research works have been conducted to further promote the use of electric vehicles by developing new 
technologies and services. However, the management of their charging planning and scheduling is still a challenging task that 
needs to be addressed. The main issue is to reroute vehicles to the suitable charging stations with less waiting time, fast charging 
process, while ensuring interesting points for their users. In this paper, a prediction model is introduced for managing and 
handling charging needs of electric vehicles. The aim is to predict the average charging rate and charging time of multiple
requests by taking into account the inter-arrival of charging requests and the charging state of each electric vehicle. Simulations 
have been conducted with high number of charging requests and obtained results show the efficiency of the proposed approach 
by studying its impact on waiting times and the occupation of charging stations. 
© 2018 The Authors. Published by Elsevier Ltd. 
This is an open access article under the CC BY-NC-ND license (
http://creativecommons.org/licenses/by-nc-nd/3.0/
). 
Keywords: Electric Vehicles, Predictive charging, Charging management.