Template-type: ReDIF-Paper 1.0
Author-Name: El Mehdi Er Raqabi
Author-Name-First: Raqabi
Author-Name-Last: El Mehdi Er
Author-Name: Wenkai Li
Author-Name-First: Li
Author-Name-Last: Wenkai
Author-Email: lwk@iuj.ac.jp
Author-Workplace-Name: IUJ Research Institute, International University of Japan
Title: An Electric Vehicle Migration Framework
Abstract: Electric vehicles (EVs), with lighter environmental footprint than traditional gasoline vehicles, are growing rapidly worldwide. Some countries such as Norway and Canada have successfully established EV networks and achieved a significant progress towards EV deployment. While the EV technology is becoming popular in developed countries, emerging countries are lacking behind mainly because of the huge investment hurdle to establishing EV networks. This paper developed an efficient Electric Vehicle Migration Framework (EVMF) aiming to minimize the total costs involved in establishing an EV network, using real world data from three major cities of Morocco: Rabat, Casablanca, and Fes. A given set of public institutions having a fleet of EVs are first grouped into zones based on clustering algorithms. MILP (Mixed Integer Linear Programming) models are developed to optimally select EV charging station locations within these organizations, with an objective to minimize the total cost. This paper can help to minimize the investment needed to establish EV networks. The transition towards EV networks can first take place in cities, especially at public institutions, followed by locations among cities. With the framework developed in this paper, policy makers can make better decisions on EV network migration.
Length: 43 pages
Creation-Date: 2022-04
Number: EMS_2022_03
File-URL: https://www.iuj.ac.jp/workingpapers/index.cfm?File=EMS_2022_03.pdf
File-Format: Application/pdf
File-Function: First version, 2022
File-Size: 1MB
Keywords: Electric vehicle, range anxiety, public transport, optimization, MILP, data mining, remote sensing, clustering.
Handle: RePEc:iuj:wpaper:EMS_2022_03