Optimizing electric vehicles station performance using AI-based decision maker algorithm

• Main Authors: Elkasrawy, M. A, Makeen, Peter, Abdellatif, Sameh O, Ghali, Hani A
• Format: Conference Proceeding
• Language: English
• Published: SPIE 20.08.2020
• ISBN: 1510637443; 9781510637443
• ISSN: 0277-786X
• DOI: 10.1117/12.2572901

This paper presses a developed methodology of estimating the total number of charging points in the Electric Vehicle Charging Station (EVCS). Three various EVCSs in the urban core, suburban area and the rural area were modeled and investigated by using an established database for fourteen different Electric Vehicles (EVs) of different manufacturers. Monte-Carlo simulation technique (MCST) was applied with high-dense iterative runs to predict the peak hour energy demand that can be occurred in the proposed three zones besides expecting the arrival interval time of the EVs across the day according to the percentage of daily demand of each station. Moreover, an imperially constructed equation is used to calculate the number of charging points in each zone by estimating the normalized arrival time with the aid of MCST. The precise estimating of the total number of charging points for each station is minimizing the charging time and the queuing delay issues.