Smart green charging scheme of centralized electric vehicle stations

Peter Makeen, Saim Memon, M. A. Elkasrawy, Sameh O. Abdullatif, Hani A. Ghali

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


This paper presses a smart charging decision-making criterion that significantly contributes in enhancing the scheduling of the electric vehicles (EVs) during the charging process. The proposed criterion aims to optimize the charging time, select the charging methodology either DC constant current constant voltage (DC-CCCV) or DC multi-stage constant currents (DC-MSCC), maximize the charging capacity as well as minimize the queuing delay per EV, especially during peak hours. The decision-making algorithms have been developed by utilizing metaheuristic algorithms including the Genetic Algorithm (GA) and Water Cycle Optimization Algorithm (WCOA). The utility of the proposed models has been investigated while considering the Mixed Integer Linear Programming (MILP) as a benchmark. Furthermore, the proposed models are seeded using the Monte Carlo simulation technique by estimating the EVs arriving density to the EVS across the day. WCOA has shown an overall reduction of 13% and 8.5% in the total charging time while referring to MILP and GA respectively.

Original languageEnglish
Pages (from-to)490-498
Number of pages9
JournalInternational Journal of Green Energy
Issue number5
Early online date27 Jul 2021
Publication statusPublished - 1 Mar 2022
Externally publishedYes


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