Enhancing the Efficiency of Electric Vehicles Charging Stations Based on Novel Fuzzy Integer Linear Programming

Shahid Hussain, Reyazur Rashid Irshad, Fabiano Pallonetto, Qasim Jan, Saurabh Shukla, Subhasis Thakur, John G. Breslin, Mousa Marzband, Yun-Su Kim, Muhammad Ahmad Rathore, Hesham El-Sayed

Research output: Contribution to journalArticlepeer-review


The electric vehicles (EVs) charging stations (CSs) at public premises have higher installation and power consumption costs. The potential benefits of public CSs rely on their efficient utilization. However, the conventional charging methods obligate a long waiting time and thereby deteriorate their efficiency with low utilization. This paper suggests a novel fuzzy integer linear programming and a heuristic fuzzy inference approach (FIA) for CSs utilization. The model introduces the underlying fuzzy inference system and a detailed formulation for obtaining the optimal solution. The developed fuzzy inference incorporates the uncertain and independent available power, required state-of-charge, and dwell time from the power grid and EVs domains and correlates them into weighted control variables. The FIA automates the service provision for the EVs with the most urgent requirements by resolving the objective function utilizing the weighted control variables, thereby optimizing the waiting time and the CSs utilization. To evaluate the effectiveness of the proposed FIA, several case studies were conducted, corresponding to different parking capacities and installations of CSs. Moreover, the simulations were conducted on EVs with varying battery capacities, and their performance was evaluated based on several metrics, including average waiting time, utilization of CSs, fairness, and execution time. The simulation results have confirmed that the effectiveness of the proposed FIA scheduling method is considerably higher than that of the other methods discussed.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusPublished - 18 May 2023


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