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Centralized Ranking-Based Approach to the Assignment of Electric Vehicles to Charging Stations
Moh’d, Fawzi Abdul Fattah Mohammed
Moh’d, Fawzi Abdul Fattah Mohammed
Date
2024-10
Advisor
Type
Thesis
Degree
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35.232-2024.47a Fawzi Abdul Fattah Mohammed Moh’d.pdf
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Description
A Master of Science thesis in Electrical Engineering by Fawzi Abdul Fattah Mohammed Moh’d entitled, “Centralized Ranking-Based Approach to the Assignment of Electric Vehicles to Charging Stations”, submitted in October 2024. Thesis advisor is Dr. Mohamed Hassan and thesis co-advisor is Dr. Ahmed Osman-Ahmed. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).
Abstract
The rapid growth of electric vehicles (EVs) has spurred the need for efficient EV-to-charging station (CS) assignment approaches. In this thesis, we provide a balanced user-utility EV assignment approach based on a ranking method, while addressing two alternative ranking methods, user-oriented and utility-oriented versions, where each serves a designated application. The main performance metric of evaluation is the average service time, defined as the average time a user spends from initiating the request until terminating the recharging service. Our approach contrasts with most EV-related studies that tend to prioritize one aspect over another, such as sacrificing user convenience for utility benefits or vice versa. Instead, we aim to balance both utility and user convenience adhering to predefined key performance indicator standards while offering alternatives that improve each aspect individually. The methodology we use depends on defining a ranking parameter between the requesting EV and all reachable charging stations and an assignment approach consisting of a central aggregator with a request accumulation period to facilitate the management of a dynamic population of EVs. The proposed ranking assignment method is compared to that of other dynamic assignment methods which are the nearest-station method, join-the-shortest queue method, and the benchmark Lyapunov EV assignment method. Our study proceeds to investigate the influence of heterogeneous EV populations on the average system time, aiming to uncover insights into the heterogeneous effects. The challenge lies in effectively managing the distribution of each EV brand in the population and addressing the varied request arrival rates stemming from diverse battery capacities. Understanding these dynamics is essential for evaluating our approach's performance under real-world conditions.