Optimizing Tesla Charging Station Matching with Diverse Driver Preferences: Solving the Tie-Breaking Challenge

The rise of electric vehicles (EVs) has underscored a critical infrastructure challenge: the rather large difference between the growing need for charging stations and the number of them in existence. Existing allocation methods, which mainly involve proximity-based methods, do not take the variation of drivers into consideration, so there are inefficiencies. Specifically, this paper focuses on approaching the matching problem by the Gale-Shapley Deferred Acceptance (DA) method and its ability to untie the tie-breaking problem in preference-based systems for matching drivers with the Tesla charging stations. Thus, if AC factors include charging speed, cost, and the driver's urgency level, the DA algorithm will have a more efficient distribution of scarce resources for different requirements. More specifically, the study proves that with a higher level of matching, there is increased efficiency and justice in the distribution of charging stations, thus helping to improve the experience of the users and increase the utilization of resources.