We present an online algorithm for scheduling the charging of Electric Vehicles (EVs) in a Charging Station, aiming to optimize the overall quality of service through sum of weighted completion time minimization. Upon arrival of each EV, the algorithm generates a menu of service-price options. By letting the EV users pick their most preferable option, the algorithm offers guaranteed quality of service, achieves near optimal performance, and prevents the users from acting strategically.

Along with high penetration of Electric Vehicles (EVs), charging stations are required to service a large amount of charging requests while accounting for constraints on the station's peak electricity consumption. To this end, a charging station needs to make online charging scheduling decisions often under limited future information. An important challenge relates to the prioritization of EVs that have unknown valuations for different levels of charging services. In this paper, we take into consideration the inability of EV users to express these valuations in closed-form utility functions. We consider a paradigm where a menu of possible charging schedules and corresponding prices is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV's charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs - charging station interaction.

We present an online algorithm for scheduling the charging of Electric Vehicles (EVs) in a Charging Station, aiming to optimize the overall quality of service through sum of weighted completion time minimization. Upon arrival of each EV, the algorithm generates a menu of service-price options. By letting the EV users pick their most preferable option, the algorithm offers guaranteed quality of service, achieves near optimal performance, and prevents the users from acting strategically.