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With the increase in adoption of Electric Vehicles (EVs), proper utilizationof the charging infrastructure is an emerging challenge for service providers.Overstaying of an EV after a charging event is a key contributor to lowutilization. Since overstaying is easily detectable by monitoring the powerdrawn from the charger, managing this problem primarily involves designing anappropriate "penalty" during the overstaying period. Higher penalties dodiscourage overstaying; however, due to uncertainty in parking duration, lesspeople would find such penalties acceptable, leading to decreased utilization(and revenue). To analyze this central trade-off, we develop a novel frameworkthat integrates models for realistic user behavior into queueing dynamics tolocate the optimal penalty from the points of view of utilization and revenue,for different values of the external charging demand. Next, when the modelparameters are unknown, we show how an online learning algorithm, such as UCB,can be adapted to learn the optimal penalty. Our experimental validation, basedon charging data from London, shows that an appropriate penalty can increaseboth utilization and revenue while significantly reducing overstaying.

Managing Overstaying Electric Vehicles in Park-and-Charge Facilities