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

Artificial Intelligence;Computer Science and Game Theory