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.

本文提出了一种电动汽车充电的过度停留问题的解决方案，通过设计恰当的 “罚款” 惩罚过度停留者，采用不同的值以满足外部充电需求，从利用率和收入的角度找到最优罚款，实现了通过使用在线学习算法来学习最优罚款的方法，但需要进一步实验验证。