  Our work delves into user behaviour at Electric Vehicle(EV) charging stations
during peak times, particularly focusing on how impatience drives balking (not
joining queues) and reneging (leaving queues prematurely). We introduce an
Agent-based simulation framework that incorporates user optimism levels
(pessimistic, standard, and optimistic) in the queue dynamics. Unlike previous
work, this framework highlights the crucial role of human behaviour in shaping
station efficiency for peak demand. The simulation reveals a key issue: balking
often occurs due to a lack of queue insights, creating user dilemmas. To
address this, we propose real-time sharing of wait time metrics with arriving
EV users at the station. This ensures better Quality of Service (QoS) with
user-informed queue joining and demonstrates significant reductions in reneging
(up to 94%) improving the charging operation. Further analysis shows that
charging speed decreases significantly beyond 80%, but most users prioritize
full charges due to range anxiety, leading to a longer queue. To address this,
we propose a two-mode, two-port charger design with power-sharing options. This
allows users to fast-charge to 80% and automatically switch to slow charging,
enabling fast charging on the second port. Thus, increasing fast charger
availability and throughput by up to 5%. As the mobility sector transitions
towards intelligent traffic, our modelling framework, which integrates human
decision-making within automated planning, provides valuable insights for
optimizing charging station efficiency and improving the user experience. This
approach is particularly relevant during the introduction phase of new
stations, when historical data might be limited.
