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.

翻译：本研究深入探讨高峰时段电动汽车充电站中的用户行为，重点关注不耐烦情绪如何导致用户放弃排队（balking）及中途离队（reneging）。我们提出一个基于智能体的仿真框架，将用户乐观程度（悲观型、标准型、乐观型）纳入排队动力学建模。与既有研究不同，该框架凸显了人类行为在高峰需求下对站点效率的关键塑造作用。仿真揭示了一个核心问题：因缺乏排队信息引发的弃队行为，使用户陷入决策困境。为此，我们提出向抵达充电站的电动汽车用户实时共享等待时间指标。这种信息驱动的排队加入机制可确保用户获得更优的服务质量，实验表明该方法能显著减少离队行为（最高达94%），从而改善充电运营效率。进一步分析表明，充电速度在电量超过80%后显著降低，但受里程焦虑影响，多数用户仍倾向于充满电，导致排队时间延长。针对此问题，我们提出一种双模式双端口充电桩设计，支持功率共享选项。该设计允许用户快速充电至80%后自动切换为慢充模式，同时释放第二端口进行快速充电。因此，快速充电桩的可用性与吞吐量可提升5%。随着交通领域向智能化演进，本研究的建模框架将人类决策整合至自动化规划中，为优化充电站效率与提升用户体验提供重要洞见，尤其适用于历史数据有限的新建充电站导入阶段。