Despite the rapid expansion of electric vehicle (EV) charging networks, questions remain about their efficiency in meeting the growing needs of EV drivers. Previous simulation-based approaches, which rely on static behavioural rules, have struggled to capture the adaptive behaviours of human drivers. Although reinforcement learning has been introduced in EV simulation studies, its application has primarily focused on optimising fleet operations rather than modelling private drivers who make independent charging decisions. To address the gap, we propose a multi-stage reinforcement learning framework that simulates charging demand of private EV drivers across a national-scale road network. We validate the model against real-world data and identify the training stage that most closely reflects actual driver behaviour, which captures both the adaptive behaviours and bounded rationality of private drivers. Based on the simulation results, we also identify critical 'charging deserts' where EV drivers consistently have low state of charge. Our findings also highlight recent policy shifts toward expanding rapid charging hubs along motorway corridors and city boundaries to meet the demand from long-distance trips.

翻译：尽管电动汽车（EV）充电网络迅速扩张，但其能否有效满足日益增长的电动汽车驾驶员需求仍存疑问。以往基于仿真的研究方法依赖静态行为规则，难以捕捉人类驾驶员的适应性行为。尽管强化学习已被引入电动汽车仿真研究，但其应用主要集中于优化车队运营，而非模拟独立做出充电决策的私人驾驶员。为弥补这一空白，我们提出一个多阶段强化学习框架，用于模拟全国尺度路网中私人电动汽车驾驶员的充电需求。我们利用真实数据验证了该模型，并确定了最能反映实际驾驶员行为的训练阶段，该阶段同时捕捉了私人驾驶员的适应性行为和有限理性。基于仿真结果，我们还识别出关键的“充电荒漠”区域，即电动汽车驾驶员在该区域持续处于低电量状态。我们的研究结果进一步指出，近期政策转向沿高速公路走廊和城市边界扩展快速充电枢纽，以满足长途出行需求。