The transition to Electric Vehicles (EVs) demands intelligent, congestion-aware infrastructure planning to balance user convenience, economic viability, and traffic efficiency. We present a joint optimisation framework for EV Charging Station (CS) placement and pricing, explicitly capturing strategic driver behaviour through coupled non-atomic congestion games over road networks and charging facilities. From a Public Authority (PA) perspective, the model minimises social cost, travel times, queuing delays and charging expenses, while ensuring infrastructure profitability. To solve the resulting Mixed-Integer Nonlinear Programme, we propose a scalable two-level approximation method, Joint Placement and Pricing Optimisation under Driver Equilibrium (JPPO-DE), combining driver behaviour decomposition with integer relaxation. Experiments on the benchmark Sioux Falls Transportation Network (TN) demonstrate that our method consistently outperforms single-parameter baselines, effectively adapting to varying budgets, EV penetration levels, and station capacities. It achieves performance improvements of at least 16% over state-of-the-art approaches. A generalisation procedure further extends scalability to larger networks. By accurately modelling traffic equilibria and enabling adaptive, efficient infrastructure design, our framework advances key intelligent transportation system goals for sustainable urban mobility.

翻译：向电动汽车（EV）的转型需要智能、拥堵感知的基础设施规划，以平衡用户便利性、经济可行性和交通效率。我们提出了一种联合优化框架，用于电动汽车充电站（CS）的选址与定价，该框架通过将道路网络及充电设施上的耦合非原子拥堵博弈纳入考量，显式地刻画了驾驶员的策略性行为。从公共管理机构（PA）的视角出发，该模型在确保基础设施盈利性的同时，最小化社会总成本、出行时间、排队延误及充电费用。为求解由此产生的混合整数非线性规划，我们提出了一种可扩展的两级近似方法——驾驶员均衡下的联合选址与定价优化（JPPO-DE），该方法将驾驶员行为分解与整数松弛相结合。在基准的Sioux Falls交通网络（TN）上的实验表明，我们的方法始终优于单参数基线，能有效适应不同的预算、电动汽车渗透率和充电站容量，其性能提升较现有最优方法至少达到16%。我们进一步通过一种泛化流程，将该方法的可扩展性延伸至更大规模的网络。通过精确建模交通均衡并实现自适应、高效的基础设施设计，我们的框架推进了面向可持续城市出行的关键智能交通系统目标。