The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure. Charging-network design requires decisions across three interdependent layers: Planning, which determines where and how much infrastructure to build; Scheduling, which governs charging dispatch, pricing, and grid interaction; and Behavior, which captures how users choose stations, charging times, and charging durations. Existing studies have advanced each layer substantially, but the literature remains fragmented, and cross-layer interactions are often treated through simplifying assumptions. This survey develops a three-layer Planning-Scheduling-Behavior (PSB) framework to organize EV charging research according to decision horizon, actor objective, and coupling structure. We further identify a fidelity-tractability tradeoff, termed the PSB trilemma: each layer is computationally difficult in isolation, and realistic integration across layers generally requires reducing the fidelity of at least one layer. Reviewing the three pairwise-coupling literatures - Planning-Scheduling, Scheduling-Behavior, and Planning-Behavior - we show that the omitted third layer is typically fixed exogenously or represented by a static aggregate surrogate. These simplifications enable tractability but impose distinct costs: they can obscure long-term investment feedback, temporal grid and emissions dynamics, or heterogeneous user response and equity outcomes. Building on this diagnosis, we identify open challenges in emerging charging technologies, behavioral incentives, equity metrics, and city-scale learning-based methods that balance fidelity, interpretability, and policy relevance.

翻译：随着电动汽车的快速发展，交通电气化的主要约束正从车辆普及转向充电基础设施的部署与运营。充电网络设计需在三个相互依存的层面进行决策：规划层决定充电设施的选址与建设规模；调度层管控充电时序、电价及电网互动；行为层反映用户对充电站、充电时刻和充电时长的选择偏好。现有研究在单层面已取得显著进展，但文献体系仍较为分散，跨层交互常被简化假设处理。本综述构建了规划-调度-行为（PSB）三层分析框架，依据决策时间尺度、主体目标和耦合结构组织电动汽车充电研究。我们进一步揭示了精度-可解性权衡关系，即PSB三重困境：每个层面单独求解即具有较高计算复杂度，而现实场景下的跨层整合通常需要至少降低一个层面的精度。通过梳理规划-调度、调度-行为和规划-行为三组两两耦合研究文献，我们发现被忽略的第三层面通常被外生给定或采用静态聚合代理模型表示。这种简化虽保障了可解性，但产生不同代价：可能模糊长期投资反馈机制、时序电网与排放动态、或异构用户响应与公平性结果。基于上述诊断，我们识别了新兴充电技术、行为激励机制、公平性指标及平衡精度、可解释性与政策相关性的城域尺度学习方法中的开放挑战。