The electric vehicle routing problem with time windows (EVRPTW) extends the classical VRPTW by introducing battery capacity constraints and charging station decisions. Existing benchmark datasets are often static and lack verifiable feasibility, which restricts reproducible evaluation of learning-based routing models. We introduce SynthCharge, a parametric generator that produces diverse, feasibility-screened EVRPTW instances across varying spatiotemporal configurations and scalable customer counts. While SynthCharge can currently generate large-scale instances of up to 500 customers, we focus our experiments on sizes ranging from 5 to 100 customers. Unlike static benchmark suites, SynthCharge integrates instance geometry with adaptive energy capacity scaling and range-aware charging station placement. To guarantee structural validity, the generator systematically filters out unsolvable instances through a fast feasibility screening process. Ultimately, SynthCharge provides the dynamic benchmarking infrastructure needed to systematically evaluate the robustness of emerging neural routing and data-driven approaches.

翻译：带时间窗的电动汽车路径规划问题（EVRPTW）在经典VRPTW基础上引入了电池容量约束与充电站决策。现有基准数据集通常是静态的且缺乏可验证的可行性，这限制了对基于学习的路径规划模型进行可重复评估。我们提出SynthCharge，一种参数化生成器，能够在不同时空配置与可扩展客户数量下生成多样化、经过可行性筛选的EVRPTW实例。虽然SynthCharge当前可生成多达500个客户的大规模实例，但我们的实验聚焦于5至100个客户的规模。与静态基准测试集不同，SynthCharge将实例几何结构与自适应能量容量缩放及里程感知的充电站布局相结合。为保证结构有效性，该生成器通过快速可行性筛选流程系统性地过滤不可解实例。最终，SynthCharge为系统评估新兴神经路径规划及数据驱动方法的鲁棒性提供了所需的动态基准测试基础设施。