在全球能源转型与新能源汽车产业高速发展的背景下，换电站作为关键补能基础设施，其布局优化对提升服务效率与电力基础设施效能至关重要。该文以 B 市营运类乘用车换电市场为研究对象，通过市场调研获取换电出租车运营数据，引入混合队列模型，建立基于动态稀释效应的饱和度预测模型，同时提出了耦合泰森多边形与粒子群算法的融合算法。基于上述方法，构建了“预测-优化-布局”的协同规划体系，量化了政策敏感性与供需的互动关系。经蒙特卡洛模拟验证了 2025 年理论新增换电站 23 座的预测可靠性；通过“换电站-充电枪”协同配置 (65 座换电站+4 把充电枪) 将用户平均等待时间控制在 10 min 内，实际建站需求优化至 13 座；利用动态空间划分与全局寻优耦合，解决了高密度城区站址优化难题。研究成果为换电网络规划提供了兼顾服务效率与投资成本的可落地方案，也可为分布式电力基础设施优化提供参考。

Against  the  backdrop  of  global  energy  transition  and  the  rapid  development  of  the  new  energy  vehicle industry,  as  critical  refueling  infrastructure,  the  optimal  layout  of  battery  swap  stations  is  essential  for  enhancing  both service efficiency and power infrastructure effectiveness. This study focuses on the operational passenger vehicle battery swap market in City B. Operational data of battery-swap taxis are obtained through market research. A hybrid queueing model  is  introduced  to  establish  a  saturation  prediction  model  based  on  dynamic  dilution  effects.  Additionally,  a  fusion algorithm  coupling  the  Voronoi  diagram  with  the  particle  swarm  optimization  algorithm  is  proposed.  Based  on  the aforementioned methods, a “prediction-optimization-layout” collaborative planning framework is constructed, quantifying policy  sensitivity  and  supply-demand  interactions.  The  reliability  of  the  prediction  of  23  theoretically  new  battery  swap stations by 2025 is further verified through Monte Carlo simulation. Through the coordinated allocation of battery swap stations and charging guns (65 stations + 4 charging guns), the average user waiting time is controlled within 10 min, and the actual station construction demand is optimized to 13 stations. By integrating dynamic spatial partitioning with global optimization, the challenge of site optimization in high-density urban areas is addressed. The research findings provide an implementable solution for battery swap network planning that balances service efficiency and investment costs and also offer valuable insights for optimizing distributed power infrastructure.