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.