Modeling the Location and Deployment Problem of Battery Swapping Stations for an Electric Scooter Company

The increasing adoption of electric scooters in urban areas has prompted new challenges in energy management and infrastructure optimization. Efficient deployment of Battery Swapping Stations (BSS) is crucial to support this growing demand while maintaining sustainable and convenient services for users. This study addresses the problem of optimally locating BSS and allocating batteries to meet dynamic customer demands. The problem is a variant of the Facility Location Problem (FLP) but with additional complexities, such as battery availability constraints, station capacity limitations, and time-sensitive customer demand patterns. To tackle these challenges, we propose a Mixed-Integer Linear Programming (MILP) model to optimize station deployment and customer assignments, minimizing both station opening and assignment costs. Two experimental studies were conducted. The first analyzed the impact of station opening and customer distance costs on the number of open stations and the objective function value. The second study examined the influence of the number of batteries per station on operational efficiency. Results demonstrate valuable insights for companies, enabling more effective decision-making for station deployment and battery management.