Modeling Vehicle Queueing at Battery Swapping Station in a Random Environment

With the rapid development of Electric Vehicles (EVs), range anxiety has become one of the core bottlenecks restricting their large-scale popularization. Among various energy replenishment solutions, battery swapping station (BSS) stand out due to their unique advantages: they can complete battery swapping in a matter of minutes, providing users with an experience close to refueling traditional internal combustion engine vehicles. This significantly alleviates range anxiety and improves vehicle utilization efficiency. However, the prevalent queueing phenomenon in BSS operations directly impairs user experience and reduces station efficiency. Therefore, there is an urgent need to establish an accurate queueing model to optimize resource allocation and service strategies. Aiming at this demand, this study develops a stochastic queueing model for EVs in BSS under stochastic environments, deriving analytical expressions for key system performance indicators. This lays a solid theoretical foundation for the quantitative evaluation of system performance. Meanwhile, numerical experiments are conducted to deeply explore the impacts of different EV arrival patterns and the number of spare battery chargers on the queueing dynamics of EVs. The research findings provide important theoretical support for the scientific planning of BSS, contributing to improving service efficiency and user satisfaction. This model is expected to be integrated into the intelligent transportation system platform to collaboratively optimize the energy replenishment network, promoting the efficient and sustainable development of EV energy supply infrastructure.