Macroscopic Modeling and Hierarchical Control of Battery Swapping Stations

Battery swapping offers a compelling alternative to fast charging for large EV fleets. By decoupling charging from vehicle dwell time, battery swapping stations (BSS) can charge batteries slower, reducing grid strain and extending battery life, while enabling quick vehicle turnaround. In this work, we present a hierarchical control architecture for large-scale BSS that addresses the computational limits of conventional integer programming approaches. By adopting a macroscopic model that represents battery states as a continuous distribution, our method captures nonlinear battery dynamics without sacrificing tractability. In this framework, the upper level optimizes station power procurement in response to market prices, while the lower level enforces realistic charging constraints across hundreds of batteries. This design enables robust operation under stochastic customer arrivals, ensures high service quality, and ultimately maximizes BSS profit, offering a practically scalable solution for heavy-duty EV fleets.