Scaling Up Battery Swapping Services in Cities

Battery swapping for electric vehicle refueling is reviving and thriving. Despite a captivating sustainable future where swapping batteries will be as convenient as refueling gas today, tensions are mounting in practice (beyond the traditional “range anxiety” issue): On one hand, it is desirable to maximize battery proximity and availability to customers. On the other hand, it is undesirable to incur too many batteries which are environmentally detrimental. Additionally, power grids for battery charging are not accessible everywhere. To reconcile these tensions, some cities are embracing an emerging infrastructure network: Decentralized swapping stations replenish charged batteries from centralized charging stations. In this paper, we model this new urban infrastructure network. This task is complicated by non-Poisson swaps (observed from real data), and by the intertwined stochastic operations of swapping, charging, stocking and circulating batteries among swapping and charging stations. We show that these complexities can be captured by analytical models. We next propose a new location-inventory model for citywide deployment of hub charging stations, which jointly determines the location, allocation and reorder quantity decisions with a non-convex non-concave objective function. We solve this problem exactly and efficiently by exploiting the hidden submodularity and combining constraint-generation and parameter-search techniques. Even for solving convexified problems, our algorithm brings a speedup of at least three orders of magnitude relative to Gurobi solver. The major insight is twofold: Centralizing battery charging may harm cost-efficiency and battery asset-lightness; however, this finding is reversed if foreseeing that decentralized charging will have limited access to grids permitting fast charging. We also identify planning and operational flexibilities brought by centralized charging. In a broader sense, this work deepens our understanding about how mobility and energy are coupled in future smart cities.