A Study on the Optimization of Site Selection and Capacity Allocation for New Energy Vehicle Swapping Stations in Urban Areas

Abstract

Urban battery swapping station (BSS) planning is difficult because site opening, service-module allocation, user assignment, battery degradation pressure, travel burden, and congestion are tightly coupled. A plan that minimizes investment alone may create long queues, whereas a plan that only reduces waiting can overbuild costly and underused capacity. This study formulates the urban BSS siting–sizing problem as an operation-aware mixed-integer nonlinear model and evaluates feasible plans with payoff-table global-criterion normalization. To search this rugged planning space, we propose a Stable Portfolio Hyper-Heuristic (SPHH) that combines Greedy construction, BO-guided large-neighborhood search, simulated annealing, and optimized annealing with reheating, followed by feasible-incumbent preservation and non-worsening post-processing. The formal campaign contains 18 synthetic cases, 10 independent repeats, and five algorithms, yielding 900 optimization records. Across the 18 cases, SPHH produced the lowest mean GC score and reduced the mean GC value on average relative to the baseline algorithms. Nonparametric Friedman and Holm-adjusted Wilcoxon tests confirmed statistically significant differences among methods, although the advantage over the strongest baseline was not universal. These results indicate that SPHH is most useful as an offline planning selector that improves recommendation stability when additional computation is acceptable, rather than as a universally faster optimizer.