Scalable No-Stockout Charging Scheduling for Battery Swapping Under Time-of-Use Prices
2026-07-27 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors studied how to efficiently manage battery swapping stations where vehicles exchange used batteries for charged ones. They created a mathematical model to plan charging schedules, ensuring no customer runs out of batteries while keeping electricity costs low. Their approach includes a simpler, faster version and a heuristic method that works well in tests, saving about half the charging cost compared to immediate charging. They also tested their methods on real-world data, confirming they can serve all swaps and still cut energy costs significantly.
battery swapping stationmixed-integer linear programcharging scheduleheuristic methodenergy cost optimizationno-stockout constraintoperational researchrolling horizon control
Authors
Eunbin Cho, Junki Cho, Hakjin Lee, Jaehoon Sim, Junghoon Seo
Abstract
A battery-swapping station must provide every arriving vehicle with a charged battery while minimizing the time-of-use cost of recharging returned units. Coordinating heterogeneous compatibility, vehicle-specific return times, and finite charger capacity requires service-aware recharge decisions across the planning horizon. We formulate a per-battery mixed-integer linear program that captures these operational features under a hard no-stockout constraint and derive a provably equivalent reduced form with fewer explicit binary variables. In the synthetic scaling study, a price-guided battery-path heuristic returned a full-service schedule for every instance; regime-level median solve times ranged from 0.24 to 8.0 seconds. Its median cost premiums were 7-8% over certified reference costs at the Small and Medium scales, and its certified ex post optimality-gap upper bounds were 9-12% at the Large and xlarge scales. For each operational baseline, the certified reference schedules reduced charging-energy cost by 50-60% on instances that the baseline fully served and for which a certified reference was available. In a 30-day replay of 1,002 swaps recorded at a commercial station, the reduced-model and heuristic rolling controllers served every swap and reduced charging-energy cost by approximately 50% relative to immediate charging.