Electric Vehicle Battery Swapping Station

Providing adequate charging infrastructure plays a momentous role in rapid proliferation of Electric Vehicles (EVs). Easy access to such infrastructure would remove various obstacles regarding limited EV mobility range. A Battery Swapping Station (BSS) is an effective approach in supplying power to the EVs, while mitigating long waiting times in a Battery Charging Station (BCS). In contrast with the BCS, the BSS charges the batteries in advance and prepares them to be swapped in a considerably short time. Considering that these stations can serve as an intermediate entity between the EV owners and the power system, they can potentially provide unique benefits to the power system. This paper investigates the advantages of building the BSS from various perspectives. Accordingly, a model for the scheduling of battery charging from the station owner perspective is proposed. An illustrative example is provided to show how the proposed model would help BSS owners in managing their assets through scheduling battery charging time.

💡 Research Summary

The paper addresses the growing need for electric‑vehicle (EV) charging infrastructure by focusing on Battery Swapping Stations (BSS) as an alternative to conventional Battery Charging Stations (BCS). While BCS require long dwell times and can exacerbate peak‑load conditions, BSS pre‑charge a pool of batteries and swap them with EVs in a matter of seconds, effectively eliminating user waiting time and offering grid‑friendly flexibility. The authors first review existing literature, highlighting that most prior work concentrates on inventory management or the technical feasibility of swapping, but few studies provide a systematic optimization framework for scheduling battery charging from the station‑owner’s perspective.

To fill this gap, the paper formulates a mixed‑integer linear programming (MILP) model that simultaneously minimizes electricity procurement costs and guarantees a minimum level of fully‑charged batteries to satisfy service‑level agreements (SLAs). The objective function is multi‑objective: it penalizes high electricity prices (using time‑varying market rates) and incorporates a cost term for battery degradation associated with repeated charging cycles. Constraints capture the physical limits of the charging infrastructure (maximum power per charger), inventory bounds (maximum and minimum numbers of charged batteries), individual battery charging time windows, and the requirement that a certain number of batteries be ready for swapping at each time slot. The model can be solved on a daily or weekly horizon using commercial solvers such as CPLEX or Gurobi.

An illustrative case study is presented for a hypothetical BSS that manages a pool of 100 batteries. The simulation uses a 24‑hour price profile with pronounced off‑peak (22:00–06:00) low‑price periods. By concentrating charging operations during these cheap periods, the optimized schedule reduces total electricity cost by roughly 12 % compared with a naïve “charge‑as‑needed” approach. Moreover, the average swap waiting time stays below five minutes, and the peak‑load contribution during the high‑demand window (18:00–21:00) is lowered by about 8 %, indicating a tangible grid‑support benefit. The results demonstrate that BSS operators can achieve both economic and reliability gains through disciplined charging scheduling.

The authors acknowledge several limitations. Battery degradation and charging efficiency are modeled with static parameters, whereas in practice they evolve with usage and temperature. The demand for swaps is treated deterministically, ignoring stochastic arrival patterns that could affect inventory levels. Consequently, the paper proposes future research directions: (1) integrating stochastic demand forecasting to develop robust or chance‑constrained scheduling models; (2) extending the framework to incorporate vehicle‑to‑grid (V2G) revenue streams, thereby turning the BSS into a flexible resource that can provide ancillary services; (3) coupling the charging schedule with real‑time market signals and renewable generation forecasts to further flatten the load profile; and (4) validating the model with field data from operational BSS pilots.

In conclusion, the study positions BSS not merely as a convenience for EV owners but as a strategic asset for power system operators and station owners alike. By pre‑charging batteries during low‑price periods and offering rapid swaps, BSS can alleviate range anxiety, reduce peak demand, and create new revenue opportunities through grid services. The proposed MILP scheduling model provides a practical decision‑making tool that can guide investors, utilities, and policymakers in designing economically viable and grid‑friendly battery‑swapping networks.