The increasing importance of electric vehicles lies in their lower emissions compared to fossil fuel vehicles. However, challenges like long charging times and range anxiety hinder their widespread adoption. Battery swapping stations offer a practical solution to expedite EV refueling, reducing wait times and range concerns. This research proposes a battery-swapping architecture that provides battery-swapping services to electric vehicles while exploring additional revenue sources and cost reductions. The model uses batteries of the battery swapping station as a battery energy storage system, supplying power to mobile or stationary loads during grid or renewable energy source downtime. By offering cost-effective electricity during peak hours or non-availability, the model demonstrates up to a 35% reduction in consumer electricity costs during peak hours and an 8.8% reduction in overall costs during 24-hour operation. The implementation combines linear programming with machine learning to forecast renewable energy output and electric vehicle energy demand, considering flexible battery charging and discharging controls and degradation processes. These optimization results show the potential of the proposed model to boost battery swapping station income and cut costs, contributing significantly to the electric vehicle market’s growth.
Keywords battery swapping station, electric vehicles, battery energy storage system, energy arbitrage, energy food and transportation nexus, optimization