Nisara Wongutai and Natthanan Promsuk
Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 472-504
Abstract
Environmental pollution, particularly from greenhouse gas emissions, has emerged as one of the most pressing global challenges, with the transportation sector being a major contributor. Electric Vehicles (EVs) are increasingly promoted as a sustainable solution to mitigate these emissions; however, their adoption is hindered by one critical limitation—the long charging time, which ranges from several hours for conventional chargers to around 30 minutes for fast chargers. To address this limitation, the concept of an EV Battery Swapping Service (BSS) has been introduced, enabling rapid battery replacement within minutes. The mobility of Battery Swapping Vans (BSVs) further enhances flexibility by overcoming the geographic constraints of fixed charging stations. This study proposes a Battery Swapping Service Request Scheduling (BSSRS) model utilizing the Minimum Waiting Time and Priority Satisfaction (MWT-PS) strategy. Using a simulation dataset of 20 service points with one BSV traveling at a constant speed of 40 km/h to service 19 EVs, the results demonstrate that the MWT-PS algorithm significantly improves service efficiency. Compared to traditional scheduling methods, the MWT-PS reduced the total Euclidean distance to 544.79 kilometers and shortened the overall service duration to 13.62 hours, outperforming both First-Come First-Serve (FCFS) and Highest Credit First (HCF) algorithms. These findings highlight the potential of the proposed scheduling approach to enhance EV adoption by making energy replenishment faster, more efficient, and more sustainable.