Predictive Charging Management for Battery Swapping Stations of Electric Scooters in Busan

Electric scooters (ESs) offer a sustainable alternative to urban logistics but face challenges such as limited range and long charging times. Battery swapping station (BSS) addresses these issues by enabling quick battery exchanges, reducing operational downtime and improving efficiency. Unlike conventional BSS, this study specifically focuses on BSSs of ESs, which show different usage behaviors from large electric vehicles and pose distinct operational challenges due to their smaller battery capacities and higher frequency of exchanges. This study proposes a predictive charging management algorithm designed for multiple BSSs of ESs. By predicting hourly demand and dynamically controlling fast and slow charging, the algorithm minimizes unnecessary fast charging, ensuring efficient resource utilization while maintaining battery availability. Simulation using a digital twin model demonstrated the delivery of resource-and cost-efficient battery exchange solutions while satisfying ES riders with predictive charging management algorithm. These findings underscore the algorithm's adaptability to optimize energy consumption and resource allocation, paving the way for its integration into smart city infrastructures.