Activity-Based travel chain simulation for Battery-Swapping demand of electric micromobility vehicles

Abstract

Intelligent battery-swapping services for electric micromobility vehicles (EMVs) are expanding from To-business to To-customer sides, offering consumers greater convenience while addressing charging challenges. This study proposes an activity-based travel chain simulation framework to predict battery-swapping demand for ordinary and delivery EMVs. A case study in Nanjing City shows that temporal distribution of EMV battery-swapping demand is related to travel patterns, while spatial distribution is correlated with EMV generation and traffic zones. Sensitivity analysis examines the impact of swapping penetration, initial power, and swapping threshold on the swapping demand. Findings suggest that increasing swapping penetration by 1% leads to a rise of 692 and 139 in one-day swapping demand for ordinary and delivery EMVs, respectively. Furthermore, higher initial power leads to lower one-day swapping demand, while increasing the swapping threshold leads to higher swapping demand. These findings offer important insights for battery-swapping station planning and operation management.