Battery swapping demand simulation for electric micromobility vehicles considering multi-source information interaction and behavior decision

The battery swapping mode has the advantages of convenience and battery controllability, which can alleviate charging problems with electric micromobility vehicles (EMVs). The layout of battery swapping facilities and scheduling management can be carried out by accurately analyzing the high-resolution spatial-temporal distribution of battery swapping demand for EMV. This study establishes a prediction model framework for battery swapping demand of EMV using Monte Carlo simulation based on travel chains considering multi-source information interaction and behavior decision. Using real residential travel survey data of Nanning City, China and an empirical analysis with the city as a case study, the results show that the prediction framework proposed in this study is reliable. The temporal distribution of EMV battery swapping demand is closely related to the spatial distribution of travel. In addition, the demand characteristics of both centralized and decentralized battery swapping stations are evaluated separately. The peak intensity of the centralized mode is 18% greater than that of the decentralized mode when the battery swapping penetration is 35%. When the battery swapping penetration rate is low, decentralized mode can meet the peak swapping demand, and as the penetration rate increases, the effect of centralized mode is reflected. Finally, the total swapping path time considering multi-source information interaction and behavior decision is reduced by 7.8%. These findings allow for the study of battery swapping station planning and transportation planning.