In order to improve the accuracy of the description of the coupling relationship between users and traffic in the study of electric vehicle charging demand prediction, a method of electric vehicle charging demand prediction based on city grid attribute division is proposed. The fusion of online vehicle trip data and Python-based urban interest point data is used to accurately divide the study area into functional areas, and then, the characteristics data such as travel patterns of residents and high frequency driving paths are mined. The path selection behavior of electric vehicle users is considered, and a two-layer path selection model based on limited rationality of users is constructed by combining road traffic data. The driving and charging characteristics of electric vehicles are considered, and a complete charging demand prediction model is built. And the model is applied to the Second Ring Road of Chengdu to verify the feasibility of charging demand in different areas and scenarios.
