基于有限信息的电动汽车用户充电行为特征识别OA北大核心CSTPCD

With the widespread adoption of electric vehicles(EVs),the charging behavior of EV users has become a critical focus area.However,EV users often exhibit low enthusiasm for participating in vehicle-to-grid(V2G)interactions,making it difficult to effectively motivate their involvement in load balancing and frequency regulation.Moreover,user behavior data are complex and limited,posing challenges for the accurate analysis of user behavior.This study proposes a model for identifying the charging behavior characteristics of EV users based on limited information to formulate differentiated incentive strategies.First,it outlines the fundamental characteristics of user charging behavior and proposes incentive strategies tailored to different user types.Subsequently,a classification model for user charging behavior is developed.It then details the steps for identifying user charging behavior and designs a model for recognizing these characteristics based on a cloud model and fuzzy Petri nets.Finally,the model is validated through a case study using limited user data from a specific charging facility.The results of the case study demonstrate that the proposed model successfully categorizes EV users into different types,thereby achieving the goals of targeted incentive strategies.This model offers an effective tool to better understand user behavior,optimize energy management,and provide personalized incentive strategies,thereby encouraging more active participation in V2G interactions and energy scheduling.It further promotes the sustainable development of electric vehicles.