电力建设2024，Vol.45Issue(10)：69-77,9.DOI:10.12204/j.issn.1000-7229.2024.10.007
基于有限信息的电动汽车用户充电行为特征识别
Identification of Charging Behavior Characteristics of Electric Vehicle Users Based on Limited Information
摘要
Abstract
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.关键词
电动汽车/用户充电行为/特征识别/云模型/模糊Petri网Key words
electric vehicles/user charging behavior/feature recognition/cloud model/fuzzy Petri net分类
信息技术与安全科学引用本文复制引用
石天琛,杨烨,刘明光,王文,王佳妮,刘敦楠..基于有限信息的电动汽车用户充电行为特征识别[J].电力建设,2024,45(10):69-77,9.基金项目
This work is Supported by National Natural Science Foundation of China(No.72171082). 国家自然科学基金面上项目(72171082) （No.72171082）
