EV Charging Behavior Analysis and Load Prediction via Order Data of Charging Stations

Shiqian Wang, Bo Liu, Qiuyan Li, Ding Han, Jianshu Zhou () and Yue Xiang
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Shiqian Wang: State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China
Bo Liu: State Grid Henan Electric Power Company, Zhengzhou 450052, China
Qiuyan Li: State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China
Ding Han: State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China
Jianshu Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yue Xiang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Sustainability, 2025, vol. 17, issue 5, 1-16

Abstract: To understand the charging behavior of electric vehicle (EV) users and the sustainable use of the flexibility resources of EV, EV charging behavior analysis and load prediction via order data of charging stations was proposed. The user probability distribution model is established from the characteristic dimensions of EV charging initial time, initial state of charge, power level, and charging time. Under the conditions of specific districts, seasons, multiple EV types, and specific weather, the Monte Carlo simulation method is used to predict the EV load distribution at the physical level. The correlation between users’ willingness to charge and the electricity price is analyzed, and the logistic function is used to establish the charging load prediction model on the economic level. Taking a city in Henan Province, China, as an example, the calculation results show that the EV charging load distribution varies with the district, season, weather, and EV type, and the 24 h time-of-use (TOU) electricity price and EV quantity distribution are analyzed. The proposed method can better reflect EV charging behavior and accurately predict EV charging load.

Keywords: behavior analysis of electric vehicle; charging load prediction; order data; Monte Carlo; logistics function (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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