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Article

Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior

School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
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World Electr. Veh. J. 2025, 16(9), 502; https://doi.org/10.3390/wevj16090502
Submission received: 16 July 2025 / Revised: 2 September 2025 / Accepted: 4 September 2025 / Published: 6 September 2025
(This article belongs to the Special Issue Sustainable EV Rapid Charging, Challenges, and Development)

Abstract

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network coupling framework is established based on a road network model with multi-source information fusion. Second, considering the multiple-intersection features of urban road networks, a time-flow model is developed. A time-optimal path selection method is designed based on the topological structure of the road network. Then, an EV driving energy consumption model is developed, accounting for both the mileage energy consumption and air conditioning energy consumption. Next, the user travel characteristics are finely modeled under two scenarios: working days and rest days. A user charging decision model is established using a fuzzy logic inference system, taking into account the state of charge (SOC), average electricity price, and parking duration. Finally, the Monte Carlo method is applied to simulate user travel and charging behavior. A simulation of the spatiotemporal distribution of the EV charging load was conducted in a specific area of Jiangning District, Nanjing. The simulation results show that there is a significant difference in the time distribution of EV charging loads between working days and rest days, with peak-to-valley differences of 3100.8 kW and 3233.5 kW, respectively.
Keywords: electric vehicle; charging load; road network; travel characteristics; charging decision electric vehicle; charging load; road network; travel characteristics; charging decision

Share and Cite

MDPI and ACS Style

Bian, H.; Tang, X.; Ji, K.; Zhang, Y.; Xie, Y. Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior. World Electr. Veh. J. 2025, 16, 502. https://doi.org/10.3390/wevj16090502

AMA Style

Bian H, Tang X, Ji K, Zhang Y, Xie Y. Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior. World Electric Vehicle Journal. 2025; 16(9):502. https://doi.org/10.3390/wevj16090502

Chicago/Turabian Style

Bian, Haihong, Xin Tang, Kai Ji, Yifan Zhang, and Yongqing Xie. 2025. "Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior" World Electric Vehicle Journal 16, no. 9: 502. https://doi.org/10.3390/wevj16090502

APA Style

Bian, H., Tang, X., Ji, K., Zhang, Y., & Xie, Y. (2025). Prediction of Electric Vehicle Charging Load Considering User Travel Characteristics and Charging Behavior. World Electric Vehicle Journal, 16(9), 502. https://doi.org/10.3390/wevj16090502

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