Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

Abstract

The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load.