Electric Vehicle Charging Load Prediction Based on Dynamic Energy Consumption Model and User Psychology

In allusion to the energy consumption variation and charging decision during the trip of household electric vehicles (abbr. EV) and electric taxi in the city, considering realtime traffic network, air temperature and user psychology an EV charging load prediction model based on dynamic energy consumption model and user psychology was proposed. Firstly, according to the trip chain theory a spatio-temporal transfer model of household EV travel was established, and on the basis of trip order data and origin-destination analysis method the trip rule of electric taxi was simulated. Secondly, according to the charging-discharging experimental data of lithium battery the influence of different temperature on battery capacity was analyzed, and the power consumption occurred in the EV driving was analyzed and a refined air conditioning energy consumption model and mileage energy consumption model were constructed. On this basis the anchoring effect was led in to analyze the impact of user psychology on charging decision, thus a charging decision model, in which the user’s subjective wishes was taken into account, was established. Finally, taking actual road network within the third ring road of Chengdu as example, the Monte Carlo method was utilized to conduct the spatio-temporal prediction of EV’s charging demand under different scenarios. Simulation results show that the proposed models and method are effective.