Xiang-Yu Liu

As the market share of electric vehicles (EVs) increases year by year, their charging load forecasting has become a research hotspot and a difficulty. Aiming at the shortcomings of the current research on the charging probability prediction of EVs with different power levels, this paper proposes a multi-power-level EV charging probability prediction method. Firstly, based on the characteristics of electric vehicles, the power of charging facilities, and the travel habits of owners, the SOC mathematical models of charging start time, as well as the start and end state of charge, are established, and the different charging power selection models are established in combination with the parking time. Then, the Monte Carlo simulation method is used to predict the charging probability of electric vehicles with different power levels on typical dates such as working days, weekends, and holidays.

With the rapid development of electric vehicles (EVs) in Chinese cities, accurately forecasting the number of EVs used by urban residents in the next five years and more long term is beneficial for the government to adjust industrial policies of EVs, guide the rational planning of urban charging facilities and supporting distribution network, and achieve the rational and orderly development of the EV industry. The paper considers the advantages of using the grey GM(1,1) prediction model to predict the short-term ownership of EVs by urban residents. Then, by forecasting the number of EV users in a certain city in the future and predicting the number of private vehicles in the future, the boundary conditions for long-term year ownership of EVs by residents are determined. Combined with historical data and short-term forecast data generated by the grey prediction model, the model parameters that include the innovation coefficient and imitation coefficient of the Bass model are trained using a genetic algorithm. Finally, the Bass model is used for medium- to long-term ownership forecasting from 2023 to 2040. The prediction error for the target year is provided. The simulation results indicate that the ownership of resident EVs in this city will experience rapid growth in the next five years.