Fast charging demand forecasting based on the intelligent sensing system of dynamic vehicle under EVs-traffic-distribution coupling

Abstract

In order to promote the efficient utilization of clean energy, in this paper, a fast charging demand prediction model based on the intelligent sensing system of dynamic electric vehicles (EVs) under EVs-Traffic-Distribution coupling is proposed. The established model consists of two parts: electric vehicle dynamic driving model and fast charging demand prediction model based on state transition intelligent sensing. The dynamic driving model of electric vehicle reflects the impact of traffic conditions on driving speed and power consumption, as well as the impact of vehicle driving on the road network under various states such as driving, parking and charging. In the fast charging demand prediction model based on state transition intelligent sensing, the intelligent sensing system can recommend the optimal path to users according to real-time road conditions and vehicle driving conditions. Taking a planning area as an example, the Monte Carlo simulation method is used for fast charging load forecasting. The simulation results verify the correctness and effectiveness of the proposed model.