Contrapose the spatial-temporal transfer random of electric vehicles

temperature and traffic conditions are taken into consideration

and a spatial-temporal distribution prediction method of charging load for urban electric vehicle based on Markov decision process(MDP)random path simulation is proposed. Firstly

the types of electric vehicles are classified according to their spatial transfer and charging characteristics. Secondly

the real-time dynamic random path simulation based on Monte Carlo method and Markov decision theory are used to establish the spatial-temporal transfer model of various electric vehicles. The model of temperature and traffic energy is established according to the measured data of electric vehicles

and the energy consumption is obtained accordingly. Finally

taking a typical urban area as an example

the charging load is calculated under different temperatures and different traffic conditions. The simulation results show that the charging load of the nodes with fast charging in the region is higher

and ambient temperature rise or deterioration of traffic congestion can lead to an increase in charging load.