Based on the large-scale influx of electric vehicles in mountain cities, this paper needs to predict the charging load of electric vehicles, and lay the foundation for the planning of electric vehicle charging and swapping facilities. This paper proposes a spatiotemporal prediction method of electric vehicle charging load considering the characteristics of mountain cities: 1) The wavelet neural network method based on historical traffic data is used to predict the traffic flow in a certain period of time in the future; 2) Considering the influence of traffic flow, the mountain cities are constructed respectively. Flow-density-speed model, road section impedance model; 3) Based on the constructed speed and impedance model and the improved Freud path search algorithm, plan the driving path of electric vehicles and construct the electric vehicle power consumption model; 4) Use random Probabilistic analysis method was used to obtain the electric vehicle charging load prediction method. In order to verify the feasibility and effectiveness of the model and method: 1) Construct a traffic road network, simulate and calculate the accuracy of the traffic flow prediction method, and compare the charging load of the mountain city and the plain city. 2) The results of the calculation example show that due to the large power consumption in mountainous cities and the high demand for electric vehicle charging, the charging time of electric vehicles lags behind that in plain cities, and the load size increases significantly in space.
Prediction of temporal and spatial distribution of electric vehicle charging load considering the characteristics of mountainous cities
2022-12-09
1849855 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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