Open AccessSpatial-temporal Distribution Prediction of Charging Load for Electric Vehicle based on Dynamic Traffic Flow
Author(s) -
Yug Song,
Shunfu Lin
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1346/1/012019
Subject(s) - electric vehicle , monte carlo method , traffic flow (computer networking) , transfer (computing) , automotive engineering , computer science , simulation , flow (mathematics) , engineering , power (physics) , mathematics , statistics , physics , geometry , computer security , quantum mechanics , parallel computing
On the basis of velocity-flow-density relationship and traffic-energy consumption relationship, this paper proposes a prediction method of the spatial and temporal characteristic of electric vehicle charging load using the traffic data. By analyzing the residential travel data, a probability model was built to generate trip chains of a day, which contain destination and start time. Then vehicle transfer model was used to simulate driving vehicles on the roads and SOC could be calculated by the road condition and temperature. Drivers would charge vehicles when SOC is below the charging threshold. Finally, by using the Monte Carlo method, charging load of a real traffic model was calculated according to charging demand from all electric vehicles at different time and location during the area.