Prediction of temporal and spatial distribution of electric vehicle charging load considering traffic flow

Tingting Xu; Xiaorui Hu; Bin Zhou; Huicai Wang; Lei Chi; Peng Li; Xin Zhao; Fangxing Chen; Hongyu Long

doi:10.1117/12.2652465

10 November 2022 Prediction of temporal and spatial distribution of electric vehicle charging load considering traffic flow

Tingting Xu, Xiaorui Hu, Bin Zhou, Huicai Wang, Lei Chi, Peng Li, Xin Zhao, Fangxing Chen, Hongyu Long

Author Affiliations +

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 1233110 (2022) https://doi.org/10.1117/12.2652465
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

Electric vehicle load forecasting is the basis for the safe and stable operation of the distribution network, and it is also a prerequisite for the planning and layout of electric vehicle infrastructure. Firstly, considering the characteristics of electric vehicles as participants in the transportation network and the characteristics and mobile load characteristics of electric vehicles as vehicles, a method for forecasting the temporal and spatial distribution of electric vehicle charging load considering traffic flow is proposed. This method first establishes a road network model that considers the flow-density-speed model and the road section impedance and node impedance based on the traffic flow of the road section based on the characteristics of the urban road network multiple intersections and the traffic flow of each section. Secondly, introduce the time function of charging probability and Freud's path search algorithm to assign start and end nodes and plan the driving path for electric vehicles to simulate its dynamic driving process and charging behavior. Finally, a simulation experiment of charging load prediction is carried out with a typical regional road network. The result shows that the distribution of electric vehicle charging load in different functional areas is different and the time distribution is uneven, which verifies the effectiveness and feasibility of the proposed method.

Citation Download Citation

Tingting Xu, Xiaorui Hu, Bin Zhou, Huicai Wang, Lei Chi, Peng Li, Xin Zhao, Fangxing Chen, and Hongyu Long "Prediction of temporal and spatial distribution of electric vehicle charging load considering traffic flow", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 1233110 (10 November 2022); https://doi.org/10.1117/12.2652465

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Roads

Computer simulations

Networks

Resistance

Error analysis

Monte Carlo methods

Neural networks

Show All Keywords

Keywords/Phrases

Search In:

Publication Years