An electric vehicle fast charging station site selection and sizing method based on a travel probability matrix

A technology of electric vehicles and probability matrix, applied in combustion engines, internal combustion piston engines, data processing applications, etc., can solve the problems of not considering the complexity of time and space distribution of charging demand, unreasonable distribution of charging stations, etc.

Pending Publication Date: 2019-05-03
ZHENGZHOU UNIV
View PDF6 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is: in order to solve the problem that the location of the existing electric vehicle charging stations is too subjective and does not consider the complexity of the time-space distribution of charging demand, resulting in unreasonable distribution of charging stations, the present invention provides a travel probability-based The method of location selection and capacity determination of electric vehicle fast charging stations based on matrix,

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An electric vehicle fast charging station site selection and sizing method based on a travel probability matrix
  • An electric vehicle fast charging station site selection and sizing method based on a travel probability matrix
  • An electric vehicle fast charging station site selection and sizing method based on a travel probability matrix

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0076] This embodiment provides a method for selecting the location and capacity of electric vehicle fast charging stations based on the travel probability matrix, such as figure 1 As shown, the time-space distribution matrix of fast charging demand for electric vehicles is first obtained to provide a reference for charging station planning, and then according to the optimization model of charging station location and capacity, the particle swarm algorithm and Voronoi diagram are used to analyze and optimize the time-space distribution matrix of fast charging demand , reasonably plan the fast charging station, including the following steps:

[0077] S1: Get the road network parameters in the planning area, the road network parameters include road network topology, road length, road grade, road capacity, road saturation at different times of the day and road traffic flow of different types of electric vehicles, such as figure 2 As shown in , it is a schematic diagram of the to...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an electric vehicle fast charging station site selection constant volume method based on a travel probability matrix, and relates to the technical field of electric vehicle public charging facility planning, and the method comprises the steps: obtaining road network parameters in a planning area; obtaining an OD travel matrix according to different types of electric vehicleroad traffic flow backstepping so as to obtain an OD travel probability matrix; Monte Carlo method, OD travel probability matrix and improvement speed. The flow model simulates a driving track of theelectric vehicle in one day to obtain a quick charge demand space-time distribution matrix in one day; the space-time distribution matrix of the fast charging demand is analyzed and optimized according to the site selection and constant volume optimization model of the charging station; the charging station position and the charger configuration number are obtained; according to the invention, the electric vehicle fast charging demand space-time distribution matrix is utilized, the Voronoi diagram is adopted to divide the service range of the charging station, the optimal position of the charging station is determined by improving the particle swarm algorithm, and the capacity of each charging station is optimized by utilizing the queuing theory, so that the planning result of the electric vehicle fast charging station is more accurate and scientific.

Description

technical field [0001] The invention relates to the technical field of public charging facility planning for electric vehicles, and more specifically relates to a method for selecting the location and capacity of fast charging stations for electric vehicles based on a travel probability matrix. Background technique [0002] The energy crisis and global warming are increasingly valued by countries all over the world. The electric vehicle industry has ushered in a huge development opportunity. It is estimated that the number of electric vehicles in my country will reach 5 million in 2020. With the increasing penetration rate of electric vehicles, Urban charging facilities will be built on a large scale. [0003] Whether the planning of electric vehicle charging stations is reasonable or not will directly affect people's charging satisfaction, which in turn will affect the development of the electric vehicle industry in the future. Make special plans. [0004] Many scholars at...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/04G06Q10/06
CPCY02T10/40
Inventor 姜欣冯永涛金阳
Owner ZHENGZHOU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products