Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3465-3471.doi: 10.13229/j.cnki.jdxbgxb.20220986

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Automatic positioning method of electric vehicle charging based on machine vision

Dong-yuan GE1(),Wen-jiang XIANG2,Jian LI1,En-chen LIU1,3,Xi-fan YAO4   

  1. 1.School of Mechanical and Automotive Engineering,Guangxi University of Science and Technology,Liuzhou 545006,China
    2.School of Mechanical and Energy Engineering,Shaoyang University,Shaoyang 422004,China
    3.Academy for Engineering and Technology,Fudan University,Shanghai 200433,China
    4.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,China
  • Received:2022-08-03 Online:2023-12-01 Published:2024-01-12

Abstract:

To address the issues of position deviation and inaccurate positioning accuracy in current automatic positioning methods for electric vehicle charging,a machine vision-based automatic positioning method for electric vehicle charging was proposed. The machine vision system in the WSN sensor acquired the driving status of the electric vehicle, extracted environmental images and data information, denoised the charging socket and background using the Hue component and Canny operator in the HSI color model, performed edge detection to determine the center of the coil, calculated the equivalent side length of the charging coil, and accomplished automatic positioning of the electric vehicle through the nonlinear least squares method in machine vision. The experimental results demonstrated that under relative displacements of 3 mm, 5 mm, and 7 mm, the proposed method achieved the same positioning accuracy as the average of multiple positioning attempts, which were 8.24, 8.99, and 8.12. The proposed method demonstrates high positioning accuracy and good positioning performance, as the average positioning accuracy matches the single positioning accuracy. Currently, it is concluded that the proposed method exhibits reliable positioning accuracy.

Key words: electric vehicle charging automatic positioning, wireless sensor network, color model, edge detection, least square method

CLC Number: 

  • TN911.73

Fig.1

Current-carrying DC diagram"

Fig.2

Square coil model"

Table 1

Coil single positioning accuracy of different methods"

方法接收线圈定位精度
d=3 mmd=5 mmd=7 mm
本文8.248.998.12
文献[46.086.275.21
文献[56.076.895.26
文献[65.736.735.41

Table 2

Average value of coil multiple positioning accuracy of different methods"

方法接收线圈定位精度
d=3 mmd=5 mmd=7 mm
本文8.248.998.12
文献[45.095.294.20
文献[55.085.884.27
文献[64.745.744.40

Fig.3

Charging positioning result of the four methods"

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