Deep Learning Based Automatic Charging Identification and Positioning Method for Electric Vehicle

Electric vehicle charging identification and positioning is critically important to achieving automatic charging.In terms of the problem of automatic charging for electric vehicles,a dual recognition and positioning method based on deep learning is proposed.The method is divided into two parts:global recognition and localization and local recognition and localization.In the specific implementation process,the collected pictures of electric vehicle charging attitude are classified and labeled.It is trained with the improved YOLOv4 networkmodel and the corresponding detectionmodel is obtained.The contour of the electric vehicle is extracted by the BiSeNet semantic segmentation algorithm.The minimum external rectangle is used for positioning of the electric vehicle.Based on the location relationship between the charging port and the electric vehicle,the rough location information of the charging port is obtained.The automatic charging equipment moves to the vicinity of the charging port,and the camera near the charging gun collects pictures of the charging port.The model is detected by the Hough circle,the KM algorithmis used for featurematching,and the homography matrix is used to solve the attitude.The results show that the dual identification and location method based on the improved YOLOv4 algorithm proposed in this paper can accurately locate the charging port.The accuracy of the charging connection can reach 80%.It provides an effective way to solve the problems of automatic charging identification and positioning of electric vehicles and has strong engineering practical value.

Design and Application of Automatic Test System of Soil Water Characteristic Curve

[Objective] In order to better meet the requirement of crops on a more and more accurate water content under various planting environment of modern agri-culture, an automatic test system of soil water characteristic curve was designed by combining the conceptions of soil moisture content and soil water potential. [Method] Electronic soil moisture tension meter was used to determine the real-time tension value of soil moisture in the tested container, and the electronic Weigh sensor was used to determine soil Weigh. Minusing method was used to calculate soil moisture content, based on which the soil water characteristic curve was plotted. [Result] Through the filed survey of 2 different kinds of soil in Jiangsu Province, the results were as fol ows： soil of different composition showed different trend in soil water characteristic curve that the soil water characteristic relation of the sandy soil in the old course of the Yel ow River in Xuzhou was Y=-0.000 2X3＋0.027 7X2-1.644 5X＋38.161, R2=0.991 9; while the soil water characteristic relation of the saline-alkali soil in Jinhai Farm of Dafeng was Y=-0.00 2X2-0.426X＋39.905, R2=0.991 3. [Con-clusion] The automatic test system of soil water characteristic curve soil water char-acteristics curve could reflect soil moisture content and soil water potential, as wel as reflect the effectiveness of soil water to plant growth, providing basis for the sci-entific irrigation.