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A Method For Detecting Metal Foreign Objects In An Array Differential Coil Of A Wireless Power Transmission System

Posted on:2022-06-25Degree:MasterType:Thesis
Country:ChinaCandidate:Z Y FuFull Text:PDF
GTID:2512306494490454Subject:Electrical engineering
Abstract/Summary:PDF Full Text Request
In recent years,the magnetic coupling wireless power transmission technology has developed rapidly.At present,wireless power transmission technology can provide efficient,green and convenient wireless charging for electric vehicles,industrial robots,etc.However,it is inevitable that the foreign objects enter in open space between the transmitting and receiving coils of wireless power transmission system.The metal foreign objects have the most serious impact on the system due to magnetic and conductive properties.When metal foreign objects of different materials and sizes intervene between the transmitting and receiving coils,it will affect the system transmission efficiency,frequency and other parameters,even cause the system to be detuned.In addition,due to the eddy current effect formed inside the metal foreign objects,part of the magnetic field energy is converted into other forms of energy loss,causing the metal foreign objects overheated,endangering the safety of the system and users.This paper studies the metal foreign objects in the interventional wireless power transmission system,using circuit theory,electromagnetic field theory and finite element simulation to explore the disturbance mechanism of the metal foreign objects to the system,and proposes a metal foreign objects detection method based on the array differential coils.1.Analyzing and comparing the advantages and disadvantages of different detection methods in domestic and foreign literature,and propose an array type differential coil metal foreign objects detection structure that has less impact on wireless power transmission systems.Based on the circuit theory,compare and analyze the power of the differential-coil and the single-coil absorption system,and derive the expression of the coil output voltage change after different kinds of metal foreign objects intervene on the surface of the differential detection coil;2.Using finite element simulation,a wireless power transmission system model with an array differential coil detection structure is established,the power loss of the differential coil and the traditional single coil is compared.Explore the influence of standard size ferromagnetic and non-ferromagnetic metal foreign objects on the output voltage of the detection coil,and optimize the parameters of the differential coil;3.Designing the coil output voltage signal acquisition module,and build a wireless power transmission system with array differential coils.Random drop experiments are carried out on cans,steel wire balls,standard iron,aluminum,copper sheets and three kinds of coins of standard foreign objects,the output voltage test of detection coils,and the type identification experiment of metal foreign objects.4.In order to solve the problem that small-sized metal foreign objects can not be detected by upper computer,BP neural network training model is constructed and used to train the sudden change voltage after the paper clip is involved in the coil,so as to realize the detection of small size such as paper clip.Aiming at the problem of metal foreign objects detection in the wireless power transmission system,this paper proposes a multi-group array type differential coil metal foreign objects detection structure composed of the same structure in reverse series.This detection structure has advantages of high detection accuracy and small impact on system transmission efficiency.The simulation experiment shows that the average power absorbed by each single coil is 0.09 W with the transmission power of 180 W,which is much larger than the average power absorbed by the differential coil.The experimental results show that the detection accuracy of the paper clip based on BP neural network is 85.7%.
Keywords/Search Tags:Wireless power transfer, Metal foreign object detection, Array type, BP neural network
PDF Full Text Request
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