Research On Online Detection Method Of Loss Of Magnetic Loss In Permanent Magnet Synchronous Motors For Electric Vehicles

With the advocacy of energy saving and environmental protection,new energy vehicles have achieved rapid development,Permanent magnet synchronous motor has been widely used in the drive system of new energy vehicles in China due to its advantages such as low pollution,high power and small size.However,due to the complex operating environment of electric vehicles and the characteristics of the structure of the permanent magnet synchronous motor itself,it is easy to cause the loss of magnetism in the motor.In order to ensure the normal operation of the car,on-line monitoring and diagnosis of permanent magnet synchronous motor loss magnetism is very important.Therefore,this dissertation focuses on the study of loss-of-magnetism fault diagnosis method of permanent magnet synchronous motor under the condition of electric vehicle.A cross-platform permanent magnet synchronous motor on-line monitoring software for loss-of-magnetism fault status was designed.This article mainly focuses on permanent magnet synchronous motors in electric vehicles.Firstly,the study of loss-of-magnetism fault diagnosis methods at home and abroad.Secondly,after analyzing the structure of permanent magnet synchronous motor,the performance of permanent magnetic material,the analysis of the drive system,and the causes of the loss of magnetism in the permanent magnet synchronous motor,Set up a uniform loss-of-magnetization fault model,a local loss-of-excitation fault model,and a loss-of-magnetism failure model under high temperature for a permanent magnet synchronous motor.Through simulation experiments,the causes of the loss of magnetization,the characteristics of the faults,and the effects of faults in the case of loss of magnetism are analyzed.Then propose fault diagnosis based on three-phase current as fault data acquisition.In order to further extract the fault eigenvectors,three-layer decomposition is carried out using wavelet packet decomposition of three-phase currents.Then use the energy of each frequency band as the loss-of-magnetization fault feature vector.Finally,the method of training least squares support vector machine with eigenvectors and real-time diagnosis of the motor loss-loss fault is studied.In the research process,we propose an improved particle swarm optimization algorithm for the disadvantages of the support vector machine parameters.And before and after the optimization of the algorithm for experimental verification.Finally,the experimental platform was built to verify the experimental results.The experimental results show that the fault diagnosis method proposed in this paper can effectively and rapidly perform the fault diagnosis and monitoring of permanent magnet synchronous motor loss.In the Qt development framework,a cross-platform software for on-line monitoring and diagnosis of loss-of-magnetization faults in permanent magnet synchronous motors was designed using mixed programming of C++ and QML.Implement the algorithm and monitoring functions presented in this article.This software can monitor the running status of the permanent magnet synchronous motor and diagnose and alarm when a fault occurs.Software can run on multiple platforms.This article focuses on PC and embedded platforms for testing.