| In order to protect the environment and save the resource, most countries’ governments propel the development of electric vehicle. Permanent magnet synchronous motor(PMSM) is widely applied in the electric vehicle motor driving system, because of its high efficiency, high power density and broad speed range. It’s important for vehicle safety to diagnose PMSM fault. This paper mainly studies on diagnostic method of PMSM electrical fault for electric vehicle, and designs the hardware of an on-line fault diagnostic system.Firstly, this paper simply introduces the overall composition of PMSM driving system. Then this paper analyzes the signature and type of PMSM electrical fault. The simulation model of PMSM driving system is established by Matlab/Simulink. It simulates the PMSM usual electrical fault such as phase to phase short-circuit, phase to ground short-circuit and demagnetization fault. A 2D FEA PMSM model is established by Ansoft, and the model is used to simulate in-turn short-circuit fault. This paper studies on fault diagnostic method which based on wavelet decomposition and integrated neural network. There are four sets of simulation sample data. Three sets of simulation sample data are used to train the neural network, another one is used to test the neural network. The simulation result shows that the fault diagnostic method is efficient and fast, and it achieves the target.This paper establishes a motor bench, and some relevant electrical faults of PMSM are simulated by experiment. Current data and voltage data from healthy PMSM and fault PMSM are acquired by using a self-designed DAQ software. These data are used in the fault diagnosis method. The result validates the reliability of the fault diagnosis method based on wavelet decomposition and integrated neural network. Finally, this paper designs the hardware of an on-line diagnostic system according to the demand. Debugging result shows that the function of the hardware is good. |
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