Permanent magnet motors are widely used in the driving system of electric vehicles because of their high power density and small size. However, the permanent magnet motor is prone to demagnetization due to the complex operating environment of electric vehicles and characteristics of permanent magnet motors. To ensure the normal operation of vehicles, the demagnetization fault diagnosis of permanent magnets should develop. In this study, the thermal behavior of a permanent magnet motor under local demagnetization fault is discussed. Results show that the demagnetization of permanent magnets has a significant effect on the temperature of the motor; thus, the temperature is added into input signals of the permanent magnet demagnetization fault diagnosis. The healthy state of the permanent magnet is predicted by the back propagation (BP) neural network to realize the demagnetization fault diagnosis. First, the demagnetization test platform is built to test the performance of the motor, and the current, torque, and temperature of the motor are measured according to different demagnetization degrees. Second, the demagnetization simulation model of the motor is established by the finite-element method, and the air gap magnetic field, loss, and temperature of the motor are analyzed. Lastly, the temperature, current, speed, and torque signals are selected as input signals of the neural network model, and the demagnetization rate of the rotor is taking as the output signal. The neural network prediction model is established and trained, and the good generalization ability is obtained.

中文翻译：

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基于温度特征量的电动汽车永磁电机退磁故障诊断

永磁电机以其功率密度高、体积小等优点被广泛应用于电动汽车的驱动系统中。但是，由于电动汽车复杂的运行环境和永磁电机的特性，永磁电机容易退磁。为保证车辆正常运行，应发展永磁体退磁故障诊断。在这项研究中，讨论了永磁电机在局部退磁故障下的热行为。结果表明，永磁体退磁对电机温度有显着影响；因此，将温度加入到永磁体退磁故障诊断的输入信号中。通过反向传播（BP）神经网络预测永磁体的健康状态，实现退磁故障诊断。首先搭建退磁测试平台，测试电机的性能，根据不同的退磁程度测量电机的电流、扭矩、温度。其次，采用有限元法建立了电机的退磁仿真模型，对电机的气隙磁场、损耗、温度进行了分析。最后选取温度、电流、转速和转矩信号作为神经网络模型的输入信号，转子退磁率作为输出信号。建立并训练了神经网络预测模型，获得了良好的泛化能力。