Performance Analysis And Parameter Identification Of IPMSM In Vehicle Based On Inductance Model

In the control system of permanent magnet synchronous motor, permanent magnet synchronous motor is a multi-variable and strong coupling nonlinear system, and its parameters will change with the change of temperature and service life. It is effective to improve the performance of the motor control system by tracking the change of the parameters of the motor timely and giving corresponding adjustments. Therefore, in order to obtain high-precision motor control, the parameters of the motor need to be estimated and identified in real time. All the work of this paper focuses on the influence of the motor’s inductance on the performance of the control system.The main work of this paper is as follows:1. In this paper, an interior permanent magnet synchronous motor(IPMSM) is taken as the research object, and weak magnetic control model of IPMSM is built in the MATLAB/Simulink software, the motor inductance parameters are obtained without considering the saturated inductance and cross coupling by using finite element method, and the inductance parameters of permanent magnet synchronous motor control system performance influence is analyzed in different calculation model. The simulation results show that using the control system considering the saturation of the magnetic circuit can broaden the operating range of the motor’s constant power area and improve the maximum operating speed of the motor.2. The identification speed of model reference adaptive method is slow but its identification accuracy is high, and the identification accuracy of recursive least squares with forgetting factor is low but its identification speed is fast. Thus, combining the characteristics of the two parameter identification methods, an improved model reference adaptive method is proposed. The simulation results show that the improved model reference adaptive method can improve the recognition speed and accuracy compared with the model reference adaptive method and the forgetting factor recursive least square method. At the same time, when the inductance, obtained by the improved model reference adaptive identification method, is fed back to the motor control model, the simulation results show that the response speed of the weak magnetic control model with online parameter identification function is faster and its stability is better compared with the normal control model.