Multi-parameter Identification of IPMSM Based on Extended Kalman Filter

This work focuses on the parameter identification of interior permanent magnet synchronous motors (IPMSM). Given the significant impact of motor parameter variations on the operating performance of the motor, the extended Kalman filter (EKF) algorithm is applied to the online identification of the motor's stator resistance, rotor flux, and d-q axis inductances. In this work, the mathematical model of the IPMSM in the d-q rotating coordinate system is firstly established, the EKF algorithm for parameter identification is sequentially expounded, and the corresponding multi-parameter identification model for IPMSM is constructed finally. The advantages of the proposed identification method are verified by simulation with different temperature environments and compared with the classical recursive least squares method with a forgetting factor. The simulation results show that the identification accuracy of the EKF algorithm for each parameter at different temperatures is higher than that of the FF-RLS algorithm, effectively verifying the superiority of the EKF algorithm in IPMSM parameter identification.