Torque Control of Interior Permanent Magnet Synchronous Motor Based on Online Parameter Identification Using Sinusoidal Current Injection

Interior permanent magnet synchronous motor (IPMSM) is applied in automotive drive because of its high efficiency and high power density. The accurate torque control of motor, however, is still subject to further investigation and improvement. In order to improve the performance of IPMSM applied in electric vehicles, a feedback torque control method based on online parameter identification is developed in this research. In order to control the instantaneous electromagnetic torque accurately, stator resistance, rotor flux, and dq-axis inductance are identified simultaneously with a recursive least square algorithm. In this process, sinusoidal disturbance current is injected into d-axis to solve the rank-deficient problem. Furthermore, a simulation model is developed based on this torque control method. The simulation results show that the torque control method developed in this research can perform better than the torque control method based on fixed parameters. On the other hand, a test rig is developed in this research to verify the feasibility of torque control method and the effectiveness of simulation model. The measured results show that these parameters can be identified accurately with the online parameter identification method developed in this research, and the robustness of this online parameter identification method can be verified in the experiment. Furthermore, the torque control method is verified with this test rig, and the results show that the measured torque can accurately follow the torque command rapidly with little fluctuation, indicating that the method developed in this research has the features of high accuracy, high robustness and short response time.

Automated summary

This research developed a feedback torque control method based on online parameter identification to improve the performance of interior permanent magnet synchronous motor (IPMSM) used in electric vehicles. By using the recursive least square algorithm, the stator resistance, rotor flux, and dq-axis inductance of the motor are identified simultaneously. The simulation and experimental results both demonstrate that this method has the features of high accuracy, high robustness, and short response time.