SIMULATION STUDY OF SPMSM VECTOR CONTROL BASED ON FINITE ELEMENT PARAMETER VERIFICATION AND PARAMETER-MATCHED PI TUNING
Keywords:
Surface-mounted permanent magnet synchronous motor, Finite element parameter check, Parameter matching PI, Vector control, Local gain sweep, Joint simulationAbstract
This study develops a traceable RMxprt/Maxwell–Simulink workflow that links finite-element parameter verification with parameter-matched PI tuning for a surface-mounted permanent magnet synchronous motor (SPMSM). A 1.5 kW, 4-pole, 24-slot motor is first examined at the rated point, and its flux density, no-load back electromotive force, dq-axis inductance, and cogging-torque characteristics are checked. The verified control parameters, Rs=2.85 Ω, Ld=Lq=8.2 mH, ψf=0.32 Wb, and J=0.0012 kg·m², are then transferred to a discrete dq-axis vector-control model. Manual PI, parameter-matched PI, and locally optimized PI schemes are compared under the same startup, load-step, sampling, and limiting conditions. Parameter matching reduces the speed drop from 191.40 to 111.11 r/min and the recovery time from 0.01354 to 0.00766 s; local optimization further reduces them to 95.14 r/min and 0.00568 s. Parameter-perturbation tests for resistance, inductance, flux linkage, and inertia show that the matched controller preserves stronger disturbance rejection within the tested range. The proposed workflow provides a reproducible connection among motor parameters, controller gains, and closed-loop dynamic performance.References
[1] Monadi M, Nabipour M, Akbari-Behbahani F, et al. Speed control techniques for permanent magnet synchronous motors in electric vehicle applications toward sustainable energy mobility: A review. IEEE Access, 2024, 12: 119615-119632.
[2] Bae Y, Kim J M. Real-time PI gain auto-tuning for SPMSM drives based on time-domain response characteristics. Energies, 2025, 18(18): 4899.
[3] Shi S, Guo L, Chang Z, et al. Current controller based on active disturbance rejection control with parameter identification for PMSM servo systems. IEEE Access, 2023, 11: 46882-46891.
[4] Zhou S, Wang D, Du M, et al. Double update intelligent strategy for permanent magnet synchronous motor parameter identification. Computers, Materials & Continua, 2023, 74(2): 3391-3404.
[5] Liu H, Niu W, Guo Y. Direct torque control for PMSM based on the RBFNN surrogate model of electromagnetic torque and stator flux linkage. Control Engineering Practice, 2024, 148: 105943.
[6] Xiao F, Chen Z, Chen Y, et al. A finite control set model predictive direct speed controller for PMSM application with improved parameter robustness. International Journal of Electrical Power & Energy Systems, 2022, 143: 108509.
[7] Wang L, Zhang S, Zhang C, et al. An improved deadbeat predictive current control based on parameter identification for PMSM. IEEE Transactions on Transportation Electrification, 2024, 10(2): 2740-2753.
[8] Li X, Zhang S, Cui X, et al. Novel deadbeat predictive current control for PMSM with parameter updating scheme. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2022, 10(2): 2065-2074.
[9] Pillay P, Krishnan R. Modeling, simulation, and analysis of permanent-magnet motor drives, part I: The permanent-magnet synchronous motor drive. IEEE Transactions on Industry Applications, 1989, 25(2): 265-273.
[10] Holtz J. Pulsewidth modulation for electronic power conversion. Proceedings of the IEEE, 1994, 82(8): 1194-1214.