A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory - Songyi Wang; Xinjian Wang (Applied Sciences, 2025)
Traditional permanent-magnet synchronous motor (PMSM) models assume constant inductance parameters in the dq frame, attributing torque ripple solely to local non-sinusoidal disturbances while neglecting nonlinear effects like iron saturation, flux linkage spatial harmonics, and inter-axis mutual coupling. These simplifications limit such models to predicting average torque but fail to capture harmonic components. To overcome these limitations, this study develops a nonlinear PMSM model using differential inductance theory and constructs a physics-informed neural network (PINN) surrogate trained on finite-element data. The proposed hybrid framework demonstrates high-fidelity torque prediction, validated against finite-element simulations, and provides insights into harmonic generation mechanisms under saturation and spatial field distortions.