A Material-Informed Multiphysics Modeling Framework for Full Range Performance Estimation of PMSMs in Electric Vehicles

Accurate prediction of operational performance in permanent magnet synchronous motors (PMSMs), where core loss characteristics are a decisive indicator, is essential for advancing electric vehicle (EV) powertrains, particularly in areas such as design optimization, efficiency enhancement, and real-time control. Targeting complex working environment inside EVs, this paper presents an integrated modelling framework capable of reliably estimating both steady-state and dynamic PMSM performance under varying operational conditions with fast calculation. Specifically, moving beyond conventional equivalent circuit models considering core loss, the proposed approach introduces a novel formulation of resistance parameters that comprehensively captures the influence of multiple factors such as operating frequency, load current, thermal effects, and carrier harmonics, while it successfully isolates material-level nonlinear factors in core loss and utilizes a custom-developed 2D tester to conduct electromagnetic-thermal coupled field measurements on core material properties, thereby providing deeper insights into the performance evolution of core loss under highly coupled fields across a wider operating range. Moreover, by analyzing the hysteresis, eddy current and anomalous loss components of core loss, relationships between pulse width modulation and sinusoidal excitations are established, leading to revisions of the proposed models. Furthermore, this framework incorporates a lumped parameter thermal network based on a weighted multiple tracking extended Kalman filter algorithm to track real-time temperature, allowing for accurate loss prediction even under continuously varying operating conditions. Finally, extensive experimental and numerical results based on a 20 kW prototype demonstrate the model’s strong generality across identical stator materials and its high accuracy in predicting PMSM performance under various EV operating conditions, effectively supporting the development of next-generation electric motors.