Monitoring critical temperatures in permanent magnet synchronous motors (PMSM) is essential to prevent device failures or excessive motor life-time reduction due to thermal stress. A lumped-parameter thermal network (LPTN) consisting of four nodes is designed to model the most important motor parts, i.e., the stator yoke, stator winding, stator teeth, and the permanent magnets. An empirical approach based on the comprehensive experimental training data and a particle swarm optimization are used to identify the LPTN parameters of a 60-kW automotive traction PMSM. Varying parameters and physically motivated constraints are taken into account to extend the model scope beyond the training data domain. Here, a so-called global identification technique for linear parameter-varying systems is innovatively applied to a thermal motor model for the first time. The model accuracy is cross-validated with independent load profiles, and a maximum estimation error (worst-case) of 8 degrees C regarding all considered motor temperatures is achieved. Also, a comprehensive residual statistical analysis proves suitable estimation results in terms of model robustness and accuracy.