Design and empirical identification of a lumped parameter thermal network for permanent magnet synchronous motors with physically motivated constraints

Abstract

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 comprehensive experimental training data and an global particle swarm optimisation are used to identify the LPTN parameters of a 60 kW automotive traction PMSM. Here, varying parameters and physically motivated constraints are taken into account to extend the model scope beyond the training data domain. The model accuracy is cross-validated with independent load profiles and a maximum estimation error of 5 ° C regarding all considered motor temperatures is achieved.