Global Identification of a Low-Order Lumped-Parameter Thermal Network for Permanent Magnet Synchronous Motors


Monitoring vital temperatures in permanent magnet synchronous motors (PMSM) is crucial to prevent device failures or excessive motor life-time reduction thanks to thermal stress. A lumped-parameter thermal network (LPTN) consisting of four nodes is intended to model the foremost important motor components, i.e., the stator yoke, stator winding, stator teeth, and therefore the permanent magnets. An empirical approach primarily based on the excellent experimental coaching 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 under consideration to extend the model scope beyond the coaching data domain. Here, a so-known as world 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 freelance load profiles, and a maximum estimation error (worst-case) of eight°C relating to all considered motor temperatures is achieved. Additionally, a comprehensive residual statistical analysis proves suitable estimation leads to terms of model robustness and accuracy.

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