Permanent Magnet Synchronous Machine Temperature Estimation using
Low-Order Lumped-Parameter Thermal Network with Extended Iron Loss Model
Abstract
Since temperature rise in electric machines is mainly
due to power losses during electro-mechanical power conversion,
temperature estimation is highly attached to power loss modelling. In
this contribution, an extended iron loss model is introduced with a
direct identification methodology in the context of temperature
estimation. The iron loss model is implemented as part of a fourth-order
lumped-parameter thermal network (LPTN), which is parametrised using
empirical measurements and global identification. Once parameters are
identified using training data, the LPTN model is validated using three
unseen profiles cross-validation. Satisfactory estimation is achieved
with the average mean squared error of 2.1 K2 and the error bias close
to zero.