PERMANENT MAGNET SYNCHRONOUS MACHINE TEMPERATURE ESTIMATION USING LOW-ORDER LUMPED-PARAMETER THERMAL NETWORK WITH EXTENDED IRON LOSS MODEL

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 fourthorder 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.