Estimation of the Magnet Temperature via a Lumped Parameter Thermal Network in Real Time for the Control of PMSM

Abstract

Interior Permanent Magnet Synchronous Machines (IPMSMs) are the preferred choice for battery electric vehicles. Knowledge of the permanent magnet temperatures is crucial for ensuring safe operation and optimal use of these machines, as the reversible and irreversible demagnetization of the magnet material is temperature dependent. Accurate knowledge of the temperature of the permanent magnets can both extend the overload range, thus increasing the power density of the machine and exploit the effect of thermal field weakening in high-speed regions. Therefore, we present a method for estimating the permanent magnet temperature using a lumped parameter thermal network (LPTN) model. Consisting of 17 nodes, the LPTN represents the IPMSM components with a low level of discretization and separates heat transfer mechanisms within the entire machine in radial and axial direction with T-structure thermal elements. An electromagnetic thermal Finite Element Analysis (FEA) model is used to validate the LPTN to be able to adapt the control software to the electrical machine at an early design stage. Also, this FEA model is used to validate the LPTN, since a prototype with measurement data is not yet available. Finally, the method is verified with five different operating points, ranging from nominal speed to high speed and from low load to high load. This LPTN can be used in model-in-the-loop control application before an actual prototype of the IPMSM is available, resulting in an optimal use of development time.