Automatic Model Order Reduction Technique for Real-time Temperature Monitoring of Oil-Cooled Electric Machines

Permanent magnet synchronous machines (PMSMs) are widely used in various applications as high-power-dense and highly efficient drives. For utilizing the unused potential of present PMSMs towards a reduced component size, light weight, and increasing power density, thermal management is key. With the goal of real-time temperature monitoring on embedded hardware, a workflow is introduced to automatically derive a reduced order model on the basis of the Craig-Bampton method using linear time parameter-varying systems. The time varying parameters are identified with an optimization approach based on experimental data. Finally, the reduced thermal model meets the target accuracy of 10 °C for several operation points and several cooling conditions such as variations in oil flow rate and oil temperature. Moreover, the real-time capability of the model is demonstrated for a low-cost micro-controller unit.