Abstract
This paper is about deriving suitable lumped parameter thermal networks for modeling the transient thermal characteristics of electric machines under variable load conditions. The network should allow for an accurate estimation of the temperatures of critical machines’ components. In best case, the model can be run in real time to adapt the motor control based on the load history and maximum permissible temperatures. Consequently, the machine’s capabilities can be exhausted at best considering a highly-utilized drive. The model further shall be as simple as possible while guaranteeing a decent accuracy of the predicted temperatures. A lumped parameter thermal network is selected and its characteristics are explained in detail. Besides the model selection and the optimization of its critical parameters through an evolutionary optimization strategy, an experimental setup will be described in detail. The model accuracy is evaluated for both static and dynamic test cycles with changing load torque and speed requirements. Finally, the significant improvement of the accuracy of the predicted motor temperatures is presented and the results are compared with measurements.
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