Being able to predict temperature rise inside a machine is as important as predicting its performance and life. Because temperature measurements and computational thermal simulations can be time consuming, thermal paths inside the through-flow universal motor were described by means of simple lumped parameter thermal network. Once the model was built, its unknown convection coefficients were tuned with the genetic algorithm tool in MatLab. The model has been applied and successfully verified with measurements on two different types of a vacuum cleaner motor. Taking account of impeller losses as one of the model inputs makes temperature estimates more accurate regardless of machine’s operational regime.