Temperature estimation of electric machines using a hybrid model of feed-forward neural and low-order lumped-parameter thermal networks

Accurate temperature estimation of parts of electric machines using a lumped-parameter thermal network (LPTN) requires knowledge of power loss and thermal parameters. However, modeling parameter varying model inputs (power loss for each node and convective thermal conductances) based on physical equations using only measurement signals available during normal drive operation as inputs is not tangible. Hence, a black-box feed-forward neural network (FFNN) based model is introduced as part of the low-order LPTN which is parametrized using empirical measurements dataset using a newly introduced step-by-step system identification instead of a global identification. Then, the proposed hybrid model performance is evaluated using three different unseen cross-validation data sets, which granted an average maximum absolute error of 5 K.