  This paper proposes a data-driven supervised machine learning (ML) for online
thermal modeling of electrically excited synchronous motors (EESMs). EESMs are
desired for EVs due to their high performance, efficiency, and durability at a
relatively low cost. Therefore, obtaining precise EESM temperature estimations
are significantly important, because online accurate temperature estimation can
lead to EESM performance improvement and guaranteeing its safety and
reliability. In this study, in addition to the default inputs' data, EESM
losses data is leveraged to improve the performance of the proposed ML approach
for thermal modeling. Exponentially weighted moving averages and standard
deviations of the inputs are also incorporated in the learning process to
consider the memory effect for modeling a dynamical thermal model. Using the
experimental data of an EESM prototype, the performance of ordinary least
squares (OLS) method is evaluated through a complete training, testing and
cross-validation process. Finally, simulation results will provide the key
performance metrics of OLS for EESM thermal modeling.
