Title:Application of Thermal Neural Networks on a Small-Scale Electric MotorAuthor(s):Wilhelm Kichgässner, Daniel Wöckinger, Oliver Wallscheid, Gerd Bramerdorfer, Joachim BöckerAbstract:In the automotive market and automation industry, the race for ever increasing power and energy densities of electric motors leads to an endeavor of developing real-time capable thermal models. The precise thermal monitoring of critical motor components promises material reduction and higher material utilization. Among classical, expert-based, lumpedparameter thermal networks (LPTNs) and black-box, data-driven machine learning approaches, synergies have been identified in the recent literature. In this work, one of these synergies – thermal neural networks (TNN) – are evaluated on a test bench equipped with a prototypical 110Wpermanent magnet synchronous motor that features thermal sensors. The demonstrated cross-validation score of the TNN with an average mean squared error of 0.48 K² and absolute estimation errors of under 2 ◦C for 98.6% of all samples excels a data-driven classic LPTN that acts as baseline. The TNN features roughly five times more parameters than the expert-based LPTN, but is optimized in a fraction of the time with no geometry or material information involved.Booktitle:IKMT2022, Innovative Kleinantriebs-und Kleinmotorentechnik, Linz, ÖsterreichPage Reference:6 page(s)Publishing:9/2022