Electric motors run hot - and knowing their internal temperature is mission-critical. But here’s the catch: you can’t place sensors where it matters most. So how do engineers protect magnets from demagnetization, maximize power, and extend motor life? In this episode, Tobias Moroder, Data Scientist, and Georg Goeppert, Systems Engineer at Schaeffler, break down Thermal Neural Networks (TNNs) - a hybrid modeling approach that fuses physics with machine learning to accurately estimate temperatures inside E-Motors. Here's what you’ll learn:
Why virtual sensing is essential for modern electric drives
How TNNs merge lumped-parameter thermal models with neural networks
Why hybrid models outperform black-box AI in safety-critical systems
How this method scales to batteries, power electronics, and full vehicle systems
How Schaeffler’s TNN implementation works in MATLAB - now open-sourced
A clear, practical deep dive at the intersection of AI, physics, and automotive engineering.
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