Development of a digital twin to predict temperature behavior of an electric machine

In electrified powertrains, accurate temperature prediction of electric machines is essential for avoiding thermal derating, improving efficiency, and enabling condition monitoring. However, direct sensing of critical internal temperatures is often limited by cost, packaging constraints, and functional-safety requirements. This motivates the use of virtual-sensor concepts and data-driven digital twins. This work develops a data-driven digital twin for predicting electric machine temperature behavior under highly dynamic vehicle load conditions. Measurements were acquired on a full-vehicle road-to-rig test bench, enabling reproducible excitation of realistic driving profiles while instrumenting the rear traction machine with additional thermal sensing. Signals were filtered and down-sampled from 50 to 1 Hz, and a vehicle-plausible feature set (e.g. vehicle speed, road gradient, and wheel-load-machine parameters) was used to support later in-vehicle deployment. Three model classes were investigated: (i) a cascaded Gaussian process regression with periodic re-initialization, and recurrent neural networks (long short-term memory (ii), gated recurrent units (iii)). The results show a clear trade-off between short-horizon accuracy and long-horizon prediction capability. On the test set, Gaussian process regression achieved the highest short-term accuracy (MAE 0.4 K, MaxAE 1.5 K) with low memory demand (41,904 bytes), whereas long short-term memory and gated recurrent units enabled stable long-horizon prediction at high throughput, but with larger maximum errors (MaxAE 19.5 K and 12.2 K, respectively). Overall, the results quantify the trade-off between forecast horizon, accuracy, and embedded feasibility for digital-twin-based temperature prediction in vehicle operation.