# Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning

arXiv: https://arxiv.org/abs/2001.06246 · HTML: https://mlsanity.com/paper/2001.06246/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning](https://mlsanity.com/paper/2103.16323/) (2021)
- [Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends](https://mlsanity.com/paper/2110.05403/) (2021)
- [Can Transformers Predict Vibrations?](https://mlsanity.com/paper/2402.10511/) (2024)
- [A physics-informed Bayesian optimization method for rapid development of electrical machines](https://mlsanity.com/paper/2503.00420/) (2025)

## Abstract (arXiv)

Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or sensor-based methods still prove unfeasible in a commercial context. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Lack of precise temperature estimations leads to lesser device utilization and higher material cost. In this work, several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles. The range of selected algorithms covers as diverse approaches as possible with ordinary and weighted least squares, support vector regression, k-nearest neighbors, randomized trees and neural networks. Having test bench data available, it is shown that ML approaches relying merely on collected data meet the estimation performance of classical thermal models built on thermodynamic theory, yet not all kinds of models render efficient use of large datasets or sufficient modeling capacities. Especially linear regression and simple feed-forward neural networks with optimized hyperparameters mark strong predictive quality at low to moderate model sizes.