DeepAI AI Chat
Log In Sign Up

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

by   Wilhelm Kirchgässner, et al.

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.


page 1

page 2

page 7


Petrophysical property estimation from seismic data using recurrent neural networks

Reservoir characterization involves the estimation petrophysical propert...

A machine learning based plasticity model using proper orthogonal decomposition

Data-driven material models have many advantages over classical numerica...

Designing architectured ceramics for transient thermal applications using finite element and deep learning

Topologically interlocking architectures can generate tough ceramics wit...

Multiblock-Networks: A Neural Network Analog to Component Based Methods for Multi-Source Data

Training predictive models on datasets from multiple sources is a common...

Accurate Prediction of Global Mean Temperature through Data Transformation Techniques

It is important to predict how the Global Mean Temperature (GMT) will ev...

A semi-parametric model for ice accumulation rate and temperature based on Antarctic ice core data

In this paper, we present a semiparametric model for describing the effe...