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<p>Accurately estimating electrical machine temperatures, particularly the magnet temperature, remains a challenging task. Data-driven approaches can be a new method if they offer highly accurate temperature predictions with compact models suitable for real-time embedded systems. This paper presents an investigation on thermal neural networks, a data-driven modeling approach. The main goal of this study is to investigate the benefits and drawbacks of this approach. Firstly, the model architecture and the input dataset from simulations in the ANSYS MotorCAD design software are presented, followed by the training routine. The initial analysis evaluates the best number of nodes for the model, already indicating its strong performance, with a maximum absolute error of the permanent magnet temperature of 1.37 K on an unknown validation dataset. The result of this first analysis is the base model with four internal nodes. This study then explores methods to enhance the physical interpretability of this thermal neural network. Firstly, a sparsing technique reduces the model’s parameters by removing connections that lack physical meaning. Secondly, in an experiment the ambient node is eliminated and a virtual node with constant temperature is introduced to isolate the influence of the ambient node. This model with virtual node has a maximum absolute error of the permanent magnet temperature of 2.75 K and offers faster training with fewer parameters than the base model. The paper finally outlines key steps toward creating a more diverse training dataset, ensuring real-world applicability and usability for temperature-based control systems in permanent magnet synchronous machines.</p>

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Study on the Interpretability of Thermal Neural Networks for Permanent Magnet Synchronous Machines

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Anna Isabel Ramones,
  • Samuel Kieling,
  • Tobias Brinkmann,
  • Christian Monissen,
  • Markus Elisenbarth,
  • Jakob Andert

摘要

Accurately estimating electrical machine temperatures, particularly the magnet temperature, remains a challenging task. Data-driven approaches can be a new method if they offer highly accurate temperature predictions with compact models suitable for real-time embedded systems. This paper presents an investigation on thermal neural networks, a data-driven modeling approach. The main goal of this study is to investigate the benefits and drawbacks of this approach. Firstly, the model architecture and the input dataset from simulations in the ANSYS MotorCAD design software are presented, followed by the training routine. The initial analysis evaluates the best number of nodes for the model, already indicating its strong performance, with a maximum absolute error of the permanent magnet temperature of 1.37 K on an unknown validation dataset. The result of this first analysis is the base model with four internal nodes. This study then explores methods to enhance the physical interpretability of this thermal neural network. Firstly, a sparsing technique reduces the model’s parameters by removing connections that lack physical meaning. Secondly, in an experiment the ambient node is eliminated and a virtual node with constant temperature is introduced to isolate the influence of the ambient node. This model with virtual node has a maximum absolute error of the permanent magnet temperature of 2.75 K and offers faster training with fewer parameters than the base model. The paper finally outlines key steps toward creating a more diverse training dataset, ensuring real-world applicability and usability for temperature-based control systems in permanent magnet synchronous machines.