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March 8, 2026Open Access

Advanced Temperature Prediction for Electric Motors: A Review from Physical Foundations to Physics-Informed Intelligence

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Authors

YHYaofei HanQZQian ZhangYLYongfeng Liu

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Overview

Review highlights temperature prediction technologies for electric motors, suggesting advancements in methodology and application.

Key Points

  • The review aims to clarify and categorize technologies for predicting temperatures in electric motors.
  • Revisiting physical foundations of motor thermal behavior and loss generation.
  • Categorizing estimation methodologies into physics-based models, observer approaches, and data-driven frameworks.
  • Comparative analysis of mathematical principles, computational trade-offs, and deployment constraints.
  • Focusing on hybrid methods that integrate reduced-order networks and learning-based corrections.
  • Emphasized the importance of time-varying boundary conditions on motor temperature.
  • Identified data-driven methods as a strong area for development despite information bottlenecks.
  • Highlighted the potential of uncertainty-aware estimation techniques for improved prediction accuracy.

Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/69ada8c2bc08abd80d5bbf9chttps://doi.org/10.3390/machines14030305
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