Neural Dynamics Model for Temperature Estimation of Permanent Magnet Synchronous Motor

Accurate temperature estimation of permanent magnet synchronous motors is the basis for ensuring safe operation and designing effective thermal management strategies. Model-based estimation methods exhibit superior real-time performance, but the intricate modeling process requires substantial expert...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 74; no. 8; pp. 11993 - 12003
Main Authors: Liao, Xinyuan, Chen, Shaowei, Long, Yunxiang, Zhao, Shuai
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Artificial intelligence
Computational modeling
control-oriented modeling
Effectiveness
Estimation
Microcontrollers
Permanent magnets
Real time
stability constraints
STM32 microcontroller
System dynamics
System thermal dynamics
Temperature measurement
Temperature sensors
Thermal management
Thermal stability
Training
Vehicle dynamics
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:Accurate temperature estimation of permanent magnet synchronous motors is the basis for ensuring safe operation and designing effective thermal management strategies. Model-based estimation methods exhibit superior real-time performance, but the intricate modeling process requires substantial expert knowledge and lacks versatility. To the contrary, data-driven estimation methods, while offering flexibility, often lack physical implications in terms of system dynamics. This paper proposed a structured linear neural dynamics model for motor temperature estimation. The stability of the data-driven pipeline is facilitated by the Perron-Frobenius theorem, and the smooth evolution of the state between time steps is achieved by utilizing a physics-informed loss term. The effectiveness of the proposed method is verified by temperature estimation tasks at the edge end, and its practicality is demonstrated by measuring the memory and power usage of a deployed STM32 microcontroller.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3551375