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Neural Dynamics Model for Temperature Estimation of Permanent Magnet Synchronous Motor

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Bibliographic Details
Title: Neural Dynamics Model for Temperature Estimation of Permanent Magnet Synchronous Motor
Authors: Xinyuan Liao, Shaowei Chen, Yunxiang Long, Shuai Zhao
Source: Liao, X, Chen, S, Long, Y & Zhao, S 2025, 'Neural Dynamics Model for Temperature Estimation of Permanent Magnet Synchronous Motor', IEEE Transactions on Vehicular Technology, vol. 74, no. 8, pp. 11993-12003. https://doi.org/10.1109/TVT.2025.3551375
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: STM32 microcontroller, control-oriented modeling, Stability Constraints, Artificial Intelligence, STM32 Microcontroller, System thermal dynamics, stability constraints, artificial intelligence, System Thermal Dynamics, Control-Oriented Modeling
Description: 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.
Document Type: Article
ISSN: 1939-9359
0018-9545
DOI: 10.1109/tvt.2025.3551375
Access URL: https://vbn.aau.dk/da/publications/5268e6bd-61f2-4193-abf0-1bed56b638f9
https://doi.org/10.1109/TVT.2025.3551375
http://www.scopus.com/inward/record.url?scp=105000150855&partnerID=8YFLogxK
Rights: IEEE Copyright
Accession Number: edsair.doi.dedup.....c06c4ee849cb0f222e2cf0f5bb9f8773
Database: OpenAIRE
Description
Abstract: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.
ISSN:19399359
00189545
DOI:10.1109/tvt.2025.3551375