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Electrical Engineering

15.03.2025 | Original Paper

A new temperature prediction method for PMSM based on a nested depth neural network

verfasst von: Yuefeng Cen, Hao Guo, Xucheng Li, Gang Cen, Cheng Zhao, Yongping Cai

Erschienen in: Electrical Engineering

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Abstract

Temperature monitoring of permanent magnet synchronous motors which is an important driving component of electric vehicles, is closely related to the performance of permanent magnet synchronous motors. A deep neural network-based temperature prediction method for permanent magnet synchronous motors is proposed in this study. The core component temperature features of permanent magnet synchronous motors obtained from the test bench are selected using maximal information coefficient (MIC) method, and then the core component temperature is predicted using a nested deep neural network composed of one-dimensional convolutional neural networks (1D-CNN) and gated recurrent unit (GRU) neural networks. The effectiveness of the proposed prediction method is verified by experimental comparison with long short-term memory (LSTM), GRU, CNN-GRU(CGRU), and other models.

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Titel: A new temperature prediction method for PMSM based on a nested depth neural network
verfasst von: Yuefeng Cen
Hao Guo
Xucheng Li
Gang Cen
Cheng Zhao
Yongping Cai
Publikationsdatum: 15.03.2025
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-025-03040-8

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