Analytical Calculation of 3D Rotor Eddy Current Loss in High-Speed Permanent Magnet Motors Considering the Multi-Layer Rotor Structure
IEEE Transactions on Magnetics(2025)
College of Electrical and Information Engineering
Abstract
This article presents an analytical method to calculate 3D rotor eddy current loss (RECL) in high-speed permanent magnet motors (HSPMSMs) by correcting each harmonic loss component of 2D RECL. First, a 3D analytical model of a multi-layer rotor, accounting for the armature reaction field, is established based on the equivalent current sheet. The 3D governing equation is derived in stationary coordinate, and stator current is periodically extended in the axial direction. The end factors for correcting each time-spatial harmonic RECL are derived as the ratio of the RECLs for infinite and finite rotor lengths. To account for the influence of slotting effect and the magnetic field induced by the permanent magnet (PM), 3D RECL is obtained by correcting the series formed 2D RECL calculated using an accurate subdomain model. The accuracy of the proposed method is then verified through finite element analysis (FEA) at different motor sizes and frequencies. Finally, a rotor-locked test is carried out on an HSPMSM prototype, validating the effectiveness of the proposed analytical method.
MoreTranslated text
Key words
Eddy current loss,high speed permanent magnet motor,loss separation method,3D analytical model
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined