| Title: | Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networks |
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| Authors: | ID Garmut, Mitja (Author)
ID Steentjes, Simon (Author)
ID Petrun, Martin (Author) |
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| Files: |  1-s2.0-S0952197625017555-main.pdf (2,87 MB)
MD5: 7C98EC9FF2E83BCEFDA518A48D63F8B1
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%. |
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| Keywords: | interior permanent magnet synchronous machine, artificial neural network, metamodel, multi-objective optimization, finite element method |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 15.05.2025 |
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| Article acceptance date: | 08.07.2025 |
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| Publication date: | 25.07.2025 |
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| Publisher: | Elsevier Science |
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| Year of publishing: | 2025 |
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| Number of pages: | 15 str. |
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| Numbering: | vol. 160, [article no.] 111753 |
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| PID: | 20.500.12556/DKUM-94206  |
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| UDC: | 621.31 |
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| ISSN on article: | 1873-6769 |
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| COBISS.SI-ID: | 244872195 |
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| DOI: | 10.1016/j.engappai.2025.111753 |
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| Copyright: | © 2025 The Authors |
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| Publication date in DKUM: | 08.08.2025 |
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| Views: | 267 |
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| Downloads: | 41 |
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| Metadata: |    |
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| Categories: | Misc.
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