| Title: | Integrating Multi-Physics Modeling within Multi-Objective Optimization to Enhance the Performance and Efficiency of Permanent Magnet Synchronous Machines : doktorska disertacija |
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| Authors: | ID Garmut, Mitja (Author)
ID Petrun, Martin (Mentor) More about this mentor...  |
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| Files: |  DOK_Garmut_Mitja_2025.pdf (17,79 MB)
MD5: CAE7F9C8C6386556D270FAE487F90B99
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| Language: | English |
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| Work type: | Doctoral dissertation |
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| Typology: | 2.08 - Doctoral Dissertation |
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| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
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| Abstract: | This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency.
The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process.
Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy.
The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods.
Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method.
By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations.
In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design. |
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| Keywords: | Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC) |
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| Place of publishing: | Maribor |
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| Place of performance: | Maribor |
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| Publisher: | [M. Garmut] |
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| Year of publishing: | 2025 |
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| Number of pages: | XXXIX, 145 str. |
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| PID: | 20.500.12556/DKUM-91651 |
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| UDC: | 621.313.8-048.34(043.3) |
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| COBISS.SI-ID: | 235945731 |
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| Publication date in DKUM: | 15.05.2025 |
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| Views: | 239 |
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| Downloads: | 249 |
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| Metadata: |    |
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| Categories: | KTFMB - FERI
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