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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.
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)
Published in DKUM: 15.05.2025; Views: 0; Downloads: 27
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Abstract: The focus of this Master's thesis was to increase the output-power density of a fractional-horsepower BLDC drive. Different stator segmentation topologies were analyzed and evaluated for this purpose. The presented analysis was performed by using various models with different complexity levels, where a Magnetic Equivalent Circuit (MEC) model and a 2D transient Finite Element Method (FEM) model combined with a power-loss model, were applied systematically. Characteristic behavior of the BLDC drive was obtained in this way. The models were validated with measurement results obtained on an experimental test drive system. The influence of the weakening of the magnetic flux density and flux linkage, due to segmentation were analyzed based on the validated models. Furthermore, the increase of the thermal-stable output power and efficiency was rated, due to the consequently higher slot fill factor. Lastly, a detailed iron-loss analysis was performed for different stator topologies. The performed analysis showed that segmentation of the stator can enable a significant increase of the output power of the discussed BLDC drives, where the positive effects of segmentation outweigh the negative ones from the electromagnetic point of view. Segmentation, however, also impacts other domains, such as Mechanical and Thermal, which was out of the scope of this thesis, and will be performed in the future.
Keywords: fractional-horsepower BLDC drive, stator segmentation, fill factor increase, thermal-stable output power, Finite Element Method model
Published in DKUM: 17.11.2020; Views: 1373; Downloads: 48
Full text (1,69 MB)

Keywords: tovorni transport, tovorni tokovi, prevoz blaga, optimiranje transportnih tokov
Published in DKUM: 01.10.2018; Views: 1088; Downloads: 0