This thesis investigates high torque density brushless machines, with a particular focus on axial flux permanent magnet (AFPM) motors, aiming to enhance electromechanical performance in the context of sustainable development and increasing electrification. Chapter 1 outlines the study’s foundations, presenting AFPM machines as a promising solution, quasi-3D modelling as an efficient approach to reduce computational cost, and segmented Halbach arrays as an innovative strategy to improve performance. It also introduces a novel parametric method for defining permanent magnet arrays, enabling a systematic and intuitive description of arbitrary configurations. Chapter 2 examines AFPM design principles, focusing on component arrangement and system integration, and providing the theoretical and methodological tools required to manage design complexity. Chapter 3 develops the theoretical framework based on Maxwell’s equations and introduces two 2D electromagnetic models: a hybrid Fourier–Bessel model and a linearised quasi-3D analytical model. These approaches, supported by advanced techniques such as conformal mapping, establish a rigorous analytical basis for studying AFPM machines and segmented Halbach arrays. Chapter 4 applies these methods to multiple case studies, exploring different configurations and their design implications. It also presents an experimental platform for characterising permanent magnets and analysing segmented Halbach arrays through automated measurements. Additional aspects include axial force evaluation, magnet skewing, and the impact of magnet geometry on electromagnetic and thermal performance, particularly in automotive applications. Overall, the thesis integrates analytical, numerical, and experimental approaches, highlighting the potential of segmented Halbach arrays and providing robust tools for future developments in high torque density electrical machines.
Electromechanical design of high torque density brushless motors
LANDI, GIOVANNI
2026
Abstract
This thesis investigates high torque density brushless machines, with a particular focus on axial flux permanent magnet (AFPM) motors, aiming to enhance electromechanical performance in the context of sustainable development and increasing electrification. Chapter 1 outlines the study’s foundations, presenting AFPM machines as a promising solution, quasi-3D modelling as an efficient approach to reduce computational cost, and segmented Halbach arrays as an innovative strategy to improve performance. It also introduces a novel parametric method for defining permanent magnet arrays, enabling a systematic and intuitive description of arbitrary configurations. Chapter 2 examines AFPM design principles, focusing on component arrangement and system integration, and providing the theoretical and methodological tools required to manage design complexity. Chapter 3 develops the theoretical framework based on Maxwell’s equations and introduces two 2D electromagnetic models: a hybrid Fourier–Bessel model and a linearised quasi-3D analytical model. These approaches, supported by advanced techniques such as conformal mapping, establish a rigorous analytical basis for studying AFPM machines and segmented Halbach arrays. Chapter 4 applies these methods to multiple case studies, exploring different configurations and their design implications. It also presents an experimental platform for characterising permanent magnets and analysing segmented Halbach arrays through automated measurements. Additional aspects include axial force evaluation, magnet skewing, and the impact of magnet geometry on electromagnetic and thermal performance, particularly in automotive applications. Overall, the thesis integrates analytical, numerical, and experimental approaches, highlighting the potential of segmented Halbach arrays and providing robust tools for future developments in high torque density electrical machines.| File | Dimensione | Formato | |
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Study_of_high_torque_density_brushless_motors_A.pdf
embargo fino al 14/05/2029
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46.54 MB | Adobe PDF |
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https://hdl.handle.net/20.500.14242/367788
URN:NBN:IT:UNIPI-367788