Electrical machine initial design and computational tool

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dc.contributor.authorUlvekar, Akash Chandrakant
dc.contributor.authorShenoy, Koushik Damodara
dc.contributor.departmentChalmers tekniska högskola / Institutionen för mekanik och maritima vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mechanics and Maritime Sciencesen
dc.contributor.examinerSedarsky, David
dc.contributor.supervisorRadecki, Przemyslaw
dc.date.accessioned2025-02-18T12:41:27Z
dc.date.available2025-02-18T12:41:27Z
dc.date.issued2025
dc.date.submitted
dc.description.abstractElectric and hybrid electric vehicles widely use Interior Permanent Magnet Synchronous Machines (IPMSM) due to their high torque and power densities. The initial design phase of these electrical machines are critical as they affect the performance, efficiency and costeffectiveness. This thesis explores the development and application of a Python-based computational tool for the initial design of IPMSMs, aiming to achieve optimal machine performance while reducing dependency on iterative Finite Element Method (FEM) solutions. The scope of the research was to develop a robust framework that bridges theoretical design principles with practical computational methods, aiding innovation in electrical machine engineering. An analytical approach was adopted for the preliminary design, focusing on the relationship between machine sizing and performance parameters. Preliminary designs were derived using geometrical constraints and mathematical equations. These designs were validated through Magnetic Equivalent Circuit (MEC) models and sensitivity analysis, with results further corroborated by FEM simulations. The study is centered on two case studies: a MotorCAD template and a journal model. The Python tool demonstrated high accuracy, with sizing parameters closely matching those from MotorCAD and the journal model. Sensitivity analysis was conducted to evaluate the impact of variations in air-gap thickness, magnet width, pole-arc to pole-pitch ratio, and magnet strength on machine performance. The results showcased consistent trends between the Python tool and MotorCAD, emphasizing the tool’s reliability and robustness. The computational tool made it computationally efficient by enabling rapid prototyping and reduced iteration time. This work highlights how the tool can efficiently and accurately design IPMSM, providing a solid foundation for future advancements in electrical machine engineering.
dc.identifier.coursecodeMMSX30
dc.identifier.urihttp://hdl.handle.net/20.500.12380/309133
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectanalytical approach
dc.subjectfinite element method
dc.subjectIPMSM
dc.subjectmachine sizing
dc.subjectmagnetic equivalent circuit
dc.subjectMotorCAD
dc.subjectpython-based computational tool
dc.titleElectrical machine initial design and computational tool
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster's Thesisen
dc.type.uppsokH
local.programmeAutomotive engineering (MPAUT), MSc
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