Year: 2025
Type: conference-paper
Abstract: This paper introduces a neural network based optimization framework for Interior Permanent Magnet Synchronous Machines (IPMSMs) in squarewave drive applications (i.e., Brushless Direct Current or BLDC drives) for enhanced machine performance. The focus was on reducing torque ripple while enhancing or maintaining the torque-to-current ratio. A Finite Element Method model and a Design of Experiments are employed to generate training data for neural networks. The networks enabled a multi-objective optimization, producing two designs that significantly reduce torque ripple and simultaneously increase average torque. A significant decrease in simulation time alongside an innovative approach to designing IPMSMs for squarewave drives is presented.
Authors: Mitja Garmut, Simon Steentjes, Martin Petrun
Institutions: University of Maribor, Hilti (Liechtenstein)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410377138