Work

On the Iron Loss Reduction Design Improvement of an Axial Flux Permanent Magnet Motor

Year: 2025

Type: article

Abstract: Reducing iron loss in axial flux permanent magnet (AFPM) motors is critical for improving efficiency. This study proposes a design-optimization procedure that combines 3D finite-element analysis (FEA) data with an artificial neural network (ANN) surrogate. For four design variables—airgap length, rotor back-yoke thickness, stator slot width, and stator slot depth—the search bounds were defined to avoid tooth and back-yoke saturation, and the corresponding space was sampled to construct a dataset. Using this dataset, the ANN was trained and then used to explore low-iron loss solutions. On an independent validation set, ANN predictions showed high agreement with 3D-FEA reference values, enabling rapid evaluation of many design candidates. As a result of the optimization, total iron loss decreased relative to the baseline, and torque increased by 3 Nm. These results demonstrate that the ANN-based surrogate model can reliably perform geometry-dependent iron loss optimization in AFPM motors, providing a fast and accurate alternative to repetitive 3D-FEA evaluations.

Source: Actuators

DOI: 10.3390/act14120595

Authors: Seung-Mi Oh, Kan Akatsu, Dong-Woo Lee, Ho-Joon Lee

Institutions: Dankook University, Yokohama National University, Cheongju University

Language: English

Topic: Electric Motor Design and Analysis

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,896