Application-Oriented Electrical Steel Selection for Advanced Drives: Enhancing Resource Efficiency

IEEE Transactions on Magnetics, pp. 1

Abstract

Electrical steel selection is a key lever in electric drive performance and resource efficiency, as material properties directly influence machine performance. Conventional design approaches typically define the machine geometry first and select the material afterward, potentially limiting the utilization of the available material properties. When machine geometry and other design parameters are however fixed, material redesign offers a practical opportunity to improve performance without requiring a complete machine redesign. This paper investigates the influence of six industrial non-oriented electrical steel grades with sheet thicknesses of 0.20 and 0.35 mm on the performance of an electric mobility application. A simulation study is conducted based on magnetic characterization, including magnetic loss modeling and magnetization modeling and is complemented by extensive material characterization of grain size, alloying content, electrical resistivity, and crystallographic texture to establish links between the measured material parameters and the simulated machine performance. The objective is not merely to compare the performance of individual steel grades, but to assess what conventional material classifications reveal—and where they fail to capture application-relevant behavior. The results show that drive-cycle performance depends strongly on the underlying material parameters and the resulting distribution of individual loss components. In particular, sheet thickness becomes increasingly important at high-speed operation, while grain size and alloying content provide additional levers for balancing magnetic performance and processing effort. The findings provide a basis for application-oriented material selection beyond nominal grade classifications, offering machine designers opportunities to identify performance improvements in existing designs and supporting more targeted material design and improved resource utilization in electric machine design.

Authors 3

1. Nora Leuning Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. A. Douiri Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Simon Steentjes Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University