Modeling of Scalar Dependencies of Soft Magnetic Material Magnetization for Electrical Machine Finite-Element Simulation

IEEE Transactions on Magnetics, vol. 56, pp. 1–4

Abstract

The magnetization behavior and thus the form of the magnetization curve of electrical steel strongly depend on the direction of the magnetic field, frequency of excitation, external mechanical stress, and cut edge effect. These factors influence the performance of electrical machines and need to be considered in advanced machine design processes or numerical modeling. Most of the aforementioned effects occur locally in the machine and, therefore, need to be described locally. It is crucial to characterize the material under realistic conditions for adequate identification and quantification of the influences. In this article, modeling and simulation of soft magnetic material are performed based on a detailed magnetic characterization, considering magnetization amplitude, angle with respect to the rolling direction of magnetization, mechanical stress, and cut edge effect. The dependent soft magnetic material characteristics are derived from the magnetic measurement data and concluded into interpolation surfaces. Subsequently, these surfaces are used to simulate a synchronous machine designed for a traction drive of an electric vehicle by finite-element simulation. One of the main challenges is the correct determination of the local material properties, depending on the operating point, which influences global quantities such as losses and torque. This article provides a methodology to consider different local influences on the magnetization behavior of electrical steel in a finite-element simulation, thus offering the potential for improving electromagnetic circuit design.

Authors 7

1. Xiao Xiao Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines (IEM), RWTH Aachen University, Aachen, Germany

2. Fabian Müller Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines (IEM), RWTH Aachen University, Aachen, Germany

3. Gregor Bavendiek Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines (IEM), RWTH Aachen University, Aachen, Germany

4. Nora Leuning Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines (IEM), RWTH Aachen University, Aachen, Germany

5. Pengfei Zhang

   Tsinghua University

   Affiliation as printed

   Department of Electrical Engineering, State Key Laboratory of Power System, Tsinghua University, Beijing, China

6. Jun Zou

   Tsinghua University

   Affiliation as printed

   Department of Electrical Engineering, State Key Laboratory of Power System, Tsinghua University, Beijing, China

7. Kay Hameyer Aachen Institute of Electrical Machines (IEM)

   RWTH Aachen University

   Affiliation as printed

   Institute of Electrical Machines (IEM), RWTH Aachen University, Aachen, Germany