Analyse of the flux density and iron loss distributions in segmented magnetic circuits made with mixed electrical steel grades

Abstract

Reducing the rate of CO2 emissions for hybrid electric vehicles (HEV) and improving the autonomy for full battery electric vehicle (BEV) constitute major challenges for motor manufacturers. That can be done by losses reduction in each part of the electric powertrains. In the electric motors, using high grades magnetic material and optimal design allows reducing iron losses in the important area of the Torque-Speed plane. That is why segmented stator cores mixing grain oriented electrical steel (GOES) and non-oriented electrical steel (NOES) constitute a serious way of improvement. The goal of the paper is to present the existing structures with, for instance GOES teeth and NOES yoke, and to analyze the contribution of the high grade steel in the automotive motor improvement, in terms of efficiency, masse and volume.GOES has strong advantages in terms of iron losses and flux density at saturation, when it is magnetized in the Rolling Direction (RD). GOES has the drawback of being very anisotropic and the performance is not so interesting in the Transverse Direction, compared to NOES, especially in term of iron losses at high induction and high operational frequencies [1]. That implies to adapt the machine geometry to the specificities of this steel [2] [3] [4]. Another disadvantage of GOES is the sensitivity to the manufacturing processes: bending, cutting, compacting… [3]. From a micro-structural point of view, cutting the sheets causes a destruction of the grains close to the cutting border. At a macroscopic scale, that reduces the magnetic performance, depending not only on the cutting technique but also the dimensions of the sheet [5].First, in the full paper, the authors present the existing magnetic circuit structures and how the constraints linked to the high anisotropy ratio has been taken into account. The electric motor analyzed mixes GOES teeth and NOES yoke, as shown in Fig.1.Second, the question of the modelling is posed. The full analytic simulation of the motor is not really adapted taking into account the complexity of the anisotropy. The Finite Element software applications integrate now both anisotropy and saturation, but not always in 3D. The strong difficulty is that the magnetic characteristics differ as a function of the magnetization angle. As a result, the results are not enough accurate in term of flux density distribution. The loss models, when they are implemented for GOES, are not reliable when the sheet is magnetized in multiple directions.Third, in order to analyse the contribution of the elements constituting the magnetic circuit, the authors propose a device, shown in Fig. 2, for numerical and experimental tests. It is designed in order to reproduce the phenomena occurring in the electrical motors, especially in the region between the teeth and the yoke. To do that, it combines two Epstein frames modified to magnetize different magnetic structures. The goal is to analyse the flux densities in the magnetic circuit and the iron losses, depending on several parameters as different geometries of junctions between the segments. The angle and the depth of insertion of the teeth into the yoke will be analysed. It allows also to evaluate the effect of the flux passing not directly in a teeth, but only the yoke. That happens in fractional slot permanent magnet machines and, in that case, the TD is fully solicited. Then, the nature and the grades of the materials will be tested with 0,20mm GOES and NOES. Effect of manufacturing will also be shown with experimental test made with cut laminations.Fourth, an existing full NO steel reference motor will be numerically compared with an improved machine made with mixed NOES and GOES. The comparison will be made on local quantities (flux density, etc) as well as on integral quantities (Flux, EMF, Electromagnetic torque and losses). The authors will also present simulation results showing the efficiency improved by the use of GOES.  Fig.1  Fig. 2