Advanced Soft- and Hard-Magnetic Material Models for the Numerical Simulation of Electrical Machines
Advanced Soft- and Hard-Magnetic Material Models for the Numerical Simulation of Electrical Machines
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DOI：
10.1109/tmag.2018.2865096
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发表时间：
2018-11-01
影响因子：
2.1
通讯作者：
Hameyer, Kay
中科院分区：
文献类型：
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作者：
Leuning, Nora;Elfgen, Silas;Hameyer, Kay
旋转电机的数值模拟需要对软磁和硬磁材料进行精确的建模，以便在设计阶段就可以沿着转矩-速度图预测其运行特性。只有当模型能够代表实际的材料行为时，才能充分利用最合适的材料选择和同步几何适应的潜力。准确预测各种频率和磁通密度下软磁材料的铁损耗，以及制造过程中的铁损耗衰减，对电机设计具有重要意义。因此，需要适应先进的材料模型并检查其准确性，以进一步改进建模并使其能够进步。本文将概述电机研究所目前在旋转电机仿真中应用的软磁和硬磁材料建模方法。最后以某牵引传动为例，给出了所建模型的应用实例。对于机器建模，使用了内部求解器pyMOOSE。为了确定与制造工艺有关的损失，采用了考虑材料退化的铁损失模型，并结合机器整个工作范围的机器仿真方案。在这里，将各种仿真方法结合起来，形成整个计算工具链，以获得整个操作范围内的准确结果。
Accurate modeling of soft- and hard-magnetic materials for the numerical simulation of rotating electrical machines is required to allow predictions on the operational characteristics along the torque-speed map, already in the design stage. The full potential of most appropriate material selection and concurrent geometry adaption can only be utilized if models can represent actual material behavior. The accurate prediction of iron losses of soft- magnetic materials for various frequencies and magnetic flux densities, as well as the degradation due to manufacturing is eminent for the design of electrical machines. Therefore, advanced material models need to be adapted and their accuracy examined to further improve the modeling and enable progression. This paper will give an overview of the current modeling approaches applied at the Institute of Electrical Machines for soft- and hard-magnetic materials in the simulation of rotating electrical machines. A case example in the form of a traction drive is presented to which the models are applied. For the machine modeling, the inhouse solver pyMOOSE is utilized. In order to determine the losses with regard to manufacturing processes, the iron-loss model with material degradation is used in combination with a machine simulation scheme of the entire operating range of the machine. Here, various simulation approaches are combined to form the entire computational toolchain to obtain accurate results in the entire operational range.