The uncertainty of electric machines and drives is inherent in its manufacturing and assembly. Robust design optimization finds the design under these uncertainties that results in a minimal variance while satisfying all design constraints. To obtain an accurate result, the statistical model is significantly important. Moreover, several uncertainties from a single source can be involved owing to multiple components in the electrical machines and drives. However, this can drastically increase the numerical cost for the conventional robust design optimization technique. In this work an uncertainty characterization method is developed by using a percentile bootstrap interval to consider the experimental results from few prototypes and a kriging surrogate model to reduce the computational cost. Then the sample-based robust design optimization is applied to the surface-mounted permanent magnet synchronous motor. It is seen that the developed methods can efficiently work to minimize both the mean and variance of the cogging torque while satisfying other design constraints.

中文翻译：

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利用自举法不确定性表征表面贴装永磁同步电机的鲁棒设计优化


电机和驱动器的不确定性是其制造和装配所固有的。稳健的设计优化可以在这些不确定性下找到设计，从而在满足所有设计约束的同时实现最小方差。为了获得准确的结果，统计模型非常重要。此外，由于电机和驱动器中有多个组件，可能会涉及来自单一来源的多个不确定性。然而，这会大大增加传统鲁棒设计优化技术的数值成本。在这项工作中，通过使用百分位数引导区间来考虑少数原型的实验结果和克里金代理模型以降低计算成本，开发了一种不确定性表征方法。然后将基于样本的鲁棒设计优化应用于表面贴装永磁同步电机。可以看出，所开发的方法可以有效地最小化齿槽转矩的平均值和方差，同时满足其他设计约束。