This article focuses on the robust optimization of a permanent-magnet (PM) synchronous machine while considering a driving cycle. The robustification is obtained by considering geometrical uncertainties caused by manufacturing inaccuracies, uncertainties linked to different driving styles, and uncertainties related to ambient parameters such as traffic and weather conditions. The optimization goal is to minimize the PM’s volume while maintaining the machine performance, i.e., the energy efficiency over the driving cycle and the maximal torque. The magnetic behavior of the machine is described by a partial differential equation (PDE) and is simulated by the finite-element method, employing an affine decomposition to avoid the reassembling of the system of equations due to the changing geometry. Sequential quadratic programming is used for the optimization, and stochastic collocation is applied to compute the moments of stochastic quantities. The robustness of the optimized configurations is validated by a Monte Carlo sampling. It is found that the uncertainties have a significant influence on the optimal PM configuration.

中文翻译：

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考虑不确定驱动循环的永磁同步电机的鲁棒优化

本文重点介绍在考虑驱动循环的同时对永磁 (PM) 同步电机进行稳健优化。通过考虑由制造不准确引起的几何不确定性、与不同驾驶方式相关的不确定性以及与交通和天气条件等环境参数相关的不确定性，获得了稳健性。优化目标是在保持机器性能的同时最小化 PM 的体积，即整个驾驶循环的能效和最大扭矩。电机的磁性能由偏微分方程 (PDE) 描述，并由有限元方法模拟，采用仿射分解以避免由于几何变化而重新组装方程组。使用顺序二次规划进行优化，并应用随机搭配来计算随机量的矩。优化配置的稳健性通过蒙特卡罗采样进行验证。结果表明，不确定性对最优 PM 配置有显着影响。