Robust Optimization of a Permanent-Magnet Synchronous Machine Considering Uncertain Driving Cycles

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.