  This work focuses on the robust optimization of a permanent magnet (PM)
synchronous machine while considering a driving cycle. The robustification is
obtained by considering uncertainties of different origins. Firstly, there are
geometrical uncertainties caused by manufacturing inaccuracies. Secondly, there
are uncertainties linked to different driving styles. The final set of
uncertainties is linked to ambient parameters such as traffic and weather
conditions. The optimization goal is to minimize the PM's volume while
maintaining a desired machine performance measured by the energy efficiency
over the driving cycle and the machine's 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 reassembling of the system of equations due to the changing PM geometry.
The Sequential Quadratic Programming algorithm is used for the optimization.
Stochastic collocation is applied to compute moments of stochastic quantities.
The robustness of the optimized configurations is validated by a Monte Carlo
sampling. It is found that the uncertainties in driving style and road
conditions have significant influence on the optimal PM configuration.
