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

TL;DR

This paper presents a robust optimization approach for a permanent magnet synchronous machine that accounts for manufacturing, driving style, and environmental uncertainties to minimize volume while ensuring performance.

Contribution

It introduces a comprehensive robust optimization framework incorporating multiple uncertainty sources using PDE-based magnetic modeling and stochastic methods.

Findings

Uncertainties in driving style and road conditions significantly impact optimal design.

The optimization reduces PM volume while maintaining efficiency and torque.

Validation confirms robustness of the optimized configurations.

Abstract

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.