Design Optimization of Traction Motors using a Quasi-Monte Carlo-based Two-Step Method

In the past decade, various approximation techniques have been thoroughly studied to minimize the computational cost for design optimization of permanent magnet synchronous machines using finite element analysis (FEA). However, most approximation techniques face the 'curse of dimensionality' problem, in which computation time increases exponentially with the number of input parameters considered. In this paper, we present a two-step design optimization approach based on artificial neural networks (ANNs) and quasi-Monte Carlo (QMC) methods for traction motor applications. First, a surrogate model is constructed using ANN to approximate the predictions by FEA. The proposed two-step method improves the estimation accuracy by starting with a small number of initial samples and then iteratively selecting additional samples near the Pareto front obtained based on the initial sampling. This allows for significant expansion and refinement of the Pareto front. Finally, the results of the proposed algorithm were compared with those of non-dominated sorting genetic algorithm II (NSGA-II). The results show that comparable results are obtained at a significantly lower computational cost.