On the use of surrogate models for drive cycle automotive electrical machine design

Surrogate models have become a widely used solution for reducing computation times along design processes. In this work, a Gaussian Process surrogate model is built and used to predict the performance and losses of an electrical machine in a fast manner. This approach is relevant, especially for drive cycle calculations that rapidly generate rising computation costs if they are computed using physical models, especially finite elements analysis. We present in detail the established method and a comparison of the obtained results with finite elements results.