  The optimization of electric machines at multiple operating points is crucial
for applications that require frequent changes on speeds and loads, such as the
electric vehicles, to strive for the machine optimal performance across the
entire driving cycle. However, the number of objectives that would need to be
optimized would significantly increase with the number of operating points
considered in the optimization, thus posting a potential problem in regards to
the visualization techniques currently in use, such as in the scatter plots of
Pareto fronts, the parallel coordinates, and in the principal component
analysis (PCA), inhibiting their ability to provide machine designers with
intuitive and informative visualizations of all of the design candidates and
their ability to pick a few for further fine-tuning with performance
verification. Therefore, this paper proposes the utilization of t-distributed
stochastic neighbor embedding (t-SNE) to visualize all of the optimization
objectives of various electric machines design candidates with various
operating conditions, which constitute a high-dimensional set of data that
would lie on several different, but related, low-dimensional manifolds.
Finally, two case studies of switched reluctance machines (SRM) are presented
to illustrate the superiority of then t-SNE when compared to traditional
visualization techniques used in electric machine optimizations.
