Surrogate Model-Based Drive Cycle Modeling and Optimization of Synchronous Reluctance Machines for Electric Vehicles

The design of traction machines for electric vehicles (EVs) typically aims to minimize total energy losses across an entire drive cycle, a process that can be highly time-consuming. To enhance the time efficiency in drive cycle-oriented optimization, this work proposes a surrogate model-based methodology for optimizing drive cycle energy loss, integrating both vehicle dynamics requirements and machine constraints. Initially, multiple modeling techniques for drive cycle energy loss, including point-by-point (PbP) search, loss map (LossMap), and k-means clustering, are assessed to identify the most accurate method. Subsequently, three prediction approaches using Kriging model are employed within the optimization framework. The results demonstrate that the PbP search method provides the highest accuracy in modeling drive cycle energy loss, while the predicted lookup table-driven sequential energy loss prediction (PLUT-SELP) achieves the optimal Pareto front among the prediction methods. Furthermore, while the PLUT-SELP method significantly improves the Pareto front and reduces computation time by 47.1% compared to the full-FEA optimization routine, the alternative methods hybrid input energy loss prediction (HIELP) and the combination of direct energy loss prediction (DELP) with PLUT-SELP achieve further time reductions of 50.7% and 53.9%, respectively, with a slight trade-off in optimization quality.