Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm
- Motor design can be said as multi-modal optimization problem, as many performances should be considered. In addition, a time-consuming finite element method (FEM) is required for accurate analysis of the motor, and such computational burden becomes worse when the FEM is applied to multi-modal optimization problem. In this paper, adaptive-sampling kriging algorithm (ASKA) is proposed to relieve the computation cost of multi-modal optimization problem. The ASKA utilizes kriging interpolation model with generated samples by Compact Search Sampling (CSS) and Exclusive Space-filling Method (ESM). The
CSS improves the accuracy of the solutions by generating samples near the expected solutions, and the ESM guarantees the diversity of solutions by generating samples far from existing samples, avoiding solution-near area. Using CSS and ESM, the ASKA adjusts the number of samples effectively and reduces function call considerably. The superior performance of the ASKA was verified by mathematical test functions with complex objective function regions. To validate the feasibility of actual electric machines, the ASKA was applied to optimal design of permanent magnet assisted synchronous reluctance motors for electric vehicles and optimum design with diminished torque ripple is derived.
- 손지창; 안종민; 임재원; 임동국
- Issued Date
- Electric vehicles; kriging; multi-modal optimization; optimal design; permanent magnet
- IEEE ACCESS
- Citation Volume
- Citation Number
- Citation Start Page
- Citation End Page
Appears in Collections:
- Engineering > Aerospace Engineering
- Authorize & License
- Files in This Item:
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.