2021
DOI: 10.1109/access.2021.3054920
|View full text |Cite
|
Sign up to set email alerts
|

Optimal Design of PMa-SynRM for Electric Vehicles Exploiting Adaptive-Sampling Kriging Algorithm

Abstract: 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 multimodal 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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
9
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
9

Relationship

3
6

Authors

Journals

citations
Cited by 21 publications
(9 citation statements)
references
References 47 publications
0
9
0
Order By: Relevance
“…The design variables are shown in Fig. 10 and are selected as the pole arc to pole pitch ratio (alpha) and the angle of the magnet (m θ ), as the torque ripple varies according to the shape of the magnet placement [31], [32].…”
Section: Design Optimization Of the Ipmsm For Hev Application Using I...mentioning
confidence: 99%
“…The design variables are shown in Fig. 10 and are selected as the pole arc to pole pitch ratio (alpha) and the angle of the magnet (m θ ), as the torque ripple varies according to the shape of the magnet placement [31], [32].…”
Section: Design Optimization Of the Ipmsm For Hev Application Using I...mentioning
confidence: 99%
“…Therefore, it is essential to select the appropriate number of samples. For this paper, appropriate initial samples were selected by referring to the [25].…”
Section: Surrogate Model Creationmentioning
confidence: 99%
“…The more kriging grid, the higher accuracy of the solution; however, the reason why the grid cannot be infinitely finely divided is because all the values of all grid points must be compared, which can take a long time. The proper kriging grid setting is required considering the accuracy of the solution and interpretation time [25]. Therefore, in this paper, the kriging grid is not finely divided from the beginning of algorithm, it is divided later in the algorithm to reduce the interpretation time and increase the accuracy of the solution.…”
Section: Subdivided Kriging Gridmentioning
confidence: 99%
“…This algorithm was applied to minimize the cogging torque of an axial flux permanent magnet machine (AFPMSM), and three local solutions were obtained [16,17]. Moreover, Yoo et al [18] and Son et al [19] suggested and developed a computationally efficient algorithm that was applied for the multi-modal optimization of an interior permanent magnet synchronous motor (IPMSM) and a permanent magnet assisted synchronous reluctance motor (PMa-SynRM). Previous studies have focused on heuristic algorithms to address the multi-modal problem by finding multiple optima.…”
Section: Introductionmentioning
confidence: 99%