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2023 | OriginalPaper | Chapter

Self-Adaptive Construction Algorithm of a Surrogate Model for an Electric Powertrain Optimization

Authors : Marvin Chauwin, Hamid Ben Ahmed, Melaine Desvaux, Damien Birolleau

Published in: ELECTRIMACS 2022

Publisher: Springer International Publishing

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Abstract

This article presents a generic and self-adaptive construction algorithm for a surrogate model. This method makes use of two major tools: Latin HyperCube, which serves to efficiently spread a large number of samples; and Kriging, which is very efficient for surrogate modeling in the domain of black box models. The efficiency of this method is investigated in the case of a finite element model of a surface permanent magnet synchronous machine. During this study, Kriging surrogate models are compared with various samples in terms of both accuracy of construction and calculation speed. Next, the self-adaptative algorithm is applied in order to derive an accuracy criterion in a minimal amount of time and compare one with a Kriging model built using the same number of samples, yet without our tool to determine any accuracy lost due to the black box feature of the model and the hypotheses used.

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Metadata
Title
Self-Adaptive Construction Algorithm of a Surrogate Model for an Electric Powertrain Optimization
Authors
Marvin Chauwin
Hamid Ben Ahmed
Melaine Desvaux
Damien Birolleau
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-24837-5_43