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

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Marvin Chauwin
Hamid Ben Ahmed
Melaine Desvaux
Damien Birolleau
Published in
ELECTRIMACS 2022
Publisher
Springer International Publishing

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Abstract

The chapter introduces a self-adaptive algorithm for constructing surrogate models to optimize electric powertrain systems. It addresses the complexity of multiphysics modeling, which includes mechanical, electromagnetic, and thermal principles. The algorithm aims to reduce computational time by using Kriging to estimate model outputs accurately. The method involves creating a sampling plan, computing the model on each sample, and optimizing the surrogate model parameters. The chapter also discusses the efficiency of Kriging and the use of Latin HyperCube for distributing samples effectively. Enrichment techniques, both OFF-Line and ON-Line, are explored to improve the accuracy of the surrogate models. The Sub-Latin HyperCube method is highlighted as a tool to add new samples efficiently, reducing the time required to achieve the desired accuracy. The chapter concludes by emphasizing the potential of these tools to create high-speed computing surrogate models, though further local accuracy improvements may be needed.
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previous chapter A Comparative Study of Existing Approaches for Modeling the Incident Irradiance on Bifacial Panels
next chapter Optimization of Neural Network-Based Load Forecasting by Means of Whale Optimization Algorithm
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
Book
ELECTRIMACS 2022

Print ISBN: 978-3-031-24836-8

Electronic ISBN: 978-3-031-24837-5

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-24837-5_43
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