Optimal drive cycle current supply of a wound field automotive electrical machine using surrogate models

Surrogate models have become a widely used solution for reducing computation times along design processes. In this work, a Gaussian Process surrogate model is built and used to predict the performance and losses of a wound field electrical machine in a fast manner. This approach is relevant, especia...

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Bibliographic Details
Published inScience and technology for energy transition Vol. 79; p. 2
Main Authors Mazloum, Rebecca, Hlioui, Sami, Laurent, Luc, Belhadi, M’Hamed, Mermaz-Rollet, Guillaume, Gabsi, Mohamed
Format Journal Article
LanguageEnglish
Published IFP Énergies nouvelles (IFPEN), Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA) 2024
EDP Sciences
Subjects
drive cycle
electrical machine
Engineering Sciences
excitation current
gaussian process
metamodeling
Excitation current
Gaussian process
Drive cycle
Electrical machine
Metamodeling
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Summary:Surrogate models have become a widely used solution for reducing computation times along design processes. In this work, a Gaussian Process surrogate model is built and used to predict the performance and losses of a wound field electrical machine in a fast manner. This approach is relevant, especially for drive cycle calculations that rapidly generate rising computation costs if they are computed using physical models, especially finite elements analysis. We present in detail the established method and a comparison of the obtained results with finite elements results. In addition, a detailed analysis of the optimized current supply is presented, and the advantages of variable excitation current are highlighted.
ISSN:2804-7699
2804-7699
DOI:10.2516/stet/2023041