Analysis of inductive power transfer systems by metamodeling techniques
This paper presents some metamodeling techniques to analyze the variability of the performances of an inductive power transfer (IPT) system, considering the sources of uncertainty (misalignment between the coils, the variation in air gap, and the rotation on the receiver). For IPT systems, one of th...
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| Published in: | Comptes rendus. Physique Vol. 25; no. S1; pp. 125 - 139 |
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Académie des sciences (Paris)
01.01.2024
Académie des sciences |
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| ISSN: | 1631-0705, 1878-1535 |
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| Abstract | This paper presents some metamodeling techniques to analyze the variability of the performances of an inductive power transfer (IPT) system, considering the sources of uncertainty (misalignment between the coils, the variation in air gap, and the rotation on the receiver). For IPT systems, one of the key issues is transmission efficiency, which is greatly influenced by many sources of uncertainty. So, it is meaningful to find a metamodeling technique to quickly evaluate the system’s performances. According to the comparison of Support Vector Regression, Multigene Genetic Programming Algorithm, and sparse Polynomial Chaos Expansions (PCE), sparse PCE is recommended for the analysis due to the tradeoff between the computational time and the accuracy of the metamodel. |
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| AbstractList | This paper presents some metamodeling techniques to analyze the variability of the performances of an inductive power transfer (IPT) system, considering the sources of uncertainty (misalignment between the coils, the variation in air gap, and the rotation on the receiver). For IPT systems, one of the key issues is transmission efficiency, which is greatly influenced by many sources of uncertainty. So, it is meaningful to find a metamodeling technique to quickly evaluate the system's performances. According to the comparison of Support Vector Regression, Multigene Genetic Programming Algorithm, and sparse Polynomial Chaos Expansions (PCE), sparse PCE is recommended for the analysis due to the tradeoff between the computational time and the accuracy of the metamodel.
Ce papier présente différentes techniques de métamodélisation afin d'analyser la variabilité des performances d'un système de transfert de puissance par induction (IPT), en tenant compte des sources d'incertitude (décentrage des bobines, la variation de l'entrefer et la rotation du récepteur). Pour les systèmes IPT, l'une des questions clés est l'efficacité de la transmission, qui est fortement influencée par les nombreuses sources d'incertitude. Il est donc important de déterminer une technique de métamodélisation susceptible d'évaluer rapidement les performances du système. Trois techniques de métamodélisation sont comparées : la régression à vecteurs de support, l'algorithme de programmation génétique multigénique et les développements du chaos polynomial (PCE), il ressort que la technique PCE est recommandée pour une telle analyse en raison du compromis entre le temps de calcul et la précision du métamodèle. This paper presents some metamodeling techniques to analyze the variability of the performances of an inductive power transfer (IPT) system, considering the sources of uncertainty (misalignment between the coils, the variation in air gap, and the rotation on the receiver). For IPT systems, one of the key issues is transmission efficiency, which is greatly influenced by many sources of uncertainty. So, it is meaningful to find a metamodeling technique to quickly evaluate the system’s performances. According to the comparison of Support Vector Regression, Multigene Genetic Programming Algorithm, and sparse Polynomial Chaos Expansions (PCE), sparse PCE is recommended for the analysis due to the tradeoff between the computational time and the accuracy of the metamodel. |
| Author | Le Bihan, Yann Bensetti, Mohamed Pei, Yao Pichon, Lionel |
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| Keywords | L Wireless power transfer Metamodels Polynomial chaos expansions Support vector regression Multigene genetic programming algorithm. Mots-clés. Transfert d'énergie sans contact Métamodèles Développements du chaos polynomial Régression à vecteurs de support L |
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