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Multiobjective optimization based on polynomial chaos expansions in the design of inductive power transfer systems

Multiobjective optimization based on polynomial chaos expansions in the design of inductive power... The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment.Design/methodology/approachIn this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs.FindingsAccording to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time.Originality/valueThe multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

Multiobjective optimization based on polynomial chaos expansions in the design of inductive power transfer systems

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0332-1649
eISSN
0332-1649
DOI
10.1108/compel-10-2021-0393
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to decrease the computation time that the large number of simulations involved in a parametric sweep when the model is in a three-dimensional environment.Design/methodology/approachIn this paper, a new methodology combining the PCE and a controlled, elitist genetic algorithm is proposed to design IPT systems. The relationship between the quantities of interest (mutual inductance and ferrite volume) and structural parameters (ferrite dimensions) is expressed by a PCE metamodel. Then, two objective functions corresponding to mutual inductance and ferrite volume are defined. These are combined together to obtain optimal parameters with a trade-off between these outputs.FindingsAccording to the number of individuals and the generations defined in the optimization algorithm in this paper, it needs to calculate 20,000 times in a 3D environment, which is quite time-consuming. But for PCE metamodel of mutual inductance M, it requires at least 100 times of calculations. Afterward, the evaluation of M based on the PCE metamodel requires 1 or 2 s. So compared to a conventional optimization based on the 3D model, it is easier to get optimized results with this approach and it saves a lot of computation time.Originality/valueThe multiobjective optimization based on PCEs could be helpful to perform the optimization when considering the system in a realistic 3D environment involving many parameters with low computation time.

Journal

COMPEL: Theinternational Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Oct 3, 2022

Keywords: Inductive power transfer; Multiobjective optimization; Polynomial chaos expansions; Surrogate optimization

References