Fast Shielding Optimization of an Inductive Power Transfer System for Electric Vehicles

The shielding design is one of the most difficult phases in developing an inductive power transfer system (IPT) for electric vehicles. In this aspect, the combination of metamodeling with a multiobjective optimization algorithm provides an efficient approach. Here, Polynomial Chaos Expansions (PCE)...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 91227 - 91234
Main Authors: Pei, Yao, Pichon, Lionel, Le Bihan, Yann, Bensetti, Mohamed, Dessante, Philippe
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aluminum
Chaos
Electric vehicles
Engineering Sciences
Ferrites
Finite element analysis
Finite element method
Genetic algorithms
Inductance
inductive power transfer system
Magnetic shielding
Metamodels
multigene genetic programming algorithm
Multiple objective analysis
Optimization
Optimization algorithms
particle swarm algorithm
Particle swarm optimization
polynomial chaos expansion
Polynomials
Power transfer
Power transmission
Receivers
Shielding design
Three-dimensional displays
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The shielding design is one of the most difficult phases in developing an inductive power transfer system (IPT) for electric vehicles. In this aspect, the combination of metamodeling with a multiobjective optimization algorithm provides an efficient approach. Here, Polynomial Chaos Expansions (PCE) and Multigene Genetic Programming Algorithm (MGPA) methods are used and compared to describe the mutual inductance of the IPT system in the function of the design variables on the shielding. These metamodels are obtained based on a number of 3D Finite Element Method (FEM) computations. Then, a multiobjective optimization algorithm coupled with the PCE metamodeling technique is applied to determine the optimal design variables for a practical shielding design when considering the magnetic coupling as well as the cost of the shielding as objective functions. Such a multiobjective optimization algorithm based on a particle swarm algorithm coupled with a metamodel on PCE method is proposed, leading to improve around 104 % of the mutual inductance <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> and save 14 % of the cost <inline-formula> <tex-math notation="LaTeX">C </tex-math></inline-formula> for the shielding compared to the initial design.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3198953