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

Abstract

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 $M$ and save 14 % of the cost $C$ for the shielding compared to the initial design.

Keywords

• Shielding design
• polynomial chaos expansion
• multigene genetic programming algorithm
• particle swarm algorithm
• inductive power transfer system