Abstract
The design and optimization of coils for Inductive Power Transfer (IPT) systems is an iterative process conducted in Finite Element (FE) tools that takes a lot of time and computational resources. In order to overcome such limitations in the design process, new empirical equations for the evaluation of the self-inductance and mutual inductance values are proposed in this work. By means of a multi-objective genetic programming algorithm, the self-inductance, the mutual inductance and the coupling factor values obtained from FE simulations of IPT link are accounted by analytical equations, based on the geometric parameters defining the IPT link. The behavioral modeling results are compared with both FE-based and experimental results, showing a good accuracy.
Highlights
The design of an Inductive Power Transfer (IPT) link involves system-level specifications that must be matched with a proper choice of architectures and components [1]
We proved the accuracy of the Finite Element Method (FEM)-based data obtained for the built IPT link prototypes against the experimental measurements of the resultant self-inductance and mutual inductance values
We can see that for all the cases studied the self-inductance, mutual inductance and coupling factor errors are within ±15%, and for the majority of cases strictly included within ±10%, corroborating the good accuracy of the behavioral models and the valued chance of using these formulas to design IPT links without using FEM simulations
Summary
The design of an Inductive Power Transfer (IPT) link involves system-level specifications that must be matched with a proper choice of architectures and components [1]. The chosen architecture has an impact in the resonance method [3][4] All these requirements must be taken into account in optimizing the system’s components, such as the magnetic link (coupled coils). Our goal is to analytically relate the coil inductances to the design variables of real IPT links, by means of the so-called “behavioral models” These models are identified once for all by using a multi-objective Genetic Programming Algorithm (GPA) and a limited set of FEM-based numerical solutions. This approach provides a solution to all the above issues, by lowering the computational cost, adding qualitative information to the design solutions, and providing a modeling tool that can be generalized to a given class of coils.
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