Wireless Power Transfer Systems Optimization Using Multiple Magnetic Couplings

Abstract

Multiple magnetic couplings used to increase the link distance in wireless power transfer systems (WPTSs) are not new. An efficient power transfer in conditions of an extended link distance requires a series connection of the intermediate coils. However, all four connections of the emitter and receiver coils are equally possible. This present paper conducts an extensive analysis of WPTSs utilizing three magnetic couplings. The type of connection of the emitter and receiver coils represented the criterion utilized for the WPTS optimization assessment. The first step requires the determination of the schematic of the sinusoidal equivalent circuit. Then, one synthesizes the functions describing the system performances (e.g., the amount of delivered active power or efficiency) by applying the entirely symbolic and or the hybrid symbolic-numerical formalism. The output of such functions consists of appropriate representation in the frequency domain, based upon Laplace state variable equations (SVE) or complex or Laplace modified nodal equations (MNE). The dependency of the WPTS performance on the number of magnetic couplings and their parameters included a study on resistive loss minimization. The minimization applies to the intermediate coils, whereas the outcomes are the active delivered power and the power transfer efficiency—the first study case aimed at a comparison between two distinct WPTSs: three magnetic couplings versus two. The second case of the study compared the WPTSs having a series connection of three magnetic couplings with those built with the emitter-receiver resonators in parallel. One determined the normalized sensitivities as frequency functions, which depend on circuit resistances, load resistance and the coupling factor between the second and the third coil. The optimization algorithms are suitable for computing optimal parameters of the given circuit to ensure maximum and minimum values of the performance value. Good simulation examples followed the proposed optimization techniques.