This paper presents a novel approach to optimize selective wireless power transmission to multiple receivers utilizing a single transmitter. The primary focus is on achieving target power levels at specific resonant frequencies on each receiver while ensuring maximum transmission efficiency. To address this, an analytical model based on magnetic coupling between coils is employed to explore different solutions, and a Genetic Algorithm (GA) is applied to identify the optimal configuration. The application of a GA for system design optimization is introduced, which proves highly effective, especially when dealing with multiple receivers where analytical models become difficult to manage. The GA-based approach facilitates the discovery of numerical solutions that might be challenging to deduce analytically. The validity of the proposed approach is confirmed through simulation and experimental measurements.

Optimizing power transfer in selective wireless charging systems: A genetic algorithm-based approach / Bertolini V.; Corti F.; Intravaia M.; Reatti A.; Cardelli E.. - In: JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS. - ISSN 0304-8853. - ELETTRONICO. - 587:(2023), pp. 1-9. [10.1016/j.jmmm.2023.171340]

Optimizing power transfer in selective wireless charging systems: A genetic algorithm-based approach

Corti F.;Intravaia M.;Reatti A.;
2023

Abstract

This paper presents a novel approach to optimize selective wireless power transmission to multiple receivers utilizing a single transmitter. The primary focus is on achieving target power levels at specific resonant frequencies on each receiver while ensuring maximum transmission efficiency. To address this, an analytical model based on magnetic coupling between coils is employed to explore different solutions, and a Genetic Algorithm (GA) is applied to identify the optimal configuration. The application of a GA for system design optimization is introduced, which proves highly effective, especially when dealing with multiple receivers where analytical models become difficult to manage. The GA-based approach facilitates the discovery of numerical solutions that might be challenging to deduce analytically. The validity of the proposed approach is confirmed through simulation and experimental measurements.
2023
587
1
9
Bertolini V.; Corti F.; Intravaia M.; Reatti A.; Cardelli E.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1337295
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