Optimization of wireless charging system by multiverse algorithm combining adaptive compression factor and Cauchy variation

The transmission efficiency and power of the existing Magnetically Coupled Resonant Wireless Power Transfer (MCR WPT) system are affected by distance, load, coil, and other factors and cannot reach the optimal state simultaneously. This paper proposes a multi-objective parameter optimization of the system to improve its performance. By analyzing the LCC-S topology, the main parameters affecting the charging efficiency and output power are studied from the perspective of an equivalent circuit. The Multi-Objective Multi-Verse Optimizer (MOMVO) algorithm is employed to alter the growth mode of wormhole refreshment probability from linear to logarithmic, thereby enhancing the algorithm's capacity for effective search. Furthermore, the introduction of an adaptive compression factor and Cauchy's variance serves to achieve a balance between global and local convergence, thus enhancing the overall efficacy of the algorithm and escape from the local extremes. This achieves multi-objective parameter optimization while analyzing the optimized model. A physical platform is built to conduct experiments based on the optimized parameters. The results demonstrate that the optimized MCR-WPT system enhances its long-distance transmission performance. The optimal transmission distance of the system is 0.25 m, and the maximum output power is 127 W. Finally, the enhanced model's efficacy is substantiated through the construction of a prototype system.