Novel Metamaterial and AI-based Multi-Objective Optimization of Coil Parameters for Efficient Wireless Power Transfer

Range limitation, low transfer power (TP) and poor Power Transfer Efficiency (PTE) are some of the issues affecting the adoption of Wireless Power Transfer (WPT). However, the discovery of materials with negative refractive index, otherwise called meta-materials, has proven a viable solution. The inherent metamaterial's magneto-inductive wave coupled with its negative refractive index causes evanescent wave amplification which results in a high density magnetic field. In this manuscript, a Ferrite-core metamaterial WPT model is presented. Finite element Analysis (FEA) of the proposed design was conducted in ANSYS simulation environment. The simulation results show that the proposed Ferrite-core metamaterial generates higher mutual inductance and received power than a conventional WPT design. Further, the model achieves higher mutual inductance and transmit power with small core radii than large core radii, thus presenting a cost saving benefits in material fabrication. Artificial intelligence (AI)-based optimization of coil parameters was performed in MATLAB to improve the amount of power received, and enhance Power Transfer Efficiency (PTE). The Matlab results were cross-verified with LTSpice results and both show close agreement.