Abstract
A Hybrid CSA-QNN approach is proposed in this manuscript for grid-connected PV with an efficient inverter-based wireless electric vehicle (EV) battery charger. The proposed hybrid method combines the performance of both the circle search algorithm (CSA) and quantum neural networks (QNN), commonly named the CSA-QNN technique. The Circle Search Algorithm helps find the best charging spot by creating a virtual circle, while the Quantum Neural Network optimizes the overall power flow and charging efficiency. Together, these technologies contribute to making wireless charging for EVs more efficient and convenient. The major goal of the manuscript is the design of a wireless EV battery charger with PV integration. Wireless EV charging systems (WEVCS) may be a feasible alternative technology for charging EVs without a plug-in problem. The CSA-QNN method is performed in the MATLAB platform and it is compared to different existing approaches. The CSA-QNN method shows better results than the existing approaches like the Salp Swarm Algorithm (SSA), Wild horse optimizer (WHO), and Particle Swarm optimization (PSO).
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