Optimizing Wireless Power Transfer for Underwater Vehicles: a Neural Network Method for Distance Prediction and Impedance Matching

This study presents a novel approach utilizing Neural Networks (NN) to estimate the distance between coils in a Wireless Power Transfer (WPT) system specifically designed for underwater vehicles. The methodology incorporates an automatic impedance matching mechanism, validated through simulated and experimental data, achieving low predictive error rates. The NN is trained on impedance measurements or scattering parameters collected from the transmitter side, enabling accurate distance estimation between the coils in challenging underwater environments. Furthermore, this framework facilitates the identification of optimal capacitance values to enhance circuit performance and achieve ideal impedance matching for underwater applications. The results indicate significant improvements in WPT efficiency for underwater vehicles, underscoring the potential of NN applications in optimizing wireless energy transfer systems in marine settings. This research contributes to advancing the operational capabilities of underwater vehicles, allowing for prolonged missions without the need for frequent recharging or tethering to surface power sources.