volume | PIER Journals

Abstract

Magnetically Coupled Resonant (MCR) Wireless Power Transfer (WPT) systems suffer power-transfer efficiency (PTE) degradation due to misalignment of the transmitter/receiver coil pair across multiple degrees of freedom (DOF) simultaneously. Previous methods track lateral offset or rotational drift individually; none have reported correcting combined lateral, vertical, and angular misalignment in real time. The proposed hybrid physics-machine learning model combines an analytical model for a mutual-inductance circuit with a trained feedforward-network (FFN) residual-correction layer to estimate the state of the matching network that produces a maximum PTE when there is simultaneous 3-DOF misalignment. The method is evaluated for a representative 6.78 MHz, 3-turn, 40 mm-diameter coil pair, using a numerical electromagnetic model (Neumann-integral mutual inductance evaluation), and an exact model of a series-series (S-S) resonant circuit. The value of the nonlinear cross coupling term between lateral and tilt that is neglected by a separable analytical model increases by ~18% to 79% of the true value of the mutual inductance as misalignment increases, and the value of a learned correction increases strongly with the size of the disturbance. Similarly, the single axis baseline has an average efficiency gap from the optimal (1.30 percentage points), which is only 49% closed by using the extended analytical model, and 89% closed with the trained residual layer. The shallow FFN is shown to be better for this correction than the degree-2 polynomial ridge regressor in an ablation. It is also observed that the carrier-frequency retuning has a very small impact on the efficiency in this topology, with the state of the matching network being the major lever. A warning, however, is that the correction may sometimes make the worst-case deficit worse at the corners of the envelope where there are fewer samples, meaning that the samples have to be denser, or an extrapolation safeguard needs to be included before deployment.

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Dr. Gundapaneni Srilatha

Dr. Bikkina Siva Chakra Avinash

Dr. Pendurthy Anthony Sunny Dayal

Dr. Manumula Srinubabu

Dr. Manikonda Venkateswara Rao

Dr. Rangarao Orugu