A Real-Time-Link-Adaptive Operation Scheme for Maximum Energy Storage Efficiency in Resonant CM Wireless Power Receivers

The development, analysis, and experimental validation of an energy storage algorithmic scheme for performance optimization of resonant inductive power receivers are presented. Motivated by the crucial role of efficient energy storage in the next generation of brain-implantable devices, we introduce an energy management strategy in the design of wireless powering links, in which, the key performance measure is the energy stored during a limited time interval rather than the average energy delivered to the load. The presented strategy is proven analytically to yield the theoretically-maximum energy storage efficiency over a pre-determined period of time. Additionally, thanks to the algorithm's closed-form solution, the optimization can be done in real time, offering the potential for a solution that is adaptive to any variations in physical (e.g., coil separation, Rx rotation, etc.) and/or electrical (e.g., Q-factor, media conductivity, etc.) properties of the link, conditional to a low-power circuit implementation for its evaluation. The efficacy and precision of the solution obtained from the presented analytical model is confirmed with CAD-based simulation results, and later validated using experimental measurements. Our experimental results for two links with different characteristics (resonance frequency, coils size and separation, etc.) show a 52.5% and 67.5% improvement in overall energy storage efficiency compared to the standard CM receiver design in which resonance-to-charging switching is performed when the receiver's LC tank energy accumulation starts to saturate. This is while the presented method does not require any calibration and is designed to be employed by any generic current-mode receiver.

Automated summary

This study introduces an energy storage algorithmic scheme for performance optimization of resonant inductive power receivers. The algorithm is proven to achieve theoretically-maximum energy storage efficiency over a pre-determined period of time and is adaptive to variations in physical and electrical properties of the link, offering potential for real-time optimization. Experimental results confirm significant improvements in energy storage efficiency compared to standard designs.