Optimizing Wireless Power Transfer From Multiple Transmit Coils

Abstract

The widespread need for ubiquitous power delivery is driving the commercialization of inductive wireless power transfer (WPT). Wireless power transfer systems, however, are plagued by low efficiency. To combat this, we propose a new approach to maximize the efficiency of inductive WPT using multiple coil charging systems. The use of multiple coils can potentially allow the system to efficiently adapt to magnetic field propagation conditions, similar to the way multiple antennas are used to adapt to channel conditions in wireless communication systems. We consider a multiple-input single-output WPT system using near-field inductive coupling. While such systems have been extensively studied in previous work using lumped resistance, inductance, and capacitance (RLC) circuit models to analyze their behavior, the difficulty of constructing tractable and realistic circuit models has limited the ability to accurately predict and optimize the performance of these systems. The main innovation in this paper is to take a more abstract approach to modeling the WPT system as a linear circuit whose input-output relationship is expressed in terms of a small number of unknown parameters that can be thought of as transimpedances and gains. The crucial advantage of this approach is the economy of the representation, i.e., the number of unknown model parameters can be much smaller than the number of lumped circuit elements required for a complete and accurate RLC circuit representation. We present simple derivations for the optimal voltage excitations to be applied at the transmitters to maximize power transfer efficiency as well as suboptimal excitations which are less computationally intensive. A simple procedure, which we call circuit sounding, for estimating the unknown parameters using a small set of direct measurements is described. We outline a series of experiments with four transmit coils and two receive coils that verify the model and show that the optimal solution can achieve higher efficiencies than those of previously known methods.