Phased Array Wireless Power Phase Optimization
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Solution Overview
Problem
Conventional wireless power transfer using phased arrays is inefficient due to the need for close proximity of coils and significant power reduction with increased distance, and existing phase optimization methods are inefficient and prone to measurement noise, leading to incorrect phase selection.
Innovation Solution
A method for setting phases of transmit elements in a phased array that involves measuring power at a receiving unit, defining a basis function, and selecting coefficients to minimize the difference between computed and measured power values, allowing for optimized phase determination and electromagnetic signal transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional magnetic inductive wireless power transfer is used with coils placed in close proximity, then power transfer efficiency is improved, but the transferred power diminishes strongly as distance increases
Solution Approach 1:
The transmit array is divided into multiple independently controllable transmit elements (antennas) that can be individually phased and amplitude-modulated. This segmentation allows the system to create multiple beam patterns and focus energy at different distances, overcoming the limitation of single-coil magnetic induction that only works at close range.
Solution Approach 2:
The system dynamically changes the phase and amplitude parameters of each transmit element based on the distance to the receiving device. By adjusting these parameters, the system optimizes power transfer efficiency for different transmission distances, transitioning from near-field magnetic coupling to far-field radiative transfer as needed.
2Measurement precision
If exhaustive phase optimization methods are used to determine optimal phases, then measurement accuracy is improved, but the time required for phase optimization increases significantly
Solution Approach 1:
The system performs preliminary characterization of the wireless power transfer channel by measuring power at a limited set of phase points. These preliminary measurements are used to fit a basis function that models the power-phase relationship, providing an initial estimate that guides subsequent optimization and reduces the need for exhaustive measurements.
Solution Approach 2:
Instead of performing exhaustive measurements for every possible phase combination, the system creates a mathematical model (basis function) that copies or approximates the power-phase relationship. This model can then predict optimal phases without requiring complete measurement of all phase states, significantly reducing measurement time while maintaining accuracy.
3Measurement precision
If exhaustive phase optimization methods are used to determine optimal phases, then phase selection accuracy is improved, but the system becomes more susceptible to measurement noise
Solution Approach 1:
The system uses basis functions to create a smoothed mathematical representation of the power-phase relationship. This copying approach filters out measurement noise by fitting a continuous function to the data, allowing accurate phase determination even when individual power measurements contain noise.
Solution Approach 2:
The system uses measured power values as feedback to iteratively refine the basis function coefficients and phase selections. By continuously comparing predicted power (from the basis function) with actual measurements, the system can correct for noise and converge on the true optimal phases, improving reliability.
4Productivity
If fewer measurement points are used in phase optimization, then the time required for optimization is reduced, but the accuracy of power transfer optimization deteriorates
Solution Approach 1:
The system performs preliminary channel characterization using a limited set of measurement points. These preliminary measurements are sufficient to fit the basis function parameters, which then serve as a foundation for determining optimal phases without requiring exhaustive measurements at all possible phase points.
Solution Approach 2:
The system changes the approach from direct measurement of all phase points to parameter estimation using a mathematical model. By fitting basis function parameters to limited measurements, the system can accurately predict optimal phases across the entire phase space without needing to measure every point, maintaining accuracy while improving speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the time required for phase optimization and improves the accuracy of power transfer by using a basis function to determine the optimal phases for maximum power delivery, even at increased distances and with reduced measurement points, thereby enhancing the efficiency of wireless power transfer.
Implementation Method 1
transmitting an electromagnetic signal from the first transmit element
Implementation Method 2
measuring a power of the electromagnetic signal at a receiving unit
Data Source
AI summary
A method of setting phases of a multitude of transmit elements of a phased array includes, in part, setting a phase of a first transmit element to N different values during each of N different time intervals, transmitting an electromagnetic signal from the first transmit element at each of the N time intervals, measuring a power of the electromagnetic signal at a receiving unit during each of the N time intervals, and selecting coefficients of a basis function such that a difference between a power value computed by the basis function and the measured power value associated with each of the N phases is smaller than a threshold value. The threshold value is optionally defined by a minimum of the sum of squares of the difference between a power value computed by the basis function and the power value for each of the N phases.