AI Wireless Charging Control for Dynamic IoT Sensor Positioning
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Solution Overview
Problem
Current wireless charging systems for IoT sensors face inefficiencies due to limited charging distance, fixed power consumption, and energy wastage, particularly in dynamic environments, as they lack accurate location detection and prioritization of power delivery.
Innovation Solution
An AI-driven wireless power transmitter and receiver system that uses RF signals to dynamically estimate the location of IoT sensors, optimize power consumption, and prioritize charging based on received and stored power levels, employing convolutional neural networks and backscatter modulation for efficient beam steering and power adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If RF signals are used for remote wireless charging, then charging distance is extended, but location detection accuracy deteriorates
Solution Approach 1:
The system employs feedback mechanisms where the wireless power receiver detects RF signals and transmits back information about received power levels and location data. The wireless power transmitter continuously receives this feedback and adjusts beam steering and power delivery accordingly, enabling accurate location detection despite extended charging distances
Solution Approach 2:
The patent introduces an intermediary AI algorithm that processes RF signal characteristics to estimate receiver location. This intermediary processing layer translates raw signal data into accurate location information, resolving the contradiction between extended range and detection precision
2Device complexity
If fixed power consumption is used in wireless power receiver, then power management is simplified, but energy waste increases in dynamic environments
Solution Approach 1:
The wireless power receiver dynamically adjusts its power consumption based on real-time conditions. The system monitors received power levels and environmental factors, then adapts power usage accordingly, transitioning from fixed to dynamic power management to reduce energy waste in changing environments
Solution Approach 2:
The system changes operational parameters of the wireless power receiver based on detected conditions. When received power is sufficient, the receiver reduces its power consumption; when power is limited, it adjusts accordingly. This parameter adaptation resolves the contradiction between management simplicity and energy efficiency
3Productivity
If AI algorithms are implemented for location estimation and prioritization, then charging efficiency is improved, but device complexity increases
Solution Approach 1:
The AI algorithm performs multiple functions including location estimation, priority determination, and beam steering control within a single integrated system. This multi-functional approach improves charging efficiency while limiting the increase in overall system complexity by consolidating AI capabilities
Solution Approach 2:
The wireless power transmitter autonomously uses AI algorithms to estimate receiver locations and determine charging priorities without external intervention. The system self-manages the complexity of AI processing internally, providing improved charging efficiency while keeping the user-facing interface simple
4Device complexity
If beam steering is performed without accurate location detection, then power delivery is simplified, but power delivery accuracy deteriorates
Solution Approach 1:
The system performs preliminary location estimation using AI algorithms before executing beam steering. By estimating the receiver's location in advance based on RF signal characteristics, the system prepares accurate beam direction information, ensuring precise power delivery without overly complicating the beam steering execution
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 system maximizes wireless power charging efficiency, minimizes energy waste, and rapidly adjusts to changes in sensor location and power states, enabling efficient and flexible charging of IoT devices in dynamic environments.
Implementation Method 1
an RF signal detector configured to detect one or more characteristics of an RF signal received through an RF receiving antenna
Implementation Method 2
a backscatter modulator configured to encode received-power information received from the RF reception controller into an amplitude modulation (AM) signal
Data Source
AI summary
Disclosed are an artificial intelligence algorithm-based wireless power transmitter, wireless power receiver, and wireless power charging system that are capable of high-speed response to environmental changes and that can optimize the power efficiency of a wireless power receiver, estimate a dynamic location from a signal received from the wireless power receiver using artificial intelligence technology, and dynamically transmit wireless power to a prioritized wireless power receiver according to a power state.