Adaptive Power Estimation for Wearables Using Dynamic Correction
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Solution Overview
Problem
Current wearable computing devices face inaccuracies and imprecision in power estimation due to reliance on OCV/SoC curves and lack of incorporation of specialized sensors, leading to inconsistent and fluctuating battery life estimates, which are exacerbated by size, cost, and power constraints.
Innovation Solution
A power estimation system that measures electrical potential and current power consumption, generates an adjustment table tailored to the specific power source, and periodically modifies it to account for changes in temperature and other factors, ensuring accurate and consistent battery life estimation with low power consumption and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If OCV/SoC curve mapping is used for power estimation, then the system can provide battery power estimation without additional sensors, but the estimation accuracy and precision deteriorates due to inherent errors in the curve mapping approach
Solution Approach 1:
The system continuously monitors actual power consumption and compares it with estimated power from the OCV/SoC curve, using the difference as feedback to generate correction values that adjust future estimates. This closed-loop feedback mechanism progressively improves estimation accuracy while maintaining the simplicity of the original curve-based approach.
Solution Approach 2:
The system dynamically adjusts estimation parameters by generating correction values based on monitored power consumption patterns, temperature changes, and usage conditions. These parameter adjustments allow the system to adapt the OCV/SoC curve estimates to actual device behavior, significantly improving precision without adding complex hardware.
2Measurement precision
If sensors or specialized components are incorporated to correct estimation errors, then power estimation accuracy improves, but device size, cost, and power consumption increase
Solution Approach 1:
The system uses existing device components (CPU, memory, sensors already present for other functions) to monitor power consumption and generate correction values. Rather than requiring dedicated power measurement hardware, the system repurposes existing resources to self-correct estimation errors, avoiding additional size, cost, and power overhead.
Solution Approach 2:
The power estimation system leverages existing multi-functional components in the wearable device. The same sensors and processing units used for other device functions are also utilized for power monitoring and correction, eliminating the need for specialized power measurement hardware and reducing overall device complexity.
3Reliability
If correction values are generated based on monitored power consumption and temperature, then estimation consistency improves across varying conditions, but processing requirements and power consumption increase
Solution Approach 1:
The system updates correction values periodically rather than continuously, adjusting the frequency of corrections based on usage conditions. During stable operation, corrections are applied less frequently, reducing processing overhead. During dynamic usage patterns or temperature changes, the system increases correction frequency to maintain accuracy, optimizing the balance between reliability and power consumption.
4Measurement precision
If the system continuously monitors and adjusts power estimates, then estimation accuracy under varying conditions improves, but computational overhead and power consumption increase
Solution Approach 1:
The system dynamically adjusts its monitoring and correction frequency based on operational conditions. During periods of stable power consumption and temperature, the system reduces correction frequency to minimize processing overhead. When detecting significant changes in usage patterns or environmental conditions, the system automatically increases monitoring intensity, optimizing the trade-off between accuracy and processing efficiency.
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
The system provides accurate power source estimation, maintaining consistency and enabling confident operation triggering and disabling of processes, such as shut-down procedures and connectivity searches, despite temperature variations, with reduced resource usage.
Implementation Method 1
measuring an electrical potential of a power source
Implementation Method 2
determining a current power consumption on the power source
Data Source
AI summary
Systems, devices, media, and methods are presented for adaptively estimating power. The systems and methods measure an electrical potential of a power source coupled to a wearable computing device and determine a current power consumption on the power source. The systems and methods identify a current slope value mapping the electrical potential to an estimated capacity percentage based on the measured electrical potential. The systems and methods determine a correction value based on the current power consumption and the current slope value and generate a current capacity value from the electrical potential and the correction value. The systems and methods cause presentation of a representation of the current capacity value within a power indicator and control one or more processes operating within the wearable computing device based on the current capacity value.