Application-Specific Battery Profiles for Mobile Power Management
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Solution Overview
Problem
Current power management systems for mobile devices, such as those used in warehouse management systems, provide inaccurate indications of battery life due to reliance on single device operational metrics like voltage, failing to account for specific usage patterns and workflow variations, leading to inefficiencies and increased costs.
Innovation Solution
A method and system that generate application-specific battery profiles by aggregating battery usage data, including runtime, current, voltage, and temperature parameters, to provide more accurate predictions of battery life, taking into account specific work applications, user efficiency, and device characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power management systems monitor only single device operational parameters (voltage, current), then the system complexity remains low, but the measurement precision of battery life indication deteriorates
Solution Approach 1:
The patent segments the power management approach by creating separate battery profiles for different work applications. Each profile contains specific runtime data for that application type, allowing accurate battery life prediction without requiring a single complex universal model. The system divides the monitoring into application-specific segments rather than attempting one comprehensive solution.
Solution Approach 2:
The system performs preliminary actions by pre-generating battery profiles based on historical runtime data for different work applications before actual use. When a device is assigned a work application, the corresponding pre-computed profile is applied, eliminating the need for real-time complex calculations and providing immediate accurate battery life estimates.
2Measurement precision
If application-specific battery profiles are generated by aggregating data from multiple devices, then the battery life prediction accuracy improves, but the data processing and storage requirements increase
Solution Approach 1:
The patent applies local quality by creating specialized battery profiles tailored to specific work applications rather than using a generic profile for all devices. Each profile contains only the relevant runtime data characteristics for its designated application type, optimizing the data structure to contain only necessary information rather than comprehensive data from all sources.
Solution Approach 2:
The system creates simplified copies of battery behavior patterns through profiles that capture essential runtime characteristics without storing complete historical data from every device. Each profile is a distilled representation of aggregated data that reproduces the key patterns needed for accurate prediction while occupying minimal storage space.
3Reliability
If constant parameter measurements are performed to monitor battery status, then the reliability of power management improves, but the energy consumption increases
Solution Approach 1:
The system performs preliminary computation of battery profiles during periods when the device is not in active use or when aggregated data is available. By pre-calculating the battery life predictions based on historical patterns, the system avoids performing complex real-time measurements and calculations during critical operational periods, thereby reducing energy consumption while maintaining reliability.
Solution Approach 2:
The system uses feedback from historical runtime data and actual battery performance to continuously refine and update battery profiles. This feedback mechanism allows the system to learn from past measurements and improve prediction accuracy without requiring constant new measurements, as the profiles adapt to reflect actual usage patterns over time.
Data Source
AI summary
A method and system of managing power usage of devices including selectively executing a program application on a plurality of battery powered devices. Battery usage data is generated for a battery in one or more of the devices during execution of the work application. The battery usage data includes the run-time of the battery for the work application being executed. The data is aggregated and stored for the plurality of devices in memory. An application specific battery profile is generated using the stored battery usage data. The application specific battery profile is associated with the work application being run by the client devices.