OLED Battery Life Prediction Using On Pixel Ratio Profiles
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Solution Overview
Problem
Existing battery life estimation methods in portable information handling systems are inaccurate, particularly for OLED displays, as they fail to account for variations in content type and usage patterns, leading to overestimation of remaining battery life.
Innovation Solution
A battery life prediction system using On Pixel Ratio (OPR) calculations, which correlates file types with accurate OLED OPR values to provide users with precise battery life estimates by comparing session time series with stored profiles and applying data forecasting when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery life estimation methods are used (assumming continuous current usage pattern), then the calculation is simple, but the accuracy of battery life prediction deteriorates
Solution Approach 1:
The system performs preliminary actions by creating OPR profiles and storing time series data before actual battery life prediction is needed. Historical OPR data is collected and organized into profiles during normal operation, so when prediction is required, the system can quickly match the current session to a pre-existing profile rather than calculating from scratch. This preliminary data collection and organization resolves the contradiction by preparing accurate prediction data in advance without adding complexity during the actual prediction moment.
Solution Approach 2:
The system uses copying by creating OPR profiles that represent typical usage patterns for different content types. Instead of directly measuring and calculating battery life for every unique usage scenario, the system creates representative copies (profiles) of usage patterns and matches current sessions to these profiles. This allows accurate prediction without requiring complex real-time analysis of every possible usage variation, thus improving accuracy while managing system complexity.
2Measurement precision
If OPR profiling system with database storage is implemented, then battery life prediction accuracy improves, but device complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting OPR data during normal operation and populating the database without requiring external intervention. The profiling system runs autonomously, matching sessions to profiles and updating the database as new usage patterns are observed. This self-service approach improves prediction accuracy through continuous data collection while managing complexity by automating the process rather than requiring manual configuration or external system support.
Solution Approach 2:
The OPR profiling system serves multiple functions: it collects historical data, creates profiles for different content types, matches current sessions to appropriate profiles, and provides battery life predictions. This multi-functionality improves overall system efficiency and accuracy while managing complexity by consolidating multiple functions into a single integrated profiling system rather than requiring separate systems for each function.
3Measurement precision
If real-time OPR monitoring and profile matching is performed, then prediction accuracy for dynamic content improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing OPR data into structured profiles during normal operation. Time series data is collected and organized into content-type-specific profiles before prediction is needed. When a prediction is required, the system quickly matches the current session to a pre-existing profile rather than performing complex real-time analysis, thus improving accuracy while minimizing the time lost during the actual prediction moment.
Solution Approach 2:
The system applies segmentation by dividing OPR profiles into separate categories based on content type (video, images, mixed content, etc.). Each content type has its own profile with specific OPR characteristics. This segmentation allows the system to quickly identify and apply the appropriate profile for the current content being displayed, improving prediction accuracy for different content types while reducing processing time by avoiding the need to analyze all possible content variations.
Data Source
AI summary
A system and method for predicting remaining battery life for a portable information handling systems include determining power consumption for an OLED display based on an on pixel ratio (OPR) for each pixel. When a user starts a session of an application and a file, file metadata may be used to search a database storing OPR profiles, with each OPR profile including one or more time series associated with one or more instances of the application executing the file. For static files, a time series in an OPR profile may be used to predict power consumption and calculate battery life. For dynamic files, periodically measuring OPR data may be used to predict power consumption. For some dynamic files, historic OPR values and periodic measurements may be used to predict power consumption. The user may be provided with a list of files usable during the calculated battery life.