Target-Based Power Management for Mobile Devices
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Solution Overview
Problem
Current power management systems for mobile computing devices are inefficient in extending battery life, leading to user dissatisfaction and potential manufacturer issues, as they fail to effectively predict and adjust power usage based on user needs and device capabilities.
Innovation Solution
The implementation of a target-based power management system that allows users to set target run-times and charge-times, which automatically determines and adjusts power management actions to achieve these goals by analyzing usage patterns and available energy, including changes to display, processor settings, and feature usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If traditional power management systems are used, then device operation is simple, but battery life is not extended effectively and power management efficiency is poor
Solution Approach 1:
The system performs preliminary actions by predicting future power requirements based on scheduled tasks and calendar events before power depletion occurs. It proactively adjusts power management settings in advance to ensure sufficient power availability, rather than reactively responding to low battery conditions.
Solution Approach 2:
The power management system dynamically adapts to changing user needs and device usage patterns. It continuously monitors scheduled tasks, battery charge status, and power consumption rates, then adjusts power allocation and management strategies in real-time to optimize battery life extension.
2Use of energy by moving object
If power management adjusts settings to extend battery life, then power consumption is reduced, but user control and flexibility are limited
Solution Approach 1:
The system incorporates feedback mechanisms by continuously monitoring actual power consumption, battery charge status, and task completion status. It uses this feedback to refine predictions and adjust power management actions, ensuring that power consumption is optimized while maintaining alignment with user needs and preferences.
Solution Approach 2:
The power management system performs self-service by automatically predicting power requirements, selecting appropriate power management actions, and adjusting device settings without requiring continuous user intervention. This automation reduces power consumption while maintaining user control through configurable preferences and thresholds.
3Measurement precision
If the system predicts power usage based on tasks, then power management accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments power prediction into discrete task-based units. It analyzes power consumption requirements for individual scheduled tasks and calendar events separately, then aggregates these predictions to determine overall power needs. This segmentation simplifies the prediction process while maintaining accuracy.
Solution Approach 2:
The power management system serves multiple functions: it predicts power usage, schedules tasks, monitors battery status, and adjusts device settings. By integrating these functions into a unified system, it achieves accurate power prediction without proportionally increasing complexity, as shared components and data structures serve multiple purposes.
4Duration of action of moving object
If the system provides detailed power management control, then battery life is extended, but user satisfaction may decrease due to perceived complexity
Solution Approach 1:
The system performs self-service by automatically managing power optimization without requiring users to understand or configure complex settings. It handles power prediction, task scheduling, and setting adjustments autonomously, extending battery life while presenting a simple interface to users.
Solution Approach 2:
The system provides feedback to users about power management decisions and their impact on battery life. This transparency helps users understand the value of automated control, maintaining satisfaction by demonstrating tangible benefits without exposing underlying complexity.
Data Source
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AI summary
A computing device has an energy storage device system with one or more energy storage devices. A target run-time is obtained, which refers to how long the computing device is to run given the current amount of energy in the energy storage device(s). A predicted power usage over the target run-time is determined, and what, if any, power management actions to take in order to achieve the target run-time are determined. The power management actions are then taken. A target charge-time is also obtained, which refers to how long the computing device is to take to charge the energy storage device(s) to a threshold level (e.g., 100% or fully charged). A predicted power gain over the target charge-time is determined, and what, if any, power management actions to take in order to achieve the target charge-time are determined. The power management actions are then taken.