Battery Charge Time Estimation via Usage Data Tracking
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Solution Overview
Problem
Users of mobile computing devices face challenges in accurately determining the remaining battery life, leading to unexpected battery depletion and user dissatisfaction.
Innovation Solution
A system that monitors various state information of a computing device, updates usage data to track energy storage device drain or charge, estimates capacity drain or charge rates, and provides feedback on the estimated remaining time for discharge or charging based on current state values and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery life estimation methods are used, then the device can operate continuously, but the remaining battery life estimation is inaccurate leading to unexpected depletion
Solution Approach 1:
The system performs preliminary actions by continuously monitoring device states and pre-calculating capacity drain rates for various state combinations before actual battery depletion occurs. Historical usage data is collected and stored in advance, allowing the system to predict remaining battery life accurately based on current states rather than waiting for battery depletion to occur.
Solution Approach 2:
The system dynamically adapts battery life estimation by continuously updating the monitoring of device states (processor usage, screen brightness, network activity) and recalculating capacity drain rates based on current conditions. The estimation is not static but adjusts in real-time as device usage patterns change, improving accuracy throughout the battery's discharge cycle.
2Measurement precision
If detailed state monitoring and historical data tracking are implemented, then battery life estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the battery life estimation problem by dividing device operation into distinct state combinations (processor states, screen states, network states). Each state combination has its own capacity drain rate profile stored in a table structure. This segmentation allows complex monitoring to be broken down into manageable, pre-calculated state profiles rather than continuous complex calculations.
Solution Approach 2:
The system creates simplified copies of battery behavior by pre-calculating and storing capacity drain rates for various state combinations in a lookup table. Instead of performing complex real-time physics calculations, the system uses these pre-computed models (copies) of battery drain behavior under different conditions to quickly estimate remaining battery life based on current states.
Data Source
AI summary
Usage data of a device is maintained over some time period, such as a couple weeks or a couple months. This usage data reflects an amount of energy that is used or stored for different time durations each having a various combinations of different state values of the device, such as a screen state, a power source state, a power mode state, a battery saver state, a processor consumption state, and so forth. This provides a record of a capacity drain rate or capacity charge rate for the battery for each combination of state values. At any given time, the device can determine the current state values of the device. The capacity drain rates or capacity charge rates for previous time durations having the same state values as the current state values are used to determine an estimated capacity drain rate or capacity charge rate for the device.