Mobile Terminal Power-Saving Control Based on Usage Prediction
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Solution Overview
Problem
Existing technologies do not effectively utilize the remaining battery capacity of secondary batteries in mobile information terminals, lacking in user convenience due to insufficient prediction of future usage and charging opportunities.
Innovation Solution
A mobile information terminal equipped with a controller and memory apparatus that predicts electricity usage and power charging opportunities, setting power saving modes to reduce consumption based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the terminal operates in normal power mode to maintain user experience, then performance and functionality are improved, but battery consumption increases reducing operational duration
Solution Approach 1:
The terminal dynamically adjusts power consumption characteristics by switching between normal and power-saving modes based on predicted battery status. The controller monitors remaining battery capacity and automatically transitions operational modes to optimize the balance between user convenience and battery duration, making the system adaptive rather than static.
Solution Approach 2:
The system performs preliminary prediction of battery depletion timing and proactively switches to power-saving mode before the battery is fully depleted. This advance action allows the terminal to prepare for low-power operation, maintaining user convenience as much as possible while extending the overall battery operational duration by preventing complete exhaustion.
2Duration of action of moving object
If the terminal switches to power-saving mode to extend battery life, then battery operational duration is improved, but performance and user experience deteriorate
Solution Approach 1:
The terminal dynamically adjusts power consumption characteristics by switching between normal and power-saving modes based on predicted battery status. The controller monitors remaining battery capacity and automatically transitions operational modes to optimize the balance between user convenience and battery duration, making the system adaptive rather than static.
Solution Approach 2:
The system changes operational parameters by adjusting power consumption levels based on predicted battery depletion timing. When battery depletion is predicted soon, the terminal modifies performance parameters to extend operational duration. When battery status is healthy, parameters are adjusted to maximize user convenience, creating a flexible parameter adaptation strategy.
3Loss of information
If the terminal provides detailed battery status information to users, then information accuracy is improved, but system complexity increases
Solution Approach 1:
The terminal implements a feedback mechanism where the controller continuously monitors battery status, predicts depletion timing, and communicates this information to the user. This closed-loop feedback system provides accurate battery status information while managing complexity through automated prediction algorithms rather than requiring complex manual tracking systems.
Solution Approach 2:
The power management system operates autonomously by automatically monitoring battery status, predicting depletion timing, and switching between operational modes without requiring complex user intervention. The system serves itself by managing power consumption based on its own monitored data, reducing the need for complex external control mechanisms.
Data Source
AI summary
The objective is to provide a mobile information terminal wherein the battery remaining of a secondary battery can be effectively used.Therefore, the configuration is such that a mobile information terminal includes a second battery, a controller, and a memory apparatus, wherein the controller performs prediction of electricity usage and power charging opportunity prediction from schedule be memorized in the memory apparatus, and sets power saving mode with different power saving level to reduce power consumption, based on the prediction of electricity power usage and the charging opportunity prediction.