Adaptive Battery Life Extension via Predictive Power Management
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Solution Overview
Problem
Conventional power management systems for portable devices fail to effectively extend battery life by not accounting for external factors and often reduce performance too late in the battery's depletion cycle, leading to inadequate power conservation.
Innovation Solution
An adaptive battery life extension system that utilizes predictive, drain, and direct models to estimate future battery conditions, allowing the operation manager to defer operations when battery conditions are poor, and adjust power management actions based on user behavior and charging patterns, ensuring optimal battery utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If user level power management reduces power consumption to extend battery life, then battery duration is improved, but device performance deteriorates
Solution Approach 1:
The system performs preliminary power management actions by predicting future battery conditions using multiple models before battery depletion occurs. The predictive model forecasts battery level, the drain model predicts discharge rate, and the direct model provides current level, enabling the system to proactively adjust power consumption patterns and defer non-critical operations in advance, rather than reacting when battery is already low.
Solution Approach 2:
The system dynamically adjusts power management strategies based on real-time battery conditions and predictions. The operation manager continuously monitors battery state, evaluates predicted future conditions from multiple models, and adaptively modifies device operations and power consumption patterns to extend battery life while maintaining acceptable performance levels throughout the battery cycle.
2Ease of operation
If conventional power management systems make decisions from deep within the system, then hardware state control is improved, but ability to account for external factors deteriorates
Solution Approach 1:
The system implements feedback by continuously monitoring actual battery performance and comparing it with predictions from the predictive, drain, and direct models. This feedback loop enables the system to learn from discrepancies between predicted and actual battery behavior, adjusting future predictions and power management decisions to better account for external factors such as user behavior patterns and environmental conditions.
Solution Approach 2:
The patent introduces an intermediary layer between hardware control and external factors through the operation manager and predictive models. This intermediary analyzes external inputs (user behavior, charging patterns) and translates them into informed power management decisions, bridging the gap between internal hardware state control and external environmental factors.
3Loss of energy
If power management actions are performed only when battery is very low, then power consumption reduction is achieved, but timing of power conservation deteriorates
Solution Approach 1:
The system performs preliminary power conservation actions by predicting future battery depletion based on current consumption patterns and external factors. Instead of waiting until battery is very low, the predictive models forecast future battery levels and enable the operation manager to proactively defer non-critical operations and adjust power consumption patterns in advance, optimizing the timing of power conservation actions throughout the battery cycle.
Data Source
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AI summary
According to one embodiment, a first battery number is determined representing a battery condition of a battery of a mobile device using a predictive model, where the predictive model is configured to predict future battery conditions based on a past battery usage of the battery. A second battery number is determined representing the battery condition using a drain model, where the drain model is configured to predict a future battery discharge rate based on a past battery discharge rate. A third battery number is determined representing the battery condition based on a current battery level corresponding to a remaining life of the battery at the point in time. Power management logic performs a power management action based on the battery condition derived from at least one of the first battery number, the second battery number and the third battery number.