Intelligent Battery Cycling for Longevity via Predictive Charge Control
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Solution Overview
Problem
Rechargeable batteries experience reduced longevity due to internal stresses that vary with charge level, particularly when maintained in 'high stress' zones, conflicting with consumer demand for maximum charge capacity when unplugged.
Innovation Solution
An intelligent battery cycling system that includes a charge interrupt predictor, a charge cycle modeler, and a charge cycling controller to predict power source disconnect events and control battery charging/discharging, maximizing time in low-stress charge zones while ensuring a high charge state when disconnected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the battery is maintained at high charge levels (greater than 90%) to meet consumer demand for maximum charge capacity, then the available charge capacity increases, but the internal stress on the battery increases and longevity decreases
Solution Approach 1:
The system performs preliminary actions by predicting future power source disconnect events and proactively adjusting the battery charge level before the disconnect occurs. The charge cycle modeler creates models that reduce charge level to a first target zone (e.g., 40-60%) during periods when power is available, then increases charge level to a second target zone (e.g., 80-100%) before predicted disconnect events, thereby preparing the battery in advance to meet user needs while minimizing stress exposure time.
Solution Approach 2:
The system dynamically adjusts the battery charge level based on real-time predictions and changing conditions. The charge cycling controller continuously monitors power source connection status, predicted disconnect events, and battery state, then dynamically transitions between first and second target zones as needed. This dynamic approach allows the battery to adapt its charge level optimally rather than maintaining a static charge state.
2Reliability
If the battery is maintained at low charge levels (around 50%) to reduce internal stress and extend longevity, then the battery longevity increases, but the available charge capacity decreases and user convenience is reduced
Solution Approach 1:
The system performs preliminary charging actions by predicting future power source disconnect events and proactively increasing the battery charge level to the second target zone (e.g., 80-100%) before the predicted disconnect occurs. This ensures that when users actually unplug the battery, it is in a high-charge state providing maximum convenience, while the battery spends the majority of time in the low-stress first target zone (e.g., 40-60%).
Solution Approach 2:
The system uses feedback from the charge interrupt predictor about predicted disconnect events and actual power source connection status to continuously adjust charging behavior. The charge cycling controller receives feedback about whether predicted disconnects occurred and whether charge level targets were achieved, then refines future charge cycle models to better balance longevity and user convenience.
3Reliability
If frequent charge level adjustments are made to maximize time in low-stress zones, then the battery longevity extends, but the system complexity increases
Solution Approach 1:
The battery management system performs self-service by autonomously predicting disconnect events, modeling optimal charge cycles, and controlling charging without requiring user intervention. The charge interrupt predictor, charge cycle modeler, and charge cycling controller work together as an integrated self-managing system that automatically balances longevity optimization with user convenience, eliminating the need for complex user interfaces or manual configuration.
Data Source
AI summary
A system for intelligent cycling of a battery for improved lifetime longevity includes a charge interrupt predictor executable to predict a likelihood of a power source disconnect event interrupting a current flow to the battery within one or more segments of a future time interval. The system also includes a charge cycle model executable to model a charge cycle for the battery within the future time interval based on the charge interrupt prediction. The system further includes a charge cycling controller that controls battery circuitry to charge or discharge the battery in accord with the charge cycle model.