Adaptive Battery Charge Limits Based on Predicted Usage
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Solution Overview
Problem
Battery wear is increased when charged to high states of charge, reducing battery capacity, and existing technologies do not effectively manage battery charging to minimize this wear based on user usage patterns.
Innovation Solution
An electronic device with a power system that includes a battery and processors programmed to predict battery usage using prior data, such as user calendar events and charging intervals, to delay charging to a full state of charge or charge to a lower state based on expected usage, thereby reducing wear by minimizing time spent at high charge levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the battery is charged to full state of charge, then the battery capacity is maximized for usage, but the battery wear increases due to time spent at high states of charge
Solution Approach 1:
The charging system dynamically adjusts the charge limit based on predicted usage patterns rather than using a fixed full charge approach. The processor continuously monitors usage data and modifies the target state of charge in real-time, making the charging behavior adaptive and dynamic to balance capacity availability with battery wear reduction
Solution Approach 2:
The system performs preliminary prediction of future usage patterns before making charging decisions. By analyzing historical usage data and calendar events in advance, the system determines the optimal charge limit beforehand, ensuring sufficient capacity is available while avoiding unnecessary time at high charge states that would increase wear
2Reliability
If the battery charging is delayed to reduce time at high charge levels, then battery wear is reduced, but the battery may not have sufficient capacity for intended usage
Solution Approach 1:
The system incorporates feedback loops where the processor continuously monitors actual usage patterns against predicted patterns and adjusts the charge limit accordingly. This feedback mechanism ensures that the battery maintains sufficient capacity for actual usage needs while optimizing wear reduction, preventing both over-charging and under-charging scenarios
Solution Approach 2:
The system changes the charge limit parameter dynamically based on multiple factors including predicted usage, calendar events, and usage patterns. By adjusting this critical parameter rather than maintaining a fixed charge level, the system optimizes the balance between having sufficient capacity and minimizing time at high charge states
3Duration of action of stationary object
If the battery is charged to a lower state of charge to reduce wear, then battery longevity is improved, but the charging routine complexity increases
Solution Approach 1:
The system performs self-service by automatically analyzing usage patterns and determining optimal charge limits without requiring user intervention. The processor autonomously monitors calendar events, usage data, and charging history to make intelligent charging decisions, eliminating the need for complex user configuration while achieving optimized charging
Data Source
AI summary
An electronic device can include a power system including a battery and one or more processors programmed to: detect that the electronic device has been connected to a power source, predict using prior usage data of the electronic device whether battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source exceeds a threshold, and if the predicted battery usage between an expected time of disconnection from the power source and a next expected time of connection to the power source does not exceed the threshold, charge the battery to a state of charge less than the full state of charge of the battery.