Battery Fuel Gauge Capacity and Resistance Correction
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Solution Overview
Problem
Rechargeable batteries in devices like implantable medical devices and smartphones experience capacity fade and increased internal resistance over time, leading to erroneous battery state information provided by fuel gauges, which can result in unexpected power depletion if not accurately updated.
Innovation Solution
A host device with processors and memory predicts battery capacity and resistance values using a predictive model, calculates differences with stored values, and updates them in the fuel gauge's memory if the error exceeds a predetermined threshold, ensuring accurate battery state representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If battery capacity and resistance values are stored in fuel gauge memory without updates, then device complexity is reduced, but measurement precision deteriorates due to capacity fade and resistance changes over time
Solution Approach 1:
The system performs preliminary characterization of the battery to establish initial capacity and resistance values, then uses predictive models to estimate future values before actual measurements are needed. This allows the fuel gauge to maintain accurate battery state information without requiring continuous complex monitoring, as the predictive values are prepared in advance.
Solution Approach 2:
The system changes the parameters stored in fuel gauge memory from static factory values to dynamically updated predictive values that reflect actual battery aging. By periodically updating capacity and resistance parameters based on elapsed time and usage, the system maintains measurement precision without requiring continuous complex monitoring infrastructure.
2Measurement precision
If battery capacity values are frequently updated using predictive models, then measurement precision is improved, but loss of time increases due to continuous monitoring and calculation
Solution Approach 1:
Instead of continuous monitoring, the system updates battery capacity and resistance values at periodic intervals based on elapsed time since battery characterization. This periodic updating approach maintains measurement precision while significantly reducing the time and computational resources required compared to continuous monitoring, as updates occur only when necessary based on time-based triggers.
3Reliability
If predictive models are used to estimate battery parameters, then reliability is improved by preventing overestimation of remaining capacity, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system introduces a predictive model as an intermediary between the battery and the fuel gauge display. This model estimates battery capacity and resistance values based on simple inputs like elapsed time and usage patterns, providing reliable battery state information without requiring complex real-time monitoring hardware. The predictive model acts as a mediator that translates basic measurements into accurate battery state predictions.
Data Source
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AI summary
In some examples, a host device includes a battery capacity and/or battery resistance predictive model. The host device may predict a battery capacity value and/or batter resistance value of a rechargeable battery and compare the predicted battery capacity and/or predicted battery resistance to a battery capacity value and/or battery resistance value stored in a fuel gauge. The host device may overwrite the battery capacity value and/or battery resistance value stored in the fuel gauge with the predicted battery capacity value and/or the predicted battery resistance value if the difference is greater than a maximum error threshold.