Battery Power Capability Correction for Aging Prediction Drift
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Solution Overview
Problem
Existing battery models fail to accurately predict power capability due to aging and environmental changes, leading to inaccurate estimation of remaining charge and power consumption, resulting in unexpected device shutdowns or reduced user experience.
Innovation Solution
Implement a processor-based system that uses voltage measurements, cutoff voltages, and predictive horizons to apply correction factors to battery power capability, incorporating a battery model and machine-learning algorithms to regulate power consumption and prevent underestimation or overestimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a battery model is used to predict power capability, then power capability can be determined to operate the electronic device, but the prediction becomes inaccurate over time as the battery ages
Solution Approach 1:
The system continuously monitors actual battery performance and compares it with predicted values from the battery model. When discrepancies are detected (indicating battery aging), the system updates the battery model parameters to reflect current battery state, thereby maintaining prediction accuracy over time.
Solution Approach 2:
The battery model parameters are dynamically adjusted based on battery age, charge cycles, and environmental conditions. By changing model parameters to reflect the battery's current state, the system maintains accurate power capability predictions even as the battery degrades.
2Reliability
If the remaining charge and power capability are underestimated, then the electronic device enters low power mode prematurely, but user experience deteriorates due to reduced performance
Solution Approach 1:
The system dynamically adjusts power mode transitions based on real-time battery state assessment. Instead of using fixed thresholds, the system continuously evaluates actual battery performance and adjusts operational modes accordingly, preventing premature transitions to low power mode while ensuring timely transitions when needed.
3Productivity
If the power capability and remaining charge are overestimated, then the electronic device continues computationally intensive operations, but the device may shut down without user notification
Solution Approach 1:
The system performs preliminary assessments of battery capacity before allowing computationally intensive operations. By proactively evaluating whether the battery can sustain required power levels, the system prevents situations where the device would shut down unexpectedly during critical operations.
4Device complexity
If the battery model does not update to account for aging, then the model remains simple, but power capability predictions become inaccurate
Solution Approach 1:
The battery model automatically updates itself by monitoring its own prediction accuracy and detecting discrepancies between predicted and actual battery performance. This self-updating mechanism maintains prediction accuracy without requiring external intervention or complex manual calibration procedures.
Data Source
AI summary
Based on changes in a battery (e.g., age, temperature) of an electronic device, battery power prediction and correction logic of the electronic device may correct a power capability and/or regulate power associated with the battery. For example, the battery power prediction and correction logic may operate the battery to supply up to a maximum of a power capability with an applied correction factor based on a voltage measurement and a cutoff voltage associated with the battery.