Battery State Prediction via Precursor Pattern Monitoring
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Solution Overview
Problem
Current battery management systems fail to predict abrupt reductions in charging capacity effectively, leading to sudden battery failure and inadequate time for corrective action, as they require extensive analysis and computational power, making online detection impractical.
Innovation Solution
A battery state measuring method and management system that monitors change patterns of precursors such as first and second derivatives of charging capacity and useful life, issuing alerts when pre-configured patterns are detected, allowing for timely corrective action before battery failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If elaborate analysis and extensive crunching are performed to detect abrupt reduction in charging capacity, then prediction accuracy is improved, but device complexity and computational power requirements increase
Solution Approach 1:
The patent extracts only the most critical precursors (first derivative and second derivative of charging capacity, and useful life) from the vast amount of battery data, eliminating the need for elaborate analysis of all battery parameters. This selective extraction maintains prediction accuracy while significantly reducing computational complexity.
Solution Approach 2:
The patent pre-calculates and stores the first derivative, second derivative, and useful life values during normal battery operation. When prediction is needed, these pre-computed values are immediately available for pattern matching, eliminating the need for extensive real-time crunching and reducing computational power requirements.
2Measurement precision
If elaborate analysis is performed to detect abrupt reduction in charging capacity, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
The patent pre-computes critical parameters (first derivative, second derivative, useful life) during normal battery operation and stores them for immediate retrieval. This preliminary action enables instant pattern matching when prediction is needed, achieving both high accuracy and rapid detection without time loss.
Solution Approach 2:
The system continuously monitors the pre-computed precursor values and immediately compares them against pre-configured patterns. This real-time feedback mechanism ensures that when an abrupt reduction pattern is detected, the prediction is made instantly without requiring time-consuming post-processing analysis.
3Reliability
If online detection of abrupt reduction is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements online detection by monitoring only the three most critical precursors (first derivative, second derivative, and useful life) rather than analyzing all battery parameters continuously. This selective monitoring maintains high reliability for detecting abrupt reductions while keeping the online system simple and manageable.
Solution Approach 2:
The system uses pre-configured patterns stored in memory that automatically match against current precursor values. This self-service mechanism eliminates the need for complex real-time analysis algorithms, allowing reliable online detection with minimal system complexity.
Data Source
AI summary
Provided are a battery state measuring method and battery management system, which predict a time point when charging capacity of a battery is to be relatively abruptly reduced. The battery state measuring method includes: monitoring a change of at least one precursor related to the charging capacity of the battery with respect to a number of charging cycles undergone by the battery; and predicting that an abrupt reduction in the charging capacity of the battery is imminent when the change of the at least one precursor follows at least one pre-configured pattern of the battery that has undergone a critical number of charging cycles.