Battery Abnormality Diagnosis Through Idle-Voltage Slope Analysis
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Solution Overview
Problem
Existing secondary battery systems lack comprehensive diagnosis methods to identify abnormality types beyond traditional over-voltage or under-voltage conditions, which can lead to fires without warning, necessitating a new approach to detect and classify battery abnormalities during idle periods.
Innovation Solution
A battery abnormality diagnosis apparatus and method that utilizes regression analysis to classify abnormality types by analyzing voltage behavior during idle periods, employing a voltage obtaining unit, analyzing unit, and diagnosing unit to identify idle long-time relaxation, post-charging, and post-discharging idle voltage abnormalities based on slope differences and reference values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnosis methods (over-voltage/under-voltage detection) are used, then simple and easy to implement, but cannot detect abnormalities in mid-voltage ranges leading to fire hazards
Solution Approach 1:
The patent changes the diagnosis parameter from traditional voltage threshold detection (over-voltage/under-voltage) to analyzing voltage slope changes and idle period behavior patterns. By monitoring how voltage changes over time rather than just its absolute values, the system can detect abnormalities in mid-voltage ranges that traditional methods miss, thereby improving fire prevention capability without requiring completely new hardware.
Solution Approach 2:
The patent replaces the mechanical/threshold-based diagnosis approach with a mathematical analysis approach using regression equations and slope calculations. Instead of simple voltage threshold comparison, the system uses computational analysis of voltage trends and idle period characteristics to diagnose abnormalities, substituting complex mathematical processing for traditional threshold-based detection.
2Measurement precision
If comprehensive voltage monitoring is implemented, then abnormality detection accuracy improves, but measurement and processing complexity increases
Solution Approach 1:
The patent segments the voltage monitoring task into distinct phases: charging period monitoring, discharging period monitoring, and idle period monitoring. Each phase has specific diagnostic criteria (e.g., voltage slope during charging, voltage behavior during idle periods). By dividing the continuous voltage monitoring into discrete temporal segments with specific analysis rules, the system achieves high detection accuracy while managing processing complexity through structured approach.
Solution Approach 2:
The patent performs preliminary analysis by calculating voltage slopes and identifying idle periods before making abnormality diagnoses. The system pre-processes voltage data to extract meaningful features (slope values, idle period durations) that simplify the subsequent diagnosis process. This preliminary feature extraction reduces the complexity of real-time decision-making while maintaining high detection accuracy.
Data Source
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AI summary
Provided is a battery abnormality diagnosis apparatus including a voltage obtaining unit obtaining a voltage of a battery cell, an analyzing unit calculating estimation information for estimating the voltage of the battery by analyzing the voltage of the battery cell, and a diagnosing unit diagnosing abnormality of the battery cell by analyzing the estimation information.