Battery Cell Voltage Deviation Analysis for Abnormal Cell Detection
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Solution Overview
Problem
Existing battery management systems struggle to accurately identify abnormal battery cells due to diverse causes of abnormalities and wide ranges of battery cell parameter values, leading to increased false positives in identifying normal cells as abnormal.
Innovation Solution
A method using battery cell voltage deviation to identify abnormal cells by comparing measurements to a median value and calculating an interquartile range (IQR) to set an outlier criterion, distinguishing between idle and charging modes to enhance detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery management systems use fixed threshold methods to identify abnormal cells, then the detection process is simple, but the accuracy is low and false positives increase due to diverse causes of abnormalities and wide ranges of battery cell parameter values
Solution Approach 1:
The patent changes the detection parameter from fixed voltage thresholds to dynamic voltage deviation values. By calculating the difference between individual cell voltages and the average voltage, and then comparing this deviation to a dynamic threshold based on the interquartile range (IQR), the system adapts to the actual voltage distribution of the battery pack. This resolves the contradiction by improving detection accuracy through parameter transformation while maintaining reasonable system complexity.
Solution Approach 2:
The patent introduces dynamic thresholds based on voltage deviation and IQR calculation instead of static voltage thresholds. The outlier criterion is dynamically determined as Q1 - 4.5×IQR or Q3 + 4.5×IQR, where Q1 and Q3 are the first and third quartiles of voltage deviations. This dynamic approach allows the system to adapt to varying battery conditions, improving accuracy without excessive complexity.
2Reliability
If the system uses wide voltage thresholds to accommodate diverse battery cell variations, then false positives decrease, but the ability to detect actual abnormalities is reduced
Solution Approach 1:
The patent transforms the detection parameter from absolute voltage to voltage deviation from the average. This parameter change allows the system to simultaneously achieve wide thresholds (reducing false positives) and high sensitivity (detecting abnormalities). By measuring how much each cell deviates from the group average rather than comparing to a fixed voltage, the system naturally adapts to battery variations while maintaining detection sensitivity.
Solution Approach 2:
The patent creates a reference model of normal battery behavior through statistical analysis (calculating average voltage, quartiles, and IQR from the battery pack data). This statistical model serves as a dynamic copy of normal operation, allowing the system to distinguish true abnormalities from normal variations. The outlier criterion based on 4.5×IQR provides a reliable threshold that reduces false positives while maintaining sensitivity.
3Measurement precision
If the system performs detailed statistical analysis including IQR calculation and outlier criterion setting, then detection accuracy improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies partial statistical analysis by focusing on key metrics (average voltage, quartiles, and IQR) rather than comprehensive analysis of all possible parameters. The outlier criterion using 4.5×IQR provides sufficient accuracy without requiring exhaustive statistical methods. This partial approach balances detection accuracy with processing efficiency, avoiding unnecessary computational overhead.
Data Source
AI summary
Disclosed herein is an abnormal battery cell diagnosis device and method for identifying an abnormal battery cell. The abnormal battery cell diagnosis device includes a voltage measurement module configured to measure an initial open circuit voltage (OCV) or closed circuit voltage (CCV) of each battery cell, a timer configured to determine a minimum rest time for a battery cell of a battery pack, and a processor configured to calculate an inter quartile range (IQR) based on the initial OCV or CCV depending on whether the minimum rest time is satisfied, to set an outlier criterion, and to detect an abnormal battery cell using the set outlier criterion.