Battery Cell Voltage Ranking for Hidden Abnormal Cell Detection
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Solution Overview
Problem
Existing battery cell diagnosis methods fail to detect abnormal cells when their voltage is within a normal range, leading to undetected defects.
Innovation Solution
A battery management system that periodically measures cell voltages, assigns target cells based on highest and lowest voltage rankings, and diagnoses abnormalities by comparing these voltages over time, identifying cells with voltages equal to or less than the minimum or greater than the maximum voltage as abnormal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cell voltage is compared to predefined maximum and minimum values, then diagnosis simplicity is improved, but detection accuracy deteriorates because abnormal cells with voltage within normal range cannot be detected
Solution Approach 1:
The patent segments the battery cells into different diagnosis target groups based on their voltage rankings. Specifically, cells with highest voltage (first through m-th cells) and cells with lowest voltage (n-m+1-th through n-th cells) are designated as first and second diagnosis target cells, respectively. This segmentation allows different comparison strategies for different cell groups, improving detection accuracy while maintaining operational simplicity.
Solution Approach 2:
The patent changes the diagnostic parameter from fixed threshold comparison to dynamic relative comparison. Instead of comparing each cell's voltage to absolute maximum and minimum thresholds, the system compares voltages within ranked groups, where the diagnosis threshold is determined by the actual voltage distribution among cells. This parameter change enables detection of abnormal cells that maintain voltage within the overall normal range but exhibit abnormal relative behavior.
2Measurement precision
If all battery cells are monitored continuously, then detection accuracy is improved, but energy consumption and system complexity increase
Solution Approach 1:
The patent applies partial action by monitoring only a subset of battery cells as diagnosis target cells rather than continuously monitoring all cells. Specifically, only the first m cells (highest voltage group) and the last m cells (lowest voltage group) are designated as diagnosis targets. This partial monitoring approach maintains detection accuracy for abnormal cells while reducing energy consumption and computational burden compared to full-cell continuous monitoring.
Solution Approach 2:
The patent implements periodic action by conducting diagnosis at specific intervals rather than continuous monitoring. The system performs diagnosis at wake-up times and at predetermined periodic intervals, comparing voltages of diagnosis target cells against thresholds. This periodic diagnostic approach reduces energy consumption while maintaining adequate detection capability, as battery cell voltage changes occur gradually and do not require continuous monitoring.
3Measurement precision
If diagnosis targets are selected based on voltage ranking, then detection accuracy for abnormal cells is improved, but device complexity increases due to additional sorting and assignment operations
Solution Approach 1:
The patent applies universality by using the voltage ranking operation for multiple purposes. The same ranking process that identifies cells for balancing operations also identifies diagnosis target cells. The cell voltage values are sorted to determine both the balancing targets and the diagnosis targets, allowing one computational operation to serve multiple diagnostic and control functions, thereby reducing overall system complexity despite the added sorting step.
Data Source
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AI summary
A battery system includes: a battery pack including a plurality of battery cells; and a battery management system for setting numbers to the battery cells by measuring cell voltages of the respective battery cells at a wake-up time, detecting a maximum cell voltage and a minimum cell voltage by periodically measuring the cell voltage of the respective battery cells after the wake-up, comparing the minimum cell voltage and the cell voltage of at least one first diagnosis target battery cell, comparing the maximum cell voltage and the cell voltage of at least one second diagnosis target battery cell, and diagnosing whether the diagnosis target battery cell is abnormal according to results of the comparison.