Battery Cell Voltage Diagnosis Using Time-Series Vector Analysis
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Solution Overview
Problem
Existing methods for diagnosing voltage abnormalities in battery cells are inadequate, particularly for lithium batteries, as they fail to accurately detect abnormalities when voltage differences between time points are small or influenced by temperature and State Of Health (SOH), and cannot identify issues like lithium plating.
Innovation Solution
A battery diagnosis apparatus and method using a voltage sensing circuit, storage medium, and control circuit to analyze voltage time series data, calculating diagnosis vectors and factors to identify voltage abnormalities through simple mathematical calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple voltage difference threshold method is used to diagnose battery cell abnormalities, then the device complexity is reduced and ease of operation is improved, but the measurement precision and reliability of voltage abnormality detection deteriorate
Solution Approach 1:
The patent transforms the diagnosis approach from using simple voltage difference thresholds to using voltage slope (rate of change of voltage with respect to time) as the diagnostic parameter. This parameter change enables detection of abnormalities like lithium plating that do not manifest as large voltage differences but cause characteristic voltage degradation patterns over time, thereby improving measurement precision without requiring complex hardware
Solution Approach 2:
The patent introduces a temporal dimension by analyzing the rate of change of voltage (dv/dt) rather than just static voltage differences. This adds a time derivative dimension to the diagnosis, allowing detection of dynamic voltage degradation behaviors that static threshold methods cannot capture, thus improving detection accuracy while maintaining simple computational implementation
2Ease of operation
If voltage abnormality diagnosis relies only on comparing voltage differences between time points, then the ease of operation is improved, but the reliability of detecting subtle abnormalities deteriorates
Solution Approach 1:
The patent changes the diagnostic parameter from voltage difference (ΔV) to voltage slope (dV/dt). This parameter transformation maintains computational simplicity while significantly improving reliability for detecting subtle abnormalities such as lithium plating, which produce characteristic voltage degradation patterns that are captured by the rate of change rather than absolute differences
3Measurement precision
If the voltage threshold for abnormality detection is set low to improve measurement precision, then the reliability of detection is improved, but false positives increase due to normal voltage variations from temperature and SOH changes
Solution Approach 1:
The patent transitions from using absolute voltage thresholds to using voltage slope thresholds. This parameter change allows setting sensitive detection thresholds because the voltage slope during normal operation (affected by temperature and SOH) is typically small and stable, whereas abnormal conditions like lithium plating produce distinct voltage slope patterns. This maintains high detection sensitivity while reducing false positives
Data Source
AI summary
A battery diagnosis apparatus including a voltage sensing circuit, a storage medium and a control circuit records first to Nth voltage time series data of first to Nth battery cells, selects a set of first voltages measured at a first time and a set of second voltages measured at a second time, determines a a first and second average position vectors, determines a difference between the first and second average position vectors as a diagnosis reference vector, for an ith battery cell determines a first and second diagnosis position vectors, determines a difference between the first and second diagnosis position vectors as an ith diagnosis vector, determines an ith diagnosis factor based on a magnitude of a cross product of the diagnosis reference vector and the ith diagnosis vector, and diagnoses the ith battery cell as exhibiting a voltage abnormality when the ith diagnosis factor exceeds a threshold value.