Battery Cell Voltage Diagnosis Using Moving Average Deviation
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Solution Overview
Problem
Conventional battery management systems struggle to accurately diagnose abnormal battery cells due to noise interference in voltage deviations, particularly when micro-disconnections occur, leading to potential safety issues like ignition and performance deterioration.
Innovation Solution
A battery management apparatus and method that calculates deviations between long and short moving averages of battery voltages, applies threshold adjustments, and uses normalization and skewness calculations to distinguish between noise and abnormal conditions, enabling accurate diagnosis of abnormal battery cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voltage deviation diagnosis is performed using conventional methods, then battery abnormality detection is attempted, but noise interference prevents accurate threshold adjustment and false diagnoses occur
Solution Approach 1:
The patent extracts and removes noise components from voltage deviation measurements by comparing individual cell deviations against the distribution of all cell deviations. By separating the noise signal from the true abnormality signal through statistical analysis, the system achieves both high measurement precision and reliable diagnosis without false positives
Solution Approach 2:
The system uses feedback by continuously monitoring the distribution of voltage deviations across all battery cells and adjusting the diagnosis threshold dynamically. The controller compares each cell's deviation against the learned normal distribution pattern, providing adaptive feedback that improves both measurement precision and diagnosis reliability over time
2Ease of operation
If a fixed threshold is used for abnormality diagnosis, then the diagnosis process is simple, but micro-disconnections and noise cause false positives and reduced detection accuracy
Solution Approach 1:
The patent transforms the static fixed threshold into a dynamic adaptive threshold that changes based on the observed distribution of voltage deviations. The system calculates statistical parameters (mean and standard deviation) from real-time data and adjusts the diagnosis threshold accordingly, maintaining operational simplicity while dramatically improving detection accuracy for micro-disconnections and noise conditions
3Reliability
If individual battery cell voltage deviation is monitored, then potential abnormalities are detected, but noise from long and short moving average differences creates false alarms
Solution Approach 1:
The patent introduces an intermediary statistical analysis layer between the raw voltage deviation measurement and the final diagnosis. By using the distribution characteristics (mean and standard deviation) of all cell deviations as an intermediary reference, the system filters out noise from moving average calculations while preserving true abnormality signals, thereby improving reliability without generating false alarms
Data Source
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AI summary
A battery management apparatus according to an embodiment disclosed herein includes a voltage measurement unit configured to measure a voltage of each of a plurality of batteries and a controller configured to calculate a first deviation, which is a deviation between a long moving average and a short moving average of a battery voltage for each of the plurality of batteries, calculate a second deviation, which is a deviation between a long moving average and a short moving average of an average voltage of the plurality of battery cells, and calculate a first diagnosis deviation between the first deviation and the second deviation for each of the plurality of battery cells, calculate an accumulative deviation by accumulating the first diagnosis deviation when the first diagnosis deviation of at least one of the plurality of batteries exceeds a threshold value, and diagnose at least one of the plurality of batteries as an abnormal battery, based on the accumulative deviation.