Battery Cell Degradation Diagnosis from Standby SOC Drift
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Solution Overview
Problem
Conventional methods for diagnosing battery cell abnormal deterioration using State of Health (SOH) values have low reliability due to errors of 5% or more compared to the actual state, leading to inaccurate diagnosis.
Innovation Solution
A battery cell abnormal deterioration diagnosis apparatus and method that calculates the State of Charge (SOC) change amount and average for each cell, using these values to diagnose abnormal deterioration by comparing them to reference values, regardless of cell voltages, during standby periods, and without requiring specific charging or discharging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional SOH modeling techniques and algorithms are used to diagnose battery abnormal deterioration, then the diagnosis can be performed, but the reliability is low with errors of 5% or more compared to actual state
Solution Approach 1:
The invention changes the diagnostic parameter from SOH (State of Health) to SOC change amount (State of Charge). By monitoring how much the SOC changes during standby periods when no charge/discharge occurs, the system can detect abnormal self-discharge that indicates cell deterioration. This parameter change eliminates the 5%+ errors inherent in conventional SOH modeling techniques while maintaining diagnostic capability.
Solution Approach 2:
The invention extracts and isolates the SOC change amount during standby periods from the overall battery operation data. By focusing specifically on the SOC change that occurs when the battery is not being charged or discharged, the system separates the self-discharge component from other factors, enabling more accurate detection of abnormal deterioration without the errors present in comprehensive SOH models.
2Measurement precision
If conventional SOH diagnosis method is used, then the trend of state change can be checked, but the complexity of the system increases without significant improvement in accuracy
Solution Approach 1:
The invention extracts only the essential information needed for deterioration detection - the SOC change amount during standby periods - from the complex battery operation data. By focusing on this specific extracted parameter rather than analyzing comprehensive SOH models, the system achieves accurate deterioration detection with significantly reduced computational complexity and simpler implementation.
Solution Approach 2:
Instead of trying to accurately model the complex SOH parameters to detect deterioration, the invention inverts the approach by monitoring what happens when the battery should remain stable (during standby). By detecting abnormal SOC changes during periods when no charge/discharge should occur, the system achieves simple yet accurate deterioration detection without complex modeling.
Data Source
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AI summary
The present disclosure relates to an apparatus and method for diagnosing abnormal deterioration of a battery cell, and provides a battery cell abnormal deterioration diagnosis apparatus including an SOC calculation unit configured to calculate a state of charge (SOC) of a plurality of battery cells, a change amount calculation unit configured to calculate, for each of the plurality of battery cells, an SOC change amount average that is a change amount of the calculated SOC during a preset period and an SOC change amount average that is an average of the SOC change amount of all the plurality of battery cells, and an abnormal deterioration diagnosis unit configured to diagnose abnormal deterioration of each of the plurality of battery cells using the SOC change amount and the SOC change amount average thereby accurately diagnosing abnormal deterioration of battery cells.