Battery Cell Deterioration Detection Using Capacity Deviation Statistics
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Solution Overview
Problem
Existing secondary battery systems lack effective methods for early detection of abnormally deteriorated cells, which can lead to safety issues such as thermal runaway and ignition.
Innovation Solution
A method and apparatus for detecting abnormally deteriorated cells by calculating statistical values representing capacity deviations at different time points, using initial and current capacity sigmas, and comparing these values with thresholds to identify outlier cells through Grubbs' test.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis of capacity deviation is performed to detect abnormally deteriorated cells, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The detection method segments the battery module into individual cells and performs statistical analysis on capacity deviations of each cell relative to the group. By dividing the monitoring task into cell-level measurements and group-level statistical comparison, the system achieves high detection accuracy without requiring overly complex computational resources. The control device calculates capacity deviations for each cell and compares them against statistical thresholds derived from the overall cell population.
Solution Approach 2:
The system uses the battery module's own operational data (charge/discharge cycles, capacity measurements) to perform self-diagnosis. The control device leverages existing monitoring infrastructure to collect capacity data and automatically performs statistical analysis to identify deteriorated cells, eliminating the need for external diagnostic equipment or complex additional sensing systems.
2Reliability
If continuous monitoring of capacity deviation is implemented, then early detection capability is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring of capacity deviations at scheduled intervals during battery operation rather than continuous real-time monitoring. The control device performs statistical analysis at defined checkpoints (e.g., after charge/discharge cycles or at predetermined time intervals), which maintains early detection capability while significantly reducing computational load and energy consumption compared to continuous monitoring.
Solution Approach 2:
The monitoring system leverages existing continuous operational data (charge/discharge currents, voltages) that are already being measured for battery management purposes. By overlaying statistical analysis on top of this continuous data stream at periodic intervals, the system maintains the continuity of useful monitoring action without requiring separate dedicated power consumption for detection purposes.
Data Source
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AI summary
An abnormally deteriorated cell detection apparatus and method that detects an abnormally deteriorated cell are described. The method includes obtaining a first statistical value representing a capacity deviation at a first time point for the plurality of cells, obtaining a second statistical value representing a capacity deviation at a second time point for the plurality of cells, comparing the first statistical value and the second statistical value to detect an abnormally deteriorated cell.