Secondary Battery Abnormality Prediction via Parameter Deviation
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Solution Overview
Problem
Existing secondary battery systems lack the ability to predict abnormalities in batteries before they become critical, leading to potential equipment failures and significant losses, as they only notify users after an abnormality is detected, not before.
Innovation Solution
An abnormality prediction system that uses parameter values such as voltage, temperature, or output current to identify secondary batteries that are likely to become abnormal by comparing their values to averages and thresholds, allowing for early detection and prevention of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional abnormality detection methods are used, then the system can identify batteries in abnormal state, but the abnormality is detected only after it has occurred, causing equipment failure and loss
Solution Approach 1:
The system performs preliminary actions by continuously monitoring parameter values and comparing them against learned normal patterns before abnormalities occur. The abnormality prediction unit proactively identifies potential failures by detecting deviations from normal parameter trajectories, enabling maintenance before actual breakdown happens.
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring parameter values, comparing them with stored normal range data, and adjusting predictions based on the degree of deviation. This closed-loop feedback enables the system to adaptively identify abnormal patterns and improve prediction accuracy over time.
2Measurement precision
If the system monitors all parameter values continuously, then early abnormality detection is enabled, but the system complexity and measurement requirements increase
Solution Approach 1:
The system employs self-service by utilizing the battery's own operational parameters (voltage, temperature, current) that are already naturally present during normal operation. These parameters serve as both the operating data and the diagnostic indicators, eliminating the need for additional sensors or complex measurement infrastructure.
Solution Approach 2:
The system focuses on monitoring changes in parameter values over time rather than absolute values. By detecting deviations from normal parameter trajectories and patterns, the system can identify abnormalities using simple threshold comparisons and pattern matching, reducing computational complexity while maintaining detection accuracy.
Data Source
AI summary
An abnormality prediction system for secondary batteries according to the present invention includes: a parameter value detection portion that detects parameter values each corresponding to each of a plurality of secondary batteries to determine whether all the parameter values are normal or not; and a singular state determination portion that determines, if a difference between a reference value calculated by use of all the parameter values determined to be normal by the parameter value detection portion and at least one of the parameter values is not less than a threshold value, the secondary battery corresponding to the parameter value with the difference not less than the threshold value to be in a state different from those of the other secondary batteries out of the plurality of secondary batteries.