Battery Cell Defect Detection via Latent Variable Distribution Analysis
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Solution Overview
Problem
Existing battery systems lack effective methods to early detect defects such as short-circuit or lithium precipitation, which can lead to fires, necessitating a solution for proactive identification of defective battery cells.
Innovation Solution
A battery diagnosis apparatus and method using an information obtaining unit to gather feature data, a controller to extract latent variables, and perform primary and secondary determinations based on feature values and distribution charts to identify defective cells, utilizing pre-trained autoencoders and machine learning models for accurate defect detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional battery testing methods are used, then the diagnosis process is simple, but defect detection accuracy is insufficient and cannot early identify defects like short-circuit or lithium precipitation
Solution Approach 1:
The diagnosis process is segmented into multiple stages: initial activation stage testing, primary determination using autoencoder, and secondary determination using distribution analysis. This segmentation allows complex defect detection to be broken down into manageable steps, improving accuracy without overwhelming system complexity.
Solution Approach 2:
The patent performs preliminary testing during the initial activation stage of battery cells, before full deployment. By extracting feature data early when defects like short-circuit or lithium precipitation first manifest, the system can identify problematic cells proactively, improving detection accuracy while maintaining reasonable system complexity.
2Reliability
If comprehensive feature data analysis is performed on all battery cells, then defect detection reliability improves, but the time and computational resources required increase significantly
Solution Approach 1:
The patent applies partial action by focusing analysis on the initial activation stage feature data rather than continuous monitoring throughout the battery lifecycle. The autoencoder extracts key latent variables from this critical period, providing reliable defect detection without requiring exhaustive analysis of all operational phases, thus reducing time loss.
Solution Approach 2:
The patent replaces traditional mechanical/electrical testing methods with machine learning-based analysis. The pre-trained autoencoder and distribution-based determination algorithms automatically process feature data and identify defects, significantly reducing diagnosis time while maintaining or improving reliability compared to manual or traditional automated testing.
3Object-affected harmful factors
If early defect detection is implemented during initial activation, then fire risk is reduced, but the complexity of the testing apparatus increases
Solution Approach 1:
The patent introduces an intermediary machine learning system (autoencoder with distribution analysis) that mediates between raw feature data and defect identification. This intermediary layer processes data during the initial activation stage to detect early signs of defects that could lead to fire, reducing fire risk while managing apparatus complexity through intelligent data processing rather than complex hardware.
Data Source
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AI summary
A battery diagnosis apparatus according to an embodiment disclosed herein includes an information obtaining unit configured to obtain feature data of each of a plurality of battery cells and a controller configured to extract a latent variable from the feature data, perform a primary determination as to whether each of the plurality of battery cells is defective, based on a feature value corresponding to the latent variable, and perform a secondary determination as to whether each battery cell is defective, based on a result of the primary determination and a distribution of the feature value.