Battery Voltage Drop Clustering for Low-Voltage Defect Screening
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Solution Overview
Problem
Conventional methods for screening low-voltage defective lithium secondary batteries fail to reliably distinguish between good and defective products due to manufacturing deviations, leading to potential misclassification during the screening process.
Innovation Solution
A method involving pre-filtering to remove outlier data, clustering based on similar characteristics, correcting voltage drop dispersion by temperature and period, and using anomaly detection algorithms to screen low-voltage defective batteries, improving the reliability of detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a specific threshold is selected from the dispersion of voltage drop data and tray-scale standard scaling is performed, then the screening process can be simplified, but the reliability of defect detection deteriorates due to manufacturing deviations accumulating and blurring the boundary between good and defective products
Solution Approach 1:
The patent segments the battery screening process into multiple unit processes (charging process, aging process, discharge process) rather than using a single tray-scale screening. By dividing the screening into smaller temporal segments and analyzing voltage drop dispersion at each stage, the method prevents accumulation of manufacturing deviations and maintains reliable defect detection throughout the activation process
Solution Approach 2:
The patent performs preliminary screening actions at each unit process stage (charging, aging, discharge) before final classification. By continuously monitoring and comparing voltage drop dispersion against reference values established from normal batteries at each stage, the system identifies defective batteries early and prevents deviation accumulation that would blur detection boundaries
2Ease of manufacture
If conventional threshold-based screening is used, then the screening method is simple to implement, but the precision of voltage drop measurement deteriorates due to increased dispersion from manufacturing deviations
Solution Approach 1:
The patent employs dynamic reference value adjustment where the reference voltage drop dispersion is continuously updated and adapted at each unit process stage based on real-time measurements from normal batteries. This dynamic approach maintains measurement precision despite manufacturing variations, unlike static threshold methods that deteriorate in precision as deviations accumulate
3Productivity
If tray-scale standard scaling is performed on all batteries, then processing efficiency is maintained, but the detectability of defective batteries deteriorates due to increased dispersion of open circuit voltage and voltage drop
Solution Approach 1:
The patent segments batteries into different groups based on their unit process characteristics (charging, aging, discharge stages) and applies targeted screening to each segment. This segmentation maintains processing efficiency by using automated tray-scale methods within each stage while improving detectability by comparing each segment against stage-specific reference values, preventing dispersion from blurring defect boundaries
Solution Approach 2:
The patent performs preliminary classification and reference value establishment for each unit process before main screening. By pre-identifying normal battery characteristics at each stage and setting stage-specific reference dispersions, the system maintains high processing efficiency while ensuring defective batteries are detected with high reliability through stage-appropriate comparison criteria
Data Source
AI summary
Disclosed herein relates to a method for screening a low-voltage defective battery including: (a) a pre-filtering step of collecting data and removing secondary batteries having an outlier data in real time during an activation process of multiple secondary batteries; (b) a clustering step of clustering based on similar characteristics or records for multiple pre-filtered secondary batteries; (c) a correction step of measuring the amount of voltage drop (ΔOCV) for each cluster and correcting a dispersion of the amount of voltage drop (ΔOCV) according to a temperature or a period; and (d) a screening step of screening low-voltage defective batteries based on a corrected dispersion of the amount of voltage drop.