AI Battery Cell Sorting Using Standardized Cross-Tray Criteria

The patent establishes a universal determination criterion that applies across all trays by standardizing derived variable data sets using average and standard deviation from the entire batch. This universal criterion eliminates the need for separate tray-by-tray inspections while maintaining detection reliability, as the standardized data can be compared against a common threshold established from all trays collectively.

The patent merges the inspection criteria across multiple trays by combining their derived variable data sets and calculating batch-level average and standard deviation. This merging creates a unified determination criterion that simultaneously evaluates all trays, reducing total inspection time while maintaining the reliability of detecting low-voltage cells across the entire batch.

The patent transforms tray-specific deviation detection into a higher-dimensional analysis by standardizing data across all trays using batch-level statistics. Instead of examining each tray in isolation, the system creates a multi-dimensional view where tray-specific deviations are captured through standardized scores that reflect both intra-tray and inter-tray variations simultaneously, enabling efficient universal screening without losing precision.

The patent changes the parameters for evaluation by introducing standardized derived variables that are normalized using batch-level average and standard deviation. This parameter transformation allows tray-specific deviations to be detected through standardized scores that account for both within-tray and between-tray variations, maintaining measurement precision while achieving universal applicability.