Battery Quality Detection With Dynamic Voltage Ranges
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Solution Overview
Problem
Conventional battery quality detection methods struggle with accuracy when battery voltage changes abruptly and become overly lenient when voltage stabilizes, leading to misjudgments and reduced precision.
Innovation Solution
A battery quality detection method that dynamically adjusts the standard detection range using image processing to maintain strict quality control during stable voltage and provide a larger range during abrupt changes, incorporating upper and lower limit curves derived from test data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed upper voltage limit is used for battery detection, then the detection method is simple and easy to implement, but the detection accuracy deteriorates when battery voltage changes rapidly
Solution Approach 1:
The patent transforms the static fixed voltage limit into a dynamic detection range that adapts to different battery states. The system calculates real-time upper and lower limit curves based on the battery's current voltage and charging current, allowing the detection boundaries to dynamically adjust as the battery charges. This resolves the contradiction by making the detection method both simple (automated calculation) and accurate (adaptive to changing conditions).
Solution Approach 2:
The patent changes the detection parameters from a single fixed voltage threshold to multiple time-varying parameters including upper limit curves, lower limit curves, and state-of-charge thresholds. These parameters are continuously updated based on battery performance data, enabling accurate detection across different charging stages while maintaining implementation simplicity through automated parameter adjustment.
2Device complexity
If a fixed upper voltage limit is used, then the detection criteria are consistent and easy to set, but the method becomes overly lenient when battery voltage stabilizes near rated voltage
Solution Approach 1:
The patent applies different detection strictness levels to different battery states. During rapid voltage changes, the detection range is wider to accommodate normal variations. When voltage stabilizes near rated voltage, the detection becomes stricter with narrower ranges around the upper and lower limit curves. This local adaptation of detection quality ensures reliable anomaly detection without false positives while maintaining consistent automated criteria.
Solution Approach 2:
The system dynamically adjusts the detection range width based on the battery's operational state. The upper and lower limit curves are continuously calculated and adjusted, creating a dynamic detection envelope that tightens when the battery is near full charge and relaxes during transient phases. This dynamic approach maintains both criteria consistency and detection reliability.
3Reliability
If the detection range is widened to accommodate rapid voltage changes, then fewer false positives occur, but detection precision deteriorates during stable voltage periods
Solution Approach 1:
The patent implements a dynamic detection range that automatically widens or narrows based on the battery's charging phase. During rapid voltage changes, the range between upper and lower limit curves is naturally wider, reducing false positives. During stable voltage periods near full charge, the range tightens, maintaining high detection precision. This dynamic adjustment resolves the contradiction by making the range adaptive rather than fixed.
Solution Approach 2:
The patent segments the charging process into different phases (rapid change phase and stable phase) and applies appropriate detection strictness to each segment. The system identifies transition points and adjusts the detection range accordingly, ensuring that each phase is evaluated with appropriate tolerance levels, thus eliminating false positives without sacrificing precision when needed.
Data Source
AI summary
The present invention provides a battery quality detection method, comprising the following steps: selecting a plurality of test data with a data concentration greater than a first threshold; determining an upper limit curve and a lower limit curve for the selected test data; setting a standard detection range between the upper limit curve and the lower limit curve; obtaining a battery characteristic waveform of a battery; and comparing the battery characteristic waveform with the standard detection range to evaluate the battery's quality.