Electrode Plate Misalignment Detection Using Channel Attention
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Solution Overview
Problem
Existing lithium battery cell detection methods struggle with inaccurate positioning and misalignment detection between anode and cathode plates, leading to lithium precipitation and safety risks due to insufficient misalignment in the battery cell structure.
Innovation Solution
A battery cell electrode plate detection method involving feature extraction with a channel attention mechanism to embed position information, followed by electrode plate detection and determination of misalignment using a neural network approach, including convolution and attention operations to enhance feature extraction and detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing methods are used for electrode plate detection, then the detection process is simple, but the positioning accuracy and misalignment detection accuracy are insufficient
Solution Approach 1:
The patent replaces conventional mechanical/image processing detection methods with a neural network-based automated detection system. The neural network model automatically learns feature representations from electrode plate images, substituting traditional manual feature extraction and processing methods, thereby improving positioning accuracy while managing system complexity through software-based automation.
Solution Approach 2:
The patent transforms the detection approach by changing from fixed threshold-based image processing to adaptive neural network parameter learning. The model learns optimal feature detection parameters automatically from training data, enabling accurate positioning and misalignment detection without manual parameter tuning, thus improving measurement precision.
2Quantity of substance
If the anode plate and cathode plate are closely aligned to maximize energy density, then the battery capacity increases, but lithium precipitation occurs leading to safety risks
Solution Approach 1:
The patent implements a feedback mechanism where the neural network detects the actual misalignment between anode and cathode plates, and this information feeds back to quality control systems. Based on the detected misalignment measurements, production parameters can be adjusted to maintain optimal spacing, preventing lithium precipitation while maximizing battery capacity.
Solution Approach 2:
The patent performs misalignment detection during the production process itself, allowing preliminary identification of potential lithium precipitation risks before they manifest as actual safety problems. This enables preventive adjustment of electrode plate positioning to avoid lithium precipitation while maintaining high energy density.
3Manufacturing precision
If traditional detection methods are used, then the production flow is fast, but the misalignment detection accuracy is insufficient leading to quality issues
Solution Approach 1:
The patent replaces slow, manual, or conventional step-by-step image processing methods with a trained neural network model that performs detection in a single pass. The model has learned to directly identify electrode plate positions and misalignment from images, eliminating multiple processing stages and achieving both high precision and fast detection speed suitable for production environments.
Data Source
AI summary
This disclosure relates to the field of battery technologies, and provides a battery cell electrode plate detection method and apparatus, and an electronic device. The method includes: performing feature extraction on a to-be-detected battery cell electrode plate image to obtain an electrode plate feature, where the electrode plate feature includes a channel attention feature embedded with position information; performing electrode plate detection based on the electrode plate feature to obtain an electrode plate detection result; and determining an amount of misalignment between an anode plate and a cathode plate based on position information of the anode plate and the cathode plate in the electrode plate detection result. According to the technical solution provided in this application, the amount of misalignment between the cathode plate and the anode plate can be detected.