Battery Overhang Calculation via Deep Learning Segmentation
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Solution Overview
Problem
Existing methods for calculating the overhang of batteries, such as using X-ray images, are limited by low versatility, complexity, and accuracy, making it difficult to control the overhang for optimal electrochemical performance.
Innovation Solution
A deep learning-based method and device that involves obtaining a training sample image set, training a neural network to create a segmentation network model, detecting the battery's object detection image, and calculating the overhang using top coordinates of the electrodes, significantly simplifying and improving the accuracy of the calculation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray images are used to calculate battery overhang, then measurement capability is provided, but calculation accuracy remains low
Solution Approach 1:
The patent replaces traditional mechanical/image-processing-based overhang calculation methods with a deep learning neural network model. The model automatically extracts electrode boundaries and calculates overhang from battery images, substituting complex manual calculation and image processing mechanisms with an intelligent system that achieves higher accuracy while simplifying the operational complexity.
Solution Approach 2:
The patent creates a digital model (neural network) that learns from labeled training images to replicate the boundary detection and overhang calculation process. Instead of directly processing raw images through complex algorithms, the system uses trained model predictions that copy the expertise of manual annotation into an automated, high-accuracy calculation process.
2Adaptability or versatility
If traditional methods are used for overhang calculation, then existing technology is maintained, but versatility is limited
Solution Approach 1:
The patent develops a universal deep learning model that can handle various battery types, electrode configurations, and image formats through a single trained system. The neural network architecture is designed to be adaptable to different battery structures while maintaining consistent high accuracy, making the method versatile across multiple applications rather than requiring separate specialized methods for each case.
3Manufacturing precision
If engineering accuracy restrictions are applied, then optimal electrochemical performance cannot be achieved, but manufacturing feasibility is maintained
Solution Approach 1:
The patent implements a feedback mechanism where the deep learning model accurately measures the actual overhang in manufactured batteries, providing precise data on alignment deviations. This feedback enables manufacturers to adjust their fabrication processes to achieve optimal electrode alignment, closing the loop between measurement and manufacturing control to simultaneously improve precision and ease of manufacture.
Data Source
AI summary
A deep learning-based method for calculating an overhang of a battery includes the following steps: obtaining a training sample image set; training a neural network according to the training sample image set to obtain a segmentation network model; detecting an object detection image of the battery to be detected according to the segmentation network model to obtain a corresponding first binarized image; obtaining top coordinates of each of a positive electrode and a negative electrode of the battery to be detected according to the first binarized image; and calculating the overhang of the battery to be detected according to the top coordinates.