Battery Electrode Profile Prediction for Non-Destructive Degradation Analysis

The patent uses a neural network model trained on disassembled electrode data to generate synthetic electrode profiles without actual disassembly. The model learns from a dataset of positive and negative electrode profiles obtained through conventional methods, then reproduces these profiles computationally, achieving both accuracy and speed by replacing physical disassembly with digital simulation.

The patent performs preliminary training of the neural network model using a dataset of electrode profiles obtained through conventional disassembly methods. This pre-trained model can then rapidly generate profiles for degraded batteries without requiring actual disassembly, thus improving productivity while maintaining measurement precision through the learned patterns.

The patent replaces physical disassembly with computational copying through neural network simulation. The model generates synthetic electrode profiles that replicate the characteristics of actual electrodes without requiring physical access to the battery internals, thereby eliminating explosion risks while maintaining profile accuracy through learned electrochemical patterns.

The patent introduces a neural network model as an intermediary between the battery and the profile measurement process. Instead of directly disassembling the battery, the model acts as a mediator that processes battery data and generates electrode profiles computationally, thus protecting against safety hazards while achieving measurement objectives.

The patent uses the neural network model to copy and reproduce electrode profiles for degraded batteries based on overall battery profile data and degradation indicators. This computational approach eliminates time-consuming disassembly and reassembly processes while maintaining profile accuracy through the model's learned understanding of electrode behavior under various degradation conditions.

The patent replaces the mechanical disassembly and physical measurement system with a computational neural network system. The neural network processes battery data and generates electrode profiles through algorithms rather than physical manipulation, significantly reducing time loss while maintaining measurement precision through sophisticated pattern recognition.