Battery Electrode Plate Design Using ML Characteristic Prediction
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Solution Overview
Problem
Existing methods for manufacturing battery electrode plates do not effectively predict and optimize electrode characteristics based on design values, leading to inefficiencies in the manufacturing process.
Innovation Solution
A system utilizing a computing device with a machine-learning model to predict electrode plate characteristics, such as tortuosity and ionic resistance, by analyzing design factors, and providing design support for optimizing the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manufacturing methods are used for battery electrode plates, then the manufacturing process is simple, but the time and cost to achieve optimal design conditions are excessive
Solution Approach 1:
The patent applies preliminary action by pre-training a machine learning model using design data from previous electrode plate manufacturing processes. This pre-trained model can then rapidly predict optimal design conditions for new electrode plates without requiring time-consuming trial-and-error experiments, thus achieving optimal characteristics in advance and reducing development time
Solution Approach 2:
The patent uses copying by creating a virtual model (machine learning model) that replicates the relationship between design conditions and electrode plate characteristics based on historical data. This virtual model allows designers to simulate and predict outcomes without physical prototyping, significantly reducing the time and cost to achieve optimal design conditions
2Manufacturing precision
If traditional manufacturing methods are used for battery electrode plates, then the process requires extensive trial and error, but this increases manufacturing cost and time
Solution Approach 1:
The patent replaces the mechanical trial-and-error manufacturing process with an information-based machine learning system. Instead of physically manufacturing multiple prototype electrode plates to test different designs, the system uses a trained model to predict optimal characteristics, substituting physical experimentation with computational analysis to improve manufacturing efficiency
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between design inputs and manufacturing outcomes. This model acts as a mediator that processes design parameters and predicts electrode plate characteristics, eliminating the need for direct trial-and-error experimentation and thereby improving productivity
Data Source
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AI summary
The present disclosure relates to systems (1) and methods for manufacturing a battery electrode plate. The system (1) comprises a computing device configured to receive, from the client device, a target process factor among a plurality of process factors associated with manufacturing a battery electrode plate, predict, via a machine-learning model, a change in a characteristic of the battery electrode plate based on a change in a design value of the target process factor, generate information for selecting the target process factor based on predicting the change of the characteristic of the battery electrode plate, and transmit the information to the client device for manufacturing the battery electrode plate.