Battery Process Data Analysis for ML-Based Factor Impact Prediction
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Solution Overview
Problem
Existing battery manufacturing processes face challenges in identifying and quantifying the impact of various process factors on battery performance due to their complex correlations, making it difficult to optimize the manufacturing process effectively.
Innovation Solution
A data processing apparatus and method utilizing machine learning-based performance prediction models to analyze battery manufacturing processes, identifying key process factors and their influence on battery performance by collecting and preprocessing data, constructing multiple models, and outputting analysis information through a GUI for intuitive visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based performance prediction models are constructed to identify process factors affecting battery performance, then the ability to analyze and quantify process factor impacts is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the battery manufacturing process into multiple unit processes (electrode coating, electrode rolling, assembly, activation, EOL) and analyzes process factors for each unit process separately. This segmentation allows the complex analysis problem to be divided into manageable parts, improving identification precision while controlling system complexity through modular analysis
Solution Approach 2:
The patent introduces machine learning-based performance prediction models as intermediaries between raw process data and performance analysis. These models serve as mediators that automatically process complex correlations among process factors, enabling precise identification of impactful factors without requiring direct complex analysis of all process parameters
2Loss of information
If data from multiple unit processes is collected and analyzed to identify process factors, then the comprehensiveness of analysis is improved, but the difficulty of detecting and measuring relationships increases
Solution Approach 1:
The patent implements feedback mechanisms where performance prediction models continuously learn from actual battery performance data. The models receive feedback by comparing predicted performance with measured performance, automatically adjusting to accurately capture complex correlations among process factors from multiple unit processes, thereby maintaining information completeness while reducing analysis difficulty
Solution Approach 2:
The patent transforms raw process data into meaningful performance predictions by changing parameters through machine learning model training. The models learn optimal parameter relationships from historical data, converting complex multi-process data into actionable insights about which process factors significantly impact battery performance
Data Source
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AI summary
Disclosed is a data processing apparatus for analyzing a battery manufacturing process according to embodiments of the present invention, which includes at least one processor; and a memory configured to store at least one instruction executed by the at least one processor. Here, the at least one instruction may include an instruction to collect process data for each process factor for a plurality of batteries; an instruction to construct a machine learning-based performance prediction model for predicting battery performance using the process data for each process factor; an instruction to generate analysis information indicating an effect of one or more process factors on a performance prediction value of the performance prediction model; and an instruction to output the generated analysis information through a predefined graphical user interface (GUI).