Battery Cell Performance Prediction Using ML Design Factors
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Solution Overview
Problem
Existing methods for predicting battery cell performance require costly expert intervention and simulation resources, making it difficult to proactively respond to battery performance changes.
Innovation Solution
A method and system using machine learning models to predict battery cell performance by receiving design factors, generating visual representations, and integrating multiple models into a pipeline to analyze charge/discharge cycles, allowing for detailed battery lifespan evaluation without direct measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation techniques are used to predict battery cell performance, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the battery cell that replicates its physical behavior and degradation patterns. This digital model allows performance prediction without requiring complex physical simulations or direct measurement of every battery parameter, thereby maintaining prediction accuracy while reducing system complexity.
Solution Approach 2:
The patent introduces sensor data and machine learning algorithms as intermediaries between the physical battery and the prediction system. These intermediaries translate complex physical phenomena into simplified digital representations that can be processed efficiently, reducing the need for complex simulation infrastructure.
2Measurement precision
If expert intervention is used for battery performance evaluation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The battery performance evaluation system performs self-assessment through embedded sensors and automated machine learning models. The system continuously monitors its own state and predicts performance metrics without requiring external expert intervention, enabling real-time evaluation while maintaining high accuracy through automated algorithms.
Solution Approach 2:
The system performs preliminary performance evaluation continuously in the background using machine learning models trained on historical data. This allows performance predictions to be ready in advance before actual expert review is needed, significantly reducing the time experts need to spend on evaluation while maintaining measurement precision.
3Measurement precision
If direct measurement of battery performance is performed, then measurement precision is improved, but loss of substance and cost increase
Solution Approach 1:
Instead of physically testing and consuming battery materials through extensive direct measurement and degradation testing, the patent uses a digital twin to create virtual copies of performance data. This allows accurate performance assessment without physically stressing or consuming battery materials in the process.
Solution Approach 2:
The patent introduces machine learning models and sensor data as intermediaries that predict performance metrics without requiring direct physical measurement of every parameter. This intermediary layer enables accurate performance evaluation while minimizing the need for physical battery disassembly, testing, and material consumption.
Data Source
AI summary
The present disclosure relates a method for predicting battery cell performance, including: receiving one or more design factors for a target battery, determining performance-related prediction data for the target battery based on the received one or more design factors and by using a machine learning model, generating a visual representation indicating performance of the target battery based on the determined performance-related prediction data, and outputting the generated visual representation.