Electrolyte Concentration Analysis via Spectral Machine Learning
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Solution Overview
Problem
Current methods for characterizing electrolyte components in lithium-ion cells are expensive, time-consuming, and unable to accurately determine the concentration of electrolyte components, especially in high-voltage cells where electrolyte degradation is a major cause of failure.
Innovation Solution
A computer-implemented method using a spectrometer to capture and analyze the spectrum of an electrolyte sample, employing machine learning models based on spectral features to determine the concentration of electrolyte components, which is more efficient and cost-effective than traditional analytical tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive analytical tools such as NMR spectrometers, GC-MS, HPLC, or ICP-OES are used to characterize electrolyte components, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical and chemical analytical systems (NMR, GC-MS, HPLC, ICP-OES) with a spectroscopic system combined with machine learning algorithms. The spectrometer captures spectral data that is then processed through ML models to determine electrolyte component concentrations, substituting physically complex instrumentation with a more streamlined optical measurement and computational approach.
Solution Approach 2:
The patent creates a virtual model or copy of the electrolyte composition through spectral fingerprints. Instead of physically separating and analyzing components through complex chromatographic or spectroscopic methods, the system captures the overall spectral signature and uses machine learning to infer individual component concentrations from this spectral copy, significantly simplifying the analytical process.
2Measurement precision
If traditional analytical methods are used to determine electrolyte component concentrations, then measurement accuracy is improved, but loss of time increases due to significant analysis time required
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models using spectral data from electrolyte samples with known compositions. This training phase creates a reference database that allows rapid prediction of component concentrations from new spectral measurements without requiring time-consuming traditional analysis procedures for each sample.
Solution Approach 2:
The patent substitutes time-intensive mechanical separation and detection processes (chromatography, mass spectrometry) with rapid spectral acquisition followed by computational analysis. The spectrometer quickly captures spectral data, and machine learning algorithms rapidly process this data to deliver concentration measurements, reducing analysis time from hours to minutes or seconds.
3Measurement precision
If chromatography-based methods are used to analyze electrolyte components, then measurement precision for organic portions is improved, but object-generated harmful factors worsen due to inability to measure water-soluble portions and high temperature decomposition products
Solution Approach 1:
The patent implements a universal analytical approach where a single spectrometer-based system can simultaneously analyze all electrolyte components including organic solvents, water-soluble salts, and thermal decomposition products. The machine learning model is trained to recognize spectral signatures of diverse components, making the system multi-functional and eliminating the need for separate analytical methods for different component types.
Solution Approach 2:
The patent replaces chromatography-based mechanical separation methods with spectroscopic detection combined with computational analysis. This substitution allows direct measurement of all components in the electrolyte without requiring physical separation, enabling detection of water-soluble portions and high temperature decomposition products that cannot be analyzed by traditional chromatographic methods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method allows for rapid and accurate characterization of electrolyte components, overcoming the limitations of existing techniques by providing a cost-effective and time-efficient means to determine the concentration of electrolyte components in lithium-ion cells, thereby improving the assessment of electrolyte degradation.
Implementation Method 1
providing, to a spectrometer, instructions to capture a spectrum of a sample solution of the electrolyte and generate a signal
Data Source
AI summary
A computer-implemented method for determining a concentration of a component of an electrolyte in a lithium-ion or for a lithium-ion cell is provided. The method includes providing, to a spectrometer, instructions to capture a spectrum of a sample solution of the electrolyte and generate a signal. The method includes analyzing the signal to determine one or more spectral features of the spectrum. The method includes preparing a database of spectra corresponding to solutions having predetermined concentrations of the component of the electrolyte wherein the database includes a plurality for spectral features for each solution. The method further includes determining a machine learning (ML) model using the database of spectra. The method includes determining the concentration of the component of the electrolyte in the sample solution using the machine learning model.