Method and device for detecting electrolyte on line based on Fourier transform near infrared spectrum technology

Abstract

The invention discloses an online electrolyte detection method and device based on a Fourier transform near infrared spectrum technology, and relates to the technical field of electrolyte production, the method comprises the following steps: S1, spectrum acquisition; s2, carrying out spectrum analysis; s3, data processing and analysis; s4, outputting and monitoring a result; the device is composed of a control cabinet, an optical fiber and a flow cell, wherein the control cabinet comprises a Fourier transform near-infrared spectrometer and an industrial personal computer. According to the method and the device for online detection of the electrolyte based on the Fourier transform near infrared spectrum technology, online monitoring of five indexes of the electrolyte can be realized by using one device, the detection efficiency and the real-time performance are greatly improved, the Fourier transform near infrared spectrum technology is adopted, and the detection accuracy is greatly improved. The method has the characteristics of short time consumption, low power consumption, high accuracy, high sensitivity and high resolution, the performance of electrolyte detection is remarkably improved, simultaneous detection of multiple samples is supported, and the detection flux is improved.

Description

Technical Field

[0001] This invention relates to the field of electrolyte production technology, specifically to a method and apparatus for online detection of electrolytes based on Fourier transform near-infrared spectroscopy. Background Technology

[0002] Lithium-ion battery electrolyte is the carrier of ions in the battery. It is generally composed of lithium salt and organic solvent. The electrolyte plays a role in conducting ions between the positive and negative electrodes of the lithium-ion battery, ensuring the high voltage and high specific energy of the battery. Electrolytes are generally prepared from high-purity organic solvents, lithium salt electrolytes, and necessary additives under specific conditions and in specific proportions. The electrolyte has always been central to the performance and stability of lithium-ion batteries. Water content, free acid, color, density, and conductivity are important parameters for evaluating electrolyte performance. Online monitoring and control are required during the production process.

[0003] Typically, the five electrolyte parameters are measured using different detection principles. For example, density is measured using a densitometer, conductivity using a conductivity meter, moisture using a coulometric Karl Fischer moisture analyzer, acidity using potentiometric titration, and colorimetry using a platinum-cobalt colorimetric method. Implementing online detection for all five parameters would result in a very large and complex detection device.

[0004] Currently, only color and density can be detected online, while moisture, acidity, and conductivity cannot. These can only be detected online and must be extracted and taken to the laboratory for offline analysis. This not only fails to achieve real-time online detection and control of the electrolyte, but also, because it requires multiple cavitation treatments of the electrolyte, it is very likely to introduce external contamination, such as moisture intrusion and contaminant dust carried by various samplers, leading to inaccurate electrolyte detection, time consumption, and errors caused by human operation. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for online detection of electrolytes based on Fourier transform near-infrared spectroscopy, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] On the one hand, an online detection method for electrolyte based on Fourier transform near-infrared spectroscopy is provided, including the following steps:

[0008] S1. Spectral Acquisition: The electrolyte sample is introduced into the flow cell and circulated or flowed through the flow pipe to ensure that the sample flows through the area between the two fiber optic probes. The Fourier transform near-infrared spectrometer (hereinafter referred to as the spectrometer) emits near-infrared light into the electrolyte in the flow cell through the optical fiber and receives the transmitted or reflected spectral signals.

[0009] S2, Spectral Analysis: The interferometer module of the spectrometer receives the light signal and analyzes the spectrum, and the spatial domain spectrum is recorded by the detector as light intensity information;

[0010] S3. Data Processing and Analysis: The industrial control computer receives spectral data and uses built-in algorithms and AI models to process and analyze it, predicting sample information, including indicators such as moisture, acidity, color, density, and conductivity.

[0011] S4. Results Output and Monitoring: The industrial control computer transmits the analysis results to the DCS central control system, providing real-time data and alarm information for operators to monitor key indicators in the electrolyte production process. If any abnormality is detected, the system can automatically trigger an alarm and promptly notify the operators to take appropriate measures.

[0012] Furthermore, in step S1, before the electrolyte sample is introduced into the flow cell, the spectrometer is turned on and automatically calibrated using the self-test module to ensure stable instrument status and improve measurement accuracy.

[0013] Furthermore, in step S1, the sample light module of the spectrometer emits near-infrared light, which is transmitted to the flow cell through an optical fiber. The optical fiber probe collimates the light and transmits it through the electrolyte sample in the flow cell. Another optical fiber then receives the transmitted or reflected light signal and transmits it back to the spectrometer. The sample light module provides a collimated beam and a near-infrared spectrum.

[0014] Furthermore, in step S1, the electrolyte sample absorbs near-infrared light of a specific wavelength to form a spectrum containing sample information.

[0015] Furthermore, in step S2, the interference module receives the optical signal returned from the electrolyte sample and converts it into an interferogram. The interferogram is a function containing all the frequency and intensity information of the sample light. The intensity distribution of the sample light according to frequency can be calculated through Fourier transform, thereby realizing the detection and analysis of various indicators of the electrolyte sample.

[0016] Furthermore, in step S3, the industrial control computer performs Fourier transform processing to analyze the spectral characteristics of the electrolyte, and calculates various indicators of the electrolyte based on the spectral characteristics. Specifically, the Fourier transform is used to calculate the intensity distribution of the sample light according to frequency to achieve high-resolution spectral analysis. The interferogram is converted into a spectrum to obtain the absorption spectrum of the electrolyte sample in the near-infrared region, thereby obtaining the absorption characteristics of the electrolyte in the near-infrared spectral region. Then, the five indicators of the electrolyte are quantitatively analyzed, namely moisture, acidity, color, density, and conductivity.

[0017] Furthermore, in step S3, based on the spectral analysis results, various performance indicators of the electrolyte sample are calculated, and their compliance with production requirements is evaluated. The calculation methods for each indicator are as follows:

[0018] Moisture content: Quantitative detection of moisture is performed using the absorption peaks in the near-infrared spectral region.

[0019] Colorimetry: Colorimetry is measured using the spectral information of the electrolyte and the colorimetric calculation formula.

[0020] Density: Density is quantitatively analyzed by measuring the absorbance of the electrolyte;

[0021] Acidity and conductivity: Since they are closely related to the density of the electrolyte, they are also quantitatively analyzed using a spectrometer.

[0022] Furthermore, the Fourier transform spectrometer works by splitting the sample light into two beams to form a certain optical path difference, and then combining them to produce interference. The resulting interference pattern function contains all the frequency and intensity information of the sample light.

[0023] On the other hand, an online electrolyte detection device based on Fourier transform near-infrared spectroscopy is provided, which is applied to the online electrolyte detection method based on Fourier transform near-infrared spectroscopy as described above. The device consists of a control cabinet, optical fiber and flow cell, wherein the control cabinet includes a Fourier transform near-infrared spectrometer (hereinafter referred to as spectrometer) and an industrial control computer.

[0024] Spectrometer: Responsible for the acquisition and preliminary processing of spectra;

[0025] Industrial control computer: Connected to the spectrometer, used for data processing and analysis of spectral data, and integrating large AI models for sample information prediction;

[0026] The optical fiber is used to connect the spectrometer and the flow cell. The connection between the spectrometer and the flow cell is made by two optical fibers. One optical fiber connects the sample light output end of the spectrometer to one end of the flow cell, and the other optical fiber transmits the light signal from the flow cell back to the input end of the spectrometer.

[0027] The flow cell consists of two fiber optic probes and a flow channel, as detailed below:

[0028] Fiber optic probe: Includes a fiber optic collimating lens, used for transmitting and receiving optical signals;

[0029] Flow channel: Two sapphire windows are provided on the side wall to allow the collimated light from the fiber optic probe to pass through the flow cell and then be received by the fiber optic cable and transmitted to the spectrometer for analysis.

[0030] Furthermore, the spectrometer comprises a sample light module, an interferometer module, a self-test module, a colorimeter, a channel switching module, a detector, and a control module, as detailed below:

[0031] Sample optical module: provides collimated beam and near-infrared spectrum for the spectrometer;

[0032] Interference module: used for spectrum analysis, recording the spatial domain spectrum as light intensity information by the detector;

[0033] Self-test module: Used for automatic calibration of the spectrometer to ensure instrument stability and accuracy;

[0034] Colorimeter: Analyzes the visible light spectrum of the electrolyte to analyze colorimetric information;

[0035] Channel switching module: Enables switching of light for different samples, supporting simultaneous detection of multiple samples;

[0036] Detector and control module: Collects light intensity information and drives various motion mechanisms.

[0037] This invention provides a method and apparatus for online detection of electrolytes based on Fourier transform near-infrared spectroscopy, which has the following advantages:

[0038] This invention enables online monitoring of five electrolyte parameters using a single device, significantly improving detection efficiency and real-time performance. Employing Fourier transform near-infrared spectroscopy, it features short processing time, low power consumption, high accuracy, high sensitivity, and high resolution, significantly enhancing electrolyte detection performance. It also supports simultaneous detection of multiple samples, increasing throughput and making it suitable for multi-batch electrolyte testing needs in large-scale production processes. Furthermore, this invention includes an online self-calibration function, eliminating the need for offline operation, ensuring the accuracy of test results and the stability of the instrument, while also facilitating maintenance and reducing the risk of production interruptions due to instrument malfunction. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the online layout of an online electrolyte detection device based on Fourier transform near-infrared spectroscopy technology according to the present invention;

[0040] Figure 2This is a diagram illustrating the composition of an online electrolyte detection device based on Fourier transform near-infrared spectroscopy technology according to the present invention.

[0041] Figure 3 This is a schematic diagram of the logic flow of an online detection method for electrolyte based on Fourier transform near-infrared spectroscopy technology according to the present invention. Detailed Implementation

[0042] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0043] like Figures 1-3 As shown, an online detection method for electrolyte based on Fourier transform near-infrared spectroscopy includes the following steps:

[0044] S1, Spectral Acquisition:

[0045] Turn on the Fourier transform near-infrared spectrometer (hereinafter referred to as the spectrometer), use the self-test module to perform automatic calibration to ensure the instrument is stable, then introduce the electrolyte sample into the flow cell, and circulate or flow through the flow pipe to ensure that the sample flows through the area between the two fiber optic probes. Then the spectrometer emits near-infrared light into the electrolyte in the flow cell through the optical fiber and receives the transmitted or reflected spectral signals.

[0046] In this embodiment, the sample light module of the spectrometer emits near-infrared light, which is transmitted to the flow cell via optical fiber. The optical fiber probe collimates the light and transmits it through the electrolyte sample in the flow cell. Another optical fiber then receives the transmitted or reflected light signal and transmits it back to the spectrometer. The electrolyte sample absorbs near-infrared light of a specific wavelength, forming a spectrum containing sample information.

[0047] S2, Spectral Analysis:

[0048] The spectrometer's interferometer module receives the light signal and analyzes the spectrum, recording the spatial domain spectrum as light intensity information using a detector. In this step, the interferometer module receives the light signal returned from the electrolyte sample and converts it into an interferogram. The interferogram is a function containing all the frequency and intensity information of the sample light. Through Fourier transform, the intensity distribution of the sample light according to frequency can be calculated, thereby enabling the detection and analysis of various indicators of the electrolyte sample.

[0049] The principle of Fourier transform spectrometer is to split the light emitted from the sample into two beams to form a certain optical path difference, and then combine them to produce interference. The resulting interference pattern function contains all the frequency and intensity information of the sample light.

[0050] S3. Data Processing and Analysis:

[0051] The industrial control computer receives spectral data and processes and analyzes it using built-in algorithms and AI models to predict sample information, including indicators such as moisture, acidity, color, density, and conductivity.

[0052] In this embodiment, the industrial control computer performs Fourier transform processing to analyze the spectral characteristics of the electrolyte. Based on these spectral characteristics, it calculates various indicators of the electrolyte. Specifically, the Fourier transform is used to calculate the intensity distribution of the sample light according to frequency, achieving high-resolution spectral analysis. The interferogram is converted into a spectrum to obtain the absorption spectrum of the electrolyte sample in the near-infrared region. Because the organic solvent molecules in the electrolyte exhibit vibrational and rotational spectral absorption characteristics within the near-infrared spectral range, they can form unique absorption spectra. This absorption spectrum is a characteristic of the electrolyte in the near-infrared region, reflecting the interaction between various components (such as organic solvents and water) in the electrolyte and near-infrared light. Therefore, by measuring and analyzing the absorption spectrum of the electrolyte sample in the near-infrared region, we can gain a deeper understanding of the absorption characteristics of the electrolyte in the near-infrared spectral region, and then quantitatively detect and analyze key parameters such as the electrolyte density and acidity.

[0053] Based on the spectral analysis results, the performance indicators of the electrolyte sample were calculated, and their compliance with production requirements was evaluated. The calculation methods for each indicator are as follows:

[0054] Moisture content: Quantitative detection of moisture is performed using the absorption peaks in the near-infrared spectral region.

[0055] Colorimetry: Colorimetry is measured using the spectral information of the electrolyte and the colorimetric calculation formula.

[0056] Density: Density is quantitatively analyzed by measuring the absorbance of the electrolyte;

[0057] Acidity and conductivity: Since they are closely related to the density of the electrolyte, they are also quantitatively analyzed using a spectrometer.

[0058] S4. Results Output and Monitoring:

[0059] The industrial control computer transmits the analysis results to the DCS central control system, providing real-time data and alarm information for operators to monitor key indicators in the electrolyte production process. If an abnormality is detected, the system can automatically trigger an alarm and promptly notify the operators to take appropriate measures. The integrated system design enables real-time transmission and monitoring of detection data, ensuring real-time control of the production process.

[0060] like Figure 1As shown, an online electrolyte detection device based on Fourier transform near-infrared spectroscopy technology is applied to the online electrolyte detection method based on Fourier transform near-infrared spectroscopy technology described above. The device consists of a control cabinet, optical fiber, and flow cell. The control cabinet includes a Fourier transform near-infrared spectrometer (hereinafter referred to as spectrometer) and an industrial control computer.

[0061] Spectrometer: Responsible for spectral acquisition and preliminary processing; the spectrometer consists of a sample light module, an interferometer module, a self-test module, a colorimeter, a channel switching module, a detector, and a control module, as detailed below:

[0062] Sample optical module: provides collimated beam and near-infrared spectrum for the spectrometer;

[0063] Interference module: used for spectrum analysis, recording the spatial domain spectrum as light intensity information by the detector;

[0064] Self-test module: Used for automatic calibration of the spectrometer to ensure instrument stability and accuracy;

[0065] Colorimeter: Analyzes the visible light spectrum of the electrolyte to analyze colorimetric information;

[0066] Channel switching module: Enables switching of light for different samples, supporting simultaneous detection of multiple samples;

[0067] Detector and control module: Collects light intensity information and drives various motion mechanisms;

[0068] Industrial control computer: Connected to the spectrometer, used for data processing and analysis of spectral data, and integrating large AI models for sample information prediction;

[0069] Optical fibers are used to connect the spectrometer and the flow cell. The connection between the spectrometer and the flow cell is made by two optical fibers. One optical fiber connects the sample light output end of the spectrometer to one end of the flow cell, and the other optical fiber transmits the light signal from the flow cell back to the input end of the spectrometer.

[0070] The flow cell consists of two fiber optic probes and a flow channel, as detailed below:

[0071] Fiber optic probe: Includes a fiber optic collimating lens, used for transmitting and receiving optical signals;

[0072] Flow channel: Two sapphire windows are provided on the side wall to allow the collimated light from the fiber optic probe to pass through the flow cell and then be received by the fiber optic cable and transmitted to the spectrometer for analysis.

[0073] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy, characterized in that, Includes the following steps: S1. Spectral Acquisition: The electrolyte sample is introduced into the flow cell and circulated or flowed through the flow pipe. The Fourier transform near-infrared spectrometer emits near-infrared light into the electrolyte in the flow cell through an optical fiber and receives the transmitted or reflected spectral signals. S2, Spectral Analysis: The interferometer module of the spectrometer receives the light signal and analyzes the spectrum, and the spatial domain spectrum is recorded by the detector as light intensity information; S3. Data Processing and Analysis: The industrial control computer receives spectral data and uses built-in algorithms and AI models to process and analyze it, predicting sample information, including moisture, acidity, color, density, and conductivity. S4. Results Output and Monitoring: The industrial computer transmits the analysis results to the DCS central control system, providing real-time data and alarm information; In step S3, the industrial control computer performs Fourier transform processing to analyze the spectral characteristics of the electrolyte and calculates various indicators of the electrolyte based on the spectral characteristics. Specifically, the intensity distribution of the sample light according to frequency is calculated by Fourier transform, the interference diagram is converted into a spectrum, and the absorption spectrum of the electrolyte sample in the near-infrared region is obtained. Thus, the absorption characteristics of the electrolyte in the near-infrared spectral region are obtained, and then the five indicators of the electrolyte, namely moisture, acidity, color, density and conductivity, are quantitatively analyzed.

2. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, In step S1, before the electrolyte sample is introduced into the flow cell, the spectrometer is turned on and automatically calibrated using the self-test module.

3. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, In step S1, the sample light module of the spectrometer emits near-infrared light, which is transmitted to the flow cell through an optical fiber. The optical fiber probe collimates the light and transmits it through the electrolyte sample in the flow cell. Then, another optical fiber receives the transmitted or reflected light signal and transmits it back to the spectrometer.

4. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, In step S1, the electrolyte sample absorbs near-infrared light of a specific wavelength to form a spectrum containing sample information.

5. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, In step S2, the interference module receives the optical signal returned from the electrolyte sample and converts it into an interferogram, which is a function containing all the frequency and intensity information of the sample light.

6. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, In step S3, based on the spectral analysis results, various performance indicators of the electrolyte sample are calculated, and their compliance with production requirements is evaluated. The calculation methods for each indicator are as follows: Moisture content: Quantitative detection of moisture is performed using the absorption peaks in the near-infrared spectral region. Colorimetry: Colorimetry is measured using the spectral information of the electrolyte and the colorimetric calculation formula. Density: Density is quantitatively analyzed by measuring the absorbance of the electrolyte; Acidity and conductivity: quantitative analysis was performed using a spectrometer.

7. The method for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 1, characterized in that, The Fourier transform spectrometer works by splitting the light emitted from the sample into two beams with a certain optical path difference, and then combining them to produce interference. The resulting interference pattern function contains all the frequency and intensity information of the sample light.

8. An apparatus for online detection of electrolytes based on Fourier transform near-infrared spectroscopy, applied to the method for online detection of electrolytes based on Fourier transform near-infrared spectroscopy as described in any one of claims 1-7, characterized in that, The device consists of a control cabinet, optical fiber, and flow cell. The control cabinet includes a Fourier transform near-infrared spectrometer and an industrial computer. The Fourier transform near-infrared spectrometer is referred to as a spectrometer. Spectrometer: Responsible for the acquisition and preliminary processing of spectra; Industrial control computer: Connected to the spectrometer, used for data processing and analysis of spectral data, and integrating large AI models for sample information prediction; The optical fiber is used to connect the spectrometer and the flow cell. The connection between the spectrometer and the flow cell is made by two optical fibers. One optical fiber connects the sample light output end of the spectrometer to one end of the flow cell, and the other optical fiber transmits the light signal from the flow cell back to the input end of the spectrometer. The flow cell consists of two fiber optic probes and a flow channel, as detailed below: Fiber optic probe: Includes a fiber optic collimating lens, used for transmitting and receiving optical signals; Flow channel: Two sapphire windows are provided on the side wall to allow the collimated light from the fiber optic probe to pass through the flow cell and then be received by the fiber optic cable and transmitted to the spectrometer for analysis.

9. The device for online detection of electrolyte based on Fourier transform near-infrared spectroscopy according to claim 8, characterized in that, The spectrometer consists of a sample light module, an interferometer module, a self-test module, a colorimeter, a channel switching module, a detector, and a control module, as detailed below: Sample optical module: provides collimated beam and near-infrared spectrum for the spectrometer; Interference module: used for spectrum analysis, recording the spatial domain spectrum as light intensity information by the detector; Self-test module: Used for automatic calibration of the spectrometer to ensure instrument stability and accuracy; Colorimeter: Analyzes the visible light spectrum of the electrolyte to analyze colorimetric information; Channel switching module: Enables switching of light for different samples, supporting simultaneous detection of multiple samples; Detector and control module: Collects light intensity information and drives various motion mechanisms.

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