Lithium-ion battery recycled raw material composition analysis method and system based on XRF and XRD

Abstract

The present invention proposes a method and system for analyzing the composition of recycled raw materials of lithium-ion batteries based on XRF and XRD, which belongs to the field of lithium battery technology. First, a disassembled, crushed and sorted lithium-ion battery powder sample is obtained, and then the elemental composition of the lithium-ion battery powder sample is analyzed based on XRF, and the X-ray diffraction pattern of the lithium-ion battery powder sample is obtained based on XRD, and the X-ray diffraction pattern data is subjected to noise suppression processing; finally, based on the processed X-ray diffraction pattern, the phase structure of the lithium-ion battery powder sample is analyzed. The present invention uses a noise suppression method to process X-ray diffraction pattern data, solves problems such as the ambiguity of the position of continuous peaks in the pattern and the deviation of continuous peak values, and improves the accuracy of phase analysis; in addition, by setting a cyclic sliding window and controlling the attenuation speed of the Gaussian weight, the amount of repeated calculations of the noise suppression method is reduced, and the efficiency and processing speed of the processing method are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium batteries, and in particular relates to a method and system for analyzing the composition of recycled raw materials for lithium-ion batteries based on XRF and XRD. Background Art

[0002] The accelerated development of the lithium-ion battery industry has led to the rise of the waste lithium-ion battery recycling industry. Systematic research on the sources and composition of recycled raw materials from waste lithium-ion batteries will facilitate their rapid recycling and secondary use. The structure of a lithium-ion battery primarily consists of the outer casing, positive electrode, negative electrode, separator, and electrolyte, each accounting for approximately 26%, 4%, 15%, 30%, and 25% of the total battery volume. Recycled raw materials for waste lithium-ion batteries generally refer to materials recovered from the outer casing, positive electrode, negative electrode, separator, and electrolyte of waste lithium-ion batteries, primarily including valuable materials such as nickel, cobalt, and lithium.

[0003] Existing techniques typically use a combination of X-ray fluorescence spectrometry (XRF) and X-ray diffractometers (XRD) to analyze the composition of recycled raw materials from used lithium-ion batteries. First, fluorescent X-rays are irradiated onto a powdered sample (commonly referred to as black powder) obtained after disassembly, crushing, and sorting of lithium-ion batteries. Based on Moraes' theorem, knowing or measuring the wavelength of the fluorescent X-rays allows the identification of the elements in the sample (essentially any element with a content above 0.01%, with the exception of light elements with lower atomic numbers in the periodic table and other elements with lower fluorescence intensity). X-ray diffractometers (XRD) are then used to examine the sample's physical phase and analyze its primary components. However, in the process of using X-ray diffractometer (XRD) to detect sample phases, problems such as fuzzy positions of continuous peaks and continuous peak deviations in the detected X-ray diffraction patterns are often caused due to various reasons, such as poor crystallinity of the sample itself, high content of amorphous phase, strong X-ray absorption of certain components in the sample, low power of the X-ray generator in XRD, and design defects of the high-voltage transformer core and winding in XRD, which in turn affects the accuracy of sample phase detection. Summary of the Invention

[0004] In order to solve the above problems existing in the prior art, the present invention proposes a method and system for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD, which improves the accuracy of the composition analysis of lithium-ion battery recycled raw materials through image processing.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD, comprising the following steps:

[0006] S1: Obtain lithium-ion battery powder samples after disassembly, crushing and sorting;

[0007] S2: Analyzing the elemental composition of the lithium-ion battery powder sample based on XRF;

[0008] S3: obtaining an X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, and performing noise suppression on the X-ray diffraction pattern data;

[0009] S4: Based on the processed X-ray diffraction pattern, perform a phase structure analysis on the lithium-ion battery powder sample, and then obtain the type of the recovered lithium-ion battery according to the elemental composition and phase composition of the lithium-ion battery powder sample.

[0010] Furthermore, the X-ray diffraction pattern data is subjected to noise suppression processing in S3, which specifically includes the following steps: S31: acquiring X-ray diffraction pattern data; S32: initializing the parameters of the noise suppression processing method and allocating memory; S33: setting the first circular sliding window width, performing the first circular sliding window processing on the acquired X-ray diffraction pattern data, and calculating the signal difference cumulative sum matrix after the circular sliding window; S34: setting the second circular sliding window width, calculating the difference between the data points in the signal difference cumulative sum matrix based on the second circular sliding window width, and further calculating the Gaussian weight; S35: acquiring the X-ray diffraction pattern data after noise suppression based on the Gaussian weight.

[0011] Furthermore, the signal difference accumulation matrix after the circular sliding window in S33 is calculated according to the following formula:

[0012] ;

[0013] Wherein, SD represents the signal difference cumulative sum matrix, csum{} represents the cumulative sum function, SIG represents the acquired X-ray diffraction pattern data, i1 represents the first data index, i1=[W1+1, W1+2,…, N], i2 represents the second data index, i2=[1, 2,…, N-W1], W1 represents the width of the first cyclic sliding window, W1 is greater than 1 and cyclically decreases, the step value is -1, and N represents the total length of the acquired X-ray diffraction pattern data SIG.

[0014] Furthermore, the calculation of the difference between the data points in the signal difference accumulation matrix in S34 is specifically:

[0015] ;

[0016] Where DT represents the difference between the data points in the signal difference accumulation matrix SD, W2 represents the second cyclic sliding window width, i3 represents the third data index, i3=[W2+2, W2+3, ..., N-W2].

[0017] Furthermore, the Gaussian weight is calculated in S34, specifically:

[0018] ;

[0019] Where Wgt represents the Gaussian weight, DT represents the difference between the signal difference accumulation and the data points in the matrix SD, H represents the intermediate variable, k1 represents the first proportional coefficient, and W1 represents the width of the first cyclic sliding window.

[0020] Furthermore, the step of obtaining the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight in S35 is specifically as follows:

[0021] ;

[0022] Wherein DNOSIG() represents the X-ray diffraction pattern data after noise suppression, i3=[W2+2, W2+3,…, N-W2], Wgt represents the Gaussian weight, SIG represents the acquired X-ray diffraction pattern data, i4 represents the fourth data index, i4=[1, 2,…, N].

[0023] The present invention also provides a lithium-ion battery recycled raw material composition analysis system based on XRF and XRD, which is used to perform the above-mentioned lithium-ion battery recycled raw material composition analysis method based on XRF and XRD. It is characterized in that it includes a data acquisition module, an XRF analysis module, an XRD analysis module and a raw material composition analysis module. The input end of the data acquisition module is used to obtain lithium-ion battery powder sample data after disassembly, crushing and sorting. The output end of the data acquisition module is connected to the input end of the XRF analysis module and the input end of the XRD analysis module. The output end of the XRF analysis module and the output end of the XRD analysis module are connected to the input end of the raw material composition analysis module. The output end of the raw material composition analysis module outputs the lithium-ion battery recycled raw material composition analysis result.

[0024] Compared with the prior art, the beneficial technical effects of the present invention are: (1) the noise suppression method is used to process X-ray diffraction pattern data to solve the problems of position ambiguity and continuous peak deviation of the spectrum, thereby improving the accuracy of phase analysis; (2) by setting a cyclic sliding window and controlling the attenuation speed of the Gaussian weight, the amount of repeated calculations of the noise suppression method is reduced, thereby improving the efficiency and processing speed of the processing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0026] Figure 1 Flowchart of the method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD in the present invention;

[0027] Figure 2 This is a flow chart of noise suppression processing for X-ray diffraction pattern data in the present invention;

[0028] Figure 3 This is a diagram showing the analysis results of the X-ray diffraction pattern of a sample using the lithium-ion battery recycled raw material composition analysis method based on XRF and XRD of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] The following first describes the concepts involved in this application with reference to the accompanying drawings. It should be noted that the following description of each concept is intended only to make the content of this application easier to understand and does not limit the scope of protection of this application. At the same time, the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict. The following detailed description of this application will be made with reference to the accompanying drawings and in conjunction with the embodiments.

[0031] In conjunction with the instructions Figure 1-2The present invention proposes a composition analysis method for lithium-ion battery recycled raw materials based on XRF and XRD, comprising the following steps: S1: obtaining a lithium-ion battery powder sample after disassembly, crushing and sorting to obtain valuable metals. The positive electrode materials of lithium-ion batteries are typically lithium cobalt oxide, lithium manganese oxide, lithium nickel cobalt manganese oxide, lithium iron phosphate, etc., which are mixed with conductive agents and binders and coated on aluminum foil to form the positive electrode sheet. The negative electrode material is typically graphite or mesophase carbon microbeads (MCMB), which are mixed with conductive agents and binders and coated on copper foil to form the negative electrode sheet. The separator is generally made of polyolefin material and its function is to separate the positive and negative electrodes from each other while allowing lithium ions to diffuse and transport in the electrolyte. The separator has high resistivity and chemical resistance, which can effectively prevent internal short circuits in the battery and maintain battery safety and stability. The electrolyte is usually a mixture of organic carbonates and lithium salts. Its function is to provide lithium ion conduction channels and maintain the chemical and thermal stability of the electrolyte to prevent electrolyte decomposition and thermal runaway of the battery. It can be seen that lithium-ion batteries, especially nickel-cobalt-manganese ternary lithium batteries, contain large amounts of valuable metals such as nickel, cobalt, and lithium, and have good disassembly and recycling value. Therefore, the present invention can provide a reference for the subsequent acquisition of valuable metals through the analysis of the components of lithium-ion battery regeneration raw materials.

[0032] S2: Analyze the elemental composition of the lithium-ion battery powder sample based on XRF; for example, for a gray-black powder sample with uniform color, no odor, no agglomeration, and no obvious inclusions, semi-quantitative analysis using X-ray fluorescence spectroscopy shows that its elemental composition mainly includes 10.6% nickel, 49.3% cobalt, and 6% manganese. It can be concluded that the sample mainly contains nickel, cobalt, and manganese. For another yellow-green solid particle sample with no inclusions, agglomeration, easy to break agglomerates, uniform color, and no odor, semi-quantitative analysis using X-ray fluorescence spectroscopy shows that its elemental composition mainly includes 51.32% nickel oxide, 7.18% manganese oxide, and 3.04% magnesium oxide. It can be concluded that the sample mainly contains nickel, manganese, and magnesium. In this step, the elemental composition of the disassembled lithium-ion battery powder sample is analyzed based on XRF. The valuable metal element composition of the sample can be obtained based on the analysis results to determine its recycling value and serve as a verification of the physical structure detection.

[0033] S3: Obtain an X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, perform noise suppression on the X-ray diffraction pattern data, and then distinguish different lithium-ion battery types based on the X-ray diffraction pattern after noise suppression; specifically includes the following sub-steps: S31: Obtain X-ray diffraction pattern data; S32: Initialize parameters of the noise suppression processing method and allocate memory; the parameters mainly include a first proportional coefficient k1.

[0034] S33: Setting a first circular sliding window width, performing a first circular sliding window process on the acquired X-ray diffraction pattern data, and calculating a signal difference cumulative sum matrix after the circular sliding window; the signal difference cumulative sum matrix after the circular sliding window is calculated according to the following formula:

[0035] ;

[0036] Wherein, SD represents the signal difference cumulative sum matrix, csum{} represents the cumulative sum function, SIG represents the acquired X-ray diffraction pattern data, i1 represents the first data index, i1=[W1+1, W1+2, ..., N], i2 represents the second data index, i2=[1, 2, ..., N-W1], W1 represents the width of the first cyclic sliding window, W1 is greater than 1 and cyclically decreases with a step value of -1, and N represents the total length of the acquired X-ray diffraction pattern data SIG. In the present invention, the width of the first cyclic sliding window is preferably set to 600.

[0037] S34: Setting a second cyclic sliding window width, calculating the difference between the data points in the signal difference accumulation matrix based on the second cyclic sliding window width, and further calculating the Gaussian weight; calculating the difference between the data points in the signal difference accumulation matrix, specifically:

[0038] ;

[0039] Wherein, DT represents the difference between the data points in the signal difference accumulation matrix SD, W2 represents the width of the second circular sliding window, and i3 represents the third data index, i3=[W2+2, W2+3, ..., N-W2]. In the present invention, the width of the second circular sliding window is preferably set to 10.

[0040] The calculation of Gaussian weight is as follows:

[0041] ;

[0042] Wherein, Wgt represents the Gaussian weight, DT represents the difference between the data points in the signal difference accumulation matrix SD, H represents the intermediate variable, k1 represents the first proportional coefficient, and W1 represents the width of the first cyclic sliding window. In the present invention, the first proportional coefficient k1 is preferably set to 0.03.

[0043] S35: Obtaining noise-suppressed X-ray diffraction pattern data based on the Gaussian weights. Specifically:

[0044] ;

[0045] Wherein DNOSIG() represents the X-ray diffraction pattern data after noise suppression, i3=[W2+2, W2+3,…, N-W2], Wgt represents the Gaussian weight, SIG represents the acquired X-ray diffraction pattern data, i4 represents the fourth data index, i4=[1, 2,…, N].

[0046] S4: Based on the processed X-ray diffraction pattern, the phase structure of the lithium-ion battery powder sample is detected. Different elements or groups in the sample have unique diffraction characteristics. Even if their constituent elements are the same, as long as the structure is slightly different, the diffraction spectrum will show obvious differences in the number of diffraction peaks, angular position, relative intensity order and shape of the diffraction peaks. Therefore, based on the Bragg equation, the diffraction angle (2θ)-diffraction intensity curve of the sample is obtained through the X-ray diffraction pattern. By comparing the diffraction peaks of the two with the standard card, a certain phase structure contained in the sample can be confirmed, and then different types of lithium-ion batteries can be distinguished. For example, for a gray-black powder sample with uniform color, no odor, no lumps, and no obvious inclusions, X-ray diffraction analysis was used to measure that the main phases of the sample are LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 O2 and graphite, it can be determined that the sample comes from a mixture of lithium cobalt oxide batteries and nickel cobalt manganese ternary lithium batteries, and the graphite has not been effectively removed. For another sample, the main phase is LiNi 0.65 Co 0.25 Mn 0.1 O2, it can be determined that the sample comes from a nickel-cobalt-manganese ternary lithium battery and the graphite has been effectively removed. In this step, the physical phase of the lithium-ion battery powder sample is analyzed based on XRD. The physical phase composition of the valuable metal elements of the sample can be obtained according to the analysis results to determine the source of the sample, such as lithium iron phosphate battery, nickel-cobalt-manganese ternary lithium battery, lithium cobalt oxide battery, lithium manganese oxide battery, etc. At the same time, the composition of the valuable metal elements and the physical phase composition can also be verified with each other to ensure the accuracy of XRF and XRD analysis.

[0047] Through the above-mentioned XRF and XRD analysis of the present invention, the elemental composition and phase structure of the lithium-ion battery powder sample can be detected. The metal components in the sample are obtained by elemental composition detection, and the main phase of the sample is obtained by phase structure detection. The two are mutually verified to obtain the type of recycled lithium-ion battery, making necessary preparations for subsequent reuse.

[0048] The present invention also provides a lithium-ion battery recycled raw material composition analysis system based on XRF and XRD, which is used to perform the above-mentioned lithium-ion battery recycled raw material composition analysis method based on XRF and XRD. It is characterized in that it includes a data acquisition module, an XRF analysis module, an XRD analysis module and a raw material composition analysis module. The input end of the data acquisition module is used to obtain the disassembled lithium-ion battery powder sample data, the output end of the data acquisition module is connected to the input end of the XRF analysis module and the input end of the XRD analysis module, the output end of the XRF analysis module and the output end of the XRD analysis module are connected to the input end of the raw material composition analysis module, and the output end of the raw material composition analysis module outputs the lithium-ion battery recycled raw material composition analysis result.

[0049] In conjunction with the instructions Figure 3 The X-ray diffraction pattern of a sample analyzed using the XRF and XRD-based lithium-ion battery recycled raw material composition analysis method of the present invention is shown in the figure. The horizontal axis is the 2θ angle and the vertical axis is the intensity. As can be seen from the figure, the positions of the various high diffraction peaks are well restored and displayed, and several other lower diffraction peaks are relatively realistically retained, and the deviation of the continuous peaks is small. According to the X-ray diffraction pattern, it can be determined that the main phases of the sample are LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 The XRF and XRD-based lithium-ion battery regeneration raw material composition analysis method of the present invention solves the problems of fuzzy positions and continuous peak deviations of the spectrum caused by noise in the prior art, thereby improving the accuracy of phase analysis.

[0050] The embodiments and / or implementation methods described above are only used to illustrate the preferred embodiments and / or implementation methods for realizing the technology of the present invention, and do not impose any form of limitation on the implementation methods of the technology of the present invention. Any person skilled in the art may make slight changes or modifications to other equivalent embodiments without departing from the scope of the technical means disclosed in the content of the present invention, but they should still be regarded as technologies or embodiments that are essentially the same as the present invention.

[0051] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of this application, they can also make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for analyzing the composition of recycled raw materials for lithium-ion batteries based on XRF and XRD, characterized in that: The steps include: S1: Obtain lithium-ion battery powder samples after disassembly, crushing and sorting; S2: Analyzing the elemental composition of the lithium-ion battery powder sample based on XRF; S3: obtaining an X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, and performing noise suppression on the X-ray diffraction pattern data; S4: analyzing the phase structure of the lithium-ion battery powder sample based on the processed X-ray diffraction pattern, and then determining the type of the recovered lithium-ion battery according to the elemental composition and phase composition of the lithium-ion battery powder sample; The noise suppression process for the X-ray diffraction pattern data in S3 specifically includes the following steps: S31: Acquire X-ray diffraction pattern data; S32: Initialize the parameters of the noise suppression processing method and allocate memory; S33: setting a first circular sliding window width, performing a first circular sliding window process on the acquired X-ray diffraction pattern data, and calculating a signal difference cumulative sum matrix after the circular sliding window process; S34: Setting a second cyclic sliding window width, calculating differences between data points in the signal difference accumulation matrix based on the second cyclic sliding window width, and further calculating Gaussian weights; S35: Obtaining noise-suppressed X-ray diffraction pattern data based on the Gaussian weight.

2. The method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD according to claim 1, characterized in that: In the S2, the valuable metal element composition of the lithium-ion battery powder sample is obtained based on XRF.

3. The method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD according to claim 1, characterized in that: The signal difference accumulation matrix after the circular sliding window in S33 is calculated according to the following formula: ； Wherein, SD represents the signal difference cumulative sum matrix, csum{} represents the cumulative sum function, SIG represents the acquired X-ray diffraction pattern data, i1 represents the first data index, i1=[W1+1, W1+2,…, N], i2 represents the second data index, i2=[1, 2,…, N-W1], W1 represents the width of the first cyclic sliding window, W1 is greater than 1 and cyclically decreases, the step value is -1, and N represents the total length of the acquired X-ray diffraction pattern data SIG.

4. The method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD according to claim 3, characterized in that: The calculation in S34 of the difference between the accumulated signal and the data points in the matrix is ​​specifically: ； Wherein, DT represents the difference between the data points in the signal difference accumulation matrix SD, W2 represents the second cyclic sliding window width, i3 represents the third data index, i3=[W2+2, W2+3, ..., N-W2].

5. The method for analyzing the composition of lithium-ion battery recycled raw materials based on XRF and XRD according to claim 4, characterized in that: The Gaussian weight is calculated in S34, specifically: ； Wherein, Wgt represents the Gaussian weight, DT represents the difference between the data points in the signal difference accumulation matrix SD, H represents the intermediate variable, k1 represents the first proportional coefficient, and W1 represents the width of the first cyclic sliding window.

6. The method for analyzing components of lithium-ion battery recycled raw materials based on XRF and XRD according to claim 5, characterized in that: The step S35 of obtaining the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight is specifically as follows: ； Among them, DNOSIG() represents the X-ray diffraction pattern data after noise suppression, i3=[W2+2, W2+3,…, N-W2], Wgt represents the Gaussian weight, SIG represents the acquired X-ray diffraction pattern data, i4 represents the fourth data index, i4=[1, 2,…, N].

7. A lithium-ion battery recycled raw material composition analysis system based on XRF and XRD, used to perform the lithium-ion battery recycled raw material composition analysis method based on XRF and XRD according to any one of claims 1 to 6, characterized in that: The system comprises a data acquisition module, an XRF analysis module, an XRD analysis module and a raw material composition analysis module. The input end of the data acquisition module is used to acquire sample data of lithium-ion battery powder after disassembly, crushing and sorting. The output end of the data acquisition module is connected to the input end of the XRF analysis module and the input end of the XRD analysis module. The output end of the XRF analysis module and the output end of the XRD analysis module are connected to the input end of the raw material composition analysis module. The output end of the raw material composition analysis module outputs the analysis results of the composition of the lithium-ion battery recycled raw materials.

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