Lithium ion battery regenerated raw material component analysis method and system based on XRF and XRD

Abstract

The invention provides a lithium ion battery regenerated raw material component analysis method and system based on XRF and XRD, and belongs to the technical field of lithium batteries. The method comprises the following steps: firstly, obtaining a disassembled, crushed and sorted lithium ion battery powder sample, then analyzing the elemental composition of the lithium ion battery powder sample based on XRF, obtaining an X-ray diffraction pattern of the lithium ion battery powder sample based on XRD, and carrying out noise suppression treatment on X-ray diffraction pattern data; and finally, based on the processed X-ray diffraction pattern, analyzing the phase structure of the lithium ion battery powder sample. According to the method, X-ray diffraction pattern data is processed by utilizing a noise suppression method, so that the problems of fuzzy positions of continuous peaks of the pattern, continuous peak value deviation and the like are solved, and the accuracy of phase analysis is improved; in addition, through the arrangement of the circulating sliding window and the control of the attenuation speed of the Gaussian weight, the repeated calculation amount of the noise suppression method is reduced, and the efficiency and the 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 of lithium-ion batteries based on XRF and XRD. Background Art

[0002] The accelerated development of the lithium-ion battery industry has driven the rise of the recycled lithium-ion battery recycling industry. Systematic research on the sources and compositions of recycled raw materials of waste lithium-ion batteries is beneficial to the rapid recycling and secondary utilization of waste lithium-ion batteries. The structure of a lithium-ion battery mainly includes a housing, a positive electrode, a negative electrode, a separator, an electrolyte, etc., and the proportion of each part is about 26%, 4%, 15%, 30% and 25% respectively. The recycled raw materials of waste lithium-ion batteries generally refer to the raw materials recovered from the housing, positive electrode, negative electrode, separator, electrolyte, etc. of waste lithium-ion batteries, and mainly include valuable materials such as nickel, cobalt, and lithium.

[0003] In the prior art, X-ray fluorescence spectrometers (XRF) and X-ray diffractometers (XRD) are usually combined to analyze the composition of recycled raw materials of waste lithium-ion batteries. First, fluorescent X-rays are irradiated on the powdered sample obtained after disassembling, crushing and sorting the lithium-ion battery (generally called black powder). Based on Moseley's theorem, as long as the wavelength of the fluorescent X-rays is known or measured, the types of elements in the sample can be analyzed (elements with a content of more than 0.01% can basically be analyzed, except for light elements with a small atomic number in the periodic table and other elements with low fluorescence radiation intensity); then, an X-ray diffractometer (XRD) is used to detect the sample phase and analyze the main components in the sample. However, during the process of using an X-ray diffractometer (XRD) to detect the sample phase, due to various reasons such as poor crystallinity of the sample itself, high non-crystalline phase content, strong X-ray absorption of certain components in the sample, low power of the X-ray generator in the XRD, and design defects of the magnetic core and winding of the high-voltage transformer in the XRD, problems such as blurred positions of the continuous peaks and deviations of the continuous peak values in the detected X-ray diffraction pattern will occur, thereby affecting the accuracy of the 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 recycled raw materials of lithium-ion batteries based on XRF and XRD, and improves the accuracy of analyzing the composition of recycled raw materials of lithium-ion batteries through image processing.

[0005] To achieve the above object, the present invention adopts the following technical solutions: A method for analyzing the composition of recycled raw materials of lithium-ion batteries based on XRF and XRD includes the following steps: S1: Obtain the powdered sample of the lithium-ion battery after disassembly, crushing and sorting; S2: Analyze the elemental composition of the lithium-ion battery powder sample based on XRF; S3: Obtain the X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, and perform noise suppression processing on the X-ray diffraction pattern data; S4: Based on the processed X-ray diffraction pattern, perform phase structure analysis of the lithium-ion battery powder sample, and then obtain the type of the recycled lithium-ion battery according to the elemental composition and phase composition of the lithium-ion battery powder sample.

[0006] Further, the noise suppression processing of the X-ray diffraction pattern data in S3 specifically includes the following steps: S31: Obtain the X-ray diffraction pattern data; S32: Initialize the parameters of the noise suppression processing method and allocate memory; S33: Set the first cyclic sliding window width, perform the first cyclic sliding window processing on the obtained X-ray diffraction pattern data, and calculate the cumulative sum matrix of the signal differences after the cyclic sliding window; S34: Set the second cyclic sliding window width, calculate the differences between the data points in the cumulative sum matrix of the signal differences based on the second cyclic sliding window width, and further calculate the Gaussian weight; S35: Obtain the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight.

[0007] Further, the cumulative sum matrix of the signal differences after the cyclic sliding window in S33 is calculated according to the following formula: ; where SD represents the cumulative sum matrix of the signal differences, csum{} represents the cumulative sum function, SIG represents the obtained 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 decreases cyclically with a step value of -1, and N represents the total length of the obtained X-ray diffraction pattern data SIG.

[0008] Further, the calculation of the differences between the data points in the cumulative sum matrix of the signal differences in S34 is specifically: ; where DT represents the differences between the data points in the cumulative sum matrix SD of the signal differences, W2 represents the width of the second cyclic sliding window, and i3 represents the third data index, i3 = [W2 + 2, W2 + 3,..., N - W2].

[0009] Further, the calculation of the Gaussian weight in S34 is specifically: ; Where Wgt represents the Gaussian weight, DT represents the difference between data points in the signal difference accumulation sum matrix SD, H represents an intermediate variable, k1 represents a first proportionality coefficient, and W1 represents the width of the first circular sliding window.

[0010] Further, obtaining the denoised X-ray diffraction pattern data based on the Gaussian weight in S35 is specifically as follows: ; Where DNOSIG() represents the denoised X-ray diffraction pattern data, i3 = [W2 + 2, W2 + 3,..., N - W2], Wgt represents the Gaussian weight, SIG represents the obtained X-ray diffraction pattern data, and i4 represents the fourth data index, i4 = [1, 2,..., N].

[0011] The present invention also provides an analysis system for the components of the regenerated raw materials of lithium-ion batteries based on XRF and XRD, which is used to execute the above-mentioned method for analyzing the components of the regenerated raw materials of lithium-ion batteries based on XRF and XRD. It is characterized by including a data acquisition module, an XRF analysis module, an XRD analysis module, and a raw material component analysis module. The input end of the data acquisition module is used to acquire the data of the lithium-ion battery powder sample after disassembly, crushing, and sorting. The output end of the data acquisition module is connected to the input ends of the XRF analysis module and the XRD analysis module. The output ends of the XRF analysis module and the XRD analysis module are connected to the input end of the raw material component analysis module. The output end of the raw material component analysis module outputs the analysis result of the components of the regenerated raw materials of lithium-ion batteries.

[0012] The beneficial technical effects of the present invention compared with the prior art are as follows: (1) Using the denoising method to process the X-ray diffraction pattern data, solving problems such as the position ambiguity of continuous peaks and the deviation of continuous peak values in the pattern, and improving the accuracy of phase analysis; (2) By setting the circular sliding window and controlling the attenuation speed of the Gaussian weight, reducing the repeated calculation amount of the denoising method, and improving the efficiency and processing speed of the processing method. Description of the Drawings

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other implementation drawings can be obtained by extending according to the provided drawings without creative efforts.

[0014] Figure 1 It is a flowchart of the method for analyzing the components of the regenerated raw materials of lithium-ion batteries based on XRF and XRD in the present invention; Figure 2It is the flowchart for noise suppression processing of X-ray diffraction pattern data in the present invention; Figure 3 It is the analysis result diagram of the X-ray diffraction pattern of a certain sample by using the method for analyzing the components of the recycled raw materials of lithium-ion batteries based on XRF and XRD of the present invention. Specific embodiments

[0015] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0016] First, the concepts involved in the present application will be described in conjunction with the accompanying drawings. It should be noted here that the descriptions of the following concepts are only for making the content of the present application easier to understand, and do not represent the limitation of the protection scope of the present application; at the same time, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0017] Combined with the specification appendix Figure 1-2 The present invention provides a method for analyzing the components of the recycled raw materials of lithium-ion batteries based on XRF and XRD, including the following steps: S1: Obtain the powder sample of the lithium-ion battery after disassembly, crushing and sorting to obtain valuable metals. The positive electrode material of the lithium-ion battery usually adopts lithium cobaltate, lithium manganate, lithium nickel cobalt manganate, lithium iron phosphate, etc., which are mixed with conductive agents and binders, etc., and coated on the aluminum foil to form the positive electrode sheet; the negative electrode material usually adopts graphite or mesocarbon microbeads (MCMB), which are mixed with conductive agents and binders, etc., and coated on the copper foil to form the negative electrode sheet; the separator is generally made of polyolefin materials, and its function is to isolate the positive and negative electrodes from each other, and at the same time allow lithium ions to diffuse and transport in the electrolyte; the separator has a high resistivity and chemical corrosion resistance, and can effectively prevent internal short circuit of the battery and maintain the safety and stability of the battery; the electrolyte usually adopts a mixture of organic carbonates and lithium salts, and its function is to provide a lithium ion conduction channel and maintain the chemical stability and thermal stability of the electrolyte to prevent the decomposition of the electrolyte and the thermal runaway phenomenon of the battery; it can be seen that lithium-ion batteries, especially nickel-cobalt-manganese ternary lithium batteries, contain a large amount of valuable metals such as nickel, cobalt, and lithium, and have good disassembly and recycling value. Therefore, through the analysis of the components of the recycled raw materials of lithium-ion batteries, the present invention can provide a reference for subsequent obtaining valuable metals.

[0018] S2: Analyze the elemental composition of the lithium-ion battery powder sample based on XRF. For example, for a grayish-black powder sample with uniform color, no peculiar smell, no caking, and no obvious inclusions, semi-quantitative analysis by X-ray fluorescence spectroscopy shows that its elemental composition mainly includes nickel with a content of 10.6%, cobalt with a content of 49.3%, and manganese with a content of 6%. Then it can be determined that the sample mainly contains elements such as nickel, cobalt, and manganese. For another yellowish-green solid particle sample with no inclusions, agglomeration phenomenon, easy-to-break agglomerates, uniform color, and no peculiar smell, semi-quantitative analysis by X-ray fluorescence spectroscopy shows that the elemental composition mainly includes nickel oxide with a content of 51.32%, manganese oxide with a content of 7.18%, and magnesium oxide with a content of 3.04%. Then it can be determined that the sample mainly contains elements such as nickel, manganese, and magnesium. In this step, analyzing the elemental composition of the disassembled lithium-ion battery powder sample based on XRF can obtain the valuable metal element components of the sample according to the analysis results, determine the recycling value, and at the same time serve as a verification for the phase structure detection.

[0019] S3: Obtain the X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, perform noise suppression processing on the X-ray diffraction pattern data, and then distinguish different types of lithium-ion batteries according to the noise-suppressed X-ray diffraction pattern. Specifically, it includes the following sub-steps: S31: Obtain the X-ray diffraction pattern data; S32: Initialize the parameters of the noise suppression processing method and allocate memory; the parameters mainly include the first proportionality coefficient k1.

[0020] S33: Set the width of the first circular sliding window, perform the first circular sliding window processing on the obtained X-ray diffraction pattern data, and calculate the cumulative sum matrix of the signal differences after circular sliding window; the cumulative sum matrix of the signal differences after circular sliding window is calculated according to the following formula: ; where SD represents the cumulative sum matrix of the signal differences, csum{} represents the cumulative sum function, SIG represents the obtained 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 circular sliding window, W1 is greater than 1 and decreases cyclically with a step value of -1, and N represents the total length of the obtained X-ray diffraction pattern data SIG. In the present invention, the width of the first circular sliding window is preferably set to 600.

[0021] S34: Set the width of the second circular sliding window, calculate the differences between the data points in the cumulative sum matrix of the signal differences based on the width of the second circular sliding window, and further calculate the Gaussian weights; calculating the differences between the data points in the cumulative sum matrix of the signal differences is specifically: ; Among them, DT represents the difference between data points in the signal difference accumulation sum matrix SD, W2 represents the width of the second cyclic sliding window, i3 represents the third data index, and i3 = [W2 + 2, W2 + 3, ……, N - W2]. In the present invention, the width of the second cyclic sliding window is preferably set to 10.

[0022] Specifically, calculating the Gaussian weight is as follows: ; Among them, Wgt represents the Gaussian weight, DT represents the difference between data points in the signal difference accumulation sum matrix SD, H represents an intermediate variable, k1 represents the first proportionality coefficient, and W1 represents the width of the first cyclic sliding window. In the present invention, the first proportionality coefficient k1 is preferably set to 0.03.

[0023] S35: Obtain the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight. Specifically: ; Among them, DNOSIG() represents the noise-suppressed X-ray diffraction pattern data, i3 = [W2 + 2, W2 + 3, ……, N - W2], Wgt represents the Gaussian weight, SIG represents the obtained X-ray diffraction pattern data, and i4 represents the fourth data index, i4 = [1, 2, ……, N].

[0024] S4: Based on the processed X-ray diffraction pattern, perform the detection of the phase structure of the lithium-ion battery powder sample. Different elements or groups in the sample have unique diffraction characteristics. Even if they have the same constituent elements, as long as the structure is slightly different, their diffraction patterns will show obvious differences in the number of diffraction peaks, angular positions, relative intensity order, and the shape of the diffraction peaks. Therefore, based on Bragg's equation, by obtaining the diffraction angle (2θ)-diffraction intensity curve of the sample from the X-ray diffraction pattern and comparing the diffraction peaks of the two with the standard card, the phase structure contained in the sample can be confirmed, and then different types of lithium-ion batteries can be distinguished. For example, for a grayish-black powder sample that is uniform in color, odorless, without lumps, and without obvious inclusions, by using X-ray diffraction analysis, the main phases of the sample are measured to be LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 O2 and graphite, then it can be determined that this sample is from a mixture of lithium cobalt oxide batteries and lithium nickel cobalt manganese ternary lithium batteries, and the graphite has not been effectively removed. For another sample, the main phases are LiNi 0.65 Co 0.25 Mn 0.1If there is O2, it can be determined that the sample is from a lithium nickel cobalt manganese ternary lithium battery and the graphite has been effectively removed. In this step, by analyzing the phase of the lithium-ion battery powder sample based on XRD, the phase composition of the valuable metal elements in the sample can be obtained according to the analysis results, and the source of the sample can be determined, such as lithium iron phosphate battery, lithium 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 phase composition can also be mutually verified to ensure the accuracy of the XRF and XRD analyses. Through the above XRF and XRD analyses 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 can be obtained through elemental composition detection, and the main phases of the sample can be obtained through phase structure detection. The two are mutually verified to obtain the type of the recycled lithium-ion battery, making necessary preparations for subsequent reuse.

[0025] The present invention also provides a lithium-ion battery recycled raw material composition analysis system based on XRF and XRD, which is used to execute 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 acquire the data of the disassembled lithium-ion battery powder sample. The output end of the data acquisition module is connected to the input ends of the XRF analysis module and the XRD analysis module. The output ends of the XRF analysis module and 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 result of the lithium-ion battery recycled raw material composition.

[0026] Combined with the specification appendix Figure 3 , the analysis result diagram of the X-ray diffraction pattern of a certain sample using the lithium-ion battery recycled raw material composition analysis method based on XRF and XRD of the present invention is shown. The abscissa is the 2θ angle, and the ordinate is the intensity. It can be seen from the figure that the positions of each high diffraction peak are well restored and displayed, and several other lower diffraction peaks are also relatively realistically retained, and the deviation of the continuous peaks is small. According to the X-ray diffraction pattern, the main phases of the sample are LiCoO2, LiNi 0.333 Co 0.333 Mn 0.333 O2 and graphite. The lithium-ion battery recycled raw material composition analysis method based on XRF and XRD of the present invention solves the problems of the blurred position of the continuous peaks of the spectrum and the deviation of the continuous peak values caused by noise in the prior art, and improves the accuracy of phase analysis.

[0027] The above-described embodiments and / or implementation manners are merely used to illustrate the preferred embodiments and / or implementation manners for implementing the technology of the present invention, and do not impose any formal restrictions on the implementation manners of the technology of the present invention. Any person skilled in the art, without departing from the scope of the technical means disclosed in the content of the present invention, may make some modifications or changes to other equivalent embodiments, but should still be regarded as the same technology or embodiment as the present invention in essence.

[0028] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application. The above is only the preferred implementation manner of the present application. It should be noted that due to the limited nature of written expression and the objectively infinite specific structures, for those of ordinary skill in the art, without departing from the principles of the present application, several improvements, refinements or changes can be made, or the above technical features can be combined in an appropriate manner; these improvements, refinements, changes or combinations, or directly applying the concept and technical solution of the invention to other occasions without improvement, should all be regarded as the protection scope of the present application.

Claims

1. A method for analyzing the components of recycled raw materials for lithium-ion batteries based on XRF and XRD, characterized in that, It includes the following steps: S1: Obtain the powder sample of the lithium-ion battery after disassembly, crushing and sorting; S2: Analyze the elemental composition of the lithium-ion battery powder sample based on XRF; S3: Obtain the X-ray diffraction pattern of the lithium-ion battery powder sample based on XRD, and perform noise suppression processing on the X-ray diffraction pattern data; S4: Based on the processed X-ray diffraction pattern, analyze the phase structure of the lithium-ion battery powder sample, and then obtain the type of the recycled lithium-ion battery according to the elemental composition and phase composition of the lithium-ion battery powder sample.

2. The method for analyzing the components of the recycled raw materials of the lithium-ion battery based on XRF and XRD according to claim 1, wherein In S2, obtain the valuable metal element composition of the lithium-ion battery powder sample based on XRF.

3. The method for analyzing the composition of recycled raw materials for lithium-ion batteries based on XRF and XRD according to claim 1, wherein, In S3, the noise suppression processing of the X-ray diffraction pattern data specifically includes the following steps: S31: Obtain the X-ray diffraction pattern data; S32: Initialize the parameters of the noise suppression processing method and allocate memory; S33: Set the width of the first circular sliding window, perform the first circular sliding window processing on the obtained X-ray diffraction pattern data, and calculate the cumulative sum matrix of the signal differences after the circular sliding window; S34: Set the width of the second circular sliding window, calculate the differences between the data points in the cumulative sum matrix of the signal differences based on the width of the second circular sliding window, and further calculate the Gaussian weight; S35: Obtain the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight.

4. The method for analyzing the components of the recycled raw materials of a lithium-ion battery based on XRF and XRD according to claim 3, characterized in that, The cumulative sum matrix of the signal differences after the circular sliding window in S33 is calculated according to the following formula: ； where SD represents the cumulative sum matrix of the signal differences, csum{} represents the cumulative sum function, SIG represents the obtained 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 circular sliding window, W1 is greater than 1 and decreases cyclically with a step value of -1, and N represents the total length of the obtained X-ray diffraction pattern data SIG.

5. The method for analyzing the composition of the regenerated raw materials of the lithium-ion battery based on XRF and XRD according to claim 4, wherein, In S34, calculating the differences between the data points in the cumulative sum matrix of the signal differences is specifically: ； where DT represents the differences between the data points in the cumulative sum matrix SD of the signal differences, W2 represents the width of the second circular sliding window, and i3 represents the third data index, i3 = [W2 + 2, W2 + 3,..., N - W2].

6. The method for analyzing the components of the recycled raw materials of lithium-ion batteries based on XRF and XRD according to claim 5, wherein In S34, calculating the Gaussian weight is specifically: ； where Wgt represents the Gaussian weight, DT represents the differences between the data points in the cumulative sum matrix SD of the signal differences, H represents an intermediate variable, k1 represents the first proportionality coefficient, and W1 represents the width of the first circular sliding window.

7. The method for analyzing the composition of the recycled raw materials of a lithium-ion battery based on XRF and XRD according to claim 6, characterized in that, In S35, obtaining the noise-suppressed X-ray diffraction pattern data based on the Gaussian weight is specifically: ； where DNOSIG() represents the noise-suppressed X-ray diffraction pattern data, i3 = [W2 + 2, W2 + 3,..., N - W2], Wgt represents the Gaussian weight, SIG represents the obtained X-ray diffraction pattern data, and i4 represents the fourth data index, i4 = [1, 2,..., N].

8. An XRF- and XRD-based component analysis system for lithium-ion battery recycling raw materials, which is used to perform the XRF- and XRD-based component analysis method for lithium-ion battery recycling raw materials according to any one of claims 1-7, 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 acquire the data of the lithium-ion battery powder sample 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 result of the lithium-ion battery recycled raw material composition.

Images