Method for detecting homogeneity of dry mixing and related apparatus

Abstract

The embodiment of the application discloses a dry mixing uniformity detection method and related equipment. The method comprises the following steps: determining a spectrum signal of dry mixing; determining a first characteristic signal and a second characteristic signal based on the spectrum signal; determining a concentration ratio of the dry mixing according to the first characteristic signal and the second characteristic signal; and determining the uniformity of the dry mixing based on a plurality of concentration ratios of the dry mixing in the mixing process. The application can realize real-time evaluation of the fiberization uniformity of the dry mixing.

Description

Technical Field

[0001] This application belongs to the field of batteries and relates to dry electrode preparation technology, and in particular to a method and related equipment for detecting the uniformity of dry mixing. Background Technology

[0002] In dry electrode fabrication technology, the uniform dispersion of active materials, conductive agents, and binders is a key factor determining electrode consistency and battery electrochemical performance. However, because the dry mixing and fiberization process involves the dry mixing of multiple materials and high shear forces, the distribution and degree of fiberization of materials in the mixing chamber change in real time. Related technologies lack effective online monitoring methods to assess the uniformity of fiberization in real time. Summary of the Invention

[0003] This application provides a method and related equipment for detecting the uniformity of dry-mixed materials, in order to improve the poor real-time evaluation effect of the fiberization uniformity of dry-mixed materials.

[0004] In a first aspect, embodiments of this application provide a method for detecting the uniformity of dry mixing, the method comprising: determining the spectral signal of the dry mixing; determining a first characteristic signal and a second characteristic signal based on the spectral signal; determining the concentration ratio of the dry mixing based on the first characteristic signal and the second characteristic signal; and determining the uniformity of the dry mixing based on multiple concentration ratios of the dry mixing during the mixing process.

[0005] In some embodiments, the first characteristic signal includes the carbon-fluorine bond vibration peak of the binder in the dry mixture within a first wavenumber range, and the second characteristic signal includes the phosphorus-oxygen bond vibration peak of the active substance in the dry mixture within a second wavenumber range.

[0006] In some embodiments, determining the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal includes: determining the first signal intensity corresponding to the first characteristic signal and the second signal intensity corresponding to the second characteristic signal; and determining the concentration ratio based on the first signal intensity and the second signal intensity.

[0007] In some embodiments, the first signal intensity includes at least one of the vibration peak height and the vibration peak area, and the second signal intensity includes at least one of the vibration peak height and the vibration peak area.

[0008] In some embodiments, determining the uniformity of dry mixing based on multiple concentration ratios during the mixing process includes: determining the dispersion of dry mixing based on multiple concentration ratios; and determining uniformity as achieved when the dispersion is less than or equal to a dispersion threshold.

[0009] In some embodiments, determining the dispersion of a dry mixture based on multiple concentration ratios includes: determining a relative standard deviation based on multiple concentration ratios; and determining the dispersion of the dry mixture based on the relative standard deviation.

[0010] In some embodiments, after acquiring the spectral signal of the dry mixture, the method further includes: preprocessing the spectral signal, the preprocessing including at least baseline correction and background subtraction, the baseline correction representing baseline drift correction of the spectral signal, and the background subtraction representing subtraction of tilted background absorption in the spectral signal introduced by the conductive agent in the dry mixture.

[0011] In some embodiments, the method further includes: if the uniformity indicator indicates that the dry mixture is not yet uniform, continuing to perform the mixing operation on the dry mixture until the uniformity indicator indicates that the dry mixture is uniform, and then stopping the continued mixing operation on the dry mixture.

[0012] Secondly, embodiments of this application provide a uniformity detection system for dry mixing. The system includes: a mixing device configured to prepare a dry mixture; a spectral detection device configured to acquire spectral signals of the dry mixture; and an electronic device communicatively coupled to the spectral detection device, including a memory and one or more processors, wherein the one or more processors are configured to: determine the spectral signals of the dry mixture; determine a first characteristic signal and a second characteristic signal based on the spectral signals; determine the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal; and determine the uniformity of the dry mixture based on multiple concentration ratios of the dry mixture during the mixing process.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory; one or more processors; and one or more programs, the one or more programs being stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing a method for detecting the uniformity of dry mixing according to any one of the foregoing.

[0014] In the dry mixing uniformity detection method provided in this application embodiment, the spectral signal of the dry mixing is determined; based on the spectral signal, a first characteristic signal and a second characteristic signal are determined; based on the first characteristic signal and the second characteristic signal, the concentration ratio of the dry mixing is determined; and based on multiple concentration ratios of the dry mixing during the mixing process, the uniformity of the dry mixing is determined. Using this application, the fiberization uniformity of dry mixing can be evaluated in real time. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a structural diagram of a dry mixing uniformity detection system provided in an exemplary embodiment of this application.

[0017] Figure 2 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application.

[0018] Figure 3 This is a flowchart of a method for detecting the uniformity of dry mixing provided by an exemplary embodiment of this application.

[0019] Figure 4 This is a flowchart illustrating the determination of concentration ratios provided in an exemplary embodiment of this application.

[0020] Figure 5 This is a schematic diagram of the infrared spectrum of polytetrafluoroethylene (PTFE) provided in an exemplary embodiment of this application.

[0021] Figure 6 This is an infrared spectrum diagram of dry mixing provided in an exemplary embodiment of this application.

[0022] Figure 7 This is a graph showing the change of concentration ratio over time during the mixing process, as provided in the exemplary embodiment of this application.

[0023] Figure 8 This is a graph showing the trend of the relative standard deviation of the concentration ratio within different mixing time windows provided in the exemplary embodiments of this application.

[0024] Figure 9 This is a final mixing scanning electron microscope (SEM) image provided by an exemplary embodiment of this application.

[0025] Figure 10 This is a schematic diagram of the structure of a dry mixing uniformity detection device provided in an exemplary embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application's specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, "at least one" means one or more. "More than one" means two or more. For example, at least one of a, b, or c can represent seven cases: a, b, c, a and b, a and c, b and c, and a, b, and c.

[0029] With the global push for energy conservation and emission reduction, lithium-ion batteries, due to their environmentally friendly characteristics, have been widely used in energy storage systems, electric vehicles, and other fields. Traditional battery electrode manufacturing methods primarily utilize wet processes, which involve dispersing active materials, conductive agents, and binders in a solvent to obtain a powder slurry, followed by coating and drying processes to obtain the finished electrode. However, this wet process has the following technical drawbacks: firstly, high energy consumption during the drying process; secondly, the solvents used... It is usually toxic and unfriendly to operators and the environment; third, the solvent recovery system requires a large investment and occupies a large area; fourth, this method is difficult to prepare thick electrodes, and the obtained thick electrodes are prone to cracking, delamination and insufficient flexibility.

[0030] To avoid the aforementioned problems, dry electrode technology has emerged. This technology forms a self-supporting film by mixing active materials, conductive agents, and binders without using solvents, and then using shear force to induce fibrosis in the binder. Compared to wet processes, dry electrode technology promises to solve problems such as high cost, environmental unfriendliness, and difficulty in fabricating high-quality, thick-loaded electrodes.

[0031] However, in the dry electrode fabrication process, the uniform dispersion of active materials, conductive agents, and binders is a key factor determining electrode consistency and battery electrochemical performance. Because the dry mixing and fiberization processes involve the dry mixing of multiple materials under high shear forces, the distribution and degree of fiberization within the mixing chamber change in real time. However, related technologies lack effective online monitoring methods, making it impossible to assess the uniformity of fiberization in real time. This problem restricts the mass application and quality inspection of dry electrode technology in actual production.

[0032] In some examples, the microstructure of the dry blend is characterized using scanning electron microscopy (SEM) to observe the uniformity of fiberization. In other examples, the dry blend is rolled into a film using a roll-forming areal density measurement method, and the areal density distribution of the film is used to indirectly determine the uniformity of fiberization. Furthermore, a powder rheometer shear test can be used to evaluate the uniformity of fiberization by measuring the flow behavior and torque changes of the dry blend at different shear rates.

[0033] However, the methods described in the examples above are all offline detection methods, which have problems such as detection lag, cumbersome operation, and poor sample representativeness, making it difficult to meet the actual needs of online, efficient, and accurate detection of fiber uniformity at the front end of dry mixing.

[0034] In other examples, infrared spectroscopy is used to determine the uniformity of continuous mixtures. The core principle is to characterize the overall mixing uniformity of the material by calculating the standard deviation and average value of the absorbance at each wavelength point.

[0035] However, the method described in the above examples has the following limitations: First, the judgment index lacks material specificity and cannot accurately identify which component is unevenly distributed; second, the method is mainly applied to semi-solid or high-viscosity slurry systems, which differ from the material state of dry electrode powder mixing systems; third, it lacks a real-time judgment mechanism for batch-type high-speed mixing processes of dry electrodes.

[0036] In view of the above problems, embodiments of this application provide a method for detecting the uniformity of dry mixing, which involves determining the spectral signal of the dry mixture; determining a first characteristic signal and a second characteristic signal based on the spectral signal; determining the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal; and determining the uniformity of the dry mixture based on multiple concentration ratios during the mixing process. Using this application, the uniformity of binder fiber dispersion during the dry electrode mixing process can be specifically and quantitatively monitored online.

[0037] Some embodiments will now be described with reference to the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0038] Combination Figure 1 This application provides a structural diagram of a dry mixing uniformity detection system according to an exemplary embodiment. The dry mixing uniformity detection system 1 includes a mixing device 10, a spectral detection device 20, and an electronic device 30. The spectral detection device 20 is located at the viewing window of the mixing device 10, and the spectral detection device 20 and the electronic device 30 are communicatively coupled. The functions of each device in the dry mixing uniformity detection system 1 are described below.

[0039] The mixing equipment 10 is configured to prepare a dry mixture. Exemplarily, in the fiberization process of the dry electrode, the active material and the conductive agent are first placed in the mixing equipment 10 for premixing, and then the binder is added to the premixed electrode mixture to obtain a dry mixture. The mixing equipment 10 performs high-speed shear dispersion on the dry mixture to promote the fiberization of the binder.

[0040] The spectral detection device 20 is configured to acquire the spectral signal of the dry mixture and transmit the spectral signal to the electronic device 30. The spectral detection device 20 may include spectral acquisition devices such as infrared probes, near-infrared fiber optic probes, or hyperspectral imaging systems, for acquiring the spectral signal of the dry mixture in real time in a non-contact manner.

[0041] Electronic device 30 includes, but is not limited to, any electronic product with high computing power and capable of data processing, such as a personal computer or tablet computer. Electronic device 30 is configured to: determine the spectral signal of the dry mixture; determine a first characteristic signal and a second characteristic signal based on the spectral signal; determine the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal; and determine the uniformity of the dry mixture based on multiple concentration ratios during the mixing process.

[0042] In the dry mixing uniformity detection system 1 provided in the embodiments of this application, a mixing device 10 is used to prepare a dry mixture, a spectral detection device 20 is used to collect the spectral signal of the dry mixture, and an electronic device 30 is used to analyze the spectral signal to determine the fiberization uniformity of the dry mixture. Through the cooperation of the above three devices, the fiberization uniformity of the dry mixture can be evaluated in real time.

[0043] Please see Figure 2 , Figure 2This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. The electronic device 30 includes a communication unit 301, a memory 302, a processor 303, an input / output (I / O) interface 304, and a bus 305. The processor 303 is coupled to the communication unit 301, the memory 302, and the input / output interface 304 via the bus 305.

[0044] In some embodiments, the communication unit 301 may include a wired communication module and / or a wireless communication module. The communication unit 301 is used to realize data interaction between the various components within the electronic device 30, and to realize information transmission and reception between the electronic device 30 and external devices. For example, the communication unit 301 is used to receive spectral signals transmitted by the spectral detection device 20 and transmit the spectral signals to the processor 303.

[0045] In some embodiments, memory 302 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). Memory 302 is used to store one or more computer programs. The one or more computer programs are configured to be executed by processor 303. The one or more computer programs include multiple instructions that, when executed by processor 303, implement a method for detecting the uniformity of dry mixing materials, which is executed on electronic device 30.

[0046] In some embodiments, the processor 303 provides computational and control capabilities; for example, the processor 303 is used to execute computer programs stored in the memory 302 to implement the above-described method for detecting the uniformity of dry mixtures. In some embodiments, the processor 303 determines the uniformity of the dry mixture online by analyzing the acquired spectral signals.

[0047] In some embodiments, the input / output interface 304 is used to provide a channel for user input or output. For example, the input / output interface 304 can be used to connect various input / output devices, such as a mouse, keyboard, touch device, display screen, etc., so that users can enter information or visualize information.

[0048] To more clearly illustrate the method for detecting the uniformity of dry mixing provided in this application, the following will describe the method in detail through multiple embodiments. It should be noted that multiple embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0049] Figure 3This is a flowchart illustrating an exemplary embodiment of a method for detecting the uniformity of dry mixing, which is applied to electronic devices (e.g., Figure 1 Electronic devices 30). Figure 3 As shown, the method for detecting the uniformity of dry mixing can include the following steps. The order of the steps in the flowchart can be changed according to different needs.

[0050] S31, determine the spectral signal of the dry mixing process.

[0051] In some embodiments, during the fiberization process of the dry electrode, the active material and conductive agent are first pre-mixed in a mixing device, and then a binder is added to the pre-mixed electrode mixture to obtain a dry mixture. The mixing device performs high-speed shear dispersion on the dry mixture to promote the fiberization of the binder. The electrode mixture includes uniformly mixed active material and conductive agent, and the dry mixture includes active material, conductive agent, and binder. To achieve quantitative analysis of the dry materials, the mass ratio of active material, binder, and conductive agent in the dry mixture can be predetermined. The mass ratio can be set according to actual needs; for example, the mass ratio of active material, binder, and conductive agent can be 90:5:5.

[0052] In some embodiments, during the mixed fiberization process, an infrared spectrum of the dry-mixed material is acquired using a spectral detection device to obtain a spectral signal. The spectral signal characterizes a composite spectral signal containing superimposed spectral information of active substances, conductive agents, and binders.

[0053] S32, based on the spectral signal, determine the first feature signal and the second feature signal.

[0054] In some embodiments, the active material, conductive agent, and binder have different spectral response characteristics in the spectral signal. For example, the active material corresponds to sharp and narrow characteristic peaks in the spectral signal, the binder corresponds to medium-width characteristic peaks in the spectral signal, and the conductive agent corresponds to broad, sloping background absorption. Based on this, a first characteristic signal and a second characteristic signal are extracted from the spectral signal, respectively. The first characteristic signal includes the carbon-fluorine bond vibration peak of the binder in the dry mixture within a first wavenumber range, and the second characteristic signal includes the phosphorus-oxygen bond vibration peak of the active material in the dry mixture within a second wavenumber range.

[0055] The binder exhibits a more pronounced carbon-fluorine bond vibration peak in the first wavenumber range of the spectral signal, while the active material shows a more pronounced phosphorus-oxygen bond vibration peak in the second wavenumber range. The first wavenumber range is determined based on the specific type of binder and the characteristic peak shift patterns of that binder under different purity levels and particle sizes. For example, by collecting spectra from multiple batches of samples of a specific type of binder, the actual distribution range of the binder's characteristic peaks in each batch is statistically analyzed, and the first wavenumber range is determined based on these multiple actual distribution ranges. Similarly, the second wavenumber range is determined based on the specific type of active material and the characteristic peak shift patterns of that active material under different purity levels and particle sizes. For example, by collecting spectra from multiple batches of samples of a specific type of active material, the actual distribution range of the active material's characteristic peaks in each batch is statistically analyzed, and the second wavenumber range is determined based on these multiple actual distribution ranges.

[0056] For example, the type of adhesive may include polytetrafluoroethylene. Polyvinylidene fluoride polyethylene Styrene-butadiene rubber (SBR), etc. Taking polytetrafluoroethylene (PTFE) as an example, the first wavenumber range includes... The types of active substances include lithium iron phosphate. ), ternary materials (Nickel Cobalt Manganese Oxide, NCM), lithium cobalt oxide (Lithium Cobalt Oxide, Lithium manganese oxide (Lithium Manganese Oxide) Lithium manganese iron phosphate (LiFePO4) ), etc. The active material is lithium iron phosphate (LiFePO4). For example, the range of the second wave includes .

[0057] In this application embodiment, the carbon-fluorine bond vibration peak of the binder in the dry mixture in the first wavenumber range and the phosphorus-oxygen bond vibration peak of the active substance in the dry mixture in the second wavenumber range are selected. The specific information of each component can be separated from the composite spectral signal, thereby realizing the qualitative identification and quantitative analysis of multiple components in the dry mixture.

[0058] S33, determine the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal.

[0059] In some embodiments, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the determination of concentration ratios provided in an exemplary embodiment of this application. Determining the concentration ratio of the dry mixture based on a first characteristic signal and a second characteristic signal includes: determining a first signal intensity corresponding to the first characteristic signal and a second signal intensity corresponding to the second characteristic signal; and determining the concentration ratio based on the first signal intensity and the second signal intensity.

[0060] The ratio of the first signal intensity to the second signal intensity is defined as the concentration ratio. The first signal intensity is denoted as I_PTFE, and the second signal intensity is denoted as I_LFP. The concentration ratio is denoted as R, and can be determined using the following formula 1: Formula 1: Concentration ratio R = I_PTFE / I_LFP.

[0061] This application embodiment determines the first signal intensity corresponding to the first feature signal and the second signal intensity corresponding to the second feature signal; based on the first signal intensity and the second signal intensity, it determines the concentration ratio, which can reduce common-mode interference caused by optical path fluctuations and / or changes in the surface state of materials during the spectral acquisition process.

[0062] Please continue reading. Figure 4 The first signal intensity includes at least one of vibration peak height and vibration peak area, and the second signal intensity includes at least one of vibration peak height and vibration peak area. For example, both the first signal intensity and the second signal intensity can be vibration peak height, or both can be vibration peak area.

[0063] The binder is polytetrafluoroethylene (PTFE), and the active material is lithium iron phosphate (LiFePO4). Taking 1100 as an example, the first signal strength includes polytetrafluoroethylene (PTFE) in the first wavenumber range. 1300 The vibration peak height and vibration peak area at a first preset value are both considered. The first preset value can be set according to actual needs; for example, the first preset value can be 1150. and nearby values, 1200 The values ​​are in and around the same range. The second signal strength includes lithium iron phosphate. Second wave number range 940 1140 The vibration peak height and vibration peak area at the second preset value are both considered. The second preset value can be set according to actual needs; for example, the second preset value can be 1035. And the values ​​in and around it.

[0064] This application uses the vibration peak height or vibration peak area as the signal intensity, which can reduce the measurement deviation caused by peak position shift or peak shape asymmetry and improve the accuracy of signal intensity measurement.

[0065] S34, Based on multiple concentration ratios during the mixing process, determine the uniformity of dry mixing.

[0066] As mentioned earlier, the active material and the conductive agent have been mixed evenly in the mixing equipment beforehand. Thus, the uniformity of dry mixing is characterized by the degree of fiberization uniformity of the binder in the mixed powder composed of the active material and the conductive agent.

[0067] In some embodiments, the uniformity of dry mixing is determined based on the degree of dispersion of multiple concentration ratios R values ​​obtained during the continuous mixing process. A higher degree of dispersion indicates lower uniformity of the dry mixing, and a lower degree of dispersion indicates higher uniformity. For example, determining the uniformity of dry mixing based on multiple concentration ratios during the mixing process includes: determining the degree of dispersion of the dry mixing based on the multiple concentration ratios; determining uniformity as "uniform" when the degree of dispersion is less than or equal to a dispersion threshold; and determining non-uniformity when the degree of dispersion is greater than the dispersion threshold.

[0068] The dispersion threshold can be set according to actual needs. For example, the dispersion threshold can be 2%, 3%, 5%, etc., and there is no restriction here.

[0069] In some embodiments, determining the dispersion of a dry mixture based on multiple concentration ratios includes: determining a relative standard deviation based on multiple concentration ratios; and determining the dispersion of the dry mixture based on the relative standard deviation.

[0070] The degree of dispersion characterizes one or more of the relative standard deviation, variance, standard deviation, and range of multiple concentration ratios. Taking the degree of dispersion characterizing the relative standard deviation of multiple concentration ratios as an example, for instance, if n concentration ratios are continuously collected during the mixing process, denoted as... Calculate the relative standard deviation (RSD) of the data set. If the RSD is less than or equal to the dispersion threshold, the dry mixture is considered homogeneous. If the RSD is greater than the dispersion threshold, the dry mixture is considered inhomogeneous.

[0071] In the dry mixing uniformity detection method provided in this application embodiment, the concentration ratio is constructed based on the unique infrared characteristic peaks of the active substances and binder materials in the dry mixing, avoiding common-mode interference caused by optical path fluctuations and / or changes in the surface state of the materials, thereby realizing real-time monitoring of the fiberization uniformity of the binder and thus achieving real-time evaluation of the fiberization uniformity of the dry mixing.

[0072] In some embodiments, after acquiring the spectral signal of the dry mixture, the spectral signal can be preprocessed. Preprocessing includes at least baseline correction and background subtraction. Baseline correction refers to correcting baseline drift in the spectral signal; for example, it can be achieved using asymmetric least squares. Background subtraction refers to removing tilted background absorption in the spectral signal introduced by the conductive agent in the dry mixture.

[0073] The embodiments of this application improve the accuracy of signal feature extraction by performing baseline correction on the spectral signal and / or eliminating the tilted background absorption caused by conductive agents (such as carbon black), thereby improving the accuracy of detecting the uniformity of interfering mixtures.

[0074] In some embodiments, when the uniformity indicator shows that the dry mixture is not yet uniform, the mixing equipment continues to perform the mixing operation on the dry mixture until the uniformity indicator shows that the dry mixture is uniform, at which point the mixing operation is stopped. For example, when the uniformity of the dry mixture is determined to be uniform based on the degree of dispersion, the electronic equipment can send a stop command to the mixing equipment, causing the mixing equipment to stop performing the mixing operation on the dry mixture, such as a high-speed shear dispersion operation.

[0075] In this embodiment of the application, when the dry mixture is already uniform, stopping the mixing operation can effectively maintain the uniform particle size distribution of the obtained dry mixture.

[0076] This application uses polytetrafluoroethylene (PTFE) as the adhesive and lithium iron phosphate as the active material. Taking carbon black as an example of a conductive agent, in order to determine the infrared characteristic peaks that can specifically and quantitatively detect the fiberization uniformity of dry-process blends, a Fourier transform infrared spectrometer can be used to collect the infrared spectra of the adhesive polytetrafluoroethylene (PTFE) and the infrared spectra of the dry-process blends, respectively. Among these, lithium iron phosphate in the dry-process blends... The mass ratio of polytetrafluoroethylene (PTFE) and carbon black can be 90:5:5.

[0077] Please see Figure 5 , Figure 5 This is a schematic diagram of the infrared spectrum of polytetrafluoroethylene (PTFE) provided in an exemplary embodiment of this application.

[0078] Polytetrafluoroethylene (PTFE) was placed in a Fourier transform infrared spectrometer at a resolution of 4... The scan wavenumber range is 4000–400. Under the given conditions, the infrared spectrum of polytetrafluoroethylene (PTFE) was obtained by scanning 32 times and taking the average value.

[0079] like Figure 5 As shown, the horizontal axis represents the wavenumber range, and the vertical axis represents the transmittance. Polytetrafluoroethylene (PTFE) exhibits typical C0 values ​​in the infrared spectrum. F-bond stretching vibration absorption: at 1201.23 A strong and sharp absorption peak appears at 1145.91. A shoulder of moderate intensity appears at 1100. The two peaks mentioned above are located at 1100. 1300 area.

[0080] Please see Figure 6 , Figure 6 This is an infrared spectrum diagram of a dry mixing process provided in an exemplary embodiment of this application. As described above, the dry mixing process includes the active material lithium iron phosphate. The mass ratio of the adhesive polytetrafluoroethylene (PTFE) and the conductive agent carbon black can be set according to actual needs, for example, lithium iron phosphate. The mass ratio of polytetrafluoroethylene (PTFE) and conductive agent carbon black can be 90:5:5.

[0081] The dry-mixed material was placed in a Fourier transform infrared spectrometer at a resolution of 4... The scan wavenumber range is 4000–400. Under the given conditions, the sample was scanned 32 times, and the average value was taken to obtain the infrared spectrum of the dry mixture.

[0082] like Figure 6 As shown, the horizontal axis represents the wavenumber range, and the vertical axis represents transmittance. In the infrared spectral signal of the dry-process mixture, two sets of non-overlapping characteristic absorptions can be observed: at 1027.67... A distinct absorption peak appears at this point, which is attributed to lithium iron phosphate. middle P of ions O-type asymmetric stretching vibration. At 1135.67 The C of polytetrafluoroethylene (PTFE) can be identified at this location. F shoulder peak (corresponding to 1145.91 for pure polytetrafluoroethylene PTFE) Peak). Due to the baseline tilt caused by the conductive agent carbon black and the low content of polytetrafluoroethylene (PTFE), 1201 The main peak at 1135 exhibits a broad, weak absorption pattern, but... 1145 The acromion at that location remains stable and measurable.

[0083] Lithium iron phosphate The characteristic peak is at 1028 Nearby, the characteristic peak of polytetrafluoroethylene (PTFE) is at 1136. Nearby, the wavenumber interval between the two characteristic peaks is approximately 108. There is absolutely no spectral overlap. Therefore, even if only the shoulder peak of polytetrafluoroethylene (PTFE) is used to represent the binder signal, the concentration ratio R = I_PTFE(1135) can be calculated. 1145 ) / I_LFP(1020 1040 Its value still shows a good linear relationship with the actual ingredient ratio.

[0084] Therefore, for the selected sample polytetrafluoroethylene (PTFE), 1135 1160 Region C The F-shoulder peak is used as the first characteristic signal, and 1145 is used as the first characteristic signal. The peak height or peak area near the selected lithium iron phosphate sample is used as the first signal intensity. 1020 1040 P in the region The O peak is used as the second characteristic signal, with 1028 The peak height or area near the peak is used as the second signal intensity. The wavenumber separation between the two peaks is clear, and they can be stably extracted in actual dry mixing processes, providing a reliable spectral basis for online monitoring.

[0085] Please refer to the following: Figures 7 to 9 , Figure 7 This is a graph showing the change of concentration ratio over time during the mixing process, as provided in the exemplary embodiment of this application. Figure 8 This is a graph showing the trend of the relative standard deviation of the concentration ratio within different mixing time windows provided in the exemplary embodiments of this application. Figure 9 This is a final mixing scanning electron microscope (SEM) image provided by an exemplary embodiment of this application.

[0086] In some embodiments, lithium iron phosphate is selected as the active material. Its average particle size D50 is 0.5 μm. Super P carbon black is used as the conductive agent, and polytetrafluoroethylene (PTFE) is used as the binder. The mass ratio of the above three components is... Conductive agent: PTFE = 90:5:5.

[0087] The mixing process is carried out using a mixing device with a volume of 2L and a mixing speed of 3000rpm.

[0088] To monitor changes in the chemical information of the powder surface in real time during the mixing process, an online Fourier transform infrared (FTIR) spectrometer can be used for detection. The FTIR spectrometer is equipped with an attenuated total reflection (ATR) diamond accessory and a near-infrared fiber optic probe. The fiber optic probe is installed at the viewing window of the mixing equipment to acquire the powder surface spectrum in a non-contact manner. The spectral acquisition time interval is set to 2 seconds.

[0089] The dry-mixed material is placed in a mixing device for high-speed shear dispersion to promote binder fiberization. During the mixing and fiberization process, the spectral signal of the dry-mixed material is acquired. After preprocessing the spectral signal (such as baseline correction and / or background subtraction), the signal is analyzed based on a wavenumber range of 1135. 1160 The characteristic signal was used to determine I_PTFE, and in the wavenumber range of 1020 1040 The characteristic signal is used to determine I_LFP, and R = I_PTFE / I_LFP is calculated. The dispersion RSD is calculated every 10 consecutive R values ​​collected. When the RSD is lower than the dispersion threshold (e.g., 2%) for 3 consecutive times, it is determined that the dry mixing has been uniformly mixed and the mixing equipment is stopped.

[0090] like Figure 7 As shown, the horizontal axis represents time, and the vertical axis represents the concentration ratio. Figure 7 As shown by the blue curve, the initial stage of mixing (e.g., 0) Within 1 minute, the R-value fluctuated dramatically, reaching a high of 0.74 and a low of 0.34. As mixing continued, the R-value gradually converged, stabilizing in the range of 0.52–0.56 after 2 minutes. From 4 minutes to 12 minutes, the R-value stabilized around 0.53, corresponding to the theoretical feed ratio reference line of R=0.54. Figure 7 As shown by the yellow dashed line. Figure 8 As shown, the horizontal axis represents time, and the vertical axis represents the relative standard deviation. Figure 8 As shown by the blue curve, the RSD rapidly decreased from 19.0% at the initial stage of mixing: 12.0% at 2.5 min, 8.0% at 5 min, 4.0% at 7.5 min, and 2.5% at 10 min, continuing its downward trend. When the RSD first fell below 2% (approximately 10.5 min), it was determined that the dry mixing process had achieved uniform mixing, and the mixing equipment was shut down. Figure 9As shown, SEM verification revealed that the PTFE fiber network in the final sample from the dry mixing process was uniformly and densely distributed. Compared to the traditional fixed-time method, the average mixing time required in this application is reduced by approximately 15%, and the finished product yield reaches 100%.

[0091] Please see Figure 10 , Figure 10 This is a schematic diagram of a dry mixing uniformity detection device provided in an exemplary embodiment of this application. In some embodiments, the dry mixing uniformity detection device 100 may include multiple functional modules composed of computer program segments. The computer programs of each program segment in the dry mixing uniformity detection device 100 may be stored in the memory of the electronic device 30 and executed by at least one processor to perform (see details). Figure 3 (Description) Function for detecting the uniformity of dry mixing.

[0092] In some embodiments, the dry mixing uniformity detection device 100 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a signal determination module 101, a feature determination module 102, a ratio determination module 103, and a uniformity determination module 104. As used in this application, a module refers to a series of computer program segments that can be executed by at least one processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module will be detailed in subsequent embodiments.

[0093] The signal determination module 101 can be used to determine the spectral signal of dry mixing.

[0094] The feature determination module 102 can be used to determine a first feature signal and a second feature signal based on the spectral signal.

[0095] The ratio determination module 103 can be used to determine the concentration ratio of dry mixture based on the first characteristic signal and the second characteristic signal.

[0096] The uniformity determination module 104 can be used to determine the uniformity of dry mixing based on multiple concentration ratios during the mixing process.

[0097] It is understood that the uniformity detection device 20 for dry mixing and the uniformity detection method for dry mixing in the above embodiments belong to the same inventive concept. The specific implementation of each module of the uniformity detection device 20 for dry mixing corresponds to each step of the uniformity detection method for dry mixing in the above embodiments, and will not be repeated here.

[0098] The module division described above is a logical functional division, and other division methods may be used in actual implementation. Furthermore, the functional modules in the various embodiments of this application can be integrated into the same processing unit, or each module can exist physically separately, or two or more modules can be integrated into the same unit. The integrated modules described above can be implemented in hardware or in a combination of hardware and software functional modules.

[0099] then Figure 2 In the description of electronic device 30, communication unit 301 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more wired communication solutions such as Universal Serial Bus (USB) and Controller Area Network (CAN). The wireless communication module may provide one or more wireless communication solutions such as Wireless Fidelity (Wi-Fi), Bluetooth (BT), mobile communication networks, frequency modulation (FM), near field communication (NFC), and infrared (IR).

[0100] In some embodiments, memory 302 may include one or more random access memory (RAM) and one or more non-volatile memory (NVM). The RAM can be directly read and written by the processor 303 and can be used to store executable programs (e.g., machine instructions) of other running programs, as well as user and application data. The RAM may include static random-access memory (SRAM), dynamic random-access memory (DRAM), synchronous dynamic random-access memory (SDRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.

[0101] In some embodiments, the non-volatile memory may also store executable programs and user and application data, and can be pre-loaded into random access memory for direct reading and writing by the processor 303. The non-volatile memory may include disk storage devices and flash memory.

[0102] In some embodiments, processor 303 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors. It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 30. In other embodiments of this application, the electronic device 30 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0103] This application also provides a computer-readable storage medium storing a computer program, which includes program instructions. When the program instructions are executed, the method implemented can refer to the methods in the above embodiments of this application.

[0104] The computer-readable storage medium can be the internal memory of the electronic device described in the above embodiments, such as the hard disk or memory of the electronic device. Alternatively, the computer-readable storage medium can be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device.

[0105] In some embodiments, a computer-readable storage medium may include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of the electronic device, etc.

[0106] Computer-readable storage media may mainly include a stored program area and a stored data area. The stored program area may store the operating system, an application program required for at least one function, etc.; the stored data area may store data created based on the use of the electronic device 30, etc.

[0107] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause an electronic device or processor to execute portions of the methods of the various embodiments of this application.

[0108] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0109] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0111] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within this application. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other elements or, and the singular does not exclude the plural. Multiple elements or devices recited in the specification may also be implemented by a single element or device in software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any particular order.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application.

Claims

1. A method for detecting the uniformity of dry mixing, characterized in that, The methods include: Determine the spectral signal of the dry mixture; Based on the spectral signal, the first feature signal and the second feature signal are determined; The concentration ratio of the dry mixture is determined based on the first characteristic signal and the second characteristic signal; The uniformity of the dry mixture is determined based on multiple concentration ratios during the mixing process.

2. The method for detecting the uniformity of dry mixing as described in claim 1, characterized in that, The first characteristic signal includes the carbon-fluorine bond vibration peak of the binder in the dry mixture within a first wavenumber range, and the second characteristic signal includes the phosphorus-oxygen bond vibration peak of the active substance in the dry mixture within a second wavenumber range.

3. The method for detecting the uniformity of dry mixing as described in claim 2, characterized in that, Determining the concentration ratio of the dry mixture based on the first characteristic signal and the second characteristic signal includes: Determine the first signal strength corresponding to the first feature signal and the second signal strength corresponding to the second feature signal; The concentration ratio is determined based on the first signal intensity and the second signal intensity.

4. The method for detecting the uniformity of dry mixing as described in claim 3, characterized in that, The first signal intensity includes at least one of the vibration peak height and the vibration peak area, and the second signal intensity includes at least one of the vibration peak height and the vibration peak area.

5. The method for detecting the uniformity of dry mixing as described in claim 1, characterized in that, Determining the uniformity of the dry mixture based on multiple concentration ratios during the mixing process includes: The degree of dispersion of the dry mixture is determined based on the multiple concentration ratios. If the degree of dispersion is less than or equal to the degree of dispersion threshold, the uniformity is determined to be uniform.

6. The method for detecting the uniformity of dry mixing as described in claim 5, characterized in that, Determining the dispersion of the dry mixture based on the multiple concentration ratios includes: The relative standard deviation is determined based on the aforementioned concentration ratios; The degree of dispersion of the dry mixture is determined based on the relative standard deviation.

7. The method for detecting the uniformity of dry mixing as described in claim 1, characterized in that, After determining the spectral signal of the dry mixture, the method further includes: The spectral signal is preprocessed, and the preprocessing includes at least baseline correction and background subtraction. The baseline correction means that the spectral signal is baseline drift corrected, and the background subtraction means that the skewed background absorption introduced by the conductive agent in the dry mixture is removed from the spectral signal.

8. The method for detecting the uniformity of dry mixing as described in claim 1, characterized in that, The method further includes: If the uniformity indicator shows that the dry mixture is not yet uniform, continue the mixing operation on the dry mixture until the uniformity indicator shows that the dry mixture is uniform, then stop continuing the mixing operation on the dry mixture.

9. A system for detecting the uniformity of dry mixing, characterized in that, The system includes: The mixing equipment is configured to prepare dry mixtures; A spectral detection device is configured to acquire the spectral signal of the dry mixture; An electronic device, communicatively coupled to the spectral detection device, includes a memory and one or more processors, said one or more processors being configured to: The spectral signal of the dry mixture is determined; based on the spectral signal, a first characteristic signal and a second characteristic signal are determined; based on the first characteristic signal and the second characteristic signal, the concentration ratio of the dry mixture is determined; based on multiple concentration ratios of the dry mixture during the mixing process, the uniformity of the dry mixture is determined.

10. An electronic device, characterized in that, include: Memory; One or more processors; as well as One or more programs, the programs being stored in the memory and configured to be executed by the one or more processors, the programs including instructions for performing a method for detecting the uniformity of dry mixing according to any one of claims 1 to 8.

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