Thickness detection method, equipment and detection device for coating foil used in new energy batteries

Abstract

This application relates to the technical field of thickness detection for battery coating foils, and specifically to a method, equipment, and device for detecting the thickness of coating foils for new energy batteries. The method includes: dividing multiple wavelength windows, determining the thickness fluctuation value based on the degree of dispersion and confusion of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window; measuring the similarity between the spectral intensity graphs at each acquisition moment and its adjacent previous and subsequent acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values ​​to determine the flatness index; and determining the thickness deviation factor by analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity graph of the battery coating foil, thereby detecting the thickness of the battery coating foil. This application aims to improve the accuracy of battery coating foil thickness detection and the overall performance of the battery.

Description

Technical Field

[0001] The present application relates to the technical field of thickness detection of battery coating foils, and specifically to a method, equipment and detection device for thickness detection of coating foils for new energy batteries. Background Art

[0002] As the requirements for battery performance in new energy vehicles, energy storage and other fields continue to increase, traditional foil materials can no longer meet the demand, and coated foil materials have emerged. Its development has gone through a diversified stage from ordinary aluminum foil to carbon-coated foil, and then to today's high-density aluminum foil and composite coated aluminum foil. By coating the surface of the aluminum foil with carbon materials, graphene, carbon nanotubes, etc., coated foil effectively enhances the adhesion between the current collector and the active material, reduces the interfacial resistance, and improves the battery's charge and discharge efficiency and cycle life. For example, carbon-coated aluminum foil can optimize the performance of lithium iron phosphate batteries, while graphene-coated aluminum foil further enhances battery performance due to its two-dimensional structural advantages. Its significance is significant. It not only improves the overall performance of the battery and meets the market demand for high-energy-density and high-safety batteries, but also promotes the upgrading of new energy battery technology and the development of the industry. It provides strong support for improving the endurance of new energy vehicles and the efficient operation of energy storage systems. It is an indispensable part of the advancement of new energy battery technology.

[0003] When coating battery foil, the fluidity of the slurry significantly affects the coating thickness distribution. Poor slurry fluidity, such as excessive viscosity or surface tension, can lead to uneven coating thickness. For example, at the start and end points of coating, the slurry accumulates due to inertia, forming a "thick edge" phenomenon. This uneven thickness distribution can cause numerous problems, such as reduced coating adhesion and incomplete curing, which in turn affects the coating's conductivity and mechanical properties. Coated foils are typically very thin, and thickness variations caused by slurry inhomogeneity can be on the micron level. Existing detection technologies, such as grayscale meters, can provide a preliminary assessment of coating uniformity, but their accuracy is limited, making it difficult to accurately measure even small thickness variations. Spectral detection techniques, which simultaneously monitor multiple components, struggle to capture localized variations in coating thickness in real time during dynamic measurements. This inability to accurately identify even small thickness variations on the coating surface can obscure defect signatures in the spectral image, increasing the rate of missed detections and thus reducing the overall performance of new energy batteries. Summary of the Invention

[0004] In a first aspect, an embodiment of the present application provides a method for detecting the thickness of a coating foil for a new energy battery, the method comprising the following steps:

[0005] S1: Place the battery coating foil on a conveyor belt and use a thickness gauge to obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period;

[0006] S2: Divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to the different wavelengths. Determine the thickness fluctuation value of the battery coating foil at each acquisition moment according to the degree of dispersion and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window.

[0007] S3: Measure the similarity between the spectral intensity graphs at each acquisition moment and those at the previous and subsequent acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values, and determine the flatness index of the battery coating foil at each acquisition moment;

[0008] S4: By analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil, the thickness deviation factor of the battery coating foil is determined, specifically:

[0009] S401: Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and select the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment;

[0010] S402: extracting and counting the total number of peaks in the spectral intensity graph at each acquisition moment, dividing the window centered at the change moment, and determining the thickness deviation factor of the battery coating foil by analyzing the dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation;

[0011] S5: Based on the thickness deviation factor, the thickness of the battery coating foil is detected.

[0012] Preferably, the acquisition process of the multiple wavelength windows is:

[0013] For the spectral intensity graph at each acquisition moment, the preset length is used as the size of the wavelength window, and the starting end point of the wavelength window is aligned with the position of the shortest wavelength on the horizontal axis of the spectral intensity graph. Move along the wavelength growth direction of the horizontal axis of the spectral intensity graph in one step each time until the end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity graph, and all wavelength windows are obtained.

[0014] Preferably, the method for determining the thickness fluctuation value of the battery coating foil at each collection moment is:

[0015] Calculate the variance and information entropy of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window respectively, and use the product of the variance and the information entropy as the light intensity product of each wavelength window;

[0016] The average value of the product of the light intensities of all wavelength windows at each acquisition moment is taken as the thickness fluctuation value of the battery coating foil at each acquisition moment.

[0017] Preferably, the method for determining the flatness index of the battery coating foil at each collection moment is:

[0018] Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent preceding and following acquisition moments, and record them as a first similarity and a second similarity respectively. The average of the first similarity and the second similarity is used as the comprehensive similarity of the battery coating foil at each acquisition moment.

[0019] Calculate the fractal dimension of the spectral intensity graph at each acquisition time as the complexity index of the battery coating foil at each acquisition time;

[0020] The flatness index of the battery coating foil at the acquisition time i The expression is: Where, 、 represent the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i-1, and the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i+1, respectively; 、 Respectively represent the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i-1, and the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i+1; represents the comprehensive similarity of the battery coating foil at the acquisition time i; exp( ) represents the exponential function with a natural constant as the base.

[0021] Preferably, the process of filtering out the change moments from all the collected moments is:

[0022] The flatness index of the battery coating foil at all sampling moments within a preset time period is fitted to obtain a fitting curve, all maximum values ​​on the fitting curve are extracted, and the maximum value corresponding to the sampling moment is used as the change moment.

[0023] Preferably, the method for determining the thickness variation of the battery coating foil at each variation moment is:

[0024] The coordinates of all peaks in the spectral intensity graph at each acquisition moment are extracted, and the coordinates of all peaks are fitted to obtain a fitting straight line. The mean square error between the fitting straight line and the coordinates of all peaks is used as the deviation value at each acquisition moment.

[0025] The average of the deviation values ​​of all acquisition moments within the window of each change moment is taken as the thickness variation of the battery coating foil at each change moment.

[0026] Preferably, the thickness deviation factor of the battery coating foil is expressed as: Where, Indicates the thickness deviation factor of the battery coating foil; represents the mean of the smoothing indices of all acquisition moments within the window of change moment m; Indicates the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition moments within the window of the change moment m; represents the thickness variation of the battery coating foil at the change moment m; M represents the number of all change moments within the preset time length; norm() represents the normalization function.

[0027] Preferably, the thickness of the battery coating foil is detected, including:

[0028] If the thickness deviation factor of the battery coating foil is greater than a preset threshold, the thickness of the battery coating foil is abnormal; otherwise, the battery coating thickness is normal.

[0029] In a second aspect, an embodiment of the present application provides a device for detecting thickness of a coating foil for a new energy battery, the device comprising:

[0030] The coated foil data acquisition module is used to place the battery coated foil on the conveyor belt and obtain the spectral intensity map of the battery coated foil at each acquisition time within a preset time period through a thickness gauge;

[0031] The coating foil weight acquisition module is used to divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to the different wavelengths. The module determines the thickness fluctuation value of the battery coating foil at each acquisition moment based on the degree of dispersion and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window.

[0032] The similarity between the spectral intensity graphs at each acquisition moment and those at the adjacent preceding and following acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values ​​are measured to determine the flatness index of the battery coating foil at each acquisition moment.

[0033] By analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil, the thickness deviation factor of the battery coating foil is determined, specifically:

[0034] Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and filter out the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment;

[0035] Extracting and counting the total number of peaks in the spectral intensity graph at each acquisition moment, dividing the window centered at the change moment, and determining the thickness deviation factor of the battery coating foil by analyzing the degree of dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation;

[0036] The coating foil thickness detection module is used to detect the thickness of the battery coating foil based on the thickness deviation factor.

[0037] In a third aspect, an embodiment of the present application also provides a device for detecting the thickness of coated foil for new energy batteries, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned methods for detecting the thickness of coated foil for new energy batteries are implemented.

[0038] As can be seen from the above embodiments, the method for detecting thickness of a coating foil for a new energy battery provided in the embodiments of the present application has at least the following beneficial effects:

[0039] The present application constructs a thickness fluctuation value by analyzing the degree of discreteness and disorder of the light intensity values ​​in the local area of ​​the spectral intensity graph of the battery coating foil, quantifies the change in the uniformity of the reflected light of the battery coating foil at the position, and thus reflects the uniformity of the coating foil surface; further, by comparing the similarity of the spectral intensity graphs at adjacent moments, the difference between the complexity of the spectral intensity graphs, and the difference between the thickness fluctuation values, a flatness index is constructed to comprehensively evaluate the flatness of the coating foil; further, by analyzing the distribution of peaks in the spectral intensity graph, a thickness variation is constructed to reflect the degree of thickness variation at different positions of the coating foil; further, by integrating the thickness variation, the flatness index and the degree of discreteness of the number of peaks, a thickness deviation factor is constructed to comprehensively reflect the thickness variation of the coating surface, improve the accuracy of identifying small thickness variations on the coating surface, reduce the probability of missing defects in the back-end of the battery coating foil, improve the accuracy of coating foil thickness detection, and improve the overall performance of the battery. The present application ensures the uniformity of the coating foil thickness by analyzing the thickness variation at different positions of the battery coating foil, and improves the overall performance of the new energy battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 A flowchart of a method for detecting thickness of a coating foil for a new energy battery provided in one embodiment of the present application;

[0042] Figure 2 A schematic diagram of a thickness deviation factor extraction process provided in one embodiment of the present application;

[0043] Figure 3 Block diagram of a coating foil thickness detection system for new energy batteries provided in one embodiment of the present application. DETAILED DESCRIPTION

[0044] To further illustrate the technical means and effectiveness of this application to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the method, equipment, and device for detecting the thickness of coated foil for new energy batteries proposed in this application, including their specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0045] Unless defined otherwise, 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.

[0046] The following describes in detail the specific scheme of the thickness detection method, equipment and detection device for the coating foil for new energy batteries provided in this application with reference to the accompanying drawings.

[0047] See also Figure 1 , which shows a flowchart of a method for detecting thickness of a coating foil for a new energy battery provided by an embodiment of the present application, the method comprising the following steps:

[0048] S1: Place the battery coating foil on a conveyor belt and use a thickness gauge to obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period.

[0049] A laser thickness gauge with an optical spectrum analyzer is used to dynamically measure the battery layer to be inspected. Specifically, a uniformly moving horizontal conveyor belt is installed below the inspection area of ​​the thickness gauge. In this embodiment, the conveyor belt moves at a speed of 150 m / min to ensure the accuracy of the measurement results. The battery layer to be inspected is placed on the conveyor belt, and the thickness gauge is turned on to detect the thickness of the battery layer to be inspected.

[0050] When the laser irradiates the surface of the foil, the surface material absorbs and reflects the light according to its physical and chemical properties. The absorption and reflection characteristics of different materials are manifested as different spectral features on the spectrum. The spectrum intensity graph reflected from the foil surface is collected by a spectrometer. In this embodiment, the collection frequency of the spectrum intensity graph is f, and the spectrum intensity graph of the battery coating foil at each collection time within a preset time length is obtained.

[0051] It should be noted that the acquisition frequency f of the spectral intensity graph is manually set. In this embodiment, the acquisition frequency f of the spectral intensity graph is 2 kHz. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0052] Furthermore, it should be understood that the preset time in this embodiment is the time required for the battery coating foil to start being scanned by the laser in the thickness gauge and for the battery coating foil to complete being scanned by the thickness gauge.

[0053] S2: Divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to different wavelengths. Determine the thickness fluctuation value of the battery coating foil at each acquisition moment according to the degree of discreteness and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window.

[0054] When testing foil thickness, if the foil surface is rough, oxidized, or affected by contaminants, it will increase light scattering, making the reflected light intensity values ​​uneven at different wavelengths, resulting in obvious intensity fluctuations or uneven distribution in the spectral intensity diagram.

[0055] Therefore, in order to analyze the changes in the uniformity of the foil reflected light at different positions on the battery, the degree of unevenness of the reflected light at each collection moment is quantified. The specific process is as follows:

[0056] For the spectral intensity graph at each acquisition moment, the preset length is used as the size of the wavelength window, and the starting end point of the wavelength window is aligned with the position of the shortest wavelength on the horizontal axis of the spectral intensity graph. Move along the wavelength growth direction of the horizontal axis of the spectral intensity graph in one step each time until the end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity graph, and all wavelength windows are obtained.

[0057] It should be noted that the value of the preset length is artificially set. In this embodiment, the value of the preset length is 10 nm. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0058] Further, the variance and information entropy of all light intensity values ​​in the corresponding area in the spectral intensity graph of each wavelength window are calculated respectively, and the product of the variance and the information entropy is used as the light intensity product of each wavelength window;

[0059] The average value of the product of the light intensities of all wavelength windows at each acquisition moment is taken as the thickness fluctuation value of the battery coating foil at each acquisition moment.

[0060] The method for calculating information entropy is a well-known technology, and its specific calculation process will not be described in detail.

[0061] According to the thickness fluctuation value of the battery coating foil at each acquisition moment, it can be understood that the larger the variance of all light intensity values ​​in the corresponding area of ​​the spectral intensity where the wavelength window is located, the more violent the fluctuation of the spectral intensity within the wavelength window, and the worse the uniformity of the coating surface; and the larger the information entropy of all light intensity values ​​in the corresponding area of ​​the spectral intensity where the wavelength window is located, the more complex the spectral intensity distribution within the wavelength window and the worse the uniformity; in summary, if the variance is larger and the information entropy is larger, the thickness fluctuation value of the battery coating foil is larger, indicating that the thickness of the battery coating foil at the current acquisition moment is more uneven; conversely, if the variance is smaller and the information entropy is smaller, the thickness fluctuation value of the battery coating foil is smaller, indicating that the thickness of the battery coating foil at the current acquisition moment is more uniform.

[0062] S3: Measure the similarity between the spectral intensity graphs at each acquisition moment and those at the adjacent previous and subsequent acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values, and determine the flatness index of the battery coating foil at each acquisition moment.

[0063] During the dynamic measurement of battery coating thickness, dynamic measurement can capture real-time changes in the coating as the conveyor moves. Spectral intensity maps at different acquisition moments reflect the physical and chemical properties of the coating surface at different locations. Dynamic analysis can effectively identify localized inhomogeneities in coating thickness, such as spectral intensity fluctuations caused by oxidation, contamination, or coating defects. Furthermore, by comparing spectral intensity maps at multiple moments, a more comprehensive assessment of the overall uniformity of the coating can be made, rather than just the local characteristics at a single moment. This multi-moment analysis method can reduce the impact of random errors and improve the reliability and repeatability of measurement results.

[0064] For two consecutively acquired spectral intensity graphs, since the conveyor belt is in constant motion, the two consecutively acquired spectral intensity graphs are spatially continuous, reflecting the physical and chemical properties of the layer at adjacent locations on the conveyor belt. This continuity allows for the identification of changes in layer thickness at adjacent locations by comparing and analyzing the two spectral intensity graphs. By comparing the thickness fluctuation values ​​at two adjacent acquisition moments, the inhomogeneity of the spectral intensity graphs at these two adjacent acquisition moments can be analyzed. The specific process is as follows:

[0065] Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent preceding and following acquisition moments, and record them as a first similarity and a second similarity respectively. The average of the first similarity and the second similarity is used as the comprehensive similarity of the battery coating foil at each acquisition moment.

[0066] It should be noted that there are many methods for measuring the similarity between spectral intensity maps. In this embodiment, the cosine similarity between the spectral intensity maps is used as the similarity between the spectral intensity maps. In actual application, as other implementation methods, the implementer may also use other methods such as the inverse of the Euclidean distance to measure the similarity between the spectral intensity maps. This embodiment does not impose any special restrictions on the selection of the method for measuring the similarity between the spectral intensity maps.

[0067] The method for calculating the cosine similarity between spectral intensity maps is a well-known technique, and the specific calculation process will not be described in detail.

[0068] Furthermore, the fractal dimension of all light intensity values ​​in the spectral intensity graph at each acquisition moment is calculated as the complexity index of the battery coating foil at each acquisition moment;

[0069] It should be noted that there are many methods for calculating fractal dimension. In this embodiment, the box counting method is used to calculate the fractal dimension of all light intensity values ​​in the spectral intensity diagram. In actual application, as another implementation method, the implementer may also use the Hurst exponent to calculate the fractal dimension. Regarding the selection of the method for calculating the fractal dimension, this example does not impose any special restrictions.

[0070] The calculation method of the fractal dimension is a well-known technology, and the specific calculation process will not be described in detail.

[0071] By measuring the differences between thickness fluctuation values ​​and complex indexes at different acquisition times, and combining the comprehensive similarity, the flatness index of the battery coating foil at each acquisition time is determined, specifically:

[0072] The flatness index of the battery coating foil at the acquisition time i The expression is: Where, 、 represent the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i-1, and the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i+1, respectively; 、 Respectively represent the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i-1, and the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i+1; represents the comprehensive similarity of the battery coating foil at the acquisition time i; exp( ) represents the exponential function with a natural constant as the base.

[0073] According to the flatness index of the battery coating foil at each acquisition time, it can be understood that the smaller the difference in thickness fluctuation values ​​at adjacent acquisition times, and the smaller the ratio between the differences in thickness fluctuations at different adjacent acquisition times, that is, The smaller it is, the smaller the difference in thickness variation between adjacent positions on the coated foil is, and the more uniform the thickness distribution of the coated foil between adjacent positions is; the smaller the difference between the complex indices at adjacent acquisition moments is, and the smaller the ratio of the differences between the complex indices at different adjacent acquisition moments is, that is, The smaller the value, the more uniform the thickness distribution between adjacent positions on the coating foil. The greater the comprehensive similarity, the smaller the difference between the spectral intensity graphs at adjacent moments. The larger the value, the more uniform the thickness distribution between adjacent positions on the battery coating foil. In summary, if The smaller, The smaller, and The larger the value is, the greater the flatness index of the battery coating foil is, indicating that the surface of the battery coating foil is smoother at the current acquisition moment;

[0074] On the contrary, the greater the difference in thickness fluctuation values ​​at adjacent acquisition moments, and the greater the ratio between the differences in thickness fluctuations at different adjacent acquisition moments, that is, The larger the value, the greater the difference in thickness variation between adjacent positions on the coated foil, and the more uneven the thickness distribution of the coated foil between adjacent positions; the greater the difference between the complex indices at adjacent acquisition moments, and the greater the ratio of the differences between the complex indices at different adjacent acquisition moments, that is, The larger the value is, the more uneven the thickness distribution between adjacent positions on the coating foil is; the smaller the comprehensive similarity is, the greater the difference between the spectral intensity graphs at adjacent moments is, that is, The smaller it is, the more uneven the thickness distribution between adjacent positions on the battery coating foil is; in summary, if The bigger, The bigger, and The smaller it is, the smaller the flatness index of the battery coating foil is, indicating that the surface of the battery coating foil is more uneven at the current acquisition moment.

[0075] At this point, by analyzing the differences in thickness characteristics between battery coating foils at adjacent acquisition moments, the flatness index of the battery coating foil is obtained, which is used to evaluate the unevenness of the battery coating foil.

[0076] S4: Determine the thickness deviation factor of the battery coating foil by analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil.

[0077] Since the uniformity and thickness of the coated foil directly depend on the coating accuracy, the viscosity and fluidity of the slurry during the coating process make it difficult to spread the slurry evenly, which can easily cause uneven thickness distribution when the foil is coated. The thickness of the coated foil is usually very thin, and the thickness variation caused by the uneven slurry may be at the micron level, making detection more difficult.

[0078] Therefore, by analyzing the changing trend and distribution characteristics of the light intensity value in the spectral intensity diagram, the thickness uniformity of the battery coating foil can be reflected. The specific process is as follows:

[0079] S401: Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and filter out the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment.

[0080] The flatness index of the battery coating foil at all sampling moments within a preset time period is fitted to obtain a fitting curve, all maximum values ​​on the fitting curve are extracted, and the maximum value corresponding to the sampling moment is used as the change moment.

[0081] It should be noted that there are many commonly used fitting methods. Least squares fitting is used in this embodiment. In actual application, as other implementation methods, the implementer may also use other fitting methods such as polynomial fitting. Regarding the selection of fitting methods, this embodiment does not impose any special restrictions.

[0082] Among them, the least square fitting is a well-known technology, and the specific process of fitting the flattening index using the least square fitting is not described in detail.

[0083] Furthermore, the coordinates of all peaks in the spectral intensity graph at each acquisition moment are extracted, and the coordinates of all peaks are fitted to obtain a fitting straight line. The mean square error between the fitting straight line and the coordinates of all peaks is used as the deviation value at each acquisition moment.

[0084] The mean of the deviation values ​​of all acquisition moments within the window of each change moment is taken as the thickness variation of the battery coating foil at each change moment; the larger the mean square error, that is, the larger the deviation value, the worse the regularity of the peak distribution, the worse the uniformity of the coating thickness, and the greater the thickness variation; conversely, the smaller the mean square error, that is, the smaller the deviation value, the better the regularity of the peak distribution, the more uniform the thickness of the coating foil, and the smaller the thickness variation.

[0085] It should be understood that there are many commonly used linear fitting methods. In this embodiment, the least squares method is used to fit the coordinates of all peaks to obtain a fitting straight line. In actual application, as other implementation methods, the implementer may also use other fitting methods such as linear regression. Regarding the selection of the fitting method, this embodiment does not impose any special restrictions.

[0086] The calculation method of the mean square error is a well-known technology, and its specific calculation process will not be repeated here.

[0087] So far, by analyzing the thickness variation degree of the battery coating foil at different positions, the thickness variation degree of the battery coating foil is obtained.

[0088] S402: Extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window centered at the change moment, and determine the thickness deviation factor of the battery coating foil by analyzing the degree of dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation.

[0089] Different coating thicknesses result in different levels of absorption of specific wavelengths of light. Thicker coatings generally absorb more light, resulting in a stronger absorption peak. Conversely, thinner coatings absorb less light, resulting in a weaker absorption peak. Variations in coating thickness can also affect the wavelength range of light absorbed or transmitted, causing slight shifts in the position of the absorption peak.

[0090] Based on the above analysis, the thickness deviation factor of the battery coating foil is determined as follows:

[0091] The total number of peaks in the spectral intensity graph at each acquisition moment was extracted and counted, and a window was divided with the change moment as the center. The window size included 1001 light intensity values. In particular, if the window size was larger than the total length of the acquired data, the mean interpolation method was used to fill in the missing data.

[0092] It should be noted that the window size is manually set and this implementation does not impose any special restrictions.

[0093] The expression of the thickness deviation factor C of the battery coating foil is: Where, represents the mean of the smoothing indices of all acquisition moments within the window of change moment m; Indicates the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition moments within the window of the change moment m; represents the thickness variation of the battery coating foil at the change moment m; M represents the number of all change moments within the preset time length; norm() represents the normalization function.

[0094] It should be noted that there are many methods for measuring the degree of data dispersion. In this embodiment, the variance of the total number of peaks in the spectral intensity graph at all acquisition moments in the window of the changing moment m is used as the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition moments in the window of the changing moment m. In actual application, as other implementation methods, the implementer may also adopt other methods for measuring the degree of data dispersion, such as variance and dispersion coefficient. This embodiment does not impose any special restrictions on the selection of methods for measuring the degree of data dispersion.

[0095] According to the thickness deviation factor of the battery coating foil, it can be understood that if the mean value of the flatness index of all acquisition moments in the window of the change time m is smaller, it means that the unevenness of the battery coating foil in the window is more obvious, and there may be more defects, oxidation or contamination, which leads to thickness deviation in the window, and the thickness deviation factor is larger; and the greater the discreteness of the total number of peaks in the spectral intensity graph at all acquisition moments in the window of the change time m, the greater the discreteness of the total number of peaks in the window, that is, the greater the influence of the spectral intensity on the thickness change in the window, the larger the thickness deviation factor; the greater the thickness variation of the battery coating foil at the change time m, the greater the regularity of the peak distribution, the worse the uniformity of the battery coating thickness, and thus the larger the thickness deviation factor; in summary, if the mean value of the flatness index is smaller, the discreteness of the total number of peaks is greater, and the thickness variation is greater, then the thickness deviation factor of the battery coating foil is larger, indicating that the thickness deviation of the battery coating foil is more obvious and there are more defects of uneven thickness;

[0096] On the contrary, if the mean value of the flatness index is larger, the dispersion of the total number of peaks is smaller, and the thickness variation is smaller, then the thickness deviation factor of the battery coating foil is smaller, indicating that the thickness deviation of the battery coating foil is less obvious and the thickness distribution of the battery coating foil is more uniform.

[0097] So far, the thickness deviation factor of the battery coating foil is obtained by analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil.

[0098] Preferably, the thickness deviation factor extraction process diagram provided in this embodiment is as follows: Figure 2 shown.

[0099] S5: Based on the thickness deviation factor, the thickness of the battery coating foil is detected.

[0100] The thickness deviation factor of the battery coating foil is obtained from S1-S4. The overall thickness deviation factor of the battery coating foil can directly reflect the thickness variation of the coating foil surface. In the dynamic laser thickness measurement process, the larger the thickness deviation factor, the more obvious the thickness unevenness on the coating surface. Therefore, by monitoring the size of the thickness deviation factor, the degree of influence of the surface quality on the measurement accuracy can be judged in real time. Specifically:

[0101] If the thickness deviation factor of the battery coating foil is greater than the preset threshold, the thickness of the battery coating foil is abnormal; conversely, if the thickness deviation factor of the battery coating foil is less than or equal to the preset threshold, the battery coating thickness is normal.

[0102] It should be noted that the value of the preset threshold is set manually. In this embodiment, the value of the preset threshold is 0.5. The implementer can also set it by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0103] Based on the same inventive concept as the above method, the embodiment of the present application also provides a coating foil thickness detection device for new energy batteries, comprising:

[0104] The coated foil data acquisition module is used to place the battery coated foil on the conveyor belt and obtain the spectral intensity map of the battery coated foil at each acquisition time within a preset time period through a thickness gauge;

[0105] The coating foil weight acquisition module is used to divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to the different wavelengths. The module determines the thickness fluctuation value of the battery coating foil at each acquisition moment based on the degree of dispersion and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window.

[0106] The similarity between the spectral intensity graphs at each acquisition moment and those at the adjacent preceding and following acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values ​​are measured to determine the flatness index of the battery coating foil at each acquisition moment.

[0107] By analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil, the thickness deviation factor of the battery coating foil is determined, specifically:

[0108] Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and filter out the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment;

[0109] Extracting and counting the total number of peaks in the spectral intensity graph at each acquisition moment, dividing the window centered at the change moment, and determining the thickness deviation factor of the battery coating foil by analyzing the degree of dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation;

[0110] The coating foil thickness detection module is used to detect the thickness of the battery coating foil based on the thickness deviation factor.

[0111] The block diagram of the coating foil thickness detection device for new energy batteries provided in the embodiment of the present application is as follows: Figure 3 shown.

[0112] Based on the same inventive concept as the above method, an embodiment of the present application also provides a device for detecting the thickness of coated foil for new energy batteries, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned methods for detecting the thickness of coated foil for new energy batteries are implemented.

[0113] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0114] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0115] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for detecting thickness of coating foil for new energy batteries, characterized in that: The method comprises the following steps: S1: Place the battery coating foil on a conveyor belt and use a thickness gauge to obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period; S2: Divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to the different wavelengths. Determine the thickness fluctuation value of the battery coating foil at each acquisition moment according to the degree of dispersion and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window. S3: Measure the similarity between the spectral intensity graphs at each acquisition moment and those at the previous and subsequent acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values, and determine the flatness index of the battery coating foil at each acquisition moment; S4: By analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil, the thickness deviation factor of the battery coating foil is determined, specifically: S401: Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and select the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment; S402: extracting and counting the total number of peaks in the spectral intensity graph at each acquisition moment, dividing the window centered at the change moment, and determining the thickness deviation factor of the battery coating foil by analyzing the dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation; S5: Detecting the thickness of the battery coating foil based on the thickness deviation factor; The thickness deviation factor of the battery coating foil is expressed as: Where, Indicates the thickness deviation factor of the battery coating foil; represents the mean of the smoothing indices of all acquisition moments within the window of change moment m; Indicates the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition moments within the window of the change moment m; represents the thickness variation of the battery coating foil at the change time m; M represents the number of all change moments within the preset time length; norm() represents the normalization function; The method for determining the flatness index of the battery coating foil at each collection time is: Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent preceding and following acquisition moments, and record them as a first similarity and a second similarity respectively. The average of the first similarity and the second similarity is used as the comprehensive similarity of the battery coating foil at each acquisition moment. Calculate the fractal dimension of the spectral intensity graph at each acquisition time as the complexity index of the battery coating foil at each acquisition time; The flatness index of the battery coating foil at the acquisition time i The expression is: Where, 、 represent the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i-1, and the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i+1, respectively; 、 Respectively represent the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i-1, and the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i+1; represents the comprehensive similarity of the battery coating foil at the acquisition time i; exp( ) represents the exponential function with a natural constant as the base.

2. The method for detecting thickness of coating foil for new energy batteries according to claim 1, wherein: The acquisition process of the multiple wavelength windows is as follows: For the spectral intensity graph at each acquisition moment, the preset length is used as the size of the wavelength window, and the starting end point of the wavelength window is aligned with the position of the shortest wavelength on the horizontal axis of the spectral intensity graph. Move along the wavelength growth direction of the horizontal axis of the spectral intensity graph in one step each time until the end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity graph, and all wavelength windows are obtained.

3. The method for detecting thickness of coating foil for new energy batteries according to claim 1, wherein: The method for determining the thickness fluctuation value of the battery coating foil at each collection time is: Calculate the variance and information entropy of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window respectively, and use the product of the variance and the information entropy as the light intensity product of each wavelength window; The average value of the product of the light intensities of all wavelength windows at each acquisition moment is taken as the thickness fluctuation value of the battery coating foil at each acquisition moment.

4. The method for detecting thickness of coating foil for new energy batteries according to claim 1, wherein: The process of filtering out the change moments from all the collected moments is as follows: The flatness index of the battery coating foil at all sampling moments within a preset time period is fitted to obtain a fitting curve, all maximum values ​​on the fitting curve are extracted, and the maximum value corresponding to the sampling moment is used as the change moment.

5. The method for detecting thickness of coating foil for new energy batteries according to claim 1, wherein: The method for determining the thickness variation of the battery coating foil at each variation moment is as follows: The coordinates of all peaks in the spectral intensity graph at each acquisition moment are extracted, and the coordinates of all peaks are fitted to obtain a fitting straight line. The mean square error between the fitting straight line and the coordinates of all peaks is used as the deviation value at each acquisition moment. The average of the deviation values ​​of all acquisition moments within the window of each change moment is taken as the thickness variation of the battery coating foil at each change moment.

6. The method for detecting thickness of coating foil for new energy batteries according to claim 1, wherein: The thickness detection of the battery coating foil comprises: If the thickness deviation factor of the battery coating foil is greater than a preset threshold, the thickness of the battery coating foil is abnormal; otherwise, the battery coating thickness is normal.

7. A device for detecting thickness of coating foil for new energy batteries, which implements the method for detecting thickness of coating foil for new energy batteries as claimed in claim 1, characterized in that: The device comprises: The coated foil data acquisition module is used to place the battery coated foil on the conveyor belt and obtain the spectral intensity map of the battery coated foil at each acquisition time within a preset time period through a thickness gauge; The coating foil weight acquisition module is used to divide the spectral intensity graph of the battery coating foil at each acquisition moment into multiple wavelength windows according to the different wavelengths. The module determines the thickness fluctuation value of the battery coating foil at each acquisition moment based on the degree of dispersion and disorder of all light intensity values ​​in the corresponding area of ​​the spectral intensity graph of each wavelength window. The similarity between the spectral intensity graphs at each acquisition moment and those at the adjacent preceding and following acquisition moments, the difference in complexity between the spectral intensity graphs, and the difference in thickness fluctuation values ​​are measured to determine the flatness index of the battery coating foil at each acquisition moment. By analyzing the distribution characteristics of the spectral intensity values ​​in the spectral intensity diagram of the battery coating foil, the thickness deviation factor of the battery coating foil is determined, specifically: Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time period, and filter out the change moments from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity graph at each acquisition moment to determine the thickness change of the battery coating foil at each change moment; Extracting and counting the total number of peaks in the spectral intensity graph at each acquisition moment, dividing the window centered at the change moment, and determining the thickness deviation factor of the battery coating foil by analyzing the degree of dispersion of the total number of peaks in all spectral intensity graphs within the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness variation; A coating foil thickness detection module, configured to detect the thickness of the battery coating foil based on the thickness deviation factor; The thickness deviation factor of the battery coating foil is expressed as: Where, Indicates the thickness deviation factor of the battery coating foil; represents the mean of the smoothing indices of all acquisition moments within the window of change moment m; Indicates the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition moments within the window of the change moment m; represents the thickness variation of the battery coating foil at the change time m; M represents the number of all change moments within the preset time length; norm() represents the normalization function; The method for determining the flatness index of the battery coating foil at each collection time is: Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent preceding and following acquisition moments, and record them as a first similarity and a second similarity respectively. The average of the first similarity and the second similarity is used as the comprehensive similarity of the battery coating foil at each acquisition moment. Calculate the fractal dimension of the spectral intensity graph at each acquisition time as the complexity index of the battery coating foil at each acquisition time; The flatness index of the battery coating foil at the acquisition time i The expression is: Where, 、 represent the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i-1, and the difference between the thickness fluctuation values ​​at the acquisition time i and the acquisition time i+1, respectively; 、 Respectively represent the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i-1, and the difference between the complex index of the battery coating foil at the acquisition time i and the acquisition time i+1; represents the comprehensive similarity of the battery coating foil at the acquisition time i; exp( ) represents the exponential function with a natural constant as the base.

8. A device for detecting thickness of coated foil for new energy batteries, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for detecting the thickness of the coated foil for new energy batteries according to any one of claims 1 to 6 are implemented.

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