Method, equipment and device for detecting thickness of coating foil for new energy battery

Abstract

The invention relates to the technical field of battery coating foil thickness detection, in particular to a coating foil thickness detection method, device and device for a new energy battery, and the method comprises the steps: dividing a plurality of wavelength windows, and according to the dispersion degree and the confusion degree of all light intensity values in a corresponding region of a spectral intensity diagram in which each wavelength window is located, calculating the thickness of the coating foil for the new energy battery; determining a thickness fluctuation value; measuring the similarity between the spectral intensity diagrams at each collection moment and the adjacent front and back collection moments, the complexity difference between the spectral intensity diagrams and the thickness fluctuation value difference, and determining a flatness index; the thickness deviation factor is determined by analyzing the distribution characteristics of the spectral intensity values in the spectral intensity diagram of the battery coating foil, and the thickness of the battery coating foil is detected. The invention 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 battery coating foil thickness detection, and specifically to a coating foil thickness detection method, equipment and detection device 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 foils can no longer meet the demand, and coated foils have emerged. Its development has gone through a diversified stage from ordinary aluminum foil to carbon-coated foil, and then to today's high-ratio aluminum foil, composite coated aluminum foil, etc. Coated foils effectively enhance the adhesion between the current collector and the active material by coating carbon materials, graphene, carbon nanotubes, etc. on the surface of aluminum foil, reduce the interface resistance, and improve the charging and discharging efficiency and cycle life of the battery. For example, carbon-coated aluminum foil can optimize the performance of lithium iron phosphate batteries, and graphene-coated aluminum foil further improves battery performance due to its two-dimensional structural advantages. It is of great significance. 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, providing strong support for the improvement of 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 the coated foil for batteries, the fluidity of the slurry has a significant impact on the coating thickness distribution. If the slurry has poor fluidity, such as too high viscosity or too large surface tension, the coating thickness will be uneven. For example, at the starting and ending points of coating, the slurry accumulates due to inertia, forming a "thick edge" phenomenon. This uneven thickness distribution will cause many problems, such as reduced coating adhesion and incomplete curing, which in turn affects the conductivity and mechanical properties of the coating. The thickness of the coated foil is usually very thin, and the thickness variation caused by uneven slurry may be at the micron level. Existing detection technologies such as grayscale detection can make a preliminary judgment on the uniformity of the coating, but its accuracy is limited and it is difficult to accurately measure small thickness changes. Spectral detection technology is used to detect multiple components at the same time. However, spectral-based thickness detection technology is difficult to capture local changes in coating thickness in real time during dynamic measurement, and cannot accurately identify small thickness changes on the coating surface, so that the back-end defect features are submerged in the spectral image, resulting in an increase in the missed detection rate, thereby 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 thickness of a coating foil for a new energy battery, the method comprising the following steps: S1: Place the battery coating foil on a conveyor belt and obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period through a thickness gauge; S2: Divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located; S3: measure the similarity between the spectral intensity graphs at each acquisition moment and 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; 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, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectrum intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment; S402: extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree; S5: Based on the thickness deviation factor, the thickness of the battery coating foil is detected.

[0005] Preferably, the acquisition process of the multiple wavelength windows is: For the spectral intensity diagram 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 diagram. Move along the wavelength growth direction of the horizontal axis of the spectral intensity diagram in a step size each time until the end end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity diagram, and all wavelength windows are obtained.

[0006] Preferably, the method for determining the thickness fluctuation value of the battery coating foil at each acquisition 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 light intensity product of all wavelength windows at each collection time is taken as the thickness fluctuation value of the battery coating foil at each collection time.

[0007] Preferably, the method for determining the flatness index of the battery coating foil at each acquisition moment is: Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent acquisition moments, and record them as the first similarity and the second similarity respectively, and take the average of the first similarity and the second similarity 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: ; In the formula, , They respectively 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 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 an exponential function with a natural constant as the base.

[0008] Preferably, the process of selecting the change moment from all the acquisition moments is: The flatness index of the battery coating foil at all sampling moments within a preset time is fitted to obtain a fitting curve, all maximum values ​​on the fitting curve are extracted, and the maximum value corresponds to the sampling moment as the change moment.

[0009] Preferably, the method for determining the thickness variation of the battery coating foil at each variation moment is: The coordinates of all peaks in the spectral intensity diagram at each acquisition time are extracted, and the coordinates of all peaks are fitted to obtain a fitting straight line, and the mean square error between the fitting straight line and the coordinates of all peaks is used as the deviation value at each acquisition time; The average of the deviation values ​​of all acquisition moments within the window at each change moment is taken as the thickness variation degree of the battery coating foil at each change moment.

[0010] Preferably, the thickness deviation factor of the battery coating foil is expressed as: ; In the formula, Indicates the thickness deviation factor of the battery coating foil; represents the mean of the smoothing index of all acquisition moments within the window of change moment m; Represents the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition times within the window of the change time m; represents the thickness variation of the battery coating foil at the variation time m; M represents the number of all variation times within the preset time length; norm() represents the normalization function.

[0011] Preferably, the thickness of the battery coating foil is detected, including: 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 thickness of the battery coating is normal.

[0012] In a second aspect, an embodiment of the present application provides a coating foil thickness detection device for new energy batteries, the device comprising: The coated foil data acquisition module is used to place the battery coated foil on the conveyor belt and obtain the spectral intensity diagram of the battery coated foil at each acquisition time within a preset time through a thickness gauge; The coating foil weight acquisition module is used to divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located; The similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent 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, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment; Extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree; The coating foil thickness detection module is used to detect the thickness of the battery coating foil based on the thickness deviation factor.

[0013] In a third aspect, an embodiment of the present application also provides a device for detecting the thickness of a coated foil for a new energy battery, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned methods for detecting the thickness of a coated foil for a new energy battery are implemented.

[0014] It can be seen from the above embodiments that the thickness detection method of the coating foil for new energy batteries provided in the embodiments of the present application has at least the following beneficial effects: This application constructs a thickness fluctuation value by analyzing the discreteness and disorder of the light intensity values ​​in the local area of ​​the spectral intensity map 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 surface of the coating foil; further, by comparing the similarity of the spectral intensity maps at adjacent moments, the difference between the complexity of the spectral intensity maps, 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 map, a thickness variation is constructed to reflect the thickness variation at different positions of the coating foil; further, by integrating the thickness variation, the flatness index and the 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 missed detection of defects at the back end of the battery coating foil, improve the accuracy of coating foil thickness detection, and improve the overall performance of the battery. This application ensures the uniformity of the thickness of the coating foil and improves the overall performance of the new energy battery by analyzing the thickness variation at different positions of the battery coating foil. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. 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 creative work.

[0016] 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; Figure 2 A schematic diagram of a thickness deviation factor extraction process provided by an embodiment of the present application; Figure 3 A block diagram of a coating foil thickness detection system for new energy batteries provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the thickness detection method, equipment and detection device for the coated foil for new energy batteries proposed in this application, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] 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.

[0019] The specific scheme of the coating foil thickness detection method, equipment and detection device for new energy batteries provided in this application is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a method for detecting the thickness of a coating foil for a new energy battery provided by an embodiment of the present application, the method comprising the following steps: S1: Place the battery coating foil on a conveyor belt and obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period through a thickness gauge.

[0021] A laser thickness gauge with a spectrum analyzer is used to dynamically measure the battery layer to be tested. Specifically, a uniformly moving horizontal conveyor belt is installed below the detection area of ​​the thickness gauge. In this embodiment, the moving speed of the conveyor belt is 150m / min to ensure the accuracy of the measurement result. The battery layer to be tested is placed on the conveyor belt, and the thickness gauge is turned on to detect the thickness of the battery layer to be tested.

[0022] When the laser is irradiated onto the surface of the foil, the material on the surface 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 spectral intensity graph reflected from the surface of the foil is collected by a spectrometer. In this embodiment, the collection frequency of the spectral intensity graph is f, and the spectral intensity graph of the battery coating foil at each collection time within a preset time length is obtained.

[0023] 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 by himself according to the specific situation. This embodiment does not impose any special restrictions.

[0024] In addition, it should be understood that the preset time length in this embodiment is the time length 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 at the end.

[0025] S2: Divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located.

[0026] When testing the thickness of foil, if the foil surface is rough, oxidized, or affected by contaminants, the light scattering will increase, making the reflected light intensity value uneven at different wavelengths, resulting in obvious intensity fluctuations or uneven distribution in the spectral intensity diagram.

[0027] Therefore, in order to analyze the change in the uniformity of the reflected light of the foil at different positions of the battery, the unevenness of the reflected light at each collection moment is quantified. The specific process is as follows: For the spectral intensity diagram 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 diagram. Move along the wavelength growth direction of the horizontal axis of the spectral intensity diagram in a step size each time until the end end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity diagram, and all wavelength windows are obtained.

[0028] 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.

[0029] Further, the variance and information entropy of all light intensity values ​​in the corresponding area in the spectral intensity diagram 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; The average value of the light intensity product of all wavelength windows at each collection time is taken as the thickness fluctuation value of the battery coating foil at each collection time.

[0030] Among them, the calculation method of information entropy is a well-known technology, and its specific calculation process will not be repeated here.

[0031] 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 in the wavelength window, the more violent the fluctuation of the spectral intensity in 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 in the wavelength window, the more complex the spectral intensity distribution in 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 is more uneven at the current acquisition moment; 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 is more uniform at the current acquisition moment.

[0032] S3: Measure the similarity between the spectral intensity graphs at each acquisition moment and 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.

[0033] In the process of dynamically measuring the thickness of the battery coating, dynamic measurement can capture the real-time changes of the coating during the movement of the conveyor belt, and the spectral intensity diagrams at different acquisition moments reflect the physical and chemical properties of the coating surface at different locations. Through dynamic analysis, local inhomogeneities in coating thickness can be effectively identified, such as spectral intensity fluctuations caused by oxidation, contamination, or coating defects. In addition, by comparing spectral intensity diagrams at multiple moments, the overall uniformity of the coating can be more comprehensively evaluated, rather than just the local characteristics of a single moment. The multi-moment analysis method can reduce the impact of random errors and improve the reliability and repeatability of the measurement results.

[0034] For the two continuously collected spectral intensity graphs, since the conveyor belt is in a uniform motion process, the two continuously collected spectral intensity graphs are continuous in space, reflecting the physical and chemical properties of the layer at adjacent positions on the conveyor belt. This continuity allows the change of the layer thickness at adjacent positions to be identified by comparing and analyzing the two spectral intensity graphs. By comparing the thickness fluctuation values ​​at two adjacent acquisition moments, the inhomogeneity change of the spectral intensity graph at two adjacent acquisition moments can be analyzed. The specific process is as follows: Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent acquisition moments, and record them as the first similarity and the second similarity respectively, and take the average of the first similarity and the second similarity as the comprehensive similarity of the battery coating foil at each acquisition moment; It should be noted that there are many methods for measuring the similarity between spectral intensity graphs. In this embodiment, the cosine similarity between the spectral intensity graphs is used as the similarity between the spectral intensity graphs. 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 graphs. Regarding the selection of the method for measuring the similarity between the spectral intensity graphs, this embodiment does not impose any special restrictions.

[0035] The method for calculating the cosine similarity between spectral intensity graphs is a well-known technique, and the specific calculation process will not be described in detail.

[0036] Further, the fractal dimension of all light intensity values ​​in the spectral intensity graph at each acquisition time is calculated as the complexity index of the battery coating foil at each acquisition time; 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 other implementation methods, 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.

[0037] The calculation method of the fractal dimension is a well-known technology, and the specific calculation process will not be described in detail.

[0038] By measuring the differences between the thickness fluctuation values ​​at different acquisition times, the differences between the complex indexes, and combining the comprehensive similarity, the flatness index of the battery coating foil at each acquisition time is determined, specifically: The flatness index of the battery coating foil at the acquisition time i The expression is: ; In the formula, , They respectively 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 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 an exponential function with a natural constant as the base.

[0039] 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 indexes at adjacent acquisition moments is, and the smaller the ratio of the differences between the complex indexes at different adjacent acquisition moments is, that is, The smaller the value, the more uniform the thickness distribution between adjacent positions on the coated 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 flatter at the current acquisition moment; 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 indexes at adjacent acquisition moments, and the greater the ratio of the differences between the complex indexes at different adjacent acquisition moments, that is, The larger the value is, the more uneven the thickness distribution between adjacent positions on the coated 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 larger the The smaller it is, the smaller the flatness index of the battery coating foil is, which means that the surface of the battery coating foil is more uneven at the current acquisition moment.

[0040] So far, 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.

[0041] 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.

[0042] 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 during foil coating. The thickness of the coated foil is usually very thin, and the thickness variation caused by uneven slurry may be at the micron level, making detection more difficult.

[0043] Therefore, by analyzing the change trend and distribution characteristics of the light intensity value in the spectral intensity diagram, the thickness uniformity of the battery coating foil is reflected. The specific process is as follows: S401: Evaluate the change trend of the flatness index of the battery coating foil at all acquisition moments within a preset time length, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment.

[0044] The flatness index of the battery coating foil at all sampling moments within a preset time is fitted to obtain a fitting curve, all maximum values ​​on the fitting curve are extracted, and the maximum value corresponds to the sampling moment as the change moment.

[0045] 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.

[0046] Among them, the least square fitting is a well-known technology, and the specific process of fitting the smoothing index using the least square fitting will not be repeated.

[0047] Further, the coordinates of all peaks in the spectral intensity diagram at each acquisition time are extracted, and the coordinates of all peaks are fitted to obtain a fitting straight line, and the mean square error between the fitting straight line and the coordinates of all peaks is used as the deviation value at each acquisition time; 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 coating foil thickness, and the smaller the thickness variation.

[0048] 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 peak values ​​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.

[0049] The calculation method of the mean square error is a well-known technique, and the specific calculation process will not be described in detail.

[0050] 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.

[0051] S402: extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree.

[0052] Different coating thicknesses will have different degrees of absorption of specific wavelengths of light. Thicker coatings generally absorb more light, resulting in a greater intensity of the absorption peak. Conversely, thinner coatings absorb less light, resulting in a smaller intensity of the absorption peak, and changes in the thickness of the coating will affect the wavelength range of light it absorbs or transmits, resulting in a slight shift in the position of the absorption peak.

[0053] Based on the above analysis, the thickness deviation factor of the battery coating foil is determined as follows: The total number of peaks in the spectral intensity diagram at each acquisition moment was extracted and counted, and a window was divided with the change moment as the center, and 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.

[0054] It should be noted that the value of the window size is manually set and this implementation does not impose any special restrictions.

[0055] The expression of thickness deviation factor C of battery coating foil is: ; In the formula, represents the mean of the smoothing index of all acquisition moments within the window of change moment m; Represents the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition times within the window of the change time m; represents the thickness variation of the battery coating foil at the variation time m; M represents the number of all variation times within the preset time length; norm() represents the normalization function.

[0056] It should be noted that there are many methods for measuring the degree of data discreteness. 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 discreteness 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, implementers may also adopt other methods for measuring the degree of data discreteness, such as variance and dispersion coefficient. Regarding the selection of methods for measuring the degree of data discreteness, this embodiment does not impose any special restrictions.

[0057] 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 pollution, 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 diagram at all acquisition moments in the window of the change time m, it means that the discreteness of the total number of peaks in the window is greater, that is, the greater the influence of the spectral intensity on the thickness change in the window, the greater 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 greater 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; On the contrary, if the mean value of the flatness index is larger, the discreteness of the total number of peak values ​​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.

[0058] So far, 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 obtained.

[0059] Preferably, the schematic diagram of the thickness deviation factor extraction process provided in this embodiment is as follows: Figure 2 shown.

[0060] S5: Based on the thickness deviation factor, the thickness of the battery coating foil is detected.

[0061] 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 change of the coating foil surface. In the dynamic laser thickness measurement process, the larger the thickness deviation factor, the more obvious the uneven thickness on the coating surface. Therefore, by monitoring the size of the thickness deviation factor, the influence of the surface quality on the measurement accuracy can be judged in real time, specifically: 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.

[0062] It should be noted that the value of the preset threshold is artificially set. 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.

[0063] 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, including: The coated foil data acquisition module is used to place the battery coated foil on the conveyor belt and obtain the spectral intensity diagram of the battery coated foil at each acquisition time within a preset time through a thickness gauge; The coating foil weight acquisition module is used to divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located; The similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent 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, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment; Extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree; The coating foil thickness detection module is used to detect the thickness of the battery coating foil based on the thickness deviation factor.

[0064] 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.

[0065] 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 a coated foil for a new energy battery, 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 a coated foil for a new energy battery are implemented.

[0066] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0068] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting thickness of coated 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 obtain the spectral intensity graph of the battery coating foil at each acquisition time within a preset time period through a thickness gauge; S2: Divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located; S3: measure the similarity between the spectral intensity graphs at each acquisition moment and 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; 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, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectrum intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment; S402: extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree; S5: Based on the thickness deviation factor, the thickness of the battery coating foil is detected.

2. The method for detecting thickness of coating foil for new energy batteries according to claim 1, characterized in that: The acquisition process of the multiple wavelength windows is: For the spectral intensity diagram 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 diagram. Move along the wavelength growth direction of the horizontal axis of the spectral intensity diagram in a step size each time until the end end point of the wavelength window is aligned with the position of the longest wavelength on the horizontal axis of the spectral intensity diagram, and all wavelength windows are obtained.

3. The method for detecting thickness of coated foil for new energy batteries according to claim 1, characterized in that: The method for determining the thickness fluctuation value of the battery coating foil at each acquisition 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 light intensity product of all wavelength windows at each collection time is taken as the thickness fluctuation value of the battery coating foil at each collection time.

4. The method for detecting thickness of coating foil for new energy batteries according to claim 1, characterized in that: The method for determining the flatness index of the battery coating foil at each acquisition time is: Calculate the similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent acquisition moments, and record them as the first similarity and the second similarity respectively, and take the average of the first similarity and the second similarity 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: ; In the formula, , They respectively 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 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 an exponential function with a natural constant as the base.

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

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

7. The method for detecting thickness of coated foil for new energy batteries according to claim 1, characterized in that: The expression of the thickness deviation factor of the battery coating foil is: ; In the formula, Indicates the thickness deviation factor of the battery coating foil; represents the mean of the smoothing index of all acquisition moments within the window of change moment m; Represents the degree of dispersion of the total number of peaks in the spectral intensity graph at all acquisition times within the window of the change time m; represents the thickness variation of the battery coating foil at the variation time m; M represents the number of all variation times within the preset time length; norm() represents the normalization function.

8. The method for detecting thickness of coating foil for new energy batteries according to claim 1, characterized in that: 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 thickness of the battery coating is normal.

9. A coating foil thickness detection device for new energy batteries, which realizes the coating foil thickness detection method 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 diagram of the battery coated foil at each acquisition time within a preset time through a thickness gauge; The coating foil weight acquisition module is used to divide multiple wavelength windows according to different wavelengths in the spectrum intensity diagram of the battery coating foil at each acquisition time, and determine the thickness fluctuation value of the battery coating foil at each acquisition time according to the discreteness and confusion degree of all light intensity values ​​in the corresponding area of ​​the spectrum intensity diagram where each wavelength window is located; The similarity between the spectral intensity graphs at each acquisition moment and the adjacent previous and subsequent 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, and select the change moment from all acquisition moments; within the window of each change moment, analyze the fitting of all peaks in the spectral intensity diagram at each acquisition moment, and determine the thickness change degree of the battery coating foil at each change moment; Extract and count the total number of peaks in the spectral intensity graph at each acquisition moment, divide the window with the change moment as the center, and determine the thickness deviation factor of the battery coating foil by analyzing the discrete degree of the total number of peaks in all spectral intensity graphs in the window at each change moment and the average distribution of the flatness index at all acquisition moments, combined with the thickness change degree; The coating foil thickness detection module is used to detect the thickness of the battery coating foil based on the thickness deviation factor.

10. A coating foil thickness detection device 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 thickness of coated foil for new energy batteries according to any one of claims 1 to 8 are implemented.

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