A preparation method for reducing poor EL brightness of photovoltaic modules and photovoltaic modules

Abstract

The present application discloses a preparation method and a photovoltaic module for reducing poor EL brightness of a photovoltaic module, and relates to the technical field of photovoltaic module preparation. The method comprises: performing PL measurement on a cell to obtain a PL image; obtaining a grayscale mean L of the entire PL image; dividing the PL image into a plurality of graphics of equal area; obtaining a grayscale mean L of each graphic. i , and analyze and calculate the grayscale mean L of each graphic i The mean value of the grayscale difference ratio δ between the preset area and other graphics i ; Set the preset difference ratio threshold and calculate the mean difference ratio of all graphics δ i The proportion value σ is higher than the preset difference ratio threshold; the cells are sorted according to the grayscale mean L and the proportion value σ; the photovoltaic modules are prepared for the cells with the same properties after sorting. The present application uses the analysis data of PL images to sort the cells and prepare the photovoltaic modules, and the EL brightness and darkness of the photovoltaic modules are significantly reduced, and the proportion of high-power gears of the photovoltaic modules is significantly increased.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic module preparation, and in particular to a preparation method and a photovoltaic module for reducing EL brightness and darkness problems of a photovoltaic module. Background Art

[0002] In photovoltaic module manufacturing technology, in order to achieve higher module conversion efficiency, the efficiency of the cells is graded and cells of the same efficiency grade are placed in the same module. However, due to defects in the silicon wafer or cell itself, parameter selection or error of the measuring machine, etc., the prepared photovoltaic modules may have poor EL (Electroluminescence) image brightness.

[0003] Poor EL brightness has a significant impact on the performance and life of photovoltaic modules. It may cause the overall photovoltaic conversion efficiency of the module to decrease, and the module cannot fully utilize the light energy when working, resulting in energy loss. In addition, poor EL brightness may also accelerate the aging and damage of the module. Local overheating and current concentration may cause the material performance inside the module to degrade, and even cause hot spot effects, which may cause the module to burn out in severe cases.

[0004] Patent application CN106206869A discloses a cell grading method for solving the EL brightness problem of crystalline silicon solar modules. The method reduces the poor EL brightness by grading twice, first grading the cell efficiency Eta, and then grading the open circuit voltage Uoc. However, since the cell efficiency Eta and the open circuit voltage Uoc are actually two strongly correlated coefficients, the high or low values ​​of the two are actually mainly related to the quality of the cell. The cause of the poor EL brightness is not only related to this, but may also be related to other factors. In addition, with the development of TOPCon (Tunnel Oxide Passivated Contact) cells and TBC (Tunnel Back Contact) cells, the EL brightness problem is also solved. With the development of high-efficiency batteries such as tunnel oxide back contact, battery efficiency has been qualitatively improved. For example, in 2016, the market was mainly occupied by PERC batteries, and its mass production efficiency was about 20.5%; and in 2024, the mass production efficiency of Topcon batteries was 25.8-26.3%; because the measurement of battery efficiency and open circuit voltage is based on the average value of the entire cell, the higher the value of battery efficiency and open circuit voltage, the more likely it is that the battery efficiency and open circuit voltage on the entire cell will be uneven. Therefore, even if the same efficiency and open circuit voltage gear are used, there will be a large difference in quality between cells. As a result, this method cannot achieve the phenomenon of reducing poor EL brightness.

[0005] like Figure 7As shown in FIG. 7 , the EL image of the photovoltaic module prepared in the prior art is shown. It can be clearly seen from the figure that the photovoltaic module contains 156 cells. The EL image shows that these cells not only have different EL brightness with adjacent cells, but also have different EL brightness of a single cell. Therefore, how to reduce EL brightness has always been a research hotspot for photovoltaic modules. Summary of the invention

[0006] The purpose of the present application is to provide a method for preparing a photovoltaic module that reduces EL brightness and darkness. In the first aspect of the present application, a method for preparing a photovoltaic module that reduces EL brightness and darkness is provided, the method comprising:

[0007] Perform PL measurement on the cell to obtain a PL image;

[0008] Obtaining a grayscale mean value L of the entire PL image;

[0009] dividing the PL image into a plurality of graphics of equal area;

[0010] Get the grayscale mean L of each of the graphics i , and analyze and calculate the grayscale mean L of each of the graphics i The mean value of the difference ratio δ between the grayscale mean values ​​of other graphics in the preset area i ;

[0011] Set a preset difference ratio threshold and calculate the mean difference ratio of all the graphics i A proportion value σ higher than the preset difference ratio threshold;

[0012] Sorting the battery cells according to the grayscale mean value L and the proportion value σ;

[0013] Photovoltaic modules are prepared for the cells with the same properties after sorting.

[0014] In an optional manner, the shape is a rectangle, a circle, a polygon or an irregular shape.

[0015] In an optional manner, the area of ​​the pattern is ≤4 mm 2 .

[0016] In an alternative approach, we need to calculate the mean of the difference ratios δ i The graphic to be analyzed is set in the preset area, and the area of ​​the preset area is 10 to 100 times the area of ​​the graphic to be analyzed.

[0017] In an optional manner, the preset difference ratio threshold is ≤10%.

[0018] In an optional manner, the method for sorting the battery cells according to the grayscale mean value L and the proportion value σ is:

[0019] Setting a preset proportion threshold, the number of the preset proportion thresholds is n, n≥1, comparing the proportion value σ with the preset proportion threshold, and dividing the proportion value σ into at least two proportion levels;

[0020] Setting a preset grayscale threshold, the number of the preset grayscale thresholds is m, m≥1, comparing the grayscale mean L with the preset grayscale threshold, and dividing the grayscale mean L into at least two grayscale levels;

[0021] The battery cells having the same proportion level and the same gray level are identified as battery cells having the same properties.

[0022] In an optional manner, the proportion value σ is 0% to 100%;

[0023] When n=1, the preset percentage threshold is ≥90%;

[0024] When n=2, the preset proportion threshold includes a first preset proportion threshold and a second preset proportion threshold, the difference between the first preset proportion threshold and the second preset proportion threshold is ≥20%; the first preset proportion threshold is ≥90%; and the second preset proportion is ≥60%.

[0025] In an optional manner, the grayscale mean L is 0-255;

[0026] The m≥2, the preset grayscale thresholds include at least two, and the difference between any two of the preset grayscale thresholds is ≤60.

[0027] In an optional embodiment, the battery cell is a Topcon battery.

[0028] The second aspect of the present application discloses a photovoltaic module, which is prepared by the preparation method for reducing EL brightness and darkness of the photovoltaic module according to the first aspect of the present application, and the EL brightness and darkness of the photovoltaic module is low.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) This application uses PL image analysis data to sort the cells and prepare photovoltaic modules, which not only effectively reduces the EL brightness problem, but also significantly increases the proportion of high-power gears of the photovoltaic modules.

[0031] (2) The sorting method of the present application uses PL images for analysis. Compared with EL images, the measurement of EL images requires a probe to contact the cell to obtain the EL image. The measurement probe is expensive, and contacting the cell will interfere with the information quality of the cell, causing deviations in the quality of subsequent photovoltaic modules. The acquisition of PL images can be obtained using a PL measuring instrument without a probe or contact. It is low in cost, does not contact the cell, and does not affect the preparation of subsequent photovoltaic modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0033] Figure 1 A schematic flow chart of a preparation method for reducing EL brightness difference of the present application is shown.

[0034] Figure 2 A PL image of one embodiment of a cell is shown.

[0035] Figure 3 A schematic diagram showing the division of a PL image of the present application into a plurality of graphics of equal area;

[0036] Figure 4 A schematic diagram showing the graphics and preset areas of the present application.

[0037] Figure 5 An EL image of the photovoltaic module according to Example 2 of the present application is shown.

[0038] Figure 6 An EL image of the photovoltaic module of Comparative Example 1 of the present application is shown.

[0039] Figure 7 The EL image of the photovoltaic module prepared in the prior art is shown.

[0040] Reference numerals:

[0041] 1-PL image of the cell;

[0042] 10-Graphics, 101-Graphics in the first quadrant; 102-Graphics in the second quadrant; 103-Graphics in the third quadrant, 103-Graphics in the fourth quadrant;

[0043] 20-preset area, 201-preset area for graphics in the first quadrant; 202-preset area for graphics in the second quadrant; 203-preset area for graphics in the third quadrant, 204-preset area for graphics in the fourth quadrant. DETAILED DESCRIPTION

[0044] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0045] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "multiple" means two or more, unless otherwise clearly and specifically defined. "Several" means one or more, unless otherwise clearly and specifically defined.

[0046] like Figure 7 As shown in FIG. 7 , an EL image of a photovoltaic module prepared in the prior art is an EL image of a photovoltaic module prepared by grading the cell efficiency Eta and the open-circuit voltage Uoc. It can be clearly seen from the figure that not only are there differences in EL brightness between these cells and adjacent cells, but there are also differences in EL brightness between a single cell.

[0047] Based on this, the first aspect of the present application discloses a preparation method for reducing the poor brightness of EL in photovoltaic modules, such as Figure 1 As shown, the method includes:

[0048] Perform PL image measurement on the battery cell to obtain a PL image;

[0049] In this step, the battery cell can be a single crystal silicon wafer or a multi-crystalline silicon wafer, preferably a single crystal silicon wafer; the battery cell includes a silicon wafer body, and the conductivity type of the silicon wafer body is P type or N type.

[0050] In a specific embodiment, the cell is a Topcon cell, the silicon wafer of which is a single crystal silicon wafer and the conductivity type is N-type. Due to the high efficiency of the Topcon cell, the cell efficiency and open circuit voltage of the entire cell are prone to unevenness, making the EL brightness problem particularly prominent.

[0051] PL (Photoluminescence) images are obtained by measuring with a PL measuring instrument. For example, a PL measuring instrument from Semilab can be used to measure the PL of a cell to obtain a PL image of the cell. Figure 2 As shown, Figure 2 The PL image of a cell measured by a PL measuring instrument is shown in the figure. As can be seen from the figure, the PL image is a black and white image, and the grayscale value of the PL image varies depending on the quality of the cell.

[0052] Get the grayscale mean L of the entire PL image;

[0053] In this step, the grayscale mean is the grayscale mean of the entire cell. The grayscale value is a numerical value that is well known in computer image processing and is used to represent image brightness or color depth. The value is usually 0-255, where 0 represents black and 255 represents white. The grayscale mean of the entire PL image of this application is characterized by this grayscale value. The range of the grayscale mean is 0 to 255, where white is 255 and black is 0. The grayscale mean L of the PL image can be calculated through image processing, or it can be obtained by extracting the grayscale mean data from the background of the PL image. First, by measuring the grayscale mean L of the entire cell, an initial evaluation of the overall performance of the cell can be made. For example Figure 3 As shown in the figure, a PL image of a complete battery cell is shown. By image processing or extracting data in the background of the PL image, the grayscale mean value of the entire PL image can be obtained.

[0054] dividing the PL image into a plurality of graphics of equal area;

[0055] In this step, the PL image can be processed by image processing software, such as Figure 3 As shown, Figure 3 The PL image of the whole cell is divided into a plurality of graphics of equal area 10 by using image processing software. It is worth noting that, since the cell has chamfers, there may be a certain difference between the area of ​​the graphics in the chamfered area and the area of ​​the graphics in the non-chamfered area; in addition, due to the area of ​​the cell, there may be a certain difference between the area of ​​the graphics near the edge area of ​​the cell and the area of ​​the graphics in the non-edge area; the difference in area within the range of 5% belongs to the protection scope of this application and will not affect the final effect of this application.

[0056] In an optional manner, the shape may be a rectangle, a circle, a polygon or an irregular shape;

[0057] Preferably, the pattern is rectangular, for example, a square or a rectangle. Since the battery sheet is generally positive or rectangular, it is easier to obtain the PL image by dividing it into a plurality of patterns of equal area.

[0058] In an optional embodiment, the area of ​​the pattern is ≤ 4 mm 2 . For example, it can be 4 mm 2 , 3 mm 2 , 2.25 mm 2 , 2mm 2 , 1 mm 2 , 0.25 mm 2 , 0.09 mm 2, 0.04 mm 2 or 0.01 mm 2 If the area of ​​the graphic is too large, the subsequent results will be inaccurate and cannot fully represent the subsequent EL brightness.

[0059] Get the grayscale mean L of each of the graphics i , and analyze and calculate the grayscale mean L of each of the graphics i The mean value of the difference ratio δ between the grayscale mean values ​​of other graphics in the preset area i ;

[0060] In this step, the grayscale mean L of each graph is obtained i , grayscale mean L i The acquisition of can be obtained by image processing or extracting the grayscale mean data of the PL image background; for example, the distribution map of the grayscale value of the entire PL image can be calculated by image processing, and the grayscale mean L on each graph can be calculated by the distribution map. i For another example, the grayscale mean data of the PL image background can be extracted. The background data of the PL image of some PL testers includes the distribution diagram of the grayscale value of the entire battery cell. By dividing the PL image of the battery cell into graphics, all the grayscale values ​​on each graphic can be obtained. By calculating the average value, the grayscale value L of each graphic can be obtained. i .

[0061] Get the grayscale mean L of each graphic i , by calculating the grayscale mean L of each graphic i The mean of the difference ratio with other graphics can be used to determine the uniformity of each graphic with respect to other graphics.

[0062] The purpose of setting the preset area is to analyze the difference in uniformity between each figure and other figures within a relative area range, so as to obtain the uniformity of this figure and other figures.

[0063] Need to calculate the difference ratio mean δ i The graphics to be analyzed may be set in a preset area or not. It is only necessary to obtain the difference in uniformity between a single graphic and other graphics by setting the preset area.

[0064] Preferably, it is necessary to calculate the mean of the difference ratio δ i The graphics to be analyzed are set in the preset area, for example, Figure 3 As shown, the preset area 20 includes 27 graphics, including the graphic 10 to be analyzed.

[0065] More preferably, the area of ​​the preset region is 10 to 100 times the area of ​​the to-be-analyzed graph. For example, it may be 10 times, 15 times, 20 times, 25 times, 26 times, 30 times, 35 times, 40 times, 45 times, 50 times, 55 times, 60 times, 70 times, 80 times, 90 times or 100 times, etc. In a specific embodiment, Figure 3 As shown, the preset area 20 includes 27 graphics, and the area of ​​the preset area is 27 times the area of ​​a single graphic 10. When the area of ​​the preset area is too large compared to the area of ​​the graphics to be analyzed, the mean value of the difference ratio of the graphics to be analyzed is δ i will be smaller; if the area of ​​the preset area is too small compared to the area of ​​the figure to be analyzed, the mean value of the difference ratio of the figure to be analyzed is δ i There will be deviations; both will lead to low accuracy in characterizing the uniformity of the analyzed figure throughout the entire figure.

[0066] The position of the graphics to be analyzed in the preset area may not be fixed. In a specific embodiment, Figure 3 As shown, the graphic setting 10 to be analyzed is set at the rightmost end of the bottom of the preset area 20. Of course, the graphic setting 10 to be analyzed can also be set at the rightmost end of the top of the preset area 20; the graphic setting 10 to be analyzed can also be set at the leftmost end of the top of the preset area 20; or, the graphic setting 10 to be analyzed can also be set at the center of the preset area 20. It only needs to ensure that during the measurement process of the same batch of battery cells, the position of the graphic to be analyzed in the preset area is a unified setting rule.

[0067] In an optional implementation, on a PL image, the area of ​​the preset region and the area of ​​the pattern are fixed. Depending on the position of the PL image, the position of the pattern to be analyzed in the preset region may not be fixed. In a specific embodiment, Figure 4As shown, the PL image is divided into four quadrants, namely, quadrants I, II, III and IV, by the axial center line and radial center line of the PL image. The direction of the axis of the PL image is set as the first direction, and the radial direction is set as the second direction. In quadrants I, II, III and IV, the position of the figure to be analyzed in each quadrant in the preset area is different. In quadrant I, the figure to be analyzed 101 is the first in the first direction and the first in the second direction of the preset area 201; in quadrant II, the figure to be analyzed 102 is the last in the first direction and the first in the second direction of the preset area 202; in quadrant III, the figure to be analyzed 103 is the first in the first direction and the last in the second direction of the preset area 203; in quadrant IV, the figure to be analyzed 104 is the last in the first direction and the last in the second direction of the preset area 204; it is worth noting that in the setting of the above preset area, the figure to be analyzed is fixed in the quadrant, but the preset area is not limited to the quadrant, as shown in FIG. Figure 4 In the figure 101 in the first quadrant close to the central axis of the PL image, the preset area 201 covers the first, second, third and fourth quadrants. This does not affect the calculation of the entire result. It only needs that the position of the figure to be analyzed is in the quadrant and the position of the figure to be analyzed and the preset area are relatively fixed.

[0068] Calculate the grayscale mean L of each of the graphics i The difference ratio mean δi between the grayscale mean of each graphic in the preset area and the grayscale of other graphics in the preset area is obtained by comparing the grayscale mean of each graphic with the grayscale of other graphics in the preset area. i ;For example Figure 3 In the example, there are 27 graphics in the preset area, including 1 graphic to be analyzed 10. First, the grayscale uniformity L of the 27 graphics to be analyzed is calculated. i , where the grayscale mean value of the image 10 to be analyzed is set to L 10 , L 10 Calculate with the other 26 grayscale means to get L 10 The difference ratio of the mean δ 10 , the calculation formula for the mean of the single difference ratio is: (L 10 Difference between the grayscale mean value of other graphics / L 10 ) × 100%. The figure 10 to be analyzed has a difference ratio value with each of the other figures, so there are 26 difference ratio values ​​in total. After averaging these 26 difference ratio values, we can get L 10 The difference ratio of the mean δ 10 . Difference ratio mean δ i The smaller the value, the higher the uniformity of a single graph and other graphs; the mean of the difference ratio δ iThe larger the value, the worse the uniformity of a single pattern compared to other patterns. Each pattern in the entire PL image will get a difference ratio mean δ i Finally, the whole cell will get a number of difference ratios equal to the number of graphics. i .

[0069] Set a preset difference ratio threshold and calculate the mean difference ratio of all the graphics i A proportion value σ higher than the preset difference ratio threshold;

[0070] In this step, through the calculation in the previous step, each graph of the entire cell has a difference ratio mean δ i , the difference ratio mean δ i Characterizes the uniformity of the pattern and other patterns in the predetermined area. If the average difference ratio δ i The higher the proportion of smaller values, the better the uniformity of the entire cell. i The lower the proportion of smaller values, the worse the uniformity of the entire battery cell.

[0071] The preset difference ratio threshold is used to calculate the mean difference ratio of each pattern in the entire cell. i The proportion of graphics with good uniformity.

[0072] In an optional embodiment, the preset difference ratio threshold is ≤10%. For example, it can be 10%, 9%, 8%, 7%, 6%, or 4%. The preset difference ratio threshold can be obtained based on a large amount of empirical data. The preset difference ratio threshold cannot be too large, otherwise the accuracy of characterizing EL brightness is reduced. The preset difference ratio threshold cannot be too small, otherwise some battery components that do not have EL brightness will be screened out, resulting in excessive cost.

[0073] The mean value of the difference ratio of each graph is δ i The values ​​are compared with the preset difference ratio threshold, and finally the difference ratio mean δ higher than the preset difference ratio threshold is obtained. i The proportion value σ.

[0074] Sorting the battery cells according to the grayscale mean value L and the proportion value σ;

[0075] After obtaining the grayscale mean L of the entire cell and the mean value of the difference ratio of each pattern of the entire cell δ i The battery cells are sorted when the proportion value σ is within the preset difference ratio threshold;

[0076] Among them, the battery cells are sorted by the grayscale mean L; the grayscale mean L is 0-255;

[0077] By setting a preset grayscale threshold, the grayscale mean is divided into at least two levels. For example, 120 can be set as the preset grayscale threshold. The battery cell with a grayscale mean L higher than the preset grayscale threshold 120 is in the first level, and the battery cell with a grayscale mean L lower than the preset grayscale threshold 120 is in the second level.

[0078] In an optional manner, the number of preset grayscale thresholds is m, m≥1, the grayscale mean L is compared with the preset grayscale threshold, and the grayscale mean L is divided into at least two grayscale levels.

[0079] In a specific embodiment, m≥2, the preset grayscale thresholds include at least two, and the difference between any two of the preset grayscale thresholds is ≤60. For example, two preset grayscale thresholds can be set, and the two preset grayscale thresholds are 100 and 150 respectively, thereby the battery cells can be divided into three grades according to the grayscale mean L: one grade less than 100, one grade between 100 and 150, and one grade greater than 150; three preset grayscale thresholds can be set, and the three preset grayscale thresholds are 80, 140 and 200 respectively, thereby the battery cells can be divided into four grades according to the grayscale mean L: one grade less than 80, one grade between 80 and 139, one grade between 140 and 200, and one grade greater than 200; four preset grayscale thresholds can be set, and the four preset grayscale thresholds are 70 and 150 respectively. , 130, 190 and 250, thus the battery cells can be divided into five grades according to the grayscale mean L: less than 70, 70-129, 130-189, 190-249 and greater than 255; four preset grayscale thresholds can be set, and the four preset grayscale thresholds are 60, 105, 150, 195 and 240 respectively, thus the battery cells can be divided into six grades according to the grayscale mean L: less than 60, 60-104, 105-149, 150-194, 195-240 and greater than 240; etc.

[0080] Among them, by setting a preset proportion threshold, the proportion value σ is divided into at least two levels. For example, 90% can be set as the preset proportion threshold, then the battery cell proportion value σ is higher than the preset proportion threshold of 90% and is in the first level, and the battery cell proportion value σ is less than the preset proportion threshold of 90% and is in the second level.

[0081] In an optional manner, the number of preset proportion thresholds is n, n≥1, the proportion value σ is compared with the preset proportion threshold, and the proportion value σ is divided into at least two grayscale levels.

[0082] In a specific embodiment, n=1, and the preset proportion threshold is ≥90%; for example, it can be 90% or 95%; for example, the preset proportion threshold can be set to 90%, and the battery cells can be divided into two levels: a level greater than or equal to 90% and a level less than 90%.

[0083] In a specific embodiment, when n=2, the preset proportion threshold includes a first preset proportion threshold and a second preset proportion threshold, and the first preset proportion threshold differs from the second preset proportion threshold by at least 20%; the first preset proportion threshold is ≥90%; for example, it can be 90%, 95%; the second preset proportion threshold is greater than or equal to 60%, for example, it can be 60%, 65%, 70% or 75%. In this way, the battery cells can be divided into three grades. In a more specific embodiment, the first preset proportion threshold is 90% and the second preset proportion threshold is 60%, then the battery cells can be divided into three grades: greater than or equal to 90% is a grade, less than 90% to greater than or equal to 60% is a grade, and less than 60% is a grade.

[0084] Photovoltaic modules are prepared for the cells with the same properties after sorting.

[0085] In this step, by classifying the grayscale mean L and the proportion value σ of the battery cells, the battery cells with the same proportion value level and the same grayscale mean level are identified as battery cells with the same properties, and the battery cells with the same properties are used to prepare the same photovoltaic module.

[0086] The second aspect of the present application discloses a photovoltaic module, which is prepared by the preparation method for reducing EL brightness and darkness of the photovoltaic module according to the first aspect of the present application, and the EL brightness and darkness of the photovoltaic module is low.

[0087] The following describes in detail the preparation method for reducing EL poor brightness of photovoltaic modules according to the first aspect of the present application and the photovoltaic module disclosed in the second aspect with two specific embodiments. It is worth noting that the following description is only an exemplary description and not a specific limitation of the invention.

[0088] Example 1

[0089] A batch of Topcon cells were selected. The cells used N-type monocrystalline silicon wafers as the body and the size of the cells was 182×182mm. The PL image of the cells was measured using Semilab's PL measuring instrument, and the grayscale mean L of the entire PL image and the distribution map of the entire PL grayscale mean were extracted from the background data of the PL measuring instrument.

[0090] like Figure 4 As shown, the PL image is divided into four quadrants by the axial center line and the radial center line of the PL image, and the direction of the axis of the PL image is set as the first direction, and the radial direction is set as the second direction;

[0091] The entire cell is divided into 1444 square figures, including 38 figures in the first direction and 38 figures in the second direction, and the preset area is set to the area of ​​27 figures; in the first quadrant, the figure 101 to be analyzed is set to be the first in the first direction and the first in the second direction of the preset area 201; in the second quadrant, the figure 102 to be analyzed is set to be the last in the first direction and the first in the second direction of the preset area 202; in the third quadrant, the figure 103 to be analyzed is set to be the first in the first direction and the last in the second direction of the preset area 203; in the fourth quadrant, the figure 104 to be analyzed is set to be the last in the first direction and the last in the second direction of the preset area 204;

[0092] First, the grayscale mean L of each figure is obtained through image processing i Then, the grayscale mean of each figure in the preset area is measured with the grayscale mean of the other 26 figures. i , the mean value of the difference ratio of each graph is δ i The calculation formula is Σ(the difference between the grayscale mean of the figure to be analyzed and the grayscale mean of other figures / L 10 )×100% / 26; a total of 1444 difference ratio means δ i .

[0093] Set the preset difference ratio threshold to 10%; calculate the mean value of 1444 difference ratios δ i The mean value of the difference ratio of more than 10% i The proportion value σ.

[0094] Set 8 preset grayscale thresholds, namely 60, 80, 100, 120, 140, 160, 180, and 200; divide the battery cells into 9 levels according to the preset grayscale thresholds; the 9 levels are: 0-59, 60-79, 80-99, 100-119, 120-139, 140-159, 160-179, 179-200, and 201-255.

[0095] Set two preset ratio thresholds, each of which is 90%; divide the battery cells into two levels according to the preset ratio thresholds; the two levels are ≥90% and <90% respectively.

[0096] Table 1 below shows the results of sorting 5376 cells according to the above method. Among them: (1) The number of cells with a preset percentage threshold ≥ 90% is 3789, and the number of cells with a preset percentage threshold < 90% is 1587. (2) The grayscale mean L of the cells is mainly distributed in the four ranges of 80-99, 100-119, 120-139 and 140-159.

[0097] Table 1 The number of cells in each level sorted by the sorting method of Example 1

[0098]

[0099] Example 2

[0100] In Example 2, all the cells in Example 1 were used to prepare photovoltaic modules, and the EL image and EL defect ratio of the photovoltaic modules were measured using Optitech's OPT-M960B EL measuring instrument, as shown in Table 2, and the proportion of the entire batch of photovoltaic modules at each power level was obtained. Figure 5 This is the EL image of the photovoltaic module prepared in Example 2.

[0101] Comparative Example 1

[0102] In Comparative Example 1, Topcon cells from the same batch as those in Example 1 were selected, and photovoltaic modules were prepared by combining cell efficiency sorting with Uoc sorting.

[0103] The battery efficiency Eta is set at 0.1%, and the open circuit voltage Uoc is set at 1mv. Photovoltaic cells with the same battery efficiency Eta level and the same open circuit voltage Uoc level are used to prepare the same photovoltaic module.

[0104] The EL image and EL defect ratio of the photovoltaic modules were measured using Optitech's OPT-M960B EL meter, and the proportion of the entire batch of photovoltaic modules at each power level was obtained. Figure 6 This is the EL image of the photovoltaic module prepared in Comparative Example 1.

[0105] Figure 5 and Figure 6 The photovoltaic modules prepared in Example 2 and Comparative Example 1 are respectively, wherein: Figure 5 (a) and Figure 6 (a) is a 625W photovoltaic module. Figure 5 (b) and Figure 6 (b) is a 630W PV module. Figure 5 (c) and Figure 6 (c) is a 635W PV module. Figure 5 As can be seen from (a), (b) and (c), no matter which gear is used, the brightness of the EL image of the photovoltaic module prepared by the present application is very uniform, and there is basically no EL brightness difference; Figure 6 From (a), (b) and (c), we can see that no matter which gear, Figure 6 There is an obvious difference in the brightness and darkness of the EL image, and the EL brightness-darkness ratio is high.

[0106] Table 2 below shows the relevant data of the photovoltaic modules prepared in Example 2 and Comparative Example 1. It can be seen from Table 2 that: (1) Compared with Comparative Example 1, the EL brightness and darkness of Example 2 has been basically improved, and there is no EL brightness and darkness problem. (2) Compared with Comparative Example 1, for the same batch of cells, the number of high-end photovoltaic modules in Example 2 prepared by the present application accounts for a larger proportion. This is mainly because in the present application, cells with similar electrical performance data are prepared in the same photovoltaic module, and the proportion of high-power gears of the photovoltaic modules prepared in this way is higher, which further proves that the present application is more accurate in sorting cells.

[0107] Table 2 Relevant data of photovoltaic modules prepared in Example 2 and Comparative Example 1

[0108]

[0109] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the present application for which protection is claimed, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in mutually different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0110] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A preparation method for reducing the poor brightness of EL in photovoltaic modules, characterized in that: The method comprises: Perform PL measurement on the cell to obtain a PL image; Obtaining a grayscale mean value L of the entire PL image; dividing the PL image into a plurality of graphics of equal area; Get the grayscale mean L of each of the graphics i , and analyze and calculate the grayscale mean L of each of the graphics i The mean value of the difference ratio δ between the grayscale mean values ​​of other graphics in the preset area i ; The difference ratio mean δ i The calculation formula is Σ(the grayscale mean value L of each of the above graphics i The difference between the grayscale mean of the other graphics / the grayscale mean of each graphic L i ) × 100% / the number of other graphics in the preset area; Set a preset difference ratio threshold and calculate the mean difference ratio of all the graphics i A percentage value σ higher than the preset difference ratio threshold; the preset difference ratio threshold is ≤10%; The battery cells are sorted according to the grayscale mean value L and the proportion value σ; wherein, Setting a preset proportion threshold, the number of the preset proportion thresholds is n, n≥1, comparing the proportion value σ with the preset proportion threshold, and dividing the proportion value σ into at least two proportion levels; Setting a preset grayscale threshold, the number of the preset grayscale thresholds is m, m≥1, comparing the grayscale mean L with the preset grayscale threshold, and dividing the grayscale mean L into at least two grayscale levels; Identifying the battery cells having the same proportion level and the same gray level as battery cells having the same attribute; Photovoltaic modules are prepared for the cells with the same properties after sorting.

2. The preparation method according to claim 1, characterized in that The graphic is a rectangle, a circle, a polygon or an irregular graphic.

3. The preparation method according to claim 1, characterized in that: The area of ​​the graphic is ≤4mm 2 .

4. The preparation method according to claim 1, characterized in that: Need to calculate the difference ratio mean δ i The graphic to be analyzed is set in the preset area, and the area of ​​the preset area is 10 to 100 times the area of ​​the graphic to be analyzed.

5. The preparation method according to claim 1, characterized in that: The proportion value σ is 0%~100%; When n=1, the preset percentage threshold is ≥90%; When n=2, the preset proportion threshold includes a first preset proportion threshold and a second preset proportion threshold, and the difference between the first preset proportion threshold and the second preset proportion threshold is ≥20%; The first preset proportion threshold is ≥90%, and the second preset proportion threshold is ≥60%.

6. The preparation method according to claim 1, characterized in that: The grayscale mean L is 0-255; The m≥2, the preset grayscale thresholds include at least two, and the difference between any two of the preset grayscale thresholds is ≤60.

7. The preparation method according to claim 1, characterized in that: The battery cell is a Topcon battery.

8. A photovoltaic module, characterized in that: The preparation method is adopted according to any one of claims 1 to 7.

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