Binning method combining solar cell EL image detection results with IV test results

Abstract

The present invention discloses a method for combining solar cell EL image detection results with IV test results for grading. The method comprises obtaining the power generation efficiency / power of a cell from an IV volt-ampere characteristic test, obtaining a corresponding cell defect analysis from an EL image analysis, matching the cell power generation efficiency / power to a corresponding efficiency range, listing data on different defect characteristics based on the cell defect analysis, prioritizing the different defect characteristics, performing a final grading calculation based on the grading results of the different defect characteristics, and sequentially performing a weighted defect grading calculation on the cells based on the defect characteristic priority grading. By combining the cell EL image detection results with the IV test results for grading, the present invention can obtain the final grading result by inputting the detection data. This simplifies the original process of performing two independent gradings and transforms the original subjective judgment of the staff into data calculation, thereby making the grading results more scientific and accurate.

Description

Technical Field

[0001] The present invention relates to the field of solar cells, and in particular to a grading method combining EL image detection results and IV test results of solar cells. Background Art

[0002] Testing and grading of solar cells is the last step in their production process. Currently, the main electrical performance tests conducted in the industry include EL image analysis and IV volt-ampere characteristic testing. EL image analysis uses a high-resolution infrared camera to capture near-infrared images of components to obtain and determine component defects. IV volt-ampere characteristic testing is mainly used to obtain the conversion efficiency of components.

[0003] The invention of CN201810794758.5 ​​proposes a method for detecting cell defects using EL image analysis, and the invention of CN201510888919.3 proposes a method for IV testing. However, in traditional production, the results of these two tests are categorized separately, that is, the IV test is first categorized and then the EL test is categorized. Some degraded products after EL categorization need to be manually re-evaluated in combination with the IV results, which results in a huge workload for categorization. At the same time, due to manual re-evaluation, the subjective influence is relatively large, resulting in subjective errors in the categorization results, which is inconvenient for production companies to control product quality and yield. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose a binning method that combines the EL image detection results of solar cells with the IV test results.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: comprising the following steps:

[0006] S1, obtain the power generation efficiency / power of the battery cell from the IV volt-ampere characteristic test;

[0007] S2, obtain the corresponding cell defect analysis from EL image analysis;

[0008] S3, matching the power generation efficiency / power of the battery into the corresponding efficiency range;

[0009] S4, based on the cell defect analysis, lists the data of different defect characteristics, each group of defect characteristics corresponds to a defect category;

[0010] S5, prioritizing different defect features, and sequentially categorizing the defect features into first-priority defect features, second-priority defect features, ..., and Nth-priority defect features;

[0011] S6, performing defect bin weighting calculation on the battery cells in sequence according to the defect feature priority level, and performing the bin weighting calculation for different groups of defect features independently;

[0012] S7, obtaining the classification results of different defect characteristics;

[0013] S8, performing final bin calculation based on the binning results of different defect characteristics;

[0014] S9, obtaining a final grading result, wherein the final grading result includes the grading level of the battery cell and corresponding defect feature information.

[0015] Preferably, there are 10 preset efficiency intervals.

[0016] Preferably, a weighting coefficient is correspondingly set between each group of efficiency intervals and defect characteristics.

[0017] Preferably, the classification result is provided with four gears, abcd, wherein gear a is the highest gear and gear d is the lowest gear.

[0018] Preferably, the defect feature number calculation needs to be performed once in S4.

[0019] Preferably, when the number of defect characteristics is 0, the battery cell is classified into grade a, and when the number of defect characteristics is greater than a preset value, the battery cell is classified into grade d.

[0020] Preferably, the defect characteristics are subjected to a parameter calculation before the grading weighted calculation is performed.

[0021] Preferably, the priority classification is classified according to different situations.

[0022] Preferably, the final classification result is the worst classification result.

[0023] Preferably, the grading results of different groups of defect features are the same, and the final grading result is the grading result of the defect feature with the highest priority.

[0024] The present invention has the following beneficial effects:

[0025] 1. The present invention combines the EL image detection results of the cell with the IV test results to classify the cells. The final classification results can be obtained by inputting the detection data, which simplifies the original process of two independent classifications, saves workload, and is more convenient to use.

[0026] 2. This invention sets weighted coefficients to indicate the tolerance of different power generation efficiencies / powers to different defect characteristics, thereby transforming the original subjective judgment of the staff into data calculation, making the grading results more scientific;

[0027] 3. The grading results obtained by the present invention can not only reflect the quality level of the battery cell, but also reflect the severity of the defect characteristics, thereby helping manufacturers to identify process defects in their products;

[0028] 4. Priority grading allows manufacturers to classify battery cells more appropriately for their application, thereby retaining more high-quality batteries and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the main steps of the present invention;

[0030] Figure 2 This is a flow chart for calculating the grading results of the present invention;

[0031] Figure 3 This is a defect characteristic parameter calculation flow chart of the present invention;

[0032] Figure 4 This is the efficiency range and weighting coefficient interface diagram of the present invention. DETAILED DESCRIPTION

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0034] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or components referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention; the terms "first", "second", and "third" are only used for descriptive purposes and should not be understood as indicating or implying relative importance. In addition, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, an indirect connection through an intermediate medium, or it can be internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] Reference Figure 1-3 , an embodiment provided by the present invention includes the following steps:

[0036] S1, obtain the power generation efficiency / power of the battery cell from the IV volt-ampere characteristic test;

[0037] The power generation efficiency / power of the battery cell is obtained through IV volt-ampere characteristic testing. The power generation efficiency / power corresponds to the power generation effect of the battery cell. The battery cell with high power generation efficiency / power has better power generation effect.

[0038] S2, obtain the corresponding cell defect analysis from EL image analysis;

[0039] EL image analysis mainly determines the production process defects of battery cells. In the actual production process, there are twenty to thirty types of battery cell defects, but in actual measurements, there are only a few types of battery cell process defects.

[0040] S3, matching the power generation efficiency / power of the battery into the corresponding efficiency range;

[0041] In actual production, the defect tolerance for cells with different power generation efficiency / power is different. For the same defect, cells with high power generation efficiency / power can have a higher defect tolerance. By setting the efficiency range, cells with different power generation efficiency / power are divided into different efficiency ranges. After weighted calculation, cells with high power generation efficiency / power can be reasonably classified.

[0042] S4, based on the cell defect analysis, lists the data of different defect characteristics, with each group of defect characteristics corresponding to a defect category;

[0043] In actual production, a battery cell may have one or more defect characteristics, or may not have any defect characteristics.

[0044] S5, prioritizing different defect features, and sequentially classifying the defect features into first-priority defect features, second-priority defect features, ..., and Nth-priority defect features;

[0045] The tolerance for different defect features is also different. Generally speaking, the tolerance for the defect features with the greatest harm is the lowest, and the tolerance for defects with lower harm is higher. Therefore, when performing priority classification, the defect features with the highest harm are generally set to have the highest priority.

[0046] S6, performing defect bin weighting calculation on the cells in sequence according to the defect feature priority level, and performing bin weighting calculation on different groups of defect features independently;

[0047] Different defect characteristics are calculated independently of each other.

[0048] S7, obtaining the classification results of different defect characteristics;

[0049] Each set of defect features will generate a corresponding classification result, which generally reflects the severity of the defect.

[0050] S8, performing final bin calculation based on the binning results of different defect characteristics;

[0051] S9, obtaining a final grading result, which includes the grading level of the battery cell and corresponding defect feature information.

[0052] In the actual binning process, different binning results may be obtained due to different defect characteristics. The final binning result is to take a final binning result from all the binning results. This binning result is the binning of the battery cell, and the corresponding defect characteristic is the defect with the greatest severity.

[0053] Furthermore, there are 10 preset efficiency intervals.

[0054] The setting of efficiency range is based on the actual product. In the actual production process, the power generation efficiency / power of battery cells from different manufacturers is different, and the power generation efficiency / power of different types of battery cells is also different. Therefore, the division of efficiency range is artificially set to facilitate the guidance of different manufacturers to set and use according to their own conditions.

[0055] Furthermore, a weighting coefficient is set correspondingly between each group of efficiency intervals and defect characteristics.

[0056] In actual use, the defect tolerance of cells with different power generation efficiencies / powers is different. Therefore, a weighting coefficient needs to be set for cells with different power generation efficiencies / powers. This weighting coefficient reflects the cell's tolerance for defect characteristics. Therefore, in actual use, different efficiency ranges have different weighting coefficients for the same defect characteristics, and the same efficiency range has different weighting coefficients for different defect characteristics. This weighting coefficient can be set by yourself, making it convenient for different manufacturers to use.

[0057] The grading results are set into four gears: a, b, c, and d, where a is the highest gear and d is the lowest gear.

[0058] The grading results indicate different grades of different battery cells, with grade A being the highest grade, corresponding to the best quality battery cells, and grades B, C, and D corresponding to battery cells of good to poor quality.

[0059] Furthermore, a defect feature number calculation needs to be performed in S4.

[0060] The number of defect features corresponds to the type of defect features, and calculating the number of defect features can directly classify particularly good (no defect features) and particularly low quality (many defect features), which is conducive to simplifying data processing.

[0061] Furthermore, when the number of defect characteristics is 0, the battery cell is classified into grade a, and when the number of defect characteristics is greater than a preset value, the battery cell is classified into grade d.

[0062] If the number of defect features is 0, it means that the battery cell has no defect features, so the battery cell can be directly classified into grade A. At the same time, a preset value can also be set. When the defect features are greater than the preset value, it means that there are too many defect features on the battery cell, and it will be directly classified into grade D.

[0063] Furthermore, the defect characteristics are subjected to a parameter calculation before the bin weighting calculation is performed.

[0064] The parameters of parameter calculation can be set to further screen defect characteristics. For example, for the defect characteristic of scratches, if the parameter is set to be greater than 2mm, then scratches smaller than 2mm will not be included in the grading weighted calculation.

[0065] Furthermore, the priority levels are graded according to different situations.

[0066] In the actual production process, different manufacturers have different equipment and processes, and their products have different defect characteristics. Therefore, some defect characteristics are more harmful, but they are not necessarily the defect characteristics with the highest priority. When in use, the priority level can be set according to different situations, which is conducive to different manufacturers to find the largest process defect characteristics of their products and solve them accordingly.

[0067] Furthermore, the final classification result is the worst classification result.

[0068] The worst grading result indicates that the severity of the defect feature is the highest. For example, a battery cell has three groups of defect features: scratches, boat marks and hidden cracks. According to the grading of scratches, the battery cell is classified as grade A, according to the grading of boat marks, the battery cell is classified as grade B, and according to the grading of boat marks, the battery cell is classified as grade C. Then the grading of this battery cell is grade C for hidden cracks.

[0069] The grading results of different groups of defect features are the same, and the final grading result is the grading result of the defect feature with the highest priority.

[0070] When the classification results are the same, according to the priority principle, for example, there are three groups of defect characteristics: scratches, boat marks and hidden cracks, which are all classified as Class B. If scratches have the highest priority, the final classification result will be Class B scratches.

[0071] Working principle: Combine the IV (volt-ampere) electrical performance parameters with the EL pattern detection results. First, obtain the power generation efficiency / power and defect feature data of the battery cell through IV (volt-ampere) characteristic test and EL pattern detection. Then, divide the battery cell into different efficiency intervals according to the power generation efficiency / power. A weighting coefficient is set for each defect feature in each efficiency interval. Therefore, the grading result of the corresponding defect feature can be obtained after weighted calculation. Then, the grading results of all defect features are calculated to obtain the final grading result of the battery cell.

[0072] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A binning method combining solar cell EL image detection results with IV test results, characterized by: The following steps are involved: S1, obtain the power generation efficiency / power of the battery cell from the IV volt-ampere characteristic test; S2, obtain the corresponding cell defect analysis from EL image analysis; S3, matching the power generation efficiency / power of the cell into a corresponding efficiency range; a weighting coefficient is set correspondingly between each set of the efficiency range and the defect characteristic; the weighting coefficient reflects the cell's tolerance to the defect characteristic; S4, based on the cell defect analysis, lists the data of different defect characteristics, each group of defect characteristics corresponds to a defect category; S5, prioritizing different defect features, and sequentially categorizing the defect features into first-priority defect features, second-priority defect features, ..., and Nth-priority defect features; S6, performing defect bin weighting calculation on the battery cells in sequence according to the defect feature priority level, and performing the bin weighting calculation for different groups of defect features independently; S7, obtaining the classification results of different defect characteristics; S8, performing final bin calculation based on the binning results of different defect characteristics; S9, obtaining a final grading result, wherein the final grading result includes the grading level of the battery cell and corresponding defect feature information.

2. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: There are 10 preset efficiency intervals.

3. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: The classification results are provided with four gears, abcd, wherein gear a is the highest gear and gear d is the lowest gear.

4. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: In the above-mentioned S4, the number of defect features needs to be calculated once.

5. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 4, characterized in that: When the number of defect characteristics is 0, the battery cell is classified into grade a; when the number of defect characteristics is greater than a preset value, the battery cell is classified into grade d.

6. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: The defect characteristics are subjected to a parameter calculation before the bin weighted calculation is performed.

7. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: The priority classification is classified according to different situations.

8. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: The final classification result is the worst classification result.

9. The method for combining EL image detection results and IV test results of a solar cell with a binning method according to claim 1, characterized in that: The classification results of different groups of defect features are the same, and the final classification result is the classification result of the defect feature with the highest priority.

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