Battery hot spot risk assessment method and device

Abstract

The invention provides a battery hot spot risk assessment method and device, and relates to the photovoltaic technical field, and the battery hot spot risk assessment method comprises the steps: obtaining a reverse bias EL image of a battery piece; the reverse bias EL image is input into an evaluation model, a battery hot spot risk evaluation result output by the evaluation model is obtained, and the battery hot spot risk evaluation result comprises temperature distribution of the battery piece under the reverse bias voltage. The risk of screening hot spots or the cost of removing hot spot battery pieces can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic technology, and in particular to a method and device for assessing battery hot spot risk. Background Art

[0002] When photovoltaic modules are used outdoors, one or more cells (cells can also be called batteries) in a group of cells in a string often produce current mismatch due to shielding. The shielded cells will become the load in the circuit, bearing the reverse bias voltage generated by the other cells in the group of cells, and consuming electrical energy to generate heat, causing the temperature of the shielded cells to rise sharply. On the module, the temperature of a certain cell is much higher than the normal operating temperature, and it appears as a high-temperature spot or area in the infrared thermal imaging image, which is the module hot spot.

[0003] Currently, most of the focus is on monitoring hot spots in components. Thermal imaging technology is used to retrieve high-temperature areas or hot spots in products that have been prepared into components. If the component is found to be unqualified in the hot spot test, that is, the hot spot temperature is higher than the quality inspection standard, even if only one battery cell is unqualified, the entire component must be scrapped, resulting in high costs for screening hot spot risks or removing hot spot batteries. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a battery hot spot risk assessment method, apparatus and computer equipment, which can reduce the cost of screening hot spot risks or removing hot spot battery cells.

[0005] In a first aspect, an embodiment of the present invention provides a battery hot spot risk assessment method, the method comprising:

[0006] Obtain the reverse EL image of the cell;

[0007] The reverse bias EL image is input into the evaluation model to obtain the battery hot spot risk assessment result output by the evaluation model. The battery hot spot risk assessment result includes the temperature distribution of the battery cell under the reverse bias voltage.

[0008] The beneficial effects of this application are: hot spot risk assessment can be achieved at the battery end, high-risk battery cells can be removed in advance, and the screening cost is greatly reduced compared to the component end; it is adapted to the existing production line and only needs to be equipped with an EL test module, and the machine modification cost is low. Therefore, it can reduce the cost of screening hot spot risks or removing hot spot cells.

[0009] Optionally, the reverse biased EL image is input into the evaluation model to obtain the battery hot spot risk assessment result output by the evaluation model, including:

[0010] Obtaining a first gray value image according to the reverse-biased EL image;

[0011] Data processing is performed on the first gray value image to obtain a battery hot spot risk assessment result.

[0012] Advantages of the present application: The leakage current distribution and relative intensity of the solar cell have an impact on the hot spot temperature, which can be used to evaluate the risk of hot spots at the cell level; for the EL image, the gray value represents the EL intensity. Once the relationship between the EL intensity and the leakage current is established, the reverse-biased EL image can be converted into a leakage current distribution map, thereby obtaining the evaluation result of the battery hot spot risk.

[0013] Optionally, obtaining a first gray value map from the reverse-biased EL image includes:

[0014] Converting the reverse-biased EL image into a pixel gray value map to obtain a second gray value map;

[0015] Removing the background noise in the second gray value map to obtain the first gray value map.

[0016] Advantages of the present application: By removing the background noise and only retaining the effective signals, the accuracy of the evaluation result of the battery hot spot risk can be improved.

[0017] Optionally, performing data processing on the first gray value map to obtain the evaluation result of the battery hot spot risk, including:

[0018] Taking the nth root of the gray value in the first gray value map to obtain a leakage current distribution map;

[0019] Performing regional integration on the leakage current distribution map to obtain the evaluation result of the battery hot spot risk.

[0020] Advantages of the present application: According to the relationship between the leakage current and the EL intensity, taking the nth root of the gray value (representing the EL intensity) to obtain a leakage current distribution map; obtaining the evaluation result of the battery hot spot risk by performing regional integration on the leakage current distribution map, which is convenient for users to view the heating position and hot spot temperature of the battery under reverse bias voltage.

[0021] Optionally, performing regional integration on the leakage current distribution map to obtain the evaluation result of the battery hot spot risk, including:

[0022] Dividing the leakage current distribution map into regions;

[0023] Integrating the leakage current in each region of the leakage current distribution map to obtain the evaluation result of the battery hot spot risk.

[0024] Advantages of the present application: The evaluation model considers the correction of heat transfer by the leakage current density distribution through regional integration of the leakage current distribution map, and can well predict the heating position and hot spot temperature of the solar cell under reverse bias voltage.

[0025] Optionally, dividing the leakage current distribution map into regions includes:

[0026] Divide the leakage current distribution map into regions in units of the first numerical value * the first numerical pixel.

[0027] Advantageous effects of the present application: Divide the leakage current distribution map into regions in units of the first numerical value * the first numerical pixel, so that each divided region is a square region. After regional integration, the data corresponding to each square region in the obtained battery hot spot risk assessment result is presented in the form of a column, making the display of the battery hot spot risk assessment result more intuitive and beautiful, and facilitating users to view the heating position and hot spot temperature of the battery under reverse bias voltage.

[0028] Optionally, integrate the grayscale values of each region in the leakage current distribution map to obtain the battery hot spot risk assessment result, including:

[0029] Integrate the leakage current of each region in the leakage current distribution map;

[0030] Multiply the integrated value of each region by the relative coefficient corresponding to the region to obtain the battery hot spot risk assessment result.

[0031] Advantageous effects of the present application: After regional integration, multiply the integrated value of each region by the relative coefficient corresponding to each region. Since the same leakage current occurs in the center or corner of the battery cell, its heat generation and temperature rise effects are different, so the relative coefficient is a correction for heat dissipation and temperature rise. The evaluation model comprehensively considers the leakage current density distribution and the correction of the battery edge effect on heat transfer, and can well predict the heating position and hot spot temperature of the battery under reverse bias voltage, so as to screen out risk battery cells.

[0032] Optionally, it further includes:

[0033] Determine whether to reject the battery cell according to the battery hot spot risk assessment result.

[0034] Advantageous effects of the present application: Since the evaluation model can well predict the heating position and hot spot temperature of the battery under reverse bias voltage, the risk battery cells that need to be rejected can be accurately screened out according to the battery hot spot risk assessment result.

[0035] Optionally, it further includes:

[0036] Obtain the infrared image of the battery cell under a constant reverse bias voltage;

[0037] According to the infrared image and the battery hot spot risk assessment result, obtain the reliable verification result of the evaluation model.

[0038] Advantageous effects of the present application: The reliability of the evaluation model can be verified through the infrared image of the battery cell under a constant reverse bias voltage.

[0039] On the other hand, an embodiment of the present invention provides a battery hot spot risk assessment device, and the device includes:

[0040] An acquisition module, configured to acquire the reverse-biased EL image of a battery cell;

[0041] A processing module, configured to input the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model, where the battery hot spot risk assessment result includes the temperature distribution of the battery cell under reverse bias voltage.

[0042] Advantages of the present application: Hot spot risk assessment can be realized at the battery end, high-risk battery cells can be removed in advance, and the screening cost is greatly reduced compared with the module end; it is adapted to the existing production line, only an EL test module needs to be equipped, and the machine transformation cost is low. Therefore, the cost of screening hot spot risk or removing hot spot cells can be reduced.

[0043] On the other hand, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, and when the program runs, it controls the device where the storage medium is located to execute the above battery hot spot risk assessment method.

[0044] On the other hand, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the above battery hot spot risk assessment method are implemented.

[0045] In the technical solution of a battery hot spot risk assessment method and device provided by an embodiment of the present invention, the battery hot spot risk assessment method includes: acquiring the reverse-biased EL image of a battery cell; inputting the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model, where the battery hot spot risk assessment result includes the temperature distribution of the battery cell under reverse bias voltage. The cost of screening hot spot risk or removing hot spot battery cells can be reduced. Description of the Drawings

[0046] Figure 1 It is a flowchart of a battery hot spot risk assessment method provided by an embodiment of the present invention;

[0047] Figure 2 It is a schematic diagram of the IV characteristic curve of a battery;

[0048] Figure 3 It is a schematic diagram of the battery hot spot risk assessment process in an embodiment of the present invention;

[0049] Figure 4 For Figure 1 It is a flowchart of inputting the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model;

[0050] Figure 5 For Figure 4Flow chart of obtaining the first grayscale value image according to the reverse-biased EL image;

[0051] Figure 6 For Figure 4 Flow chart of performing data processing on the first grayscale value image to obtain the battery hot spot risk assessment result in;

[0052] Figure 7 For Figure 6 Flow chart of performing regional integration on the leakage current distribution map to obtain the battery hot spot risk assessment result in;

[0053] Figure 8 Schematic diagram of the infrared images of different battery cells and the battery hot spot risk assessment results in the embodiments of the present invention;

[0054] Figure 9 Schematic structural diagram of a battery hot spot risk assessment device provided by an embodiment of the present invention;

[0055] Figure 10 Schematic diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0056] For better understanding of the technical solutions of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0058] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms of "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0059] It should be understood that the term " / and / " used herein is only a description of the associated relationship of the associated objects, indicating that three relationships may exist, for example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.

[0060] Figure 1 Flow chart of a battery hot spot risk assessment method provided by an embodiment of the present invention, as Figure 1 shown, the method includes: Step 101 - Step 102.

[0061] Step 101: Obtain the reverse-biased EL image of the cell.

[0062] In this step, an electro-luminescence (EL) tester can be used to collect the reverse-biased EL image of the cell, and then the reverse-biased EL image is stored in a computer device. When the computer device executes the battery hot spot risk assessment method provided in the embodiments of the present invention, the reverse-biased EL image is obtained according to the storage location of the reverse-biased EL image.

[0063] Among them, the cell can be a tunnel oxide passivating contact (TOPCON) solar cell.

[0064] In the embodiments of the present invention, the non-uniform heating during the hot spot formation process is caused by the local leakage current of the cell. The reverse-biased EL image contains information about the leakage current, and the leakage current is positively correlated with the EL intensity.

[0065] Exemplarily, Figure 2 is a schematic diagram of the IV characteristic curve of the battery. In a string of m solar cells, if one of the cells is shaded, as Figure 2 shown, the red curve is the current-voltage (IV) characteristic curve of a shaded cell, the gray curve is the IV characteristic curve of the remaining (m - 1) non-shaded cells, and the black curve is the IV characteristic curve of the (m - 1) non-shaded cells and 1 shaded cell. Among them, the heating power P of a shaded cell (also called a shadow cell) can be expressed as:

[0066] P = U rev × I′ m (1)

[0067] I′ m = I rev + I′ sc (2)

[0068] Among them, U rev is the reverse bias voltage of the shadow cell, I′ m is the maximum power point current of the cell, and I rev and I′ sc are the leakage current and short-circuit current of the shadow cell.

[0069] Generally speaking, the heat generated by the hot spot is unevenly distributed in the sunshade groove, which can be divided into uniform heating and non-uniform heating. The uniformly heated part is generated by the photo-generated current, while the non-uniformly heated part is generated by the non-uniform leakage current under the reverse bias caused by the micro-defects of the cell. The power P1 of the uniform heating and the power P2 of the non-uniform heating can be expressed as:

[0070] P1 = U rev × I′ sc (3)

[0071] P2 = U rev × I rev (4)

[0072] Reverse bias voltage U rev is determined by the number of a string of batteries, and the leakage current is related to the battery itself. Since non-uniform heating easily causes local heat concentration, resulting in an increase in the internal temperature of the battery, the distribution and relative intensity of the leakage current should have an impact on the hot spot temperature and can be used as a starting point for evaluating the hot spot risk at the battery level.

[0073] Exemplarily, Figure 3 is a schematic diagram of the battery hot spot risk assessment process in an embodiment of the present invention. Taking a TOPCON solar cell as an example, first, an EL image of the cell is obtained as the original data. Figure 3 (a) and Figure 3 (b) are EL images under forward and reverse bias voltages, namely the forward bias EL image and the reverse bias EL image, respectively. Figure 3 In the reverse bias EL image shown in (b), some white dots appear, indicating the positions of the leakage current.

[0074] Among them, the forward bias EL image is used for positioning, and the reverse bias EL image is input into the evaluation model for battery hot spot risk assessment.

[0075] Step 102: Input the reverse bias EL image into the evaluation model to obtain the battery hot spot risk assessment result output by the evaluation model. The battery hot spot risk assessment result includes the temperature distribution of the cell under the reverse bias voltage.

[0076] In some possible embodiments, as Figure 4 shown, step 102 includes: step 1021 - step 1022.

[0077] Step 1021: Obtain a first grayscale value map according to the reverse bias EL image.

[0078] In some possible embodiments, as Figure 5 shown, step 1021 includes: step S11 - step S12.

[0079] Step S11: Convert the reverse bias EL image into a pixel grayscale value map to obtain a second grayscale value map.

[0080] As Figure 3 shown, as Figure 3 (d) is the second grayscale value map. By converting the reverse bias EL image shown in Figure 3 (b) into the one shown in Figure 3As shown in the pixel grayscale value diagram of (d), it can be found that high grayscale value spikes appear at the positions where the white dots are located in the reverse-biased EL image.

[0081] Step S12: Remove the background noise in the second grayscale value diagram to obtain the first grayscale value diagram.

[0082] As Figure 3 shown, as Figure 3 (e) is the first grayscale value diagram. After removing the background noise from the second grayscale value diagram shown in Figure 3 (d), the first grayscale value diagram only retains the effective signals and contains leakage current information, which can improve the accuracy of the battery hot spot risk assessment result.

[0083] Step 1022: Perform data processing on the first grayscale value diagram to obtain the battery hot spot risk assessment result.

[0084] In some possible embodiments, as Figure 6 shown, step 1022 includes: step S21 - step S22.

[0085] Step S21: Take the nth root of the grayscale values in the first grayscale value diagram to obtain the leakage current distribution diagram.

[0086] To analyze the local leakage current of the solar cell, the reverse-biased EL image is used as the original data, which contains the information of the leakage current. Theoretically, once the relationship between the EL intensity and the leakage current is established, the reverse-biased EL image can be converted into a leakage current distribution diagram. For the EL image, the grayscale value represents the EL intensity, that is, the number of photons collected per unit time. The photon generation rate can be expressed as:

[0087]

[0088] where E is the photon energy, x is the position on the cell, h is the Planck constant, c is the speed of light, α(E) is the photon absorption coefficient, k is the Boltzmann constant, q is the electron charge, V a is the bias voltage applied to the cell, d is the cell thickness, and L n is the electron diffusion length. During the EL test, the only artificially set parameter is the applied bias voltage, and equation (5) can be expressed as a function of V a :

[0089]

[0090] On the other hand, the injected current J can be expressed by the equation:

[0091]

[0092] Among them, J0 is the dark saturation current, and n is the diode ideality factor. From Equations (6) and (7), the relationship between the injection current J and the EL intensity R can be obtained:

[0093]

[0094] Among them, C and C′ are constants for a specific type of solar cell. It should be noted that in the relationship between R and J, a diode ideality factor n appears. When the EL intensity R is the photon generation rate under reverse bias voltage, the injection current J is the leakage current, and Equation (8) provides a theoretical basis for the grayscale value data processing step S21 in the evaluation model.

[0095] As Figure 3 shown, taking the nth root of the grayscale values in the first grayscale value map shown in Figure 3 (e) obtains a leakage current distribution map as shown in Figure 3 (f). The values in the leakage current distribution map can represent the intensity of the leakage current.

[0096] Step S22: Perform regional integration on the leakage current distribution map to obtain the battery hot spot risk assessment result.

[0097] As Figure 3 shown, performing regional integration on the leakage current distribution map shown in Figure 3 (f) obtains the battery hot spot risk assessment result as shown in Figure 3 (g).

[0098] In some possible embodiments, as Figure 7 shown, step S22 includes: step S31 - step S32.

[0099] Step S31: Divide the leakage current distribution map into regions.

[0100] In the embodiments of the present invention, the leakage current distribution map is divided into regions in units of the first value * the first value of pixels, so that each divided region is a square region.

[0101] Exemplarily, the first value is 100, and dividing the leakage current distribution map shown in Figure 3 (f) into regions in units of 100 * 100 pixels can obtain 4 × 8 different square regions.

[0102] Step S32: Integrate the leakage current in each region of the leakage current distribution map to obtain the battery hot spot risk assessment result.

[0103] In some possible embodiments, the leakage current in each region of the leakage current distribution map is integrated to directly obtain the battery hot spot risk assessment result. After dividing the leakage current distribution map into regions in the embodiments of the present invention, by integrating the leakage current in each region of the leakage current distribution map, the battery hot spot risk assessment result is directly obtained, taking into account the correction of heat transfer by the leakage current density distribution, and can well predict the heating position and hot spot temperature of the battery cell under reverse bias voltage.

[0104] After the embodiments of the present invention divide the leakage current distribution map into regions in units of the first numerical value * the first numerical value pixel and integrate each region, the data corresponding to each square region in the battery hot spot risk assessment result shown in Figure 3 (g) is presented in the form of a column, making the display of the battery hot spot risk assessment result more intuitive and beautiful, facilitating the user to view the heating position and hot spot temperature of the battery cell under reverse bias voltage.

[0105] In some possible embodiments, after integrating the leakage current in each region of the leakage current distribution map, the integrated value of each region is further multiplied by a relative coefficient corresponding to the region to obtain the battery hot spot risk assessment result. Since the same leakage current occurs at the center or corner of the battery cell, the heat generation and temperature rise effects are different, so the relative coefficient is a correction for heat dissipation and temperature rise. The evaluation model comprehensively considers the leakage current density distribution and the correction of heat transfer by the battery edge effect, and can well predict the heating position and hot spot temperature of the battery cell under reverse bias voltage, thereby screening out risky battery cells.

[0106] Exemplarily, as Figure 3 (g) shows, the battery hot spot risk assessment result shows the output values of each region. The larger the output value, the higher the risk of hot spot formation, indicating that under reverse bias voltage, this region heats up faster and can reach a higher temperature.

[0107] In the embodiments of the present invention, the battery cells that can be eliminated can also be determined according to the battery hot spot risk assessment result. Since the evaluation model can well predict the heating position and hot spot temperature of the battery cell under reverse bias voltage, the risky battery cells that need to be eliminated can be accurately screened out according to the battery hot spot risk assessment result.

[0108] In order to verify the reliability of the evaluation model in the embodiments of the present invention, an infrared image (also called a thermal imaging map) of the battery cell under a constant reverse bias voltage can be obtained, and a reliable verification result of the evaluation model can be obtained according to the infrared image and the battery hot spot risk assessment result. To characterize the heat generated by the leakage current, a constant reverse bias voltage of -16V is applied to the solar cell, and its infrared image is captured with an infrared camera, as shown in Figure 3 (c). The infrared image shows that the heat is mainly concentrated on one side of the battery cell, resulting in two high temperature points of 150.7°C and 82.4°C.Figure 3 (c) The measured temperature distribution shown is Figure 3 in good agreement with the output of the evaluation model shown in (g), verifying the reliability of the evaluation model and making it applicable for predicting the heating location and temperature. Therefore, based on the output of the evaluation model, it is considered that the cells with concentrated heating points and high temperatures can be effectively removed, thereby reducing the hot spot risk.

[0109] Meanwhile, a fixed reverse bias voltage (-16V) is applied to the cells, and the heat distribution on their surfaces is captured by an infrared thermal imager to obtain the maximum temperature on the surface of each cell.

[0110] Exemplarily, Figure 8 FIG. is a schematic diagram of the infrared images and the battery hot spot risk evaluation results of different cells in the embodiments of the present invention. As Figure 8 shown, Figure 8 (a)- Figure 8 (d) are the thermal images of different cells measured under reverse bias voltage, which can reflect the temperature and heat distribution on the cell surfaces. Figure 8 (e)- Figure 8 (h) are the evaluation results output after the corresponding cells are calculated by the evaluation model. The relative high and low of the evaluation results in each region can be used as a prediction of the temperature distribution on the cell surface under reverse bias voltage.

[0111] By comparing the infrared images with the output values of the evaluation model, it can be found that there is a good consistency between the relative high and low of the output parameters in each region on the cell and the temperature distribution in the infrared images. The evaluation model fully considers the heat transfer conditions at the above different positions, corrects the temperature distribution of the cells by using regional integration and relative coefficients, and finally achieves a good prediction of the hot spot temperature and location.

[0112] The embodiments of the present invention consider that: although occlusion is likely to cause the cells to generate heat and increase in temperature, the temperatures reached by different cells under the same occlusion conditions are different, that is, their hot spot risks are different. Therefore, differentiating the cells with high and low hot spot risks at the battery end and removing the cells that are prone to generate high temperatures (high hot spot temperatures) under reverse bias voltage can, to a certain extent, reduce the hot spot temperature of the module.

[0113] In an embodiment of the present invention, an evaluation model for assessing the hot spot risk of TOPCon cells based on reverse-biased EL images is established. The evaluation model takes the reverse-biased EL image of the cell as the only input data. After gray value numerical processing and regional weight integration, it outputs the evaluation result of the battery hot spot risk, which reflects the hot spot temperature distribution of the cell under reverse bias voltage. Based on this evaluation result, the cells with higher temperature under reverse bias voltage can be screened out, realizing the elimination of high-hot-spot-risk cells at the battery end without affecting the production of the module. It is adapted to the existing industrial production. Hardware-wise, only an EL tester is required, and the cost of machine transformation is low. Since the evaluation model only needs to input the reverse-biased EL image, and EL equipment is very common in photovoltaic manufacturing, the method of the embodiment of the present invention can be directly applied to existing photovoltaic manufacturers without reconstructing the production line and can be quickly applied to the production line.

[0114] An embodiment of the present invention proposes a method for quickly estimating the hot spot risk of TOPCon cells based on reverse-biased EL images. By comprehensively considering the leakage current density distribution and the correction of heat transfer by the battery edge effect, an evaluation model is established. According to the output of the evaluation model, the heating position and hot spot temperature of the cell under reverse bias voltage can be well predicted, so as to screen out the risky cells.

[0115] In the technical solution of a battery hot spot risk assessment method provided by an embodiment of the present invention, the battery hot spot risk assessment method includes: obtaining the reverse-biased EL image of the cell; inputting the reverse-biased EL image into the evaluation model to obtain the evaluation result of the battery hot spot risk output by the evaluation model, and the evaluation result of the battery hot spot risk includes the temperature distribution of the cell under reverse bias voltage. It can reduce the cost of screening hot spot risks or eliminating hot spot cells.

[0116] Figure 9 It is a schematic structural diagram of a battery hot spot risk assessment device provided by an embodiment of the present invention, as Figure 9 shown, the device includes:

[0117] An acquisition module 11, configured to acquire the reverse-biased EL image of the cell;

[0118] A processing module 12, configured to input the reverse-biased EL image into the evaluation model to obtain the evaluation result of the battery hot spot risk output by the evaluation model, and the evaluation result of the battery hot spot risk includes the temperature distribution of the cell under reverse bias voltage.

[0119] In some possible embodiments, the processing module 12 is specifically configured to: obtain a first gray value map according to the reverse-biased EL image; perform data processing on the first gray value map to obtain the evaluation result of the battery hot spot risk.

[0120] In some possible embodiments, the processing module 12 is specifically configured to: convert the reverse-biased EL image into a pixel grayscale value map to obtain a second grayscale value map; remove background noise in the second grayscale value map to obtain a first grayscale value map.

[0121] In some possible embodiments, the processing module 12 is specifically configured to: take the nth root of the grayscale values in the first grayscale value map to obtain a leakage current distribution map; perform regional integration on the leakage current distribution map to obtain a battery hot spot risk assessment result.

[0122] In some possible embodiments, the processing module 12 is specifically configured to: divide the leakage current distribution map into regions; integrate the leakage current in each region of the leakage current distribution map to obtain a battery hot spot risk assessment result.

[0123] In some possible embodiments, the processing module 12 is specifically configured to: divide the leakage current distribution map into regions in units of the first numerical value * the first numerical value pixels.

[0124] In some possible embodiments, the processing module 12 is specifically configured to: integrate the leakage current in each region of the leakage current distribution map; multiply the integrated numerical value of each region by a relative coefficient corresponding to the region to obtain a battery hot spot risk assessment result.

[0125] In some possible embodiments, the processing module 12 is further configured to: determine whether to reject the battery cell according to the battery hot spot risk assessment result.

[0126] In some possible embodiments, the acquisition module 11 is further configured to: acquire an infrared image of the battery cell under a constant reverse bias voltage; the processing module 12 is further configured to: obtain a reliable verification result of the evaluation model according to the infrared image and the battery hot spot risk assessment result.

[0127] In the technical solution provided by the embodiment of the present invention, a reverse-biased EL image of a battery cell is acquired; the reverse-biased EL image is input into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model, and the battery hot spot risk assessment result includes the temperature distribution of the battery cell under the reverse bias voltage. It can reduce the cost of screening hot spot risks or rejecting hot spot battery cells.

[0128] An embodiment of the present application provides a storage medium, where the storage medium includes a stored program, and when the program runs, it controls the device where the storage medium is located to execute the above battery hot spot risk assessment method.

[0129] An embodiment of the present application provides a computer device, including a memory and a processor, the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the above battery hot spot risk assessment method are implemented.

[0130] Figure 10 A schematic structural diagram of a computer device provided by an embodiment of the present application is shown as Figure 10 shown. The computer device 20 includes: a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and executable on the processor 21. When the computer program 23 is executed by the processor 21, it implements the battery hot spot risk assessment method in the embodiment. To avoid repetition, it will not be elaborated here one by one.

[0131] The computer device 20 includes, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that Figure 10 merely examples of the computer device 20 do not constitute a limitation on the computer device 20. It may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device 20 may also include input / output devices, network access devices, buses, etc.

[0132] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0133] The memory 22 may be an internal storage unit of the computer device 20, such as the hard disk or memory of the computer device 20. The memory 22 may also be an external storage device of the computer device 20, such as a plug-in hard disk equipped on the computer device 20, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 22 may also include both the internal storage unit and the external storage device of the computer device 20. The memory 22 is used to store the computer program and other programs and data required by the computer device 20. The memory 22 may also be used to temporarily store data that has been output or will be output.

[0134] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0135] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0138] The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0139] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for evaluating the risk of battery hot spots, characterized in that, The method includes: Obtaining a reverse-biased EL image of a solar cell; Inputting the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model, where the battery hot spot risk assessment result includes the temperature distribution of the solar cell under reverse bias voltage.

2. The battery hot spot risk assessment method according to claim 1, wherein The step of inputting the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model includes: Obtaining a first grayscale value map according to the reverse-biased EL image; Performing data processing on the first grayscale value map to obtain the battery hot spot risk assessment result.

3. The battery hot spot risk assessment method according to claim 2, wherein The step of obtaining a first grayscale value map according to the reverse-biased EL image includes: Converting the reverse-biased EL image into a pixel grayscale value map to obtain a second grayscale value map; Removing background noise in the second grayscale value map to obtain the first grayscale value map.

4. The battery hot spot risk assessment method according to claim 2 or 3, characterized in that, The step of performing data processing on the first grayscale value map to obtain the battery hot spot risk assessment result includes: Taking the nth root of the grayscale values in the first grayscale value map to obtain a leakage current distribution map; Performing regional integration on the leakage current distribution map to obtain the battery hot spot risk assessment result.

5. The battery hot spot risk assessment method according to claim 4, wherein, The step of performing regional integration on the leakage current distribution map to obtain the battery hot spot risk assessment result includes: Dividing the leakage current distribution map into regions; Integrating the leakage current in each region of the leakage current distribution map to obtain the battery hot spot risk assessment result.

6. The battery hot spot risk assessment method according to claim 5, wherein The step of dividing the leakage current distribution map into regions includes: Dividing the leakage current distribution map into regions with a unit of first value * first value pixels.

7. The battery hot spot risk assessment method according to claim 5, wherein The step of integrating the grayscale values in each region of the leakage current distribution map to obtain the battery hot spot risk assessment result includes: Integrating the leakage current in each region of the leakage current distribution map; Multiplying the integrated numerical value of each region by a relative coefficient corresponding to the region to obtain the battery hot spot risk assessment result.

8. The battery hot spot risk assessment method according to claim 1, characterized in that, It further includes: Determining whether to reject the solar cell according to the battery hot spot risk assessment result.

9. The battery hot spot risk assessment method according to claim 1, wherein It further includes: Obtaining an infrared image of the solar cell under a constant reverse bias voltage; Obtaining a reliable verification result of the evaluation model according to the infrared image and the battery hot spot risk assessment result.

10. A battery hot spot risk assessment device, characterized in that, It includes: An acquisition module for obtaining a reverse-biased EL image of a solar cell; A processing module for inputting the reverse-biased EL image into an evaluation model to obtain a battery hot spot risk assessment result output by the evaluation model, where the hot spot risk assessment result includes the temperature distribution of the solar cell under reverse bias voltage.

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