Battery detection method, device, apparatus and readable storage medium

Abstract

The application discloses a battery detection method, device and equipment and a readable storage medium. The battery detection method comprises the following steps: acquiring projection data of a glue layer on a surface of a battery monomer, the glue layer comprising at least one target glue layer; acquiring a coating area of the target glue layer on the surface of the battery monomer according to the projection data of the glue layer on the surface of the battery monomer; and determining a first detection result of the target glue layer according to the coating area of the target glue layer and a preset glue layer coating area. The battery detection method can realize nondestructive detection of the battery, improve the detection efficiency, and effectively intercept defective products.

Description

Technical Field

[0001] This application relates to the field of batteries, specifically to a battery testing method, apparatus, device, and readable storage medium. Background Technology

[0002] With the continuous development of science and technology, batteries are being used more and more widely in daily production and life. Battery safety is also receiving increasing attention. Related technologies aim to ensure the structural stability of battery cells during use or transportation, and to avoid the risk of battery cells detaching, which could lead to electrical safety issues.

[0003] Currently, adhesive layers are often coated on the surface of individual battery cells to secure them. Ensuring that the adhesive layer on the battery cell surface meets the required standards is fundamental to ensuring battery electrical safety. Therefore, an effective testing method is needed to inspect the adhesive layer coated inside the battery. Summary of the Invention

[0004] In view of the above problems, this application provides a battery testing method, apparatus, device and readable storage medium, which can effectively detect whether the adhesive layer coated on the surface of the cell meets the coating requirements.

[0005] In a first aspect, embodiments of this application provide a battery detection method, including:

[0006] Obtain projection data of the adhesive layer on the surface of the battery cell, wherein the adhesive layer includes at least one target adhesive layer;

[0007] Based on the projection data of the adhesive layer on the surface of the battery cell, determine the coating area of ​​the target adhesive layer on the surface of the battery cell;

[0008] The first test result of the target adhesive layer is determined based on the coating area of ​​the target adhesive layer and the preset coating area of ​​the adhesive layer.

[0009] In the technical solution of this application embodiment, by using the projection data of the adhesive layer on the surface of the battery cell, at least one target adhesive layer on the surface of the battery cell can be quickly analyzed. Next, based on the projection data of the surface of the battery cell, the coating area of ​​the target adhesive layer on the surface of the battery cell can be quickly obtained, thereby achieving efficient and accurate acquisition of the internal information of the battery to be tested. Then, based on the preset adhesive layer coating area, the first detection result of the target adhesive layer can be quickly determined, effectively improving the detection efficiency of the battery and reducing the detection cost.

[0010] In some embodiments, determining the coating area of ​​the target adhesive layer on the surface of the battery cell based on the projection data of the adhesive layer on the surface of the battery cell includes:

[0011] The first imaging information of the target adhesive layer on the surface of the battery cell is determined from the adhesive layer projection data;

[0012] The coating area of ​​the target adhesive layer is determined based on the first imaging information and the preset image area algorithm.

[0013] In this embodiment, by further obtaining the first imaging information of the target adhesive layer from the adhesive layer projection data, the detection accuracy of the target adhesive layer can be improved, making the coating area of ​​the target adhesive layer calculated based on the first imaging information and the preset image area algorithm more accurate, thereby reducing calculation errors.

[0014] In some embodiments, determining the coating area of ​​the target adhesive layer based on the first imaging information and a preset image area algorithm further includes:

[0015] Based on the preset adhesive layer recognition model, the coating features of the target adhesive layer in the first imaging information are pre-detected to obtain the second detection result of the target adhesive layer.

[0016] When the second detection result of the target adhesive layer includes the first detection information, the coating area of ​​the target adhesive layer is determined according to the first imaging information of the target adhesive layer and the preset image area algorithm.

[0017] In this embodiment of the application, before determining the target adhesive coating area, the coating features of the target adhesive layer in the first imaging information are pre-detected according to a preset adhesive layer recognition model to obtain a second detection result. Then, based on whether the second detection result includes the first detection information, it can be determined whether to continue to detect the target adhesive layer, thereby reducing the amount of computation in the battery detection process and improving the speed and accuracy of battery detection.

[0018] In some embodiments, when the second detection result of the target adhesive layer includes the second detection information, a first detection result of the target adhesive layer is generated, wherein the first detection result includes the second detection information.

[0019] In this embodiment of the application, when the second detection result, including the second detection information, is obtained through pre-detection, it can be determined that there is an obvious coating defect in the target adhesive layer. Thus, the battery being tested can be directly determined to be a defective product without further detection calculations, thereby improving the detection speed and accuracy of the battery.

[0020] In some embodiments, obtaining the projection data of the adhesive layer on the surface of a battery cell includes:

[0021] Acquire first location information, wherein the first location information includes the relative location information between the battery, the transmitter, and the detector;

[0022] Based on the location information, determine the two-dimensional projection data of the battery;

[0023] According to the preset filtering function, the two-dimensional projection data are filtered to obtain the filtered two-dimensional projection data.

[0024] Weighted back projection calculations are performed on the filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

[0025] In this embodiment of the application, based on the above calculation process, the adhesive layer projection data on the surface of the battery cell can be obtained, which can make the adhesive layer projection data more accurate, reduce data calculation errors, and obtain high-quality adhesive layer projection data, so as to improve the accuracy of battery detection.

[0026] Secondly, this application provides a battery testing device, the device comprising:

[0027] The acquisition module is used to acquire the projection data of the adhesive layer on the surface of the battery cell, wherein the adhesive layer includes at least one target adhesive layer;

[0028] The acquisition module is also used to determine the coating area of ​​the target adhesive layer on the surface of the battery cell based on the projection data of the adhesive layer on the surface of the battery cell.

[0029] The processing module is used to determine the first detection result of the target adhesive layer based on the coating area of ​​the target adhesive layer and the preset coating area of ​​the adhesive layer.

[0030] In the technical solution of this application embodiment, at least one target adhesive layer on the surface of the battery cell can be quickly analyzed by using the projection data of the adhesive layer on the surface of the battery cell. Next, based on the projection data of the surface of the battery cell, the coating area of ​​the target adhesive layer on the surface of the battery cell can be quickly obtained, thereby achieving efficient and accurate acquisition of the internal information of the battery. Then, based on the preset adhesive layer coating area, the first detection result of the target adhesive layer can be quickly determined, effectively improving the detection efficiency of the battery and reducing the detection cost.

[0031] In some embodiments, the processing module is further configured to determine first imaging information of the target adhesive layer on the surface of the battery cell from the adhesive layer projection data;

[0032] The processing module is also used to determine the coating area of ​​the target adhesive layer based on the first imaging information and the preset image area algorithm.

[0033] In this embodiment, by further obtaining the first imaging information of the target adhesive layer from the adhesive layer projection data, the detection accuracy of the target adhesive layer can be improved, making the coating area of ​​the target adhesive layer calculated based on the first imaging information and the preset image area algorithm more accurate, thereby reducing calculation errors.

[0034] In some embodiments, the processing module is further configured to perform pre-detection on the coating features of the target adhesive layer in the first imaging information according to a preset adhesive layer recognition model, so as to obtain a second detection result of the target adhesive layer;

[0035] The processing module is also used to determine the coating area of ​​the target adhesive layer based on the first imaging information of the target adhesive layer and a preset image area algorithm when the second detection result of the target adhesive layer includes the first detection information.

[0036] In this embodiment of the application, before determining the target adhesive coating area, the coating features of the target adhesive layer in the first imaging information are pre-detected according to a preset adhesive layer recognition model to obtain a second detection result. Then, based on whether the second detection result includes the first detection information, it can be determined whether to continue to detect the target adhesive layer, thereby reducing the amount of computation in the battery detection process and improving the speed and accuracy of battery detection.

[0037] In some embodiments, the processing module is further configured to generate a first detection result of the target adhesive layer when the second detection result of the target adhesive layer includes the second detection information, wherein the first detection result includes the second detection information.

[0038] In this embodiment of the application, when the second detection result, including the second detection information, is obtained through pre-detection, it can be determined that there is an obvious coating defect in the target adhesive layer. Thus, the battery being tested can be directly determined to be a defective product without further detection calculations, thereby improving the detection speed and accuracy of the battery.

[0039] In some embodiments, the acquisition module is further configured to acquire first location information, wherein the first location information includes relative location information between the battery, the transmitter, and the detector;

[0040] The processing module is also used to determine the two-dimensional projection data of the battery based on the location information;

[0041] The processing module is also used to filter the two-dimensional projection data according to the preset filtering function to obtain the filtered two-dimensional projection data.

[0042] The processing module is also used to perform weighted back projection calculations on the filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

[0043] In this embodiment of the application, based on the above calculation process, the adhesive layer projection data on the surface of the battery cell can be obtained, which can make the adhesive layer projection data more accurate, reduce data calculation errors, and obtain high-quality adhesive layer projection data, so as to improve the accuracy of battery detection.

[0044] Thirdly, embodiments of this application provide a battery testing device, which includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor. When the program or instructions are executed by the processor, they implement the steps of the method described in the first aspect.

[0045] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0046] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0048] Figure 1 This is a schematic flowchart of a battery detection method according to some embodiments of this application;

[0049] Figure 2 This is a schematic diagram of battery projection for some embodiments of this application;

[0050] Figure 3 This is a schematic diagram of a battery detection system according to some embodiments of this application;

[0051] Figure 4 This is a schematic diagram of the structure of a battery detection device according to some embodiments of this application;

[0052] Figure 5 This is a schematic diagram of the hardware structure of a battery testing device according to some embodiments of this application. Detailed Implementation

[0053] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0055] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0056] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0057] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0058] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0059] Currently, with the continuous development of science and technology, batteries are being used more and more widely in daily production and life. For example, batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power, and solar power plants, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, and electric cars, as well as in military equipment and aerospace. As the application fields of batteries continue to expand, the market demand for them is also constantly increasing.

[0060] In related technologies, most batteries are based on individual cells as the smallest unit, which are assembled to create a usable battery. During assembly, adhesives are used to bond the individual cells to the side panels, securing them and ensuring structural strength and stability. Before use, the internal adhesive coating of the battery undergoes testing, including performance and appearance tests. Performance tests include basic tests such as charging, discharging, heating, and vibration, as well as safety tests. Appearance tests check for damage, corrosion, leakage, swelling, and other abnormalities on the battery surface. Internally, the quality of the adhesive coating on the individual cell surfaces is crucial for ensuring the battery's structural strength and stability.

[0061] The inventors of this application have noted that currently, there is no effective and rapid detection method to determine whether the coating state of the adhesive on the surface of the battery cell meets the coating standards. In particular, when dealing with batch assembly of batteries, the detection efficiency is low, which is not conducive to intercepting defective products.

[0062] To achieve non-destructive testing of batteries and reduce testing costs, especially when dealing with mass-produced batteries, this application proposes a battery testing method. By using projection data of the adhesive layer on the surface of a battery cell, at least one target adhesive layer on the surface of the battery cell can be quickly analyzed. Next, based on the projection data of the battery cell surface, the coating area of ​​the target adhesive layer on the battery cell surface can be quickly obtained, thereby achieving efficient and accurate acquisition of the internal information of the battery to be tested. Then, based on the preset adhesive layer coating area, the first test result of the target adhesive layer can be quickly determined, effectively improving battery testing efficiency and reducing testing costs.

[0063] In the battery testing method disclosed in this application, a battery refers to a physical module comprising one or more individual battery cells. For example, the battery mentioned in this application may include a battery module or a battery pack. A battery generally includes a casing for encapsulating one or more individual battery cells. Individual battery cells may be cylindrical, flat, cuboid, or other shapes, and this application does not limit the shapes.

[0064] During battery testing, the battery cells may include, for example, lithium-ion secondary battery cells, lithium-ion primary battery cells, lithium-sulfur battery cells, sodium-lithium-ion battery cells, sodium-ion battery cells, or magnesium-ion battery cells, etc., and the embodiments of this application are not limited to this.

[0065] During battery testing, the battery cells may include, for example, lithium-ion secondary battery cells, lithium-ion primary battery cells, lithium-sulfur battery cells, sodium-lithium-ion battery cells, sodium-ion battery cells, or magnesium-ion battery cells, etc., and the embodiments of this application are not limited to this.

[0066] The battery detection method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0067] Please see Figure 1 This application provides a schematic flowchart of a battery detection method, as shown in the embodiments below. Figure 1 As shown, the above method may include the following steps 110 to 130.

[0068] Step 110: Obtain the adhesive layer projection data on the surface of the battery cell, wherein the adhesive layer includes at least one target adhesive layer.

[0069] Step 120: Obtain the coating area of ​​the target adhesive layer on the surface of the battery cell based on the projection data of the adhesive layer on the surface of the battery cell.

[0070] Step 130: Determine the first test result of the target adhesive layer based on the coating area of ​​the target adhesive layer and the preset coating area of ​​the adhesive layer.

[0071] In step 110 above, the assembled battery to be tested may include at least one battery cell. When the battery includes multiple battery cells, the multiple battery cells may be connected in series and / or in parallel via electrode terminals for various applications. To protect the battery cells and ensure the safety of the battery during application, such as reducing the impact of external shocks, heat, vibration, and other factors on battery life, one or more battery cells are usually electrically connected together and placed in a frame to obtain an assembled battery.

[0072] For example, battery cells are typically connected to attachment members via an adhesive layer. Optionally, the attachment members may be provided with or constituted by support members. Specifically, for example, a support member may be a component that provides support for the battery cell or resists the weight of the battery cell, and it may typically be attached to, for example, the bottom wall or bottom of the battery cell's housing, supporting or securing the battery cell thereto. Furthermore, the attachment member may also be any other suitable component within the battery.

[0073] Optionally, the attachment components can be attached to the battery cells with an adhesive layer. For example, an adhesive layer can be applied to the surface of the battery cell. After the adhesive layer cures, the adhesive layer will use the adhesive force and cohesive force to bond the corresponding adhesive surfaces on the battery cell and the attachment components together in a surface bonding manner.

[0074] For example, the adhesive layer can be a hot melt adhesive, which is non-sticky at room temperature and becomes sticky when heated. The hot melt adhesive can be selected from, but is not limited to, polyethylene, polypropylene, copolymers of ethylene and other olefins, polyesters, polyurethanes, styrene and its block copolymers.

[0075] In some embodiments, the adhesive layer includes at least one target adhesive layer. The adhesive layer can be applied to the surface of the battery cell according to specific coating requirements. For example, if the battery cell needs to be bonded to the side plate, the adhesive can be applied to the side plate first, and then the side plate can be directly bonded to one side of the outer surface of the battery cell. Alternatively, the adhesive layer shape and thickness required by the user can be determined by using a sol-gel tank between the side of the battery cell and the side plate.

[0076] For example, the shape of the adhesive layer on the surface of the battery cell can be circular, elliptical, rectangular, polygonal, or other regular or irregular shapes. Different adhesive layers can be spaced at equal intervals or not.

[0077] In this embodiment of the application, by obtaining the projection data of the adhesive layer on the surface of the battery cell in the battery to be tested, the coating state of each adhesive layer inside the battery can be understood in detail.

[0078] The adhesive layer projection data can be extracted from the complete projection data of the battery. For example, the complete projection data of the battery can be obtained through computed tomography (CT) technology.

[0079] In step 120 above, there may be one or more target adhesive layers on the surface of the battery cell. For example, on the surface of a battery cell, one target adhesive layer required for the battery design may be applied to bond it to the attachment component, or multiple target adhesive layers required for the battery design may be applied.

[0080] Since the adhesive layer projection data on the surface of a battery cell can include the coating state of each adhesive layer, the coating area of ​​the target adhesive layer on the surface of the battery cell can be obtained by analyzing the adhesive layer projection data. It is understandable that when there are multiple target adhesive layers, the coating area of ​​each target adhesive layer can be obtained.

[0081] After obtaining the coating area of ​​the target adhesive layer on the surface of the battery cell, the first detection result of the target adhesive layer can be quickly determined according to step 130 above.

[0082] In step 130 above, before testing the battery, a preset adhesive coating area can be set according to the adhesive coating standards that the surface of the individual battery cells needs to meet, thereby improving the practicality of the battery testing system. The preset adhesive coating area can be a specific value or a range of values, and is not specifically limited here.

[0083] After obtaining the coating area of ​​the target adhesive layer, the first detection result can be obtained quickly online by comparing the coating area of ​​the target adhesive layer with the preset coating area of ​​the adhesive layer, that is, the detection process of the target adhesive layer in the battery is completed.

[0084] For example, first and second detection information can be preset. The first detection information indicates a pass, and the second detection information indicates a fail. Specifically, for a first detection result obtained based on the coating area of ​​the target adhesive layer, if the first detection result includes the first detection information, the target adhesive layer can be considered to meet the coating standard. Accordingly, it can be determined that the adhesive coating in the currently inspected battery meets the requirements for a good product. If the first detection result includes the second detection information, the currently inspected battery can be considered a defective product and needs to be intercepted to prevent it from entering the production line.

[0085] In this embodiment of the application, for the battery to be tested, at least one target adhesive layer on the surface of the battery cell can be quickly analyzed by using the projection data of the adhesive layer on the surface of the battery cell. Then, based on the projection data of the battery cell surface, not only can the coating area of ​​the target adhesive layer on the surface of the battery cell be obtained quickly and accurately, but also the internal information of the battery can be obtained non-destructively, realizing non-destructive testing of the battery. Then, based on the preset adhesive layer coating area, the first test result of the target adhesive layer can be quickly determined, effectively improving the testing efficiency of the battery and reducing the testing cost.

[0086] In some embodiments of this application, step 120 above may include: determining first imaging information of the target adhesive layer on the surface of the battery cell from the adhesive layer projection data; and determining the coating area of ​​the target adhesive layer according to the first imaging information and a preset image area algorithm.

[0087] Specifically, in actual batteries, each battery cell can be coated with a target adhesive layer of different shapes. To improve detection accuracy, first imaging information of the target adhesive layer on the surface of the battery cell can be acquired. For example, the edge of the target adhesive layer can be detected by pre-setting the coating shape, thereby determining the first imaging information of the target adhesive layer from the adhesive layer projection data; in another example, the first imaging information of the target adhesive layer can also be determined by acquiring a grayscale image of the adhesive layer projection data and detecting the gradient change of grayscale values ​​in the grayscale image.

[0088] After obtaining the first imaging information of the target adhesive layer, the first imaging information is analyzed and calculated using a preset image area algorithm, thereby achieving quantitative analysis of the target adhesive layer and obtaining the actual coating area of ​​the target adhesive layer.

[0089] In this embodiment, by further obtaining the first imaging information of the target adhesive layer from the adhesive layer projection data, the detection accuracy of the target adhesive layer can be improved, making the coating area of ​​the target adhesive layer calculated based on the first imaging information and the preset image area algorithm more accurate, thereby reducing calculation errors.

[0090] In some embodiments of this application, in order to improve the detection speed and accuracy of the battery, the process of obtaining the coating area of ​​the target adhesive layer may further include the following steps:

[0091] Based on the preset adhesive layer recognition model, the coating features of the target adhesive layer in the first imaging information are pre-detected to obtain the second detection result of the target adhesive layer.

[0092] When the second detection result of the target adhesive layer includes the first detection information, the coating area of ​​the target adhesive layer is determined according to the first imaging information of the target adhesive layer and the preset image area algorithm.

[0093] Specifically, after obtaining the first imaging information of the target adhesive layer, the coating features of the target adhesive layer corresponding to different coating standards can be different adhesive layer edge information, or the color depth information of the target adhesive layer in the imaging information, etc., which will not be listed here.

[0094] By using a preset adhesive layer recognition model, the coating features of the target adhesive layer in the first imaging information can be identified, and it can be determined whether the corresponding coating standard can be met, thereby quickly obtaining the second detection result of the target adhesive layer.

[0095] Optionally, the first detection information can be used to indicate that the detection has passed. Therefore, when the second detection result of the target adhesive layer includes the first detection information, it can be considered that the coating features of the target adhesive layer in the first imaging information can meet the corresponding coating standards and the target adhesive layer has passed the pre-detection. Next, the coating area of ​​the target adhesive layer can be determined according to the first imaging information of the target adhesive layer and the preset image area algorithm.

[0096] Based on the embodiments of this application, before determining the target adhesive layer coating area, a second detection result can be obtained by pre-detecting the coating features of the target adhesive layer in the first imaging information according to a preset adhesive layer recognition model, thereby completing the preliminary screening of the target adhesive layer and helping to improve the detection speed. Next, based on whether the second detection result includes the first detection information, it can be determined whether to continue detecting the target adhesive layer, thereby reducing the computational load in the battery detection process and improving the speed and accuracy of battery detection.

[0097] In some embodiments of this application, when the second detection result of the target adhesive layer includes the second detection information, a first detection result of the target adhesive layer is generated, wherein the first detection result includes the second detection information.

[0098] Optionally, the second detection information can be used to indicate a failed detection. When the second detection result of the target adhesive layer includes the second detection information, it can be assumed that the coating characteristics of the target adhesive layer in the first imaging information do not meet the corresponding coating standards. For example, the target adhesive layer may have obvious coating defects. Therefore, the target adhesive layer has not passed the pre-detection stage, and the battery being tested can be directly considered defective. Optionally, at this time, the detection of the target adhesive layer in the battery can be stopped, and the battery being tested can be directly intercepted to prevent defective batteries from entering the production line.

[0099] According to the embodiments of this application, when the second detection result, including the first detection information, is obtained through the pre-detection stage, the detected battery can be directly determined to be a defective product without further detection calculations, thereby improving the detection speed of batteries and the accuracy of good product detection.

[0100] In some embodiments of this application, step 110 above may specifically be:

[0101] First position information is obtained, including the relative position information between the battery, the transmitter, and the detector; based on the position information, multiple two-dimensional projection data of the battery are determined; the two-dimensional projection data are filtered according to a preset filtering function to obtain multiple filtered two-dimensional projection data; weighted back projection calculation is performed on the multiple filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

[0102] The emitter is used to emit rays, for example, X-rays. The detector is used to receive the two-dimensional projection of the battery onto the emitter. During the projection process, the emitter can emit rays onto the battery from different projection angles to obtain complete battery projection data.

[0103] Specifically, Figure 2 This is a schematic diagram of battery projection provided in an embodiment of this application, for reference. Figure 2 As shown, the emission source is S, which emits rays that pass through the battery, and the detector receives the two-dimensional projection of the battery. For example, Figure 2 The transmitter S can rotate around the battery along the dotted line. Correspondingly, during the rotation of the transmitter, the detector can receive projection data of the transmitter at different projection angles.

[0104] Based on this, by establishing a three-dimensional coordinate system centered at point O and based on x, y, and z, it is possible to conveniently represent different projection angles of the corresponding transmitter, as well as the relative position information between the battery, transmitter, and projection receiver detector. Continuing with... Figure 2 As shown, the rays emitted by the emission source S can be cone-beam rays, where (a, b, c) can represent the coordinate position of each point on the battery to be detected, γ and α are the angles between the center line of the cone beam and the two sides of the cone beam, respectively, β is the angle between the battery and the detector, and R is the distance between the emission source S and the battery.

[0105] For example, the second projection data may include the projection of each point on the battery, i.e., the projection point, which can be represented by p'(β, a, b). For example, the projection point can be calculated according to formula (1).

[0106]

[0107] in, The pre-weighting factor can be calculated according to formula (2).

[0108]

[0109] To improve the accuracy of the projection data and reduce calculation errors, the two-dimensional projection data can be filtered according to a preset filtering function to obtain multiple filtered two-dimensional projection data. For example, the filtered two-dimensional projection data can be represented as p″(β,a,b): where the filtered two-dimensional projection data can be calculated according to formula (3).

[0110] p″(β,a,b)=p′(β,a,b)*h(a) (3)

[0111] Weighted back projection calculation is performed on multiple filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell. The weighted back projection calculation process can be performed according to formula (4).

[0112]

[0113] For simplicity, in formula (4)

[0114] In the embodiments of the application, based on the above calculation process, the adhesive layer projection data on the surface of the battery cell can be obtained, which can make the adhesive layer projection data more accurate, reduce data calculation errors, and obtain high-quality adhesive layer projection data. This is to improve the accuracy of battery testing.

[0115] To better understand the battery detection method provided in the embodiments of this application, embodiments of the above battery detection in practical applications are provided here for illustration. Figure 3 This is a schematic diagram of a battery detection system provided in an embodiment of this application, with reference to... Figure 3 As shown, the battery testing system can include an emission source, a testing stage, and a detector. Within the system, the emission source and detector can rotate relative to the testing stage, facilitating the acquisition of two-dimensional projection data corresponding to different projection angles. The positional relationship between the emission source, testing stage, and detector can be adjusted according to testing requirements to maximize single-shot imaging of the battery located on the testing stage. Furthermore, the parameters of the emitted rays can be adjusted according to testing needs, including parameters such as tube voltage, tube current, magnification, exposure time, number of stacked frames, and finite angle, enabling the detector to generate high-resolution images of the low-density adhesive layer.

[0116] Optionally, during the testing process, the battery to be tested can be placed on the testing table using a transfer cart.

[0117] After the battery to be tested is placed on the testing stage, a radiation source emits rays. The radiation source and detector can rotate relative to the testing stage to obtain two-dimensional projection data corresponding to different projection angles. After filtering the two-dimensional projection data, a weighted back-projection calculation can be performed on the multiple filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell, thereby realizing the reconstruction of the battery's three-dimensional projection data.

[0118] To improve the speed and accuracy of battery detection, the following step involves pre-detecting the coating features of the target adhesive layer in the first imaging information based on a preset adhesive layer recognition model, thus determining the second detection result of the target adhesive layer. Whether the second detection result includes the first detection information is then used to determine whether to continue detecting the target adhesive layer.

[0119] When the second detection result of the target adhesive layer includes the second detection information, it can be determined that there is an obvious coating defect in the target adhesive layer. Therefore, the tested battery can be directly identified as a defective product without further detection calculations, thereby improving the detection speed and accuracy of the battery.

[0120] When the second detection result of the target adhesive layer includes the first detection information, in order to reduce calculation errors, the first imaging information of the target adhesive layer on the surface of the battery cell can be determined from the adhesive layer projection data. Then, based on the first imaging information of the target adhesive layer and a preset image area algorithm, the coating area of ​​the target adhesive layer can be determined. Finally, based on the preset adhesive layer coating area, the first detection result of the target adhesive layer can be quickly determined, effectively improving the detection efficiency of the battery and reducing the detection cost.

[0121] The battery detection device according to embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0122] Figure 4 This is a schematic diagram of the structure of a battery detection device provided in an embodiment of this application. Figure 4 As shown, the battery detection device 400 includes an acquisition module 410 and a processing module 420.

[0123] The acquisition module 410 is used to acquire the projection data of the adhesive layer on the surface of the battery cell, wherein the adhesive layer includes at least one target adhesive layer.

[0124] The processing module 420 is also used to determine the coating area of ​​the target adhesive layer on the surface of the battery cell based on the adhesive layer projection data on the surface of the battery cell.

[0125] The processing module 420 is used to determine the first detection result of the target adhesive layer based on the coating area of ​​the target adhesive layer and the preset coating area of ​​the adhesive layer.

[0126] In the technical solution of this application embodiment, by using the projection data of the adhesive layer on the surface of the battery cell, at least one target adhesive layer on the surface of the battery cell can be quickly analyzed. Next, based on the projection data of the surface of the battery cell, the coating area of ​​the target adhesive layer on the surface of the battery cell can be quickly obtained, thereby achieving efficient and accurate acquisition of the internal information of the battery to be tested. Then, based on the preset adhesive layer coating area, the first detection result of the target adhesive layer can be quickly determined, effectively improving the detection efficiency of the battery and reducing the detection cost.

[0127] In some embodiments, the processing module 420 is further configured to determine first imaging information of the target adhesive layer on the surface of the battery cell from the adhesive layer projection data;

[0128] The processing module 420 is also used to determine the coating area of ​​the target adhesive layer based on the first imaging information and the preset image area algorithm.

[0129] In this embodiment, by further obtaining the first imaging information of the target adhesive layer from the adhesive layer projection data, the detection accuracy of the target adhesive layer can be improved, making the coating area of ​​the target adhesive layer calculated based on the first imaging information and the preset image area algorithm more accurate, thereby reducing calculation errors.

[0130] In some embodiments, the processing module 420 is further configured to perform pre-detection on the coating features of the target adhesive layer in the first imaging information according to a preset adhesive layer recognition model, and obtain a second detection result of the target adhesive layer;

[0131] The processing module 420 is further configured to determine the coating area of ​​the target adhesive layer based on the first imaging information of the target adhesive layer and a preset image area algorithm when the second detection result of the target adhesive layer includes the first detection information.

[0132] In this embodiment of the application, before determining the target adhesive coating area, the coating features of the target adhesive layer in the first imaging information are pre-detected according to a preset adhesive layer recognition model to obtain a second detection result. Then, based on whether the second detection result includes the first detection information, it can be determined whether to continue to detect the target adhesive layer, thereby reducing the amount of computation in the battery detection process and improving the speed and accuracy of battery detection.

[0133] In some embodiments, the processing module 420 is further configured to generate a first detection result of the target adhesive layer when the second detection result of the target adhesive layer includes the second detection information, wherein the first detection result includes the second detection information.

[0134] In this embodiment of the application, when the second detection result, including the second detection information, is obtained through pre-detection, it can be determined that there is an obvious coating defect in the target adhesive layer. Thus, the battery being tested can be directly determined to be a defective product without further detection calculations, thereby improving the detection speed and accuracy of the battery.

[0135] In some embodiments, the acquisition module 410 is further configured to acquire first location information, wherein the first location information includes relative location information between the battery, the transmitter, and the detector;

[0136] The processing module 420 is also used to determine the two-dimensional projection data of the battery based on the location information;

[0137] The processing module 420 is also used to perform filtering processing on the two-dimensional projection data according to the preset filtering function to obtain the filtered two-dimensional projection data.

[0138] The processing module 420 is also used to perform weighted back projection calculation on the filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

[0139] In this embodiment, based on the above calculation process, the adhesive layer projection data on the surface of the battery cell can be obtained, making the adhesive layer projection data more accurate, reducing data calculation errors, and obtaining high-quality adhesive layer projection data. This improves the accuracy of battery testing.

[0140] Other details of the battery detection device according to embodiments of this application, in conjunction with the above. Figures 1 to 3 The battery detection method described in the example is similar and can achieve the same technical effect. For the sake of brevity, it will not be elaborated further here.

[0141] Figure 5 A schematic diagram of the hardware structure of the battery testing device provided in an embodiment of this application is shown.

[0142] The battery testing device may include a processor 501 and a memory 502 storing computer program instructions.

[0143] Specifically, the processor 501 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0144] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In some instances, memory 502 may include removable or non-removable (or fixed) media, or memory 502 may be a non-volatile solid-state memory. In some embodiments, memory 502 may be internal or external to a battery device.

[0145] In some instances, memory 502 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0146] Memory 502 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0147] The processor 501 reads and executes computer program instructions stored in the memory 502 to achieve... Figures 1 to 3 The method in the illustrated embodiment achieves... Figures 1 to 3The technical effects achieved by executing the methods / steps shown in the examples are not elaborated here for the sake of brevity.

[0148] In one example, the battery testing device may also include a communication interface 503 and a bus 504. Wherein, as... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 504 and complete communication with each other.

[0149] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0150] Bus 504 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 504 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0151] The roller press can perform the battery detection method in the embodiments of this application, thereby achieving the combination Figures 1 to 3 The battery detection method and apparatus described herein.

[0152] Furthermore, in conjunction with the battery detection methods and apparatus described in the above embodiments, this application can provide a readable storage medium for implementation. This readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the battery detection methods described in the above embodiments.

[0153] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0154] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0155] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0156] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A battery testing method, characterized in that, include: The adhesive layer projection data on the surface of a battery cell is obtained, wherein the adhesive layer includes at least one target adhesive layer. The adhesive layer projection data on the surface of the battery cell is obtained by means of: using computed tomography (CT) to penetrate the encapsulated battery with X-rays to obtain the complete projection data of the battery, and extracting the adhesive layer projection data on the surface of the battery cell from the complete projection data of the battery. From the adhesive layer projection data, determine the first imaging information of the target adhesive layer on the surface of the battery cell; According to the preset adhesive layer recognition model, the coating features of the target adhesive layer in the first imaging information are pre-detected to obtain the second detection result of the target adhesive layer, wherein the coating features of the target adhesive layer include adhesive layer edge information and adhesive layer color depth information; When the second detection result of the target adhesive layer includes the first detection information, the coating area of ​​the target adhesive layer is determined according to the first imaging information of the target adhesive layer and the preset image area algorithm, wherein the first detection information indicates that there are no obvious coating defects in the target adhesive layer; The first detection result of the target adhesive layer is determined based on the coating area of ​​the target adhesive layer and the preset coating area of ​​the adhesive layer; When the second detection result of the target adhesive layer includes the second detection information, a first detection result of the target adhesive layer is generated, wherein the first detection result includes the second detection information, and the second detection information is used to indicate that the detection failed.

2. The method according to claim 1, characterized in that, The acquisition of adhesive layer projection data on the surface of the battery cell includes: Acquire first location information, wherein the first location information includes the relative location information between the battery, the transmitter, and the detector; Based on the location information, determine the two-dimensional projection data of the battery; According to the preset filtering function, the two-dimensional projection data are filtered to obtain the filtered two-dimensional projection data. Weighted back projection calculation is performed on the filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

3. A battery testing device, characterized in that, The device includes: An acquisition module is used to acquire the adhesive layer projection data on the surface of a battery cell, wherein the adhesive layer includes at least one target adhesive layer, and the adhesive layer projection data on the surface of the battery cell is acquired by: using computed tomography technology to penetrate the encapsulated battery with X-rays to obtain the complete projection data of the battery, and extracting the adhesive layer projection data on the surface of the battery cell from the complete projection data of the battery. The processing module is used to determine the first imaging information of the target adhesive layer on the surface of the battery cell from the adhesive layer projection data; The processing module is further configured to perform pre-detection on the coating features of the target adhesive layer in the first imaging information according to a preset adhesive layer recognition model, and obtain a second detection result of the target adhesive layer, wherein the coating features of the target adhesive layer include adhesive layer edge information and adhesive layer color depth information; The processing module is further configured to determine the coating area of ​​the target adhesive layer according to the first imaging information of the target adhesive layer and a preset image area algorithm when the second detection result of the target adhesive layer includes the first detection information, wherein the first detection information indicates that there are no obvious coating defects in the target adhesive layer; The processing module is used to determine the first detection result of the target adhesive layer based on the coating area and the preset adhesive layer coating area; The processing module is further configured to generate a first detection result of the target adhesive layer when the second detection result of the target adhesive layer includes second detection information, wherein the first detection result includes the second detection information, and the second detection information is used to indicate that the detection failed.

4. The apparatus according to claim 3, characterized in that, The acquisition module is further configured to acquire first location information, which includes the location information between the battery, the transmitter, and the detector. The processing module is further configured to determine the two-dimensional projection data of the battery based on the location information; The processing module is also used to filter the two-dimensional projection data according to a preset filtering function to obtain filtered two-dimensional projection data. The processing module is also used to perform weighted back projection calculation on the filtered two-dimensional projection data to obtain the adhesive layer projection data on the surface of the battery cell.

5. A battery testing device, characterized in that, The device includes: a processor, and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the battery detection method as described in any one of claims 1-2.

6. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the battery detection method as described in any one of claims 1-2.

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