Device and method for checking a component of a battery

Abstract

The invention relates to a device (10) for checking a component (12) of a battery, in particular a battery cell of the battery, wherein the device (10) comprises a sensor device (14) with at least one sensor (16) which is configured to detect an outline of the component (12) and to generate a three-dimensional image of the component (12) based on the outline.Furthermore, the device (10) comprises a computing unit (19) configured to receive the three-dimensional image of the component (12) from the sensor unit (14), to determine the external dimensions of the component (12) based on the image, and to detect, based on the external dimensions, a contamination (28) along a surface of the component (12), in particular a particle contaminating the surface, and / or to perform an evaluation to assess whether the component (12) itself meets a predetermined quality standard, in particular predetermined geometric dimensions for the component (12).

Description

[0001] The invention relates to a device and a method for checking a component of a battery, in particular a battery cell of a battery.

[0002] A battery according to the invention comprises at least one, and in particular several, electrically interconnected battery cells, wherein such a battery cell preferably provides a voltage in the range of 3.5 to 4.0 volts. Such a battery cell can, for example, be designed as a prismatic cell, a pouch cell, or a cylindrical cell. The battery is preferably configured as a so-called high-voltage battery (HV battery), which is designed to provide an electrical voltage in the range of more than 60 volts, in particular in the range of several hundred volts. Such a high-voltage battery can be arranged in a motor vehicle, where it can supply an electrical consumer, in particular a drive motor of the motor vehicle, with electrical energy.

[0003] High-voltage battery manufacturing requires adherence to stringent standards of technical cleanliness and dimensional tolerances. For example, if a metallic chip from upstream processing steps (or transferred through transport and handling) is present on the individual parts or components, it can impair battery operation depending on its type, size, and position.

[0004] A well-known approach is therefore to inspect the relevant surfaces of the parts or components for particulate contamination using a camera before assembly. Camera sensors can simultaneously capture multiple images of a component from different angles and under varying lighting conditions, which, depending on the specific design, can require a very complex and highly precise inspection system. Particle detection with cameras also has the disadvantage that the image information is only two-dimensional. This means that while the surface area of ​​the particle can be evaluated, its height, which is usually the relevant criterion for battery impairment, cannot. Furthermore, purely two-dimensional contamination (e.g., local discoloration) can lead to false rejection.

[0005] In image data processing, methods are generally known for deriving height information of detected particles or contaminants from multiple camera images. For example, EP 3 092 948 B1 describes a method for operating a device for capturing the three-dimensional geometry of objects, particularly teeth, in which a two-dimensional image of the object is first recorded with a camera. Subsequently, depth information of the object is generated from this two-dimensional image. However, this post-processing of the images can require significant computing power, depending on the specific application.

[0006] Furthermore, the geometric dimensions of the components are relevant. Battery cells, in particular, often exhibit dimensional deviations due to their complex internal structure and the electrochemical processes involved, which must be monitored. Point measurements using laser sensors are typically employed for this purpose. However, point-based measurement of the component geometry has the disadvantage of not capturing the entire topology of the component. For example, if the thickest point of a battery cell is unknown or constant, this method will not measure the actual maximum thickness.

[0007] The invention is based on the objective of accelerating and simplifying the testing of battery components.

[0008] The problem is solved by the subject matter of the independent patent claims. Advantageous embodiments of the invention are described by the dependent patent claims, the following description, and the figures.

[0009] The invention provides a device for inspecting a battery component, in particular a battery cell. The device comprises a sensor assembly with at least one sensor configured to detect or scan the outline, external geometric shape, or three-dimensional surface of the component and to generate a three-dimensional image of the component based on the outline. In other words, unlike known line-scan or matrix cameras, the sensor is configured to directly detect or record the outline or three-dimensional surface of the component and generate a three-dimensional image of the component from this measurement. Thus, after the measurement, three-dimensional information about the component is available.

[0010] Furthermore, the device comprises a computing unit configured to receive the three-dimensional image of the component from the sensor device, to determine the component's external dimensions based on the image, and to detect contamination along a surface of the component based on these external dimensions, in particular a particle contaminating the surface. Alternatively or additionally, the computing unit according to the invention is configured to perform an evaluation based on the external dimensions to assess whether the component itself meets a predetermined quality standard, in particular predetermined geometric dimensions for the component.

[0011] In other words, the computing unit has a data or communication interface through which it can receive the three-dimensional image or a data set describing the image from the sensor device. The data set can be transmitted wirelessly, for example within a WLAN network, or via a wired connection. The computing unit can be located locally on or within the device. Alternatively, the computing unit can be an external server, which can be integrated into a computer network, such as an internet-based network.

[0012] A computing device within the meaning of the invention can be understood, in particular, as a data processing device containing a processing circuit. The computing device can thus, in particular, process data to perform arithmetic operations. This may also include operations to perform indexed accesses to a data structure, for example, a lookup table (LUT).

[0013] The computing device may, in particular, include one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example, one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more systems on a chip (SoCs). The computing device may also include one or more processors, for example, one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs).The computing facility may also include a physical or virtual network of computers or other units of the aforementioned type.

[0014] The computing device may include one or more hardware and / or software interfaces and / or one or more storage units.

[0015] A storage unit can be volatile data storage, for example as dynamic random access memory (DRAM) or static random access memory (SRAM), or as non-volatile data storage, for example as read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or flash EEPROM, ferroelectric random access memory (FRAM), or magnetoresistive random access memory.It can be designed as MRAM (magnetoresistive random access memory) or as phase-change random access memory, PCRAM (phase-change random access memory).

[0016] The computing device according to the invention is configured to first determine the external dimensions of the component based on its three-dimensional image. In other words, the computing device can be configured, for example by a suitably designed computer program executed by the computing device, to extract information describing the external dimensions of the component from the data set describing the three-dimensional image. If the three-dimensional image was generated based on a point cloud, the information can, for example, include spatial coordinates of the individual points or some of the points in the point cloud.

[0017] The computing device according to the invention is further configured to detect contamination, in particular a surface-contaminating particle, along a surface of the component based on its external dimensions. For this purpose, the computing device can, for example, be configured to determine a profile of the external dimensions or a gradient along at least one surface of the component. Should a sudden, significant change occur in the profile or gradient, the computing device can recognize this change, for example, a change in a previously constant slope of the surface, as contamination if the change exceeds a predetermined tolerance range.

[0018] Alternatively or additionally, the computing device can determine whether the component itself, for example the battery cell, meets a predetermined quality standard, in particular predetermined geometric dimensions for the component. The predetermined quality standard could, for example, be a maximum thickness of the battery cell. The external dimensions can therefore not only be used to detect particles or contaminants, but also (especially independently of particle detection) be used for evaluations (e.g., rejecting battery cells if they exceed or fall below a specified thickness).

[0019] The device according to the invention offers the advantage that the external dimensions of the component and any particulate contaminants present on the component can be detected in a single step, based on a combined scan of the component or its three-dimensional surface by a sensor device. This significantly accelerates and simplifies component inspection. In particular, it enables differentiation between planar / film-like contaminants and particles, thus preventing false rejects. The three-dimensional image can also be used to determine the geometric dimensions of the component. Since the entire topology of the component can be recorded, in addition to point-specific thickness measurements, it is also possible to locate and measure the thickest point.

[0020] The invention also includes embodiments that offer additional advantages.

[0021] One embodiment provides that the sensor device has at least one optical sensor. The optical sensor could be, for example, a light section sensor, a laser scanner, and / or one or more 3D camera sensors.

[0022] Here and in the following, the term "light" can be understood to encompass electromagnetic waves in the visible, infrared, and / or ultraviolet ranges. Accordingly, the term "optical" can also be understood to refer to light as defined in this way.

[0023] Preferably, the sensor device comprises at least one active optical system. By definition, an active optical sensor system has a light source for emitting light or light pulses. The light source can, in particular, be configured as a laser, for example, an infrared laser. Furthermore, by definition, an active optical sensor system has at least one optical detector for detecting reflected components of the emitted light. The active optical sensor system is, in particular, configured to generate, process, or output one or more sensor signals based on the detected components of the light.

[0024] Light section sensors based on the principle of laser triangulation are preferred. The light section method directly generates profiles which can be assembled into a 3D image by scanning in a spatial direction (e.g., conveyor movement).

[0025] It is also possible to use other sensor technologies suitable for generating depth images or three-dimensional images. Examples include active stereoscopy and / or structured light projection, which are also based on the triangulation principle but have the advantage that no relative movement between the sensor and the test object is required. However, these tend to have disadvantages regarding resolution compared to light section sensors. Other methods, such as interferometry, optical coherence tomography, shape-from-focus, deflectometry, light field cameras, or similar techniques, are also possible. The use of these sensor types can enable component detection at a resolution in the micrometer range. This allows datasets to be provided to the computing system that enable the detection of particles or contaminants in the micrometer range.

[0026] Another well-known type of active optical system is the lidar system. Depending on its specific design and accuracy, a lidar system can also be used in connection with the present invention.

[0027] A well-known type of lidar system is the so-called laser scanner, in which a laser beam is deflected by means of a light deflection device, allowing for various deflection angles of the laser beam. The light deflection device can, for example, contain a rotatably mounted mirror. Alternatively, the light deflection device can have a mirror element with a tiltable and / or swiveling surface. The mirror element can, for example, be designed as a microelectromechanical system (MEMS). In the environment, the emitted laser beams can be partially reflected, and the reflected portions can then strike the laser scanner, in particular the light deflection device, which can direct them onto a detector unit of the laser scanner. Each optical detector of the detector unit generates a corresponding detector signal based on the portions detected by the respective optical detector.

[0028] Based on the spatial arrangement of the respective detector, together with the current position of the light deflection device, in particular its rotational or tilting and / or swiveling position, the direction of incidence of the detected reflected components can be determined. An evaluation unit can also, for example, perform a time-of-flight measurement to determine the radial distance of the reflecting object. Alternatively or additionally, a method can be used to determine the distance by evaluating the phase difference between emitted and detected light.

[0029] Other types of lidar systems are flash lidar systems. These are non-scanning systems that do not require such a light deflection arrangement. Instead, the laser light generated by the light source is scattered by an optical element, so that it is emitted in a single flash over a wide angle.

[0030] Depending on whether the sensors enable the desired resolution, other TOF (time of flight) sensors can also be used.

[0031] One embodiment provides that the three-dimensional image is generated based on a point cloud.

[0032] A point cloud can be understood as a set of points, each identified by corresponding coordinates in a two- or three-dimensional coordinate system. In the case of a three-dimensional point cloud, the three-dimensional coordinates can be determined, for example, by the direction of incidence of the reflected light and the corresponding light travel time or radial distance measured for that particular point. In other words, the three-dimensional coordinate system can be a three-dimensional polar coordinate system. However, the information can also be pre-processed to obtain three-dimensional Cartesian coordinates for each of the points. Generally, the points in a point cloud can be specified in an unordered or unsorted manner, unlike, for example, a camera image.In addition to spatial information, namely the two- or three-dimensional coordinates, the point cloud can also contain additional information or measured values ​​for the individual points, such as an echo pulse width (EPW) of the respective sensor signal.

[0033] Preferably, the sensor and / or computing device can be configured to convert the point cloud information into a grayscale-coded elevation image or grayscale image. Such an image can then be used for further image data analysis. This offers the advantage that known object recognition algorithms can be used to detect and analyze contaminants. For example, a filter and a corresponding algorithm can be applied to the original grayscale image to extract a reference plane for particle detection. Various filters and algorithms are suitable for this purpose, their size depending on the expected particle size. A difference image can then be created from the original grayscale image and the reference plane, in which the contaminants, their spatial dimensions, and their position on the component can be determined.

[0034] Alternatively or additionally, a depth image or grayscale-coded elevation image can be generated directly based on the outline, without having created a point cloud beforehand.

[0035] One embodiment provides that the computing device is configured to compare the component's external dimensions with known external dimensions of a reference body and to detect contamination based on any deviation between the component's external dimensions and the known external dimensions of the reference body. In other words, the computing device can be calibrated to the reference body in a calibration step. For this purpose, the reference body can be positioned within a predetermined detection area of ​​the sensor device, whereby its three-dimensional image can then be generated by the at least one sensor of the sensor device. Using this image, the computing device can determine the external dimensions of the reference body, in particular its thickness and / or a profile along at least one surface of the reference body.These external dimensions of the reference body can then be stored in the computing unit for future comparison with the external dimensions of components to be inspected. If such a comparison reveals a deviation between the external dimensions of the reference body and the component being inspected that exceeds a predetermined tolerance range, contamination can be detected based on this deviation. Once the dimensions of the reference body are stored or stored in the computing unit, contamination detection can be performed with very little computational effort.

[0036] Another embodiment provides that the computing device is designed to classify the contamination using an algorithm for automatic visual perception.

[0037] Automatic visual perception algorithms, also known as computer vision algorithms, machine vision algorithms, or machine vision algorithms, can be considered computer algorithms for the automatic execution of a visual perception task. A visual perception task, also referred to as a computer vision task, can be understood, for example, as a task for extracting visual information from image data. In some cases, the visual perception task can, in principle, be performed by a human who is capable of visually perceiving an image corresponding to the image data. However, in this context, visual perception tasks are also performed automatically without the need for human intervention.

[0038] A computer vision algorithm may, for example, include an image processing algorithm or an image analysis algorithm that is or has been trained using machine learning and may be based on an artificial neural network, particularly a convolutional neural network. The computer vision algorithm may, for example, include an object detection algorithm, an obstacle detection algorithm, an object tracking algorithm, a classification algorithm, a semantic segmentation algorithm, and / or a depth estimation algorithm.

[0039] Corresponding algorithms can also be performed analogously based on input data other than images that are visually perceptible to a human. For example, point clouds or images from infrared cameras, lidar systems, etc., can also be evaluated using appropriately adapted computer algorithms. Strictly speaking, these algorithms are not algorithms for visual perception, since the corresponding sensors can operate in ranges that are not visually perceptible, i.e., not perceptible to the human eye, for example, in the infrared range. Therefore, within the scope of the present invention, such algorithms are referred to as algorithms for automatic perception. Algorithms for automatic perception thus include algorithms for automatic visual perception, but are not limited to human perception.Consequently, according to this understanding, an automatic perception algorithm can include a computer algorithm for the automatic execution of a perception task, which is or has been trained, for example, through machine learning and may, in particular, be based on an artificial neural network. Such generalized automatic perception algorithms can also include object detection algorithms, object tracking algorithms, classification algorithms, and / or segmentation algorithms, such as semantic segmentation algorithms.

[0040] If an artificial neural network is used to implement an automatic visual perception algorithm, a commonly used architecture is that of a convolutional neural network (CNN). Specifically, a 2D CNN can be applied to corresponding 2D camera images. CNNs can also be used for other automatic perception algorithms. For example, 3D, 2D, or 1D CNNs can be applied to point clouds, depending on the spatial dimensions of the point cloud and the processing requirements.

[0041] The result or output of an automatic perception algorithm depends on the specific underlying perception task. For example, the output of an object detection algorithm might include one or more bounding boxes that define a spatial position and, optionally, an orientation of one or more corresponding objects or contaminants along at least one surface of a component under inspection, and / or corresponding object classes for the one or more objects. The output of a semantic segmentation algorithm applied to a camera image might include a pixel-level class for each pixel of the camera image. Similarly, the output of a semantic segmentation algorithm applied to a point cloud might include a corresponding point-level class for each of the points.The classes at the pixel level or at the point level can, for example, define an object type or contaminant type to which the respective pixel or point belongs. This allows different contaminants to be classified.

[0042] Within the scope of the present disclosure, an object recognition algorithm can be understood as a computer algorithm capable of identifying and locating one or more objects within a provided input data set, for example, an input image. In the context of the present invention, a contaminant to be detected and classified is such an object. The identification and localization can be achieved by the algorithm, for example, by defining appropriate boundary boxes or regions of interest (ROIs) and, in particular, by assigning a corresponding object class to each of the boundary boxes, wherein the object classes can be selected from a predefined set of object classes.The assignment of an object class to a bounding box can be understood as providing a corresponding confidence value or probability that the object identified within the bounding box belongs to the corresponding object class. For example, the algorithm can provide such a confidence value or probability for each object class within a given bounding box. The object class assignment might involve selecting or providing the object class with the highest confidence value or probability. Alternatively, the algorithm can simply define the bounding boxes without assigning a corresponding object class.

[0043] Another embodiment provides that the computing device is configured to provide the component's external dimensions and / or a classification of the detected contamination and / or information concerning the detected contamination to at least one downstream processing device for the component. This allows the downstream processing device to be prepared for the component's condition. For example, the downstream processing device can also be configured to reject the component for further processing, such as within the framework of a quality assurance program. This reliably prevents contaminated components from being processed further. Alternatively or additionally, the downstream processing device can be configured to generate a warning signal when a contaminated component is announced or offered to it for further processing.

[0044] Another embodiment provides that the device for checking the component includes a conveying device configured to move the component and the at least one sensor relative to each other. In other words, the conveying device can move the component along a stationary sensor and / or the at least one sensor along or around a stationary component. It is also conceivable that both the component and the at least one sensor are moved relative to each other by the conveying device, for example, rotated and / or pushed.

[0045] A further development provides for the conveyor system to be designed to move the component along the sensor device in a predetermined conveying direction, with the sensor device preferably being stationary. In this way, component inspection can be integrated into an assembly line, whereby the component can be scanned along the conveying direction, for example, by the at least one sensor of the sensor device, without interrupting or stopping the assembly line. This can advantageously accelerate the entire production process.

[0046] One embodiment provides that the device includes a cleaning unit designed to at least partially remove the contamination. The cleaning unit may, for example, include a blower that can be directed precisely at the detected contamination to remove it at least partially before the component is made available for further processing. The result of the cleaning process may, for example, be transmitted to the subsequent processing device. The cleaning unit may be configured to execute a customized cleaning program depending on the classification of the contamination.

[0047] Another aspect of the invention relates to a method for inspecting a battery component, in particular a battery cell. It is provided that a sensor device with at least one sensor generates a three-dimensional image of the component, and a computing device receives the three-dimensional image of the component from the sensor device, determines the external dimensions of the component based on the image, and detects contamination, in particular a surface-contaminating particle, along a surface of the component based on these external dimensions.

[0048] For use cases or application situations that may arise during the procedure and are not explicitly described here, it may be provided that, according to the procedure, an error message and / or a request for user feedback is issued and / or a default setting and / or a predetermined initial state is set.

[0049] The invention also includes further developments of the method according to the invention, which have features already described in connection with the further developments of the device according to the invention. For this reason, the corresponding further developments of the method according to the invention are not described again here.

[0050] As a further solution, the invention also includes a computer-readable storage medium comprising program code which, when executed by a computer or a computer network, causes it to execute an embodiment of the method according to the invention. The storage medium can be provided at least partially as a non-volatile data storage medium (e.g., as flash memory and / or as an SSD - solid state drive) and / or at least partially as a volatile data storage medium (e.g., as RAM - random access memory). The storage medium can be located within the computer or computer network. However, the storage medium can also be operated, for example, as an app store server and / or cloud server on the internet. The computer or computer network can provide a processor circuit with, for example, at least one microprocessor.The program code can be provided as binary code and / or as assembly code and / or as source code of a programming language (e.g. C) and / or as a program script (e.g. Python).

[0051] The invention also includes combinations of the features of the described embodiments. The invention therefore also includes realizations that each exhibit a combination of the features of several of the described embodiments, provided that the embodiments have not been described as mutually exclusive.

[0052] The following are exemplary embodiments of the invention described. This is illustrated by: Fig. 1 a schematic cross-sectional representation of a device for checking a component of a battery; Fig. 2 a schematic top view of a device for checking a component of a battery; Fig. 3: a schematic representation of a calibration of a device for checking a component of a battery using a reference body; Fig. 4: a schematic representation of a method for detecting contamination along a surface of a battery component; and Fig. 5: A schematic representation of a procedure for checking a component of a battery.

[0053] The exemplary embodiments described below are preferred embodiments of the invention. In these exemplary embodiments, the described components each represent individual features of the invention, which can be considered independently of one another and each further develops the invention independently. Therefore, the disclosure is intended to include combinations of features of the embodiments other than those shown. Furthermore, the described embodiments can also be supplemented by further features of the invention already described.

[0054] In the figures, identical reference symbols denote functionally equivalent elements.

[0055] Fig. Figure 1 shows a schematic cross-sectional view of a device 10 for checking a component 12 of a battery. The component 12 could, for example, be a battery cell. An example is shown in Fig. 1 as component 12 a prismatic battery cell is shown.

[0056] The device 10 of the Fig. 1 comprises a sensor device 14 with two exemplary sensors 16 arranged opposite each other, which can be designed as light section sensors. Each of the sensors 16 can have or cover a detection area 18 within which the component 12 can be detected, scanned, or sampled. In other words, a three-dimensional image of the component 12 can be generated by means of the sensors 16.

[0057] The device also includes a computing unit 19, which is designed to receive and further process the three-dimensional image of the component 12 from the sensor unit 14.

[0058] Preferably, the component 12 and the sensors 16 are arranged to be movable relative to each other. Fig. Figure 1 shows a conveying device 20, which includes, for example, a drive mechanism (not shown here) that drives a conveyor belt 22 (see Fig. 2) can drive the conveyor on which the component 12 is arranged. The conveyor 20 can operate a communication link to the sensor device 14, so that communication between the conveyor 20 and the sensor device 14 regarding scan progress is possible. In other words, a thrust that can be transmitted to the conveyor belt 22 by the drive mechanism of the conveyor 20 can be synchronized with the scan progress, so that a speed at which the component 12 moves in a conveying direction 24 ( Fig. 2) as it moves past the sensors 16, the movement can be adjusted as needed to the scan progress. The communication link can alternatively or additionally be implemented by integrating the computing unit 19. Preferably, the conveyor 20 is designed to be backlash-free and jerk-free and is equipped with an encoder that can detect the current position of the component 12 relative to the sensors 16. Alternatively or additionally, the sensors 16 can also be guided along stationary components 12 via their own linear axis.

[0059] With reference to the issues related to the Fig. The elements shown and described in section 1 Fig. 2 a schematic top view of the device 10.

[0060] To determine the dimensions of component 12, the sensors 16 are preferably calibrated. In particular, the sensors 16 can be calibrated relative to each other, i.e., their distance and orientation relative to each other can be determined. This is in Fig. 3 schematically represented by means of a simple planar body or reference body 26 of known thickness.

[0061] In the upper area of ​​the Fig. Figure 3 schematically illustrates the calibration of the sensors 16 using the reference body 26. For calibration, the reference body 26 is first moved into the detection range 18 of the sensors 16. The sensors 16 can, for example, be configured for distance measurement, preferably by measuring the angle between the projected (laser) line and the sensor 16, thereby enabling the acquisition of a height profile of the reference body 26 (triangulation principle). It is also possible to perform the distance measurement by determining the time of flight of the light emitted or radiated by the sensors 16 towards the reference body 26, which, after reflection from the reference body 26, can be detected by detector units of the sensors 16. By determining the respective distance of a sensor 16 to the reference body 26 with a known thickness, the following can be performed according to the diagram in Figure 3: Fig. In the constellation shown in Figure 3, in which the sensors 16 are opposite each other and the reference body 26 is arranged between the sensors 16, the distance and orientation of the sensors 16 to each other are determined.

[0062] Using such calibrated sensors 16, a three-dimensional image of a component 12 to be inspected can then be generated, as is shown for example in the lower area of ​​the Fig. Figure 3 is shown. Alternatively or additionally, a deviation between the external dimensions of the reference body 26 and the component 12 can be determined in a comparative measurement on the component 12 to be tested. In a further step, a contamination 28 can be identified based on the deviation (see Figure 3). Fig. 4) component 12 will be recognized.

[0063] Fig. Figure 4 shows a schematic representation of a method for detecting a contamination 28 along a surface of a component 12 of a battery. The method shown here is an exemplary embodiment of a method for image data evaluation.

[0064] The in Fig. The methods shown in section 4 can be based on a height-coded grayscale image or grayscale image of component 12. In the upper area of ​​the Fig. Figure 4 shows a schematic representation of such a grayscale or raw image. The image can be used for further image data analysis.

[0065] In the middle range of the Fig. Figure 4 schematically shows the further analysis of the raw image. Here, the geometric dimensions of component 12, in the form of its maximum thickness Dmax and width B, are determined. Furthermore, a filter and a corresponding algorithm are applied to the original grayscale image to extract a reference plane 30 for particle detection. Various filters and algorithms are suitable for this purpose, the size of which depends on the expected particle size.

[0066] From the original grayscale image and the reference plane 30, the following can then be determined according to the schematic representation in the lower area of ​​the Fig. 4. A difference image is created in which the contaminant 28, its spatial dimensions and its position on the component 12 can be determined.

[0067] With reference to the issues related to the Fig. The elements described and labelled in 1 to 4 show Fig.5 A schematic representation of a method for checking a component 12 of a battery, in particular a battery cell of the battery, wherein in step S1 a sensor device 14 with at least one sensor 16 generates a three-dimensional image of the component 12. In step S2 a computing device 19 receives the three-dimensional image of the component 12 from the sensor device 14, determines external dimensions of the component 12 based on the image (step S3), and detects a contamination 28 along a surface of the component 12 based on the external dimensions (step S4).

[0068] Overall, the examples show how a testing system for HV battery components, especially battery cells, can be provided. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] EP 3 092 948 B1

[0005]

Claims

[1] Device (10) for checking a component (12) of a battery, in particular a battery cell of the battery, comprising - a sensor device (14) with at least one sensor (16) which is designed to detect an outline of the component (12) and to generate a three-dimensional image of the component (12) based on the outline, and - a computing device (19) which is designed to receive the three-dimensional image of the component (12) from the sensor device (14), to determine external dimensions of the component (12) on the basis of the image, and on the basis of the external dimensions: - to detect a contamination (28) along a surface of the component (12), in particular a particle contaminating the surface, and / or - to carry out an assessment to evaluate whether the component (12) itself satisfies a predetermined quality measure, in particular predetermined geometric dimensions for the component (12). [2] Device (10) according to claim 1, wherein the sensor device (14) comprises at least one optical sensor (16). [3] Device (10) according to one of the preceding claims, wherein the three-dimensional image is generated on the basis of a point cloud, and / or wherein the three-dimensional image is generated as a gray-value coded height image. [4] Device (10) according to one of the preceding claims, wherein the computing device (19) is designed to compare the external dimensions of the component (12) with known external dimensions of a reference body (26) for the component (12) and - to detect the contamination (28) based on a deviation between the external dimensions of the component (12) and the known external dimensions of the reference body (26), and / or - to assess, on the basis of the deviation, whether the component (12) itself satisfies the predetermined quality standard, in particular predetermined geometric dimensions for the component (12). [5] Device (10) according to one of the preceding claims, wherein the computing device (19) is designed to classify the contamination (28) with the aid of an algorithm for automatic visual perception. [6] Device (10) according to one of the preceding claims, wherein the computing device (19) is designed to provide the external dimensions of the component (12) and / or information relating to the detected contamination (28) for at least one further processing device for the component (12) following the device (10). [7] Device (10) according to one of the preceding claims, comprising a conveyor device (20) which is designed to move the component (12) and the at least one sensor (16) relative to one another. [8] Device (10) according to claim 7, wherein the conveying device (20) is designed to move the component (12) in a predetermined conveying direction (24) along the sensor device (14), wherein the sensor device (14) is preferably designed to be stationary. [9] Device (10) according to one of the preceding claims, comprising a cleaning device which is designed to at least partially remove the contamination (28). [10] Method for testing a component (12) of a battery, in particular a battery cell of the battery, wherein - a sensor device (14) with at least one sensor (16) detects an outline of the component (12) and generates a three-dimensional image of the component (12) based on the outline, and - a computing device (19) receives the three-dimensional image of the component (12) from the sensor device (14), determines external dimensions of the component (12) on the basis of the image, and on the basis of the external dimensions: - detects a contamination (28) along a surface of the component (12), in particular a particle contaminating the surface, and / or - carries out an assessment to evaluate whether the component (12) itself satisfies a predetermined quality measure, in particular predetermined geometric dimensions for the component (12).

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