Computer-implemented method for image-based analysis of a battery precursor and / or a battery cell for an energy storage device for a motor vehicle, computer program and / or computer-readable medium, data processing device, method for producing

Abstract

A computer-implemented method for image-based analysis of a battery precursor and / or a battery cell for an energy storage device for a motor vehicle, the method comprising: acquiring an image data set, wherein the image data set represents a pictorial representation of a section through the battery precursor and / or the battery cell; automated determination of a plurality of layers of the battery precursor and / or the battery cell based on the image data set; virtually unfolding the plurality of layers to determine an analysis data set; and outputting the analysis data set.

Description

[0001] The present disclosure relates to a computer-implemented method for image-based analysis of a battery precursor and / or a battery cell for an energy storage device for a motor vehicle. The disclosure also relates to a computer program and / or computer-readable medium, a data processing device, and a method for producing a battery cell for an energy storage device for a motor vehicle.

[0002] Such an energy storage device typically comprises a plurality of battery cells or cells connected in parallel and / or series, thus forming a high-voltage storage device of the motor vehicle, also known as a traction battery. The energy storage device is configured to discharge the battery cells and provide electrical energy to operate the motor vehicle and / or to provide electrical energy externally, for example, via a charging station, and to be supplied with electrical energy via the charging station and / or through recuperation during a journey in order to charge the battery cells of the energy storage device.

[0003] During battery cell production, a battery precursor is typically produced during what is known as cell assembly. A plurality of layers, each of which can be configured as an electrode or separator, are connected to form the battery precursor, for example, by winding the layers and / or folding them onto one another.

[0004] The battery cell and / or the battery precursor can be examined using image-based analysis to characterize the layers and / or identify possible features or irregularities such as inclusions, for example, foreign bodies located between two adjacent layers. A purely manual evaluation of computed tomography data is known for this purpose. The evaluation of computed tomography data to locate inclusions, for example, is typically only possible manually, since the grayscale changes in the computed tomography data caused by the sequence of the majority of layers are comparatively strong, making features enclosed in the battery precursor difficult to detect.

[0005] Ziesche, RF, Arlt, T., Finegan, DP et al., "4D imaging of lithium batteries using correlative neutron and X-ray tomography with a virtual unrolling technique," Nat Commun 11, 777 (2020). https: / / doi.org / 10.1038 / s41467-019-13943-3 reveals that X-ray computed tomography enables the identification of mechanical degradation processes in a commercial Li / MnO2 primary battery and the indirect tracking of lithium diffusion. Furthermore, complementary neutron computed tomography revealed the direct lithium diffusion process and electrode wetting by the electrolyte. Virtual unrolling of the electrode provides deeper insight into the electrode layers and is used to detect small fluctuations that are difficult to detect with conventional three-dimensional rendering tools.

[0006] However, the "virtual unwinding" is performed by manually creating contours. This is possible with a comparatively small number of rolls, although manual creation can still result in inaccuracies in the identification of the layers.

[0007] Against the background of this prior art, one object of the present disclosure is to provide a method suitable for enriching the prior art and improving at least the above-mentioned aspects of the prior art. In particular, the object of the disclosure is to provide an improved and automated image-based analysis of a battery precursor and / or a battery cell for a motor vehicle.

[0008] The problem is solved by the features of the independent claims. The subclaims contain further developments of the disclosure.

[0009] According to one aspect of the disclosure, the object is achieved by a computer-implemented method for image-based analysis of a battery precursor and / or a battery cell for an energy storage device for a motor vehicle, the method comprising: capturing an image data set, wherein the image data set represents a pictorial representation of a section through the battery precursor and / or the battery cell; automated determination of a plurality of layers of the battery precursor and / or the battery cell based on the image data set; virtual

[0010] Unfolding the plurality of layers to determine an analysis data set; and outputting the analysis data set.

[0011] It was recognized that it is possible to analyze the battery product, i.e., the battery precursor and / or the battery cell, using the automated or computer-implemented method. First, the image dataset is acquired. The image dataset can represent the visual representation of one or more sections through the battery precursor and / or the battery cell.

[0012] The battery precursor and / or the battery cell comprises multiple layers, i.e., materially separable layers of optionally different materials. For example, the battery precursor and / or the battery cell comprises electrodes and / or one or more separators, each of which forms one or more layers depending on their arrangement. A cross-section can depict the plurality of layers and / or a portion thereof.

[0013] It was discovered that the image dataset allows for automated determination of the majority of layers. Automated determination makes it possible to reliably examine a comparatively large number of layers.

[0014] Once the layers have been determined, the majority of the layers can be unfolded. This unfolding can lead to a different representation of the majority of the layers, allowing the battery precursor and / or the battery cell to be analyzed more effectively and / or using computer implementation. For this purpose, the dimension of the representation of the majority of the layers can be reduced by unfolding. For example, a three-dimensional image data set can be converted into a two-dimensional analysis data set by unfolding. By unfolding, the analysis data set can be created which, for example, shows only slight changes in gray value in a reference state (also known as the OK state), whereas a feature or irregularity, such as a metal inclusion and / or a pore, is represented in the analysis data set by an abrupt change in the gray value around the feature.In addition, the method can be used, for example in the case of cylindrical battery precursors and / or battery cells, to prevent significant grey value deviations in the reference state due to minimal misalignment of the layers, which can hinder reliable analysis.

[0015] The word "unfolding" can describe the figurative spreading of the layers, whether by unwinding or uncoiling a cell coil or unfolding a cell stack. This means that the process can be applied to various battery precursors and / or battery cells comprising multiple layers, for example, prismatic cells and / or so-called pouch cells.

[0016] Optionally, determining the plurality of layers includes automated surface determination. It was recognized that the layers each have their own surface, which is typically visible in the image dataset and separates the layers from each other. Thus, by determining the surface areas of the layers, the arrangement of the plurality of layers can be deduced.

[0017] Optionally, determining the plurality of layers includes computer-assisted edge detection. It has been recognized that in image datasets, adjacent layers are typically separated by an edge. By detecting edges, the determination of the plurality of layers and / or surface determination can be carried out reliably and effectively.

[0018] Optionally, the method comprises: computer-assisted searching for irregularities in the battery precursor and / or the battery cell based on grayscale changes in the analysis data set; and outputting analysis information relating to the irregularities. It was recognized that the analysis data set is particularly suitable for the automated detection of irregularities, where an irregularity is, for example, a feature such as an inclusion and / or a pore. After the irregularity(ies) have been detected, corresponding analysis information can be output to characterize the irregularity(ies).

[0019] Optionally, each of the plurality of layers has a thickness of less than 350 µm, optionally less than 50 µm. Alternatively or additionally, the plurality of layers comprises more than 15 layers. It was recognized that the resolution of image data sets is sufficient for layers with a thickness of less than 50 µm. Furthermore, it is equally possible to identify layers with a greater thickness of, for example, 150 µm to 350 µm or more. The method can therefore be used, for example, for uncoated or bare layers with an exemplary density of less than 50 µm and / or coated layers with a thickness in a range of 150 µm to 350 µm. Image data sets with more than 15 layers can be effectively processed using the method.

[0020] Optionally, the battery precursor is a cell coil, and the virtual unfolding involves virtual unrolling. It was found that the cell coil is suitable for the process because the majority of the layers can be unfolded effectively. The same applies to a cylindrical battery cell. The battery precursor can also be a stack, which allows the battery cell to be a prismatic cell.

[0021] Optionally, the image dataset is three-dimensional and represents multiple sections through the battery precursor and / or the battery. This allows for effective spatial analysis of the battery precursor and / or the battery. Alternatively or additionally, the analysis dataset includes a two-dimensional representation of the unfolded plurality of layers. This allows for comprehensive and effective evaluation of the analysis dataset.

[0022] According to one aspect of the disclosure, a computer program and / or a computer-readable medium is provided. The computer program and / or the computer-readable medium comprise instructions which, when the program or instructions are executed by a data processing device, cause the device to perform the method according to the disclosure and / or steps thereof. Optionally, the computer program and / or the computer-readable medium comprises instructions which, when the program or instructions are executed by a data processing device, cause the device to perform the method steps described as advantageous or optional in order to achieve an associated technical effect.

[0023] According to one aspect of the disclosure, a data processing device is provided. The data processing device is configured to perform the method described above. Optionally, the data processing device is configured to perform a method step described as advantageous or optional and / or to implement a method feature in order to achieve an associated technical effect.

[0024] According to one aspect of the disclosure, a method for producing a battery cell for an energy storage device for a motor vehicle is provided, the method comprising: providing electrodes and a separator; cell assembly by connecting electrodes and separator to produce a battery precursor; and analyzing the battery precursor according to the method described above. Optionally, the method is carried out such that a method step described above as advantageous and / or optional is carried out and / or a method feature described above as advantageous and / or optional is realized in order to achieve an associated technical effect.

[0025] In the following, one embodiment is described with reference to the figures. Fig. 1 schematically shows a motor vehicle and a data processing device according to one aspect of the disclosure; Fig. 2 schematically shows a flow diagram of a method for image-based analysis of a battery precursor and / or a battery cell according to one aspect of the disclosure; Fig. 3 shows a schematic representation of a computer program and / or computer-readable medium according to one aspect of the disclosure; Fig. 4 shows exemplary sections of two image data sets for use in a method for image-based analysis of a battery precursor and / or a battery cell according to one aspect of the disclosure; Fig. 5 shows, by way of example and schematically, the determination of a plurality of layers and an unfolding each when carrying out a method for image-based analysis of a battery precursor and / or a battery cell according to one aspect of the disclosure; and Fig. 6 schematically shows a flow diagram of a method for manufacturing a battery cell according to one aspect of the disclosure.

[0026] Fig. Figure 1 schematically shows a motor vehicle 50 and data processing device 91 according to one aspect of the disclosure. The motor vehicle 50 is shown schematically in section (A) and the data processing device 91 is shown schematically in section (B).

[0027] The motor vehicle 50 is a land vehicle. The motor vehicle 50 is a passenger car. The motor vehicle 50 has an energy storage device 55 and an electric drive (not shown). The energy storage device 55 has a plurality of battery cells 56, which and their number are shown only schematically.

[0028] The energy storage device 55 or the battery cells 56 are configured to be supplied with electrical energy in order to charge the battery cells 56, i.e., to increase the state of charge of the battery cells 56. The energy storage device 55 or the battery cells 56 are configured to provide electrical energy for operating the motor vehicle 50 and / or the electric drive, wherein the battery cells 56 are discharged, i.e., the state of charge of the battery cells 56 decreases.

[0029] Each of the battery cells 56 has an electrode 58 configured as an anode, an electrode 58 configured as a cathode, and an electrolyte or separator 59 (see schematic indexing of the battery cell 56 below). For example, the battery cell 56 is a lithium-ion cell.

[0030] Fig. 1 (B) shows the data processing device 91. The data processing device 91 is configured to process the data processing device 91 with reference to Fig. 2 described method 100. For this purpose, the data processing device 91 is configured to generate an image data set 60 (see Fig. 4 and Fig. 5 (A)), wherein the image data set 60 represents a pictorial representation of a section through a battery precursor 56a and / or the battery cell 56. For this purpose, the battery precursor 56a and / or the battery cell 56 can be imaged, for example, using a computer tomography scanner (not shown) to generate the image data set 60. The data processing device 91 can acquire the image data set 60, for example, via a communication connection with the computer tomography scanner.

[0031] The data processing device 91 is configured, as described with reference to the following figures, to process the image data set 60 in order to analyze the battery precursor 56a and / or the battery cell 56.

[0032] The data processing device 91 is configured to generate an analysis data set 65 (see Fig. 5) to determine and output. The analysis data set 65 can, for example, be transferred to another function for further analysis and / or processing. Alternatively or additionally, the analysis data set 65 can be output via an output device in a way that is perceptible to a user, for example in order to visually inspect the battery precursor 56a and / or the battery cell 56. In addition, the data processing device 91 is configured to determine and output analysis information 66. The analysis information 66 can, for example, comprise information on irregularities 61 in the battery precursor 56a and / or the battery cell 56 and, for example, indicate the number, arrangement and / or properties, including the size, of inclusions, pores and / or other heterogeneities. The analysis information 66 can, for example, be transferred to another function for further analysis and / or processing.Alternatively or additionally, the analysis information 66 can be output via an output device in a manner perceptible to a user.

[0033] Fig. 2 schematically shows a flow diagram of a method 100 for image-based analysis of a battery precursor 56a and / or a battery cell 56 according to one aspect of the disclosure. The method 100 according to Fig. 2 is a computer-implemented method 100 for image-based analysis of a battery precursor 56a and / or a battery cell 56 for an energy storage device 55 for a motor vehicle 50. Such a battery cell 56, such an energy storage device 55 and such a motor vehicle 50 are described with reference to Fig. 1 described. Fig. 2 is made with reference to Fig. 1 described.

[0034] The procedure 100 according to Fig. 2 comprises: capturing 110 an image data set 60, wherein the image data set 60 represents a pictorial representation of a section through the battery precursor 56a and / or the battery cell 56.

[0035] The method 100 comprises: automated determination 120 of a plurality of layers 57 of the battery precursor 56a and / or the battery cell 56 based on the image data set 60 (see Fig. 5 (A)). The determination 120 of the plurality of layers 57 comprises an automated surface determination. The determination 120 of the plurality of layers 57 comprises computer-assisted edge detection.

[0036] Each of the plurality of layers 57 has a thickness of less than 350 µm, optionally less than 50 µm, and / or the plurality of layers 57 comprises more than 15 layers 57 (see Fig. 4 (B) and Fig. 5 (A)).

[0037] The method 100 comprises: virtual unfolding 130 of the plurality of layers 57 to determine an analysis data set 65 (see Fig. 5 (C) and (D)). The battery precursor 56a is a cell coil 56b, and the virtual unfolding 130 comprises a virtual unrolling.

[0038] The method 100 comprises: outputting 140 the analysis data set 65.

[0039] The method 100 comprises: computer-assisted searching 150 for irregularities 61 of the battery precursor 56a and / or the battery cell 56 based on gray value changes of the analysis data set 65.

[0040] The method 100 comprises: outputting 160 analysis information 66 concerning the irregularities 61.

[0041] The image data set 60 is three-dimensional and represents multiple sections through the battery precursor 56a. The analysis data set 65 comprises a two-dimensional representation of the unfolded plurality of layers 57.

[0042] The person skilled in the art will recognize that the method 100 according to Fig. 2 can also be performed in a different order than that shown. In particular, it is possible for steps of method 100 to be interchanged, shifted, repeated, and / or performed simultaneously.

[0043] Fig. Figure 3 shows a schematic representation of a computer program and / or computer-readable medium 200 according to one aspect of the disclosure. The computer program and / or computer-readable medium 200 comprises instructions 201 which, upon execution of the program or instructions 201 by a data processing device 91, cause the data processing device 91 to execute the method 100 and / or the steps of the method 100 according to Fig. 2 and / or optionally the method 300 and / or the steps of the method 300 according to Fig. 6 to be carried out.

[0044] The instructions 201 can be present as program code in any code or in any language, in particular in a code suitable for controlling and / or monitoring the production of battery cells 56. The computer program and / or computer-readable medium 200 can be or comprise any digital data storage device, such as a USB stick, a hard drive, a CD-ROM, an SD card, or an SSD card. The computer program does not necessarily have to be stored on such a computer-readable storage medium, but can also be accessible via the Internet or otherwise.

[0045] Fig. 4 shows exemplary sections of two image data sets 60 for use in a method 100 for image-based analysis of a battery precursor 56a and / or a battery cell 56 according to one aspect of the disclosure. Such an image data set 60 is described with reference to Fig. 1 described. Fig. 4 is made with reference to Fig. 1 to 3.

[0046] Fig. 4 (A) shows an image data set 60 of a battery cell 56 in the assembled state, for example, mounted in an energy storage device 55. More specifically, Fig. 4 (A) shows a section through a laminographic CT scan of a high-voltage battery. The battery cell 56 exhibits irregularities 61 in the form of inclusions 61' of copper particles. For example, copper inclusions 61' can typically be detected with a size of 200 µm. Aluminum inclusions 61' can, for example, be detected with a size of 300 µm.

[0047] Fig. 4 (B) also shows an image data set 60 of a battery cell 56 in the assembled state, for example mounted in an energy storage device 55. Fig. 4 (B) shows a plurality of layers 57. Each of the plurality of layers 57 has a thickness of less than 50 µm. The plurality of layers 57 of the battery cell 56 comprises more than 15 layers 57.

[0048] Fig. 5 shows, by way of example and schematically, the determination 120 of a plurality of layers 57 and an unfolding 130 each during the implementation of a method 100 for image-based analysis of a battery precursor 56a and / or a battery cell 56 according to one aspect of the disclosure. Fig. 5 divided into sections (A), (B), (C) and (D). Fig. 5 (A) shows an image data set 60. Such an image data set 60 is described with reference to Fig. 1 and Fig. 4, with the difference that Fig. 5 (A) shows a section of a cell coil 56b as a battery precursor 56a of a cylindrical battery cell 56. Fig. 5 is made with reference to Fig. 1 to 4 described.

[0049] In Fig. Figure 5 (A) shows a surface representation of the separation between material and air or between anode and cathode material. Fig. 5 (A) a plurality of layers 57 are visible. The layers 57 are determined automatically via a surface determination. The surface determination aims at the different visual representation of the plurality of layers 57 based on their nature. By the different visual representation of the plurality of layers 57, the surface of the layers 57 can be determined. The adjacent surfaces of two layers 57 are in Fig. 5 (A) as an edge. An edge refers to a change in intensity in the image data set 60 that indicates a boundary and / or a contour between two different regions in the image, here between two layers 57. The determination 120 of the plurality of layers 57 comprises computer-assisted edge detection or edge recognition. For this purpose, for example, a Laplace filter, a Sobel operator, and / or a Canny operator can be used, which calculates intensity gradients in the image data set 60 and thus identifies edges. The Sobel operator can use a convolution mask to capture the changes in intensity in the horizontal and / or vertical direction. The Canny operator enables a suitable combination of noise suppression, edge detection, and precise positioning of the edge points.

[0050] This makes the individual layers 57 visible. Also highlighted are winding positions A, B, C, D, E, and F, here after every 10th winding of the 57 layers.

[0051] Fig. Figure 5 (B) shows a spatial schematic representation of the cell coil 56a of Fig. 5 (A). The representation of the round battery cell with winding positions A, B, C, D, E, F, G, H is transferred after every 10th winding of layers 57. The winding positions A, B, C, D, E, F, G, H are for orientation purposes and are not required for method 100.

[0052] Fig. 5 © illustrates a schematic representation of a cylindrical development of the individual electrode layers according to Fig. 5 (A) and (B). The image data set 60 or the plurality of layers 57 are virtually unfolded to determine an analysis data set 65. The battery precursor 56a according to Fig. 5 is a cell winding 56b and the virtual unfolding 130 comprises a virtual unwinding according to the detected windings or layers 57.

[0053] Fig. Figure 5 (D) illustrates a two-dimensional, parameterizable cross-sectional image from the image data set 60 of the laminographic high-voltage storage CT. The image data set 60, or the majority of layers 57, were completely unfolded or unwound. While the image data set 60 is a three-dimensional representation of the cell coil 56b, the analysis data set 65 is two-dimensional. This enables a more effective search 150 for irregularities 60 in the analysis data set 65.

[0054] By virtually unwinding the windings with at least half the annular disk of a winding ring, the analysis data set 65 can be created as a 2D image from the three-dimensional image data set 60. For this purpose, the three-dimensional image data set 60 is virtually unwound around its rotation axis in such a way that at least 720 degrees of the cell winding 56b are unwound per layer 57. This prevents excessive gray value deviations in the OK winding from minimal misalignments.

[0055] Fig. 6 schematically shows a flow diagram of a method 300 for producing a battery cell 56 according to one aspect of the disclosure. The method 300 according to Fig. 6 is a method 300 for producing a battery cell 56 for an energy storage device 55 for a motor vehicle 50. Such a battery cell 56, such an energy storage device 55 and such a motor vehicle 50 are described with reference to Fig. 1, Fig. 4 and Fig. 5 described. Fig. 6 is made with reference to Fig. 1 to 5 described.

[0056] The procedure 300 according to Fig. 6 comprises: providing 310 electrodes 58 and a separator 59.

[0057] The method 300 comprises: cell assembly 320 by connecting electrodes 58 and separator 59 to produce a battery precursor 56a.

[0058] The method 300 comprises: analysis 330 of the battery precursor 56a according to the method 100 according to Fig. 2.

[0059] The person skilled in the art will recognize that the method 300 according to Fig. 6 can also be performed in a different order than that shown. In particular, it is possible for steps of method 300 to be interchanged, shifted, repeated, and / or performed simultaneously. Reference symbol (part of the description) 50 motor vehicles 55 Energy storage device 56 battery cells 56a Battery precursor 56b Cell coil 57 Location 58 Electrode 59 Separator 60 image data sets 61 irregularities 61' inclusion 65 Analysis data set 66 Analysis information 91 Data processing device 100 procedures 110 Capture 120 Determine 130 Unfold 140 Issues 150 searches 160 Issues 200 Computer program and / or computer-readable medium 201 commands 300 procedures 310 Provide 320 Cell assembly 330 Analysis A, B, C, D, E, F, G, H wrapping position QUOTES CONTAINED IN THE DESCRIPTION

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

[0000] Ziesche RF, Arlt T, Finegan DP et al. “4D imaging of lithium batteries using correlative neutron and X-ray tomography with a virtual unrolling technique,” ​​Nat Commun 11, 777 (2020). https: / / doi.org / 10.1038 / s41467-019-13943-3

[0005]

Claims

[1] Computer-implemented method (100) for image-based analysis of a battery pre-product (56a) and / or a battery cell (56) for an energy storage device (55) for a motor vehicle (50), wherein the method (100) comprises: - Acquisition (110) of an image data set (60), wherein the image data set (60) represents a pictorial representation of a section through the battery pre-product (56a) and / or the battery cell (56); - automated determination (120) of a plurality of layers (57) of the battery pre-product (56a) and / or the battery cell (56) using the image data set (60); - virtual unfolding (130) of the majority of layers (57) to determine an analysis data set (65); and - Output (140) of the analysis data set (65). [2] Method (100) according to claim 1, wherein the determination (120) of the plurality of layers (57) comprises an automated surface determination. [3] Method (100) according to claim 1 or 2, wherein determining (120) the plurality of layers (57) comprises computer-aided edge detection. [4] Method (100) according to any of the preceding claims, wherein the method (100) comprises: - computer-aided searching (150) for irregularities (61) of the battery precursor (56a) and / or the battery cell (56) based on gray value changes of the analysis data set (65); and - Output (160) of analytical information (66) concerning the irregularities (61). [5] Method (100) according to any of the preceding claims, wherein each of the plurality of layers (57) has a thickness of less than 350 µm, optionally less than 50 µm, and / or the plurality of layers (57) comprises more than 15 layers (57). [6] Method (100) according to any of the preceding claims, wherein the battery pre-product (56a) is a cell winding (56b) and the virtual unfolding (130) comprises a virtual unwinding. [7] Method (100) according to any of the preceding claims, wherein the image data set (60) is three-dimensional and represents multiple sections through the battery pre-product (56a) and / or the battery cell (56); and / or the analysis data set (65) comprises a two-dimensional representation of the unfolded plurality of layers (57). [8] Computer program and / or computer-readable medium (200) comprising instructions (201) which, when the program or instructions (201) are executed by a data processing device (91), cause the device to perform the method (100) and / or the steps of the method (100) according to any one of claims 1 to 7. [9] Data processing device (91) wherein the data processing device (91) is configured to perform the method (100) according to any one of claims 1 to 7. [10] Method (300) for manufacturing a battery cell (56) for an energy storage device (55) for a motor vehicle (50), wherein the method (300) comprises: - Providing (310) electrodes (58) and a separator (59); - Cell assembly (320) by connecting electrodes (58) and separator (59) to produce a battery precursor (56a); and - Analysis (330) of the battery precursor (56a) according to the method (100) according to one of claims 1 to 7.

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