Cylindrical battery capacity quantitative determination method and device based on CT image processing

Abstract

The invention relates to a cylindrical battery capacity quantitative determination method and device based on CT image processing, an electronic device and a storage medium, and the method comprises the steps: obtaining a CT scanning image of a battery, and carrying out the preprocessing; extracting a region of interest in the battery metal shell; carrying out binarization processing on the region of interest, and calculating a pixel proportion of a current collector; fitting a quantitative relation between the pixel ratio of the current collector and the battery capacity based on a least square method, and establishing a battery capacity calculation model; and determining the current capacity of the to-be-measured battery according to the battery capacity calculation model and the pixel proportion of the current collector. According to the method, the problems that a traditional battery capacity measuring method usually depends on charging and discharging testing, consumed time is long, the battery is damaged and the like are solved, and rapid nondestructive measurement of the battery capacity is achieved.

Description

Technical Field

[0001] The present application relates to the field of battery detection technology, and in particular to a method, device, electronic device and storage medium for quantitatively measuring the capacity of a cylindrical battery based on CT image processing. Background Art

[0002] With the rapid development of battery technology, accurate battery capacity measurement has become a crucial step in battery performance evaluation. Traditional battery capacity measurement methods typically rely on charge and discharge tests, which are time-consuming, require high-precision equipment, and damage the battery during the test. In recent years, technologies based on CT image processing have been widely used in industrial inspection, but no method for battery capacity measurement based on CT image processing has yet been developed. Therefore, a rapid, non-destructive battery capacity measurement method is urgently needed.

[0003] Traditional battery capacity measurement methods usually rely on charge and discharge tests, which are time-consuming, and no effective solution has been proposed so far. Summary of the Invention

[0004] In this embodiment, a method, device, electronic device and storage medium for quantitatively measuring the capacity of a cylindrical battery based on CT image processing are provided to solve the problem that traditional battery capacity measurement methods in related technologies generally rely on charge and discharge tests and are time-consuming.

[0005] In a first aspect, a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing is provided in this embodiment. The method comprises:

[0006] Acquire CT scan images of the battery and perform preprocessing;

[0007] Extract the region of interest inside the battery metal shell;

[0008] Binarize the region of interest and calculate the pixel ratio of the current collector;

[0009] The battery capacity calculation model was established by fitting the quantitative relationship between the current collector pixel ratio and battery capacity based on the least squares method;

[0010] The current capacity of the battery to be tested is determined based on the battery capacity calculation model and the current collector pixel ratio.

[0011] In some embodiments, the pre-processing includes grayscaling, denoising, and contrast enhancement.

[0012] In some embodiments, extracting the region of interest inside the battery metal shell includes: using Hough circle transform to detect a circular area of the battery metal shell, reducing the detection radius according to a preset value to determine the area inside the metal shell, generating a binary mask and extracting the region of interest.

[0013] In some embodiments, extracting the region of interest inside the battery metal shell further includes: adjusting the region of interest to a fixed size.

[0014] In some embodiments, a battery capacity calculation model is established based on fitting the quantitative relationship between the current collector pixel ratio and the battery capacity using the least squares method, including:

[0015] Collect CT scan images of battery samples and extract the pixel ratio of current collector;

[0016] Obtain the actual battery capacity through charge and discharge tests;

[0017] According to the current collector pixel ratio and the actual battery capacity, the quantitative relationship between the current collector pixel ratio and the battery capacity is fitted based on the least squares method to establish a battery capacity calculation model.

[0018] In some embodiments, a battery capacity calculation model is established based on fitting the quantitative relationship between the current collector pixel ratio and the battery capacity using the least squares method, including determining the optimal coefficient of the battery capacity calculation model through cross-validation.

[0019] In some embodiments, the binarization process uses a local threshold binarization method.

[0020] In a second aspect, in this embodiment, a device for quantitatively measuring the capacity of a cylindrical battery based on CT image processing is provided, the device comprising:

[0021] An image acquisition module, used to obtain CT scan images of the battery and perform preprocessing;

[0022] An image processing module is used to extract the region of interest inside the battery metal shell;

[0023] The calculation module is used to perform binarization processing on the region of interest and calculate the pixel ratio of the current collector;

[0024] A model building module is used to fit the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method to establish a battery capacity calculation model;

[0025] The capacity measurement module is used to determine the current capacity of the battery to be tested based on the battery capacity calculation model and the current collector pixel ratio.

[0026] In a third aspect, an electronic device is provided in this embodiment, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the cylindrical battery capacity quantification determination method based on CT image processing of the first aspect.

[0027] In a fourth aspect, a computer-readable storage medium is provided in this embodiment, on which a computer program is stored. When the computer program is executed by a processor, the steps of the cylindrical battery capacity quantitative determination method based on CT image processing of the first aspect are implemented.

[0028] Compared with the related art, the present embodiment provides a method, device, electronic device and storage medium for quantitative determination of cylindrical battery capacity based on CT image processing, which obtains and preprocesses the CT scan image of the battery; extracts the region of interest inside the metal shell of the battery; binarizes the region of interest and calculates the pixel ratio of the current collector; establishes a battery capacity calculation model based on the least squares method to fit the quantitative relationship between the pixel ratio of the current collector and the battery capacity; determines the current capacity of the battery to be tested based on the battery capacity calculation model and the pixel ratio of the current collector, thereby realizing rapid measurement of the battery capacity and solving the problem that the traditional battery capacity determination method usually relies on charge and discharge testing, which is time-consuming.

[0029] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0031] Figure 1 This is a hardware structure block diagram of a terminal for a method for quantitatively measuring cylindrical battery capacity based on CT image processing provided in this embodiment;

[0032] Figure 2 This is a flow chart of a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in an embodiment of the present application;

[0033] Figure 3 This is a flow chart of a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in this specific embodiment;

[0034] Figure 4 This is a battery cross-sectional CT image provided in this specific embodiment;

[0035] Figure 5 1 is a schematic diagram of a region of interest (ROI) extraction result provided in this specific embodiment;

[0036] Figure 6 This is a schematic diagram of a binarization processing result provided by this specific embodiment;

[0037] Figure 7This is a quantitative relationship diagram between the current collector pixel ratio and the battery capacity provided in an embodiment of the present application;

[0038] Figure 8 This is a structural block diagram of a cylindrical battery capacity quantification measurement device based on CT image processing in this embodiment. DETAILED DESCRIPTION

[0039] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0040] Unless otherwise defined, the technical terms or scientific terms involved in this application should have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "an", "a", "the", "these" and the like in this application do not indicate quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. Generally, the character " / " indicates that the related objects are in an "or" relationship. The terms "first," "second," "third," etc. used in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0041] The method embodiment provided in this embodiment can be executed in a terminal, a computer or a similar computing device. For example, running on a terminal, Figure 1 This is a hardware block diagram of a terminal for a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in this embodiment. Figure 1 As shown, the terminal may include one or more ( Figure 1Only one is shown) a processor 102 and a memory 104 for storing data, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0042] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a method for quantitative determination of cylindrical battery capacity based on CT image processing in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0043] The transmission device 106 is used to receive or send data via a network. The network may include a wireless network provided by the terminal's telecommunications provider. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0044] In this embodiment, a method for quantitatively measuring the capacity of cylindrical batteries based on CT image processing is provided. Figure 2 This is a flow chart of a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in an embodiment of the present application. Figure 2 As shown, the process includes the following steps:

[0045] Step S210: Acquire a CT scan image of the battery and perform preprocessing.

[0046] In this step, a CT scanner is used to obtain a cross-sectional CT scan image of the battery, and the CT scan image is grayscaled. The CT scan image is also known as a CT image.

[0047] Step S220: extracting the region of interest inside the battery metal shell.

[0048] In this step, since the cross-section of a cylindrical battery is circular, the battery area can be obtained by detecting the circular area of the battery metal shell in the image. The battery area in the image is extracted from the image as the region of interest (ROI). The specific extraction process is as follows:

[0049] (1) Circle detection stage: input grayscale image → detect circle radius range → specify the target area as dark area → balance detection sensitivity and noise suppression → control edge intensity threshold → output the detected circle center coordinates and the corresponding circle radius.

[0050] (2) Detection failure processing: If no circle is detected (centers is empty), the default parameters are manually set: center of the circle: geometric center of the image (width / 2, height / 2), radius: half the width of the short side of the image minus 10 pixels (to avoid edge overflow).

[0051] (3) Radius scaling: Reduce the detected radius by 6% (0.94 times) to ensure that the inner area of the metal shell is completely included.

[0052] (4) ROI extraction process:

[0053] 1) Generate mask

[0054] ① Generate a two-dimensional coordinate matrix (x, y) covering all pixels of the image;

[0055] ②Based on the circle equation (xa) 2 +(yb)2≤r 2 Generate a binary mask: the area that meets the conditions is set as the inner region of the circle (ROI), and the area that does not meet the conditions is set as the outer region of the circle, (a, b) is the coordinate of the center of the circle, and r is the radius.

[0056] 2) Apply a mask: Set the pixel values outside the mask to zero, retaining only the area inside the metal shell, i.e., ROI.

[0057] (5) Adjust the ROI to a fixed size (e.g., 256×256 pixels).

[0058] Step S230 , performing binarization processing on the region of interest and calculating the current collector pixel ratio.

[0059] In this step, the ROI is the internal area of the battery, including the positive and negative current collectors of the battery, the positive and negative active materials coated on the current collectors, the electrolyte, the diaphragm and the labels leading to the tabs. The CT results of the battery cross section show that since the current collector is generally made of copper or aluminum, it is generally brighter in the picture, while the other components of the battery are darker in the picture. In addition, relevant studies have found that as the battery performance deteriorates, the battery volume expands significantly, and the volume expansion is mainly caused by changes in active materials, and the volume of the current collector remains basically unchanged. Therefore, the ROI image inside the metal shell can be converted into a binary image, and the highlighted current collector area can be separated. The current battery capacity can be reflected according to the proportion of the number of current collector pixels in the ROI. Binarize the ROI, extract the current collector area, and calculate the proportion of current collector pixels. The specific steps are:

[0060] (1) Input a fixed-size ROI image.

[0061] (2) Image binarization: Use the adaptive threshold method and correctly set the sensitivity parameters. The binarization effect is confirmed by comparative analysis. After binarization, the value in the ROI area becomes 0 or 1.

[0062] (3) Pixel statistics and analysis: Count the total number of pixels in the ROI and the number of pixels with a threshold of 1 (i.e., the collector area) after binarization, and calculate the pixel ratio of the collector in the ROI.

[0063] Step S240 , fitting the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method, and establishing a battery capacity calculation model.

[0064] In this step, CT images of multiple battery samples are collected, and the pixel percentage of the current collector is determined according to steps S210 to S230. The capacity of the battery samples is measured using charge and discharge testing equipment. A least squares method is used to fit the quantitative relationship between the current collector pixel percentage and battery capacity, establishing a battery capacity calculation model.

[0065] Step S250 , determining the current capacity of the battery to be tested according to the battery capacity calculation model and the current collector pixel ratio.

[0066] In this step, a CT image of the battery to be tested is acquired, and the current collector pixel ratio in the CT image is extracted. The current capacity of the battery is calculated based on the battery capacity calculation model.

[0067] Through the above steps, a CT scan image of the battery is obtained and preprocessed; a region of interest inside the battery metal shell is extracted; the region of interest is binarized and the current collector pixel ratio is calculated; a battery capacity calculation model is established based on the least squares method to fit the quantitative relationship between the current collector pixel ratio and the battery capacity; and the current capacity of the battery under test is determined based on the battery capacity calculation model and the current collector pixel ratio. The current capacity of the battery under test can be determined without charging or discharging the battery under test, thus solving the problem that traditional battery capacity determination methods generally rely on charge and discharge testing, which is time-consuming.

[0068] In some embodiments, the pre-processing includes grayscaling, denoising, and contrast enhancement.

[0069] In some embodiments, extracting the region of interest inside the battery metal shell includes: using Hough circle transform to detect a circular area of the battery metal shell, reducing the detection radius according to a preset value to determine the area inside the metal shell, generating a binary mask and extracting the region of interest.

[0070] In some embodiments, extracting the region of interest inside the battery metal shell further includes: adjusting the region of interest to a fixed size.

[0071] In some of the embodiments, a battery capacity calculation model is established based on fitting the quantitative relationship between the current collector pixel ratio and the battery capacity using the least squares method, including: collecting CT scan images of battery samples and extracting the current collector pixel ratio; obtaining the actual battery capacity through charge and discharge tests; and establishing a battery capacity calculation model based on fitting the quantitative relationship between the current collector pixel ratio and the battery capacity using the least squares method according to the current collector pixel ratio and the actual battery capacity.

[0072] In some embodiments, a battery capacity calculation model is established based on fitting the quantitative relationship between the current collector pixel ratio and the battery capacity using the least squares method, including determining the optimal coefficient of the battery capacity calculation model through cross-validation.

[0073] In some embodiments, the binarization process uses a local threshold binarization method.

[0074] The present embodiment is described and illustrated below through specific examples.

[0075] Figure 3 This is a flow chart of a method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in this specific embodiment.

[0076] Step S1, image acquisition and preprocessing, includes:

[0077] Step S11: CT image acquisition.

[0078] An industrial CT scanner is used to perform axial cross-sectional scanning on the cylindrical battery to obtain a battery cross-sectional CT image. The battery cross-sectional CT image is as follows: Figure 4 The scanning conditions were set as follows: voltage 200 kV, current 300 μA, and exposure time 0.98 s.

[0079] Step S12: Image preprocessing.

[0080] The imread function in MATLAB is used to read the battery cross-sectional CT image (supporting PNG / JPEG format), and the color image is converted to a grayscale image using the rgb2gray function. The formula is as follows:

[0081] I gray =0.299·R+0.587·G+0.114·B

[0082] Where R, G, and B are the red, green, and blue channel pixel values, respectively. Output is an 8-bit single-channel grayscale image. If the input image is in grayscale format, skip this step.

[0083] Step S2, region of interest (ROI) extraction, includes:

[0084] Step S21: circular metal shell detection.

[0085] Use Hough circle transform imfindcircles to detect the circular area of the battery metal shell:

[0086] 1. Input grayscale image I gray .

[0087] 2. Set the detection parameters:

[0088] [centers,radii]=imfindcircles(img,[50 200],

[0089] 'ObjectPolarity','dark','Sensitivity',0.9,'EdgeThreshold',0.1);

[0090] Parameter description: Radius range [50,200]: preset based on battery size (diameter is approximately 100 to 400 pixels); 'dark': the target area is a bright circle (metal shell) against a dark background; 'Sensitivity': controls the detection sensitivity (0.9 is a higher sensitivity); 'EdgeThreshold': edge detection threshold (0.1 is a more relaxed threshold).

[0091] Step S22: Detection failure processing.

[0092] If no circle is detected (centers is empty), manually set the default parameters:

[0093] if isempty(centers)

[0094] centers=[size(img,2) / 2,size(img,1) / 2];%The center of the circle is set to the geometric center of the image

[0095] radii=min(size(img)) / 2-10; % The radius is set to half of the short side minus 10 pixels (to avoid edge overflow)

[0096] end

[0097] Step S23: radius scaling and mask generation.

[0098] Reduce the detection radius by 6% to ensure that the inner area of the metal shell is completely included:

[0099] radii=radii*0.94;

[0100] Generate binary mask and extract ROI:

[0101] [x,y]=meshgrid(1:size(img,2),1:size(img,1));

[0102] mask=(x-centers(1))^2+(y-centers(2))^2<=radii^2;

[0103] roi=img.*uint8(mask); %Set the area outside the mask to zero. The result of region of interest (ROI) extraction is as follows Figure 5 shown.

[0104] Step S24: size standardization.

[0105] Resize the ROI to a fixed size (e.g. 256×256 pixels) to eliminate size differences:

[0106] target_size = [256, 256];

[0107] roi_resized=imresize(roi,target_size,'bilinear');

[0108] Step S3, image binarization and current collector region extraction, includes:

[0109] Adopt local adaptive threshold method, combined with Otsu global threshold verification:

[0110] bw=imbinarize(roi_resized,'adaptive','Sensitivity',0.65);

[0111] Parameter optimization: 'Sensitivity' is set to 0.65 to balance noise suppression and current collector integrity (higher values preserve more details, lower values enhance contrast). The binarization results are as follows Figure 6 shown.

[0112] Step S4, current collector pixel ratio calculation and degradation analysis, includes:

[0113] Step S41: pixel statistics.

[0114] Calculate the current collector area ratio:

[0115] white_pixels=sum(bw_final(:));

[0116] total_pixels=numel(bw_final);

[0117] ratio=white_pixels / total_pixels*100;

[0118] fprintf('Pole pixel ratio: %.2f%%\n',ratio);

[0119] Step S42: constructing a capacity degradation model.

[0120] 1. Data collection:

[0121] Collect CT images of battery samples and extract the current collector pixel ratio P.

[0122] Obtain the actual battery capacity C through charge and discharge tests actual .

[0123] 2. Model training:

[0124] The least squares method is used to fit the quantitative relationship between the current collector pixel ratio P and the battery capacity C, and the battery capacity calculation model is obtained:

[0125]

[0126] Among them, the optimal coefficients a, b, and c are determined by cross-validation, and the goodness of fit R is required. 2 ≥0.95. The quantitative relationship between the current collector pixel ratio and battery capacity is shown in the figure below. Figure 7 shown.

[0127] Step S5, the battery capacity rapid measurement process, includes:

[0128] Step S51: Processing the battery to be tested.

[0129] 1. Perform a CT scan on the new battery, extract the ROI, and perform binarization according to the above process.

[0130] 2. Calculate the current collector pixel ratio P test .

[0131] Step S52: capacity prediction.

[0132] Substitute the training model to calculate the capacity:

[0133]

[0134] Output predicted value C estimated .

[0135] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0136] In this embodiment, a device for quantitatively measuring the capacity of cylindrical batteries based on CT image processing is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the details that have been described will not be repeated. The terms "module", "unit", "sub-unit", etc. used below can implement a combination of software and / or hardware for predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, implementation by hardware, or a combination of software and hardware, is also possible and conceivable.

[0137] Figure 8 This is a block diagram of a cylindrical battery capacity quantification measurement device based on CT image processing according to this embodiment. Figure 8 As shown, the device includes:

[0138] An image acquisition module 810 is used to obtain a CT scan image of the battery and perform preprocessing;

[0139] Image processing module 820, used to extract the region of interest inside the battery metal shell;

[0140] A calculation module 830 is used to perform binarization processing on the region of interest and calculate the pixel ratio of the current collector;

[0141] A model building module 840 is used to fit the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method to establish a battery capacity calculation model;

[0142] The capacity determination module 850 is used to determine the current capacity of the battery to be tested based on the battery capacity calculation model and the current collector pixel ratio.

[0143] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0144] This embodiment further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0145] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0146] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0147] S1, obtain the CT scan image of the battery and perform preprocessing;

[0148] S2, extracting the region of interest inside the battery metal shell;

[0149] S3, binarize the region of interest and calculate the pixel ratio of the current collector;

[0150] S4, based on the least squares method, the quantitative relationship between the current collector pixel ratio and the battery capacity is fitted to establish a battery capacity calculation model;

[0151] S5, determining the current capacity of the battery to be tested according to the battery capacity calculation model and the current collector pixel ratio.

[0152] It should be noted that, for specific examples in this embodiment, reference may be made to the examples described in the above embodiments and optional implementation modes, and will not be repeated in this embodiment.

[0153] In addition, in conjunction with the method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in the above embodiment, this embodiment may also provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the methods for quantitatively measuring the capacity of a cylindrical battery based on CT image processing provided in the above embodiment.

[0154] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0155] Obviously, the accompanying drawings are merely examples or embodiments of the present application. A person skilled in the art can also apply the present application to other similar situations based on these drawings without inventive effort. Furthermore, it is understandable that, although the work involved in this development process may be complex and lengthy, certain design, manufacturing, or production changes based on the technical content disclosed in this application are merely routine technical means for a person skilled in the art and should not be considered to constitute a deficiency in the disclosure of the present application.

[0156] The term "embodiment" as used in this application refers to specific features, structures, or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily mean that the embodiment is the same, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. It is understood, either explicitly or implicitly, by those skilled in the art that the embodiments described in this application can be combined with other embodiments when there is no conflict.

[0157] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for quantitatively measuring the capacity of cylindrical batteries based on CT image processing, characterized in that: The method comprises: Acquire CT scan images of the battery and perform preprocessing; Extract the region of interest inside the battery metal shell; Performing binarization processing on the region of interest and calculating the pixel ratio of the current collector; The battery capacity calculation model was established by fitting the quantitative relationship between the current collector pixel ratio and battery capacity based on the least squares method; The current capacity of the battery to be tested is determined according to the battery capacity calculation model and the current collector pixel ratio.

2. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 1, characterized in that: The preprocessing includes grayscale conversion, denoising and contrast enhancement.

3. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 1, characterized in that: The extraction of the region of interest inside the battery metal shell includes: using Hough circle transform to detect the circular area of the battery metal shell, reducing the detection radius according to a preset value to determine the area inside the metal shell, generating a binary mask and extracting the region of interest.

4. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 3, characterized in that: The extracting the region of interest inside the battery metal shell further includes: adjusting the region of interest to a fixed size.

5. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 1, characterized in that: The method of fitting the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method to establish a battery capacity calculation model includes: Collect CT scan images of battery samples and extract the pixel ratio of current collector; Obtain the actual battery capacity through charge and discharge tests; According to the current collector pixel ratio and the actual capacity of the battery, a battery capacity calculation model is established by fitting the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method.

6. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 5, characterized in that: The method of fitting the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method to establish a battery capacity calculation model includes: determining the optimal coefficient of the battery capacity calculation model through cross-validation.

7. The method for quantitatively measuring cylindrical battery capacity based on CT image processing according to claim 1, characterized in that: The binarization process adopts a local threshold binarization method.

8. A cylindrical battery capacity quantitative measurement device based on CT image processing, characterized in that: The device comprises: An image acquisition module, used to obtain CT scan images of the battery and perform preprocessing; An image processing module is used to extract the region of interest inside the battery metal shell; A calculation module, configured to perform binarization processing on the region of interest and calculate the pixel ratio of the current collector; A model building module is used to fit the quantitative relationship between the current collector pixel ratio and the battery capacity based on the least squares method to establish a battery capacity calculation model; The capacity determination module is used to determine the current capacity of the battery to be tested based on the battery capacity calculation model and the current collector pixel ratio.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively measuring the capacity of a cylindrical battery based on CT image processing according to any one of claims 1 to 7 are implemented.

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