Battery tomography database construction method based on quantum sensor

Abstract

The invention provides a battery tomography database construction method based on a quantum sensor. The method comprises the following steps: performing magnetic field detection on different layers of a lithium battery sample, recording magnetic field intensity required for detecting different layers of each lithium battery, and collecting magnetic induction intensity fed back after detection; generating a layered original vector diagram according to the fed-back magnetic induction intensity; marking the corresponding relation between the layered vector diagram of each lithium battery and the actual number of battery layers, and the corresponding relation between the magnetic field intensity and the frequency and the number of battery layers; performing feature extraction on the layered vector diagram; marking the corresponding relation between different failure mechanisms and the vector diagram characteristics of each layer of each lithium battery, and the relation between the actual size of various structural changes and the vector diagram form; and marking a defect type and a defect position according to a failure mechanism. According to the invention, data reference is provided for the internal defect detection technology of the lithium battery, and data support is provided for promoting the development of the internal defect detection technology of the lithium battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery detection, in particular to a battery tomography database construction method based on quantum sensors. BACKGROUND

[0002] For the internal defect detection of batteries, especially lithium batteries, the existing technical means include X-ray, ultrasonic wave, thermal imaging, magnetic resonance, analysis of electrochemical impedance spectrum, weak magnetic detection, magnetic field gradient detection, etc.

[0003] The judgment results of these detections need to be compared with a relatively complete battery feature database to obtain the results. However, the existing battery feature database is basically two-dimensional surface data, and basically does not involve the internal multi-dimensional features of the battery, which undoubtedly limits the development of the internal defect detection technology of lithium batteries.

[0004] Therefore, it is necessary to design a new battery tomography test method to construct a complete battery tomography database to solve the above problems. SUMMARY

[0005] In order to overcome the defects in the prior art, the purpose of the present application is to provide a battery tomography database construction method based on quantum sensors.

[0006] In order to achieve the above purpose of the present application, the present application provides a battery tomography database construction method based on quantum sensors, comprising the following steps:

[0007] A set of lithium battery samples with known internal defects is obtained, the magnetic field of each lithium battery in the set of lithium battery samples is detected at different levels, the magnetic field strength required for detecting each lithium battery at different levels is recorded, and the magnetic induction strength fed back after detection is collected by using a quantum magnetic induction component;

[0008] A layered original vector diagram is generated according to the magnetic induction strength fed back by the internal different levels of the lithium battery;

[0009] The layered original vector diagram is preprocessed and dimensionless, and the layered vector diagram of each lithium battery is obtained;

[0010] According to the magnetic field strength required for detecting each lithium battery at different levels, in combination with the actual situation of each lithium battery, the corresponding relationship between the layered vector diagram of each lithium battery and the actual battery layer number is labeled, and the corresponding relationship between the magnetic field strength and frequency and the battery layer number is labeled;

[0011] The layered vector diagram is feature extracted;

[0012] According to the actual situation of each lithium battery, the correspondence between different failure mechanisms and defect types and the vector features of each layer of each lithium battery is marked in combination with common failure mechanisms and defect labels of lithium batteries, and the actual size, position and vector pattern relationship of various structural changes;

[0013] The battery tomography database includes the layered vector diagram corresponding to all lithium batteries, and the data marked in the layered vector diagram, the type, size and position of the defect.

[0014] Optionally, the preprocessing includes:

[0015] Interlayer image alignment is performed on the layered original vector diagram;

[0016] The layered original vector diagram is denoised;

[0017] The magnetic induction intensity in the layered original vector diagram is normalized to obtain a dimensionless quantity.

[0018] Optionally, the step of performing global feature extraction on the layered vector diagram is:

[0019] The mean, variance and gradient histogram of each layer of the layered vector diagram are counted;

[0020] The deep learning features of each layer of the layered vector diagram are extracted.

[0021] Optionally, when marking the defect type and its position, the defect area in the vector diagram is identified, an interactive labeling tool is used for polygon labeling, and the target size is measured.

[0022] Optionally, when the magnetic field of each lithium battery in the lithium battery sample set is detected, and the quantum magnetic induction component is used to collect the magnetic induction intensity of the feedback after detection,

[0023] A magnetic field generating component is arranged opposite to the lithium battery for generating a controllable magnetic field horizontal to the plane of the lithium battery, which is used to detect different layers in the lithium battery;

[0024] A quantum magnetic induction component is arranged on the same side of the magnetic field generating component, and the induction surface of the quantum magnetic induction component is opposite to the lithium battery. The quantum magnetic induction component is used to collect the magnetic induction intensity of the reflected magnetic field reflected from different layers in the lithium battery;

[0025] A processing module is connected to the input end of the magnetic field generating component, and the processing module controls the magnetic field generating component to generate different magnetic field characteristics when detecting different levels in the lithium battery; the second output end of the processing module is connected to the control end of the quantum magnetic induction component to control the working condition of the quantum magnetic induction component; and the input end of the processing module is electrically connected to the output end of the quantum magnetic induction component to receive the magnetic induction intensity collected by the quantum magnetic induction component.

[0026] The present application has the following advantages:

[0027] By establishing the correspondence between the strength and frequency of the detected magnetic field and the number of battery layers, and the one-to-one correspondence between the battery failure mechanism and the layered vector diagram, the battery tomography database constructed contains a variety of information, such as the corresponding layered vector diagram of the lithium battery, the data marked in the layered vector diagram, the type, size and position of the defect, etc. The battery tomography database provides data reference for the internal defect detection technology of lithium batteries, and can realize tomographic analysis of the internal structure of the battery by combining deep learning technology, and provides data support for promoting the development of the internal defect detection technology of lithium batteries.

[0028] Additional aspects and advantages of the present application will be made apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0030] Figure 1 is a flowchart of the present application;

[0031] Figure 2 is a schematic diagram of a detection structure for detecting the magnetic field of different levels of a lithium battery. DETAILED DESCRIPTION

[0032] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, in which the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and cannot be understood as limiting the present application.

[0033] In the description of the present application, unless otherwise specified and limited, it should be noted that the terms "mounting", "connection" and "connection" should be understood in a broad sense, for example, they can be mechanical connection or electrical connection, or the communication between two elements, or direct connection or indirect connection through an intermediate medium. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0034] Example 1

[0035] like Figure 1 As shown, this invention provides a method for constructing a battery tomography database based on quantum sensors, comprising the following steps:

[0036] A sample set of lithium batteries with known internal defects was obtained. Magnetic field detection was performed on different layers of each lithium battery in the sample set. The required magnetic field strength for detecting different layers of each lithium battery was recorded, and the feedback magnetic induction intensity after detection was collected using a quantum magnetic induction device. The lithium batteries here are pre-fabricated battery packs, and the internal defects and their dimensions in each layer are known.

[0037] In this embodiment, the following structure is used to detect the magnetic field at different levels of each lithium battery in the lithium battery sample set and to collect the magnetic induction intensity fed back after the detection. For example... Figure 2 As shown, the structure includes: a magnetic field generating component, a quantum magnetic induction component, and a processing module.

[0038] The system comprises a magnetic field generating component positioned opposite the lithium battery to generate a controllable magnetic field horizontally aligned with the battery plane. This controllable magnetic field is used to detect different layers within the lithium battery. The magnetic field generating component and the lithium battery must maintain a fixed distance. A quantum magnetic induction component is positioned on the same side as the magnetic field generating component, with its sensing surface facing the lithium battery. It is used to collect the magnetic induction intensity of reflected magnetic fields from different layers within the lithium battery. The relative positions of the magnetic field generating component, the lithium battery, and the quantum magnetic induction component ensure that the magnetic field generated by the magnetic field generating component is tangential to the quantum magnetic induction component. The quantum magnetic induction component will not sense the magnetic field emitted by the magnetic field generating component, but only the reflected magnetic field from the lithium battery. The first output terminal of the processing module is connected to the input terminal of the magnetic field generating component, controlling the magnetic field generating component to generate different magnetic field characteristics when detecting different layers within the lithium battery. The second output terminal of the processing module is connected to the control terminal of the quantum magnetic induction component, controlling its operating conditions. The input terminal of the processing module is electrically connected to the output terminal of the quantum magnetic induction component, receiving the magnetic induction intensity collected by the quantum magnetic induction component.

[0039] In this embodiment, the magnetic field generating component includes two coils with opposite winding directions. The quantum magnetic induction component is located between the two coils. The three components are closely arranged to form a whole. The processing module controls the two coils to generate a controllable magnetic field.

[0040] Of course, the magnetic field generating component can also be a U-shaped coil or a C-shaped coil, and the quantum magnetic induction component is set inside the opening of the U-shaped coil or the C-shaped coil. The two are closely set together to form a whole.

[0041] In this embodiment, the working conditions of the quantum magnetic induction component, such as the size and frequency of the bias current, can be changed according to the model, parameters, etc. of the lithium battery to achieve the optimal detection effect. The magnetic field characteristics generated by the magnetic field generating component change according to different levels in the lithium battery, and the magnetic field frequency or / and magnetic induction intensity required for detecting different levels in the lithium battery is not the same, in order to achieve the optimal detection effect. The magnetic field characteristics include: the magnetic induction intensity or frequency of the magnetic field, which is determined by the size and frequency of the current on the coil. The magnetic field generating component and the quantum magnetic induction component are arranged on the detection table. Specifically, the detection table is provided with a three-axis sliding table, including an X-axis sliding rail, a Y-axis sliding rail and a Z-axis sliding rail. The quantum magnetic induction component and the magnetic field generating component are arranged on the three-axis sliding table and synchronously move on the X-axis sliding rail, the Y-axis sliding rail and the Z-axis sliding rail of the three-axis sliding table.

[0042] When detecting different batteries, the quantum magnetic induction component and the magnetic field generating component can be synchronously moved along the Z-axis to adjust the vertical distance between the quantum magnetic induction component, the magnetic field generating component and the battery to be detected.

[0043] During detection, the quantum magnetic induction component and the magnetic field generating component synchronously move on the X-axis sliding rail and the Y-axis sliding rail without priority, that is, they can be moved along the X-axis direction first and then along the Y-axis direction, or they can be moved along the Y-axis direction first and then along the X-axis direction, to detect different levels in the lithium battery point by point in a point scanning manner, and to obtain the magnetic induction intensity of the reflected magnetic field from different levels in the lithium battery point by point. The number of scanning points determines the detection accuracy. The three-axis sliding table is connected with the processing module in a control mode, and the movement of the three-axis sliding table is controlled by the processing module. The three-axis sliding table can adopt a conventional structure, and the setting position relationship between the quantum magnetic induction component, the magnetic field generating component and the three-axis sliding table can also adopt a conventional mode. It is only required to ensure that the quantum magnetic induction component and the magnetic field generating component can synchronously move on the X-axis sliding rail, the Y-axis sliding rail and the Z-axis sliding rail, and details are not described herein.

[0044] This embodiment is applicable to the case that the quantum magnetic induction component can only obtain the magnetic induction intensity of the reflection of one point or a small number of points of the lithium battery at a time. For example, the quantum magnetic induction component has only one quantum magnetic sensor.

[0045] The coil can generate a magnetic field by applying a current, and the magnetic induction intensity of the magnetic field is proportional to the current of the coil. In order to realize the detection of various internal battery defects and failure mechanisms, the processing module applies an alternating current with a frequency of 0 Hz to 10 MHz to the coil of the magnetic field generating component to form an alternating magnetic field with a frequency of 0 Hz to 10 MHz. The specific frequency can be determined according to the model, parameters, etc. of the lithium battery. The distance between the magnetic field generating component and the lithium battery is not greater than 1 cm. In order to realize the detection of the magnetic field with a strength of 10 -9The processing module controls the bias current / voltage frequency of the quantum magnetic sensor to be 0Hz to 100kHz to improve the signal-to-noise ratio of the effective signal output by the quantum magnetic sensor in response to the magnetic field. Here, it is realized by the processor in the processing module and the driving circuit. Specifically, the processor can be a single-chip microcomputer, a microprocessor, an FPGA, or a DSP, etc., the output end of which is connected with the input end of the driving circuit to generate an electric signal for controlling the working condition of the quantum magnetic sensing component and an electric signal for controlling the magnetic field generating component to generate different magnetic field characteristics, and send them to the driving circuit; the driving circuit is composed of an operational amplifier, which can be a conventional application circuit, the input end of which is electrically connected with the output end of the processor, the first output end of which is electrically connected with the input end of the magnetic field generating component to provide a driving current for the magnetic field generating component according to the electric signal for controlling the magnetic field generating component to generate different magnetic field characteristics; and the second output end of which is connected with the control end of the quantum magnetic sensing component to provide a bias current for the quantum magnetic sensing component according to the electric signal for controlling the working condition of the quantum magnetic sensing component.

[0046] The processor is connected with the control end of the three-axis sliding table to control the quantum magnetic sensing component and the magnetic field generating component to move on the X-axis sliding rail and the Y-axis sliding rail in a synchronous and non-sequential manner, so as to realize the detection of different levels in the lithium battery by the magnetic field generating component in a point-by-point scanning manner, and the acquisition of the magnetic field signal of the reflected magnetic field reflected from different levels in the lithium battery by the quantum magnetic sensing component point by point. Here, the number of scanning points determines the detection accuracy. In this embodiment, the different levels in the lithium battery refer to different levels in the Z-axis direction of the lithium battery, and when detecting point by point, the different levels in the Z-axis direction of each point of the lithium battery are detected, specifically by controlling the magnetic field generating component to generate different magnetic field characteristics by the processing module.

[0047] After the quantum magnetic sensing component collects the magnetic induction intensity of the reflected magnetic field from the lithium battery, the analog front-end circuit in the processing module acquires and processes it. Specifically, the analog front-end circuit is composed of an instrument amplifier, an operational amplifier, a phase-sensitive detector, and a low-pass filter, the input end of which is electrically connected with the output end of the quantum magnetic sensing component, and the voltage signal output by the quantum magnetic sensing component is captured and processed by the instrument amplifier, the operational amplifier, the phase-sensitive detector, and the low-pass filter in sequence to filter out the noise interference and extract the effective voltage signal reflecting the change of the magnetic induction intensity of the magnetic field as the magnetic induction intensity signal. The instrument amplifier, the operational amplifier, the phase-sensitive detector, and the low-pass filter can all be conventional application circuits.

[0048] The output end of the analog front-end circuit is electrically connected with the input end of the acquisition circuit in the processing module, the acquisition circuit is composed of an analog-to-digital converter, the acquisition circuit receives the magnetic induction intensity signal and converts it from an analog voltage quantity to a digital voltage quantity, the input end of the processor is electrically connected with the output end of the acquisition circuit, receives the digital voltage quantity signal sent by the acquisition circuit as the magnetic induction intensity, and performs signal processing, and the embodiment adopts fast Fourier transform, digital filtering and digital detection to further improve the signal-to-noise ratio of the effective signal.

[0049] After the magnetic induction intensity is collected, the magnetic field information of each scanning point is combined to display the reflection magnetic field intensity of each layer of the entire lithium battery area in the form of an image, different magnetic field frequencies correspond to different internal layers of the battery, so that the corresponding layered original vector diagram of each lithium battery is obtained.

[0050] The layered original vector diagram is preprocessed, mainly including the following three types:

[0051] Image alignment: after the battery is scanned, the layered original vector diagram is obtained, and the coordinate origin is aligned according to the x, y coordinates of the battery image, that is, the interlayer image is aligned.

[0052] Noise suppression: wavelet transform and median filtering are used to denoise the layered original vector diagram.

[0053] Standardization: the magnetic induction intensity in the layered original vector diagram is normalized, that is, the electric field intensity is divided by the electromagnetic wave velocity in the medium to obtain a dimensionless quantity.

[0054] Generally, the image alignment and noise suppression can be performed in any order during the preprocessing, and the standardization is performed last. After the layered original vector is preprocessed, the layered vector diagram of each lithium battery is obtained.

[0055] The layered vector diagram contains three-dimensional electromagnetic field distribution information. The stronger the magnetic field intensity generated by the "magnetic field generating component" and the lower the frequency, the deeper the detection depth, and vice versa, that is, each layer in the lithium battery corresponds to a required magnetic field intensity and frequency for detection, and also corresponds to a feedback magnetic induction intensity. According to this feature, combined with the actual situation of the lithium battery, the corresponding relationship between the layered vector diagram of each lithium battery and the actual battery layer number, and the corresponding relationship between the magnetic field intensity and frequency and the battery layer number are marked.

[0056] Feature extraction is performed on the layered vector diagram:

[0057] The mean, variance and gradient histogram of each vector diagram in the layered vector diagram are counted.

[0058] The deep learning features of each layer of the layered vector diagram are extracted. The deep learning features can be obtained by pre-training CNN to extract high-dimensional feature vectors, usually including hand-crafted features (texture features (GLCM), edge density, connected component analysis, etc.).

[0059] According to the actual situation of each lithium battery, such as the layer where each internal defect is located, the actual size of each internal defect corresponding to multiple structural changes, and the common failure mechanisms (multiple structural changes of active materials, active particle fragmentation, transition metal dissolution, volume expansion, electrolyte deficiency, etc.) and defects of the previously prepared battery pack, the correspondence between different failure mechanisms and defect types and the features of each layered vector diagram of each lithium battery in the layered vector diagram is labeled, as well as the actual size, position and vector graph morphology relationship of multiple structural changes.

[0060] Specifically, the defect area in the vector diagram is identified, polygon labeling is performed using an interactive labeling tool, and the target size is measured. The polygon labeling here labels the boundary of the target in the image with high precision, so that the shape and size of the target are clearly understood.

[0061] The layered vector diagram, labeled data, and measured size data are stored, thereby constructing a battery tomography database. The battery tomography database includes all lithium batteries corresponding to normalized layered vector diagrams, as well as the labeled data in the layered vector diagram, the type, size and position of the defect.

[0062] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0063] Although embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, replacements and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for constructing a battery tomography database based on quantum sensors, characterized in that, Includes the following steps: A lithium battery sample set with known internal defects was obtained. Magnetic field detection was performed on different layers of each lithium battery in the sample set. The magnetic field strength required for detection of different layers of each lithium battery was recorded. The magnetic field strength fed back after detection was collected using a quantum magnetic induction device. A layered original vector diagram is generated based on the magnetic induction intensity feedback from different layers inside the lithium battery. The original layered vector map is preprocessed and dimensionless to obtain the layered vector map of each lithium battery. Based on the magnetic field strength required to detect different layers of each lithium battery, and combined with the actual situation of each lithium battery, the corresponding relationship between the layer vector diagram of each lithium battery and its actual number of battery layers, as well as the correspondence between magnetic field strength and frequency and the number of battery layers are marked. Feature extraction from layered vector graphics; Based on the actual situation of each lithium battery, and combined with the common failure mechanisms and defects of lithium batteries, the correspondence between different failure mechanisms and defect types and the vector diagram features of each layer of each lithium battery is marked, as well as the relationship between the actual size, position and vector diagram shape of various structural changes. The battery tomographic database includes layered vector maps corresponding to all lithium batteries, as well as the data marked in the layered vector maps, including the type, size, and location of defects.

2. The method for constructing a battery tomography database based on quantum sensors according to claim 1, characterized in that, The preprocessing includes: Align the images between layers of the original layered vector graphics; Denoise the layered original vector graphics; The magnetic field strength in the layered original vector diagram is normalized to obtain a dimensionless quantity.

3. The method for constructing a battery tomography database based on quantum sensors according to claim 1, characterized in that, The steps for global feature extraction from a layered vector map are as follows: The mean, variance, and gradient histogram of each layer in a statistical stratified vector diagram; Extract deep learning features from each layer of the layered vector map.

4. The method for constructing a battery tomography database based on quantum sensors according to claim 1, characterized in that, When labeling defect types and their locations, identify the defect areas in the vector diagram, use interactive labeling tools to perform polygon labeling, and measure the target dimensions.

5. The method for constructing a battery tomography database based on quantum sensors according to claim 1, characterized in that, When magnetic field detection is performed on different layers of each lithium battery in the lithium battery sample set, and the magnetic induction intensity fed back after detection is collected using a quantum magnetic induction device, A magnetic field generating component is positioned directly opposite the lithium battery to generate a controllable magnetic field horizontal to the plane of the lithium battery. This controllable magnetic field is used to detect different layers within the lithium battery. A quantum magnetic induction component is placed on the same side as the magnetic field generating component, with the sensing surface of the quantum magnetic induction component facing the lithium battery. The quantum magnetic induction component is used to collect the magnetic induction intensity of the reflected magnetic field reflected from different layers inside the lithium battery. A processing module is connected to the input terminal of a magnetic field generating component. When detecting different layers within the lithium battery, the processing module controls the magnetic field generating component to produce different magnetic field characteristics. The second output terminal of the processing module is connected to the control terminal of a quantum magnetic induction component to control the operating conditions of the quantum magnetic induction component. The input terminal of the processing module is electrically connected to the output terminal of the quantum magnetic induction component to receive the magnetic induction intensity collected by the quantum magnetic induction component.

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