A method and system for identifying the activity level of a lithium battery

Abstract

The application discloses a method and system for identifying the activity level of a lithium battery. A plurality of battery activity discrimination coefficients are obtained according to the gray value of each pixel of each neutron image of a single lithium battery under a plurality of different SOC states. The plurality of battery activity discrimination coefficients are weighted and calculated to obtain a lithium battery activity level discrimination coefficient. The power lithium battery activity level discrimination coefficient is used to obtain the activity level of the single lithium battery. The application uses the sensitivity of a neutron beam to light elements to detect the activity of lithium elements in a lithium battery under different SOC states. The activity level of the battery can be directly, quickly and effectively measured, the lithium battery can be more appropriately utilized to obtain higher safety and prolong the service life, and the lithium battery has a better application prospect in ships.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium battery state detection, and particularly relates to a lithium battery activity level identification method and system. BACKGROUND

[0002] In recent years, many green energy technologies have been widely developed and applied in the shipping industry. The ship power lithium battery has the characteristics of zero pollution and high energy density and is applied to the ship power system. In order to better utilize the lithium battery, the activity level state of the lithium battery needs to be detected to obtain the current state of the lithium battery.

[0003] The current method for evaluating the activity level of the ship power lithium battery mainly includes the following three categories: a method based on a characteristic parameter, a model-based evaluation method, and a data-driven method. The method based on the characteristic parameter mainly establishes an offline relationship between the characteristic parameter of the ship power battery and the activity level, obtains the characteristic parameter of the battery through measurement or calculation, and finally obtains the activity level of the ship power lithium battery through the offline relationship. This method requires the battery to operate under specific conditions and is not suitable for real-world power batteries. The model-based evaluation method first establishes a reliable battery performance model. Through the equivalent circuit model of the power battery and its state equation, a filter algorithm and an observer are applied to build a model-based ship power lithium battery activity level identification method. The mathematical model established after fully considering the influencing factors is too complex, and the corresponding calculation amount is also large. The data-driven method is based on a large amount of offline data to establish and train a direct mapping relationship between the voltage, current, temperature, and other parameters of the power battery and the activity level. This method relies too much on data and is prone to overfitting. SUMMARY

[0004] According to the aging mechanism of the lithium battery, as the activity level of the lithium battery degrades, the free lithium element in the lithium battery will become less and less, and the "dead lithium element" will become more and more. The larger the lithium battery activity discrimination coefficient is, the more free lithium elements in the lithium battery, that is, the better the activity level of the lithium battery. In order to more quickly, conveniently and effectively obtain the level identification state of the lithium battery, the application proposes a method and system for detecting the activity level of the power lithium battery using neutron imaging.

[0005] One of the purposes of the application is a lithium battery activity level identification method, which includes the following steps:

[0006] S1, obtaining a plurality of battery activity discrimination coefficients according to the gray values of each pixel of each neutron image of the single lithium battery under a plurality of different SOC states;

[0007] The battery activity discrimination coefficient is based on the difference between the neutron images of the lithium battery under different SOC states to describe the activity of the single lithium battery.

[0008] The neutron image is an image obtained after the single lithium battery is subjected to neutron imaging; since the neutron beam is more sensitive to light elements, the lithium element is a lighter element, so the area with more lithium elements absorbs more neutron beams, which is reflected in the gray scale image as an area with a lower gray scale value. The gray scale image of the neutron image of the lithium battery at each SOC state reflects the distribution of lithium elements in the battery monomer at each SOC state;

[0009] S2, weighted sum calculation is performed on the plurality of battery activity discrimination coefficients to obtain a lithium battery activity level discrimination coefficient; the power lithium battery activity level discrimination coefficient is used to obtain the activity level of the single lithium battery.

[0010] In order to increase the differentiation degree of gray scale and more accurately reflect the distribution state of lithium ions, further, in the step S1, before obtaining the plurality of battery activity discrimination coefficients, further comprising: mapping calculation is performed on the gray scale of each pixel point of each neutron image to obtain the gray scale mapping value of each pixel point, and the gray scale value of each pixel point in the neutron image is replaced by the corresponding gray scale mapping value.

[0011] Further, the method for gray scale mapping processing comprises:

[0012] According to the gray scale value of each pixel point of each neutron image and the maximum gray scale value G max and the minimum gray scale value G min of all pixel points, the gray scale mapping value of each pixel point is obtained.

[0013] Further, the method for obtaining the gray scale mapping value of each pixel point comprises:

[0014]

[0015] In the formula:

[0016] G i is the gray scale value of the i-th pixel point of the neutron image;

[0017] is the gray scale mapping value of the i-th pixel point of the neutron image.

[0018] In order to obtain the difference between the distribution states of lithium elements at different SOCs, thereby indirectly obtaining the number of free lithium elements in the lithium battery, further, in the step S1, the method for obtaining the plurality of battery activity discrimination coefficients comprises:

[0019] S101, according to the SOC value, the neutron image is composed of a plurality of image groups, wherein each image group is composed of a plurality of pairs of neutron images, and the absolute value of the difference between the corresponding SOCs of each pair of neutron images is within a set range;

[0020] S102, difference operation is performed on each pair of sub-images in each image group to obtain a plurality of difference images, and a battery activity discrimination coefficient of each image group is obtained according to a gray value of each pixel point of each difference image in each image group.

[0021] Further, in the step S102, the method for obtaining the plurality of activity discrimination coefficients comprises:

[0022] The mean value of the gray value of each pixel point of each difference image in each image group is calculated, and weighted summation is performed on all the mean values to obtain the activity discrimination coefficient P of each image group. j (j∈[1,K], K is the number of groups of image groups); wherein the purpose of the weighted summation is to more accurately reflect the current number of free lithium elements in the lithium battery;

[0023] Wherein P j The calculation method comprises:

[0024]

[0025] In the formula:

[0026] P j represents the activity discrimination coefficient;

[0027] f i1 and f i1 represent the gray value of the i-th pixel point of a pair of sub-images used for difference operation;

[0028] N represents the total number of pixel points contained in the sub-image;

[0029] ω i represents a set weight, (i∈[1,K]).

[0030] A lithium battery activity level identification system for achieving the second purpose of the application comprises a sub-image acquisition module; a battery activity discrimination coefficient acquisition module; and a battery activity level discrimination coefficient acquisition module.

[0031] The sub-image acquisition module is used for performing sub-imaging on single lithium batteries in a plurality of different SOC states to obtain a plurality of sub-images.

[0032] The battery activity discrimination coefficient acquisition module is used for obtaining a plurality of battery activity discrimination coefficients according to the gray values of the pixels of the plurality of sub-images.

[0033] The battery activity level discrimination coefficient acquisition module is used for performing weighted calculation on the plurality of battery activity discrimination coefficients to obtain a battery activity level discrimination coefficient; and the lithium battery activity level discrimination coefficient is used for obtaining the activity level of the single lithium battery.

[0034] Further, the system further comprises a gray scale mapping processing module, which is used for mapping calculation on the gray scale of each pixel point of each neutron image, and replacing the gray scale value of each pixel point in the neutron image with a mapped gray scale value.

[0035] Further, the system further comprises an image group division module, which is used for grouping the neutron images into multiple image groups according to the SOC values, wherein each image group is composed of multiple pairs of neutron images, and the absolute value of the SOC difference corresponding to each pair of neutron images is within a set range.

[0036] Further, the system further comprises a difference operation module, which is used for performing difference operation on each pair of neutron images in each image group to obtain a difference image, and obtaining a battery activity discrimination coefficient of each group according to the mean value of the gray scale values of all pixel points in the difference image.

[0037] Beneficial effects:

[0038] The present application utilizes the sensitivity of neutron beams to light elements, describes the activity of single lithium battery based on the difference of neutron images of lithium batteries in different SOC states, can directly, quickly and effectively measure the battery activity level, can more appropriately utilize lithium batteries, can obtain higher safety and prolong the service life, and can make the lithium battery have a better application prospect in ships. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a flowchart of an embodiment of the method of the present application;

[0040] Figure 2 is a neutron imaging gray scale diagram;

[0041] Figure 3 is a neutron imaging mapping gray scale diagram;

[0042] Figure 4 is a SOC interval difference operation diagram;

[0043] Figure 5 is a comparison diagram of neutron mapping of lithium batteries with inconsistent activity states. DETAILED DESCRIPTION

[0044] The following detailed description is used to explain the technical solutions of the claims of the present application, so that those skilled in the art can understand the claims of the present application. The protection scope of the present application is not limited to the following specific implementation structure. The technical solutions of the claims of the present application which are different from the following specific embodiments and contain the technical solutions of the claims of the present application are also within the protection scope of the present application.

[0045] This embodiment describes one embodiment of the method of the present application by taking a square ship power lithium battery as a detection target.

[0046] The square-shaped ship power lithium battery monomer is respectively imaged by neutrons in the state of 100% SOC, 90% SOC, 80% SOC, 70% SOC, 60% SOC, 50% SOC, 40% SOC and 30% SOC. The gray neutron image of the monomer battery in each SOC state reflects the distribution diagram of the lithium element of the battery monomer in each SOC state. The area with more lithium elements absorbs more neutron beams, which is reflected in the gray value of the gray image. The gray neutron imaging gray diagram is shown in Figure 2 ;

[0047] As can be seen from Figure 2 , the gray value distinction is not large. In order to increase the gray value distinction and more accurately reflect the distribution state of lithium ions, the neutron image also needs to be gray mapped in another embodiment. The specific steps are as follows:

[0048] S1001, according to the gray value G i of each pixel point of each neutron image (i = 1, 2, …, N), N is the total number of pixel points contained in the neutron image and the maximum gray value G max = Max(G i ) and the minimum gray value G min = Min(G i ), (i = 1, 2, …, N) to get the gray mapping value of each pixel point;

[0049]

[0050] Among them, G i is the gray value of the i-th pixel point of the original neutron image; is the mapping gray value of the i-th pixel point of the neutron image.

[0051] S1002, replace the gray value G i of each pixel point of each neutron image with the corresponding gray mapping value , so as to obtain the neutron image after gray mapping; record the new gray neutron image as NP 100％ , NP 90％ , NP 80％ , NP 70％ , NP 60％ , NP 50％ , NP 40％ , NP 30％ . The neutron imaging mapping gray diagram is shown in Figure 3 .

[0052] Step 2, the method for obtaining a plurality of battery activity discrimination coefficients according to the gray value of each pixel of each neutron image is as follows:

[0053] S101. The neutron images are grouped into multiple image groups according to the SOC value, wherein each image group consists of multiple pairs of neutron images, and the absolute value of the difference in SOC between each pair of neutron images is within a set range.

[0054] In this embodiment, neutron images with 70% SOC interval, 50% SOC interval, 30% SOC interval, and 10% SOC interval are respectively grouped into four image groups; that is:

[0055] The image group with a 70% SOC interval includes neutron images corresponding to the following SOCs: {100% SOC, 30% SOC};

[0056] The image group with a 50% SOC interval includes neutron images corresponding to the following SOCs: {100% SOC, 50% SOC}, {90% SOC, 40% SOC}, {80% SOC, 30% SOC};

[0057] The image group with a 30% SOC interval includes neutron images corresponding to the following SOCs: {100% SOC, 70% SOC}, {90% SOC, 60% SOC}, {80% SOC, 50% SOC}, {70% SOC, 40% SOC}, {60% SOC, 30% SOC};

[0058] The image group with a 10% SOC interval includes neutron images corresponding to the following SOCs: {100% SOC, 90% SOC}, {90% SOC, 80% SOC}, {80% SOC, 70% SOC}, {70% SOC, 60% SOC}, {60% SOC, 50% SOC}, {50% SOC, 40% SOC}, {40% SOC, 30% SOC};

[0059] S102. Perform a difference operation on each pair of neutron images in each image group to obtain multiple difference images. Obtain the activity discrimination coefficient of the first group based on the gray value of the pixel of each difference image in each image group.

[0060] Perform a difference operation on two grayscale neutron images with a 70% SOC interval, and calculate the mean grayscale value of each pixel in the difference image. The difference operation with a 70% SOC interval is as follows: Figure 4 As shown, the activity discrimination coefficient P at a 70% SOC interval was obtained. 70 :

[0061]

[0062] In the formula:

[0063] N represents the total number of pixels contained in the neutron image;

[0064] fi 100％ , f i 30％ respectively represent the gray value of the i-th pixel point of the neutron image under the state of 100% SOC and 30% SOC respectively, and f i k％ , f 50 respectively represent the gray value of the i-th pixel point of the neutron image under the state of 100% SOC and 30% SOC respectively, and f

[0065] The difference operation is performed on each adjacent two gray neutron images in the 50% SOC interval, the mean value of the gray value of each pixel point of the difference image is calculated, and the weighted sum of all mean values is calculated by using the weight of 1 / 3, so as to obtain the activity discrimination coefficient P 50 :

[0066]

[0067] The difference operation is performed on each adjacent two gray neutron images in the 30% SOC interval, the mean value of the gray value of each pixel point of the difference image is calculated, and the weighted sum of all mean values is calculated by using the weight of 1 / 5, so as to obtain the activity discrimination coefficient P 30 :

[0068]

[0069] The difference operation is performed on each adjacent two gray neutron images in the 10% SOC interval, the mean value of the gray value of each pixel point of the difference image is calculated, and the weighted sum of all mean values is calculated by using the weight of 1 / 7, so as to obtain the activity discrimination coefficient P 10 :

[0070]

[0071] In the formula:

[0072] N represents the total number of pixel points contained in the neutron image;

[0073] , f i 100％ , f i 90％ , f i 80％ ,..., f i 40％ , f i 30％ respectively represent the gray value of the i-th pixel point of the neutron image under the state of 100% SOC, 90% SOC, 80% SOC,..., 40% SOC and 30% SOC;

[0074] The above four battery activity discrimination coefficients P 10 , P 30 , P50 , P 70 The weighted calculation is performed to obtain a lithium battery activity level discrimination coefficient J:

[0075]

[0076] According to the discrimination coefficient J, a ship power lithium battery activity level is obtained from Table 1; it should be pointed out that the levels in Table 1 are not limited to five levels, and the threshold values of each level can be adjusted according to the needs, and the present application does not limit this.

[0077] Table 1 Activity discrimination coefficient level table

[0078]

[0079] Among them, A represents that the lithium battery activity level is excellent; B represents that the lithium battery activity level is good; C represents that the lithium battery activity level is general; D represents that the lithium battery activity level is poor; and E represents that the lithium battery activity level is the worst.

[0080] According to the aging mechanism of the lithium battery, as the activity level of the lithium battery degrades, the free lithium element in the lithium battery will be less and less, and the relative "dead lithium element" will be more and more. The larger the lithium battery activity discrimination coefficient is, the more free lithium elements in the lithium battery, that is, the more excellent the lithium battery activity level is. For example, Figure 5 Fig. 2 is a comparative schematic diagram of lithium battery neutron mapping diagrams with inconsistent activity states, and it can be seen from the figure that the lithium battery neutron mapping gray scale diagram with the more excellent activity level has a larger difference under different SOC states, that is, the lithium battery activity discrimination coefficient J is larger; and the lithium battery neutron mapping gray scale diagram with the worse activity level has a smaller difference under different SOC states, that is, the lithium battery activity discrimination coefficient J is smaller.

[0081] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0082] The present application also provides a lithium battery activity level identification system, comprising a neutron image acquisition module; a battery activity discrimination coefficient acquisition module; a battery activity level discrimination coefficient acquisition module;

[0083] The neutron image acquisition module is used for neutron imaging of single lithium batteries in a plurality of different SOC states to obtain a plurality of neutron images;

[0084] The battery activity discrimination coefficient acquisition module is used for obtaining a plurality of battery activity discrimination coefficients according to the gray values of each pixel of the plurality of neutron images;

[0085] The battery activity level discrimination coefficient acquisition module is configured to perform weighted calculation on the plurality of battery activity discrimination coefficients to obtain a battery activity level discrimination coefficient; and the lithium battery activity level discrimination coefficient is configured to obtain the activity level of the single lithium battery.

[0086] In another embodiment, the system further comprises a gray scale mapping processing module configured to perform mapping calculation on the gray scale of each pixel point of each neutron image, and replace the gray scale value of each pixel point in the neutron image with a mapped gray scale value.

[0087] In another embodiment, the system further comprises an image group division module configured to divide the neutron images into a plurality of image groups according to the SOC values, wherein each image group is composed of a plurality of pairs of neutron images, and the absolute value of the SOC difference corresponding to each pair of neutron images is within a set range.

[0088] In another embodiment, the system further comprises a difference operation module configured to perform difference operation on each pair of neutron images in each image group to obtain a difference image, and obtain a battery activity discrimination coefficient of each group according to the mean value of the gray scale values of all pixel points in the difference image.

Claims

1. A method of identifying the activity level of a lithium battery, characterized by, The method comprises the following steps: S1. Obtain a plurality of battery activity discrimination coefficients according to the gray values of each pixel of each neutron image of the single lithium battery in a plurality of different SOC states; the neutron image is an image obtained after neutron imaging of the single lithium battery; the battery activity discrimination coefficient is used to describe the activity of the single lithium battery; S2. Perform weighted summation calculation on the plurality of battery activity discrimination coefficients to obtain a lithium battery activity level discrimination coefficient; The lithium battery activity level discrimination coefficient is used to obtain the activity level of the single lithium battery; In the step S1, the method for obtaining the plurality of battery activity discrimination coefficients comprises: S101. Group the neutron images into a plurality of image groups according to the SOC values, wherein each image group is composed of a plurality of pairs of neutron images, and the absolute value of the difference between the corresponding SOC values of each pair of neutron images is within a set range; S102. Perform difference operation on each pair of neutron images in each image group to obtain a plurality of difference images, and obtain the battery activity discrimination coefficient of each image group according to the gray values of the pixel points of each difference image in each image group; In the step S102, the method for obtaining the battery activity discrimination coefficient comprises: The mean value of the gray value of each pixel point of each differential image in each image group is calculated, and all the mean values are weighted and summed to obtain a battery activity discrimination coefficient P of each image group j , j ∈ [1, K], and K is the number of groups of image groups; the calculation method comprises the following steps: ； In the formula: P j represents the battery activity discrimination coefficient of the jth image group; with denotes the gray value of the i-th pixel point of a pair of neutron images for difference operation; N represents the total number of pixel points contained in the neutron image; denotes the set weight, i e [1, K].

2. The method of claim 1, wherein the step of determining the state of charge of the lithium battery is performed by using a voltage of the lithium battery. In the step S1, before obtaining the plurality of battery activity discrimination coefficients, the method further comprises: performing mapping calculation on the gray value of each pixel point of each neutron image to obtain the gray mapping value of each pixel point, and replacing the gray value of each pixel point in the neutron image with the corresponding gray mapping value.

3. The method for identifying the activity level of a lithium battery as described in claim 2, characterized in that, The method for mapping and calculating the gray scale of each pixel point of each neutron image comprises: obtaining the gray mapping value of each pixel point according to the gray value of each pixel point of each neutron image and the maximum gray value G max and the minimum gray value G min of all pixel points.

4. The method for identifying the activity level of a lithium battery as described in claim 3, characterized in that, The method for obtaining the gray mapping value of each pixel point comprises: ； In the formula: G i is the gray value of the i-th pixel point of the neutron image; is the gray scale mapping value of the i-th pixel point of the neutron image.

5. A system for identifying the state of health of a lithium battery employing the method of claim 1, comprising: a neutron image acquisition module; a battery activity discrimination coefficient acquisition module; and a battery activity level discrimination coefficient acquisition module; The neutron image acquisition module is used to perform neutron imaging on the single lithium battery in a plurality of different SOC states to obtain a plurality of neutron images; The battery activity discrimination coefficient acquisition module is used to obtain a plurality of battery activity discrimination coefficients according to the gray values of the pixels of the plurality of neutron images; The battery activity level discrimination coefficient acquisition module is used to perform weighted summation calculation on the plurality of battery activity discrimination coefficients to obtain a battery activity level discrimination coefficient; The lithium battery activity level discrimination coefficient is used to obtain the activity level of the single lithium battery.

6. The lithium battery state of charge identification system of claim 5, wherein, The method further comprises a gray mapping processing module, which is used to perform mapping calculation on the gray value of each pixel point of each neutron image, and replace the gray value of each pixel point in the neutron image with the mapped gray value.

7. The lithium battery state of charge identification system of claim 5, wherein, The method further comprises an image group division module, which is used to group the neutron images into a plurality of image groups according to the SOC values, wherein each image group is composed of a plurality of pairs of neutron images, and the absolute value of the difference between the corresponding SOC values of each pair of neutron images is within a set range.

8. The lithium battery state of charge identification system of claim 5, wherein, The method further comprises a difference operation module, which is used to perform difference operation on each pair of neutron images in each image group to obtain a difference image, and obtain the battery activity discrimination coefficient of each group according to the mean value of the gray values of all pixel points in the difference image.

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