A nondestructive detection method for battery polarization distribution and a rapid battery classification method

Abstract

A nondestructive detection method for battery polarization distribution and a method for rapid battery classification belong to the field of battery detection technology. The present invention detects the changes in the polarization distribution inside the battery by detecting the two-dimensional distribution of the external magnetic field during the relaxation process of the lithium-ion battery, thereby realizing nondestructive detection of the polarization distribution inside the battery, and can effectively analyze the electrochemical characteristics of different sites inside the battery. This method realizes rapid screening and classification of different batteries through the differences in polarization distribution characteristics inside the battery, which helps to quickly classify the consistency of batteries and screen for abnormalities. Compared with conventional electrochemical detection methods, this method allows for rapid nondestructive testing of batteries, especially for factory testing, battery pack inconsistency detection, and rapid detection of large-scale long-term storage batteries.

Description

Technical Field

[0001] The invention belongs to the technical field of battery detection, and in particular relates to a battery polarization distribution nondestructive detection method and a battery rapid classification method. Background Art

[0002] The polarization difference of lithium-ion batteries is a key factor affecting the performance of single cells and the consistency of battery packs. The internal polarization of batteries is mainly affected by battery manufacturing and design, as well as attenuation and failure during use. By detecting the internal polarization distribution and corresponding performance of batteries under different manufacturing and working conditions, the key factors affecting battery performance can be effectively analyzed, which helps to optimize battery design strategies. In addition, the internal polarization distribution characteristics of batteries can be used to quickly classify batteries, and batteries with similar performance can be quickly classified into groups to increase the performance and safety stability of battery packs. Therefore, the development of a method for detecting the internal polarization distribution of batteries can achieve rapid optimization of battery design and battery screening and classification.

[0003] At present, the methods for testing battery performance include voltage and internal resistance testing, capacity testing, pulse testing, and electrochemical impedance spectroscopy (EIS) testing. These methods mainly detect battery performance through the lumped electrical characteristics of the battery to obtain the battery health status or impedance characteristics, but it is difficult to obtain local information affected by the battery structure design or local attenuation faults. The current battery detection method is difficult to realize the test and analysis of the polarization distribution inside the battery, and the ability to obtain electrochemical information inside the battery is very limited. In addition, for battery screening and classification, the current detection methods based on electrical characteristics are also very limited. For example, although the capacity test is accurate, it is too time-consuming and not suitable for rapid screening of large quantities of batteries. Although the pulse test is fast, the information provided is limited and may not accurately reflect the long-term attenuation state of the battery. Although the electrochemical impedance spectroscopy test is rich in information, it is affected by the battery charge state and is not conducive to rapid detection. Summary of the invention

[0004] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a battery polarization distribution non-destructive detection method and a battery rapid classification method. The method performs an external planar magnetic field distribution test on the relaxation process of a lithium-ion battery after power failure, obtains the external two-dimensional magnetic field distribution of the lithium-ion battery relaxation process, maps the internal polarization distribution of the battery, and realizes non-destructive detection of the internal polarization distribution of the battery; the method realizes rapid screening and classification of different batteries through the differences in the internal polarization distribution characteristics of the battery.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A nondestructive testing method for battery polarization distribution comprises the following steps:

[0007] Step 1: When the battery under test is in a standby state, a magnetic sensor is used to test the external magnetic field distribution B(i, j) of the battery under test;

[0008] Step 2: Apply working current to the battery under test for a certain period of time and then cut off the power supply, and measure the external magnetic field distribution B of the battery during the relaxation process after power off. RT (i, j), and calculate the external magnetic field distribution B during the battery relaxation process RT The difference between (i, j) and the external magnetic field distribution B(i, j) when the battery is in a standby state is used to obtain the external magnetic field distribution ΔB generated by the battery relaxation current. RT (i, j);

[0009] Step 3: External magnetic field distribution ΔB generated by battery relaxation current RT (i, j) Two-dimensional image visualization is used to map the two-dimensional polarization distribution characteristics inside the battery, ΔB RT The greater the deviation of (i, j) from 0, the greater the polarization.

[0010] Furthermore, the external magnetic field distribution ΔB generated by the battery relaxation current RT The calculation method of (i, j) is ΔB RT , n(i, j) = B RT (i, j)-B(i, j).

[0011] Furthermore, the magnetic sensor includes a Hall sensor, a fluxgate sensor, a giant magnetoresistance sensor or an anisotropic magnetoresistance sensor.

[0012] Furthermore, when conducting external magnetic field distribution tests, magnetic shielding equipment is used to provide magnetic shielding protection for the tested battery, and magnetic shielding devices of various shapes made of various high magnetic permeability metals are used to shield external magnetic field interference to improve detection accuracy. In an environment without magnetic shielding protection, the complexity of the device is reduced.

[0013] Further, the battery includes a laminated battery or a wound battery. The battery includes a lithium ion battery or a sodium ion battery. The lithium ion battery is any type of commercial battery, the positive electrode of the lithium ion battery includes lithium nickel cobalt manganese oxide, nickel cobalt aluminum manganese oxide, lithium cobalt oxide, lithium manganese oxide or lithium iron phosphate, and the negative electrode includes graphite or SiO x .

[0014] Furthermore, when testing the external magnetic field distribution of the battery under test, the test is performed on the battery surface or on a plane at a fixed height from the battery surface.

[0015] Furthermore, when testing the external magnetic field of the tested battery, a single magnetic sensor is used for scanning testing or a plurality of identical magnetic sensors are used to form an array for coverage testing.

[0016] Furthermore, in step 2, the time for applying the working current to the battery under test is adjusted according to actual needs, and the magnitude of the applied current is within a normal range acceptable to the battery under test.

[0017] A method for rapid battery classification comprises the following steps:

[0018] S1. Perform an external magnetic field distribution test on all the batteries to be classified according to steps 1 and 2 in the battery polarization distribution nondestructive testing method to obtain the external magnetic field distribution ΔB generated by the relaxation current of all the batteries under test. RT，n (i, j), where n is the number of each battery, n = 1, 2, 3, ..., n;

[0019] S2, the external magnetic field distribution ΔB generated by the relaxation current of each battery under test RT，n (i, j) is calculated by absolute value to obtain |ΔB RT，n (i, j)|, and solve for |ΔB of each tested battery RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n ;

[0020] S3, with mean μ RT，n and standard deviation σ RT，n and M n =μ RT，n +σ RT，n is the degree of deviation for each battery; according to the degree of deviation M n Judgement, M. n The greater the difference, the greater the difference in polarization distribution inside the battery;

[0021] S4. Set the number of categories that all tested batteries need to be classified into, and use the K-means clustering method to use the deviation degree of each battery to achieve consistent classification and abnormal screening of batteries.

[0022] Furthermore, the absolute value of the external magnetic field distribution of the measured battery |ΔB RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n The calculation method is,

[0023] Where N is the number of measurement points of the external magnetic field of the battery.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The present invention detects the changes in the polarization distribution inside the battery by detecting the two-dimensional distribution of the external magnetic field during the relaxation process of the lithium-ion battery, thereby realizing non-destructive detection of the polarization distribution inside the battery and effectively analyzing the electrochemical characteristics of different sites inside the battery.

[0026] 2. The present invention realizes rapid screening and classification of different batteries through the difference in polarization distribution characteristics inside the battery, which is helpful for rapid consistency classification and abnormal screening of batteries. Compared with conventional electrochemical detection methods, this method allows rapid non-destructive testing of batteries, especially for factory testing, battery pack inconsistency detection and rapid detection of large-scale long-term storage batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is the flow chart of the nondestructive testing method for polarization distribution of the battery;

[0028] Figure 2 A flowchart of the rapid battery classification method;

[0029] Figure 3 Schematic diagram of the placement of the battery under test;

[0030] Figure 4 ΔB is the external magnetic field distribution of the battery RT，1 (i, j) The internal polarization distribution obtained by mapping;

[0031] Figure 5 It is the classification test result of all the tested batteries. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] Example 1

[0034] This embodiment provides a nondestructive detection method for battery polarization distribution. Figure 1 As shown, the method comprises the following specific steps:

[0035] Step 1: When the battery under test is in a standby state, a magnetic sensor is used to test the external magnetic field distribution B(i, j) of the battery under test;

[0036] The plane formed by the length direction and width direction of the battery is the xy plane; i and j represent the coordinates of any point in the x direction and the coordinates of any point in the y direction of the battery respectively.

[0037] In this embodiment, the battery under test is a 5Ah soft-pack lithium-ion battery with a SOH of 100%. Figure 3 The figure is a schematic diagram of the placement of the battery during the test; the magnetic field distribution test equipment uses a multi-axis mobile platform to load a three-axis fluxgate sensor to scan the battery under test at a plane 5 mm above the battery, and uses a 5-layer magnetic shielding barrel for magnetic shielding protection. According to the test method of step 1, when the sample battery is in a shelved state, the magnetic field distribution B(i, j) outside the battery is tested to obtain the background magnetic field distribution.

[0038] Step 2: Apply working current to the battery under test for a certain period of time and then cut off the power supply, and measure the external magnetic field distribution B of the battery during the relaxation process after power off. RT (i, j), and calculate the external magnetic field distribution B during the battery relaxation process RT The difference between (i, j) and the external magnetic field distribution B(i, j) when the battery is in a standby state is used to obtain the external magnetic field distribution ΔB generated by the battery relaxation current. RT (i, j);

[0039] In this embodiment, a 1C charging current is applied for 10 seconds and then the power is turned off, and the external magnetic field distribution B of the battery relaxation process after the power is turned off is measured. RT (i, j), and through difference calculation, the external magnetic field distribution ΔB generated by the battery relaxation current is obtained RT (i, j).

[0040] Step 3: External magnetic field distribution ΔB generated by battery relaxation current RT (i, j) Two-dimensional image visualization is used to map the two-dimensional polarization distribution characteristics inside the battery, the magnetic field distribution ΔB RT The greater the deviation of (i, j) from 0, the greater the polarization.

[0041] In this embodiment, the polarization distribution of the battery relaxation process can be directly mapped by the magnetic field distribution outside the battery. RT (i, j) Two-dimensional image visualization to obtain the internal polarization distribution of the battery. Battery two-dimensional magnetic field distribution ΔB RT The more the value of (i, j) deviates from 0, the greater the polarization. Figure 4 As shown, the deviation of the sample battery is very small, indicating that the polarization difference of each part of the battery is small.

[0042] Example 2

[0043] A method for rapid battery classification comprises the following steps:

[0044] S1. When the battery under test is in a standby state, a magnetic sensor is used to measure the external magnetic field distribution B1(i, j), B2(i, j), ..., Bn (i, j) to conduct the test;

[0045] Among them, B1(i, j), B2(i, j), ..., B n (i, j) is the external magnetic field distribution of the 1st, 2nd, ..., nth battery.

[0046] This embodiment selects two types of batteries to be tested, 9 batteries in a healthy state of SOH100% and 4 batteries in a healthy state of SOH90%, for classification detection, and tests the classification detection results at the battery state of charge SOC0% and SOC100%, respectively. The 9 batteries in a healthy state of 100% are numbered as battery 1, 2, 3, 4, 6, 9, 11, 12, and 13, and the 4 batteries in a healthy state of SOH90% are numbered as battery 5, 7, 8, and 10, respectively. Rapid classification detection of the two types of batteries is achieved at each state of charge; all the batteries tested are 5Ah soft-pack lithium-ion batteries. According to the test method of S1, firstly, an external magnetic field test is performed on the 13 lithium-ion batteries in the idle state at each state of charge to obtain B1(i, j), B2(i, j),…, B RT，13 (i, j).

[0047] S2. Test according to step 2 described in Example 1 and obtain the external magnetic field distribution ΔB generated by the relaxation current of all the tested batteries RT，n (i, j); External magnetic field distribution ΔB generated by relaxation current of each battery under test RT，n (i, j) is calculated by absolute value to obtain |ΔB RT，n (i, j)|, and solve for |ΔB of each tested battery RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n ;

[0048] In the embodiment, a 1C charging current is applied for 10 seconds and then the power is turned off, and the external magnetic field distribution B of the battery relaxation process after the power is turned off is measured. RT，1 (i,j), B RT，2 (i, j), …, B RT，13 (i, j), and through difference calculation, the external magnetic field distribution |ΔB generated by the battery relaxation current is obtained RT，1 (i, j)|,|ΔB RT，2 (i, j)|,…,|ΔB RT，13 (i, j)|, then, the external magnetic field distribution generated by the relaxation current of each measured battery |ΔB RT，1 (i, j)|,|ΔB RT，2 (i, j)|,…,|ΔB RT，13 (i, j)| are calculated by absolute value to obtain |ΔB RT，1 (i, j)|,|ΔBRT，2 (i, j)|,…,|ΔB RT，13 (i, j)|, and then solve the absolute value of the external magnetic field distribution of each battery under test |ΔB RT，1 (i, j)|,|ΔB RT，2 (i, j)|,…,|ΔB RT，13 The mean μ of (i, j)| RT，1 , μ RT，2 ,…,μ RT，13 and standard deviation σ RT，1 , σ RT，2 , …, σ RT，13 .

[0049] S3, with mean μ RT，n and standard deviation σ RT，n and M n =μ RT，n +σ RT，n The deviation degree of each battery. Set the number of categories that the battery needs to be classified into, and use the K-means clustering method to use the deviation degree of each battery to achieve consistent classification and abnormal screening of batteries. n Judgement, M. n The larger the difference, the greater the difference in polarization distribution inside the battery.

[0050] In this embodiment, according to the absolute value of the external magnetic field distribution of 13 batteries at each state of charge |ΔB RT，1 (i, j)|,|Δ BRT，2 (i, j)|,…,|ΔB RT，13 The mean μ of (i, j)| RT，1 , μ RT，2 ,…,μ RT，13 and standard deviation σ RT，1 , σ RT，2 , …, σ RT，13 Calculate the degree of deviation. When the number of battery classification categories is set to 2, the test results are as follows Figure 5 As shown, battery 5, battery 7, battery 8 and battery 10 are a group of similar batteries, which can be defined as first-class batteries, and batteries 1, 2, 3, 4, 6, 9, 11, 12, and 13 are a group of similar batteries, which can be defined as second-class batteries. According to further analysis of the results, it can be seen that the deviation degree of the first-class batteries deviates more from 0, while the deviation degree of the second-class batteries deviates less from 0, indicating that the first-class batteries deviate more from the normal state of healthy batteries than the second-class batteries. Since it is known that the health state SOH of the first-class batteries is 90%, the attenuation is greater, which shows that the invention can effectively realize the rapid classification and screening of batteries. Figure 5 (a) is the test result when the state of charge is 0% and Figure 5(b) The detection results are consistent when the state of charge is 100%, indicating that this method is not affected by the battery charge state.

[0051] The present invention detects the changes in the polarization distribution inside the battery by detecting the two-dimensional distribution of the external magnetic field during the relaxation process of the lithium-ion battery, thereby realizing non-destructive detection of the polarization distribution inside the battery and effectively analyzing the electrochemical characteristics of different sites inside the battery. This method realizes rapid screening and classification of different batteries through the differences in polarization distribution characteristics inside the battery, which helps to quickly classify the consistency of batteries and screen for abnormalities. Compared with conventional electrochemical detection methods, this method allows for rapid non-destructive testing of batteries, especially for factory testing, battery pack inconsistency detection, and rapid detection of large-scale long-term storage batteries.

[0052] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A nondestructive testing method for battery polarization distribution, characterized in that: The following steps are involved: Step 1: When the battery under test is in a standby state, a magnetic sensor is used to test the external magnetic field distribution B(i, j) of the battery under test; Step 2: Apply working current to the battery under test for a certain period of time and then cut off the power supply, and measure the external magnetic field distribution B of the battery during the relaxation process after power off. RT (i, j), and calculate the external magnetic field distribution B during the battery relaxation process RT The difference between (i, j) and the external magnetic field distribution B(i, j) when the battery is in a standby state is used to obtain the external magnetic field distribution ΔB generated by the battery relaxation current. RT (i, j); Step 3: External magnetic field distribution ΔB generated by battery relaxation current RT (i, j) Two-dimensional image visualization is used to map the two-dimensional polarization distribution characteristics inside the battery, ΔB RT The greater the deviation of (i, j) from 0, the greater the polarization; The battery polarization distribution nondestructive testing method is used for rapid classification of batteries. The method for rapid classification comprises the following steps: S1. Perform an external magnetic field distribution test on all the batteries to be classified according to step 1 and step 2 to obtain the external magnetic field distribution ΔB generated by the relaxation current of all the batteries under test. RT，n (i, j), where n is the number of each battery, n = 1, 2, 3, ..., n; S2, the external magnetic field distribution ΔB generated by the relaxation current of each battery under test RT，n (i, j) is calculated by absolute value to obtain |ΔB RT，n (i, j)|, and solve for |ΔB of each tested battery RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n ; S3, with mean μ RT，n and standard deviation σ RT，n and M n =μ RT，n +σ RT，n is the degree of deviation for each battery; according to the degree of deviation M n Judgement, M. n The greater the difference, the greater the difference in polarization distribution inside the battery; S4. Set the number of categories that all tested batteries need to be classified into, and use the K-means clustering method to use the deviation degree of each battery to achieve consistent classification and abnormal screening of batteries.

2. A battery polarization distribution nondestructive testing method according to claim 1, characterized in that: The external magnetic field distribution ΔB generated by the battery relaxation current RT The calculation method of (i, j) is ΔB RT (i,j)=B RT (i,j)-B(i,j).

3. The method for nondestructive testing of battery polarization distribution according to claim 1, characterized in that: The magnetic sensor includes a Hall sensor, a fluxgate sensor, a giant magnetoresistance sensor or an anisotropic magnetoresistance sensor.

4. A battery polarization distribution nondestructive testing method according to claim 1, characterized in that: When conducting external magnetic field distribution tests, magnetic shielding equipment is used to provide magnetic shielding protection for the battery under test.

5. The method for nondestructive testing of battery polarization distribution according to claim 1, characterized in that: The battery includes a laminated battery or a wound battery.

6. A nondestructive testing method for battery polarization distribution according to claim 1, characterized in that: When testing the external magnetic field distribution of the battery under test, test on the battery surface or on a plane at a fixed height from the battery surface.

7. A battery polarization distribution nondestructive testing method according to claim 1, characterized in that: When testing the external magnetic field of the battery under test, a single magnetic sensor is used for scanning testing or multiple identical magnetic sensors are used to form an array for coverage testing.

8. A battery polarization distribution nondestructive testing method according to claim 1, characterized in that: In step 2, the time for applying the working current to the battery under test is adjusted according to actual needs, and the magnitude of the applied current is within a normal range acceptable to the battery under test.

9. A method for rapid battery classification, characterized in that: The following steps are involved: S1. Perform an external magnetic field distribution test on all the batteries to be classified according to step 1 and step 2 of the nondestructive testing method for battery polarization distribution according to any one of claims 1 to 8, and obtain the external magnetic field distribution ΔB generated by the relaxation current of all the batteries to be tested. RT，n (i, j), where n is the number of each battery, n = 1, 2, 3, ..., n; S2, the external magnetic field distribution ΔB generated by the relaxation current of each battery under test RT，n (i, j) is calculated by absolute value to obtain |ΔB RT，n (i, j)|, and solve for |ΔB of each tested battery RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n ; S3, with mean μ RT，n and standard deviation σ RT，n and M n =μ RT，n +σ RT，n is the degree of deviation for each battery; according to the degree of deviation M n Judgement, M. n The greater the difference, the greater the difference in polarization distribution inside the battery; S4. Set the number of categories that all tested batteries need to be classified into, and use the K-means clustering method to use the deviation degree of each battery to achieve consistent classification and abnormal screening of batteries.

10. A battery rapid classification method according to claim 9, characterized in that: The absolute value of the external magnetic field distribution of the measured battery |ΔB RT，n The mean μ of (i, j)| RT，n and standard deviation σ RT，n The calculation method is, Where N is the number of measurement points of the external magnetic field of the battery.

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