CN118011257A - Battery polarization distribution nondestructive testing method and battery rapid classification method - Google Patents
Battery polarization distribution nondestructive testing method and battery rapid classification method Download PDFInfo
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- 238000009826 distribution Methods 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 58
- 230000010287 polarization Effects 0.000 title claims abstract description 48
- 238000009659 non-destructive testing Methods 0.000 title claims abstract description 16
- 238000012360 testing method Methods 0.000 claims abstract description 38
- 238000001514 detection method Methods 0.000 claims abstract description 26
- 238000012216 screening Methods 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000003064 k means clustering Methods 0.000 claims description 3
- 230000000284 resting effect Effects 0.000 claims description 3
- 238000012800 visualization Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 abstract description 4
- 230000002159 abnormal effect Effects 0.000 abstract description 3
- 238000000835 electrochemical detection Methods 0.000 abstract description 3
- 238000003860 storage Methods 0.000 abstract description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 11
- 229910001416 lithium ion Inorganic materials 0.000 description 11
- 238000013461 design Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000000157 electrochemical-induced impedance spectroscopy Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 101000872084 Danio rerio Delta-like protein B Proteins 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 description 1
- HFCVPDYCRZVZDF-UHFFFAOYSA-N [Li+].[Co+2].[Ni+2].[O-][Mn]([O-])(=O)=O Chemical compound [Li+].[Co+2].[Ni+2].[O-][Mn]([O-])(=O)=O HFCVPDYCRZVZDF-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- QHGJSLXSVXVKHZ-UHFFFAOYSA-N dilithium;dioxido(dioxo)manganese Chemical compound [Li+].[Li+].[O-][Mn]([O-])(=O)=O QHGJSLXSVXVKHZ-UHFFFAOYSA-N 0.000 description 1
- 239000010439 graphite Substances 0.000 description 1
- 229910002804 graphite Inorganic materials 0.000 description 1
- 238000001453 impedance spectrum Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 229910052751 metal Inorganic materials 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 150000002739 metals Chemical class 0.000 description 1
- -1 nickel cobalt aluminum manganate Chemical compound 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 229910001415 sodium ion Inorganic materials 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
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Abstract
The invention discloses a nondestructive testing method for polarization distribution of a battery and a rapid battery classification method, which belong to the technical field of battery detection. According to the method, the rapid screening and classification of different batteries are realized through the difference of the internal polarization distribution characteristics of the batteries, and the rapid consistency classification and abnormal screening of the batteries are facilitated. Compared with the conventional electrochemical detection method, the method allows rapid nondestructive detection of the battery, especially for factory testing, battery pack inconsistency detection and rapid detection of large-scale long-term storage batteries.
Description
Technical Field
The invention belongs to the technical field of battery detection, and particularly relates to a battery polarization distribution nondestructive detection method and a battery rapid classification method.
Background
The polarization difference of lithium ion batteries is a key factor affecting the performance of the cells and the consistency of the battery. The internal polarization of a cell is primarily affected by cell fabrication and design, as well as attenuation and failure during use. By detecting the internal polarization distribution and the corresponding performance of the battery under different manufacturing and different working conditions, key factors affecting the battery performance can be effectively analyzed, and the battery design strategy can be optimized. In addition, the batteries can be classified rapidly through the internal polarization distribution characteristics of the batteries, the batteries with similar performances are classified into groups rapidly, and the performance and the safety stability of the battery pack are improved. Therefore, the development of a method for detecting the polarization distribution in the battery can realize the rapid optimization of the battery design and the battery screening classification.
Current methods of testing battery performance include voltage and internal resistance testing, capacity testing, pulse testing, electrochemical Impedance Spectroscopy (EIS) testing, and the like. The method mainly detects the battery performance through the lumped electric characteristics of the battery to acquire the battery health state or impedance characteristics, but is difficult to acquire local information influenced by the structural design or local attenuation faults of the battery. The current battery detection method is difficult to realize the test analysis of the polarization distribution in the battery, and has very limited capability of acquiring electrochemical information in the battery. In addition, for battery screening classification, the current detection method based on electrical characteristics is also very limited, such as capacity test is accurate, but is too time-consuming to be suitable for rapid screening of large-scale batteries. Pulse testing is rapid, but provides limited information that may not accurately reflect the long-term decay state of the battery. The electrochemical impedance spectrum test, although the information quantity is abundant, is influenced by the charge state of the battery, and is unfavorable for rapid detection.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a battery polarization distribution nondestructive testing method and a battery rapid classification method, wherein the method is used for carrying out an external planar magnetic field distribution test on a relaxation process after a lithium ion battery is powered off to obtain an external two-dimensional magnetic field distribution mapping battery internal polarization distribution of the relaxation process of the lithium ion battery, so as to realize nondestructive testing of the battery internal polarization distribution; according to the method, different batteries are rapidly screened and classified through the difference of polarization distribution characteristics in the batteries.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A battery polarization distribution nondestructive testing method comprises the following steps:
When the battery to be tested is in a rest state, testing the external magnetic field distribution B (i, j) of the battery to be tested by adopting a magnetic sensor;
Step two, the power is cut off after the working current is applied to the battery to be tested for a certain time, the external magnetic field distribution B RT (i, j) in the relaxation process of the battery after the power is cut off is measured, and the external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery is obtained by calculating the difference value between the external magnetic field distribution B RT (i, j) in the relaxation process of the battery and the external magnetic field distribution B (i, j) in the resting state of the battery;
And thirdly, carrying out two-dimensional image visualization on external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery to map the internal two-dimensional polarization distribution characteristics of the battery, wherein the larger the delta B RT (i, j) deviates from 0, the larger the polarization.
Further, the external magnetic field distribution Δb RT (i, j) generated by the battery relaxation current is calculated by Δb RT,n(i,j)＝BRT (i, j) -B (i, j).
Further, the magnetic sensor includes a hall sensor, a fluxgate sensor, a giant magneto-resistance sensor, or an anisotropic magneto-resistance sensor.
Furthermore, when the external magnetic field distribution test is performed, the tested battery is magnetically shielded and protected by using magnetic shielding equipment, and various magnetic shielding devices which are made of various high-permeability metals and are used for shielding the external magnetic field interference are adopted, so that the detection precision is improved. The complexity of the device is reduced in the environment of no magnetic shielding protection.
Further, the battery includes a laminated battery or a wound battery. The battery comprises 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 comprises nickel cobalt lithium manganate, nickel cobalt aluminum manganate, lithium cobaltate, lithium manganate or lithium iron phosphate, and the negative electrode comprises graphite or SiO x.
Further, when the external magnetic field distribution of the tested battery is tested, the plane test with the fixed height on the surface of the battery or from the surface of the battery is performed.
Further, when testing the external magnetic field of the tested battery, a single magnetic sensor is used for scanning test or a plurality of identical magnetic sensors are used for covering test to form an array.
In the second step, the time of applying the working current to the battery to be tested is adjusted according to the actual requirement, and the applied current is within the normal range acceptable by the battery to be tested.
A method for rapid battery classification, comprising the steps of:
S1, performing external magnetic field distribution test on all the to-be-classified detected batteries according to the first step and the second step recorded in the battery polarization distribution nondestructive detection method to obtain external magnetic field distribution delta B RT,n (i, j) generated by relaxation currents of all the detected batteries, wherein n is the label of each battery, and n=1, 2,3, … and n;
S2, absolute value calculation is carried out on external magnetic field distribution delta B RT,n (i, j) generated by relaxation current of each battery to be tested to obtain delta B RT,n (i, j), and the mean value mu RT,n and standard deviation sigma RT,n of delta B RT,n (i, j) of each battery to be tested are solved;
S3, taking the sum M n＝μRT,n+σRT,n of the mean mu RT,n and the standard deviation sigma RT,n as the deviation degree of each cell; judging according to the deviation degree M n, wherein the larger the M n difference is, the higher the difference degree of the polarization distribution in the battery is;
s4, setting the number of categories to be classified of all the tested batteries, and adopting a K-means clustering method to realize consistency classification and anomaly screening of the batteries by using the deviation degree of each battery.
Further, the mean mu RT,n and standard deviation sigma RT,n of the absolute value |DeltaB RT,n (i, j) | of the external magnetic field distribution of the battery to be tested are calculated by the following method,
Wherein, the number of N battery external magnetic field measuring points.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, through detecting the two-dimensional distribution of the external magnetic field in the relaxation process of the lithium ion battery to map the change of the internal polarization distribution of the battery, the nondestructive detection of the internal polarization distribution of the battery is realized, and the electrochemical characteristics of different sites in the battery can be effectively analyzed.
2. According to the invention, the rapid screening and classification of different batteries are realized through the difference of the internal polarization distribution characteristics of the batteries, and the rapid consistency classification and abnormal screening of the batteries are facilitated. Compared with the conventional electrochemical detection method, the method allows rapid nondestructive detection of the battery, especially for factory testing, battery pack inconsistency detection and rapid detection of large-scale long-term storage batteries.
Drawings
FIG. 1 is a flow chart of a method for non-destructive testing of polarization distribution of a battery;
FIG. 2 is a flow chart of a method of rapid battery classification;
FIG. 3 is a schematic diagram of the placement of a battery under test;
FIG. 4 is an internal polarization distribution mapped by the battery external magnetic field distribution ΔB RT,1 (i, j);
Fig. 5 shows the classification test results of all the tested batteries.
Detailed Description
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings and examples, and it is apparent that the described examples are only some, but not all, of the examples of the invention, and all other examples obtained by those skilled in the art without making any inventive effort are within the scope of the present invention.
Example 1
The embodiment provides a battery polarization distribution nondestructive testing method, as shown in fig. 1, comprising the following specific steps:
When the battery to be tested is in a rest state, testing the external magnetic field distribution B (i, j) of the battery to be tested by adopting a magnetic sensor;
Wherein, a plane formed by the length direction and the width direction of the battery is an x-y plane; i and j respectively represent any point coordinate in the x direction and any point coordinate in the y direction of the battery.
In this embodiment, the battery to be tested is a lithium ion battery with 5Ah soft pack SOH of 100%, and fig. 3 is a schematic placement diagram during battery test; the magnetic field distribution testing equipment adopts a multi-axis mobile platform load three-axis fluxgate sensor to scan and test a battery to be tested on a plane 5mm away from the upper part of the battery, and adopts a 5-layer magnetic shielding barrel to carry out magnetic shielding protection. According to the testing method of the first step, when the sample battery is in a rest state, the magnetic field distribution B (i, j) outside the battery is tested, and the background magnetic field distribution is obtained.
Step two, the power is cut off after the working current is applied to the battery to be tested for a certain time, the external magnetic field distribution B RT (i, j) in the relaxation process of the battery after the power is cut off is measured, and the external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery is obtained by calculating the difference value between the external magnetic field distribution B RT (i, j) in the relaxation process of the battery and the external magnetic field distribution B (i, j) in the resting state of the battery;
In this embodiment, the external magnetic field distribution B RT (i, j) of the relaxation process of the battery after the power-off is measured after the application of the 1C charging current for 10S, and the external magnetic field distribution Δb RT (i, j) generated by the relaxation current of the battery is obtained by the difference calculation.
And thirdly, carrying out two-dimensional image visualization on external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery to map the two-dimensional polarization distribution characteristics inside the battery, wherein the larger the magnetic field distribution delta B RT (i, j) deviates from 0, the larger the polarization.
In this embodiment, the polarization distribution in the relaxation process of the battery can be mapped directly through the magnetic field distribution outside the battery, and the two-dimensional image of the external magnetic field distribution Δb RT (i, j) of the battery obtained in the second step is visualized to obtain the internal polarization distribution of the battery. The more points in the cell two-dimensional magnetic field distribution Δb RT (i, j) that deviate from 0, the greater the polarization is indicated. As shown in fig. 4, the sample cell was deviated very little, indicating that the cell had little polarization difference throughout.
Example 2
A method for rapid battery classification, comprising the steps of:
s1, when a battery to be tested is in a rest state, testing the external magnetic field distribution B 1(i,j),B2(i,j),…,Bn (i, j) of all the batteries to be tested by adopting a magnetic sensor;
Wherein B 1(i,j),B2(i,j),…,Bn (i, j) is the external magnetic field distribution of the 1,2, …, n cells.
In this embodiment, two types of batteries to be tested, including 9 SOH100% and 4 SOH90%, are selected for classification detection, and classification detection results are tested when the states of charge SOC0% and SOC100% of the batteries are respectively measured. The labels of the 9 batteries with 100% of the health states are batteries 1, 2, 3,4, 6, 9, 11, 12 and 13, and the labels of the batteries with 90% of the SOH with 4 of the health states are batteries 5, 7, 8 and 10, respectively, and the rapid classification detection of the class 2 batteries is realized under each state of charge; all the tested batteries are 5Ah soft-package lithium ion batteries. According to the test method of S1, firstly, external magnetic field tests are carried out on the lithium ion batteries in 13 rest states under each charge state to obtain B 1(i,j),B2(i,j),…,BRT,13 (i, j).
S2, testing and obtaining external magnetic field distribution delta B RT,n (i, j) generated by relaxation currents of all tested batteries according to the step II described in the embodiment 1; absolute value calculation is carried out on external magnetic field distribution delta B RT,n (i, j) generated by relaxation current of each battery to be tested to obtain delta B RT,n (i, j) I, and the mean value mu RT,n and standard deviation sigma RT,n of delta B RT,n (i, j) I of each battery to be tested are solved;
In an embodiment, after a 1C charging current is applied for 10S, the power is turned off, the external magnetic field distribution B RT,1(i,j),BRT,2(i,j),…,BRT,13 (i, j) of the relaxation process of the battery after the power is turned off is measured, the external magnetic field distribution |Δb RT,1(i,j)|,|ΔBRT,2(i,j)|,…,|ΔBRT,13 (i, j) | generated by the relaxation current of the battery is obtained through difference calculation, then the absolute value calculation is respectively performed on the external magnetic field distribution |Δb RT,1(i,j)|,|ΔBRT,2(i,j)|,…,|ΔBRT,13 (i, j) | generated by the relaxation current of each measured battery to obtain |Δb RT,1(i,j)|,|ΔBRT,2(i,j)|,…,|ΔBRT,13 (i, j) |, and then the average mu RT,1,μRT,2,…,μRT,13 and the standard deviation sigma RT,1,σRT,2,…,σRT,13 of the external magnetic field distribution absolute value |Δb RT,1(i,j)|,|ΔBRT,2(i,j)|,…,|ΔBRT,13 (i, j) | of each measured battery are respectively solved.
S3, taking the sum M n＝μRT,n+σRT,n of the mean mu RT,n and the standard deviation sigma RT,n as the deviation degree of each cell. Setting the number of categories of batteries to be classified, adopting a K-means clustering method to realize consistency classification and anomaly screening of the batteries by using the deviation degree of each battery, and judging according to the deviation degree M n, wherein the larger the difference of M n is, the higher the difference degree of polarization distribution inside the batteries is.
In the present embodiment, the degree of deviation is calculated from the mean μ RT,1,μRT,2,…,μRT,13 and standard deviation σ RT,1,σRT,2,…,σRT,13 of the external magnetic field distribution absolute values |Δb RT,1(i,j)|,|ΔBRT,2(i,j)|,…,|ΔBRT,13 (i, j) | of 13 cells at each state of charge. When the number of the battery classification categories is set to be 2, the detection results are shown in fig. 5, where the battery 5, the battery 7, the battery 8 and the battery 10 are a set of similar batteries, and may be defined as a first type of battery, and the batteries 1, 2, 3, 4, 6, 9, 11, 12 and 13 are a set of similar batteries, and may be defined as a second type of battery. Further analysis of the results shows that the first type of battery deviates more from 0, while the second type of battery deviates less from 0, indicating that the first type of battery deviates more from the normal state of a healthy battery relative to the second type of battery. As the state of health SOH of the first type of battery is known to be 90%, the attenuation is more, and the invention can effectively realize rapid classification and screening of the battery. The detection result when fig. 5 (a) is 0% of the state of charge is identical to the detection result when fig. 5 (b) is 100% of the state of charge, indicating that the method is not affected by the state of charge of the battery.
According to the invention, through detecting the two-dimensional distribution of the external magnetic field in the relaxation process of the lithium ion battery to map the change of the internal polarization distribution of the battery, the nondestructive detection of the internal polarization distribution of the battery is realized, and the electrochemical characteristics of different sites in the battery can be effectively analyzed. According to the method, the rapid screening and classification of different batteries are realized through the difference of the internal polarization distribution characteristics of the batteries, and the rapid consistency classification and abnormal screening of the batteries are facilitated. Compared with the conventional electrochemical detection method, the method allows rapid nondestructive detection of the battery, especially for factory testing, battery pack inconsistency detection and rapid detection of large-scale long-term storage batteries.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (10)
1. A battery polarization distribution nondestructive testing method is characterized in that: the method comprises the following steps:
When the battery to be tested is in a rest state, testing the external magnetic field distribution B (i, j) of the battery to be tested by adopting a magnetic sensor;
Step two, the power is cut off after the working current is applied to the battery to be tested for a certain time, the external magnetic field distribution B RT (i, j) in the relaxation process of the battery after the power is cut off is measured, and the external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery is obtained by calculating the difference value between the external magnetic field distribution B RT (i, j) in the relaxation process of the battery and the external magnetic field distribution B (i, j) in the resting state of the battery;
And thirdly, carrying out two-dimensional image visualization on external magnetic field distribution delta B RT (i, j) generated by the relaxation current of the battery to map the internal two-dimensional polarization distribution characteristics of the battery, wherein the larger the delta B RT (i, j) deviates from 0, the larger the polarization.
2. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: the external magnetic field distribution delta B RT (i, j) generated by the battery relaxation current is calculated as delta B RT(i,j)＝BRT (i, j) -B (i, j).
3. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: the magnetic sensor includes a hall sensor, a fluxgate sensor, a giant magneto-resistance sensor, or an anisotropic magneto-resistance sensor.
4. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: when the external magnetic field distribution test is carried out, the magnetic shielding equipment is used for carrying out magnetic shielding protection on the tested battery.
5. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: the battery includes a laminated battery or a wound battery.
6. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: and (3) testing the external magnetic field distribution of the tested battery, and testing the battery surface or a plane with a fixed height from the battery surface.
7. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: when testing the external magnetic field of the tested battery, a single magnetic sensor is used for scanning test or a plurality of identical magnetic sensors are used for covering test to form an array.
8. The method for non-destructive testing of battery polarization distribution according to claim 1, wherein: and step two, the time of applying working current to the tested battery is adjusted according to the actual requirement, and the applied current is within the normal range acceptable by the tested battery.
9. A method for rapid battery classification, comprising the steps of:
S1, performing external magnetic field distribution test on all the to-be-classified detected batteries according to the first and second steps in the battery polarization distribution nondestructive detection method according to any one of claims 1-8 to obtain external magnetic field distribution delta B RT,n (i, j) generated by relaxation currents of all the detected batteries, wherein n is the label of each battery, and n=1, 2,3, … and n;
S2, absolute value calculation is carried out on external magnetic field distribution delta B RT,n (i, j) generated by relaxation current of each battery to be tested to obtain delta B RT,n (i, j), and the mean value mu RT,n and standard deviation sigma RT,n of delta B RT,n (i, j) of each battery to be tested are solved;
S3, taking the sum M n＝μRT,n+σRT,n of the mean mu RT,n and the standard deviation sigma RT,n as the deviation degree of each cell; judging according to the deviation degree M n, wherein the larger the M n difference is, the higher the difference degree of the polarization distribution in the battery is;
s4, setting the number of categories to be classified of all the tested batteries, and adopting a K-means clustering method to realize consistency classification and anomaly screening of the batteries by using the deviation degree of each battery.
10. The rapid battery classification method according to claim 9, wherein: the mean mu RT,n and standard deviation sigma RT,n of the absolute value |delta B RT,n (i, j) | of the external magnetic field distribution of the battery to be tested are calculated by the following method,Wherein, the number of N battery external magnetic field measuring points.
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