A full-automatic test analysis method, system and device for an automobile battery pack

Abstract

The application relates to the technical field of automobile battery pack testing, in particular to a full-automatic test analysis method, system and equipment for an automobile battery pack, the method comprising the following steps: acquiring abnormal battery packs in all battery packs and impedance spectra of the abnormal battery packs in two tests; according to the impedance spectrum difference degrees of the abnormal battery packs in the two tests, all the abnormal battery packs are divided into defective battery packs and battery packs to be classified; according to abnormal characteristics of the battery packs to be classified in high, medium and low frequency bands when internal defects exist in the battery packs to be classified, defect factors of the battery packs to be classified in the high, medium and low frequency bands are acquired, and whether the battery packs to be classified are qualified battery packs is judged in combination with the difference degrees of impedance spectrum curves of the battery packs to be classified and qualified impedance spectrum curves. The application screens out qualified battery packs which are misjudged as defective battery packs due to environmental vibration influence by analyzing impedance spectrum characteristics when the environmental vibration influence, and improves the test result accuracy of the automobile battery pack.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile battery pack testing, and in particular to a full-automatic test analysis method, system and device for an automobile battery pack. BACKGROUND

[0002] In recent years, electric vehicles have developed rapidly. As a core component of electric vehicles, the performance and quality of a battery pack directly affect the endurance mileage, safety and reliability of the vehicle. In order to ensure that the production quality of the battery pack meets the requirements during use, the quality of the battery pack needs to be detected before it is put into use, such as testing the performance indicators of the battery pack, such as appearance, electrical performance, and charge-discharge safety, by a full-automatic test method.

[0003] Electrochemical impedance spectroscopy (EIS) testing of a battery pack is an effective method for evaluating the performance of the battery pack. By applying a small-amplitude sinusoidal voltage signal, the impedance spectrum of the battery pack is obtained, and the resistance performance of the battery pack is analyzed to obtain the resistance performance test result. Currently, a dedicated mechanical clamp is mainly used to improve the accuracy of EIS measurement. However, in the actual test environment, there is usually vibration influence (such as self-vibration of the test equipment or external vibration caused by the operation of surrounding equipment), which leads to inaccurate impedance spectrum collected, and thus qualified battery packs are easily misjudged as defective battery packs, affecting the accuracy of the battery pack test result. SUMMARY

[0004] To solve the above technical problems, the purpose of the present application is to provide a full-automatic test analysis method, system and device for an automobile battery pack, and the technical solution adopted is as follows:

[0005] In a first aspect, the present application provides a full-automatic test analysis method for an automobile battery pack, which includes the following steps:

[0006] Obtaining abnormal battery packs in all battery packs and their impedance spectra in two tests;

[0007] According to the difference between the impedance spectra of each abnormal battery pack in two tests, obtaining a vibration influence factor of each abnormal battery pack, so as to divide all abnormal battery packs into defective battery packs and battery packs to be classified;

[0008] According to the dispersion degree and fluctuation degree of the real part values of all impedance points in the high frequency band of the impedance spectrum of each battery pack to be classified in two tests, a defect factor of each battery pack to be classified in the high frequency band is obtained; according to the deviation degree of the impedance modulus values of all impedance points in the medium frequency band of the impedance spectrum of each battery pack to be classified in two tests, a defect factor of each battery pack to be classified in the medium frequency band is obtained; according to the difference between the slope of the fitting straight line corresponding to the coordinates of all impedance points in the low frequency band of the impedance spectrum of each battery pack to be classified in two tests and the standard slope, a defect factor of each battery pack to be classified in the low frequency band is obtained;

[0009] A qualified impedance spectrum is obtained, and according to the difference degree between the impedance spectrum curve of each battery pack to be classified and the qualified impedance spectrum curve, and in combination with the defect factors of each battery pack to be classified in the high, medium and low frequency bands, a defect confidence of each battery pack to be classified is obtained, so as to judge whether each battery pack to be classified is a qualified battery pack.

[0010] Preferably, the vibration influence factor of each abnormal battery pack refers to the DTW distance between the real part sequences in two tests; wherein the real part sequence in each test refers to a sequence composed of the real part values of all impedance points in the impedance spectrum of each test arranged in the order from high to low according to the frequency.

[0011] Preferably, the specific process of dividing all abnormal battery packs into defective battery packs and battery packs to be classified is: if the vibration influence factor of any abnormal battery pack is less than a preset vibration threshold, the abnormal battery pack is recorded as a defective battery pack; otherwise, the abnormal battery pack is recorded as a battery pack to be classified.

[0012] Preferably, the calculation formula of the defect factor of each battery pack to be classified in the high frequency band is: ; in the formula, wherein, d j represents the defect factor of the jth battery pack to be classified in the high frequency band, represents the mean value of the total number of extreme values of the high frequency real part sequence in two tests of the jth battery pack to be classified; represents the total number of impedance points in the high frequency band of the impedance spectrum of the jth battery pack to be classified; represents the mean value of the variance of the high frequency real part sequence in two tests of the jth battery pack to be classified; wherein the high frequency real part sequence in each test refers to a sequence composed of the real part values of all impedance points in the high frequency band of the impedance spectrum of each test arranged in the order from high to low according to the frequency.

[0013] Preferably, the acquisition method of the defect factor of each battery pack to be classified in the medium frequency band is:

[0014] The impedance modulus values of all impedance points in the medium frequency band of the impedance spectrum of each battery pack to be classified in two tests are arranged in the order from high to low respectively, and curve fitting is performed respectively;

[0015] The average of the absolute difference between the impedance modulus value of each battery pack to be classified at all impedance points in the mid-frequency band in the impedance spectrum in the two tests and the fitting value is recorded as the defect factor of each battery pack to be classified in the mid-frequency band.

[0016] Preferably, the method for obtaining the defect factor of each battery pack to be classified in the low-frequency band is: obtaining the fitting straight line of each battery pack to be classified in the low-frequency band in the impedance spectrum in the two tests and the corresponding slope, respectively; and recording the average of the absolute difference between the two slopes and the standard slope as the defect factor of each battery pack to be classified in the low-frequency band.

[0017] Preferably, the method for obtaining the defect confidence of each battery pack to be classified is:

[0018] The average of the defect factors of each battery pack to be classified in the high, mid and low frequency bands is counted;

[0019] The average of the DTW distance between the impedance spectrum curve of each battery pack to be classified in the two tests and the qualified impedance spectrum curve is counted;

[0020] The product of the two averages is recorded as the defect confidence of each battery pack to be classified.

[0021] Preferably, the specific process of judging whether each battery pack to be classified is a qualified battery pack is: if the defect confidence of any battery pack to be classified is less than a preset defect threshold, the battery pack to be classified is recorded as a qualified battery pack; otherwise, the battery pack to be classified is recorded as a defective battery pack.

[0022] In a second aspect, the embodiments of the present application provide a full-automatic test and analysis equipment for automobile battery packs, which comprises: a data acquisition module, an environmental vibration analysis module, and a qualified battery pack discrimination module.

[0023] The data acquisition module is used to acquire abnormal battery packs in all battery packs and their impedance spectrum in the two tests.

[0024] The environmental vibration analysis module is used to judge whether each abnormal battery pack is affected by environmental vibration based on the difference between the impedance spectrum of each abnormal battery pack in the two tests, and divide all abnormal battery packs into defective battery packs and battery packs to be classified.

[0025] The qualified battery pack discrimination module is used to obtain the defect confidence of each battery pack to be classified according to the impedance spectrum characteristics of each battery pack to be classified when there is an internal defect, and further judge whether each battery pack to be classified is a qualified battery pack.

[0026] In a third aspect, the embodiments of the present application further provide a full-automatic test analysis system for an automobile battery pack, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the full-automatic test analysis method for the automobile battery pack according to any one of the above embodiments when executing the computer program.

[0027] As can be seen from the above embodiments, the full-automatic test analysis method, system and device for the automobile battery pack provided by the embodiments of the present application have at least the following beneficial effects:

[0028] The present application is aimed at the problem that the existing electrochemical impedance spectroscopy test method cannot exclude the influence of test environment vibration, resulting in inaccurate impedance spectrum collection and easy identification of qualified battery packs as defective battery packs. The present application performs secondary impedance spectrum collection on the abnormal battery packs tested by the existing method, obtains a vibration influence factor according to the impedance spectrum difference degree of each abnormal battery pack in two tests, so as to screen out the battery packs affected by environmental vibration from the abnormal battery packs. By analyzing the abnormal degree of the impedance spectrum of the high, medium and low frequency bands of the battery pack to be classified, and the difference degree of the impedance spectrum in two measurements and the qualified impedance spectrum, a defect confidence is constructed, which can evaluate the possibility of the existence of internal defects of each battery pack to be classified. Finally, the threshold comparison method is used to screen out the battery packs incorrectly identified as containing defects due to the influence of environmental vibration, and accurately identify the battery packs with internal defects, thereby effectively improving the accuracy of the test results of the automobile battery pack. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0030] Figure 1 A step flow chart of a full-automatic test analysis method for an automobile battery pack provided by an embodiment of the present application;

[0031] Figure 2 A structure schematic diagram of a full-automatic test analysis device for an automobile battery pack provided by an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object, the specific implementation, structure, features and effects of the full-automatic test analysis method, system and device for automobile battery pack according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, the terms such as "comprise", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or further includes elements inherent to such article or device. Without more limitations, the element defined by the phrase "comprising one" does not exclude the presence of additional identical elements in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as understood by those skilled in the art of the technology to which the present application belongs.

[0034] The specific scheme of the full-automatic test analysis method, system and device for automobile battery pack provided by the present application is described in detail below in combination with the drawings.

[0035] Please refer to Figure 1 which shows the step flowchart of the full-automatic test analysis method for automobile battery pack provided by one embodiment of the present application, which includes the following steps:

[0036] Step one: obtain the abnormal battery pack in all battery packs and its impedance spectrum in two tests.

[0037] Firstly, use the full-automatic mechanical arm to clamp each battery pack and place it on the test table, and connect it with the test circuit. After successful connection, start testing. The test unit will scan the two-dimensional code on each battery pack in advance to obtain the battery pack specification parameter information, and then start testing. During testing, a fixed peak value, frequency changing sinusoidal voltage signal is input to each battery pack as an excitation signal. In this embodiment, the sweep frequency range of the sinusoidal voltage signal is set to 1mHz-10kHz, the sweep direction is from high frequency to low frequency, and the sweep point number is 100. Then the Nyquist-based impedance spectrum of each battery pack is output.

[0038] Further, each battery pack is tested by using the existing battery pack testing method, and if any battery pack has a defective initial test result, the corresponding battery pack is recorded as an abnormal battery pack and is subjected to a second test to obtain an impedance spectrum in the second test. In the present embodiment, the battery pack testing method refers to obtaining the electrode resistance of each battery pack by analyzing the impedance spectrum of each battery pack, and then comparing the electrode resistance with a standard electrode resistance to determine whether each battery pack is an abnormal battery pack. The battery pack testing method is a known technology in the art, and the specific implementation process is not described in detail.

[0039] At this point, all abnormal battery packs and their corresponding impedance spectra in two tests are obtained.

[0040] Step two: According to the difference between the impedance spectra of each abnormal battery pack in two tests, obtain the vibration influence factor of each abnormal battery pack, and divide all abnormal battery packs into defective battery packs and battery packs to be classified.

[0041] In the actual test environment of the battery pack, there will usually be vibration influence, such as self-vibration of the test equipment or external vibration caused by the operation of surrounding equipment. These vibrations will have a certain influence on the impedance spectrum curve of the battery pack electrical performance test, affecting the accuracy of the battery pack qualified test. Among all the obtained abnormal battery packs, there are three cases of abnormal characteristics in the impedance spectrum: caused only by internal defects of the battery pack, caused only by environmental vibration, and caused by both environmental vibration and internal defects. Among them, the abnormal characteristics caused only by environmental vibration are not real defects of the battery pack, and such abnormal battery packs are actually qualified battery packs. Therefore, such abnormal battery packs need to be screened to prevent qualified battery packs from being misjudged as defective battery packs.

[0042] If the impedance spectrum is abnormal due to defects in the internal structure of the battery pack, the internal defects are relatively stable, and the impedance spectrum abnormal segments in two tests are similar. If the battery pack is affected by environmental vibration during the test, the environmental vibration has randomness, which can destroy the stability of the contact resistance of the battery pack, causing the contact resistance to fluctuate randomly, and thus the impedance spectrum abnormal segments of the same battery pack measured in two tests are also relatively random, and the similarity of the impedance spectra in different test times is also relatively low. Therefore, the abnormal battery packs affected by environmental vibration can be screened according to the randomness of the abnormal problems of the impedance spectrum of the abnormal battery pack in multiple tests.

[0043] Based on the above analysis, taking the impedance spectrum of the ith abnormal battery pack in two tests as an example, the real part values of all impedance points in the two tests are arranged in order from high to low frequency, and the real part sequence of the ith abnormal battery pack in the two tests is obtained. The DTW distance between the real part sequences of the ith abnormal battery pack in the two tests is recorded as the vibration influence factor of the ith abnormal battery pack. The greater the value, the greater the difference in impedance spectrum abnormal characteristics between the two tests of the ith abnormal battery pack, the more random it is, the more likely the ith abnormal battery pack is affected by environmental vibration during testing, and the more likely the impedance spectrum of the ith abnormal battery pack is affected by environmental vibration or both environmental vibration and internal defects.

[0044] Further, the vibration influence factors of all abnormal battery packs are calculated, all abnormal battery pack vibration influence factors are used as input, and the maximum inter-class variance algorithm is used to output the vibration threshold, which is recorded as the preset vibration threshold. If the vibration influence factor of any abnormal battery pack is less than the preset vibration threshold, it indicates that the impedance spectrum of the abnormal battery pack is only affected by internal defects and not by environmental vibration, and the abnormal battery pack is recorded as a defective battery pack. Otherwise, it indicates that the impedance spectrum of the abnormal battery pack is affected by environmental vibration, and the abnormal battery pack is recorded as a battery pack to be classified. The obtained battery pack to be classified may be only affected by environmental vibration or both environmental vibration and internal defects. Therefore, further classification is required.

[0045] Step three: According to the dispersion degree and fluctuation degree of the real part values of all impedance points in the high frequency band of the impedance spectrum of each battery pack to be classified in two tests, the defect factor of each battery pack to be classified in the high frequency band is obtained; According to the deviation degree of the impedance modulus values of all impedance points in the medium frequency band of the impedance spectrum of each battery pack to be classified in two tests, the defect factor of each battery pack to be classified in the medium frequency band is obtained; According to the difference between the slope of the fitting straight line corresponding to the coordinates of all impedance points in the low frequency band of the impedance spectrum of each battery pack to be classified in two tests and the standard slope, the defect factor of each battery pack to be classified in the low frequency band is obtained.

[0046] When the battery pack to be classified has internal defects, such as virtual ear (poor contact), SEI film rupture, micro short circuit, and electrode clumping, the impedance value in the high frequency band of the impedance spectrum will change with the frequency, such as 10 kHz, the impedance point real part value is , 8 kHz suddenly jumps to (vibration enhancement), 6 kHz again drops to (vibration attenuation), and along the real axis, it is a jagged point; At the same time, the half-circle structure in the medium frequency band of the impedance spectrum may be damaged, and there may be burrs or breaks in the medium frequency band; In addition, the angle between the impedance curve in the low frequency band of the impedance spectrum and the real axis will deviate from 45°, that is, the slope value deviates from 1.

[0047] Based on the above analysis, the frequency band of the impedance spectrum greater than or equal to a Hz is recorded as a high frequency band, the frequency band of the impedance spectrum less than a Hz and greater than b Hz is recorded as a medium frequency band, and the frequency band of the impedance spectrum less than or equal to b Hz is recorded as a low frequency band, wherein the specific values of a and b need to be determined according to different physical processes in the impedance spectrum, and in the embodiment, a and b are respectively 1000 and 1. The impedance points of each battery pack to be classified in different frequency bands in the impedance spectrum of two tests are obtained, and in the embodiment, according to the division of different frequency band ranges, there are 15 impedance points in the high frequency band, 42 impedance points in the medium frequency band, and 43 impedance points in the low frequency band, corresponding to a total of 100 sweep frequency points.

[0048] Taking the jth battery pack to be classified as an example, the real part values of all the impedance points of the jth battery pack to be classified in the high frequency band of the impedance spectrum in two tests are arranged in order from high to low frequency, and the high frequency real part sequence of the jth battery pack to be classified in two tests is obtained. The extreme point detection algorithm is used to obtain the extreme values in the high frequency real part sequence of the jth battery pack to be classified in two tests. The more the number of extreme values, the more obvious the sawtooth shape in the high frequency band of the impedance spectrum of the jth battery pack to be classified, and the more likely the jth battery pack to be classified has internal defects. Then, the mean of the variance of the high frequency real part sequence of the jth battery pack to be classified in two tests is calculated, and the larger the value, the more likely the jth battery pack to be classified has internal defects.

[0049] As a preferred embodiment, the defect factor of each battery pack to be classified in the high frequency band is obtained according to the dispersion degree and fluctuation degree of the real part values of all the impedance points in the high frequency band of the impedance spectrum of each battery pack to be classified, which is used to represent the possibility of internal defects of each battery pack to be classified.

[0050] In the embodiment, the defect factor of the jth battery pack to be classified in the high frequency band is recorded as , and the specific expression is: ; in the formula, , represents the defect factor of the jth battery pack to be classified in the high frequency band, , represents the mean of the total number of extreme values of the high frequency real part sequence of the jth battery pack to be classified in two tests; , represents the total number of impedance points of the jth battery pack to be classified in the high frequency band of the impedance spectrum, and in the embodiment, , the value of is 15; , represents the mean of the variance of the high frequency real part sequence of the jth battery pack to be classified in two tests.

[0051] The larger the value of, the more obvious the sawtooth feature of the high frequency band of the impedance spectrum of the jth battery pack to be classified, and the more likely the jth battery pack to be classified has internal defects.

[0052] Further, for the impedance spectrum of the middle frequency band of the jth battery pack to be classified, the impedance modulus value of each impedance point is calculated according to the real part and the imaginary part of each impedance point in the impedance spectrum of the middle frequency band; the impedance modulus values of all the impedance points in the impedance spectrum of the middle frequency band of the two tests are arranged in descending order of frequency, respectively, to obtain the impedance modulus value sequence of the jth battery pack to be classified in the two tests; and the impedance modulus value sequences of the two tests are respectively taken as the input of the least square fitting algorithm to obtain the fitting curve of the impedance modulus value sequence of the two tests, and the mean of the absolute difference between the impedance modulus values of all the impedance points in the impedance spectrum of the middle frequency band of the jth battery pack to be classified in the two tests and the fitting values is taken as the defect factor of the jth battery pack to be classified in the middle frequency band, denoted as ; the greater the value is, the more obvious the burr or the breaking point of the impedance spectrum of the middle frequency band of the jth battery pack to be classified is, and the more likely the jth battery pack to be classified has internal defects. The least square fitting algorithm and the calculation of the impedance modulus value are known technologies, and will not be described in detail.

[0053] Further, for the impedance spectrum of the low frequency band of the jth battery pack to be classified, the coordinates (the horizontal and vertical coordinates correspond to the real part and the imaginary part, respectively) of all the impedance points in the impedance spectrum of the low frequency band of the two tests are respectively taken as the input of the linear regression fitting method algorithm to obtain the fitting straight line of the impedance spectrum of the low frequency band of the jth battery pack to be classified in the two tests and the corresponding slope; the absolute difference between the two slopes and the standard slope is calculated, respectively, and the mean of the two absolute differences is taken as the defect factor of the jth battery pack to be classified in the low frequency band, denoted as ; the greater the value is, the more the slope of the impedance spectrum of the low frequency band of the jth battery pack to be classified deviates from the standard slope, and the more likely the jth battery pack to be classified has internal defects. The standard impedance spectrum of the low frequency band has an angle of 45° with the x-axis, i.e. the standard slope is 1. The linear regression fitting is a known technology, and will not be described in detail.

[0054] Step four: obtaining qualified impedance spectrum, obtaining the defect confidence of each battery pack to be classified according to the difference degree of the impedance spectrum curve of each battery pack to be classified and the qualified impedance spectrum curve, and combining the defect factors of each battery pack to be classified in the high, middle and low frequency bands, to determine whether each battery pack to be classified is a qualified battery pack.

[0055] Further, the qualified impedance spectrum of the qualified battery pack is obtained; if the battery pack to be classified is a qualified battery pack, that is, only affected by environmental vibration, the random fluctuation of the tested impedance spectrum is mainly based on the qualified impedance spectrum, and the deviation from the qualified impedance spectrum range is small; if the battery pack has internal defects and is also affected by environmental vibration, the random fluctuation of the tested impedance spectrum is mainly based on the abnormal impedance spectrum, so that the tested impedance spectrum deviates from the qualified impedance spectrum. Therefore, when the battery pack to be classified has internal defects, the greater the difference between the impedance spectrum curves of the two tests and the qualified impedance spectrum curve.

[0056] Based on the above characteristics, as a preferred embodiment, according to the difference between the impedance spectrum curve of each battery pack to be classified and the qualified impedance spectrum curve, and in combination with the defect factors of each battery pack to be classified in the high, medium and low frequency bands, the defect confidence of each battery pack to be classified is obtained, which is used to represent the possibility of the existence of internal defects of each battery pack to be classified.

[0057] In this embodiment, the defect confidence of the jth battery pack to be classified is denoted as , and the specific expression is: ; in the formula, , the defect confidence of the jth battery pack to be classified, , the average of the defect factors of the jth battery pack to be classified in the high, medium and low frequency bands, , the average of the DTW distance between the impedance spectrum curves of the two tests of the jth battery pack to be classified and the qualified impedance spectrum curve.

[0058] The greater the value of , the greater the defect factors of the jth battery pack to be classified in each frequency band, and the greater the difference between the impedance spectrum curve and the qualified impedance spectrum, that is, the greater the possibility of the existence of internal defects of the jth abnormal battery pack.

[0059] According to the above method, the defect confidence of all battery packs to be classified is calculated, the defect confidence of all battery packs to be classified is taken as input, the maximum class variance algorithm is used to output the defect threshold, and the defect threshold is denoted as the preset defect threshold; if the defect confidence of any battery pack to be classified is less than the preset defect threshold, it indicates that the impedance spectrum anomaly of the battery pack to be classified is caused by environmental vibration, and the battery pack to be classified has no internal defects, then the battery pack to be classified is denoted as a qualified battery pack; otherwise, it indicates that the impedance spectrum anomaly of the battery pack to be classified is affected by both environmental vibration and internal defects, that is, the battery pack to be classified has internal defects, then the battery pack to be classified is denoted as a defective battery pack.

[0060] Further, all information of all defective battery packs is transmitted to the test result display unit to display the unqualified battery pack information, so as to facilitate further analysis and troubleshooting by the staff.

[0061] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a full-automatic test analysis equipment of an automobile battery pack provided by the embodiment. In the embodiment, each unit included in the terminal is used to execute each step in the corresponding embodiment of a full-automatic test analysis method of an automobile battery pack. Please refer to Figure 2 The full-automatic test analysis equipment includes a data acquisition module, an environmental vibration analysis module, and a qualified battery pack discrimination module.

[0062] The data acquisition module is configured to acquire abnormal battery packs in all battery packs and impedance spectra of the abnormal battery packs in two tests.

[0063] The environmental vibration analysis module is configured to determine whether each abnormal battery pack is affected by environmental vibration based on a difference degree between the impedance spectra of each abnormal battery pack in the two tests, and divide all abnormal battery packs into defective battery packs and battery packs to be classified.

[0064] The qualified battery pack discrimination module is configured to acquire a defect confidence of each battery pack to be classified according to an impedance spectrum feature of each battery pack to be classified when an internal defect exists in the battery pack, and further determine whether each battery pack to be classified is a qualified battery pack.

[0065] Based on the same inventive concept as the above method, the embodiment of the present application further provides a full-automatic test analysis system of an automobile battery pack, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor executes the computer program to implement the steps of the full-automatic test analysis method of the automobile battery pack according to any one of the above embodiments.

[0066] Each embodiment in the present application is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

[0067] It should be noted that, unless otherwise specified and limited, terms such as "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that the circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such article or device. Without more limitation, the element limited by the statement "including a" does not exclude the existence of another same element in the article or device including the element. In addition, the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0068] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

[0069] It should be understood that the application is not limited to the precise construction and compositions described above and shown in the attached drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application.

Claims

1. A full-automatic test analysis method for an automobile battery pack, characterized in that, The method comprises the following steps: Obtaining abnormal battery packs in all battery packs and their impedance spectra in two tests; According to the difference between the impedance spectra of each abnormal battery pack in two tests, obtaining the vibration influence factor of each abnormal battery pack, so as to divide all abnormal battery packs into defective battery packs and battery packs to be classified; According to the dispersion degree and fluctuation degree of the real part values of all impedance points in the high-frequency band of the impedance spectrum of each battery pack to be classified in two tests, obtaining the defect factor of each battery pack to be classified in the high-frequency band; according to the deviation degree of the impedance modulus values of all impedance points in the medium-frequency band of the impedance spectrum of each battery pack to be classified in two tests, obtaining the defect factor of each battery pack to be classified in the medium-frequency band; according to the difference between the slope of the fitting straight line corresponding to the coordinates of all impedance points in the low-frequency band of the impedance spectrum of each battery pack to be classified in two tests and the standard slope, obtaining the defect factor of each battery pack to be classified in the low-frequency band; Obtaining a qualified impedance spectrum, according to the difference between the impedance spectrum curve of each battery pack to be classified and the qualified impedance spectrum curve, and combining the defect factors of each battery pack to be classified in the high, medium and low frequency bands, obtaining the defect confidence of each battery pack to be classified, so as to judge whether each battery pack to be classified is a qualified battery pack; The vibration influence factor of each abnormal battery pack refers to the DTW distance between the real part sequences in two tests; wherein the real part sequence in each test refers to the sequence composed of the real part values of all impedance points in the impedance spectrum of each test arranged in the order of frequency from high to low; The method for obtaining the defect confidence of each battery pack to be classified is: Statistically obtaining the mean value of the defect factors of each battery pack to be classified in the high, medium and low frequency bands; Statistically obtaining the mean value of the DTW distance between the impedance spectrum curves of the battery pack to be classified in two tests and the qualified impedance spectrum curve; The product of the two mean values is recorded as the defect confidence of each battery pack to be classified.

2. The fully automatic test analysis method of an automobile battery pack according to claim 1, characterized in that, The specific process of dividing all abnormal battery packs into defective battery packs and battery packs to be classified is: if the vibration influence factor of any abnormal battery pack is less than a preset vibration threshold, the abnormal battery pack is recorded as a defective battery pack; otherwise, the abnormal battery pack is recorded as a battery pack to be classified.

3. The fully automatic test and analysis method of an automotive battery pack according to claim 1, characterized by, The calculation formula of the defect factor of each battery pack to be classified in the high frequency band is: ; in the formula, represents the defect factor of the jth battery pack to be classified in the high frequency band, represents the average value of the total number of extreme values of the high frequency real part sequence of the jth battery pack to be classified in two tests; represents the total number of impedance points of the jth battery pack to be classified in the high frequency band of the impedance spectrum; represents the average value of the variance of the high frequency real part sequence of the jth battery pack to be classified in two tests; wherein the high frequency real part sequence in each test refers to a sequence composed of the real part values of all impedance points in the high frequency band of the impedance spectrum in each test arranged in the order of frequency from high to low.

4. The fully automatic test and analysis method of an automotive battery pack according to claim 1, characterized by, The method for obtaining the defect factor of each battery pack to be classified in the medium-frequency band is: Arranging the impedance modulus values of all impedance points in the medium-frequency band of the impedance spectrum of each battery pack to be classified in two tests in the order of frequency from high to low, and respectively performing curve fitting; The mean value of the absolute difference between the impedance modulus values of all impedance points in the medium-frequency band of the impedance spectrum of each battery pack to be classified in two tests and the fitting values thereof is recorded as the defect factor of each battery pack to be classified in the medium-frequency band.

5. The fully automatic test and analysis method of an automotive battery pack according to claim 1, characterized by, The method for obtaining the defect factor of each battery pack to be classified in the low-frequency band is: respectively obtaining the fitting straight line of the low-frequency band of the impedance spectrum of each battery pack to be classified in two tests and the corresponding slope thereof; the mean value of the absolute difference between the two slopes and the standard slope is recorded as the defect factor of each battery pack to be classified in the low-frequency band.

6. The fully automatic test and analysis method of an automotive battery pack according to claim 1, characterized by, The specific process of judging whether each battery pack to be classified is a qualified battery pack is: if the defect confidence of any battery pack to be classified is less than a preset defect threshold, the battery pack to be classified is recorded as a qualified battery pack; otherwise, the battery pack to be classified is recorded as a defective battery pack.

7. A full-automatic test analysis apparatus for an automobile battery pack, characterized by comprising: The application discloses a full-automatic test and analysis method for an automobile battery pack, and relates to the technical field of automobile battery packs. A data acquisition module is configured to acquire abnormal battery packs in all battery packs and impedance spectra of the abnormal battery packs in two tests; An environmental vibration analysis module is configured to judge whether each abnormal battery pack is affected by environmental vibration based on a difference degree between the impedance spectra of each abnormal battery pack in the two tests, and divide all abnormal battery packs into defective battery packs and battery packs to be classified; A qualified battery pack discrimination module is configured to acquire defect confidence of each battery pack to be classified according to impedance spectrum characteristics of each battery pack to be classified when an internal defect exists in the battery pack, and further judge whether each battery pack to be classified is a qualified battery pack.

8. A full-automatic test analysis system for automobile battery packs, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the full-automatic test and analysis method for the automobile battery pack.

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