A method and device for screening defective batteries

Abstract

The present invention relates to the technical field of battery manufacturing, and discloses a method and device for screening defective batteries. The method includes: S10 obtaining a qualified test data as an initial test data group, sorting the a qualified test data according to the acquisition order, and using the initial test data group as a basic test data group; S20 calculating the average value and standard deviation of the basic test data group; S30 screening the test data of the current test product according to the average value and standard deviation; S40 using b qualified test data as qualified test data to supplement the basic test data group, so that the number of qualified test data in the basic test data group always remains a, and returning to step S20. The present invention screens unqualified test data by updating the average value and standard deviation of the basic test data group in real time, thereby realizing the screening of defective batteries and improving the accuracy of defective product determination.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of battery manufacturing, and particularly to a method and device for screening defective batteries. Background Art

[0002] Currently, during the manufacturing process of lithium-ion batteries, factors such as material batches and manufacturing processes will have a certain impact on the AC internal resistance of lithium-ion batteries, resulting in data fluctuations in the average value of the AC internal resistance of batteries in different batches.

[0003] The existing screening method uses a target value ± tolerance, that is, a target value and a tolerance are set, and the upper limit value and the lower limit value of the AC internal resistance are calculated according to the target value and the tolerance. Batteries with an AC internal resistance outside the upper limit value and the lower limit value are determined as defective products, and batteries with an AC internal resistance between the upper limit value and the lower limit value are determined as qualified products.

[0004] If the average value of the AC internal resistance of a certain batch of batteries is too high or the set tolerance is too small, there will be overkill, that is, there will be a situation where qualified products are misjudged as defective products; if the average value of the AC internal resistance of a certain batch of batteries is too low or the set tolerance is too large, there will be underkill, that is, there will be a situation where defective products are misjudged as qualified products; the defective products and qualified products cannot be effectively distinguished. Summary of the Invention

[0005] In view of the above problems, the embodiments of the present invention provide a method and device for screening defective batteries, which are used to solve the problem that the existing technology cannot effectively screen defective products by using a target value ± tolerance.

[0006] According to one aspect of the embodiments of the present invention, a method for screening defective batteries is provided. The method includes:

[0007] S10. Obtain a qualified test data as an initial test data group. The a qualified test data are sorted according to the acquisition order, and the initial test data group is used as a basic test data group;

[0008] S20. Calculate the average value and the standard deviation of the basic test data group;

[0009] S30. Screen the test data of the current test product according to the average value and the standard deviation, and determine the test product corresponding to the unqualified test data as a defective product;

[0010] S40. Use b qualified test data as qualified test data to supplement the basic test data group, and remove the b qualified test data with the earliest acquisition order in the basic test data group, so that the number of qualified test data in the basic test data group always remains a, and return to step S20;

[0011] Among them, both a and b are positive integers, b < a, and the value of a is greater than or equal to 32.

[0012] In an alternative implementation, the test data of the current test product is screened according to the average value and the standard deviation, which specifically includes:

[0013] Judge whether the test data of the current test product is between the difference between the average value and n times the standard deviation and the sum of the average value and n times the standard deviation. If so, determine that the test data of the current test product is qualified test data; otherwise, determine that the test data of the current test product is unqualified test data.

[0014] In an alternative implementation, the standard deviation is set to a fixed value.

[0015] In an alternative implementation, the difference between the average value and n times the standard deviation is greater than or equal to a first preset threshold, and the sum of the average value and n times the standard deviation is less than or equal to a second preset threshold;

[0016] The second preset threshold is greater than the first preset threshold;

[0017] The first preset threshold and the second preset threshold are set according to the qualified determination criteria of the product.

[0018] In an alternative implementation, n is set according to the qualified determination criteria of the product;

[0019] The setting range of n is 3 - 6.

[0020] In an alternative implementation, the current test product is a single battery product;

[0021] Or, the current test product is m battery products in a tray;

[0022] Among them, m is a positive integer, b ≤ m, m < a.

[0023] In an alternative implementation, the a qualified test data in step S10 are obtained through the following method:

[0024] Obtain the test data of the test product according to the order, and screen it through the target value and the tolerance. The test data within the range of the target value and the tolerance is used as the qualified test data until a qualified test data are obtained.

[0025] In an alternative implementation, the target value is set according to the average value of the historical test data.

[0026] In an alternative implementation, the test data is the AC internal resistance of the battery product to screen out defective products with poor battery tab welding.

[0027] According to another aspect of the embodiments of the present invention, there is provided a device for screening defective batteries, including:

[0028] a data acquisition module, a calculation module, and a determination module, which are communicatively connected to each other;

[0029] The data acquisition module is configured to obtain a qualified test data as an initial test data group, and the a qualified test data are sorted according to the acquisition order. Taking the initial test data group as a basic test data group, and sending the basic test data group to the calculation module; the data acquisition module is configured to obtain the test data of the current test product and send it to the determination module; the data acquisition module is configured to use b screened qualified test data as qualified test data to supplement the basic test data group, and remove the b qualified test data with the earliest acquisition order in the basic test data group, so that the number of qualified test data in the basic test data group always remains a;

[0030] The calculation module is configured to calculate the average value and standard deviation of the basic test data group and send them to the determination module;

[0031] The determination module is configured to screen the test data of the current test product according to the average value and standard deviation, send the screened qualified test data to the data acquisition module, and determine the test product corresponding to the unqualified test data as a defective product.

[0032] Compared with the prior art, the present invention has the following advantages:

[0033] The battery defective product screening method and device provided by the present invention, in the screening process of the current test product, by using b screened qualified test data with acquisition order closer to the test data of the current test product as qualified test data to supplement the basic test data group, and removing the b qualified test data with acquisition order farther from the test data of the current test product in the basic test data group, so that the number of qualified test data in the basic test data group always remains a, making the basic test data group include as much as possible the qualified test data of the battery products in the same batch as the current test product, and further making the average value of the basic test data group as close as possible to the average value of the product parameters of the battery products in the batch where the current test product is located, thereby reducing the influence of data fluctuations in the average values of the product parameters of different batches of batteries.

[0034] Then, based on the average value and standard deviation of the basic test data set, the test data of the current test product is screened. The test products corresponding to the unqualified test data are determined as defective products, avoiding overkill or underkill caused by data fluctuations in the average values of products in different batches due to factors such as materials and processes, and improving the accuracy of defective product determination.

[0035] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to be able to understand the technical means of the embodiment of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and understandable, the following specifically illustrates the specific implementation manners of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings are only used to illustrate the embodiments and are not considered as a limitation to the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0037] Figure 1 Shows the AC internal resistance scatter plot of the battery product determination in the background art of the present invention with the risk of overkill;

[0038] Figure 2 Shows the AC internal resistance scatter plot of the battery product determination in the background art of the present invention with the risk of underkill;

[0039] Figure 3 Shows the schematic flow chart of a method for screening defective battery products provided by an embodiment of the present invention;

[0040] Figure 4 Shows the AC internal resistance scatter plot of the determination of a single battery product in a method for screening defective battery products provided by an embodiment of the present invention;

[0041] Figure 5 Shows the AC internal resistance scatter plot of the determination of a single tray of battery products in a method for screening defective battery products provided by an embodiment of the present invention;

[0042] Figure 6 Shows the structural block diagram of a device for screening defective battery products provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Hereinafter, the exemplary embodiments of the present invention will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein.

[0044] This embodiment is to solve the problem that in the prior art, when screening defective products by the method of target value ± tolerance, there is overkill or underkill, resulting in the inability to effectively screen defective products.

[0045] Specifically, a target value and a tolerance are set. The upper limit value and the lower limit value of the AC internal resistance are calculated according to the target value and the tolerance. The batteries with the AC internal resistance outside the upper limit value and the lower limit value are determined as defective products, and the batteries with the AC internal resistance between the upper limit value and the lower limit value are determined as non-defective products. If the average value of the AC internal resistance of a batch of batteries is too high or the set tolerance is too small, there will be overkill, that is, there will be a situation where non-defective products are misjudged as defective products; if the average value of the AC internal resistance of a batch of batteries is too low or the set tolerance is too large, there will be underkill, that is, there will be a situation where defective products are misjudged as non-defective products; the defective products and non-defective products cannot be effectively distinguished.

[0046] For example, see Figure 1 As shown, the upper limit value and the lower limit value of the AC internal resistance are set as fixed values. For example, the upper limit value of the AC internal resistance of the battery is set to 0.1830 mOhm, and the lower limit value is set to 0.1650 mOhm. The test data of the AC internal resistance are judged through the fixed interval formed by the upper limit value and the lower limit value of the AC internal resistance. Then, the battery product corresponding to the test data of the AC internal resistance of 0.1831 mOhm will be misjudged as a defective product.

[0047] See Figure 2 As shown, the upper limit value and the lower limit value of the AC internal resistance are set as fixed values. For example, the upper limit value of the AC internal resistance of the battery is set to 0.2000 mOhm, and the lower limit value is set to 0.1500 mOhm. The test data of the AC internal resistance are judged through the fixed interval formed by the upper limit value and the lower limit value of the AC internal resistance. Then, the battery products corresponding to the test data of the AC internal resistance of 0.1840 mOhm, 0.1835 mOhm, and 0.1866 mOhm will be misjudged as non-defective products. These three battery products are actually all defective products. After disassembly, they are all defective products caused by the folding of the negative electrode tab resulting in poor welding of the battery tab.

[0048] Therefore, this embodiment discloses a method for screening defective battery products. The method for screening defective battery products is as Figure 3 shown, and the method includes the following steps:

[0049] S10. Obtain a qualified test data as the initial test data group. The a qualified test data are sorted according to the acquisition order, and the initial test data group is used as the basic test data group.

[0050] Specifically, this step is to limit the number of qualified test data in the initial test data group to a. The a qualified test data are sorted according to the acquisition order to form the initial test data group, and then the initial test data group is used as the basic test data group.

[0051] Preferably, a qualified test data are sorted by test time to form an initial test data group {1, 2, ……, a}.

[0052] Wherein, a represents the number of qualified test data in the initial test data group, and is a positive integer.

[0053] In some alternative embodiments, the value of a is greater than or equal to 32.

[0054] S20. Calculate the mean and standard deviation of the basic test data group.

[0055] In this step, it is necessary to calculate the mean and standard deviation of the basic test data group, that is, calculate the mean and standard deviation of a qualified test data in the basic test data group, and use the mean and standard deviation of a qualified test data as the criteria for determining whether the (a + 1)-th test data is qualified.

[0056] S30. Screen the test data of the current test product according to the mean and standard deviation, and determine the test product corresponding to the unqualified test data as a defective product.

[0057] In this step, the mean and standard deviation of the basic test data group calculated in step S20 are used as the criteria for determining whether the test data of the current test product is qualified, so as to screen the test data of the current test product according to the mean and standard deviation.

[0058] Specifically: determine whether the test data of the current test product is within the range between the difference between the mean and n times the standard deviation and the sum of the mean and n times the standard deviation. If so, it is determined that the test data of the current test product is qualified test data, and further determine that the current test product is a good product. Otherwise, that is, the test data of the current test product exceeds the range between the difference between the mean and n times the standard deviation and the sum of the mean and n times the standard deviation, it is determined that the test data of the current test product is unqualified test data, and further determine that the current test product is a defective product.

[0059] Wherein, the difference between the mean of the basic test data group and n times the standard deviation can be understood as the lower limit value for screening good battery products, and the sum of the mean of the basic test data group and n times the standard deviation can be understood as the upper limit value for screening good battery products. n is set according to the qualified determination criteria of the product, and the qualified determination criteria of the product are set according to customer requirements, as long as it can ensure that the determination criteria meet the customer's requirements.

[0060] In some alternative embodiments, the setting range of n is 3 - 6.

[0061] In some alternative embodiments, the value ranges of the mean and n times the standard deviation are limited, specifically:

[0062] The difference between the average value and n times the standard deviation is greater than or equal to the first preset threshold, and the sum of the average value and n times the standard deviation is less than or equal to the second preset threshold.

[0063] Among them, the second preset threshold is greater than the first preset threshold; and the first preset threshold and the second preset threshold are set according to the qualified judgment criteria of the product.

[0064] S40. Use the b screened qualified test data as qualified test data to supplement the basic test data group, and remove the first b qualified test data in the acquisition order in the basic test data group, so that the number of qualified test data in the basic test data group always remains a, and return to step S20.

[0065] Among them, b is a positive integer, and b < a.

[0066] In this step, the b qualified test data screened in step S30 are supplemented to the basic test data group, and the first b qualified test data in the acquisition order in the basic test data group are removed, so that the number of qualified test data in the basic test data group always remains a. Then execute step S20, calculate the average value and standard deviation of the updated basic test data group, and then screen the test data of the next or the next group of test products according to the average value and standard deviation of the updated basic test data group, and repeat the above steps in sequence.

[0067] For example, assume that the current qualified test data is the (a + 1)th. Update the (a + 1)th qualified test data to the basic test data group, and at the same time remove the first qualified test data in the basic test data group. The updated basic test data group is {2, 3, ……, a, a + 1}.

[0068] Then return to step S20, calculate the average value and standard deviation of the updated basic test data group, and use the average value and standard deviation of the updated basic test data group to determine whether the next or the next group of test data is qualified.

[0069] Assume that the next qualified test data is the (a + 2)th. Update the (a + 2)th qualified test data to the basic test data group, and at the same time remove the first qualified test data in the basic test data group after the previous update. The updated basic test data group is {3, 4, ……, a, a + 1, a + 2}.

[0070] Return to step S20 again and repeat steps S20 - S40.

[0071] In this embodiment, since the battery tabs and the adapter are usually welded by ultrasonic welding, there are risks of cold welding, over welding, tab breakage or tab folding, which may result in a high AC internal resistance of the battery product. Therefore, the test data may be the AC internal resistance of the battery product, so as to screen out defective battery tab welding products.

[0072] In the present embodiment, during the testing process of the battery products, the tests may be performed one by one or tray by tray.

[0073] In some preferred embodiments, a pallet of battery products is usually tested simultaneously, and the number of battery products in a pallet is m, where m is a positive integer and the value of m is greater than or equal to 2. The judgment criteria for the battery products in a pallet are usually the same, and the a qualified test data before the battery products in this pallet are tested are used as the basic test data group to calculate the mean and standard deviation, thereby obtaining the judgment criteria for the battery products in this pallet.

[0074] Then, one or more qualified test data are extracted from the battery products of this tray and updated to the aforementioned basic test data group to serve as the judgment criteria for the battery products of the next tray.

[0075] In this embodiment, it should be noted that a is at least 20 times greater than m, and the specific value can be set according to the actual test process.

[0076] Preferably, the value of a is a positive integer greater than or equal to 32.

[0077] The defective battery screening method provided by an embodiment of the present invention, in the screening process of the current test product, adds b qualified test data whose acquisition order is close to the test data of the current test product as qualified test data to the basic test data group, and eliminates b qualified test data in the basic test data group whose acquisition order is far from the test data of the current test product, so that the number of qualified test data in the basic test data group is always maintained at a, so that the basic test data group includes as many qualified test data of battery products in the same batch as the current test product as possible, and then makes the average value of the basic test data group as close as possible to the average value of the product parameters of the battery products in the batch where the current test product is located, thereby reducing the influence of data fluctuations on the average values ​​of product parameters of batteries in different batches.

[0078] Then, the test data of the current test product is screened according to the mean value and standard deviation of the basic test data group, and the test products corresponding to the unqualified test data are judged as defective products. This avoids over-killing or under-killing due to data fluctuations in the average values ​​of different batches of products caused by factors such as materials and processes, thereby improving the accuracy of defective product judgment.

[0079] For details, please refer toFigure 4 - Figure 5 As shown, where Figure 4 is the scatter plot of the AC internal resistance for the determination of a single battery product in a method for screening defective battery products provided in this embodiment, that is, the test of a single battery product. Each test obtains a single qualified test data until a qualified test data are obtained as the initial test data group, and the initial test data group is used as the basic test data group, where a is specifically 600; starting from the (a + 1)-th qualified test data, each time the qualified test data of the next battery product is updated to the basic test data group, and the average value and standard deviation of the basic test data group are updated, so that the average value of the basic test data group has dynamic fluctuations, that is, a moving average value is formed. Correspondingly, the upper limit value for screening good battery products also has dynamic fluctuations, that is, a moving upper limit value is formed, and the lower limit value for screening good battery products also has dynamic fluctuations, that is, a moving lower limit value is formed. The test data of the current test product is judged through the moving interval formed by the moving upper limit value and the moving lower limit value, so as to reduce the influence of data fluctuations in the average values of the product parameters of different batches of batteries.

[0080] Figure 5 is the scatter plot of the AC internal resistance for the determination of a single tray of battery products in a method for screening defective battery products provided in this embodiment, that is, the test of a single tray of battery products. Each test obtains b qualified test data of a single tray of battery products until a qualified test data are obtained as the initial test data group, and the initial test data group is used as the basic test data group, where a single tray of battery products includes 24 battery products, b < a, and a is specifically 600; starting from the (a + 1)-th qualified test data, each time the b qualified test data of the battery products in the next group of trays are updated to the basic test data group, and the average value and standard deviation of the basic test data group are updated, so that the average value of the basic test data group has dynamic fluctuations, that is, a moving average value is formed. Correspondingly, the upper limit value for screening good battery products also has dynamic fluctuations, that is, a moving upper limit value is formed, and the lower limit value for screening good battery products also has dynamic fluctuations, that is, a moving lower limit value is formed. The test data of the current test product is judged through the moving interval formed by the moving upper limit value and the moving lower limit value, so as to reduce the influence of data fluctuations in the average values of the product parameters of different batches of batteries; during the process of screening defective battery products, the results of using the determination of a single battery product and the determination of a single tray of battery products to screen defective battery products have relatively small differences, and both can effectively screen out defective products.

[0081] And in Figure 4 - Figure 5 , n takes the value of 3.5, which is a safety value obtained based on the results of a large number of battery disassembly operations to distinguish defective products and good products. The standard deviation takes a fixed value, and the fixed value comes from the test results of more than 300,000 batteries, avoiding misjudgment caused by large short-term fluctuations in the standard deviation.

[0082] In some optional embodiments, the defective battery screening method of this embodiment is further optimized.

[0083] In this embodiment, the a qualified test data in step S10 can be obtained in the following way.

[0084] Acquire test data of the test product in sequence, and screen them by target value and tolerance, and take the test data that meets the target value and tolerance range as qualified test data, until a qualified test data are obtained;

[0085] Wherein, the target value is set according to the average value of historical test data.

[0086] Then execute step S20 to calculate the mean value and standard deviation of the basic test data group, adopt the mean value and standard deviation as the standard for determining the next test data or the next group of test data, and update the b test data determined to be qualified into the basic test data group, that is, add the b qualified test data that have been screened as qualified test data to the basic test data group, and eliminate the b qualified test data that are ranked first in the basic test data group, so that the number of qualified test data in the basic test data group is always maintained at a, and repeat steps S20-S40 to realize real-time updating of the basic test data group, and then realize real-time updating of the mean value and standard deviation of the basic test data group.

[0087] The updating process of the basic test data set can refer to the specific implementation process of the above step S40, which will not be repeated here.

[0088] In some optional embodiments, the criteria for determining qualified test data of the current tested product are specifically described.

[0089] It is necessary to determine whether the test data of the current test product is between the difference between the mean value and n times the standard deviation and the sum of the mean value and n times the standard deviation, and the following formula is used to determine:

[0090] Y x~a+x-1 -nσ x~a+x-1 ≤R a+x ≤Y x~a+x-1 +nσ x~a+x-1 ;

[0091] Among them, Y represents the average value, Y x~a+x-1 represents the average value of the qualified test data from the xth to the a+x-1th, σ represents the standard deviation, σ x~a+x-1 Indicates the standard deviation of the xth to a+x-1th qualified test data, Y x~a+x-1 +nσ x~a+x-1 It represents the sum of the mean value and n times the standard deviation of the xth to a+x-1th qualified test data, Yx~a+x-1 -nσ x~a+x-1 represents the difference between the average value of the x-th to the (a + x - 1)-th qualified test data and n times the standard deviation. R represents the test data, and R a+x represents the qualified test data of the (a + x)-th test product. n represents the multiple of the standard deviation, and x is a positive integer greater than or equal to 1.

[0092] In this embodiment, it should be noted that when the (a + x)-th test data R a+x satisfies Y x~a+x-1 ±nσ x~a+x-1 range, then it is determined that the test data R a+x is qualified test data and is updated to the basic test data group. When the (a + x)-th test data R a+x exceeds Y x~a+x-1 ±nσ x~a+x-1 range, then it is determined that the test data R a+x is unqualified test data. Then, when calculating the average value and standard deviation of the basic test data group for the (a + x + 1)-th to the (a + x + a)-th in the subsequent determination, this test data is excluded. That is to say, the unqualified test data is automatically excluded from the established basic test data group. After exclusion, the corresponding number of qualified test data is filled in forward, and there are always a qualified test data as the basic test data group, so as to obtain the average value and standard deviation of the basic test data group.

[0093] In some alternative implementation manners, the embodiment of the present invention further provides a battery defective product screening device, as Figure 6 shown. The battery defective product screening device includes: a data acquisition module 100, a calculation module 200, and a determination module 300. The data acquisition module 100, the calculation module 200, and the determination module 300 are communicatively connected.

[0094] Among them, the data acquisition module 100 is used to obtain a qualified test data as the initial test data group. The a qualified test data are sorted according to the acquisition order, and the initial test data group is used as the basic test data group and sent to the calculation module 200; the data acquisition module 100 is used to obtain the test data of the current test product and send it to the determination module 300; the data acquisition module 100 is used to fill b screened qualified test data as qualified test data into the basic test data group and exclude the b qualified test data with the earliest acquisition order in the basic test data group, so that the number of qualified test data in the basic test data group always remains a.

[0095] The calculation module 200 is used to calculate the average value and standard deviation of the basic test data group and send them to the determination module 300.

[0096] The determination module 300 is configured to screen the test data of the current test product based on the average value and the standard deviation, send the qualified test data to the data acquisition module 100, and determine the test product corresponding to the unqualified test data as a defective product.

[0097] For the specific screening process of the defective battery screening device by the data acquisition module 100, the calculation module 200, and the determination module 300 for defective batteries, please refer to the above-mentioned defective battery screening method, and this embodiment will not be repeated here.

[0098] The defective battery screening device provided in this embodiment screens unqualified test data by updating the average value and the standard deviation of the basic test data group in real time, and thus realizes the screening of defective batteries, avoiding overkill or underkill caused by data fluctuations in the average value of different batches of products due to factors such as materials and processes in product parameters, and improving the accuracy of defective product determination.

[0099] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. Similarly, in order to streamline the present invention and help understand one or more of the various aspects of the present invention, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Among them, the claims following the specific implementation manners are hereby expressly incorporated into the specific implementation manners, where each claim itself is a separate embodiment of the present invention.

[0100] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from this embodiment. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive.

[0101] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In the unit claims listing several devices, several of these devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for screening defective batteries, characterized in that: The method comprises: S10. Acquire a qualified test data as an initial test data group, sort the a qualified test data according to the acquisition order, and use the initial test data group as a basic test data group; S20. Calculate the average value of the basic test data group, and set the standard deviation of the basic test data group to a fixed value; S30. Determine whether the test data of the current test product is between the difference between the mean value and n times the standard deviation and the sum of the mean value and n times the standard deviation. If so, determine that the test data of the current test product is qualified test data. Otherwise, determine that the test data of the current test product is unqualified test data, and screen the test products corresponding to the unqualified test data as defective products; S40. Add b qualified test data as qualified test data to the basic test data group, and remove the first b qualified test data in the basic test data group in the acquisition order, so that the number of qualified test data in the basic test data group is always maintained at a, and return to step S20; Wherein, a and b are both positive integers, b<a, and the value of a is greater than or equal to 32; The n is set to a fixed value.

2. The method for screening defective batteries according to claim 1, characterized in that: The difference between the average value and n times the standard deviation is greater than or equal to a first preset threshold, and the sum of the average value and n times the standard deviation is less than or equal to a second preset threshold; The second preset threshold is greater than the first preset threshold; The first preset threshold and the second preset threshold are set according to a product qualification criterion.

3. The method for screening defective batteries according to claim 1, characterized in that: The n is set according to the product qualification criteria; The setting range of n is 3-6.

4. The method for screening defective batteries according to claim 1, characterized in that: The product currently under test is a single battery product; Alternatively, the currently tested product is m battery products in a tray; Wherein, m is a positive integer, b≤m, m<a.

5. The method for screening defective batteries according to claim 1, characterized in that: The a qualified test data in step S10 are obtained in the following manner: The test data of the test product are obtained in sequence, and are screened by target value and tolerance, and the test data that meets the target value and tolerance range is taken as qualified test data, until a qualified test data are obtained.

6. The method for screening defective batteries according to claim 5, characterized in that: The target value is set according to the average value of historical test data.

7. The method for screening defective batteries according to claim 1, characterized in that: The test data is the AC internal resistance of the battery product, so as to screen out defective battery tab welding products.

8. A battery defective product screening device, characterized in that: include: A data acquisition module, a calculation module and a determination module, wherein the data acquisition module, the calculation module and the determination module are communicatively connected with each other; The data acquisition module is used to obtain a qualified test data as an initial test data group, the a qualified test data are sorted according to the acquisition order, the initial test data group is used as a basic test data group, and the basic test data group is sent to the calculation module; the data acquisition module is used to obtain the test data of the current test product and send it to the determination module; The data acquisition module is used to add b pieces of qualified test data to the basic test data group as qualified test data, and remove the b pieces of qualified test data ranked first in the basic test data group in the acquisition order, so that the number of qualified test data in the basic test data group is always maintained at a pieces; The calculation module is used to calculate the average value of the basic test data group, the standard deviation of the basic test data group is set to a fixed value, and the average value and standard deviation of the basic test data group are sent to the determination module; The determination module is used to determine whether the test data of the current test product is between the difference between the mean value and n times the standard deviation and the sum of the mean value and n times the standard deviation. If so, the test data of the current test product is determined to be qualified test data; otherwise, the test data of the current test product is determined to be unqualified test data, and the qualified test data is sent to the data acquisition module, and the test product corresponding to the unqualified test data is determined to be a defective product.

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