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 comprising: S10 obtaining a qualified test data as an initial test data group, sorting the a qualified test data according to the acquisition order, and taking 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 supplementing the b screened qualified test data as qualified test data to 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 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 field of battery manufacturing technology, and in particular to a method and device for screening defective batteries. Background Art

[0002] During the current manufacturing process of lithium-ion batteries, material batches, manufacturing processes, etc. will have a certain impact on the AC internal resistance of lithium-ion batteries, resulting in data fluctuations in the average values ​​of the AC internal resistance of batteries from different batches.

[0003] The existing screening method adopts target value ± tolerance, that is, setting the target value and tolerance, calculating the upper limit and lower limit of the AC internal resistance according to the target value and tolerance, and judging the battery with AC internal resistance outside the upper limit and lower limit as defective, and judging the battery with AC internal resistance between the upper limit and lower limit as good.

[0004] 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 over-killing, that is, good products will be mistakenly judged 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 under-killing, that is, defective products will be mistakenly judged as good products; defective products cannot be effectively distinguished from good products. Summary of the invention

[0005] In view of the above problems, an embodiment of the present invention provides a method and device for screening defective batteries, which are used to solve the problem in the prior art that the use of target value ± tolerance cannot effectively screen defective batteries.

[0006] According to one aspect of an embodiment of the present invention, a method for screening defective batteries is provided, the method comprising: S10. Obtaining a qualified test data as an initial test data group, a said qualified test data are sorted according to the acquisition order, and the initial test data group is used as a basic test data group; S20. Calculate the mean and standard deviation of the basic test data set; S30. The test data of the current test product is screened according to the mean value and the standard deviation, and the test product corresponding to the unqualified test data is determined to be a defective product; S40. Add b qualified test data as qualified test data to the basic test data group, and remove the b qualified test data in the order of acquisition 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 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.

[0007] In an optional implementation, screening the test data of the current test product according to the mean value and the standard deviation specifically includes: 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.

[0008] In an optional implementation, the standard deviation is set to a fixed value.

[0009] In an optional 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; 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.

[0010] In an optional implementation, the n is set according to a product qualification criterion; The setting range of n is 3-6.

[0011] In an optional implementation, the currently tested product 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.

[0012] In an optional implementation, the a qualified test data in step S10 are obtained by: 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.

[0013] In an optional implementation, the target value is set according to an average value of historical test data.

[0014] In an optional implementation, the test data is the AC internal resistance of the battery product, so as to screen out defective battery tab welding products.

[0015] According to another aspect of an embodiment of the present invention, a battery defective product screening device is provided, comprising: 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 screened qualified test data as qualified test data to the basic test data group, and remove the b qualified test data in the basic test data group that are ranked first in the acquisition order, so that the number of qualified test data in the basic test data group is always maintained at a; The calculation module is used to calculate the average value and standard deviation of the basic test data group and send them to the determination module; The determination module is used to screen the test data of the current test product according to the mean value and the standard deviation, send the test data that pass the screening to the data acquisition module, and determine the test product corresponding to the test data that fails the screening as a defective product.

[0016] Compared with the prior art, the present invention has the following advantages: The defective battery screening method and device provided by the present invention, in the screening process of the current test product, by adding b qualified test data whose acquisition order is closer to the test data of the current test product as qualified test data to the basic test data group, and eliminating b qualified test data in the basic test data group whose acquisition order is farther 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 the average value of the basic test data group is 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.

[0017] 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.

[0018] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings: Figure 1 A scatter plot of AC internal resistance of a battery product described in the background technology of the present invention is shown; Figure 2 A scatter plot of AC internal resistance of a battery product described in the background technology of the present invention is shown; Figure 3 A schematic diagram of a process for screening defective batteries provided by an embodiment of the present invention is shown; Figure 4 A scatter plot of AC internal resistance for determining a single battery product in a method for screening defective batteries provided by an embodiment of the present invention is shown; Figure 5 An AC internal resistance scatter plot for determining a single tray battery product in a method for screening defective batteries provided by an embodiment of the present invention is shown; Figure 6 A structural block diagram of a battery defective product screening device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although 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 to the embodiments set forth herein.

[0021] This embodiment aims to solve the problem that in the prior art, when defective products are screened by a method of target value ± tolerance, there is over-screening or under-screening, resulting in the inability to effectively screen defective products.

[0022] Specifically, the target value and tolerance are set, and the upper and lower limits of the AC internal resistance are calculated based on the target value and tolerance. Batteries with AC internal resistance outside the upper and lower limits are judged as defective products, and batteries with AC internal resistance between the upper and lower limits are judged as good 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, good products will be mistakenly judged 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, bad products will be mistakenly judged as good products; and defective products cannot be effectively distinguished from good products.

[0023] For an example, see Figure 1As shown, the upper limit and lower limit of the AC internal resistance are set to fixed values. For example, the upper limit of the AC internal resistance of the battery is set to 0.1830mOhm, and the lower limit is set to 0.1650mOhm. The test data of the AC internal resistance is judged by the fixed interval formed by the upper limit and lower limit of the AC internal resistance. The battery product corresponding to the test data of the AC internal resistance of 0.1831mOhm will be mistakenly judged as a defective product.

[0024] See also Figure 2 As shown, the upper and lower limits of the AC internal resistance are set to fixed values. For example, the upper limit of the AC internal resistance of the battery is set to 0.2000mOhm, and the lower limit is set to 0.1500mOhm. The test data of the AC internal resistance is judged by the fixed interval formed by the upper and lower limits of the AC internal resistance. Then, the battery products corresponding to the AC internal resistance test data of 0.1840mOhm, 0.1835mOhm and 0.1866mOhm will be misjudged as good products. These three battery products are actually defective products. After disassembly, they are all defective battery ear welding products caused by the folding of the negative ear.

[0025] Therefore, this embodiment discloses a method for screening defective batteries. Figure 3 As shown, the method comprises the following steps: S10. Acquire a qualified test data as an initial test data group, sort the a qualified test data according to an acquisition order, and use the initial test data group as a basic test data group.

[0026] This step specifically includes: limiting the number of qualified test data in the initial test data group to a, sorting the a qualified test data according to the acquisition order to form the initial test data group, and then using the initial test data group as the basic test data group.

[0027] Preferably, a qualified test data are sorted by test time to form an initial test data group {1, 2, ..., a}.

[0028] Wherein, a represents the number of qualified test data in the initial test data group, which is a positive integer.

[0029] In some optional embodiments, the value of a is greater than or equal to 32.

[0030] S20. Calculate the mean and standard deviation of the basic test data set.

[0031] 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 standard for determining whether the a+1th test data is qualified.

[0032] S30. The test data of the current test product is screened according to the mean value and the standard deviation, and the test product corresponding to the screened unqualified test data is determined as a defective product.

[0033] In this step, the average value 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 that the test data of the current test product is screened by the average value and standard deviation.

[0034] Specifically: determine whether the test data of the current test product is within the range of 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, and then determine that the current test product is a good product; otherwise, that is, the test data of the current test product exceeds the range of 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, determine that the test data of the current test product is unqualified test data, and then determine that the current test product is a defective product.

[0035] Among them, the difference between the average value of the basic test data group and n times the standard deviation can be understood as the lower limit value for screening good batteries, and the sum of the average value of the basic test data group and n times the standard deviation can be understood as the upper limit value for screening good batteries. n is set according to the qualified judgment standard of the product, and the qualified judgment standard of the product is set according to customer needs to ensure that the judgment standard meets the customer's requirements.

[0036] In some optional embodiments, the setting range of n is 3-6.

[0037] In some optional embodiments, the value ranges of the mean value and the n-fold standard deviation are limited, specifically: 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.

[0038] 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 a qualified judgment standard of the product.

[0039] 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 so that the number of qualified test data in the basic test data group is always kept at a, and return to step S20.

[0040] Wherein, b is a positive integer and b<a.

[0041] In this step, the b qualified test data screened out in step S30 are added to the basic test data group, and the b qualified test data ranked first in the basic test data group are eliminated, so that the number of qualified test data in the basic test data group is always maintained at a, and then step S20 is executed to calculate the average value and standard deviation of the updated basic test data group, and then the test data of the next or next group of test products are screened according to the average value and standard deviation of the updated basic test data group, and the above steps are repeated in sequence.

[0042] For example, assuming that the current qualified test data is the a+1th, the a+1th qualified test data is updated to the basic test data group, and the first qualified test data in the basic test data group is removed. The updated basic test data group is {2, 3, ..., a, a+1}.

[0043] 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 test data or the next group of test data is qualified.

[0044] Assuming that the next qualified test data is the a+2th qualified test data, update the a+2th qualified test data to the basic test data group, and remove the first qualified test data in the basic test data group updated last time. The updated basic test data group is {3, 4, ..., a, a+1, a+2}.

[0045] Return to step S20 again and repeat steps S20-S40.

[0046] 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.

[0047] In the present embodiment, during the testing process of the battery products, the tests may be performed one by one or tray by tray.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] Preferably, the value of a is a positive integer greater than or equal to 32.

[0052] 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.

[0053] 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.

[0054] For details, please refer to Figure 4-Figure 5 As shown, Figure 4 The AC internal resistance scatter plot for determining a single battery product in a method for screening defective batteries provided in the present embodiment, i.e., a single battery product test, obtains a single qualified test data in each test until a qualified test data are obtained as an initial test data group, and the initial test data group is used as a basic test data group, wherein a is specifically 600; starting from the a+1th qualified test data, the qualified test data of the next battery product is updated to the basic test data group each time, and the mean value and standard deviation of the basic test data group are updated, so that the mean value of the basic test data group has dynamic fluctuations, i.e., a moving average value is formed, and accordingly, the upper limit value for screening good batteries also has dynamic fluctuations, i.e., a moving upper limit value is formed, and the lower limit value for screening good batteries also has dynamic fluctuations, i.e., a moving lower limit value is formed, and the test data of the current test product is determined by the moving interval formed by the moving upper limit value and the moving lower limit value, thereby reducing the influence of data fluctuations on the average values ​​of product parameters of batteries from different batches.

[0055] Figure 5 The AC internal resistance scatter diagram of a single tray battery product determination in a battery defective product screening method provided in this embodiment, that is, a single tray battery product test, each test obtains b qualified test data of the single tray battery product until a qualified test data are obtained as an initial test data group, and the initial test data group is used as a basic test data group, wherein the single tray battery product includes 24 battery products, b<a, a is specifically 600; starting from the a+1th 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 mean value and standard deviation of the basic test data group are updated, so that the basic The average value of the test data group has dynamic fluctuations, that is, a moving average value is formed. Correspondingly, the upper limit value for screening good batteries also has dynamic fluctuations, that is, a moving upper limit value is formed, and the lower limit value for screening good batteries also has dynamic fluctuations, that is, a moving lower limit value is formed. The test data of the current test product is judged by the moving range formed by the moving upper limit value and the moving lower limit value, thereby reducing the impact of data fluctuations on the average values ​​of product parameters of different batches of batteries. In the process of screening defective battery products, the results of screening defective battery products by using single battery product judgment and single-disc battery product judgment are slightly different, and both can effectively screen out defective products.

[0056] And in Figure 4-Figure 5 In the figure, n is taken as 3.5, which is a safety value obtained by distinguishing defective products from good products based on the results of disassembling a large number of batteries. The standard deviation is a fixed value, which comes from the test results of more than 300,000 batteries, avoiding misjudgment caused by large short-term fluctuations in the standard deviation.

[0057] In some optional embodiments, the defective battery screening method of this embodiment is further optimized.

[0058] In this embodiment, the a qualified test data in step S10 can be obtained in the following way.

[0059] 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; Wherein, the target value is set according to the average value of historical test data.

[0060] Then execute step S20 to calculate the mean and standard deviation of the basic test data group, adopt the mean 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 and standard deviation of the basic test data group.

[0061] 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.

[0062] In some optional embodiments, the criteria for determining qualified test data of the current tested product are specifically described.

[0063] 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: Y x~a+x-1 -nσ x~a+x-1 ≤R a+x ≤Y x~a+x-1 +nσ x~a+x-1 ; 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, Y x~a+x-1 -nσ x~a+x-1 represents the difference between the average value and n times the standard deviation of the xth to a+x-1th qualified test data, R represents the test data, R a+x represents the qualified test data of the a+xth tested product, n represents the multiple of the standard deviation, and x is a positive integer greater than or equal to 1.

[0064] In this embodiment, it should be noted that when the a+xth test data R a+x Meet Y x~a+x-1 ±nσ x~a+x-1 If the test data R a+x The qualified test data is updated to the basic test data group. When the a+xth test data R a+x Beyond Yx~a+x-1 ±nσ x~a+x-1 If the test data R a+x If it is unqualified test data, then the test data will be eliminated when the mean value and standard deviation of the basic test data group from a+x+1 to a+x+a are calculated in the future. That is to say, the unqualified test data will be automatically eliminated from the constructed basic test data group, and after elimination, the corresponding number of qualified test data will be added forward, and there will always be a qualified test data as the basic test data group, thereby achieving the acquisition of the mean value and standard deviation of the basic test data group.

[0065] In some optional implementations, the present invention also provides a battery defective product screening device, such as Figure 6 As shown, the battery defective product screening device includes: a data acquisition module 100, a calculation module 200 and a determination module 300, and the data acquisition module 100, the calculation module 200 and the determination module 300 are communicatively connected with each other.

[0066] Among them, the data acquisition module 100 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 200; the data acquisition module 100 is used to obtain the test data of the current test product and send it to the judgment module 300; the data acquisition module 100 is used to add b qualified test data to the basic test data group as qualified test data, and eliminate the b qualified test data ranked first 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 at a.

[0067] 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.

[0068] The determination module 300 is used to screen the test data of the current test product according to the mean value and the standard deviation, send the test data that pass the screening to the data acquisition module 100, and determine the test product corresponding to the test data that fails the screening as a defective product.

[0069] The process of screening defective batteries by the data acquisition module 100, the calculation module 200 and the determination module 300 of the device for screening defective batteries is specifically described in the method for screening defective batteries described above, and will not be repeated in this embodiment.

[0070] The battery defective product screening device provided in this embodiment screens out unqualified test data by updating the average value and standard deviation of the basic test data group in real time, thereby screening out defective batteries, avoiding data fluctuations in the average values ​​of different batches of products due to factors such as materials and processes, resulting in over-killing or under-killing, and improving the accuracy of defective product judgment.

[0071] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of 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. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0072] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0073] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for screening defective batteries, characterized in that: The method comprises: S10. Obtaining a qualified test data as an initial test data group, a said qualified test data are sorted according to the acquisition order, and the initial test data group is used as a basic test data group; S20. Calculate the mean and standard deviation of the basic test data set; S30. The test data of the current test product is screened according to the mean value and the standard deviation, and the test product corresponding to the unqualified test data is determined to be a defective product; S40. Add b qualified test data as qualified test data to the basic test data group, and remove the b qualified test data in the order of acquisition 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 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.

2. The method for screening defective batteries according to claim 1, characterized in that: The test data of the current test product is screened according to the mean value and standard deviation, specifically including: 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.

3. The method for screening defective batteries according to claim 2, characterized in that: The standard deviation is set to a fixed value.

4. The method for screening defective batteries according to claim 2, 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.

5. The method for screening defective batteries according to claim 2, characterized in that: The n is set according to the product qualification criteria; The setting range of n is 3-6.

6. 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.

7. 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.

8. The method for screening defective batteries according to claim 7, characterized in that: The target value is set according to the average value of historical test data.

9. 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.

10. 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 and standard deviation of the basic test data group and send them to the determination module; The determination module is used to screen the test data of the current test product according to the mean value and the standard deviation, send the test data that pass the screening to the data acquisition module, and determine the test product corresponding to the test data that fails the screening as a defective product.

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