Battery screening method, electronic equipment and storage medium

Abstract

The invention discloses a battery screening method, electronic equipment and a storage medium. The battery screening method comprises the following steps: acquiring a plurality of test voltages of a plurality of to-be-tested batteries in a formation process; for any to-be-tested battery, determining a voltage deviation between each test voltage of the to-be-tested battery and a plurality of test voltages of other to-be-tested batteries based on the plurality of test voltages of the to-be-tested battery and the plurality of test voltages of the other to-be-tested batteries; and based on the voltage deviation corresponding to each test voltage of the plurality of to-be-tested batteries, screening a target battery from the plurality of to-be-tested batteries. The accuracy of battery screening can be improved.

Description

Technical Field

[0001] This application relates to the field of battery manufacturing technology, specifically to battery screening methods, electronic devices, and storage media. Background Technology

[0002] In battery manufacturing, the cell formation process is one of the key steps determining battery performance. The formation process not only affects the cell's capacity, internal resistance (DCR), and open-circuit voltage (OCV), but also directly impacts the formation quality of the solid electrolyte interphase (SEI) film. The stability of the SEI film has a significant impact on the battery's cycle life and safety. If the cell does not undergo strict moisture control before formation, even trace amounts of moisture can lead to side reactions, further affecting SEI film formation and potentially causing safety hazards in severe cases.

[0003] Currently, the factory lacks a solution for identifying the above-mentioned defective battery cells. Therefore, it is urgent to develop a new screening scheme to accurately identify potentially risky abnormal battery cells and normal battery cells, and to promptly remove abnormal battery cells during the production process, thereby reducing the probability of defective products leaving the factory and improving the product consistency and reliability of normal battery cells. Summary of the Invention

[0004] A battery screening method is provided to address the problem of improving the accuracy of battery screening during the battery manufacturing process.

[0005] Firstly, a battery screening method is provided, comprising the following steps: Acquire multiple test voltages of multiple batteries under test during the formation process; For any battery under test, based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test; Based on the voltage deviation of multiple batteries under test at each test voltage, a target battery is selected from the multiple batteries under test.

[0006] In one embodiment, the multiple test voltages include test voltages corresponding to multiple acquisition times; Based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test, including: For the test voltage of the battery under test at any acquisition time, other test voltages at the acquisition time are obtained from multiple test voltages of other batteries under test. Based on the average voltage corresponding to the test voltage at the acquisition time and other test voltages, the voltage deviation between the test voltage of the battery under test at the acquisition time and other test voltages is determined, thereby obtaining the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test.

[0007] In this embodiment, by simultaneously performing the formation process on batteries in the same batch, the voltage deviation of each battery under test at multiple test voltages is calculated. This can intuitively reflect the voltage anomalies of the batteries under test during the formation stage, thereby quickly identifying target batteries and improving the accuracy and efficiency of battery screening.

[0008] In one embodiment, selecting a target battery from multiple batteries under test based on the voltage deviation corresponding to each test voltage includes: Based on a preset voltage detection range, a target test voltage is determined from multiple test voltages corresponding to each battery under test, and the target voltage deviation corresponding to the target test voltage is obtained. Target batteries are selected based on the target voltage deviations of multiple batteries under test.

[0009] In this embodiment, by screening batteries within a preset voltage detection range, target batteries can be quickly identified, improving the accuracy and efficiency of battery screening.

[0010] In one embodiment, the target battery includes an abnormal battery; the target battery is screened based on the target voltage deviation corresponding to each of the multiple batteries under test, including: Among multiple batteries under test, those whose target voltage deviation exceeds a preset voltage deviation threshold are identified as abnormal batteries.

[0011] In this embodiment, abnormal batteries can be quickly identified by detecting abnormal batteries within a preset voltage detection range, thereby improving the accuracy and efficiency of battery screening.

[0012] In one embodiment, the target battery further includes a target normal battery; the battery screening method further includes: Among multiple batteries to be tested, those whose target voltage deviation does not exceed the preset voltage deviation threshold are identified as normal batteries, and the battery capacity corresponding to each test voltage during the formation process of multiple normal batteries is obtained. Based on the battery capacity of multiple normal batteries at each test voltage, the formation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation of multiple normal batteries at each test voltage, the voltage deviation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation curves and formation curves corresponding to multiple normal cells, the target normal cell is determined among the multiple normal cells.

[0013] In this embodiment, by using voltage deviation curves and formation curves to identify target normal cells in normal cells, it is possible to further screen cells with better SEI film consistency.

[0014] In one embodiment, the battery screening method further includes: Acquire multiple sampling voltages of the sample battery during the formation process; For any given sample battery, based on multiple sample voltages of the sample battery and multiple sample voltages of other sample batteries, determine the sample voltage deviation between each sample voltage of the sample battery and multiple sample voltages of other sample batteries; Based on the sampling voltage deviation and the sampling voltage, voltage deviation curves between the voltage deviation and the voltage are established for multiple sample batteries respectively; The voltage detection range is determined based on the voltage deviation curves corresponding to multiple sample batteries.

[0015] In this embodiment, by establishing voltage deviation curves between voltage and voltage corresponding to multiple sample batteries, and determining the voltage detection range based on the voltage deviation curves, the voltage difference between sample batteries can be quantified, and the specific voltage range with the most significant and unstable difference between batteries can be accurately identified, thereby ensuring the accuracy of battery screening based on voltage detection range.

[0016] In one embodiment, the sample battery includes normal sample batteries and abnormal sample batteries; Based on the voltage deviation curves corresponding to multiple sample batteries, a preset voltage detection range is determined, including: The voltage deviation curve of the abnormal sample battery is compared with the voltage deviation curve of the normal sample battery to obtain the curve deviation; The sampling voltage range corresponding to the curve deviation that is greater than the preset curve deviation threshold is determined as the voltage detection range.

[0017] In this embodiment, by comparing curve deviations and determining the voltage detection range, the voltage detection range can be effectively identified, thereby improving the accuracy of battery screening.

[0018] In one embodiment, the battery screening method further includes: Based on the multiple test voltages corresponding to the multiple batteries under test, the internal resistance of the batteries under test is determined. Based on the voltage deviation of multiple batteries under test at each test voltage, target batteries are selected from the multiple batteries under test, including: Based on the voltage deviation of multiple batteries under test at each test voltage and the internal resistance of the batteries under test, target batteries are selected from the batteries under test.

[0019] In this embodiment, battery screening efficiency can be improved by combining the battery's internal resistance and voltage deviation.

[0020] Secondly, this application also provides a battery sorting device, which includes: The data acquisition module is used to acquire multiple test voltages of multiple batteries under test during the formation process; The deviation calculation module is used to determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test, based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, for any battery under test. The screening module is used to screen target batteries from multiple batteries under test based on the voltage deviation corresponding to each test voltage.

[0021] Thirdly, this application also provides an electronic device, including a memory and a processor, the memory storing a computer program for controlling the processor to operate in order to perform the methods in any of the embodiments of any of the above aspects.

[0022] Fourthly, this application also provides a computer-readable storage medium including computer instructions that, when executed by a processor, implement the methods in any of the embodiments described above.

[0023] Fifthly, the present application provides a computer program product that, when executed by a processor, implements the method in any of the above-described embodiments.

[0024] Beneficial effects: By collecting multiple test voltages during the formation process of the battery under test, and for any given battery under test, determining the voltage deviation between each test voltage of the battery under test and the multiple test voltages of other batteries under test based on the multiple test voltages of the battery under test, and selecting target batteries based on the voltage deviations of multiple batteries under test at each test voltage, it is possible to identify abnormal or normal target batteries in real time during the battery formation process, improve the effective identification of target batteries, thereby ensuring the effectiveness of battery formation, improving the formation consistency of the battery SEI film, and significantly enhancing the overall safety and reliability of battery products. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic flowchart of the battery screening method provided by an exemplary embodiment of this disclosure; Figure 2 This is a schematic diagram of the voltage deviation curve and electrode lithium plating of an example battery under test provided by an exemplary embodiment of this disclosure. Figure 3 This is a schematic diagram of the voltage deviation curve and electrode lithium plating of another example battery under test provided by an exemplary embodiment of this disclosure; Figure 4 This is a schematic diagram of a battery screening device provided in an exemplary embodiment of this disclosure; Figure 5 This is an internal structural diagram of an electronic device provided by an exemplary embodiment of this disclosure. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0030] On the one hand, this embodiment provides a battery screening method, such as Figure 1 As shown, it includes the following steps: S101, acquire multiple test voltages of multiple batteries under test during the formation process; S102, for any battery under test, based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test; S103, based on the voltage deviation of multiple batteries under test at each test voltage, selects the target battery from the multiple batteries under test.

[0031] The battery under test refers to the battery that needs to be identified as having anomalies. This can be a battery undergoing formation or a battery awaiting screening after formation. The formation process refers to the initial charging and activation of the battery after manufacturing. Specific current and voltage are used to form a stable solid electrolyte interface film inside the battery, activating its performance. The test voltage is the voltage collected during the formation process of the battery under test. Voltage deviation indicates the degree to which the voltage of a single battery deviates from the group's average voltage level.

[0032] For example, batteries from the same batch are placed into a formation device. These batteries, such as lithium iron phosphate batteries, use lithium iron phosphate as the cathode material. The formation device performs formation on the batteries. The formation device is equipped with a detection module, such as a host computer or other terminal, to perform the screening function for lithium-ion batteries.

[0033] During the formation and charging process of the battery under test, the terminal collects the test voltage of the battery under test at a preset frequency to obtain multiple test voltages for each battery under test.

[0034] For any battery under test, the terminal calculates the voltage deviation between each test voltage of the battery under test and the multiple test voltages of other batteries under test, based on the multiple test voltages of the battery under test and the multiple test voltages of other batteries under test, thereby obtaining the voltage deviation between each test voltage of the multiple batteries under test and the multiple test voltages of other batteries under test.

[0035] The terminal filters target batteries from multiple test batteries based on the voltage deviation corresponding to each test voltage for each battery under test. Target batteries generally include normal batteries and abnormal batteries. Specifically, batteries with voltage deviations exceeding a preset voltage deviation threshold are identified as abnormal batteries, indicating the presence of excessive moisture that affects normal charging, thus deemed unqualified due to moisture content. Conversely, batteries with voltage deviations within the preset threshold are identified as normal batteries, indicating that their internal moisture content is within acceptable limits, considered acceptable for moisture content. Further, abnormal batteries identified during the formation process are removed from the production line or scrapped. Normal batteries continue formation processing, and after formation, they can be further filtered to obtain target normal batteries. Target normal batteries are those that meet the screening requirements from the normal battery pool.

[0036] In this embodiment, by collecting multiple test voltages during the formation process of the battery under test, and for any battery under test, determining the voltage deviation between each test voltage of the battery under test and the multiple test voltages of other batteries under test based on the multiple test voltages of the battery under test and the multiple test voltages of other batteries under test, and screening target batteries based on the voltage deviations of multiple batteries under test at each test voltage, it is possible to identify abnormal or normal target batteries in real time during the battery formation process, improve the effective identification of target batteries, thereby ensuring the effectiveness of battery formation, improving the formation consistency of the battery SEI film, and significantly improving the overall safety and reliability of battery products.

[0037] In one embodiment, the multiple test voltages include test voltages corresponding to multiple acquisition times; Based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test, including: For the test voltage of the battery under test at any acquisition time, other test voltages at the acquisition time are obtained from multiple test voltages of other batteries under test. Based on the average voltage corresponding to the test voltage at the acquisition time and other test voltages, the voltage deviation between the test voltage of the battery under test at the acquisition time and other test voltages is determined, thereby obtaining the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test.

[0038] For example, during the formation and charging process of the battery under test, the test voltage of the battery under test is collected at a preset frequency to obtain the test voltage of each battery under test at multiple collection times. For any test voltage of any battery under test at any collection time, other test voltages at that collection time are obtained from multiple test voltages of other batteries under test.

[0039] Calculate the average voltage corresponding to other test voltages of other batteries under test at the same acquisition time. Based on the test voltage of the battery under test at the same acquisition time and the average voltage of other batteries under test at the same acquisition time, determine the voltage deviation between the test voltage of the battery under test at the acquisition time and other test voltages. The specific calculation is shown in formula (1).

[0040] (1) in, This represents the voltage deviation of the i-th battery under test at time t; The test voltage of the i-th battery under test is collected at time t; This represents the test voltage collected at time t for other batteries under test j; N represents the total number of batteries under test.

[0041] This allows us to obtain the voltage deviation between each test voltage of the battery under test and multiple test voltages of other batteries under test.

[0042] In this embodiment, by simultaneously performing the formation process on batteries in the same batch, the voltage deviation of each battery under test at multiple test voltages is calculated. This can intuitively reflect the voltage anomalies of the batteries under test during the formation stage, thereby quickly identifying abnormal batteries and improving the accuracy and efficiency of battery screening.

[0043] In one embodiment, selecting a target battery from multiple batteries under test based on the voltage deviation corresponding to each test voltage includes: Based on a preset voltage detection range, a target test voltage is determined from multiple test voltages corresponding to each battery under test, and the target voltage deviation corresponding to the target test voltage is obtained. Target batteries are selected based on the target voltage deviations of multiple batteries under test.

[0044] The preset voltage detection range is a specified voltage range used to screen target batteries. Generally, the preset voltage detection range can be determined based on the reaction stage of the SEI film in the battery, such as the lithium intercalation stage of the SEI film.

[0045] For example, after obtaining the voltage deviation of multiple batteries under test at each test voltage, a preset voltage detection range is obtained. Within each test voltage of the multiple batteries under test, the test voltage located within the preset voltage detection range is determined as the target test voltage.

[0046] The target voltage deviation of multiple batteries under test at the target measurement voltage is obtained. Based on the preset voltage deviation threshold and the target voltage deviation, the target battery is selected from the multiple batteries under test.

[0047] In this embodiment, by detecting abnormal batteries within a preset voltage detection range, the target battery can be quickly identified, improving the accuracy and efficiency of battery screening.

[0048] In one embodiment, the target battery includes an abnormal battery; the target battery is screened based on the target voltage deviation corresponding to each of the multiple batteries under test, including: Among multiple batteries under test, those whose target voltage deviation exceeds a preset voltage deviation threshold are identified as abnormal batteries.

[0049] For example, within a preset voltage detection range, such as [2.0 V, 3.0 V], batteries under test whose target voltage deviation exceeds a preset voltage deviation threshold are identified as abnormal batteries, while batteries under test whose target voltage deviation does not exceed the preset voltage deviation threshold are identified as normal batteries.

[0050] Understandably, excessive moisture inside the battery during the battery formation stage can lead to abnormal SEI film growth. Specifically, when the moisture content is too high, side reactions (such as LiPF6 hydrolysis) occur in the electrolyte during the lithium intercalation stage (corresponding to the 2.1V-2.5V range), hindering lithium-ion intercalation into the negative electrode. This results in a decrease in the utilization rate of the active material in the negative electrode of the abnormal battery, leading to slower voltage feedback. Consequently, at the same SOC, the voltage response of the abnormal battery lags behind that of other normal batteries with acceptable moisture content. That is, at the same SOC, the test voltage of the abnormal battery is significantly lower than that of other batteries. Furthermore, since the voltage deviation of the abnormal battery at the same SOC is negative, the more negative the voltage deviation of the abnormal battery, the lower its test voltage compared to other batteries.

[0051] Therefore, within the preset voltage detection range, the target voltage deviation of the battery under test can be used to screen for abnormal batteries by using a preset voltage deviation threshold. The screening of abnormal batteries is shown in formula (2).

[0052] AVD abnormality (V)≤K, V∈[2.0V, 3.0V], K<0 (2) Wherein, AVD abnormal (V) represents the voltage deviation of the abnormal battery at any target test voltage; K represents the preset voltage deviation threshold, which is negative, in mV; [2.0V, 3.0V] is the voltage detection range.

[0053] In this embodiment, abnormal batteries can be quickly identified by detecting abnormal batteries within a preset voltage detection range, thereby improving the accuracy and efficiency of battery screening.

[0054] In one specific embodiment, a grading judgment rule can be set within the voltage detection range [2.0V, 3.0V]: when the target voltage deviation (AVD) of the battery is ≤K1, it is judged as the first abnormal cell; when the target voltage deviation (AVD) of the battery is ≤K2, it is judged as the second abnormal cell. Where K2 < K1, the severity of lithium plating in the second abnormal cell is greater than that in the third abnormal cell. For details, please refer to the following example, which can be applied to the quality control stage in lithium-ion battery manufacturing.

[0055] Example 1, such as Figure 2 As shown, an example voltage deviation curve and a schematic diagram of electrode lithium plating are provided for a battery under test. During the formation process of the same batch of batteries under test, the voltage deviation of each battery under test at each test voltage is calculated according to formula (1), which can be used to construct a voltage deviation curve for each battery under test. The voltage deviation curve can be as follows: Figure 2 As shown in -a, the horizontal axis of the curve represents voltage, and the vertical axis represents voltage deviation. The voltage deviation curve represents the correlation between the voltage deviation and the test voltage for each battery under test.

[0056] Then, within the voltage detection range [2.0V, 3.0V], the target voltage deviation (AVD) of each battery under test is compared using a preset voltage deviation threshold. When the preset voltage deviation threshold is -200mV, batteries under test with a target voltage deviation (AVD) less than -200mV are identified as abnormal cells. After full-charge disassembly, they can be... Figure 2 As shown in -b, each negative electrode sheet has black spots and lithium plating, accounting for 40% of the area.

[0057] Example 2, such as Figure 3 As shown, another example of a battery under test, including its voltage deviation curve and a schematic diagram of electrode lithium plating, is provided. During the formation process of another batch of batteries under test, the voltage deviation of each battery at each test voltage is calculated according to formula (1), which can be used to construct a voltage deviation curve for each battery under test. The voltage deviation curve can be as follows: Figure 3 As shown in -a, the horizontal axis of the curve represents voltage, and the vertical axis represents voltage deviation. The voltage deviation curve represents the correlation between the voltage deviation and the test voltage for each battery under test.

[0058] Then, within the voltage detection range [2.0V, 3.0V], the target voltage deviation (AVD) of each battery under test is compared using a preset voltage deviation threshold. When the preset voltage deviation threshold is -100mV, batteries under test with a target voltage deviation (AVD) less than -100mV are identified as abnormal cells. After full-charge disassembly, they can be... Figure 3 As shown in -b, each negative electrode sheet has black spots and lithium plating, accounting for 30% of the area.

[0059] In this embodiment, by comparing the voltage deviations of the same batch of batteries under test, without relying on traditional manual inspection or single-index analysis, abnormal batteries with substandard moisture content and SEI film defects can be accurately screened within a specific voltage detection range during the formation process, significantly improving screening efficiency and accuracy. Furthermore, batteries with potential safety hazards can be promptly eliminated, significantly enhancing the overall safety and reliability of the battery product.

[0060] In one embodiment, the target battery further includes a target normal battery; the battery screening method further includes: Among multiple batteries to be tested, those whose target voltage deviation does not exceed the preset voltage deviation threshold are identified as normal batteries, and the battery capacity corresponding to each test voltage during the formation process of multiple normal batteries is obtained. Based on the battery capacity of multiple normal batteries at each test voltage, the formation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation of multiple normal batteries at each test voltage, the voltage deviation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation curves and formation curves corresponding to multiple normal cells, the target normal cell is determined among the multiple normal cells.

[0061] The formation curve represents the relationship between the battery's test voltage and its capacity. The voltage deviation curve represents the relationship between the battery's test voltage and its voltage deviation.

[0062] For example, among multiple batteries to be tested, those whose target voltage deviation does not exceed a preset voltage deviation threshold are identified as normal batteries. A normal battery is one whose internal moisture content meets the standard.

[0063] At this point, the normal batteries can be further screened according to actual needs. Specifically, this can involve obtaining the battery capacity of multiple normal batteries at each test voltage during the formation process, and determining the formation curves for each normal battery based on its capacity at each test voltage. Then, determining the voltage deviation curves for each normal battery based on its voltage deviation at each test voltage.

[0064] Calculate the first curve similarity among the formation curves corresponding to multiple normal batteries, and calculate the second curve similarity among the voltage deviation curves corresponding to multiple normal batteries. Based on the first curve similarity and the second curve similarity for each normal battery, determine the target normal battery among the multiple normal batteries. Alternatively, among the multiple normal batteries, determine the normal battery whose first curve similarity is greater than a preset first similarity threshold and whose second curve similarity is greater than a preset second similarity threshold as the target normal battery.

[0065] Specifically, since the overall trends of the formation curves of normal batteries (batteries with adequate moisture content) are similar, the first curve similarity between the formation curves can be obtained by calculating the Pearson correlation coefficient between the formation curves of each normal battery. In the interval [0,1], the closer the Pearson correlation coefficient is to 1, the more similar the shapes of the two curves are, and the closer the Pearson correlation coefficient is to 0, the more opposite the trends of the two curves are.

[0066] Since the overall trend and curve differences between the voltage deviation curves of normal batteries (batteries with acceptable moisture content) are relatively large, the average distance between each test point on the voltage deviation curves corresponding to multiple normal batteries can be used as a second curve similarity between the voltage deviation curves. For example, the Euclidean distance between test point 1 on voltage deviation curve A and test point 1 on voltage deviation curve B can be calculated, as can the Euclidean distance between test point 2 on voltage deviation curve A and test point 2 on voltage deviation curve B. This yields the point distances (i.e., Euclidean distances) between multiple test points on voltage deviation curves A and B. The average distance between these multiple test points is then calculated to obtain the second curve similarity between voltage deviation curves A and B.

[0067] In this embodiment, by using voltage deviation curves and formation curves to identify target normal cells in normal cells, it is possible to further screen cells with better SEI film consistency.

[0068] In one embodiment, the battery screening method further includes: Acquire multiple sampling voltages of the sample battery during the formation process; For any given sample battery, based on multiple sample voltages of the sample battery and multiple sample voltages of other sample batteries, determine the sample voltage deviation between each sample voltage of the sample battery and multiple sample voltages of other sample batteries; Based on the sampling voltage deviation and the sampling voltage, voltage deviation curves between the voltage deviation and the voltage are established for multiple sample batteries respectively; The voltage detection range is determined based on the voltage deviation curves corresponding to multiple sample batteries.

[0069] For example, the voltage detection range can be verified using sample batteries from the same batch as the battery under test. Specifically, the sample battery can be placed in a formation device for formation, and the sampling voltage of the sample battery can be collected at multiple sampling moments during the formation process. Then, the sampling voltage deviation between each sampling voltage of the sample battery and multiple sampling voltages of other sample batteries can be calculated according to formula (1). Based on the sampling voltage deviation of each sample battery at each sampling voltage, voltage deviation curves between the voltage deviation and the voltage of each sample battery can be established.

[0070] Then, the voltage deviation curves corresponding to multiple sample batteries are compared to determine the voltage range where curve differences exist. Based on this voltage range where curve differences exist, the voltage detection range is determined.

[0071] In this embodiment, by establishing voltage deviation curves between voltage and voltage corresponding to multiple sample batteries, and determining the voltage detection range based on the voltage deviation curves, the voltage difference between sample batteries can be quantified, and the specific voltage range with the most significant and unstable difference between batteries can be accurately identified, thereby ensuring the accuracy of battery screening based on voltage detection range.

[0072] In one embodiment, the sample battery includes normal sample batteries and abnormal sample batteries; Based on the voltage deviation curves corresponding to multiple sample batteries, a preset voltage detection range is determined, including: The voltage deviation curve of the abnormal sample battery is compared with the voltage deviation curve of the normal sample battery to obtain the curve deviation; The sampling voltage range corresponding to the curve deviation that is greater than the preset curve deviation threshold is determined as the voltage detection range.

[0073] For example, the sample batteries include normal sample batteries and abnormal sample batteries. Normal sample batteries represent those with acceptable internal moisture content, while abnormal sample batteries represent those with insufficient internal moisture content (excessive moisture content). The voltage deviation curves corresponding to the multiple sample batteries include the voltage deviation curve for normal sample batteries and the voltage deviation curve for abnormal sample batteries. The voltage deviation curve includes test points, which represent the voltage deviation of the sample battery at any given voltage.

[0074] The voltage deviation curve of each abnormal sample battery is compared with the voltage deviation curve of a normal sample battery to obtain the curve deviation. Specifically, for any abnormal sample battery and the voltage deviation curve of a normal sample battery, the difference in voltage deviation between each test point is determined based on the voltage deviation at each test point on the voltage deviation curve of the abnormal sample battery and the voltage deviation at the same test point on the voltage deviation curve of the normal sample battery. This difference in voltage deviation is used to obtain the curve deviation corresponding to that abnormal sample battery.

[0075] For any abnormal sample battery, consecutive test points where the deviation exceeds a preset difference threshold (i.e., a preset curve deviation threshold) are identified. The voltage range corresponding to these consecutive test points is defined as the sampling voltage range, thus obtaining the sampling voltage range for each abnormal sample battery. The sampling voltage ranges for each abnormal sample battery are then merged to obtain the voltage detection range.

[0076] Understandably, the voltage detection range is, for example, [2.0V, 3.0V], which corresponds to the main formation period of the SEI film during the battery formation process. At this time, the electrolyte reduction and decomposition are intense, and it is extremely sensitive to the negative electrode material, electrolyte composition and current magnitude, which can easily lead to inconsistencies.

[0077] In this embodiment, by comparing curve deviations and determining the voltage detection range, the voltage detection range can be effectively identified, thereby improving the accuracy of battery screening.

[0078] In one embodiment, the battery screening method further includes: Based on the multiple test voltages corresponding to the multiple batteries under test, the internal resistance of the batteries under test is determined. Based on the voltage deviation of multiple batteries under test at each test voltage, target batteries are selected from the multiple batteries under test, including: Based on the voltage deviation of multiple batteries under test at each test voltage and the internal resistance of the batteries under test, target batteries are selected from the batteries under test.

[0079] For example, when the internal moisture content of a battery is too high, side reactions (such as LiPF6 hydrolysis) occur in the electrolyte during the lithium intercalation stage (corresponding to the 2.1V-2.5V range), hindering lithium-ion intercalation into the negative electrode. This results in a decrease in the utilization rate of the active material in the negative electrode of the abnormal battery. Besides causing the voltage response of the abnormal battery to lag behind other normal batteries with acceptable moisture content at the same SOC, it also leads to an increase in the internal resistance of the abnormal battery at the same SOC. Therefore, the internal resistance and voltage deviation of the battery under test can be combined simultaneously for screening target batteries. Target batteries include normal batteries and abnormal batteries.

[0080] After obtaining the voltage deviation of multiple batteries under test for each test voltage, the terminal calculates the internal resistance of each battery under test at multiple acquisition times based on the test voltages at multiple acquisition times. Among the multiple batteries under test, those with voltage deviations exceeding a preset voltage deviation threshold and / or internal resistance greater than a preset internal resistance threshold are identified as abnormal batteries. Alternatively, batteries with voltage deviations not exceeding the preset voltage deviation threshold and internal resistance less than the preset internal resistance threshold are identified as normal batteries.

[0081] In this embodiment, battery screening efficiency can be improved by combining the battery's internal resistance and voltage deviation.

[0082] On the other hand, this embodiment provides a battery screening device. Figure 4 This is a schematic diagram of a battery screening device according to an embodiment of this application, such as... Figure 4As shown, the battery screening device 400 includes: a data acquisition module 401, a deviation calculation module 402, and a screening module 403. The device will be described below.

[0083] The data acquisition module 401 is used to acquire multiple test voltages of multiple batteries under test during the formation process; The deviation calculation module 402 is used to determine the voltage deviation between each test voltage of the battery under test and the multiple test voltages of other batteries under test, based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, for any battery under test. The screening module 403 is used to screen target batteries from multiple batteries under test based on the voltage deviation corresponding to each test voltage.

[0084] In one embodiment, the multiple test voltages include test voltages corresponding to multiple acquisition times; the deviation calculation module 402 is further configured to, for the test voltage of the battery under test at any acquisition time, obtain other test voltages at the acquisition time from the multiple test voltages of other batteries under test; and, based on the average voltage corresponding to the test voltage at the acquisition time and other test voltages, determine the voltage deviation between the test voltage of the battery under test at the acquisition time and other test voltages, thereby obtaining the voltage deviation between each test voltage of the battery under test and the multiple test voltages of other batteries under test.

[0085] In one embodiment, the screening module 403 is further configured to determine a target test voltage among multiple test voltages corresponding to each battery under test based on a preset voltage detection range, obtain the target voltage deviation corresponding to the target test voltage, and screen target batteries based on the target voltage deviations corresponding to the multiple batteries under test respectively.

[0086] In one embodiment, the target battery includes an abnormal battery; the screening module 403 is further configured to identify, among a plurality of batteries to be tested, batteries whose target voltage deviation exceeds a preset voltage deviation threshold as abnormal batteries.

[0087] In one embodiment, the target battery further includes a target normal battery; the battery screening device 400 is further configured to, among the multiple batteries to be tested, identify the batteries whose target voltage deviation does not exceed a preset voltage deviation threshold as normal batteries, obtain the battery capacity corresponding to each test voltage of the multiple normal batteries during the formation process; determine the formation curves corresponding to the multiple normal batteries based on the battery capacity corresponding to each test voltage of the multiple normal batteries; determine the voltage deviation curves corresponding to the multiple normal batteries based on the voltage deviation curves corresponding to each test voltage of the multiple normal batteries; and identify the target normal battery among the multiple normal batteries based on the voltage deviation curves and formation curves corresponding to the multiple normal batteries.

[0088] In one embodiment, the battery screening device 400 is further configured to acquire multiple sampling voltages of a sample battery during the formation process; for any sample battery, based on the multiple sampling voltages of the sample battery and the multiple sampling voltages of other sample batteries, determine the sampling voltage deviation between each sampling voltage of the sample battery and the multiple sampling voltages of other sample batteries; based on the sampling voltage deviation and the sampling voltage, establish voltage deviation curves between the voltage deviation and the voltage corresponding to the multiple sample batteries respectively; and determine the voltage detection range based on the voltage deviation curves corresponding to the multiple sample batteries respectively.

[0089] In one embodiment, the sample battery includes a normal sample battery and an abnormal sample battery; the screening module 403 is further configured to compare the voltage deviation curve of the abnormal sample battery with the voltage deviation curve of the normal sample battery to obtain the curve deviation; and determine the sampling voltage range corresponding to the curve deviation that is greater than a preset curve deviation threshold as the voltage detection range.

[0090] In one embodiment, the battery screening device 400 is further configured to determine the battery internal resistance corresponding to each of the multiple batteries under test based on the multiple test voltages corresponding to the multiple batteries under test; and to screen target batteries among the multiple batteries under test based on the voltage deviations of the multiple batteries under test corresponding to each test voltage, including: screening target batteries among the batteries under test based on the voltage deviations of the multiple batteries under test corresponding to each test voltage and the battery internal resistance of the batteries under test.

[0091] Each module in the battery expansion force prediction device of the aforementioned energy storage equipment can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0092] Thirdly, this embodiment provides an electronic device, including a memory and a processor. The memory stores computer instructions, and when the computer instructions are executed by the processor, they implement the method of any of the above embodiments.

[0093] In one embodiment, this embodiment also provides an electronic device, which may be a server, and its internal structure diagram may be as follows. Figure 5As shown, this electronic device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer instructions stored in the non-volatile storage media. The database stores data involved in business data processing methods. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer instructions are executed by the processor, a communication configuration method for an energy storage device is implemented.

[0094] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0095] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions thereon, which are loaded by a processor to execute the arrangements in any of the methods described above. In embodiments of this application, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0096] Fifthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the steps of any of the methods described above.

[0097] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0098] The battery screening method, electronic device, and storage medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A battery screening method, characterized in that, Includes the following steps: Acquire multiple test voltages of multiple batteries under test during the formation process; For any battery under test, based on multiple test voltages of the battery under test and multiple test voltages of other batteries under test, determine the voltage deviation between each test voltage of the battery under test and multiple test voltages of the other batteries under test; Based on the voltage deviation of each of the multiple batteries under test at each test voltage, a target battery is selected from the multiple batteries under test.

2. The method according to claim 1, characterized in that, The multiple test voltages include test voltages corresponding to multiple acquisition times; The step of determining the voltage deviation between each test voltage of the battery under test and the multiple test voltages of the other batteries under test, based on multiple test voltages of the battery under test and multiple test voltages of the other batteries under test, includes: For the test voltage of the battery under test at any acquisition time, other test voltages at the acquisition time are obtained from multiple test voltages of the other batteries under test; Based on the test voltage at the acquisition time and the average voltage corresponding to the other test voltages, the voltage deviation between the test voltage of the battery under test at the acquisition time and the other test voltages is determined, thereby obtaining the voltage deviation between each test voltage of the battery under test and multiple test voltages of the other batteries under test.

3. The method according to claim 1, characterized in that, The step of selecting target batteries from the plurality of batteries under test based on the voltage deviation corresponding to each test voltage includes: Based on a preset voltage detection range, a target test voltage is determined from multiple test voltages corresponding to each battery under test, and the target voltage deviation corresponding to the target test voltage is obtained. Target batteries are selected based on the target voltage deviations corresponding to the multiple batteries under test.

4. The method according to claim 3, characterized in that, The target battery includes abnormal batteries; the process of screening target batteries based on the target voltage deviations corresponding to multiple batteries under test includes: Among the multiple batteries under test, those whose target voltage deviation exceeds a preset voltage deviation threshold are identified as abnormal batteries.

5. The method according to claim 3, characterized in that, The target battery also includes a target normal battery; The method further includes: Among the multiple batteries under test, the batteries whose target voltage deviation does not exceed the preset voltage deviation threshold are identified as normal batteries, and the battery capacity corresponding to each test voltage in the formation process of the multiple normal batteries is obtained. Based on the battery capacity of multiple normal batteries at each test voltage, the formation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation of multiple normal batteries at each test voltage, the voltage deviation curves corresponding to the multiple normal batteries are determined. Based on the voltage deviation curves and formation curves corresponding to the multiple normal batteries, a target normal battery is determined among the multiple normal batteries.

6. The method according to claim 3, characterized in that, The method further includes: Acquire multiple sampling voltages of the sample battery during the formation process; For any given sample battery, based on multiple sample voltages of the sample battery and multiple sample voltages of other sample batteries, determine the sampling voltage deviation between each sample voltage of the sample battery and multiple sample voltages of the other sample batteries; Based on the sampling voltage deviation and the sampling voltage, voltage deviation curves between the voltage deviation and the voltage are established for each of the sample batteries. The voltage detection range is determined based on the voltage deviation curves corresponding to the multiple sample batteries.

7. The method according to claim 6, characterized in that, The sample cells include normal sample cells and abnormal sample cells; The step of determining the preset voltage detection range based on the voltage deviation curves corresponding to the multiple sample batteries includes: The voltage deviation curve of the abnormal sample battery is compared with the voltage deviation curve of the normal sample battery to obtain the curve deviation; The sampling voltage range corresponding to the curve deviation that is greater than the preset curve deviation threshold is determined as the voltage detection range.

8. The method according to claim 1, characterized in that, The method further includes: Based on the multiple test voltages corresponding to the multiple batteries under test, the internal resistance of the multiple batteries under test is determined. The step of selecting target batteries from the plurality of batteries under test based on the voltage deviation corresponding to each test voltage includes: Based on the voltage deviation of the multiple batteries under test at each test voltage and the internal resistance of the batteries under test, a target battery is selected from the batteries under test.

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