Method, device, electronic device, and storage medium for identifying abnormal battery cores

Abstract

A method, device, electronic device and storage medium for identifying an abnormal battery core, the method comprising: determining whether a polarization abnormality occurs in a battery core (11) based on target characteristic data of the battery core (11), where the target characteristic data includes characteristic data related to the polarization of the battery core that occurs during a chemical formation process. On the one hand, since there is a clear difference in the target characteristic data during the chemical formation process between a battery core that has a polarization abnormality and a battery core that has no polarization abnormality, it is possible to accurately determine whether a polarization abnormality occurs in the battery core based on this. On the other hand, since the chemical formation process of the battery core occurs before the battery core is mounted in a battery pack, it is possible to identify a battery core with a polarization abnormality before the battery core is mounted, thereby reducing the risk of performance degradation of a battery system and disposal of a battery pack due to mounting of an abnormal battery core.

Description

[Technical Field]

[0001] The present application relates to the field of batteries, and in particular to a method, device, electronic device and storage medium for identifying an abnormal battery core. [Background technology]

[0002] Lithium-ion battery formation is a critical step in battery production, and the results of formation directly affect the battery's cycle life, rate performance, and high- and low-temperature performance. If polarization abnormalities occur in the battery core during battery production, these problems may not be apparent during the testing stage on the production line, but they can lead to lithium deposition and low capacity after use (post-cycling). The installation of these abnormal battery cores affects the performance of the battery system, and as battery systems become increasingly integrated, removing the abnormal battery cores becomes extremely difficult, leading to the disposal of the entire battery pack containing the abnormal battery cores. Therefore, it is necessary to identify battery cores with polarization abnormalities before installing them. Summary of the Invention

[0003] An object of the embodiments of the present application is to provide a method, device, electronic equipment, and storage medium for identifying an abnormal battery core, which are used to identify a battery core in which a polarization abnormality has occurred, before the battery core is mounted.

[0004] According to a first aspect, an embodiment of the present application provides a method for identifying an abnormal battery core, the method including determining whether a polarization abnormality has occurred in the battery core based on target characteristic data of the battery core, wherein the target characteristic data includes characteristic data related to polarization of the battery core that occurred during a chemical formation process.

[0005] In the technical solution of the embodiment of the present application, target characteristic data related to the polarization of the battery core generated during the battery core formation process is analyzed to determine whether the battery core has a polarization abnormality. In this way, on the one hand, there is a clear difference in the target characteristic data between a battery core with a polarization abnormality and a battery core without a polarization abnormality during the battery core formation process, so based on this, it is possible to accurately determine whether the battery core has a polarization abnormality and to reliably identify an abnormal battery core with a polarization abnormality. On the other hand, because the battery core formation process must occur before the battery core is installed in a battery pack in an industrial production flow, the technical solution of the embodiment of the present application can achieve the goal of identifying a battery core with a polarization abnormality before the battery core is installed, thereby reducing the risk of battery system performance degradation due to the installation of an abnormal battery core and the risk of battery pack disposal due to the installation of an abnormal battery core.

[0006] In some embodiments, the target characteristic data includes characteristic data collected during the formation process that reflects polarization differences in the battery core.

[0007] In the above technical solution, the characteristic data collected during the chemical formation process and reflecting the polarization differences of the battery cores is used as target characteristic data, and the target characteristic data can be used to distinguish between abnormal battery cores with polarization abnormalities and normal battery cores without polarization abnormalities, thereby achieving the effect of reliable identification of abnormal battery cores with polarization abnormalities.

[0008] In some embodiments, the target characteristic data includes a maximum target parameter value collected during the first stage of the formation process and / or a depolarization deviation of the target parameter during the first stage of the formation process.

[0009] In practical applications, the inventors have conducted a large number of experiments and found that when comparing battery cores with polarization abnormalities with battery cores without polarization abnormalities, there are relatively large differences in both the maximum parameter values ​​and depolarization deviations in the first stage of the formation process. Based on this, the above technical solution uses the maximum target parameter values ​​and / or depolarization deviations collected in the first stage of the formation process as target characteristic data, thereby achieving the effect of reliably identifying abnormal battery cores with polarization abnormalities.

[0010] In some embodiments, the target characteristic data includes target parameter values ​​collected during a second stage of the conversion process.

[0011] In practical application, the inventors have conducted a large number of experiments and found that when comparing battery cores with polarization abnormalities with battery cores without polarization abnormalities, there are relatively large differences in the parameter values ​​in the second stage of the chemical formation process. Based on this, in the above technical solution, the target parameter values ​​collected in the second stage of the chemical formation process are used as target characteristic data, thereby achieving the effect of reliable identification of abnormal battery cores with polarization abnormalities.

[0012] In some embodiments, the target parameter values ​​collected during the second stage of the chemical conversion process include a first target parameter value when the parameter change reaches a first process change threshold value for the first time during parameter collection during the second stage, and a second target parameter value when the parameter change reaches a second process change threshold value for the first time, wherein the first process change value and the second process change value are different.

[0013] In actual application, because the characteristics of a battery core with polarization abnormality and a battery core without polarization abnormality are different, the corresponding parameter values ​​when a certain process change value is reached in the second stage will also be different. Based on this, in the above technical solution, in the process of collecting parameters in the second stage, the first target parameter value when the parameter change first reaches the first process change value threshold and the second target parameter value when the parameter change first reaches the second process change value are used as the basis for determining whether a battery core has polarization abnormality, and by combining the two target parameter values, accurate identification of abnormal battery cores with polarization abnormality can be achieved, thereby improving the reliability of identification.

[0014] In some embodiments, determining whether a polarization abnormality has occurred in the battery core based on the target characteristic data includes determining whether a polarization abnormality has occurred in the battery core by determining whether a situation exists in which the target parameter value in the second stage is greater than a predetermined target parameter value threshold, wherein if a situation exists in which the target parameter value in the second stage is greater than the predetermined target parameter value threshold, it indicates that a polarization abnormality has occurred in the battery core, and vice versa.

[0015] In practical applications, the inventors have conducted a large number of experiments and found that battery cores with polarization abnormalities are affected by the polarization abnormality, and the target parameter values ​​of the battery cores in the second stage are often higher than the normal values. Based on this, the above technical solution can quickly determine whether the battery cores have polarization abnormalities by determining whether the target parameter values ​​in the second stage are greater than the preset target parameter value threshold, thereby realizing the rapid identification of battery cores with polarization abnormalities.

[0016] In some embodiments, determining whether a polarization abnormality has occurred in the battery core based on the target feature data includes inputting the target feature data into a pre-set identification model to obtain an identification result of whether a polarization abnormality has occurred in the battery core.

[0017] In the above technical solution, the target feature data is processed by a preset identification model, thereby utilizing the strong identification ability of the model to achieve the effect of rapid identification of battery cores with polarization abnormalities.

[0018] In some embodiments, the discrimination model is a two-dimensional Gaussian model, and determining whether a polarization abnormality has occurred in the battery core based on the target feature data includes inputting the target feature data into the two-dimensional Gaussian model, obtaining a probability density of the battery core calculated by the two-dimensional Gaussian model, and determining that a polarization abnormality has occurred in the battery core when the probability density of the battery core is smaller than a predetermined probability density threshold.

[0019] The two-dimensional Gaussian model is a model that can well reflect the distribution of various types of data. In the above technical solution, the probability density of the battery core can be obtained by using the two-dimensional Gaussian model for identification, and thus, based on the probability density of the battery core and the preset probability density threshold, it is possible to quickly distinguish between battery cores with polarization abnormality and battery cores without polarization abnormality.

[0020] In some embodiments, determining whether a polarization abnormality has occurred in the battery core based on the target characteristic data of the battery core includes determining whether a polarization abnormality has occurred in the battery core based on the target characteristic data of the battery core during a chemical formation process of the battery core.

[0021] In the above technical solution, whether polarization abnormality occurs in the battery core is identified based on the target characteristic data of the battery core during the battery core formation process, thereby realizing the identification and sorting of battery cores with polarization abnormality at the battery core formation stage, realizing the early identification of abnormal battery cores with polarization abnormality, avoiding the abnormal battery cores from continuing to undergo subsequent production flow, and reducing resource waste.

[0022] According to a second aspect, an embodiment of the present application further provides an abnormal battery core identification device, the device including an identification module for determining whether a polarization abnormality has occurred in the battery core based on target characteristic data of the battery core, wherein the target characteristic data includes characteristic data related to polarization of the battery core generated during a chemical formation process.

[0023] In some embodiments, the target characteristic data includes characteristic data collected during the formation process that reflects polarization differences in the battery core.

[0024] In some embodiments, the target characteristic data includes a maximum target parameter value collected during the first stage of the formation process and / or a depolarization deviation of the target parameter during the first stage of the formation process.

[0025] In some embodiments, the target characteristic data includes target parameter values ​​collected during a second stage of the conversion process.

[0026] In some embodiments, the target feature data includes a first target parameter value when the parameter change reaches a first process change value threshold for the first time during the process of collecting parameters in the second stage, and a second target parameter value when the parameter change reaches a second process change value for the first time, where the first process change value and the second process change value are different.

[0027] In some embodiments, the identification module is specifically used to determine whether a polarization abnormality has occurred in the battery core by determining whether a situation exists in which the target parameter value in the second stage is greater than a predetermined target parameter value threshold, where if a situation exists in which the target parameter value in the second stage is greater than a predetermined target parameter value threshold, it indicates that a polarization abnormality has occurred in the battery core, and vice versa.

[0028] In some embodiments, the identification module is specifically used to input the target feature data into a pre-set identification model to obtain an identification result of whether a polarization abnormality has occurred in the battery core.

[0029] In some embodiments, the identification model is a two-dimensional Gaussian model, and the identification module is specifically used to input the target feature data into the two-dimensional Gaussian model, obtain a probability density of the battery core calculated by the two-dimensional Gaussian model, and determine that a polarization abnormality has occurred in the battery core when the probability density of the battery core is smaller than a predetermined probability density threshold.

[0030] In some embodiments, the identification module is specifically used to determine whether a polarization abnormality has occurred in the battery core based on the target characteristic data of the battery core during the chemical formation process of the battery core.

[0031] According to a third aspect, an embodiment of the present application further provides an electronic device, the electronic device including a processor and a memory, the processor being used to realize any one of the above-mentioned methods for identifying an abnormal battery core by executing one or more instructions stored in the memory.

[0032] According to a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein one or more instructions are stored in the computer-readable storage medium, and the one or more instructions can be executed by a processor to realize any one of the above-mentioned methods for identifying an abnormal battery core. [Brief explanation of the drawings]

[0033] In order to more clearly explain the technical solutions of the embodiments of the present application, the following briefly introduces the drawings that need to be used in the embodiments of the present application. It should be understood that the following drawings only illustrate some embodiments of the present application, and should not be considered as limiting the scope. Those skilled in the art can also derive other related drawings based on these drawings without exerting any creative efforts. [Figure 1] 1 is a structural schematic diagram of a battery cell according to an embodiment of the present application; [Figure 2] 3 is a flowchart of a method for identifying an abnormal battery core according to an embodiment of the present application. [Figure 3] 1 is a flowchart of a model building method according to an embodiment of the present application. [Figure 4] 3 is a specific flowchart for realizing identification of an abnormal battery core according to an embodiment of the present application; [Figure 5] FIG. 1 is a schematic diagram of the effect of a two-dimensional Gaussian model according to an embodiment of the present application. [Figure 6] 1 is a structural schematic diagram of an abnormal battery core identification device according to an embodiment of the present application; [Figure 7] 1 is a structural schematic diagram of a model construction device according to an embodiment of the present application; [Figure 8] 1 is a structural schematic diagram of an electronic device according to an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0034] The following detailed description will be given of the embodiments of the technical solution of the present application in conjunction with the drawings. The following embodiments are only used to more clearly explain the technical solution of the present application, and are merely illustrative, and do not limit the scope of protection of the present application.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by a person skilled in the art to which this application pertains, and the terms used herein are merely for describing specific embodiments and are not intended to limit this application. The terms "comprises" and "having" and any variations thereof in the specification and claims of this application and the description of the drawings above are intended to cover a non-exclusive "comprise."

[0036] In the description of the embodiments of the present application, the technical terms "first," "second," etc. are only used to distinguish between different objects, and cannot be understood as indicating or implying relative importance, or the number, specific order, or hierarchical relationship of the technical features shown. In the description of the embodiments of the present application, "plurality" means two or more (including two), unless otherwise clearly and specifically limited.

[0037] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor does it necessarily refer to an independent or alternative embodiment that is mutually exclusive from other embodiments. Those skilled in the art can explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0038] In the description of the embodiments of this application, the term "and / or" is merely a relation describing related objects and indicates that three relations may exist. For example, A and / or B can represent three cases: A alone, a combination of A and B, and B alone. In addition, the character " / " in this specification generally indicates that the related objects before and after are in an "or" relationship.

[0039] In the description of the embodiments of the present application, unless otherwise clearly defined or limited, the technical terms "attached," "connected," "connected," "fixed," etc. should be understood in a broad sense, and may refer to, for example, a fixed connection, a detachable connection, or an integral connection, a mechanical connection, an electrical connection, a direct connection, an indirect connection via an intermediate medium, an internal communication between two elements, or an interactive relationship between two elements. Those skilled in the art will be able to understand the specific meanings of the above terms in the embodiments of the present application according to the specific circumstances.

[0040] Currently, in view of market development trends, the applications of power batteries are becoming more and more widespread, and they are widely used not only in energy storage power systems such as hydroelectric power, thermal power, wind power and solar power plants, but also in the field of electric transportation such as electric bicycles, electric motorcycles and electric cars.

[0041] The most commonly used power battery in these fields is a lithium-ion battery (i.e., a battery composed of a lithium-ion battery core). The inventors discovered that if a polarization abnormality occurs in a battery core during the production process of a lithium-ion battery, the problem may not be apparent during the testing stage of the battery core production line. However, after the battery core is put into use, problems of lithium deposition and low capacity may occur. Lithium deposition in the battery core may cause serious safety issues such as thermal runaway, and the low capacity of the battery core may seriously affect the use effect and experience of the battery core. Therefore, the installation of a defective battery core with polarization abnormality affects the performance of the battery system. Furthermore, as the integration level of battery systems increases, it becomes increasingly difficult to remove the battery core. Therefore, if a defective battery core with polarization abnormality is installed in a battery pack, even if the defective battery core is later discovered using technical means, it is difficult to remove the battery core. Therefore, the installation of the defective battery core leads to the disposal of the entire battery pack containing the abnormal battery core. Therefore, it is necessary to identify a battery core with polarization abnormality before installing the battery core.

[0042] The inventors further realized that the chemical formation of lithium-ion batteries is a critical step in battery production, and that subsequent steps (including the battery core mounting step) are initiated only after the battery core has completed chemical formation. Process data during the battery formation process (including, but not limited to, data on the battery formation steps, time, current, voltage, temperature, and air pressure) are stored in units of seconds and milliseconds. During the chemical formation process, differences may occur in the values ​​of some parameters between battery cores with and without polarization abnormalities.

[0043] Based on the above idea, in order to meet the demand for identifying battery cores with polarization abnormalities before the battery core is installed, the inventors have conducted extensive research and designed a method for identifying abnormal battery cores, which involves obtaining target characteristic data related to the polarization of battery cores generated during the battery core formation process, and determining whether the battery core has polarization abnormalities based on the target characteristic data. In this way, on the one hand, there is a clear difference in the target characteristic data between battery cores with polarization abnormalities and battery cores without polarization abnormalities during the battery core formation process, so that it is possible to accurately determine whether the battery core has polarization abnormalities and to reliably identify abnormal battery cores with polarization abnormalities. On the other hand, because the battery core formation process always occurs before the battery core is installed into a battery pack in industrial production, the goal of identifying battery cores with polarization abnormalities before the battery core is installed can be effectively achieved, reducing the risk of battery system performance degradation due to the installation of an abnormal battery core and the risk of battery packs being discarded due to the installation of an abnormal battery core.

[0044] In order to facilitate understanding of the solutions of the embodiments of the present application, some basic information regarding the embodiments of the present application will be first introduced below.

[0045] A battery pack is made up of multiple battery cells. A battery cell is the smallest unit that makes up a battery pack. A single battery pack may contain multiple battery cells. The multiple battery cells may be connected in series, parallel, or series-parallel. A series-parallel connection means that some of the multiple battery cells are connected in series and others in parallel. Multiple battery cells may be directly connected in series, parallel, or series-parallel, and the entire system consisting of the multiple battery cells may be housed in a case or other packaging so that it can be charged and discharged externally.

[0046] In practical applications, one battery pack may be used as one battery. However, multiple battery packs may be connected in series, parallel, or series-parallel to form a single whole and housed in a case or other packaging to be used as one battery. That is, one battery may have one or multiple battery packs.

[0047] Referring to Figure 1, a battery cell 100 may consist of a battery core 11, end caps 12, a case 13, and other functional components, where: The end cap 12 refers to a member that covers the opening of the case 13 and isolates the internal environment of the battery cell 100 from the external environment. The shape of the end cap 12 may be adapted to the shape of the case 13 so as to fit the case 13, but is not limited to this. Functional members such as electrode terminals 12a may be installed on the end cap 12. The electrode terminals 12a may be electrically connected to the battery core 11 to output or input electrical energy to the battery cell 100.

[0048] The case 13 is an assembly that fits with the end cap 12 to form an internal environment for the battery cell 100, where the formed internal environment may be used to accommodate the battery core 11, electrolyte, and other components.

[0049] The battery core 11 is a component where electrochemical reactions occur in the battery cell 100. One or more battery cores 11 may be included in the case 13. The battery core 11 is primarily formed by winding or stacking positive and negative electrode plates, and typically has a separator between the positive and negative electrode plates. The portions of the battery core 11 containing the active material of the positive and negative electrode plates form the main body of the battery core assembly, and the portions of the positive and negative electrode plates without the active material form the tabs 11a, respectively. The positive and negative electrode tabs may both be located at one end of the main body, or may be located at both ends of the main body. During the battery charge and discharge process, the positive and negative electrode active materials react with the electrolyte, and the tabs 11a are connected to the electrode terminals to form a current circuit. When a current flows through the battery core, if the static state is broken, the actual electrode potential within the battery core deviates from the equilibrium electrode potential. This phenomenon is called polarization.

[0050] Here, each battery cell 100 may be a secondary battery or a primary battery. The battery cell 100 may have a cylindrical shape, a flat shape, a rectangular parallelepiped shape, or any other shape.

[0051] The formation of a lithium-ion battery is a process that, after the battery is manufactured, activates the positive and negative electrode materials inside the battery core through charging and discharging, thereby improving the battery's self-discharge, charge and discharge performance, and storage performance.

[0052] In the embodiment of the present application, during formation, formation may be performed on each battery core that is not assembled into a battery cell 100, or on the battery core in an assembled battery cell 100.

[0053] Based on the above description, according to some embodiments of the present application, refer to FIG. 2, which shows a basic flowchart of a method for identifying an abnormal battery core according to an embodiment of the present application, including:

[0054] S201: Based on the target characteristic data of the battery core, determine whether a polarization abnormality occurs in the battery core.

[0055] It should be understood that in the present embodiment, the target characteristic data is characteristic data related to polarization of the battery core generated during the formation process. Exemplarily, the target characteristic data may be values ​​of parameters such as film formation peaks (dQ / dV, where dQ is the amount of current flowing in the battery core per unit time and dV is the voltage change in the battery core per unit time) corresponding to specific positions generated during the formation process, dynamic internal resistivity (V / I, where V is the voltage and I is the current), dV / dQ, etc.

[0056] In an embodiment of the present application, chemical process data (including, but not limited to, data such as battery chemical process steps, time, current, voltage, temperature, and air pressure) generated during the chemical process of the battery core during or after the chemical process is completed is introduced into an electronic device capable of executing the method for identifying an abnormal battery core according to an embodiment of the present application, so that the electronic device can obtain target characteristic data of the battery core from the chemical process data.

[0057] In the embodiments of the present application, when target characteristic data of a battery core is obtained during the chemical formation process, it can be determined whether a polarization abnormality has occurred in this battery core based on the target characteristic data of the battery core during the chemical formation stage of the battery core, thereby realizing identification and sorting of battery cores with polarization abnormalities during the chemical formation stage of the battery core, allowing abnormal battery cores with polarization abnormalities to be identified early, preventing the abnormal battery cores from entering the subsequent production flow, and reducing resource waste.

[0058] It should be understood that the parameter values ​​are constantly changing during the formation process. During the formation process, defective battery cores with polarization abnormalities and normal battery cores without polarization abnormalities may have similar parameter values ​​within some positions or sections, making it ineffective to distinguish between them based on the parameter values ​​within these positions or sections. Conversely, defective battery cores with polarization abnormalities and normal battery cores without polarization abnormalities may have distinct parameter values ​​within other positions or sections, thereby effectively distinguishing between defective and normal battery cores. The parameter values ​​within these specific positions or sections can be used as target characteristic data in the embodiments of the present application to identify whether a battery core has polarization abnormalities.

[0059] For example, the inventors have conducted extensive experiments and found that, when comparing a battery core with a polarization abnormality with a battery core without a polarization abnormality, there are relatively large differences in both the maximum parameter value and the depolarization deviation in the first stage of the formation process. Therefore, in one alternative embodiment, the target characteristic data may include the maximum target parameter value collected in the first stage of the formation process and / or the depolarization deviation of the target parameter in the first stage of the formation process. In this way, by using the maximum target parameter value and the depolarization deviation collected in the first stage of the formation process as the target characteristic data, it is possible to achieve the effect of reliably identifying an abnormal battery core with a polarization abnormality.

[0060] It should be understood that in the embodiments of the present application, the target parameters may embody formation process changes and may be parameters related to polarization of the battery core, such as film formation peak, dynamic internal resistivity, dV / dQ, etc. The target parameter value is the value of the target parameter. The maximum target parameter value is the maximum value of all collected target parameter values.

[0061] It should further be understood that the battery core may require multiple charging steps during the formation process, and the first step described in the examples of this application is the first charging step during the formation process.

[0062] It should be noted that, at the end of the first stage of the anodization process, the value of the target parameter fluctuates significantly, resulting in a "crash" phenomenon (i.e., a significant drop in the target parameter value over a short period of time). In the present embodiment, the depolarization deviation of the target parameter refers to the fluctuation in the target parameter during this "crash" phenomenon that occurs at the end of the first stage. For example, if the film formation peak fluctuates significantly from A to B at the end of the first stage of the anodization process, the depolarization deviation of the film formation peak is equal to the result of subtracting A from B.

[0063] For example, the inventors have further discovered through extensive experiments that, when comparing a battery core with a polarization abnormality with a battery core without a polarization abnormality, there are relatively large differences in the parameter values ​​during the second stage of the chemical formation process. Specifically, the parameter values ​​of a battery core with a polarization abnormality during the second stage of the chemical formation process are significantly larger than the parameter values ​​of a battery core without a polarization abnormality over a certain period of time. Therefore, in another optional embodiment, the target characteristic data may include target parameter values ​​collected during the second stage of the chemical formation process.

[0064] It should be understood that the second stage described in the examples of this application is any other charging stage in the formation process other than the first stage.

[0065] Considering that the target parameter values ​​of battery cores with and without polarization abnormalities are not always significantly different in the second stage, in the present embodiment, instead of using all target parameter values ​​collected in the second stage of the chemical conversion process as target feature data, only target parameter values ​​at specific positions may be used as target feature data, thereby reducing the number of target feature data and improving identification efficiency. For example, in the process of collecting parameters in the second stage, the first target parameter value when the parameter change first reaches a first process change value threshold and the second target parameter value when the parameter change first reaches a second process change value may be used as target feature data. Here, the first process change value and the second process change value are different.

[0066] It should be noted that the process variation value in the embodiments of the present application is the variation value of the target parameter within a set collection time interval (e.g., the collection time interval between two adjacent collection points). For example, assuming that the target parameter is a film formation peak, the process variation value may be the slope value of the film formation peak.

[0067] It should be understood that there are obvious differences in the target characteristic data between a battery core with polarization abnormality and a battery core without polarization abnormality during the chemical formation process of the battery core, so that based on the target characteristic data, it is possible to accurately determine whether a polarization abnormality has occurred in the battery core and to reliably identify an abnormal battery core with polarization abnormality.

[0068] In order to accurately determine whether a polarization abnormality has occurred in a battery core, in one alternative embodiment of the present application, it is possible to determine whether a polarization abnormality has occurred in a battery core by setting a threshold based on the difference in the range of target characteristic data between the battery cores with polarization abnormality and the battery cores without polarization abnormality, which have been compiled in advance (i.e., it is determined whether the target characteristic data is greater than or less than the preset threshold, and whether a polarization abnormality has occurred in a battery core is determined based on the relationship between the target characteristic data and the threshold between the compiled battery cores with polarization abnormality and the battery cores without polarization abnormality).

[0069] For example, assuming that the target characteristic data includes a maximum target parameter value collected in the first stage of the chemical conversion process, a target parameter threshold may be preset, and the maximum target parameter value may be compared with this target parameter threshold. If the comparison result matches the preset comparison result of a battery core with a polarization abnormality, it is determined that a polarization abnormality has occurred in this battery core. If not, it is determined that a polarization abnormality has not occurred.

[0070] Also, for example, assuming that the target characteristic data includes the depolarization deviation of the target parameter collected in the first stage of the chemical conversion process, a depolarization deviation threshold may be preset, and the depolarization deviation of the target parameter may be compared with this depolarization deviation threshold. If the comparison result matches the preset comparison result of the battery core in which a polarization abnormality has occurred, it is determined that a polarization abnormality has occurred in this battery core. If not, it is determined that a polarization abnormality has not occurred.

[0071] Also, for example, assuming that the target characteristic data includes the maximum target parameter value collected in the first stage of the formation process and the depolarization deviation of the target parameter in the first stage of the formation process, one target parameter threshold and one depolarization deviation threshold may be preset, and the maximum target parameter value may be compared with the target parameter threshold, and the depolarization deviation of the target parameter may be compared with the depolarization deviation threshold. If both comparison results match the comparison result of the battery core with a polarization abnormality, it is determined that a polarization abnormality has occurred in this battery core. Otherwise, it is determined that a polarization abnormality has not occurred.

[0072] For example, assuming that the target parameter is a film formation peak, if, in the first stage of the chemical process, the maximum value of the film formation peak is greater than a predetermined film formation peak threshold value and the polarization depolarization deviation of the film formation peak is smaller than the polarization depolarization deviation threshold value, it is determined that a polarization abnormality has occurred in the battery core.

[0073] It should be understood that the target parameter threshold and the depolarization deviation threshold may be values ​​that are set after previously conducting statistics of the target parameter values ​​and depolarization deviation in the first stage of the chemical formation process for a large number of first sample battery cores having polarization anomalies and second sample battery cores having no polarization anomalies.

[0074] Also, for example, assuming that the target characteristic data includes target parameter values ​​collected in the second stage of the chemical formation process, considering that the parameter values ​​of a battery core in which a polarization abnormality has occurred in the second stage of the chemical formation process are significantly greater than the parameter values ​​of a battery core in which a polarization abnormality has occurred within a certain period of time, it is possible to determine whether a polarization abnormality has occurred in the battery core by determining whether a situation exists in which the target parameter value in the second stage is greater than a predetermined target parameter value threshold. Here, if a situation exists in which the target parameter value in the second stage is greater than the predetermined target parameter value threshold, it indicates that a polarization abnormality has occurred in the battery core, and conversely, it indicates that a polarization abnormality has not occurred in the battery core.

[0075] Also, for example, assuming that the target characteristic data includes a first target parameter value when the parameter change first reaches a first process change value threshold and a second target parameter value when the parameter change first reaches a second process change value during the process of collecting parameters in the second stage, one first target parameter threshold may be set corresponding to the first process change value threshold and one second target parameter threshold may be set corresponding to the second process change value threshold, so that the first target parameter value is compared with the first target parameter threshold and the second target parameter value is compared with the second target parameter threshold. If both comparison results match the comparison result of the battery core with a polarization abnormality, it is determined that a polarization abnormality has occurred in this battery core. Otherwise, it is determined that no polarization abnormality has occurred.

[0076] It should be understood that the predetermined target parameter value thresholds in the above example, the first target parameter threshold and the second target parameter threshold, may be values ​​that are set after previously conducting statistics on the target parameter values ​​in the second stage of the chemical formation process for a large number of first sample battery cores having polarization anomalies and second sample battery cores having no polarization anomalies.

[0077] In order to realize an accurate determination of whether a polarization abnormality has occurred in the battery core, in another optional embodiment of the examples of the present application, an identification model may be constructed and target feature data may be input into a preset identification model to obtain an identification result of whether a polarization abnormality has occurred in the battery core.

[0078] Here, the discrimination model may be, but is not limited to, a pre-constructed two-dimensional Gaussian model. For example, the discrimination model may also be a classification model such as an SVM (Support Vector Machine) model or a CART (Classification and Regression Tree) model that further uses target feature data as input.

[0079] It should be noted that when using a classification model such as an SVM (Support Vector Machine) model or a CART (Classification and Regression Tree) model for identification, each sample battery core required for model construction must be marked in advance as either a first sample battery core with polarization anomalies or a second sample battery core without polarization anomalies. Similarly, for the aforementioned optional embodiment of identification based on a threshold, the sample battery cores used to set the threshold must also be marked in advance as either a first sample battery core with polarization anomalies or a second sample battery core without polarization anomalies. However, when using a two-dimensional Gaussian model for identification, this marking may be omitted, and the two-dimensional Gaussian model itself may be used to cluster the two types of sample battery cores. Therefore, using a two-dimensional Gaussian model for identification can reduce the workload of engineers and the difficulty of obtaining data corresponding to the sample battery cores.

[0080] It should be further explained that when the discrimination model is a two-dimensional Gaussian model, two kinds of target feature data should be provided to meet the data input requirements of the two-dimensional Gaussian model. For example, the target feature data may be the maximum target parameter value collected in the first stage of the chemical conversion process and the depolarization deviation of the target parameter in the first stage of the chemical conversion process. For example, the target feature data may be the first target parameter value when the parameter change first reaches a first process change threshold and the second target parameter value when the parameter change first reaches a second process change threshold during the parameter collection in the second stage.

[0081] The following will take the discrimination model as a two-dimensional Gaussian model as an example to further illustrate the scheme of the embodiment of the present application.

[0082] In order to ensure that accurate determination of whether polarization abnormality has occurred in the battery core can be achieved based on the two-dimensional Gaussian model, it is necessary to first select two types of target feature data, and then construct a reasonable two-dimensional Gaussian model based on these two types of target feature data.

[0083] Referring to FIG. 3, FIG. 3 illustrates a model building method according to an embodiment of the present application, which includes:

[0084] S301: Obtain two kinds of target characteristic data for each sample battery core during the formation process.

[0085] It should be noted that each sample battery core includes a first sample battery core in which a polarization anomaly exists and a second sample battery core in which a polarization anomaly does not exist.

[0086] The two kinds of target characteristic data of the first sample battery core and the second sample battery core can be collected from the production line or other routes.

[0087] S302: A two-dimensional Gaussian model is constructed based on the two kinds of target feature data of each sample battery core.

[0088] In the embodiment of the present application, the mean value and variance between two kinds of target feature data may be calculated, and the correlation coefficient between the two kinds of target feature data may be calculated. Based on the mean value and variance of the two kinds of target feature data and the correlation coefficient between the features, the mean value vector and covariance matrix required for the two-dimensional Gaussian model can be constructed. Then, a two-dimensional Gaussian model can be obtained based on the mean value vector and covariance matrix.

[0089] Considering that there may be certain abnormal data in the two types of target feature data of the acquired sample battery cores, which may affect the reliability of the constructed two-dimensional Gaussian model, one possible embodiment of the present application may first construct a preliminary two-dimensional Gaussian model based on the two types of target feature data of all sample battery cores, and then construct a final two-dimensional Gaussian model based on the two types of target feature data of each sample battery core belonging to a target cluster in the preliminary two-dimensional Gaussian model, where the target cluster is a cluster of the second sample battery core in the preliminary two-dimensional Gaussian model that indicates the absence of polarization abnormality.

[0090] It should be understood that the construction method of the two-time two-dimensional Gaussian model is as described in the above paragraph and will not be further described here.

[0091] It should be understood that in the embodiment of the present application, after constructing a two-dimensional Gaussian model based on two types of target feature data of the first sample battery core and the second sample battery core, a probability density distribution belonging to the second sample battery core is obtained in the two-dimensional Gaussian model, and a probability density threshold can be set based on this distribution.

[0092] When using the two-dimensional Gaussian model to determine whether a polarization abnormality has occurred in the battery core, two kinds of target feature data of the battery core are input into the two-dimensional Gaussian model, and then a probability density corresponding to the battery core is obtained, and this probability density is compared with a set probability density threshold. If this probability density is smaller than the probability density threshold, it can be determined that a polarization abnormality has occurred in the battery core. Conversely, it can be determined that a polarization abnormality has not occurred in the battery core.

[0093] It should be noted that the two kinds of target feature data used in constructing the two-dimensional Gaussian model should be consistent with the target feature data of the battery core collected in the subsequent process of identifying abnormal battery cores, that is, if the two kinds of target feature data used in constructing the two-dimensional Gaussian model are the maximum target parameter value collected in the first stage of the formation process and the depolarization deviation of the target parameter in the first stage of the formation process, then the target feature data of the battery core collected when identifying abnormal battery cores should also be the maximum target parameter value collected in the first stage of the formation process and the depolarization deviation of the target parameter in the first stage of the formation process. If the two types of target feature data used when constructing the two-dimensional Gaussian model are the first target parameter value when the parameter change first reaches the first process change value threshold in the process of collecting parameters in the second stage, and the second target parameter value when the parameter change first reaches the second process change value, then when identifying abnormal battery cores, the target feature data collected for the battery cores should also be the first target parameter value when the parameter change first reaches the first process change value threshold in the process of collecting parameters in the second stage, and the second target parameter value when the parameter change first reaches the second process change value.

[0094] It should be further explained that when the two types of target characteristic data are the first target parameter value when the parameter change reaches the first process change value threshold for the first time in the process of collecting parameters in the second stage, and the second target parameter value when the parameter change reaches the second process change value for the first time, in order to ensure that the target characteristic data can distinguish between battery cores with polarization abnormalities and battery cores without polarization abnormalities, the selected first process change value and second process change value may be two process change values ​​with the greatest degree of distinction between the first sample battery core and the second sample battery core.

[0095] Therefore, in order to accurately find the two process change values ​​with the greatest degree of discrimination between the first sample battery core and the second sample battery core, in an embodiment of the present application, the target parameter value corresponding to the first time each sample battery core reaches each of the preset different process change values ​​during the chemical formation process may be obtained, and the first process change value and the second process change value may be identified based on the target parameter value corresponding to each of the different process change values.

[0096] For example, based on target parameter values ​​corresponding to two different process change values, the degree of distinction between the first sample battery core and the second sample battery core corresponding to the two different process change values ​​may be calculated, and the two different process change values ​​corresponding to the maximum degree of distinction may be determined as the first process change value and the second process change value.

[0097] It should be understood that in the embodiments of the present application, an LDA (Linear Discriminant Analysis) algorithm may be adopted to calculate the degree of distinction between the first sample battery core and the second sample battery core corresponding to each of two different process change values ​​among the different process change values ​​(i.e., the inter-class distance calculated by the LDA algorithm).

[0098] For example, assuming there are three different process change values ​​A, B, and C, calculations can be performed based on the LDA algorithm to calculate the distinction ab between A and B, the distinction ac between A and C, and the distinction bc between B and C, and then determine the maximum value from ab, ac, and bc. Assuming the maximum value is ac, the first process change value and the second process change value can be determined to be A and C, respectively.

[0099] In the embodiment of the present application, in order to ensure the distinguishing effect between the first process change value and the second process change value, before adopting the LDA algorithm for calculation, the target parameter value corresponding to each process change value may be standardized to convert the target parameter value corresponding to each process change value into a standard normal distribution following N(0,1), thereby making it easier for the LDA algorithm to process.

[0100] It should be understood that for other types of discriminative models, the discriminative model may be trained based on the target feature data to obtain the trained discriminative model, and then used. The training process is consistent with the normal training process of each type of discriminative model, and therefore will not be described in detail here.

[0101] In an embodiment of the present application, if it is determined that a battery core has a polarization abnormality, the battery core may be marked, and engineers may then process and analyze the battery core based on the mark. For example, after identifying all of the battery cores in one or several lots as abnormal, the number of battery cores with polarization abnormalities may be counted based on the mark. If the counted number of battery cores with polarization abnormalities is greater than a preset threshold, it is likely that a problem such as production line contamination has caused contamination of the battery cores, resulting in polarization abnormalities, and an action such as an alarm may be taken accordingly.

[0102] It should be noted that the above solutions of the embodiments of the present application may be implemented by electronic devices with data processing capabilities, such as PLCs (Programmable Logic Controllers), computers, smartphones, servers, and other devices.

[0103] It should be understood that in the embodiment of the present application, the marked battery cores may be notified to the industrial internet by connecting the electronic device to the industrial internet, and then notified to each engineer via the industrial internet.

[0104] In the technical solution of the embodiment of the present application, whether a battery core has a polarization abnormality is determined by obtaining and analyzing target characteristic data related to the polarization of the battery core that occurs during the battery core formation process. In this way, because there are clear differences in the target characteristic data between a battery core with a polarization abnormality and a battery core without a polarization abnormality during the battery core formation process, it is possible to accurately determine whether a battery core has a polarization abnormality and reliably identify an abnormal battery core with a polarization abnormality. On the other hand, because the battery core formation process always occurs before the battery core is installed in a battery pack in an industrial production flow, the technical solution of the embodiment of the present application can effectively achieve the goal of identifying a battery core with a polarization abnormality before the battery core is installed, thereby reducing the risk of battery system performance degradation due to the installation of an abnormal battery core and the risk of battery pack disposal due to the installation of an abnormal battery core.

[0105] In order to easily understand the solution according to the embodiments of the present application, the present application will be further illustrated below by taking as an example a specific implementation process in which the execution body is PLC, the target characteristic data is the maximum film formation peak value collected in the first stage of the chemical process and the polarization depolarization deviation of the film formation peak in the first stage of the chemical process, and is identified by a two-dimensional Gaussian model.

[0106] Referring to FIG. 4, the implementation process includes:

[0107] S401: The PLC receives formation process data of sample battery cores of a certain lot or several lots that have been introduced.

[0108] Here, data can be acquired according to the chemical formation batch or according to the data acquired after chemical formation is completed each day. According to the law of large numbers, the larger the number, the more the statistical parameters conform to the data distribution rule of normal battery cores. Here, the battery cores in the batch can include a first sample battery core with polarization abnormality and a second sample battery core without polarization abnormality.

[0109] It should be noted that chemical formation process data refers to process data stored during the chemical formation process of the battery core (including, but not limited to, data such as steps, time, current, voltage, temperature, and pressure of the battery formation).

[0110] S402: Preprocess the chemical conversion process data.

[0111] This step includes removing the formation process data corresponding to the battery cores where situations such as formation termination, formation equipment sampling abnormality, formation not being involved, first stage interruption, and formation re-measurement have occurred, and obtaining film formation peak data (including the film formation peak value) from the remaining formation process data to obtain the maximum film formation peak value collected in the first stage of the formation process for each battery core and the polarization depolarization deviation of the film formation peak in the first stage of the formation process.

[0112] Here, if the chemical formation process data of a certain sample battery core shows that the number of chemical formation steps is smaller than the set required number of chemical formation steps, it is determined that the sample battery core has a chemical formation termination situation; if data corruption occurs in the chemical formation process data of a certain sample battery core, it is determined that the sample battery core has a chemical formation equipment sampling abnormality situation; if the chemical formation process data of a certain sample battery core shows that there is no current value and that the voltage value does not increase, it is determined that the sample battery core is not involved in chemical formation; if the chemical formation process data of a certain sample battery core shows that charging is terminated and then resumed in the first stage, it is determined that the sample battery core has a first stage interruption situation; and if the chemical formation process data of a certain sample battery core shows that the number of chemical formation steps is greater than the set required number of chemical formation steps, it is determined that the sample battery core has a chemical formation re-measurement situation.

[0113] It should be noted that the film formation peak value may be calculated based on data such as current and voltage in the anodization process data. After obtaining film formation peak values ​​corresponding to each sampling point in the first stage of the anodization process, the maximum film formation peak value may be selected from among them. At the same time, the polarization elimination deviation of the film formation peak may be obtained by calculating the difference between the film formation peak values ​​corresponding to both ends when the polarization elimination phenomenon occurs at the end of the first stage of the anodization process.

[0114] S403: The average value and variance of the maximum film-forming peak value, and the average value and variance of the polarization depolarization deviation of the film-forming peak are calculated, and the correlation coefficient between the maximum film-forming peak value and the polarization depolarization deviation of the film-forming peak is calculated.

[0115] S404: Based on the mean value and variance of the maximum film formation peak value and the polarization depolarization deviation of the film formation peak, and the correlation coefficient between the maximum film formation peak value and the polarization depolarization deviation of the film formation peak, the mean value vector and covariance matrix required for the two-dimensional Gaussian model are constructed to obtain a preliminary two-dimensional Gaussian model.

[0116] S405: Based on the maximum film formation peak value and the film formation peak depolarization deviation of each sample battery core belonging to the target cluster in this preliminary two-dimensional Gaussian model, the average value and variance of the maximum film formation peak value and the average value and variance of the film formation peak depolarization deviation are calculated, respectively, and the correlation coefficient between the maximum film formation peak value and the film formation peak depolarization deviation is calculated.

[0117] It should be understood that the target cluster is the cluster of the second sample cell core in the preliminary two-dimensional Gaussian model that indicates the absence of polarization anomalies.

[0118] S406: Based on the mean value and variance of the latest maximum film formation peak value and the depolarization deviation of the film formation peak, and the correlation coefficient between the maximum film formation peak value and the depolarization deviation of the film formation peak, the mean value vector and covariance matrix required for the two-dimensional Gaussian model are constructed to obtain the final two-dimensional Gaussian model.

[0119] S407: Determine a target probability density range based on the probability density region where the target cluster is located in the final two-dimensional Gaussian model.

[0120] For example, as shown in Figure 5, the area defined by three circles in Figure 5 is the area where the target cluster is located, and an engineer may determine the probability density range corresponding to each sample battery core within the range defined by any one of the circles as the target probability density range. For example, the probability density range corresponding to the sample battery core within the area defined by the outermost circle may be determined as the target probability density range. It should be understood that the abscissa in Figure 5 is the maximum film formation peak value, and the ordinate is the depolarization deviation of the film formation peak.

[0121] S408: The PLC receives the introduced formation process data of the battery core to be detected.

[0122] S409: The maximum film formation peak value and the polarization depolarization deviation of the film formation peak in the first stage of the battery core to be detected are obtained from the formation process data.

[0123] S410: Use the final two-dimensional Gaussian model to calculate the maximum film formation peak value and the depolarization deviation of the film formation peak in the first stage of the battery core to be detected, and obtain the probability density of the battery core to be detected.

[0124] S411: Determine whether the probability density of the battery core to be detected is within the target probability density range. If it is, proceed to step S412; if not, proceed to step S413.

[0125] For example, the target probability density range may be a range starting from 0. In this case, only one probability density threshold may be set to determine whether the probability density of the battery core to be detected is smaller than this probability density threshold. If it is smaller, it indicates that the probability density of the battery core to be detected is within the target probability density range, and proceed to step S412; if not, proceed to step S413.

[0126] S412: It is determined that no polarization abnormality has occurred in the battery core to be detected.

[0127] S413: Mark the battery core to be detected and report the marking result to the industrial Internet.

[0128] It should be understood that in the embodiments of the present application, rather than making a judgment based on probability density, the maximum film formation peak value and the polarization depolarization deviation of the film formation peak in the first stage of the battery core to be detected may be converted into one coordinate point in the coordinate system shown in Figure 5 as two coordinate values ​​of a single coordinate point, and the battery core to be detected may be determined to be located within a set circle (for example, the outermost circle in Figure 5). If the coordinate point is within the circle, it is determined that no polarization abnormality has occurred in the battery core to be detected. If the coordinate point is not within the circle, it is determined that a polarization abnormality has occurred in the battery core to be detected. At this time, the battery core to be detected may be marked, and the marking result may be reported to the industrial Internet.

[0129] 4 essentially includes two steps, where steps S401 to S407 are a model construction step, and steps S408 to S413 are a battery core identification step. After the final two-dimensional Gaussian model is constructed by steps S401 to S407, the final two-dimensional Gaussian model can be repeatedly applied, i.e., steps S408 to S413 can be repeatedly performed for different battery cores to be detected.

[0130] The above method can achieve the goal of identifying battery cores with polarization abnormalities before the battery cores are installed. At the same time, experimental results have shown that this method is highly robust, has low error, and is effective in practical applications.

[0131] Based on the same inventive concept, the embodiments of the present application further provide an abnormal battery core identification device 600 and a model construction device 700. As shown in Figures 6 and 7, Figure 6 illustrates an abnormal battery core identification device using the method shown in Figure 2, and Figure 7 illustrates a model construction device using the method shown in Figure 3. It should be understood that the specific functions of the devices 600 and 700 can be referred to the above descriptions, and detailed descriptions will be omitted here to avoid repetition. The devices 600 and 700 include at least one software function module that can be stored in memory in the form of software or firmware or fixed in the operating system of the devices 600 and 700. Specifically, the following is provided:

[0132] Referring to FIG. 6, the device 600 includes: An identification module 601 is included for determining whether a polarization abnormality has occurred in the battery core based on target characteristic data of the battery core, where the target characteristic data includes characteristic data related to polarization of the battery core generated during the chemical formation process.

[0133] In one alternative embodiment of the present application, the target characteristic data includes characteristic data collected during the formation process that reflects polarization differences of the battery core.

[0134] In one exemplary embodiment of the above alternative example, the target characteristic data includes a maximum target parameter value collected during the first stage of the chemical formation process and a depolarization deviation of the target parameter during the first stage of the chemical formation process.

[0135] In another exemplary embodiment of the above alternative example, the target characteristic data includes target parameter values ​​collected during a second stage of the chemical conversion process.

[0136] In the above-mentioned another exemplary embodiment, the target feature data includes a first target parameter value when the parameter change reaches a first process change value threshold for the first time during the process of collecting parameters in the second stage, and a second target parameter value when the parameter change reaches a second process change value for the first time, where the first process change value and the second process change value are different.

[0137] In the embodiment of the present application, the identification module 601 is specifically used to determine whether a polarization abnormality has occurred in the battery core by determining whether a situation exists in which the target parameter value in the second stage is greater than a preset target parameter value threshold, where if a situation exists in which the target parameter value in the second stage is greater than a preset target parameter value threshold, it indicates that a polarization abnormality has occurred in the battery core, and vice versa.

[0138] In the embodiment of the present application, the identification module 601 is specifically used to input the target feature data into a pre-set identification model to obtain an identification result of whether polarization abnormality occurs in the battery core.

[0139] In the embodiment of the present application, the identification model is a two-dimensional Gaussian model, and the identification module 601 is specifically used to input the target feature data into the two-dimensional Gaussian model, obtain the probability density of the battery core calculated by the two-dimensional Gaussian model, and determine that a polarization abnormality has occurred in the battery core when the probability density of the battery core is smaller than a preset probability density threshold.

[0140] In the embodiment of the present application, the identification module 601 is specifically used to determine whether polarization abnormality occurs in the battery core based on the target characteristic data of the battery core during the formation process of the battery core.

[0141] In an embodiment of the present application, the device 600 further includes a marking module for marking the battery core when a polarization abnormality occurs in the battery core.

[0142] Referring to FIG. 7, the device 700 includes: a second acquisition module 701 for acquiring two types of target characteristic data of each sample battery core during the chemical formation process, the target characteristic data being characteristic data related to the polarization of the battery core generated during the chemical formation process, and each sample battery core including a first sample battery core having a polarization abnormality and a second sample battery core having no polarization abnormality; and a construction module 702 for constructing a two-dimensional Gaussian model based on two kinds of target feature data of each sample battery core.

[0143] In an embodiment of the present application, the construction module 702 is specifically used to construct a preliminary two-dimensional Gaussian model based on two types of target feature data of all sample battery cores, and to construct a final two-dimensional Gaussian model based on two types of target feature data of each sample battery core belonging to a target cluster in the preliminary two-dimensional Gaussian model, where the target cluster is a cluster of the second sample battery core in the preliminary two-dimensional Gaussian model that indicates the absence of polarization anomalies.

[0144] In one alternative embodiment of the present application, the two types of target characteristic data include the maximum target parameter value collected during the first stage of the formation process and the depolarization deviation of the target parameter during the first stage of the formation process.

[0145] It should be understood that, for the sake of simplicity, some of the content described in the method section above will not be further described in the apparatus section.

[0146] An embodiment of the present application further provides an electronic device, and referring to FIG. 8, it includes a processor 801 and a memory 802, where: The processor 801 is used to execute one or more instructions stored in the memory 802 to implement the above-mentioned method for identifying an abnormal battery core or the above-mentioned model building method.

[0147] Illustratively, but not limited to, the processor 801 and the memory 802 may be connected via an internal communication bus.

[0148] As can be understood, the structure shown in Figure 8 is only schematic, and the electronic device may further include more or fewer assemblies than those shown in Figure 8, or may have a different configuration than that shown in Figure 8. For example, the electronic device may further include devices such as a data input interface, a data output interface, etc.

[0149] In the embodiment of the present application, the electronic device may be a device having data processing capabilities, such as a PLC, a computer, a smartphone, a server, etc., and is not limited to the embodiment of the present application.

[0150] It should be noted that a PLC is a digital computing controller for automation control that has a microprocessor and can load, store, and execute control instructions in its internal memory in real time. The PLC consists of functional units such as a microprocessor, internal memory, input / output interfaces, and power supply. When the electronic device is a PLC, the processor 801 is the microprocessor of the PLC, and the memory 802 is the internal memory of the PLC.

[0151] An embodiment of the present application further provides a computer-readable storage medium, such as a flexible disk, an optical disk, a hard disk, a flash memory, a U disk, a Secure Digital Memory Card (SD) card, a Multimedia Card (MMC) card, etc., in which one or more instructions for implementing each of the above steps are stored, and the one or more instructions can be executed by one or more processors to implement the abnormal battery core identification method or the model construction method. No further description will be given here.

[0152] Finally, it should be noted that the above embodiments are merely intended to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solution described in the above embodiments may still be modified or some or all of the technical features may be replaced with equivalents. Such modifications or replacements do not deviate from the essence of the corresponding technical solution from the scope of the technical solution of the embodiments of the present application, and should be included in the scope of the claims and description of the present application. In particular, as long as there is no conflict of structure or step, the technical features mentioned in the embodiments may be combined in any manner. The present application is not limited to the specific embodiments disclosed in the description, but includes all technical solutions included within the scope of the claims.

Claims

1. Inputting target characteristic data of the battery core into a predetermined identification model to obtain an identification result of whether polarization abnormality occurs in the battery core, wherein: The target feature data includes:

10. A method for identifying an abnormal battery core, comprising: a maximum target parameter value collected during a first stage of a formation process; and / or a depolarization deviation of a target parameter during the first stage of the formation process.

2. Inputting target characteristic data of the battery core into a predetermined identification model to obtain an identification result of whether polarization abnormality occurs in the battery core, wherein: The target feature data includes: The method for identifying an abnormal battery core includes target parameter values ​​collected during a second stage of the formation process.

3. The target parameter values ​​collected in the second stage of the formation process are:

3. The method for identifying an abnormal battery core as described in claim 2, wherein the process of collecting parameters in the second stage includes a first target parameter value when the parameter change reaches a first process change value threshold for the first time, and a second target parameter value when the parameter change reaches a second process change value for the first time, wherein the first process change value and the second process change value are different.

4. Determining whether a polarization abnormality has occurred in the battery core based on the target characteristic data includes: determining whether a polarization abnormality has occurred in the battery core by determining whether a condition exists in which the target parameter value in the second stage is greater than a preset target parameter value threshold; The method for identifying an abnormal battery core according to claim 2, wherein if there is a situation in which the target parameter value in the second stage is greater than the preset target parameter value threshold, it indicates that a polarization abnormality has occurred in the battery core, and conversely, it indicates that no polarization abnormality has occurred in the battery core.

5. the discriminant model is a two-dimensional Gaussian model; Determining whether a polarization abnormality has occurred in the battery core based on the target characteristic data includes: inputting the target feature data into the two-dimensional Gaussian model, and obtaining a probability density of the battery core calculated by the two-dimensional Gaussian model; The method for identifying an abnormal battery core according to claim 1 or 2, further comprising determining that a polarization abnormality has occurred in the battery core when the probability density of the battery core is smaller than a preset probability density threshold.

6. The target characteristic data of the battery core is input into a predetermined identification model to obtain an identification result of whether a polarization abnormality occurs in the battery core, The method for identifying an abnormal battery core according to any one of claims 1 to 4, characterized in that, during the chemical formation process of the battery core, target feature data of the battery core is input into a predetermined identification model, and an identification result indicating whether a polarization abnormality has occurred in the battery core is obtained.

7. An abnormal battery core identification device, An identification module is provided for inputting target characteristic data of the battery core into a preset identification model to obtain an identification result of whether polarization abnormality occurs in the battery core; Here, the target characteristic data includes the maximum target parameter value collected in the first stage of the chemical formation process, and / or the depolarization deviation of the target parameter in the first stage of the chemical formation process.

8. An abnormal battery core identification device, An identification module is provided for inputting target characteristic data of the battery core into a preset identification model to obtain an identification result of whether polarization abnormality occurs in the battery core; Here, the target characteristic data includes target parameter values ​​collected in the second stage of the chemical formation process.

9. An electronic device comprising a processor and a memory, The processor is used to execute one or more instructions stored in the memory to implement the method for identifying an abnormal battery core according to any one of claims 1 to 4.

10. 5. A computer-readable storage medium, characterized in that one or more instructions are stored in the computer-readable storage medium, and the instructions can be executed by a processor to realize the method for identifying an abnormal battery core according to any one of claims 1 to 4.

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