Abnormal battery cell detection method, device, electronic device, storage medium and program product

Abstract

The embodiments of the present disclosure provide a method, device, electronic device, storage medium and program product for detecting abnormal cells, which relate to the field of battery technology. The method comprises: determining a cell to be detected; obtaining a plurality of measurement parameters corresponding to a plurality of sequential processes in the cell detection process for the cell to be detected; for each first process, determining the prediction parameters of the first process based on the measurement parameters of the previous process of the first process and the state transition information corresponding to the first process; and determining the abnormal detection results of the cell to be detected based on the differences between the measurement parameters and the prediction parameters corresponding to each first process. The embodiments of the present disclosure effectively detect abnormal cells without puncturing the diaphragm, reduce the missed detection rate of abnormal cell detection, thereby effectively eliminating abnormal cells caused by metal foreign matter and diaphragm defects, and improve the quality and safety of the produced batteries.

Description

Technical Field

[0001] The present disclosure relates to the field of battery technology, and more specifically, to a method, device, electronic device, storage medium, and program product for detecting abnormal battery cells. Background Art

[0002] With the development of the lithium battery industry, the requirements for the production effect and energy density of lithium batteries are becoming increasingly higher, and the production process is more complicated. Therefore, it is necessary to perform abnormal detection on battery cells in the battery manufacturing process.

[0003] Existing methods for detecting battery cell anomalies include high-voltage short-circuit testing (or insulation resistance testing) and self-discharge testing. The high-voltage short-circuit test typically involves applying 200-500V AC or DC voltage to cause metal foreign matter or burrs on the electrode surface to generate a sharp discharge, generating leakage current and even puncturing the diaphragm, thereby screening out defective cells. The self-discharge test typically involves exposing the cell to high or room temperature for 3-7 days, and then determining whether there are micro-short circuits within the cell based on the voltage drop.

[0004] Existing methods for detecting abnormal battery cells can only detect abnormalities when dust or burrs pierce the diaphragm. When the diaphragm is not pierced (for example, when there is a small amount of dust), it cannot effectively detect abnormal battery cells, resulting in a high missed detection rate. Summary of the Invention

[0005] The embodiments of the present disclosure provide a method, device, electronic device, storage medium, and program product for detecting abnormal cells, which can solve the problem in the prior art that abnormal cells cannot be effectively detected without puncturing the diaphragm, resulting in a high missed detection rate. The technical solutions provided by the present disclosure are as follows:

[0006] According to one aspect of an embodiment of the present disclosure, a method for detecting abnormal battery cells is provided, the method comprising:

[0007] Determine the battery cells to be tested;

[0008] Acquire a plurality of measurement parameters of the battery cell to be tested corresponding to a plurality of sequentially performed steps in a battery cell testing process;

[0009] For each first process, based on the measurement parameters of the process preceding the first process and the state transition information corresponding to the first process, a predicted parameter of the first process is determined; wherein the first process is a process other than the first process; and the state transition information is used to represent the correlation between the measurement parameters of the first process and the measurement parameters of the process preceding the first process;

[0010] Based on the differences between the measurement parameters and the predicted parameters corresponding to each of the first processes, an abnormality detection result of the battery cell to be detected is determined.

[0011] Optionally, the state transition information includes a state transition matrix;

[0012] For each first step, the state transition matrix is ​​determined based on the following method:

[0013] Obtaining sample measured voltage values ​​of a plurality of sample battery cells in the first process, and determining at least two state intervals based on the plurality of sample measured voltage values; the sample battery cells are normal battery cells;

[0014] Based on the at least two state intervals, determining a first sample state vector corresponding to the first process, and determining a second sample state vector corresponding to the previous process;

[0015] A state transfer matrix between the first process and a previous process is determined based on the first sample state vector and the second sample state vector.

[0016] Optionally, determining a first sample state vector corresponding to the first process based on the at least two state intervals includes:

[0017] Determining, based on the plurality of sample measured voltage values ​​corresponding to the first process and the at least two state intervals, a probability corresponding to each state interval of the first process; the probability being used to represent a ratio of the number of sample measured voltage values ​​belonging to the corresponding state interval to the number of all sample measured voltage values;

[0018] Based on the probabilities corresponding to the respective state intervals, a first sample state vector corresponding to the first process is determined.

[0019] Optionally, for each first process, determining the predicted parameters of the first process based on the measured parameters of the process preceding the first process and the state transition information corresponding to the first process includes:

[0020] Determining a measurement parameter of a process preceding the first process, and determining a second state vector corresponding to the process preceding the measurement parameter based on the process preceding the measurement parameter;

[0021] Determining a first state vector corresponding to the first process based on a second state vector of a previous process and a state transfer matrix corresponding to the first process;

[0022] Based on a first state vector corresponding to the first process, a prediction parameter of the first process is determined.

[0023] Optionally, determining the measurement parameters of a process previous to the first process includes:

[0024] Obtaining a measured voltage value of the battery cell to be tested in a process previous to the first process;

[0025] A target state interval to which the measured voltage value belongs is determined, and corresponding measurement parameters are determined based on the target state interval.

[0026] Optionally, determining the abnormality detection result of the battery cell to be tested based on the difference between the measurement parameters and the prediction parameters corresponding to each first process includes:

[0027] For each first process, if the measured parameters corresponding to the first process are inconsistent with the predicted parameters, the first process is determined to be an abnormal process;

[0028] If there are at least a preset number of abnormal processes in each of the first processes, it is determined that the abnormality detection result of the battery cell to be detected is a battery cell abnormality.

[0029] Optionally, the multiple processes in the battery cell testing process include a sequentially connected cutting and rolling process, a hot pressing process, a cover plate laser welding process, a top cover pre-welding process, a laser top cover welding process and a primary liquid injection process.

[0030] According to another aspect of an embodiment of the present disclosure, there is provided an abnormal battery cell detection device, the device comprising:

[0031] A cell determination module is used to determine the cell to be tested;

[0032] A measurement parameter acquisition module, configured to acquire a plurality of measurement parameters of the battery cell to be tested corresponding to a plurality of sequentially performed steps in the battery cell testing process;

[0033] a prediction parameter acquisition module, configured to determine, for each first process, a prediction parameter of the first process based on the measurement parameter of the process preceding the first process and state transition information corresponding to the first process; wherein the first process is a process other than the first process; and the state transition information is used to represent a correlation between the measurement parameter of the first process and the measurement parameter of the process preceding the first process;

[0034] The abnormality detection module is used to determine the abnormality detection result of the battery cell to be detected based on the difference between the measurement parameters and the prediction parameters corresponding to each first process.

[0035] According to another aspect of an embodiment of the present disclosure, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of any one of the above-mentioned abnormal battery cell detection methods when executing the program.

[0036] According to another aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned abnormal battery cell detection methods are implemented.

[0037] According to one aspect of an embodiment of the present disclosure, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned abnormal battery cell detection methods are implemented.

[0038] The technical solutions provided by the embodiments of the present disclosure have the following beneficial effects:

[0039] By obtaining multiple measurement parameters corresponding to multiple processes of the battery cell to be inspected, and obtaining the predicted parameters corresponding to each first process based on the measurement parameters of the previous process of each first process, and by comparing the differences between the measurement parameters and the predicted parameters of each first process, the battery cell to be inspected is detected for abnormalities, which can effectively detect abnormal batteries without puncturing the diaphragm, reduce the missed detection rate of battery cell abnormality detection, and effectively eliminate abnormal batteries caused by metal foreign matter and diaphragm defects, thereby improving the quality and safety of the produced batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for describing the embodiments of the present disclosure.

[0041] Figure 1 A schematic diagram of an application environment for the normal cell detection method provided in an embodiment of the present disclosure;

[0042] Figure 2 A schematic flow chart of a method for detecting abnormal battery cells provided in an embodiment of the present disclosure;

[0043] Figure 3 A schematic diagram of a battery cell detection process provided by an embodiment of the present disclosure;

[0044] Figure 4 A schematic structural diagram of an abnormal battery cell detection device provided in an embodiment of the present disclosure;

[0045] Figure 5 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0046] The following describes embodiments of the present disclosure in conjunction with the accompanying drawings. It should be understood that the embodiments described below in conjunction with the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions of the embodiments of the present disclosure.

[0047] Those skilled in the art will understand that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include the plural forms. It should be further understood that the terms "including" and "comprising" used in the embodiments of the present disclosure mean that the corresponding features can be implemented as the features, information, data, steps, operations, elements, and / or components presented, but do not exclude implementation as other features, information, data, steps, operations, elements, components, and / or combinations thereof supported by the present technical field. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, the element can be directly connected or coupled to the other element, or it can refer to the element and the other element establishing a connection relationship through an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein indicates at least one of the items defined by the term, for example, "A and / or B" or "A, B" indicates implementation as "A," or implementation as "B," or implementation as "A and B."

[0048] In order to make the objectives, technical solutions and advantages of the present disclosure more clear, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0049] In the specific implementations of this disclosure, any data related to an object is involved. When this disclosure is applied to a specific product or technology, the permission or consent of the object must be obtained, and the collection, use, and processing of the relevant data must comply with the relevant laws, regulations, and standards of the relevant country and region. In other words, if any of the above-mentioned data related to an object is involved in the embodiments of this disclosure, the data must be obtained with the authorization and consent of the object and in compliance with the relevant laws, regulations, and standards of the relevant country and region.

[0050] The following describes several exemplary embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on, or combine with each other, and the same terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0051] Figure 1Schematic diagram of the application environment of the normal battery cell detection method provided in the embodiment of the present disclosure. The application environment may include a terminal 101 and a server 102. The terminal 101 sends the information of the battery cell to be detected to the server 102. The server 102 determines the battery cell to be detected; obtains multiple measurement parameters corresponding to the battery cell to be detected for multiple sequential processes in the battery cell detection process; for each first process, based on the measurement parameters of the previous process of the first process and the state transition information corresponding to the first process, determines the prediction parameters of the first process; based on the difference between the measurement parameters and the prediction parameters corresponding to each first process, determines the abnormal detection result of the battery cell to be detected, and returns the abnormal detection result to the terminal 101.

[0052] The normal cell detection method provided in the embodiment of the present disclosure can be executed by any electronic device, which can be Figure 1 The server or terminal shown.

[0053] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server or server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The terminal can be a smartphone (such as an Android phone or iOS phone), a tablet computer, a laptop computer, a digital broadcast receiver, a MID (Mobile Internet Device), a PDA (Personal Digital Assistant), a desktop computer, a smart home appliance, an in-vehicle terminal (such as an in-vehicle navigation terminal or in-vehicle computer), a smart speaker, a smartwatch, etc. The terminal and server can be connected directly or indirectly via wired or wireless communication, but are not limited to such.

[0054] Figure 2 A flow chart of a method for detecting abnormal cells provided by an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the method includes:

[0055] Step S110: determining the battery cell to be tested.

[0056] Specifically, a cell is the electricity storage part of a rechargeable battery, which can also be understood as a single battery. A battery can be composed of at least one cell. The cell to be tested can be the cell that needs to be tested for abnormalities.

[0057] Step S120 , obtaining a plurality of measurement parameters corresponding to a plurality of sequentially performed steps in the battery cell detection process for the battery cell to be detected.

[0058] Specifically, after the battery cell to be tested is determined, a Hipot test (High-Potential Test) may be performed on the battery cell to obtain a plurality of measurement parameters corresponding to a plurality of sequential steps in the battery cell testing process.

[0059] Figure 3 A schematic diagram of a cell detection process provided by an embodiment of the present disclosure, such as Figure 3 As shown, the battery cell inspection process includes the sequential connection of the coiling process, the hot pressing process, the cover laser welding process, the top cover pre-welding process, the laser top cover welding process and the primary liquid injection process.

[0060] For the multiple steps in the battery cell testing process, these steps can be converted into a state sequence based on the Hidden Markov Chain (HMM).

[0061] For each process, the measurement parameter corresponding to the process may be status information of the battery cell to be tested in the process determined based on actual measurement data of the battery cell to be tested.

[0062] Step S130: For each first process, based on the measurement parameters of the previous process and the state transition information corresponding to the first process, determine the predicted parameters of the first process; wherein the first process is a process other than the first process; the state transition information is used to characterize the correlation between the measurement parameters of the first process and the measurement parameters of the previous process.

[0063] Specifically, among the above-mentioned multiple processes, the processes except the first process (i.e., the coiling process) can be used as the first process, that is, the first process can include any one of the hot pressing process, the cover plate laser welding process, the top cover pre-welding process, the laser top cover welding process and the one-time liquid injection process.

[0064] For each first process, the measurement parameters of the process before the first process may be obtained, and the prediction parameters of the first process may be calculated based on the state transition information corresponding to the first process.

[0065] The state transition information can be used to characterize the correlation between the measurement parameters of the first process and the measurement parameters of the previous process. The process of determining the state transition information will be described in detail below. The predicted parameters of the first process can be the state information of the battery cell to be tested in the first process predicted based on the measurement parameters of the battery cell to be tested in the previous process of the first process.

[0066] Step S140 : determining an abnormality detection result of the battery cell to be detected based on the difference between the measurement parameters and the prediction parameters corresponding to each first process.

[0067] Specifically, after obtaining the measurement parameters and prediction parameters corresponding to each first process, the difference between the measurement parameters and prediction parameters corresponding to each first process can be compared to obtain the abnormality detection result of the battery cell to be tested. The abnormality detection result can include a normal battery cell and an abnormal battery cell.

[0068] Optionally, determining the abnormality detection result of the battery cell to be tested based on the difference between the measurement parameters and the predicted parameters corresponding to each first process includes:

[0069] For each first process, if the measured parameters corresponding to the first process are inconsistent with the predicted parameters, the process is determined to be an abnormal process;

[0070] If there are at least a preset number of abnormal processes in each of the first processes, it is determined that the abnormality detection result of the battery cell to be detected is a battery cell abnormality.

[0071] Specifically, for each first process, after obtaining the measurement parameters and predicted parameters of the first process, if the measurement parameters of the first process are inconsistent with the predicted parameters, it means that there is an abnormality in the battery cell to be tested during the first process, and the first process can be treated as an abnormal process; otherwise, the first process can be treated as a normal process.

[0072] After performing the above judgment on each first process, the results corresponding to each first process can be counted. If at least a preset number of abnormal processes appear in each first process, it means that the possibility of the battery cell to be detected being abnormal is relatively high, and the abnormal detection result of the battery cell to be detected is determined to be battery cell abnormality; otherwise, the abnormal detection result of the battery cell to be detected is determined to be battery cell normal.

[0073] It should be noted that when the preset number is set to a small value, cells that may have abnormalities can be effectively detected, that is, the detection rate of abnormal cells is high. However, some normal cells may be identified as abnormal cells, resulting in a high overkill rate. Therefore, the preset number can be set to be greater than half of the total number of cells in the first process. For example, when the total number of cells in the first process is 5, the preset number can be set to 3.

[0074] In the embodiment of the present disclosure, multiple measurement parameters corresponding to multiple processes of the battery cell to be inspected are obtained, and based on the measurement parameters of the previous process of each first process, the prediction parameters corresponding to each first process are obtained. By comparing the differences between the measurement parameters and the prediction parameters of each first process, the battery cell to be inspected is detected for abnormalities. Abnormal batteries without puncturing the diaphragm can be effectively detected, thereby reducing the missed detection rate of battery cell abnormality detection, thereby effectively eliminating abnormal batteries caused by metal foreign matter and diaphragm defects, and improving the quality and safety of the produced batteries.

[0075] In addition, by obtaining the measurement parameters and prediction parameters corresponding to each first step in the battery cell detection process, the status information of each step of the battery cell to be detected in the entire battery cell detection process is provided, which is conducive to further analyzing the cause of the abnormality of the battery cell to be detected when the battery cell is abnormal.

[0076] As an optional embodiment, the state transition information includes a state transition matrix;

[0077] For each first step, the state transition matrix is ​​determined based on the following method:

[0078] Obtaining sample measured voltage values ​​of a plurality of sample cells in the first process, and determining at least two state intervals based on the plurality of sample measured voltage values; the sample cells are normal cells;

[0079] Determine a first sample state vector corresponding to a first process and a second sample state vector corresponding to a previous process based on at least two state intervals;

[0080] A state transfer matrix between a first process and a previous process is determined based on the first sample state vector and the second sample state vector.

[0081] Specifically, the state transition information may be represented as a state transition matrix. For each first process, the state transition matrix corresponding to the first process may be determined based on a large amount of historical data of the first process.

[0082] For each first process, based on a large amount of historical data, sample measured voltage values ​​of multiple sample cells in the first process can be obtained, and the sample cells can be normal cells. According to the distribution of the multiple sample measured voltage values, at least two state intervals are determined, and each state interval corresponds to a state. Among them, the sample measured voltage value can be the voltage value of each phase in the three-phase power supply, such as VD1 (i.e., the first phase voltage value), VD2 (i.e., the second phase voltage value) or VD3 (i.e., the third phase voltage value). The selection of the sample measured voltage value can be specifically set according to the actual situation, and the embodiment of the present disclosure is not limited to this.

[0083] Optionally, the sample measured voltage value may be used as a state representation, a numerical range corresponding to a plurality of sample measured voltage values ​​may be determined, and the numerical range may be divided into at least two state intervals according to a distribution of the plurality of sample measured voltage values.

[0084] For example, when the sample measured voltage value is VD3, according to the process control document, the numerical range of VD3 is determined to be 0-8; after obtaining multiple VD3s of a process, according to the distribution of the multiple VD3 values, if it is detected that the VD3 values ​​are concentrated between 3-5, the numerical range of 0-8 can be divided into three state intervals S1, S2 and S3.

[0085] Among them, the three state intervals can be continuous numerical intervals, such as S1: 0-3, S2: 3-5, S3: 5-8; the three state intervals can also be discrete numerical intervals, such as S1=[0,1,2], S2=[3,4,5], S3=[6,7,8].

[0086] It should be noted that the number of state intervals and the specific range of each state interval can be set according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0087] After obtaining at least two divided state intervals, for the first process, the multiple sample measured voltage values ​​of the multiple sample battery cells in the first process can be statistically analyzed to obtain a first sample state vector corresponding to the first process. The first sample state vector can be used to characterize the distribution information of the sample measured voltage values ​​of the multiple sample battery cells in the first process in each of the two state intervals.

[0088] Similarly, for the process before the first process, multiple sample measured voltage values ​​of multiple sample battery cells in the process before the first process can be statistically analyzed to obtain a second sample state vector corresponding to the process before the first process. The second sample state vector can be used to characterize the distribution information of the sample measured voltage values ​​of the multiple sample battery cells in the process before the first process in at least two state intervals.

[0089] After obtaining a first sample state vector corresponding to a first process and a second sample state vector corresponding to a process preceding the first process, a state transfer matrix between the first process and the process preceding the first process can be obtained by performing vector operations on the first sample state vector and the second sample state vector.

[0090] As an optional embodiment, determining a first sample state vector corresponding to the first process based on at least two state intervals includes:

[0091] Determining, based on a plurality of sample measured voltage values ​​corresponding to the first process and at least two state intervals, a probability corresponding to each state interval of the first process; the probability being used to represent a ratio of the number of sample measured voltage values ​​belonging to the corresponding state interval to the number of all sample measured voltage values;

[0092] Based on the probabilities corresponding to the respective state intervals, a first sample state vector corresponding to the first process is determined.

[0093] Specifically, for each first step, statistics are collected on the multiple sample measured voltage values ​​corresponding to the first step for multiple sample cells to obtain the number of sample measured voltage values ​​belonging to each state interval (i.e., the number of sample cells belonging to the state corresponding to each state interval). For each state interval, the ratio of the number of sample measured voltage values ​​belonging to that state interval to the number of all sample measured voltage values ​​for the first step (i.e., the ratio of the number of sample cells belonging to the state corresponding to that state interval to the total number of sample cells) is used as the probability corresponding to that state interval.

[0094] The probabilities corresponding to the various state intervals are combined to obtain a first sample state vector corresponding to the first process.

[0095] Similarly, the above steps may be performed to obtain a second sample state vector corresponding to a process preceding the first process.

[0096] Optionally, the following Figure 3 The cell detection process shown in the figure is used as an example to illustrate.

[0097] The three state intervals of the coil cutting process are represented as SQ1, SQ2, and SQ3, and the corresponding probability values ​​are PQ1, PQ2, and PQ3; the three state intervals of the hot pressing process are represented as SR1, SR2, and SR3, and the corresponding probability values ​​are PR1, PR2, and PR3; the three state intervals of the cover laser welding process are represented as SG1, SG2, and SG3, and the corresponding probability values ​​are PG1, PG2, and PG3; the three state intervals of the top cover pre-welding process are represented as SD1, SD2, and SD3, and the corresponding probability values ​​are PD1, PD2, and PD3; the three state intervals of the laser top cover welding process are represented as SJ1, SJ2, and SJ3, and the corresponding probability values ​​are PJ1, PJ2, and PJ3; the three state intervals of the one-time liquid injection process are represented as SZ1, SZ2, and SZ3, and the corresponding probability values ​​are PZ1, PZ2, and PZ3.

[0098] On this basis, the state transition matrix M1 from cutting to hot pressing is expressed as:

[0099]

[0100] The state transfer matrix M2 of hot pressing to the cover is expressed as:

[0101]

[0102] The state transfer matrix M3 of the cover to top cover pre-welding is expressed as:

[0103]

[0104] The state transition matrix M4 of the top cover pre-welded to the laser top cover is expressed as:

[0105]

[0106] The state transition matrix M5 from laser capping to primary injection is expressed as:

[0107]

[0108] That is, for the adjacent first process and the previous process, when the corresponding first sample state vector and second state vector are 1*3 vectors, the corresponding state transfer matrix is ​​a 3*3 matrix.

[0109] Combined with the above example, Table 1 shows the comparison of the measured parameters and predicted parameters of each process. As shown in Table 1, since slitting is the first process, there is no corresponding predicted parameter for slitting. The measured parameters and predicted parameters corresponding to the hot pressing process are SR and SR', respectively; the measured parameters and predicted parameters corresponding to the cover plate process are SG and SG', respectively; the measured parameters and predicted parameters corresponding to the top cover pre-welding process are SD and SD', respectively; the measured parameters and predicted parameters corresponding to the laser top cover process are SJ and SJ', respectively; and the measured parameters and predicted parameters corresponding to the liquid injection process are SZ and SZ'.

[0110] Taking the hot pressing process as an example, if the VD3 value of the battery cell to be tested in the hot pressing process is 4, 4 belongs to the second state interval S2, then the measurement parameter SR of the battery cell to be tested in the hot pressing process is determined to be S2; if based on the coiling process of the battery cell to be tested and M1, the predicted voltage value of the battery cell to be tested in the hot pressing process is calculated to be 6, 6 belongs to the third state interval S3, then the predicted parameter SR' of the battery cell to be tested in the hot pressing process is determined to be S3, and it can be judged that the measured parameters and predicted parameters of the hot pressing process are inconsistent.

[0111] Table 1

[0112]

[0113] As an optional embodiment, for each first process, based on the measured parameters of the process before the first process and the state transition information corresponding to the first process, the prediction parameters of the first process are determined, including:

[0114] Determining a measurement parameter of a process preceding the first process, and determining a second state vector corresponding to the process preceding the measurement parameter based on the process preceding the measurement parameter;

[0115] Determine a first state vector corresponding to the first process based on the second state vector of the previous process and the state transfer matrix corresponding to the first process;

[0116] Based on the first state vector corresponding to the first process, a prediction parameter of the first process is determined.

[0117] Specifically, when performing abnormality detection on the battery cell to be inspected, the measurement parameters of the previous process of the first process can be obtained first, and the measurement parameters can be converted into a second state vector corresponding to the previous process. By performing vector calculation on the second state vector and the state transfer matrix corresponding to the first process, the first state vector corresponding to the first process is obtained, and the first state vector is converted into the corresponding prediction parameters.

[0118] Optionally, when converting the measured parameter into a corresponding state vector, the state interval to which the measured parameter belongs can be set to 1, and the remaining state intervals can be set to 0, thereby obtaining a state vector. For example, if the measured parameter is S2, its corresponding state vector can be [0, 1, 0]. Similarly, after obtaining the first state vector, the state interval corresponding to the largest value in the first state vector can be used as the corresponding prediction parameter.

[0119] As an optional embodiment, determining the measurement parameters of a process previous to the first process includes:

[0120] Obtaining the measured voltage value of the battery cell to be tested in the previous process of the first process;

[0121] Determine the target state interval to which the measured voltage value belongs, and determine corresponding measurement parameters based on the target state interval.

[0122] Specifically, to determine the measurement parameters of the previous process of the first process, the measured voltage value of the battery cell to be tested in the previous process can be obtained, and the measured voltage value can be used as the state representation. The state interval to which the measured voltage value belongs can be used as the target state interval, and the target state interval can be used as the corresponding measurement parameter.

[0123] For example, if the measurement parameter of the battery cell to be tested in the previous process is 4, and 4 belongs to the second state interval, then the target state interval is determined to be S2, and S2 can be used as the measurement parameter.

[0124] The above steps can be performed for each process to obtain the measurement parameters corresponding to each process.

[0125] Figure 4 A schematic diagram of the structure of an abnormal battery cell detection device provided by an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, the device of this embodiment may include:

[0126] A cell determination module 210 is used to determine a cell to be detected;

[0127] A measurement parameter acquisition module 220 is configured to acquire a plurality of measurement parameters corresponding to a plurality of sequentially performed steps in a battery cell detection process for the battery cell to be detected;

[0128] The prediction parameter acquisition module 230 is configured to determine, for each first process, the prediction parameters of the first process based on the measurement parameters of the process preceding the first process and the state transition information corresponding to the first process; wherein the first process is a process other than the first process; and the state transition information is used to represent the correlation between the measurement parameters of the first process and the measurement parameters of the process preceding the first process;

[0129] The abnormality detection module 240 is configured to determine an abnormality detection result of the battery cell to be detected based on the difference between the measurement parameters and the prediction parameters corresponding to each first process.

[0130] As an optional embodiment, the state transition information includes a state transition matrix;

[0131] The device also includes a state transition matrix determination module, which is used to:

[0132] Obtaining sample measured voltage values ​​of a plurality of sample battery cells in the first process, and determining at least two state intervals based on the plurality of sample measured voltage values; the sample battery cells are normal battery cells;

[0133] Based on the at least two state intervals, determining a first sample state vector corresponding to the first process, and determining a second sample state vector corresponding to the previous process;

[0134] A state transfer matrix between the first process and a previous process is determined based on the first sample state vector and the second sample state vector.

[0135] As an optional embodiment, when the state transition matrix determination module determines the first sample state vector corresponding to the first process based on the at least two state intervals, it is specifically configured to:

[0136] Determining, based on the plurality of sample measured voltage values ​​corresponding to the first process and the at least two state intervals, a probability corresponding to each state interval of the first process; the probability being used to represent a ratio of the number of sample measured voltage values ​​belonging to the corresponding state interval to the number of all sample measured voltage values;

[0137] Based on the probabilities corresponding to the respective state intervals, a first sample state vector corresponding to the first process is determined.

[0138] As an optional embodiment, the prediction parameter determination module, when determining the prediction parameter of the first process based on the measurement parameter of the previous process of the first process and the state transition information corresponding to the first process, is specifically configured to:

[0139] Determining a measurement parameter of a process preceding the first process, and determining a second state vector corresponding to the process preceding the measurement parameter based on the process preceding the measurement parameter;

[0140] Determining a first state vector corresponding to the first process based on a second state vector of a previous process and a state transfer matrix corresponding to the first process;

[0141] Based on a first state vector corresponding to the first process, a prediction parameter of the first process is determined.

[0142] As an optional embodiment, when determining the measurement parameters of the previous process of the first process, the prediction parameter determination module is specifically configured to:

[0143] Obtaining a measured voltage value of the battery cell to be tested in a process previous to the first process;

[0144] A target state interval to which the measured voltage value belongs is determined, and corresponding measurement parameters are determined based on the target state interval.

[0145] As an optional embodiment, when the abnormality detection module determines the abnormality detection result of the battery cell to be detected based on the difference between the measurement parameters and the prediction parameters corresponding to each first process, it is specifically configured to:

[0146] For each first process, if the measured parameters corresponding to the first process are inconsistent with the predicted parameters, the first process is determined to be an abnormal process;

[0147] If there are at least a preset number of abnormal processes in each of the first processes, it is determined that the abnormality detection result of the battery cell to be detected is a battery cell abnormality.

[0148] As an optional embodiment, the multiple processes in the battery cell inspection process include a sequentially connected cutting and rolling process, a hot pressing process, a cover plate laser welding process, a top cover pre-welding process, a laser top cover welding process and a primary liquid injection process.

[0149] The apparatus of the embodiments of the present disclosure can execute the methods provided by the embodiments of the present disclosure, and their implementation principles are similar and have corresponding technical effects. The actions performed by each module in the apparatus of each embodiment of the present disclosure correspond to the steps in the methods of each embodiment of the present disclosure. For detailed functional descriptions of each module of the apparatus, please refer to the descriptions of the corresponding methods shown above, and will not be repeated here.

[0150] In the embodiments of the present disclosure, the term "module" or "unit" refers to a computer program or portion of a computer program that has a predetermined function and works together with other related components to achieve a predetermined goal. The ... that can be implemented in whole or in part using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0151] In an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method provided in any optional embodiment of the present disclosure. Compared with the prior art, it can be achieved that: by obtaining a plurality of measurement parameters corresponding to a plurality of processes of the battery cell to be detected, and based on the measurement parameters of the previous process of each first process, the prediction parameters corresponding to each first process are obtained, and by comparing the difference between the measurement parameters and the prediction parameters of each first process, the battery cell to be detected is detected for abnormalities, which can effectively detect abnormal batteries without puncturing the diaphragm, reduce the missed detection rate of battery cell abnormality detection, and effectively eliminate abnormal batteries caused by metal foreign matter and diaphragm defects, thereby improving the quality and safety of the produced batteries.

[0152] In an alternative embodiment, an electronic device is provided, such as Figure 5 As shown, Figure 5 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which may be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present disclosure.

[0153] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, or a combination of a DSP and a microprocessor.

[0154] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0155] The memory 4003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0156] The memory 4003 is used to store the computer program for executing the embodiments of the present disclosure, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer program stored in the memory 4003 to implement the steps shown in the above method embodiments.

[0157] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), wearable devices, etc., as well as fixed terminals such as digital TVs, desktop computers, etc.

[0158] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.

[0159] The embodiments of the present disclosure further provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0160] It should be understood that, although the flowcharts of the embodiments of the present disclosure indicate the various operation steps by arrows, the order of implementation of these steps is not limited to the order indicated by the arrows. Unless otherwise clearly stated herein, in some implementation scenarios of the embodiments of the present disclosure, the implementation steps in each flowchart can be performed in other orders as required. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios where the execution times are different, the order of execution of these sub-steps or stages can be flexibly configured as required, and the embodiments of the present disclosure do not limit this.

[0161] The above description is only an optional implementation method for some implementation scenarios of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present disclosure, other similar implementation methods based on the technical ideas of the present disclosure also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method for detecting abnormal battery cells, characterized in that: include: Determine the battery cells to be tested; Acquire a plurality of measurement parameters of the battery cell to be tested corresponding to a plurality of sequentially performed steps in a battery cell testing process; For each first process, based on the measurement parameters of the process preceding the first process and the state transition information corresponding to the first process, a predicted parameter of the first process is determined; wherein the first process is a process other than the first process; and the state transition information is used to represent the correlation between the measurement parameters of the first process and the measurement parameters of the process preceding the first process; Based on the differences between the measurement parameters and the predicted parameters corresponding to each of the first processes, an abnormality detection result of the battery cell to be detected is determined.

2. The abnormal cell detection method according to claim 1, characterized in that: The state transition information includes a state transition matrix; For each first step, the state transition matrix is ​​determined based on the following method: Obtaining sample measured voltage values ​​of a plurality of sample battery cells in the first process, and determining at least two state intervals based on the plurality of sample measured voltage values; the sample battery cells are normal battery cells; Based on the at least two state intervals, determining a first sample state vector corresponding to the first process, and determining a second sample state vector corresponding to the previous process; A state transfer matrix between the first process and a previous process is determined based on the first sample state vector and the second sample state vector.

3. The abnormal cell detection method according to claim 2, characterized in that: The determining, based on the at least two state intervals, a first sample state vector corresponding to the first process includes: Determining, based on the plurality of sample measured voltage values ​​corresponding to the first process and the at least two state intervals, a probability corresponding to each state interval of the first process; the probability being used to represent a ratio of the number of sample measured voltage values ​​belonging to the corresponding state interval to the number of all sample measured voltage values; Based on the probabilities corresponding to the respective state intervals, a first sample state vector corresponding to the first process is determined.

4. The abnormal cell detection method according to claim 2, characterized in that: For each first process, determining the predicted parameters of the first process based on the measured parameters of the process preceding the first process and the state transition information corresponding to the first process includes: Determining a measurement parameter of a process preceding the first process, and determining a second state vector corresponding to the process preceding the measurement parameter based on the process preceding the measurement parameter; Determining a first state vector corresponding to the first process based on a second state vector of a previous process and a state transfer matrix corresponding to the first process; Based on a first state vector corresponding to the first process, a prediction parameter of the first process is determined.

5. The abnormal cell detection method according to claim 4, characterized in that: Determining the measurement parameters of the previous process of the first process includes: Obtaining a measured voltage value of the battery cell to be tested in a process previous to the first process; A target state interval to which the measured voltage value belongs is determined, and corresponding measurement parameters are determined based on the target state interval.

6. The abnormal cell detection method according to claim 1, characterized in that: The determining of the abnormality detection result of the battery cell to be inspected based on the difference between the measurement parameters and the prediction parameters corresponding to each of the first steps includes: For each first process, if the measured parameters corresponding to the first process are inconsistent with the predicted parameters, the first process is determined to be an abnormal process; If there are at least a preset number of abnormal processes in each of the first processes, it is determined that the abnormality detection result of the battery cell to be detected is a battery cell abnormality.

7. The abnormal cell detection method according to any one of claims 1 to 6, characterized in that: The multiple processes in the battery cell testing process include a sequentially connected cutting and rolling process, a hot pressing process, a cover plate laser welding process, a top cover pre-welding process, a laser top cover welding process and a primary liquid injection process.

8. An abnormal battery cell detection device, characterized in that: include: A cell determination module is used to determine the cell to be tested; A measurement parameter acquisition module, configured to acquire a plurality of measurement parameters of the battery cell to be tested corresponding to a plurality of sequentially performed steps in the battery cell testing process; a prediction parameter acquisition module, configured to determine, for each first process, a prediction parameter of the first process based on the measurement parameter of the process preceding the first process and state transition information corresponding to the first process; wherein the first process is a process other than the first process; and the state transition information is used to represent a correlation between the measurement parameter of the first process and the measurement parameter of the process preceding the first process; The abnormality detection module is used to determine the abnormality detection result of the battery cell to be detected based on the difference between the measurement parameters and the prediction parameters corresponding to each first process.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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