Abnormal battery detection method and device, electronic equipment and storage medium

Abstract

The invention provides an abnormal battery detection method and device, electronic equipment and a storage medium, and relates to the field of new energy power batteries. Comprising the following steps: acquiring detection time and a voltage value of a to-be-detected battery pack in each detection process; determining a voltage change parameter corresponding to each battery pack according to the detection time and the voltage value of the battery pack in each detection process; according to the voltage change parameter corresponding to each battery pack in the same batch of battery packs, determining a cluster to which each battery pack belongs and the number of batteries contained in each cluster; and under the condition that the number of the batteries contained in any cluster is smaller than a number threshold value, determining that the battery packs in any cluster are abnormal battery packs. Therefore, abnormity recognition in the battery pack production process can be achieved, the detection effect on defects with the slight change trend is enhanced, improvement and troubleshooting of the battery pack process defects are facilitated, the qualification rate and safety of the battery pack are improved, and battery accidents are effectively avoided.

Description

Technical Field

[0001] The present disclosure relates to the field of new energy power batteries, and in particular to an abnormal battery detection method, device, electronic equipment and storage medium. Background Art

[0002] For new energy vehicle power batteries, safety issues are the core concern of the industry. The occurrence of battery thermal events not only brings negative public opinion and economic losses to car companies, but also poses life and property safety risks to electric vehicle users. Faced with the frequent occurrence of new energy vehicle thermal events in the industry, it is more meaningful to intercept and prevent abnormal batteries from flowing out with higher precision during the production stage than to conduct early warning interception during the operation stage of the product. It can not only avoid safety incidents, but also save a lot of manpower and material resources for the industry. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] The first embodiment of the present disclosure provides an abnormal battery detection method, including:

[0005] Obtain the detection time and voltage value of the battery pack to be detected in each detection process;

[0006] Determining a voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process;

[0007] Determine the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs;

[0008] When the number of batteries included in any cluster is less than the number threshold, the battery pack in any cluster is determined to be an abnormal battery pack.

[0009] The second embodiment of the present disclosure provides an abnormal battery detection device, comprising:

[0010] The first acquisition module is used to obtain the detection time and voltage value of the battery pack to be detected in each detection process;

[0011] A first determination module, used to determine a voltage variation parameter corresponding to each of the battery packs according to the detection time and voltage value of the battery pack in each detection process;

[0012] A second determination module, configured to determine the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs;

[0013] The third determination module is used to determine that the battery pack in any cluster is an abnormal battery pack when the number of batteries included in any cluster is less than a quantity threshold.

[0014] The third aspect embodiment of the present disclosure proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the abnormal battery detection method proposed in the first aspect embodiment of the present disclosure is implemented.

[0015] The fourth aspect of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the abnormal battery detection method provided in the first aspect of the present disclosure is implemented.

[0016] The abnormal battery detection method, device, electronic device and storage medium provided by the present disclosure have the following beneficial effects:

[0017] In the disclosed embodiment, all battery packs of the same batch are clustered based on the voltage change parameters of each battery pack between the detection processes, and all battery packs in any cluster containing less than a threshold number of battery packs are determined as abnormal batteries, thereby completing abnormal battery detection. This enables abnormal identification in the battery pack production process, enhances the detection effect of defects with slight change trends, helps improve and troubleshoot battery pack process defects, improves the pass rate and safety of battery packs, and effectively avoids battery accidents.

[0018] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flowchart of an abnormal battery detection method provided by an embodiment of the present disclosure;

[0021] Figure 2 A flowchart of an abnormal battery detection method provided by another embodiment of the present disclosure;

[0022] Figure 3 A flowchart of an abnormal battery detection method provided by another embodiment of the present disclosure;

[0023] Figure 4 A flowchart of an abnormal battery detection method provided by another embodiment of the present disclosure;

[0024] Figure 5 A schematic diagram of the structure of an abnormal battery detection device provided by an embodiment of the present disclosure;

[0025] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0026] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0027] The abnormal battery detection method, device, electronic device, and storage medium according to the embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0028] Figure 1 A flowchart of an abnormal battery detection method provided by an embodiment of the present disclosure.

[0029] The embodiment of the present disclosure takes the abnormal battery detection method being configured in an abnormal battery detection device as an example. The abnormal battery detection device can be applied to any electronic device so that the electronic device can perform an abnormality detection function in the battery pack production process.

[0030] like Figure 1 As shown, the abnormal battery detection method may include the following steps:

[0031] Step 101, obtaining the detection time and voltage value of the battery pack to be detected in each detection process.

[0032] Among them, a battery pack is a battery system that combines multiple battery cells (i.e., battery cells) according to a certain configuration and connection method.

[0033] It should be noted that after the battery cell rolls off the production line, environmental conditions, human errors, and substandard process conditions during the production process of the battery cell to the battery pack will cause irreversible damage to the battery, affecting the safety of the battery pack. Therefore, it is necessary to perform abnormal detection on the battery pack in each process of the battery pack production process to identify defects with slight change trends, so as to effectively intercept abnormal batteries and prevent them from entering the market.

[0034] In the disclosed embodiment, each process of pack production is a detection process, and the pack production processes are in order: open circuit voltage (OCV) test of battery cell, single module communication test, PACK communication test, end of line (EOL) test of battery pack production line and vehicle electrical inspection test. The voltage value of each detection process is the battery cell voltage measured by the battery pack in the detection process. In addition, the start time of each detection process can be determined as the detection time of the detection process.

[0035] Step 102 , determining a voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process.

[0036] Among them, the voltage change parameter is used to describe the voltage change characteristics of the battery cells between various processes in the production process of the battery pack, and can include multiple voltage change values, each of which is the average daily voltage drop between two detection processes.

[0037] In the disclosed embodiments, since some battery defects are not obvious in appearance or within a short period of time, they need to be identified based on trend changes in the time dimension. Therefore, the difference between the voltage values ​​of every two detection processes and the time difference between the corresponding detection times can be calculated based on the voltage value of the battery pack in each detection process. The difference is then divided by the time difference to obtain the voltage change value between the two detection processes, thereby obtaining the voltage change parameters corresponding to each battery pack.

[0038] It should be noted that the two detection processes corresponding to the voltage change value can be two adjacent detection processes or two non-adjacent detection processes. This can make the time dimension corresponding to the voltage change parameter wider, the change more obvious, and be more conducive to detecting abnormalities.

[0039] For example, for a battery pack to be tested, the voltage change value dv1 between the battery cell coming off the line and the OCV test, the voltage change value dv2 between the OCV test and single-module communication, the voltage change value dv3 between single-module communication and dual-module communication, the voltage change value dv4 between dual-module communication and the EOL test of the pack, the voltage change value dv5 between the OCV test and dual-module communication, the voltage change value dv6 between the OCV test and the EOL test of the pack, and the voltage change value dv7 between single-module communication and dual-module communication can be determined, thereby obtaining the voltage change parameters dv1, dv2, dv3, dv4, dv5, dv6 and dv7 corresponding to the battery pack to be tested.

[0040] Step 103 , determining the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs.

[0041] In the disclosed embodiment, any clustering algorithm including but not limited to birch, k-means, DBSCAN, particle swarm, etc. can be selected to build a battery abnormality detection model. Then, using the detection model, all battery packs in the same batch are divided into multiple clusters according to the voltage change parameters corresponding to each battery pack in the same batch of battery packs, and the cluster to which each battery pack belongs and the number of batteries contained in each cluster are determined.

[0042] For example, the model uses the k-means algorithm to divide all battery packs in the same batch into K clusters, where the initial value of K can be 3. Based on the similarity measurement between the voltage change parameters corresponding to each battery pack, battery packs with similar voltage change parameters are grouped into the same cluster, and battery packs with dissimilar voltage change parameters are grouped into different clusters.

[0043] Alternatively, in an embodiment of the present application, a model may not be used, but the battery packs may be clustered directly based on the similarity between the voltage change parameters corresponding to each battery pack, divided into multiple clusters, and the number of batteries contained in each cluster may be obtained.

[0044] Step 104 : when the number of batteries included in any cluster is less than a quantity threshold, determine that the battery pack in any cluster is an abnormal battery pack.

[0045] The quantity threshold may be 1% or 2% of the total number of battery packs to be tested, etc. It may be determined based on experience or detection accuracy requirements, etc., and the present disclosure does not limit this.

[0046] In the disclosed embodiment, after determining the number of batteries contained in each cluster, the number of batteries can be compared with the number threshold. If the number of batteries contained in any cluster is less than the number threshold, it means that the voltage data of the battery pack in any cluster is abnormal. It can be determined that the battery pack in the cluster is defective and is an abnormal battery pack, thereby completing the abnormal battery detection.

[0047] In the disclosed embodiment, the detection time and voltage value of the battery pack to be detected in each detection process are first obtained, and then the voltage change parameter corresponding to each battery pack is determined according to the detection time and voltage value of the battery pack in each detection process. After that, the cluster to which each battery pack belongs and the number of batteries contained in each cluster are determined according to the voltage change parameter corresponding to each battery pack in the same batch of battery packs. In the case where the number of batteries contained in any cluster is less than the number threshold, the battery pack in any cluster is determined to be an abnormal battery pack. Therefore, by clustering all battery packs in the same batch based on the voltage change parameters of each battery pack between the detection processes, all battery packs in any cluster containing a number of battery packs less than the threshold are determined to be abnormal batteries, thereby completing the abnormal battery detection. Thereby, it is possible to realize abnormal identification in the battery pack production process, enhance the detection effect of defects with slight change trends, help improve and troubleshoot battery pack process defects, improve the qualification rate and safety of battery packs, and effectively avoid battery accidents.

[0048] It should be noted that the abnormal battery detection method proposed in the present disclosure can not only be applied to the battery pack production process, but also can be migrated to the production scenarios of other products. And the scenarios where abnormalities can be detected include but are not limited to: when the battery cell is incoming, the self-discharge rate itself is slightly higher than the group scenario; during the transportation of the battery cell, a discharge path appears, resulting in a lower battery cell voltage; during the module assembly process, the anti-static operation is not standardized, resulting in a micro short circuit; the battery cell micro short circuit caused by human error; and the voltage and temperature sampling abnormalities in the module / Pack cause slight voltage fluctuations, etc.

[0049] Figure 2 A flowchart of an abnormal battery detection method provided by an embodiment of the present disclosure is shown in FIG. Figure 2 As shown, the abnormal battery detection method may include the following steps:

[0050] Step 201, obtaining the detection time and voltage value of the battery pack to be detected in each detection process.

[0051] For a detailed description of the above step 201, please refer to other embodiments of the present disclosure and will not be repeated here.

[0052] Step 202 , determining a plurality of voltage variation values ​​corresponding to the battery pack according to the detection time and voltage value of the battery pack in each detection process.

[0053] In the disclosed embodiment, any two detection processes of the battery pack can be selected, and the difference between the voltage values ​​of the two detection processes and the time difference between the corresponding detection times can be calculated. The obtained difference is then divided by the time difference to obtain the voltage change value between the two detection processes. Thereafter, the voltage change values ​​are calculated using the other two detection processes, thereby obtaining multiple voltage change values ​​corresponding to each battery pack.

[0054] It should be noted that the two detection processes corresponding to each voltage change value may be two detection processes that are adjacent in detection order, or two detection processes that are not adjacent in detection order. Therefore, the method for determining the voltage change value may be one or more of the following:

[0055] Optionally, the voltage change value of the battery pack between each two adjacent detection steps may be determined according to the detection time and voltage value of the battery pack in each two adjacent detection steps.

[0056] Furthermore, the voltage change value of the battery pack between any two detection steps may be determined according to the detection time and voltage value of the battery pack in any two detection steps.

[0057] For example, all the inspection processes of the battery pack are, in order, process A, process B, and process C. The inspection time and voltage value of process A are t1 and v1, the inspection time and voltage value of process B are t2 and v2, and the inspection time and voltage value of process C are t3 and v3. The multiple voltage change values ​​corresponding to the battery pack may include: the voltage change value dv1=(v1-v2) / (t2-t1) corresponding to process A and process B, dv2=(v2-v3) / (t3-t2) corresponding to process B and process C, and the voltage change value dv3=(v1-v3) / (t3-t1) corresponding to process A and process C.

[0058] In the disclosed embodiment, by calculating the voltage change value for two adjacent inspection processes or any two inspection processes, the voltage change characteristics of the battery pack between the production processes can be clarified, which helps to identify defects that are not obvious in a short period of time and improve the reliability of abnormality detection.

[0059] Step 203 , determining the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the multiple voltage change values ​​corresponding to each battery pack in the same batch of battery packs.

[0060] In the embodiment of the present disclosure, the multiple voltage change values ​​are the voltage change parameters in the above embodiment, and the detailed description of step 203 can refer to the above embodiment of the present disclosure, which will not be repeated here.

[0061] Step 204 : when the number of batteries contained in any cluster is less than the number threshold, determine that the battery pack in any cluster is an abnormal battery pack.

[0062] For a detailed description of the above step 204, reference may be made to other embodiments of the present disclosure and will not be repeated here.

[0063] In the embodiments of the present disclosure, the process section where the battery abnormality occurs can be located based on the characteristic changes between different processes, and it can be checked whether it is caused by process defects, thereby providing a direction for checking and improving the process defects.

[0064] In the disclosed embodiment, by determining multiple voltage change values ​​corresponding to the battery pack based on the detection time and voltage value of the battery pack in each detection process, and then performing abnormality detection, the voltage change characteristics of the battery pack between production processes can be clarified, which helps to identify faults that are not obvious in a short period of time, reversely check defects in the production process, and improve the reliability of abnormality detection.

[0065] Figure 3 A signaling interaction diagram of an abnormal battery detection method provided by an embodiment of the present disclosure is shown in FIG. Figure 3 As shown, the abnormal battery detection method may include the following steps:

[0066] Step 301, obtaining the detection time and voltage value of the battery pack to be detected in each detection process.

[0067] Step 302 , determining a voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process.

[0068] For detailed description of the above steps 301 and 302, please refer to other embodiments of the present disclosure and will not be repeated here.

[0069] Step 303 , determining a parameter matrix corresponding to the same batch of battery packs according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs.

[0070] Among them, each row parameter in the parameter matrix is ​​the voltage change parameter corresponding to a battery pack, and each column parameter in the parameter matrix is ​​the voltage change parameter of each battery pack in the same dimension.

[0071] In the disclosed embodiment, a parameter matrix representing the voltage variation characteristics of the same batch of battery packs can be constructed, and the voltage variation parameters corresponding to each battery pack in the same batch of battery packs are listed in the matrix according to the row and column arrangement rules of the parameter matrix to obtain the parameter matrix corresponding to the same batch of battery packs.

[0072] It can be understood that the rows of the parameter matrix are the sample numbers corresponding to each battery pack in the same batch of battery packs, and the number of rows is the number of battery packs in the same batch, and the number of battery packs in a batch is not less than 10000. Each column of the parameter matrix is ​​a one-dimensional vector composed of the voltage change values ​​determined for all battery packs under the same two testing processes.

[0073] Step 304 , input the parameter matrix into a preset detection model to obtain the cluster to which each battery pack belongs and the number of batteries contained in each cluster output by the detection model.

[0074] The preset detection matrix can obtain multiple data clusters by clustering the data in the parameter matrix according to similarity. The clustering algorithms that can be selected for the detection matrix include but are not limited to birch, k-means, DBSCAN, particle swarm, etc.

[0075] In the disclosed embodiment, the parameter matrix corresponding to the same batch of battery packs can be input into a preset detection model, and all battery packs can be clustered by the detection matrix to obtain the cluster to which each battery pack belongs and the number of batteries contained in each cluster output by the detection model.

[0076] It should be noted that each cluster output by the detection model can be distinguished by a label, such as 0, 1, 2, etc.

[0077] It should be noted that because the absolute value of the difference between the voltage change parameters is small and the clustering algorithm involves distance measurement, it is not suitable for linear normalization. Therefore, the standard deviation normalization is used to process the parameter matrix in this disclosure, which can accelerate the convergence speed of the clustering algorithm, improve the performance of the detection model for anomaly detection, and improve stability.

[0078] Optionally, each row of elements in the parameter matrix may be normalized by standard deviation to obtain a normalized parameter matrix, and then the normalized parameter matrix may be input into a preset detection model.

[0079] In the embodiment of the present disclosure, the standard deviation normalization, also known as z-score normalization, is used to adjust each row of elements in the parameter matrix to a distribution with a mean of 0 and a standard deviation of 1, so that the processed parameter matrix is ​​suitable for situations where the data is approximately Gaussian distributed and distance measurement is required.

[0080] In this embodiment, by using a preset detection matrix, the battery packs in the same batch are clustered according to the parameter matrix determined by the voltage variation parameters corresponding to each battery pack in the same batch, and the cluster to which each battery pack belongs and the number of batteries contained in each cluster are obtained. Automated battery abnormality detection can be achieved, and the efficiency of battery detection can be improved.

[0081] Step 305 : When the number of batteries contained in any cluster is less than a quantity threshold, determine that the battery pack in any cluster is an abnormal battery pack.

[0082] For a detailed description of the above step 305, reference may be made to other embodiments of the present disclosure and will not be repeated here.

[0083] Step 306 , determining the detection time and voltage value of each detection process corresponding to the battery pack in any cluster as data to be verified.

[0084] In the disclosed embodiment, after determining that the battery pack in any cluster is an abnormal battery pack, the batteries with smaller outliers can be selected from the detected abnormal batteries for disassembly and analysis. Therefore, the detection time and voltage value of each detection process corresponding to the battery pack in any cluster can be determined as the data to be verified, which is used to further verify whether the battery pack is abnormal.

[0085] Step 307: Send the data to be verified to the data verification device.

[0086] In the disclosed embodiment, the data to be verified may be sent to a data verification device to automatically verify the anomaly detection result.

[0087] Step 308, when the verification result returned by the verification device indicates that the battery packs in any cluster have passed the verification, the preset detection model is updated until the number of battery packs contained in the cluster to which the battery packs in any cluster belong determined based on the detection model is greater than or equal to the quantity threshold.

[0088] In the embodiment of the present disclosure, when the verification result returned by the verification device indicates that the battery pack in any cluster has passed the verification, there is no abnormality in the battery pack. At this time, it may be because the parameters of the detection model are not accurate enough, resulting in abnormal detection failure, so the parameters of the detection model need to be adjusted and updated until the updated detection model determines that the cluster whose detection result was abnormal before is no longer abnormal, that is, the number of battery packs contained in the cluster to which the battery packs in the cluster belong is greater than or equal to the quantity threshold.

[0089] It should be noted that in the present disclosure, the model parameters can be updated according to a fixed correction gradient, or a grid search can be added to achieve automatic tuning of parameters according to the offline results of abnormal samples to complete the update of the detection model.

[0090] Optionally, the central battery pack corresponding to each cluster output by the detection model can be determined, and then the distance between the first central battery pack corresponding to any cluster and the second central battery pack corresponding to other clusters can be determined based on the voltage change parameter corresponding to each battery pack. After that, the model update gradient can be determined based on the distance, and then the preset detection model can be updated based on the update gradient.

[0091] In the disclosed embodiment, the initial value of the number of cluster centers in the detection model can be set as needed (for example, it can be 3), and then after all battery packs corresponding to each cluster are obtained by using the Euclidean distance as the similarity measure between the voltage change parameters, the central battery pack corresponding to each cluster can be determined according to the distance between the voltage change parameters of the battery packs in the cluster. Then, the distance between the first central battery pack corresponding to any cluster and the second central battery pack corresponding to other clusters can be calculated, and the first central battery pack can be classified into the cluster with the closest distance, and the average value of all data points in each cluster can be recalculated and used as the new cluster center, thereby completing an iteration of model update.

[0092] It should be noted that model updating is a multi-iteration process. When the model reaches the convergence condition, the model update can be determined to be complete. The convergence condition can be that the number of iterations reaches a preset maximum value, for example, 300.

[0093] In this embodiment, the detection time and voltage value of each detection process corresponding to the battery pack in any cluster are first determined as the data to be verified, and then the data to be verified is sent to the data verification device. When the verification result returned by the verification device indicates that the battery pack in any cluster has passed the verification, the preset detection model is updated until the number of battery packs in the cluster to which the battery pack in any cluster belongs, determined based on the detection model, is greater than or equal to the number threshold. Therefore, by further verifying the battery pack with an abnormal detection result, when it is determined that the battery pack is not abnormal, the parameters of the detection model are updated, so that the accuracy and stability of the detection model can be improved.

[0094] Figure 4 A signaling interaction diagram of an abnormal battery detection method provided by an embodiment of the present disclosure is shown in FIG. Figure 4 As shown, the abnormal battery detection method may include the following steps:

[0095] Step 401, obtaining the detection time and voltage value of the battery pack to be detected in each detection process.

[0096] Step 402 , determining a voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process.

[0097] Step 403 , determining the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs.

[0098] For detailed description of the above steps 401 to 403, reference can be made to other embodiments of the present disclosure and will not be repeated here.

[0099] Step 404 , determining a quantity threshold value according to the total quantity of batteries contained in the same batch of battery packs and at least one of the detection results corresponding to the reference batch of battery packs.

[0100] Among them, the performance parameters of the reference batch of battery packs have a matching degree greater than the matching threshold with the performance parameters of the same batch of battery packs.

[0101] In the disclosed embodiment, since the performance parameters of the reference batch of battery packs match the performance parameters of the same batch of battery packs more than the matching threshold, the voltage changes during the production process should also be similar. Therefore, according to the proportion of abnormal battery packs in the detection results of the reference battery packs, the quantity threshold should be determined as a percentage of the total number of battery packs in the batch, and then the quantity threshold is determined in combination with the total number of batteries contained in the same batch of battery packs. For example, the percentage corresponding to the quantity threshold is 1% of the total number, or 2%, etc. Alternatively, the quantity threshold can be determined only by the total number of batteries contained in the same batch of battery packs or the detection results corresponding to the reference batch of battery packs.

[0102] Step 405 : when the number of batteries contained in any cluster is less than the number threshold, determine that the battery pack in any cluster is an abnormal battery pack.

[0103] For a detailed description of the above step 405, reference can be made to other embodiments of the present disclosure and will not be repeated here.

[0104] In this embodiment, by determining the quantity threshold based on the total number of batteries contained in the same batch of battery packs and at least one of the test results corresponding to the reference batch of battery packs, it is possible to avoid setting the quantity threshold unrealistically, which may lead to inaccurate test results, and provide conditions for improving the reliability of abnormal detection results.

[0105] In order to implement the above embodiments, the present disclosure also provides an abnormal battery detection device.

[0106] Figure 5 A schematic diagram of the structure of an abnormal battery detection device provided in an embodiment of the present disclosure.

[0107] like Figure 5 As shown, the abnormal battery detection device 500 may include:

[0108] The first acquisition module 501 is used to obtain the detection time and voltage value of the battery pack to be detected in each detection process;

[0109] A first determination module 502, configured to determine a voltage variation parameter corresponding to each battery pack according to a detection time and a voltage value of the battery pack in each detection process;

[0110] A second determination module 503, configured to determine the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs;

[0111] The third determination module 504 is configured to determine that a battery pack in any cluster is an abnormal battery pack when the number of batteries included in any cluster is less than a quantity threshold.

[0112] Optionally, the first determining module 502 may be specifically configured to:

[0113] According to the detection time and voltage value of the battery pack in each detection process, a plurality of voltage change values ​​corresponding to the battery pack are determined.

[0114] Optionally, the first determining module 502 may be specifically configured to perform one or more of the following:

[0115] Determine a voltage change value of the battery pack between each two adjacent detection steps according to the detection time and voltage value of the battery pack in each two adjacent detection steps;

[0116] According to the detection time and voltage value of the battery pack in any two detection processes, the voltage change value corresponding to the battery pack between any two detection processes is determined.

[0117] Optionally, the second determining module 503 may be specifically used for:

[0118] According to the voltage variation parameters corresponding to each battery pack in the same batch of battery packs, the parameter matrix corresponding to the same batch of battery packs is determined, wherein each row parameter in the parameter matrix is ​​the voltage variation parameter corresponding to one battery pack, and each column parameter in the parameter matrix is ​​the voltage variation parameter of each battery pack in the same dimension;

[0119] The parameter matrix is ​​input into the preset detection model to obtain the cluster to which each battery pack belongs and the number of batteries contained in each cluster output by the detection model.

[0120] Optionally, the third determining module 504 may also be used to:

[0121] Determine the detection time and voltage value of each detection process corresponding to the battery pack in any cluster as data to be verified;

[0122] Sending the data to be verified to the data verification device;

[0123] When the verification result returned by the verification device indicates that the battery pack in any cluster has passed the verification, the preset detection model is updated until the number of battery packs contained in the cluster to which the battery pack in any cluster belongs determined based on the detection model is greater than or equal to the quantity threshold.

[0124] Optionally, the third determining module 504 may also be used to:

[0125] Determine the central battery pack corresponding to each cluster output by the detection model;

[0126] Determine, according to the voltage variation parameter corresponding to each battery pack, the distance between the first central battery pack corresponding to any cluster and the second central battery pack corresponding to other clusters;

[0127] According to the distance, determine the model update gradient;

[0128] Based on the update gradient, the preset detection model is updated.

[0129] Optionally, the third determining module 504 may be specifically used for:

[0130] Normalize the standard deviation of each row element in the parameter matrix to obtain a normalized parameter matrix;

[0131] The normalized parameter matrix is ​​input into the preset detection model.

[0132] Optionally, the abnormal battery detection device 500 may further include:

[0133] The fourth determination module is used to determine the quantity threshold based on the total number of batteries contained in the same batch of battery packs and at least one of the detection results corresponding to the reference batch of battery packs, wherein the performance parameters of the reference batch of battery packs match the performance parameters of the same batch of battery packs to a degree greater than the matching threshold.

[0134] The functions and specific implementation principles of the above modules in the embodiments of the present disclosure can be referred to the above method embodiments, and will not be repeated here.

[0135] The abnormal battery detection device of the disclosed embodiment clusters all battery packs of the same batch based on the voltage change parameters of each battery pack between the detection processes, and determines all battery packs in any cluster whose number of battery packs is less than a threshold as abnormal batteries, thereby completing abnormal battery detection. This enables abnormal identification in the battery pack production process, enhances the detection effect of defects with slight change trends, helps improve and troubleshoot battery pack process defects, improves the pass rate and safety of battery packs, and effectively avoids battery accidents.

[0136] In order to implement the above embodiments, the present disclosure also proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the abnormal battery detection method proposed in the above embodiments of the present disclosure is implemented.

[0137] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the abnormal battery detection method proposed in the above embodiments of the present disclosure is implemented.

[0138] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The electronic device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0139] like Figure 6 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0140] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0141] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0142] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0143] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0144] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0145] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0146] The technical solution disclosed in the present invention can realize the abnormality identification in the battery pack production process, enhance the detection effect of defects with slight change trends, help to improve and troubleshoot battery pack process defects, improve the qualification rate and safety of battery packs, and effectively avoid battery accidents.

[0147] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0148] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0149] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0150] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0151] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0152] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0153] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0154] The storage medium mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. A method for detecting abnormal batteries, characterized in that: include: Obtain the detection time and voltage value of the battery pack to be detected in each detection process; Determining a voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process; Determine the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs; When the number of batteries included in any cluster is less than the number threshold, the battery pack in any cluster is determined to be an abnormal battery pack.

2. The method according to claim 1, characterized in that Determining the voltage variation parameter corresponding to each battery pack according to the detection time and voltage value of the battery pack in each detection process includes: According to the detection time and voltage value of the battery pack in each detection process, a plurality of voltage change values ​​corresponding to the battery pack are determined.

3. The method according to claim 2, characterized in that Determining a plurality of voltage change values ​​corresponding to the battery pack according to the detection time and voltage value of the battery pack in each detection process includes one or more of the following: Determine a voltage change value of the battery pack between each two adjacent detection steps according to the detection time and voltage value of the battery pack in each two adjacent detection steps; According to the detection time and voltage value of the battery pack in any two detection processes, the voltage change value of the battery pack corresponding to the two detection processes is determined.

4. The method according to claim 1, characterized in that The step of determining the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs includes: Determine a parameter matrix corresponding to the same batch of battery packs according to the voltage variation parameters corresponding to each battery pack in the same batch of battery packs, wherein each row parameter in the parameter matrix is ​​a voltage variation parameter corresponding to a battery pack, and each column parameter in the parameter matrix is ​​a voltage variation parameter of each battery pack in the same dimension; The parameter matrix is ​​input into a preset detection model to obtain the cluster to which each battery pack belongs and the number of batteries contained in each cluster output by the detection model.

5. The method according to claim 4, characterized in that After determining that the battery pack in any one of the clusters is an abnormal battery pack, the method further includes: Determine the detection time and voltage value of each detection process corresponding to the battery pack in any one of the clusters as data to be verified; Sending the data to be verified to a data verification device; When the verification result returned by the verification device indicates that the battery packs in any cluster have passed the verification, the preset detection model is updated until the number of battery packs contained in the cluster to which the battery packs in any cluster belong, determined based on the detection model, is greater than or equal to the number threshold.

6. The method according to claim 5, characterized in that The updating of the preset detection model includes: Determine a central battery pack corresponding to each cluster output by the detection model; Determine, according to the voltage variation parameter corresponding to each of the battery packs, the distance between the first central battery pack corresponding to any one of the clusters and the second central battery packs corresponding to the other clusters; Determining a model update gradient according to the distance; Based on the update gradient, the preset detection model is updated.

7. The method according to claim 4, characterized in that The step of inputting the parameter matrix into a preset detection model includes: Performing standard deviation normalization on each row element in the parameter matrix to obtain a normalized parameter matrix; The normalized parameter matrix is ​​input into the preset detection model.

8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: The quantity threshold is determined based on the total number of batteries contained in the same batch of battery packs and at least one of the detection results corresponding to the reference batch of battery packs, wherein the performance parameters of the reference batch of battery packs match the performance parameters of the same batch of battery packs to a degree greater than the matching threshold.

9. An abnormal battery detection device, characterized in that: The device comprises: The first acquisition module is used to obtain the detection time and voltage value of the battery pack to be detected in each detection process; A first determination module, used to determine a voltage variation parameter corresponding to each of the battery packs according to the detection time and voltage value of the battery pack in each detection process; A second determination module, configured to determine the cluster to which each battery pack belongs and the number of batteries contained in each cluster according to the voltage variation parameter corresponding to each battery pack in the same batch of battery packs; The third determination module is used to determine that the battery pack in any cluster is an abnormal battery pack when the number of batteries included in any cluster is less than a quantity threshold.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the abnormal battery detection method as claimed in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the abnormal battery detection method as described in any one of claims 1 to 8 is implemented.

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