Method and device for detecting micro short circuit of energy storage battery

Abstract

The invention provides an energy storage battery micro short circuit detection method and device. The method comprises the following steps: acquiring BMS data of each battery cluster; determining a first Pearson's correlation coefficient of the battery module; acquiring a second Pearson's correlation coefficient smaller than a preset first threshold value from all the first Pearson's correlation coefficients; and obtaining candidate standing voltage data of the battery cells in the battery module corresponding to the second Pearson's correlation coefficient, and based on linear fitting of the candidate standing voltage data, determining a first battery cell with micro short circuit in the battery cluster. The second Pearson's correlation coefficient smaller than the preset first threshold value is screened out from the obtained first Pearson's correlation coefficients of the battery module, and the linear fitting operation is performed according to the second Pearson's correlation coefficient, so that the first battery cell with the micro short circuit in the battery cluster is obtained, the micro short circuit detection precision of the energy storage battery is greatly improved, and the detection efficiency of the energy storage battery is improved. And the calculation amount of the micro-short circuit state is reduced to the greatest extent, so that the complexity of the energy storage battery system is reduced, and the method has wide applicability.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage battery detection, and in particular to a method and device for detecting micro-short circuits in energy storage batteries. Background Art

[0002] During the operation of energy storage batteries, micro short circuits may occur, which may lead to safety risks such as battery overheating and battery explosion. In the existing micro short circuit detection scheme, either judgment is based on experience, which has poor accuracy; or by obtaining parameters such as open circuit voltage, current, polarization voltage, ohmic internal resistance, and establishing a corresponding relationship between parameters and charge state, to determine whether a micro short circuit has occurred, but this scheme has a huge amount of calculations, which greatly increases the complexity of the system.

[0003] It should be noted that the above introduction to the technical background is only for the convenience of providing a clear and complete description of the technical solutions of the present application and for the convenience of understanding by those skilled in the art. It cannot be considered that the above technical solutions are well known to those skilled in the art simply because they are described in the background technology section of the present application. Summary of the invention

[0004] The embodiments of the present application provide a method and device for detecting micro-short circuits in energy storage batteries.

[0005] The first embodiment of the present application provides a method for detecting micro-short circuits in energy storage batteries, comprising:

[0006] Get the BMS data of each battery cluster;

[0007] Determine the first Pearson correlation coefficient of the battery module based on preprocessing all BMS data;

[0008] Obtaining a second Pearson correlation coefficient that is less than a preset first threshold from all the first Pearson correlation coefficients;

[0009] Candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient is obtained, and based on linear fitting of the candidate static voltage data, a first battery cell in the battery cluster where a micro short circuit occurs is determined.

[0010] The second embodiment of the present application provides a micro-short circuit detection device for an energy storage battery, which is used to implement the micro-short circuit detection method for an energy storage battery provided in the first embodiment of the present application, including:

[0011] A first acquisition module, the first acquisition module is used to acquire BMS data of each battery cluster;

[0012] A second acquisition module, the second acquisition module is used to determine a first Pearson correlation coefficient of the battery module based on preprocessing all BMS data;

[0013] A third acquisition module, the third acquisition module is used to acquire a second Pearson correlation coefficient that is less than a preset first threshold from all the first Pearson correlation coefficients;

[0014] A fourth acquisition module is used to obtain candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient, and determine the first battery cell with a micro-short circuit in the battery cluster based on linear fitting of the candidate static voltage data.

[0015] The third aspect embodiment of the present application proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the energy storage battery micro-short circuit detection method proposed in the first aspect embodiment of the present application.

[0016] The fourth aspect embodiment of the present application proposes a non-temporary computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the method proposed in the first aspect embodiment of the present application.

[0017] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which implements the method proposed in the first aspect embodiment of the present application when executed by a processor in a communication device.

[0018] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0019] By obtaining the first Pearson correlation coefficient of the battery module, screening out the second Pearson correlation coefficient that is less than the preset first threshold, and performing a linear fitting operation based on the second Pearson correlation coefficient, the first battery cell where a micro-short circuit occurs in the battery cluster is obtained, thereby greatly improving the micro-short circuit detection accuracy of the energy storage battery and minimizing the amount of calculation of the micro-short circuit state, thereby reducing the complexity of the energy storage battery system, and having wide applicability.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic diagram of a method for detecting micro-short circuits in energy storage batteries provided in an embodiment of the present application;

[0023] Figure 2A schematic flow chart of another method for detecting micro-short circuits in energy storage batteries provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a micro-short circuit detection device for an energy storage battery provided in an embodiment of the present application;

[0025] Figure 4 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application;

[0026] Figure 5 The figure is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present application, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at..." or "when..." or "in response to determination".

[0030] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes a method and device for detecting micro-short circuits of energy storage batteries according to an embodiment of the present application with reference to the accompanying drawings.

[0032] Figure 1 A schematic diagram of a method for detecting micro-short circuits in energy storage batteries provided in an embodiment of the present application. Figure 1 As shown, the method includes but is not limited to the following steps:

[0033] S101, obtaining BMS data of each battery cluster.

[0034] It should be noted that in the energy storage battery system, battery clusters, battery modules and cells are indispensable components. There is a close relationship between them, and together they constitute the core components of the energy storage battery system. The cell is the basic unit of the energy storage battery system, usually composed of a positive electrode, a negative electrode and an electrolyte between the two, which is used to store and release electrical energy. The battery module is a modular component assembled from multiple cells in series or parallel, which is used to connect multiple cells into a whole to provide greater energy storage capacity and output power; the battery module also includes a bracket, a management system (such as a BMS slave board, BMS stands for Battery Management System, that is, a battery management system. The BMS slave board is also called the BMS slave control board, which is responsible for monitoring the battery module's cell voltage, cell temperature, current and other key information, and transmits this information to the battery main board to ensure accurate monitoring of the battery status), and the outer shell and other components. A battery cluster is a larger unit composed of multiple battery modules. It is used to integrate multiple battery modules together to form an integral unit to provide greater energy output and higher voltage levels. It aims to optimize the space utilization of the energy storage battery system, improve energy density, and ensure the safe and reliable operation of the battery system.

[0035] In a feasible implementation, the BMS data of the battery cluster generally involves communication with the battery management system to collect information about key parameters such as battery status, health, temperature, voltage, current, etc. First, it is necessary to identify the communication protocols and interfaces supported by the battery management system. Then ensure that the communication connection between the BMS data acquisition device and the battery management system is correct. Next, according to the communication protocol of the BMS, send appropriate requests to obtain the required data, which may include the identifier of the battery cluster, the type of data requested (such as voltage, current, temperature, etc.) and other necessary parameters, and extract the BMS data of the battery cluster required for this embodiment by parsing the response received from the battery management system, such as cell current, battery state of charge (State Of Charge, referred to as SOC), etc. After obtaining the BMS data, it is processed in real time, such as calculating the average value, abnormality detection, etc.

[0036] S102, determining a first Pearson correlation coefficient of the battery module based on preprocessing of all BMS data.

[0037] In a feasible implementation, BMS data generally includes time series data of parameters such as voltage, current, and temperature of each battery cell of each battery module in each battery cluster. BMS data inevitably includes outliers (voltage, current values ​​that exceed the normal range, etc.), missing values, etc. Therefore, the BMS data can be preprocessed accordingly, wherein outliers are removed; for missing values, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) can be used to fill in missing data points; after completing outlier cleaning and filling in missing values, the BMS data can also be aligned to ensure that all BMS data are aligned on the time axis.

[0038] Further, according to the correspondence between the battery cells, battery modules and battery clusters, the BMS data is grouped according to the battery modules, and then the first Pearson correlation coefficient between the battery modules is calculated. As an example, the BMS data of two battery modules can be selected from the selected battery cluster. Then, the average values ​​of the two battery modules on the selected variable (for example, the selected variable can be voltage) are calculated respectively. Then, the covariance of the two battery modules is calculated based on the following formula:

[0039]

[0040] Where X and Y are the BMS data sequences of the two battery modules; n is the number of data points, and are the average values ​​of X and Y respectively. Then calculate the standard deviation σ of the two battery modules respectively X and σ Y Then, the first Pearson correlation coefficient between the two battery modules is calculated based on the covariance and standard deviation of the two battery modules, where the calculation formula of the first Pearson correlation coefficient is as follows:

[0041]

[0042] Then repeat the above steps for each pair of battery modules in the selected battery cluster to obtain the first Pearson correlation coefficient between each pair of battery modules in the battery cluster. The value range of the first Pearson correlation coefficient is between -1 and +1, where +1 indicates that there is a complete positive correlation between the two variables, that is, the increase of one variable is accompanied by the increase of the other variable; -1 indicates that there is a complete negative correlation between the two variables, that is, the increase of one variable is accompanied by the decrease of the other variable; 0 indicates that there is no linear correlation between the two variables.

[0043] S103: Obtain a second Pearson correlation coefficient that is less than a preset first threshold from all first Pearson correlation coefficients.

[0044] In a feasible implementation, a preset first threshold is used to determine which first Pearson correlation coefficients are "significant" and which are not significant enough, and the setting of the first threshold should be based on the actual situation of the data. After having the first Pearson correlation coefficient and the first threshold, the next step is to perform a screening operation. This process involves comparing all the first Pearson correlation coefficients in each battery cluster with the first threshold to find the second Pearson correlation coefficient that meets the conditions. The screened second Pearson correlation coefficients are then analyzed and interpreted. The second Pearson correlation coefficient may indicate a weaker or stronger linear relationship between battery modules, or may indicate that under certain specific conditions, the performance differences between these battery modules are large. Based on these second Pearson correlation coefficients, the overall performance of the battery cluster can be further evaluated, or corresponding optimization strategies can be specified.

[0045] It should be noted that the first threshold should be set with caution. A first threshold that is too high or too low may lead to deviations in the results. In practical applications, it may be necessary to combine multiple factors to comprehensively determine the first threshold. The first Pearson correlation coefficient and the second Pearson correlation coefficient indicate the degree of linear correlation between two variables, and do not indicate a causal relationship between them. Therefore, caution is required when interpreting the results.

[0046] S104, obtaining candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient, and determining the first battery cell having a micro short circuit in the battery cluster based on linear fitting of the candidate static voltage data.

[0047] In a feasible implementation, the corresponding battery modules are determined according to the second Pearson correlation coefficient. These modules may show a weaker linear relationship, indicating that there may be performance differences or potential problems in the internal cells. For the selected battery module, the voltage data of all the internal cells in the static state are collected, that is, the candidate static voltage data, wherein the static state generally refers to the stable state of the battery cell when there is no reverse charge and discharge operation. The voltage at this time is mainly determined by the internal characteristics of the battery cell and the environmental conditions. It should be noted that data collection can be carried out directly through the battery management system, or it can be carried out in the operating environment of the energy storage battery through special testing equipment. The specific collection process should be set according to the needs and will not be repeated here.

[0048] Furthermore, the collected candidate static voltage data are subjected to data preprocessing, including removing outliers, filling missing values, and aligning data. Then, a linear regression model can be used to fit the candidate static voltage data, or the least squares method can be used to fit the candidate static voltage data, or the ridge regression method can be used to fit the candidate static voltage data, or the gradient descent method can be used to fit the candidate static voltage data, or the polynomial fitting method can be used to fit the candidate static voltage data. Specifically, the candidate static voltage data of the battery cell corresponding to the second Pearson correlation coefficient is used as the dependent variable, and time is used as the independent variable. Based on the fitting operation, the linear relationship between the candidate static voltage data is obtained, and then according to the linear relationship, the slope, intercept, and related statistics of the fitting line of each battery cell are obtained (for example, the related statistics can be R 2 Compare the slope, intercept and R of the fitted straight line of each cell. 2 If a cell has a micro-short circuit, it may show a voltage change trend different from other cells in a static state. The significant difference in the slope or intercept of the fitted straight line can be used to identify the first cell with a micro-short circuit in the battery cluster.

[0049] On the other hand, in addition to the linear fitting results, a comprehensive judgment can also be made through battery cell temperature data, historical performance records, etc. If a battery cell shows significant differences from other battery cells in terms of voltage change trends, temperature anomalies, etc., then it is likely to be the first battery cell to experience a micro short circuit.

[0050] In summary, by screening out the second Pearson correlation coefficient that is less than the preset first threshold value from the first Pearson correlation coefficient of the obtained battery module, and performing a linear fitting operation based on the second Pearson correlation coefficient, the first battery cell where a micro-short circuit occurs in the battery cluster is obtained, thereby greatly improving the micro-short circuit detection accuracy of the energy storage battery, and minimizing the amount of calculation of the micro-short circuit state, thereby reducing the complexity of the energy storage battery system, and having wide applicability.

[0051] Figure 2 This is a flow chart of another method for detecting micro short circuits in energy storage batteries provided in an embodiment of the present application. Figure 2 As shown, the method includes but is not limited to the following steps:

[0052] S201, based on a preset first sampling period and a BMS system architecture, data collection is performed on the energy storage battery to obtain BMS data of each battery cluster.

[0053] In a feasible implementation, firstly, the BMS system architecture of the energy storage battery is identified, wherein the BMS system architecture of the energy storage battery generally includes multiple levels, each level is responsible for different functions and tasks, so as to ensure the safe and efficient operation of the entire energy storage battery system. According to the specific level, the BMS system architecture of the energy storage battery includes the slave control module (the slave control module is the bottom layer of the BMS system, responsible for directly monitoring and managing a single battery or battery module. Each slave control module usually contains multiple sensors for measuring key parameters such as battery voltage and temperature, and transmitting these data to the upper module), the master control module (the master control module receives data from the slave control module for aggregation, processing and analysis. It is responsible for executing advanced control algorithms such as battery balancing control, state of charge estimation, health status assessment, etc. At the same time, the master control module is also responsible for communicating with others such as PCS, EMS, etc., exchanging battery status information and control instructions), the master control module (responsible for comprehensive monitoring and management of the entire battery system. It receives data from the master control module, performs further aggregation and analysis, and provides a user-friendly interface to display battery status information. At the same time, the master control module is also responsible for the fault diagnosis and alarm functions of the entire BMS system), communication interface, battery balancing device, and safety devices (such as overcharge protection, over-discharge protection, overheating protection, short circuit protection, etc.).

[0054] Further, as an example, the first sampling period can be set to 24 hours, and then based on the BMS system architecture, data collection is performed on the energy storage battery to obtain corresponding BMS data.

[0055] For a detailed description of step S201, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0056] S202, determining a first Pearson correlation coefficient of the battery module based on preprocessing all BMS data.

[0057] In a feasible implementation, all BMS data are formatted according to a preset data structure to obtain first intermediate data, wherein the data structure may include time, current, state of charge, voltage, etc. The corresponding data structure should be set according to specific needs, and the specific setting process will not be repeated here.

[0058] After obtaining the first intermediate data, the first intermediate data is parsed according to the BMS system architecture to obtain the second intermediate data. Optionally, according to the BMS system architecture, the first intermediate data is parsed to obtain the charging data related to the first sampling period (i.e., the charging data within 24 hours, wherein, if the current in the data structure is less than 0, it indicates that the battery cell is charged; if the current is equal to 0, it indicates that the battery cell is at rest; if the current is greater than 0, it indicates that the battery cell is discharged); then based on the charging data, the second intermediate data that meets the preset battery state of charge is screened out from all the first intermediate data. As an example, if the battery cell is discharged and the battery state of charge is greater than 0.8, the first intermediate data is marked as the second intermediate data, and then the next step is continued.

[0059] After obtaining the second intermediate data, the second intermediate data is preprocessed according to a preset second sampling period to obtain third intermediate data. Optionally, as an example, the second sampling period may be 5 seconds. According to the second sampling period, the second intermediate data is resampled (including data clearing, data padding, data alignment, etc.) to obtain sampled data. Then, according to the connection relationship of the battery modules in each battery cluster, the sampled data is grouped to obtain third intermediate data.

[0060] After obtaining the third intermediate data, based on the Pearson correlation operation on the third intermediate data, the first Pearson correlation coefficient of the battery module is obtained. Optionally, according to the battery module grouping corresponding to the third intermediate data, the window width and the amount of data in the window that need to be processed by the sliding window operation are determined. According to the window width and the amount of data in the window, the third intermediate data is processed by sliding window to obtain the time series data of the state of charge of each battery module. As an example, the window width can be 5 minutes, and the amount of data in the window can be 1000. Then the first window contains 1000 data within 5 minutes (the data is set in the form of rows, and the columns of the data are the same as the number of cells in the battery module), that is, the first window and the data contained therein can be characterized as time series data; the second window contains 1000 data within 5 minutes; and so on, the number of windows is equal to the number of cells in the battery module. Based on the Pearson correlation operation on the time series data, the first Pearson correlation coefficient of the battery module is obtained. As an example, the first window corresponding to the first battery cell contains 1000 data within 5 minutes; the second window corresponding to the second battery cell contains 1000 data within 5 minutes; the first window and the second window are subjected to Pearson correlation operation to obtain the first Pearson correlation coefficient between the first battery cell and the second battery cell in the battery module, and so on, to calculate the first Pearson correlation coefficient between all battery cells in the battery module.

[0061] For further details on step S202, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0062] S203: Obtain a second Pearson correlation coefficient that is less than a preset first threshold from all first Pearson correlation coefficients.

[0063] In a feasible implementation, based on the connection relationship of the battery modules in each battery cluster, all the first Pearson correlation coefficients are matrixed to obtain the first matrix D of the battery module, wherein the number of rows and columns of the first matrix D are equal to the number of cells in the battery module, and each element in the first matrix D is equal to the first Pearson correlation coefficient, wherein D 12 The element in the first row and second column of the first matrix D is the first Pearson correlation coefficient between the first battery cell and the second battery cell.

[0064] After obtaining the first matrix D of the battery module, the element rows in the first matrix D are summed to obtain the correlation result of each battery cell. As an example, if there are five battery cells in the battery module, the first matrix D is a 5×5 matrix, then the element D 11 +Element D 21 +Element D 31 +Element D 41 +Element D 51 The value of is the correlation result of the first battery cell in the battery module.

[0065] After obtaining the correlation results of each battery cell, a second Pearson correlation coefficient that is less than a preset first threshold is screened out from the correlation results, wherein the first threshold should be set according to a specific usage scenario, and the specific setting process will not be repeated here.

[0066] For further details on step S203, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0067] S204, obtaining candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient, and determining the first battery cell having a micro short circuit in the battery cluster based on linear fitting of the candidate static voltage data.

[0068] In a feasible implementation, based on the second Pearson correlation coefficient, multiple static voltage data of the corresponding battery cell are obtained; then, from the multiple static voltage data, the first static voltage data greater than the preset first static period (as an example, the first static period can be 6 hours) is screened out; from the first static period, the second static period in which the polarization effect is filtered out of the battery cell is screened out (as an example, in the first static period, which can be within 6 hours, there is usually time for the battery cell to undergo battery polarization, and battery polarization will cause the point to deviate from the equilibrium potential, therefore, the polarization time of the first static period is eliminated, and the specific polarization time should be obtained by referring to the battery cell manual, which will not be described here); then, according to the second static period and the preset number of data (wherein the preset number of data can be 100 0, if it is greater than 1000, proceed to the next step; if it is less than or equal to 1000, end the operation of screening the micro-short-circuited battery cells), screen out candidate static voltage data from all the first static voltage data; based on linear fitting of the candidate static voltage data, obtain the static voltage curve corresponding to each candidate static voltage data; from all the static voltage curves, screen out the first static voltage curve with a slope less than zero; according to the duration of the voltage drop corresponding to the first static voltage curve (including querying the correspondence table of the battery cell state of charge and the open circuit voltage), determine the first battery cell with a micro-short circuit in the battery cluster, specifically, divide the battery cell capacity drop value by the duration to obtain the leakage current of the battery cell; then divide the open circuit voltage by the leakage current to obtain the micro-short circuit internal resistance of the first battery cell.

[0069] For further details on step S204, please refer to the relevant contents in the above embodiment, which will not be repeated here.

[0070] In summary, by screening out the second Pearson correlation coefficient that is less than the preset first threshold value from the first Pearson correlation coefficient of the obtained battery module, and performing a linear fitting operation based on the second Pearson correlation coefficient, the first battery cell where a micro-short circuit occurs in the battery cluster is obtained, thereby greatly improving the micro-short circuit detection accuracy of the energy storage battery, and minimizing the amount of calculation of the micro-short circuit state, thereby reducing the complexity of the energy storage battery system, and having wide applicability.

[0071] Figure 3 This is a schematic diagram of the structure of a micro-short circuit detection device for energy storage batteries provided in an embodiment of the present application. Figure 3 As shown, the energy storage battery micro-short circuit detection device 300 is used to implement the energy storage battery micro-short circuit detection method provided in the embodiment of the present application. The energy storage battery micro-short circuit detection device 300 includes:

[0072] A first acquisition module 301, the first acquisition module 301 is used to acquire BMS data of each battery cluster;

[0073] A second acquisition module 302, the second acquisition module 302 is used to determine a first Pearson correlation coefficient of the battery module based on preprocessing all BMS data;

[0074] A third acquisition module 303, the third acquisition module 303 is used to acquire a second Pearson correlation coefficient that is less than a preset first threshold from all first Pearson correlation coefficients;

[0075] The fourth acquisition module 304 is used to obtain candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient, and determine the first battery cell with a micro short circuit in the battery cluster based on linear fitting of the candidate static voltage data.

[0076] Figure 4 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0077] like Figure 4 As shown, the electronic device 400 includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a memory 406 to a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 are also stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0078] The following components are connected to the I / O interface 405: a memory 406 including a hard disk, etc.; and a communication part 407 including a network interface card such as a LAN (Local Area Network) card, a modem, etc., and the communication part 407 performs communication processing via a network such as the Internet; a drive 408 is also connected to the I / O interface 405 as needed.

[0079] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 407. When the computer program is executed by the processor 401, the above-mentioned functions defined in the method of the present application are executed.

[0080] In an exemplary embodiment, a storage medium including instructions is also provided, such as a memory including instructions, and the instructions can be executed by the processor 401 of the electronic device 400 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0081] In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0082] Figure 5 The figure is a schematic diagram of the structure of another electronic device provided according to an embodiment of the present application. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 5 As shown, the electronic device 500 includes a processor 501 and a memory 502. The memory 502 is used to store program codes, and the processor 501 is connected to the memory 502 to read the program codes from the memory 502 to implement the energy storage battery micro-short circuit detection method in the above embodiment.

[0083] Optionally, the number of processors 501 may be one or more.

[0084] Optionally, the electronic device may further include an interface 503, and the number of the interface 503 may be multiple. The interface 503 may be connected to an application program and may receive data from an external device such as a sensor.

[0085] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0086] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for detecting micro-short circuit of energy storage battery, characterized in that: include: Get the BMS data of each battery cluster; Determine the first Pearson correlation coefficient of the battery module based on preprocessing all BMS data; Obtaining a second Pearson correlation coefficient that is less than a preset first threshold from all the first Pearson correlation coefficients; Candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient is obtained, and based on linear fitting of the candidate static voltage data, a first battery cell in the battery cluster where a micro short circuit occurs is determined.

2. The method according to claim 1, characterized in that The obtaining of BMS data of each battery cluster includes: Identify the BMS system architecture of energy storage batteries; Based on the preset first sampling period and BMS system architecture, data is collected on the energy storage battery to obtain the BMS data of each battery cluster.

3. The method according to claim 1 or 2, characterized in that: The method of determining the first Pearson correlation coefficient of the battery module based on preprocessing all BMS data includes: According to a preset data structure, all BMS data are formatted to obtain first intermediate data; According to the BMS system architecture, the first intermediate data is parsed to obtain second intermediate data; Preprocessing the second intermediate data according to a preset second sampling period to obtain third intermediate data; Based on performing a Pearson correlation operation on the third intermediate data, a first Pearson correlation coefficient of the battery module is obtained.

4. The method according to claim 3, characterized in that The step of parsing the first intermediate data according to the BMS system architecture to obtain the second intermediate data includes: According to the BMS system architecture, the first intermediate data is parsed to obtain charging data related to the first sampling period; Based on the charging data, second intermediate data that meets a preset battery state of charge is screened out from the first intermediate data.

5. The method according to claim 3, characterized in that: The preprocessing of the second intermediate data according to the preset second sampling period to obtain third intermediate data includes: Resampling the second intermediate data according to a preset second sampling period to obtain sampled data; The sampled data are grouped according to the connection relationship between the battery modules in each battery cluster to obtain third intermediate data.

6. The method according to claim 3, characterized in that: The step of obtaining a first Pearson correlation coefficient of the battery module based on performing a Pearson correlation operation on the third intermediate data includes: Determining the window width and the amount of data in the window that need to be subjected to a sliding window processing operation according to the battery module grouping corresponding to the third intermediate data; According to the window width and the amount of data in the window, sliding window processing is performed on the third intermediate data to obtain time series data of the state of charge of each battery module; Based on performing a Pearson correlation operation on the time series data, a first Pearson correlation coefficient of the battery module is obtained.

7. The method according to any one of claims 1 to 6, characterized in that The step of obtaining a second Pearson correlation coefficient less than a preset first threshold value from all the first Pearson correlation coefficients includes: Based on the connection relationship of the battery modules in each battery cluster, all the first Pearson correlation coefficients are matrixed to obtain the first matrix of the battery modules; Obtaining a correlation result for each battery cell based on a row summation operation on the elements in the first matrix; From the correlation results, a second Pearson correlation coefficient that is less than a preset first threshold is screened out.

8. The method according to claim 1, characterized in that The obtaining of candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient includes: Based on the second Pearson correlation coefficient, acquiring a plurality of static voltage data of the corresponding battery cell; Filtering out first static voltage data greater than a preset first static period from the plurality of static voltage data; From the first rest period, selecting a second rest period for filtering out polarization effects in the battery cell; According to the second static period and a preset data quantity, candidate static voltage data are screened out from all the first static voltage data.

9. The method according to claim 1, characterized in that: The method of determining the first battery cell having a micro short circuit in the battery cluster based on linear fitting of the candidate static voltage data includes: Based on linear fitting of the candidate static voltage data, a static voltage curve corresponding to each candidate static voltage data is obtained; Screening out a first static voltage curve having a slope less than zero from all the static voltage curves; The first battery cell having a micro short circuit in the battery cluster is determined according to the duration of the voltage drop corresponding to the first static voltage curve.

10. A micro-short circuit detection device for energy storage batteries, used to implement the micro-short circuit detection method for energy storage batteries as described in any one of claims 1 to 9, characterized in that: include: A first acquisition module, the first acquisition module is used to acquire BMS data of each battery cluster; A second acquisition module, the second acquisition module is used to determine a first Pearson correlation coefficient of the battery module based on preprocessing all BMS data; A third acquisition module, the third acquisition module is used to acquire a second Pearson correlation coefficient that is less than a preset first threshold from all the first Pearson correlation coefficients; A fourth acquisition module is used to obtain candidate static voltage data of the battery cells in the battery module corresponding to the second Pearson correlation coefficient, and determine the first battery cell with a micro-short circuit in the battery cluster based on linear fitting of the candidate static voltage data.

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