Battery fault early warning method and device, electronic equipment and storage medium

Abstract

The invention relates to a battery fault early warning method and device, electronic equipment and a storage medium, and relates to the technical field of batteries, and the method comprises the steps: obtaining a battery voltage matrix, the battery voltage matrix comprises the voltage values of n single batteries at m sampling moments, and n and m are positive integers; probability calculation is carried out on the voltage value in each sliding window in the battery voltage matrix based on sliding window calculation, and a plurality of probability matrixes are obtained; determining an early warning mode of an abnormal single cell in the battery according to the plurality of probability matrixes, wherein the abnormal single cell is a single cell in n single cells of the battery; performing abnormal early warning on the abnormal monomer based on the early warning mode of the abnormal monomer; it can be seen that the probability calculation is performed on the voltages of the plurality of cells of the battery, so that the abnormal cells in the battery can be pre-warned, the accuracy of battery fault pre-warning can be improved, and the property and personal safety of a vehicle user can be better guaranteed.

Description

Technical Field

[0001] The present disclosure relates to the field of battery technology, and in particular to a battery failure early warning method, device, electronic device and storage medium. Background Art

[0002] As the number of electric vehicles continues to increase, a series of problems have become increasingly prominent, especially battery safety issues represented by spontaneous combustion incidents have become the focus of attention. Power batteries are the power source of electric vehicles. Lithium-ion batteries have high energy density and high specific power, fast ignition speed, high combustion temperature, long duration, and are difficult to extinguish. Once a fire occurs, it will seriously threaten the life and property safety of drivers and passengers. In order to protect the property and personal safety of vehicle users, power battery fault warning is one of the key methods to ensure the safe operation of electric vehicles. Therefore, providing an effective battery fault warning method is a technical problem that needs to be solved urgently. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present disclosure provides a battery failure early warning method, device, electronic device and storage medium.

[0004] According to a first aspect of an embodiment of the present disclosure, a battery failure early warning method is provided, comprising: Obtaining a battery voltage matrix, wherein the battery voltage matrix includes voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; Performing probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices; Determine an abnormal cell in the battery according to the multiple probability matrices, the abnormal cell being a cell among n cells of the battery; An abnormal warning is performed on the abnormal monomer according to the warning method corresponding to the abnormal monomer.

[0005] Optionally, the probability calculation is performed on the voltage values ​​in each sliding window in the battery voltage matrix based on a preset sliding window to obtain a plurality of probability matrices, including: Performing probability calculation on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix; wherein the first sliding window is any sliding window in the sliding window calculation multiple sliding windows; Performing probability calculation on each column of voltage values ​​in the first sliding window to obtain a second probability matrix; A probability matrix corresponding to the first sliding window is determined according to the first probability matrix and the second probability matrix.

[0006] Optionally, determining the abnormal cells in the battery according to the multiple probability matrices includes: Performing Shannon entropy calculation on each probability matrix of the multiple probability matrices to obtain Shannon entropy matrices corresponding to the multiple probability matrices; An abnormal cell in the battery is determined according to the Shannon entropy matrix.

[0007] Optionally, determining the abnormal monomer in the battery according to the Shannon entropy matrix includes: Performing Z-score calculation on the Shannon entropy matrix to obtain an abnormal coefficient matrix, wherein the abnormal coefficient matrix includes the abnormal coefficients of the n single cells at the m sampling moments; Based on the abnormal coefficient matrix, an abnormal single cell is determined from the n single cells.

[0008] Optionally, performing probability calculation on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix includes: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the row where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The first probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0009] Optionally, performing probability calculation on each column of voltage values ​​in the first sliding window to obtain a second probability matrix includes: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the column where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The second probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0010] Optionally, obtaining the battery voltage matrix includes: Obtaining respective voltage values ​​of the n single cells of the battery at m sampling moments; The battery voltage matrix is ​​constructed based on the voltage values ​​of the n single batteries at the m sampling moments.

[0011] According to a second aspect of an embodiment of the present disclosure, a battery failure warning device is provided, characterized in that it includes: An acquisition module is used to acquire a battery voltage matrix, wherein the battery voltage matrix includes voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; A calculation module, configured to perform probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices; A determination module, configured to determine an abnormal cell in the battery according to the plurality of probability matrices, wherein the abnormal cell is a cell among the n cells of the battery; The early warning module is used to issue an abnormal early warning to the abnormal monomer according to the early warning method corresponding to the abnormal monomer.

[0012] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including: processor; a memory for storing processor-executable instructions; Among them, the processor is configured to execute the executable instructions to implement the steps of the battery failure warning method provided in the first aspect of the present disclosure.

[0013] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the program instructions are executed by a processor, the steps of the battery failure warning method provided in the first aspect of the present disclosure are implemented.

[0014] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: In the above technical scheme, a battery voltage matrix is ​​obtained, which includes the voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; the voltage values ​​in each sliding window in the battery voltage matrix are probability calculated based on the sliding window calculation to obtain multiple probability matrices; the warning method of the abnormal single cell in the battery is determined according to the multiple probability matrices, and the abnormal single cell is a single cell among the n single cells of the battery; based on the warning method of the abnormal single cell, an abnormal warning is given to the abnormal single cell; it can be seen that the present disclosure can perform probability calculation on the voltages of multiple single cells of the battery, so as to detect the abnormal single cell in the battery and give a warning, which is conducive to improving the accuracy of battery fault warning, thereby better protecting the property and personal safety of vehicle users.

[0015] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0017] Figure 1 The figure is a flow chart of a battery failure warning method according to an exemplary embodiment.

[0018] Figure 2 The figure is a flow chart of a battery failure warning method according to an exemplary embodiment.

[0019] Figure 3 The figure is a flow chart of a battery failure warning method according to an exemplary embodiment.

[0020] Figure 4 The figure is a flow chart of a battery failure warning method according to an exemplary embodiment.

[0021] Figure 5 is a schematic diagram of a battery failure warning device 500 according to an exemplary embodiment.

[0022] Figure 6 is a schematic structural diagram of an electronic device 600 according to an exemplary embodiment.

[0023] Figure 7 is a schematic structural diagram of a chip 700 according to an exemplary embodiment. DETAILED DESCRIPTION

[0024] 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, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0025] It should be noted that all actions of acquiring signals, information or data in the present disclosure are performed with the authorization given by the owner of the corresponding device.

[0026] Figure 1 FIG. 1 is a flow chart showing a battery failure warning method according to an exemplary embodiment. Figure 1 As shown, the method comprises the following steps: In step S11, a battery voltage matrix is ​​obtained, where the battery voltage matrix includes voltage values ​​of n single batteries at m sampling moments, respectively, where n and m are both positive integers.

[0027] Optionally, step S11 may include the following steps: Obtaining respective voltage values ​​of n single cells of the battery at m sampling moments; The battery voltage matrix is ​​constructed based on the voltage values ​​of the n single batteries at the m sampling moments.

[0028] Exemplarily, the vehicle power battery is composed of a plurality of single cells connected in series and parallel, which together constitute the core energy source of the electric vehicle and are responsible for storing and releasing electrical energy. The battery management system (BMS) is one of the core technologies of the vehicle, responsible for monitoring and managing the state of the battery to ensure the safety and performance stability of the battery pack. Therefore, in the embodiments included in the present disclosure, the power battery state can be detected by the battery management system to perform battery failure warning. The battery management system can collect the voltage values ​​of the n single cells of the battery at m sampling times at a specified time interval, and construct an n×m battery voltage matrix based on the voltage values ​​of the n single cells at m sampling times, wherein n represents the number of single cells contained in the target battery (i.e., the number of rows of the battery voltage matrix), and m represents the number of sampling times (i.e., the number of columns of the battery voltage matrix), and n and m are both positive integers customized according to actual conditions.

[0029] Furthermore, the present disclosure may preprocess each voltage value obtained above, and obtain the corresponding preprocessed voltage values ​​of n single cells at m sampling moments. The preprocessing here may include but is not limited to any one or more of the following combinations: missing value processing, outlier processing, data conversion, data cleaning or other customized data processing methods. Among them, missing value processing may refer to filling or deleting missing values ​​to ensure data integrity. The above-mentioned outlier value processing may refer to identifying and processing outliers to prevent them from affecting the analysis results. The above-mentioned data conversion may refer to standardization or normalization of data, so that the data has the same measurement unit and range for comparison and analysis. The above-mentioned data cleaning may refer to removing duplicate, incomplete or invalid data, etc.

[0030] For example, the battery voltage matrix above can be expressed as , where a ij Represents the voltage value of the j-th single cell at the ith sampling moment; i=1, 2, …, m; j=1, 2, …, n.

[0031] In step S12, probability calculation is performed on the voltage values ​​in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices.

[0032] Exemplarily, the present disclosure does not impose any restrictions on the window size and sliding window step size of the sliding window calculation, and the window size and sliding window step size of the sliding window can be pre-customized and selected according to actual conditions; for example, the window size can be a p×n matrix, and p<m, and the sliding window includes the voltage values ​​corresponding to the above-mentioned n single cells at p sampling moments; the sliding window step size can be a positive integer less than m. Probability calculation is performed on the voltage values ​​in any p×n sliding window, including: first, according to the number of occurrences of the voltage value equal to the first voltage value in the sliding window ÷ (p×n) in the p×n voltage values, the probability corresponding to the first voltage value is determined, wherein the first voltage value is any voltage value of the p×n voltage values; then, according to the probability corresponding to each of the voltage values, a probability matrix corresponding to the sliding window is constructed, and it can be understood that the probability matrix is ​​also a p×n-order matrix; therefore, when the window size is a p×n-order matrix, a sliding window calculation is performed on the n×m-order battery voltage matrix. Taking the first sliding window in the sliding window calculation as an example, the probability matrix corresponding to the first sliding window can be expressed as ,in, represents the probability of the jth single cell at the i-th sampling moment, i=1, 2,…, p; j=1, 2,…, n.

[0033] In addition, in addition to performing probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on the sliding window calculation, mean difference calculation, median difference calculation, variance calculation, etc. can also be performed on the voltage values ​​in each sliding window in the battery voltage matrix, wherein the mean difference calculation, median difference calculation, and variance calculation can refer to the above-mentioned probability calculation method and will not be repeated here.

[0034] In step S13, an abnormal cell in the battery is determined according to the multiple probability matrices, and the abnormal cell is a cell among the n cells of the battery.

[0035] Exemplarily, each of the multiple probability matrices can reflect the change in voltage probability of n cells of the battery at p moments. Therefore, data analysis can be performed using the multiple probability matrices to determine the single cells that may have abnormalities.

[0036] For example, the average value of each column of data in the probability matrix can be used to determine whether the cell corresponding to the column of data is abnormal. When the number of abnormalities of the cell in the multiple probability matrices is greater than a specified number, the cell can be determined to be an abnormal cell. It is understood that the present disclosure does not limit the method of determining the abnormal cell in the battery based on the multiple probability matrices. In step S14, an abnormal warning is issued to the abnormal monomer according to the warning method corresponding to the abnormal monomer.

[0037] For example, the above-mentioned warning mode in the present disclosure may be a battery failure warning mode that is pre-defined and set by the system according to actual conditions, and different warning modes may adopt different warning measures. For example, the warning mode corresponding to the abnormal cell can be determined according to the number of times the abnormal cell appears abnormal in the multiple probability matrices.

[0038] In the above technical scheme, a battery voltage matrix is ​​obtained, which includes the voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; the voltage values ​​in each sliding window in the battery voltage matrix are probability calculated based on the sliding window calculation to obtain multiple probability matrices; the warning method of the abnormal single cell in the battery is determined according to the multiple probability matrices, and the abnormal single cell is a single cell among the n single cells of the battery; based on the warning method of the abnormal single cell, an abnormal warning is given to the abnormal single cell; it can be seen that the present disclosure can perform probability calculation on the voltages of multiple single cells of the battery, so as to detect the abnormal single cell in the battery and give a warning, which is conducive to improving the accuracy of battery fault warning, thereby better protecting the property and personal safety of vehicle users.

[0039] Figure 2 FIG. 1 is a flow chart showing a battery failure warning method according to an exemplary embodiment. Figure 2 As shown, step S12 includes the following steps: In step S121, probability calculation is performed on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix; wherein the first sliding window is any sliding window among the multiple sliding windows of the sliding window calculation.

[0040] For example, there are generally two situations in which the battery cell voltage is abnormal. One is the time dimension: the voltage of the same battery cell is abnormal at a certain moment; the other is the cell dimension: a certain cell is abnormal compared with the battery voltage of other different cells. Some existing methods either only consider the abnormality of the same cell at a certain moment, or only consider the abnormality of a certain cell compared with other different cells; therefore, the abnormal information of the time dimension and the cell dimension can be comprehensively considered to improve the accuracy of battery fault warning.

[0041] In each sliding window of the n×m battery voltage matrix, the row vector represents the voltage of each cell of the n cells at the same sampling time. Therefore, probability calculation can be performed on each row of voltage values ​​in the first sliding window to obtain a first probability matrix of the time dimension.

[0042] Optionally, step S121 includes the following steps: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the row where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The first probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0043] Exemplarily, the probability corresponding to the first voltage value can be determined based on the number of times the first voltage value appears in the n voltage values ​​in the row÷n, where the first voltage value is any voltage value of the p×n voltage values; after determining the probability corresponding to each voltage value in the first sliding window, the first probability matrix can be constructed based on the probability corresponding to each voltage value, and each probability position in the first probability matrix corresponds one-to-one to the position of each voltage value in the first sliding window.

[0044] In step S122, probability calculation is performed on each column of voltage values ​​in the first sliding window to obtain a second probability matrix.

[0045] Exemplarily, in each sliding window of the n×m battery voltage matrix, the column vector represents the cell voltage of a single cell at m sampling moments. Therefore, the probability calculation can be performed on each column voltage value in the first sliding window to obtain a second probability matrix of the cell dimension.

[0046] Optionally, step S122 includes the following steps: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the column where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The second probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0047] Exemplarily, the probability corresponding to the first voltage value can be determined based on the number of times the first voltage value appears in the p voltage values ​​in the column÷p, where the first voltage value is any voltage value of the p×n voltage values; after determining the probability corresponding to each voltage value in the first sliding window, the second probability matrix can be constructed based on the probability corresponding to each voltage value, and each probability position in the second probability matrix corresponds one-to-one to the position of each voltage value in the first sliding window.

[0048] In step S123, a probability matrix corresponding to the first sliding window is determined according to the first probability matrix and the second probability matrix.

[0049] Exemplarily, the first probability matrix can be determined by the first probability matrix and the second probability matrix. For example, the probabilities of the same positions of the first probability matrix and the second probability matrix are mathematically calculated and used as the data of the same positions of the probability matrices. The data calculation can be summation, average, multiplication, etc., and the present disclosure does not impose any restrictions on this.

[0050] Figure 3 FIG. 1 is a flow chart showing a battery failure warning method according to an exemplary embodiment. Figure 3 As shown, step S13 includes the following steps: In step S131, Shannon entropy calculation is performed on each of the multiple probability matrices to obtain a Shannon entropy matrix corresponding to the multiple probability matrices.

[0051] Exemplarily, the present disclosure can calculate the Shannon entropy corresponding to each column of data of each probability matrix in the multiple probability matrices, and construct a Shannon entropy matrix corresponding to the battery voltage matrix, wherein the Shannon entropy matrix is ​​a k×n matrix, wherein n is the number of single cells and k is the number of sliding windows. Therefore, the row vector of the Shannon entropy can reflect the change of the Shannon entropy of the n single cells in the sliding window corresponding to the row, and the column vector of the Shannon entropy can reflect the change of the Shannon entropy of the single cell corresponding to the column vector in the n sliding windows.

[0052] The present disclosure does not limit the implementation method of the above-mentioned Shannon entropy calculation. Specifically, the present disclosure can use the Shannon entropy calculation formula to perform the corresponding Shannon entropy calculation. The Shannon entropy calculation formula is an existing formula, and the present disclosure does not make too many limitations and details on it.

[0053] In step S132, abnormal cells in the battery are determined according to the Shannon entropy matrix.

[0054] Exemplarily, data analysis can be performed on the column vectors in the Shannon entropy matrix to determine the abnormal cells in the battery. For example, the data analysis can be performed to determine the variance of each column of Shannon entropy data. When the variance of the column of Shannon quotient data is greater than a specified threshold, the single cell battery corresponding to the column of data is determined to be an abnormal cell. It is understood that the present application does not limit the data analysis method.

[0055] Figure 4 FIG. 1 is a flow chart showing a battery failure warning method according to an exemplary embodiment. Figure 4 As shown, step S132 includes the following steps: In step S1321, the Shannon entropy matrix is ​​Z-score calculated to obtain an abnormal coefficient matrix, which includes the abnormal coefficients of the n single cells at the m sampling moments.

[0056] Exemplarily, the Z score (also called standard score) of the present disclosure can be calculated by (entropy value - mean of entropy value) / standard deviation of entropy value, which reflects the distance of the entropy value from the mean of entropy value. The present disclosure does not limit the expression of the above-mentioned abnormal coefficient matrix, which can be a matrix with the same dimension as the above-mentioned Shannon entropy evidence, or can be expressed by a Z score (i.e., abnormal coefficient) curve, etc.

[0057] In step S1322, based on the abnormal coefficient matrix, an abnormal cell is determined from the n single cells.

[0058] Exemplarily, the abnormal coefficient matrix includes the abnormal coefficients of n single cells. The present disclosure can judge each abnormal coefficient to determine whether the corresponding single cell is abnormal at a certain sampling time, and whether an early warning is needed. Specifically, when a certain abnormal coefficient is in the preset first abnormal interval, it can be determined that the corresponding single cell has no abnormality and no early warning is needed. For example, when a certain abnormal coefficient is in the preset second abnormal interval, it can be determined that the corresponding single cell may be an abnormal cell, and the early warning method of the abnormal cell can be the first early warning method. For example, when a certain abnormal coefficient is in the preset third abnormal interval, it can be determined that the corresponding single cell is an abnormal cell, and the early warning method of the abnormal cell can be the second early warning method, etc.

[0059] Among them, the first abnormal interval, the second abnormal interval and the third abnormal interval are all different from each other, and they can be customized abnormal determination intervals according to actual conditions, for example, they can be determined by trial and error and experience. Optionally, the upper limit value of the first abnormal interval can be the same as the lower limit value of the second abnormal interval, the upper limit value of the second abnormal interval can be the same as the lower limit value of the third abnormal interval, etc.

[0060] In the above technical scheme, a battery voltage matrix is ​​obtained, which includes the voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; the voltage values ​​in each sliding window in the battery voltage matrix are probability calculated based on the sliding window calculation to obtain multiple probability matrices; the warning method of the abnormal single cell in the battery is determined according to the multiple probability matrices, and the abnormal single cell is a single cell among the n single cells of the battery; based on the warning method of the abnormal single cell, an abnormal warning is given to the abnormal single cell; it can be seen that the present disclosure can perform probability calculation on the voltages of multiple single cells of the battery, so as to detect the abnormal single cell in the battery and give a warning, which is conducive to improving the accuracy of battery fault warning, thereby better protecting the property and personal safety of vehicle users.

[0061] Figure 5 FIG. 5 is a schematic diagram of a battery failure warning device 500 according to an exemplary embodiment. Figure 5As shown, the device includes: an acquisition module 510, a calculation module 520, a determination module 530, and an early warning module 540; The acquisition module 510 is used to acquire a battery voltage matrix, which includes voltage values ​​of n single batteries at m sampling moments, where n and m are both positive integers; The calculation module 520 is used to perform probability calculation on the voltage value in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices; The determination module 530 is used to determine an abnormal cell in the battery according to the multiple probability matrices, where the abnormal cell is a cell among the n cells of the battery; The warning module 540 is used to issue an abnormal warning to the abnormal unit according to the warning method corresponding to the abnormal unit.

[0062] Optionally, the calculation module 520 is used to: Probability calculation is performed on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix; wherein the first sliding window is any sliding window in the sliding window calculation multiple sliding windows; Perform probability calculation on each column of voltage values ​​in the first sliding window to obtain a second probability matrix; A probability matrix corresponding to the first sliding window is determined according to the first probability matrix and the second probability matrix.

[0063] Optionally, the determining module 530 is configured to: Performing Shannon entropy calculation on each probability matrix in the multiple probability matrices to obtain a Shannon entropy matrix corresponding to the multiple probability matrices; The abnormal cells in the battery are determined according to the Shannon entropy matrix.

[0064] Optionally, the determining module 530 is further configured to: Performing Z-score calculation on the Shannon entropy matrix to obtain an abnormal coefficient matrix, the abnormal coefficient matrix including the abnormal coefficients of the n single cells at the m sampling moments; Based on the abnormal coefficient matrix, an abnormal cell is determined from the n single cells.

[0065] Optionally, the calculation module 520 is further configured to: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the row where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The first probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0066] Optionally, the calculation module 520 is further configured to: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the column where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The second probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

[0067] Optionally, the acquisition module 510 is used to: Obtaining respective voltage values ​​of n single cells of the battery at m sampling moments; The battery voltage matrix is ​​constructed based on the voltage values ​​of the n single batteries at the m sampling moments.

[0068] In the above technical scheme, a battery voltage matrix is ​​obtained, which includes the voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; the voltage values ​​in each sliding window in the battery voltage matrix are probability calculated based on the sliding window calculation to obtain multiple probability matrices; the warning method of the abnormal single cell in the battery is determined according to the multiple probability matrices, and the abnormal single cell is a single cell among the n single cells of the battery; based on the warning method of the abnormal single cell, an abnormal warning is given to the abnormal single cell; it can be seen that the present disclosure can perform probability calculation on the voltages of multiple single cells of the battery, so as to detect the abnormal single cell in the battery and give a warning, which is conducive to improving the accuracy of battery fault warning, thereby better protecting the property and personal safety of vehicle users.

[0069] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, and when the program instructions are executed by a processor, the steps of the battery failure early warning method provided by the present disclosure are implemented.

[0070] Figure 6 6 is a schematic diagram showing the structure of a terminal device according to an exemplary embodiment. For example, the terminal device 600 may be a palm computer, a mobile phone or other electronic devices with communication functions.

[0071] Reference Figure 6 The terminal device 600 may include one or more of the following components: a processing component 602 , a memory 604 , a power component 606 , a multimedia component 608 , an audio component 610 , an input / output interface 612 , a sensor component 614 , and a communication component 616 .

[0072] The processing component 602 generally controls the overall operation of the device 600, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 602 may include one or more processors 620 to execute instructions to complete all or part of the steps of the above-mentioned battery failure early warning method. In addition, the processing component 602 may include one or more modules to facilitate the interaction between the processing component 602 and other components. For example, the processing component 602 may include a multimedia module to facilitate the interaction between the multimedia component 608 and the processing component 602.

[0073] The memory 604 is configured to store various types of data to support operations on the device 600. Examples of such data include instructions for any application or method operating on the device 600, contact data, phone book data, messages, pictures, videos, etc. The memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0074] The power supply component 606 provides power to the various components of the device 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 600.

[0075] The multimedia component 608 includes a screen that provides an output interface between the device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 608 includes a front camera and / or a rear camera. When the terminal device 600 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0076] The audio component 610 is configured to output and / or input audio signals. For example, the audio component 610 includes a microphone (MIC), and when the device 600 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 604 or sent via the communication component 616. In some embodiments, the audio component 610 also includes a speaker for outputting audio signals.

[0077] The input / output interface 612 provides an interface between the processing component 602 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0078] The sensor assembly 614 includes one or more sensors for providing various aspects of status assessment for the device 600. For example, the sensor assembly 614 can detect the open / closed state of the device 600, the relative positioning of components, such as the display and keypad of the device 600, and the sensor assembly 614 can also detect the position change of the device 600 or a component of the device 600, the presence or absence of user contact with the device 600, the orientation or acceleration / deceleration of the device 600, and the temperature change of the device 600. The sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 614 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 614 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0079] The communication component 616 is configured to facilitate wired or wireless communication between the device 600 and other devices. The device 600 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 616 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 616 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0080] In an exemplary embodiment, the device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-mentioned battery failure warning method.

[0081] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, and the instructions can be executed by a processor 620 of the device 600 to complete the above-mentioned upper battery failure early warning method. 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.

[0082] In addition to being an independent electronic device, the above-mentioned device can also be a part of an independent electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be an IC or a collection of multiple ICs; the chip can include but is not limited to the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip, SoC), etc. The above-mentioned integrated circuit or chip can be used to execute executable instructions (or codes) to implement the above-mentioned battery fault warning method. The executable instructions can be stored in the integrated circuit or chip, or can be obtained from other devices or equipment, for example, the integrated circuit or chip includes a processor, a memory, and an interface for communicating with other devices. The executable instruction may be stored in the memory, and when the executable instruction is executed by the processor, the above-mentioned battery failure warning method may be implemented; alternatively, the integrated circuit or chip may receive the executable instruction through the interface and transmit it to the processor for execution, so as to implement the above-mentioned battery failure warning method.

[0083] Figure 7 FIG. 1 is a schematic diagram showing the structure of a chip according to an exemplary embodiment. Figure 7The chip 700 shown includes a processor 701 and an interface 702. Optionally, it may also include a memory 703. The number of the processor 701 may be one or more, and the number of the interface 702 may be multiple.

[0084] In one embodiment, for a case where a chip is used to implement the method embodiment of the present disclosure: The interface 702 is used to receive or output signals; The processor 701 is used to execute part or all of the contents of the battery failure early warning method embodiment.

[0085] It is understandable that the processor in the embodiment of the present disclosure can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DigitalSignal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0086] It is understandable that the memory in the embodiments of the present disclosure may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0087] It should be noted here that the description of the above storage medium, device and chip embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present disclosure, please refer to the description of the method embodiments of the present disclosure for understanding.

[0088] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the present disclosure. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0089] It should be understood that the present disclosure is not limited to the exact 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 disclosure is limited only by the appended claims.

Claims

1. A battery failure early warning method, characterized in that: include: Obtaining a battery voltage matrix, wherein the battery voltage matrix includes voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; Performing probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices; Determine an abnormal cell in the battery according to the multiple probability matrices, the abnormal cell being a cell among n cells of the battery; An abnormal warning is performed on the abnormal monomer according to the warning method corresponding to the abnormal monomer.

2. The method according to claim 1, characterized in that The method performs probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on the preset sliding window to obtain multiple probability matrices, including: Performing probability calculation on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix; wherein the first sliding window is any sliding window in the sliding window calculation multiple sliding windows; Performing probability calculation on each column of voltage values ​​in the first sliding window to obtain a second probability matrix; A probability matrix corresponding to the first sliding window is determined according to the first probability matrix and the second probability matrix.

3. The method according to claim 1, characterized in that The determining the abnormal cells in the battery according to the multiple probability matrices comprises: Performing Shannon entropy calculation on each probability matrix of the multiple probability matrices to obtain Shannon entropy matrices corresponding to the multiple probability matrices; An abnormal cell in the battery is determined according to the Shannon entropy matrix.

4. The method according to claim 3, characterized in that The determining the abnormal monomer in the battery according to the Shannon entropy matrix includes: Performing Z-score calculation on the Shannon entropy matrix to obtain an abnormal coefficient matrix, wherein the abnormal coefficient matrix includes the abnormal coefficients of the n single cells at the m sampling moments; Based on the abnormal coefficient matrix, an abnormal single cell is determined from the n single cells.

5. The method according to claim 2, characterized in that: The performing probability calculation on each row of voltage values ​​in the first sliding window in the battery voltage matrix to obtain a first probability matrix includes: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the row where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The first probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

6. The method according to claim 2, characterized in that The performing probability calculation on each column of voltage values ​​in the first sliding window to obtain a second probability matrix includes: Determine the probability corresponding to the first voltage value according to the number of times the first voltage value in the first sliding window appears in the column where the first voltage value is located; the first voltage value is any voltage value in the first sliding window; The second probability matrix is ​​constructed according to the probability corresponding to each voltage value in the first sliding window.

7. The method according to any one of claims 1 to 6, characterized in that The step of obtaining a battery voltage matrix comprises: Obtaining respective voltage values ​​of the n single cells of the battery at m sampling moments; The battery voltage matrix is ​​constructed based on the voltage values ​​of the n single batteries at the m sampling moments.

8. A battery failure warning device, characterized in that: include: An acquisition module is used to acquire a battery voltage matrix, wherein the battery voltage matrix includes voltage values ​​of n single cells at m sampling moments, where n and m are both positive integers; A calculation module, configured to perform probability calculation on the voltage values ​​in each sliding window in the battery voltage matrix based on sliding window calculation to obtain multiple probability matrices; A determination module, configured to determine an abnormal cell in the battery according to the plurality of probability matrices, wherein the abnormal cell is a cell among the n cells of the battery; The early warning module is used to issue an abnormal early warning to the abnormal monomer according to the early warning method corresponding to the abnormal monomer.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the executable instructions to implement the steps of the method described in any one of claims 1 to 7.

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

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