A battery capacity drop identification method and device, electronic equipment and storage medium

Abstract

The application discloses a battery capacity diving identification method and device, electronic equipment and storage medium, comprising: obtaining the current and voltage of each battery monomer in the battery on the current date; calculating the current capacity characteristic coefficient of the battery monomer according to the current and voltage, determining the diagnostic coefficient and the proportional coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, and identifying whether the battery capacity diving occurs based on the diagnostic coefficient and the proportional coefficient. The capacity characteristic coefficient represents the characteristics of the capacity of the battery monomer. According to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, the change trend of the capacity characteristic coefficient of each monomer can be analyzed, the diagnostic coefficient and the proportional coefficient are obtained, the diagnostic coefficient represents the possibility of the capacity diving of the battery monomer in the recent time period, the proportional coefficient represents the reliability of the diagnostic coefficient, the stability of identifying the battery capacity diving is enhanced, the test cost is low and the method is simple, and the method is suitable for most working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery detection, and in particular to a battery capacity drop identification method and device, electronic equipment and a storage medium. BACKGROUND

[0002] Lithium batteries have the characteristics of light weight, low discharge rate and long life, and are widely used in various electrical devices, such as electric vehicles, avionics, etc.

[0003] When the battery system has a capacity drop, the battery performance is significantly degraded, and the cause may be that an individual cell in the system has a capacity drop, at which time the system may have a major safety hazard, so accurate identification of the capacity drop plays a very key role in battery safety warning.

[0004] The current method for studying capacity drop is mostly a capacity trend identification method, which accurately estimates the SOH (State of health) of the battery system through the charging curve, and then identifies based on the change trend of the SOH. This method has high precision and can directly quantitatively evaluate the capacity degradation, but it requires high quality of charging data, requires a large amount of test data support, requires a large test cost, and the algorithm design is complex, making it difficult to apply to actual working conditions. SUMMARY

[0005] The present application provides a battery capacity drop identification method to solve the problem of the existing capacity drop identification method that requires a large amount of test data support, complex algorithm design, and difficulty in applying to actual working conditions.

[0006] In a first aspect, the present application provides a battery capacity drop identification method, comprising:

[0007] Obtaining the current date current and voltage of each battery cell in the battery;

[0008] Calculating the current capacity characteristic coefficient of the battery cell according to the current and the voltage;

[0009] Obtaining the historical capacity characteristic coefficient of the battery cell at a plurality of dates before the current date;

[0010] Determine the diagnostic coefficient and the proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, the diagnostic coefficient represents the possibility of the battery cell occurring capacity drop in the recent time period, and the proportion coefficient represents the reliability of the diagnostic coefficient;

[0011] Identify whether the battery has a capacity drop based on the diagnostic coefficient and the proportion coefficient.

[0012] In a second aspect, the present application provides a battery capacity drop identification device, comprising:

[0013] a current voltage acquisition module configured to acquire a current and a voltage of each battery cell in the battery at a current date;

[0014] a current capacity characteristic coefficient calculation module configured to calculate a current capacity characteristic coefficient of the battery cell according to the current and the voltage;

[0015] a historical capacity characteristic coefficient acquisition module configured to acquire historical capacity characteristic coefficients of the battery cell at multiple dates before the current date;

[0016] a judgment parameter determination module configured to determine a diagnostic coefficient and a proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, the diagnostic coefficient representing a possibility of the battery cell to have a capacity drop in a recent time period, and the proportion coefficient representing a reliability of the diagnostic coefficient;

[0017] a capacity drop judgment module configured to identify whether the battery has a capacity drop based on the diagnostic coefficient and the proportion coefficient.

[0018] In a third aspect, the present application provides an electronic device, comprising:

[0019] at least one processor; and

[0020] a memory connected with the at least one processor in communication; wherein,

[0021] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the battery capacity drop identification method of the first aspect of the present application.

[0022] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for enabling a processor to execute the battery capacity drop identification method of the first aspect of the present application when executed.

[0023] The battery capacity jump identification method provided by the embodiment of the present application obtains the current and voltage of each battery monomer in the battery on the current date; calculates the current capacity characteristic coefficient of the battery monomer according to the current and voltage, and obtains the historical capacity characteristic coefficients of the battery monomer on multiple dates before the current date; determines the diagnostic coefficient and the proportional coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, and identifies whether the battery has capacity jump based on the diagnostic coefficient and the proportional coefficient. The capacity characteristic coefficient represents the characteristics of the capacity of the battery monomer, and according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, the change trend of the capacity characteristic coefficient of each monomer can be analyzed to obtain the diagnostic coefficient and the proportional coefficient. The diagnostic coefficient represents the possibility of capacity jump of the battery monomer in the recent time period, that is, the possibility of capacity jump of the battery in the recent time period, and whether the capacity jump occurs can be judged according to the diagnostic coefficient; the proportional coefficient represents the reliability of the diagnostic coefficient, and the greater the proportional coefficient, the more reliable the judgment result, which can enhance the stability of identifying the battery capacity jump. On the other hand, the embodiment only needs to use the capacity characteristic coefficient and the historical capacity characteristic coefficients of multiple dates for judgment, does not need a large amount of test data support, has low test cost and high efficiency, and the method is simple and suitable for most working conditions.

[0024] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0026] Figure 1 is a flowchart of a battery capacity jump identification method provided by an embodiment of the present application;

[0027] Figure 2 is a flowchart of a battery capacity jump identification method provided by an embodiment of the present application;

[0028] Figure 3 is a current data diagram of a battery monomer in a day provided by the embodiment two of the present application;

[0029] Figure 4 is a voltage heat diagram of different battery monomers in a day provided by the embodiment two of the present application;

[0030] Figure 5A polarization difference voltage heat map of different battery monomers in a day is provided by the embodiment two of the present application.

[0031] Figure 6 A current capacity characteristic coefficient of different battery monomers in a current date is provided by the embodiment two of the present application.

[0032] Figure 7 A mean value map of capacity characteristic coefficients of different battery monomers in a recent time period is provided by the embodiment two of the present application.

[0033] Figure 8 A structural schematic diagram of a battery capacity diving identification device is provided by the embodiment three of the present application.

[0034] Figure 9 A structural schematic diagram of an electronic device is provided by the embodiment four of the present application. DETAILED DESCRIPTION

[0035] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should belong to the protection scope of the present application.

[0036] Embodiment one

[0037] Figure 1 A flow chart of a battery capacity diving identification method is provided by the embodiment one of the present application. The embodiment can be applicable to the case of detecting whether the battery has capacity diving. The method can be executed by a battery capacity diving identification device. The battery capacity diving identification device can be realized in the form of hardware and / or software. The battery capacity diving identification device can be configured in an electronic device, for example, can be arranged in a vehicle-mounted computer of a vehicle.

[0038] As shown in the figure, the battery capacity diving identification method comprises: Figure 1

[0039] S101, acquiring the current and voltage of each battery monomer in the battery in a current date.

[0040] ​The battery of the embodiment is a lithium battery, and a plurality of battery cells are included in one battery, for example, the number of battery cells can be 96, and the plurality of battery cells are connected in series. When the battery is charged and discharged, the current flowing through each battery cell is the same, and the voltage can be different. During the use of the battery, the substances in the battery cells change due to electrochemical reactions, causing the internal resistance of each battery cell to change to varying degrees, and thus the voltage of each battery cell can be different.

[0041] The battery of the embodiment can be applied to electric vehicles, automatic cleaning vehicles and other electric devices. The battery management system is generally provided in the electric device, and the battery management system can collect the data of each battery cell and send it to the external device interacting therewith, so that the current and voltage of the battery cell on the current date can be obtained from the battery management system. The battery management system can collect the data of the battery cell at a preset collection period, and obtain a frame of data set each time. Each frame of data set includes the voltage and current of each battery cell. Since all the battery cells are connected in series, one frame of data set only contains one current.

[0042] S102, calculating the current capacity characteristic coefficient of the battery cell according to the current and voltage.

[0043] In the same battery, under the condition of the same remaining capacity, the voltage value changes with the size of the discharge current. When there is no current, the voltage is the highest. When discharging, the current is positive. The larger the discharge current, the lower the voltage. When charging, the current is negative. The larger the charging current, the higher the voltage.

[0044] In the battery, if the capacity of a certain battery cell is much lower than that of other battery cells, that is, the capacity of the battery cell jumps, its internal resistance will be larger than that of other battery cells. Therefore, the performance on the voltage is that the voltage during charging is higher than that of other battery cells, and the voltage during discharging is lower than that of other battery cells. Therefore, it can be known that when the capacity of the battery cell jumps, the voltage during charging is negatively correlated with the capacity, and the voltage during discharging is positively correlated with the capacity, and the current is opposite to the voltage.

[0045] Based on the above phenomenon of voltage, current and capacity, it can be known that the voltage and current are associated with the capacity of the battery. Therefore, the capacity characteristic coefficient of each battery cell can be extracted based on the voltage and current, and the value of the capacity characteristic coefficient is normalized to a fixed range. In the fixed range, the value of the capacity characteristic coefficient is positively or negatively correlated with the possibility of the capacity jump of the battery cell.

[0046] Specifically, the capacity characteristic coefficient can be calculated by setting a capacity characteristic coefficient calculation model, inputting the current and voltage into the capacity characteristic coefficient calculation model, and calculating the capacity characteristic coefficient. For one battery monomer, the voltage and current are fused into one capacity characteristic coefficient, which is convenient for comparing with the capacity characteristic coefficients of other dates to obtain the capacity characteristic change trend, and can make the analysis of the capacity characteristic change trend more simple and easy.

[0047] The current capacity characteristic coefficient is calculated according to the current and voltage, and the battery capacity is associated with the current and voltage. Therefore, the current capacity characteristic coefficient represents the capacity characteristic of each battery monomer on the current date.

[0048] S103, obtaining historical capacity characteristic coefficients of the battery monomer on multiple dates before the current date.

[0049] The multiple dates before the current date can be consecutive dates of a preset number of days. In order to ensure data richness while reducing the burden of increasing test quantity, the preset number of days can be a value between 10 and 20, for example, 13. Therefore, the historical capacity characteristic coefficients of the battery monomer on 13 days before the current date are obtained, and the capacity characteristic coefficients of the battery monomer in the past two weeks are obtained. The historical capacity characteristic coefficients of the battery monomer on multiple dates before the current date are obtained, so as to analyze the change trend of the capacity characteristic coefficients of the battery monomer in a period of time.

[0050] It should be noted that the historical capacity characteristic coefficients are also obtained by S101-S102. After obtaining the current and voltage of the battery monomer, the capacity characteristic data can be calculated and stored, which is convenient for direct calling in the future and reduces the data processing time. For the current date and the multiple dates before the current date, the frame number of the data set obtained for each date can be different. The more the frame number of the data set, the higher the data accuracy. Therefore, the accuracy of the capacity characteristic coefficients of the battery monomer of each date can be different.

[0051] S104, determining a diagnosis coefficient and a proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient.

[0052] The diagnosis coefficient represents the possibility of capacity diving of the battery monomer in the recent time period. The recent time period refers to the current date and the multiple dates before the current date. The diagnosis coefficient is a concentrated reflection of whether the battery has capacity diving in the recent time period. It should be noted that at least one battery monomer has capacity diving, which is equivalent to the entire battery having capacity diving. Therefore, the diagnosis coefficient also represents the possibility of capacity diving of the battery in the recent time period.

[0053] The current capacity characteristic coefficient and the historical capacity characteristic coefficient correspond to the same battery cell, and thus the column number of the current capacity characteristic coefficient and each historical capacity characteristic coefficient is the same. When calculating the diagnostic coefficient, the average value of the capacity characteristic coefficients of each battery cell in the recent time period can be calculated, which represents the centralized embodiment of the change trend of the capacity characteristic coefficients of each battery cell in the recent time period. If the value of the capacity characteristic coefficient is positively correlated with the possibility of capacity diving of the battery cell, the maximum average value is taken as the diagnostic coefficient. If the value of the capacity characteristic coefficient is negatively correlated with the possibility of capacity diving of the battery cell, the minimum average value is taken as the diagnostic coefficient. The diagnostic coefficient is the diagnostic coefficient of whether the entire battery has capacity diving.

[0054] For the average value of the capacity characteristic coefficients of each battery cell in the recent time period, for example, if the first battery cell corresponds to the first column of elements in the current capacity characteristic coefficient and the historical capacity characteristic coefficient, the elements in the first column of the current capacity characteristic coefficient and the historical capacity characteristic coefficient can be taken as the capacity characteristic coefficients of the first battery cell and the average value can be calculated.

[0055] The proportion coefficient represents the reliability of the diagnostic coefficient. The higher the reliability of the diagnostic coefficient, the more reliable the judgment result. The reliability of the battery capacity diving identification result is judged according to the proportion coefficient. The proportion coefficient can be the proportion of target data in all capacity diving characteristic coefficients. The target data is the capacity diving characteristic coefficient less than the preset diagnostic threshold.

[0056] S105, identifying whether the battery has capacity diving based on the diagnostic coefficient and the proportion coefficient.

[0057] A preset diagnostic threshold range can be set. When the diagnostic coefficient falls within the preset diagnostic threshold range, it indicates that the battery has capacity diving.

[0058] A preset proportion threshold can be set. If the proportion of the target data exceeds the preset proportion, it is confirmed that more battery cells have capacity diving, and it is considered that the reliability of the battery having capacity diving is higher, which can ensure the reliability of the judgment result.

[0059] The battery capacity jump identification method of the embodiment of the present application obtains the current and voltage of each battery monomer in the battery on the current date; calculates the current capacity characteristic coefficient of the battery monomer according to the current and voltage, and obtains the historical capacity characteristic coefficients of the battery monomer on multiple dates before the current date; determines the diagnostic coefficient and the proportional coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, and identifies whether the battery has a capacity jump based on the diagnostic coefficient and the proportional coefficient. The capacity characteristic coefficient represents the characteristics of the capacity of the battery monomer. According to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, the change trend of the capacity characteristic coefficient of each monomer can be analyzed, the diagnostic coefficient and the proportional coefficient are obtained, the diagnostic coefficient represents the possibility of the capacity jump of the battery monomer in the recent time period, that is, the possibility of the capacity jump of the battery in the recent time period, and whether the capacity jump occurs can be judged according to the diagnostic coefficient; the proportional coefficient represents the reliability of the diagnostic coefficient, the greater the proportional coefficient, the more reliable the judgment result, and the stability of identifying the battery capacity jump can be enhanced. On the other hand, the embodiment only needs to use the capacity characteristic coefficient and the historical capacity characteristic coefficients of multiple dates for judgment, does not need a large amount of test data support, has low test cost and high efficiency, and the method is simple and suitable for most working conditions.

[0060] Embodiment two

[0061] Figure 2 A flowchart of a battery capacity jump identification method provided for the embodiment two of the present application, the embodiment of the present application is optimized on the basis of the above-mentioned embodiment one, as shown in the figure, Figure 2 The battery capacity jump identification method comprises the following steps.

[0062] S201, obtaining the current and voltage of each battery monomer in the battery on the current date.

[0063] Generally, the current and voltage of the battery monomer can be obtained through the battery management system of the electric device where the battery is located. The battery management system can collect the data of the battery monomer and send it to the external device interacting therewith, for example, a battery capacity jump identification device, so that the current and voltage of the battery monomer on the current date can be obtained from the battery management system. The battery management system can collect the data of the battery monomer at a preset collection period, and obtain a data set each time the collection is performed. Each data set includes the voltage and current of each battery monomer. Since all the battery monomers are connected in series, one data set only contains one current.

[0064] Exemplarily, when the number of battery monomers is 96, as shown in the figure, Figure 3 The current curve of the battery monomer in a day is shown in the figure, Figure 3 The current of the 96 battery monomers is the same, wherein the abscissa is the collection time, the collection time is from 0 o'clock to 24 o'clock in a day, and the ordinate is the current value. AsFigure 4 As shown, Figure 4 The voltage heatmap for different individual battery cells was generated throughout the day, from 00:00 to 24:00. Figure 4 In the diagram, different shades of line color represent different voltage levels. The specific voltage value corresponding to each shade of color is as follows: Figure 4 As shown in the bar chart on the right, this heat map is equivalent to a three-dimensional graph, with the three dimensions being the collection time, battery cell serial number, and voltage, respectively.

[0065] S202. Form a voltage matrix based on the voltage.

[0066] In the voltage matrix, the voltages are arranged sequentially according to the frame number of the dataset. The rows of the voltage matrix represent the voltages of different frames in the dataset, and the columns represent the voltages of different individual battery cells. The voltage matrix is ​​a multi-row, multi-column matrix. For example, in the voltage matrix, the element in the 1st row and 3rd column represents the voltage of the 3rd battery cell in the 1st frame dataset.

[0067] S203. Form a current matrix based on the current.

[0068] In the current matrix, the currents are arranged sequentially according to the frame number of the dataset. The rows of the current matrix represent the currents of different frame datasets, and the columns represent the currents of individual battery cells. The current matrix is ​​a multi-row, single-column matrix. For example, in the current matrix, the element in the 5th row represents the current of all battery cells in the 5th frame dataset.

[0069] S204. Calculate the current capacity characteristic coefficient of a single battery cell using the voltage matrix and current matrix.

[0070] The current capacity characteristic coefficient is calculated based on current and voltage. Since battery capacity is related to current and voltage, the current capacity characteristic coefficient represents the capacity characteristics of each battery cell on the current date.

[0071] In one example of this embodiment, the current capacity characteristic coefficient of a battery cell is calculated using a voltage matrix and a current matrix. Specifically, this includes: detrending the voltages in the voltage matrix to obtain a polarization difference voltage matrix; summing the elements in the polarization difference voltage matrix column-wise to obtain a voltage sum matrix; summing the elements in the current matrix to obtain a current sum; and calculating the sum of squares of the elements in the current matrix. Based on the polarization difference voltage matrix, the current matrix, the voltage sum matrix, the current sum, and the sum of squares, the current capacity characteristic coefficient of the battery cell is calculated.

[0072] For example, when the number of battery cells is 96, for Figure 4 By performing detrending processing on the voltage of each individual battery cell, the polarization difference voltage of each individual battery cell can be obtained, such as... Figure 5 As shown, Figure 5For the polarization difference voltage heat map of different battery monomers in a day, in Figure 5 The line color depth represents the size of the polarization difference voltage, and the specific values of the polarization difference voltage corresponding to different color depths are shown in the right column chart in Figure 5 The smaller the value, the darker the color, and the larger the value, the lighter the color. The color ranges from deep to light, from -0.15 to 0.05.

[0073] Deduction is to remove linear trend. By removing linear trend from data, the analysis can be focused on the fluctuation of the trend data. Linear trend usually represents the systematic increase or decrease of data. For example, sensor drift may cause systematic deviation. Although the trend may be meaningful, after removing the linear trend, the influence of the deviation of the sensor when acquiring voltage data on the later calculation is eliminated, so that the capacity characteristic coefficient in the subsequent step can more accurately represent the capacity jump feature of the battery monomer.

[0074] Specifically, the voltage of the voltage matrix is de-trended to obtain a polarization difference voltage matrix, which can include: calculating the median of each row of the voltage matrix to obtain a median matrix corresponding to the voltage matrix, and subtracting the voltage matrix from the median matrix to obtain the polarization difference voltage matrix. That is, the voltage in each frame data set is de-trended to obtain the polarization difference voltage of each battery monomer.

[0075] After de-trend processing of the voltage in the voltage matrix, the mean of the polarization difference voltage of the same frame data set (the same row) in the polarization difference voltage matrix is equal to 0 or very close to 0. Specifically, the current capacity characteristic coefficient of the battery monomer is calculated by the following formula:

[0076]

[0077] Where C diff is the matrix of the current capacity characteristic coefficient of the battery monomer, I is the current matrix, n is the frame number of the data set, V diff is the polarization difference voltage matrix, V dsum is the voltage sum matrix, I sum is the current sum, and I sq_sum is the element sum of squares.

[0078] Where n is the frame number of the data set, that is, n can be the number of rows of the current matrix or the number of rows of the voltage matrix. The value of the current capacity characteristic coefficient of each battery monomer is usually in the interval [-1, 1].

[0079] For example, the number of battery monomers is 96, and the calculated matrix C diffis a 1x96 matrix, wherein each row represents the current capacity characteristic coefficient of the battery monomer with the serial number corresponding to the column number, as shown in Figure 6 Figure 6 is the current capacity characteristic coefficient of the battery monomer with the serial number 1, which corresponds to the data in the first column of the matrix C diff , which is -0.4; the current capacity characteristic coefficient of the battery monomer with the serial number 90 corresponds to the data in the 90th column of the matrix, which is -0.38.

[0080] The matrix of the current capacity characteristic coefficients is a single-row matrix, and the elements in the matrix of the current capacity characteristic coefficients correspond to the current capacity characteristic coefficients of each battery monomer in turn. For a battery monomer, the voltage and current are fused into a capacity characteristic coefficient, which facilitates comparison with the capacity characteristic coefficients of other dates to obtain the capacity characteristic change trend, and can make the analysis of the capacity characteristic change trend more simple and easy to implement. Moreover, the embodiment sets multiple parameters and formulas to calculate the capacity characteristic coefficient, so that the identification of the battery capacity drop is more accurate.

[0081] S205, acquire the historical capacity characteristic coefficients of the battery monomers on multiple dates before the current date.

[0082] The multiple dates before the current date can be consecutive dates for a preset number of days, and the historical capacity characteristic coefficients of the battery monomers on the multiple dates before the current date are acquired, so as to analyze the change trend of the capacity characteristic coefficients of the battery monomers in a period of time.

[0083] S206, stack the current capacity characteristic coefficient and the historical capacity characteristic coefficient into a capacity characteristic coefficient matrix.

[0084] , wherein the rows of the capacity characteristic coefficient matrix represent the capacity characteristic coefficients of all battery monomers in different dates, and the columns of the capacity characteristic coefficient matrix represent the capacity characteristic coefficients of different battery monomers in different dates. That is, the capacity characteristic data of all battery monomers in the first date are arranged in the first row in turn, the capacity characteristic data of all battery monomers in the second date are arranged in the second row in turn, and so on.

[0085] Exemplarily, when the number of battery monomers is 96 and the recent time period is 14 days, the calculated capacity characteristic coefficient matrix is a 14x96 matrix, wherein each row represents the capacity characteristic coefficient of each battery monomer in a day, and each column represents the capacity characteristic coefficient of the battery monomer with the serial number corresponding to the column number, for example, the data in the 4th row and the 5th column can represent the capacity characteristic coefficient of the battery monomer with the serial number 5 on the 4th day.

[0086] ​S207, average the capacity characteristic coefficient matrix by column, and take the minimum average value as the diagnosis coefficient.

[0087] In the capacity characteristic coefficient matrix, one column of data represents all capacity characteristic coefficients of one battery cell in the latest time period. The average of the capacity characteristic coefficient matrix by column can obtain the centralized representation of the capacity characteristic coefficients of all battery cells in the latest time period.

[0088] For example, when the number of battery cells is 96, the average of the capacity characteristic coefficient matrix by column can obtain a 1x96 average matrix, wherein each column value represents the average of the capacity characteristic coefficient of the battery cell with the corresponding serial number in the latest time period, as shown in Figure 7 . Figure 7 The average of the capacity characteristic coefficient of the 96 battery cells in the latest time period is shown in the figure, wherein the average of the capacity characteristic coefficient of the battery cell with serial number 1 is the data in the first column of the average matrix, which is -0.42; the average of the capacity characteristic coefficient of the battery cell with serial number 89 is the data in the 89th column of the average matrix, which is -0.944. It can be seen from Figure 7 that the average of the capacity characteristic coefficient of the battery cell with serial number 89 is the smallest, so the average value is taken as the diagnosis coefficient of the whole battery, that is, the diagnosis coefficient of the battery is -0.944.

[0089] S208, determine the number of target rows in the capacity characteristic coefficient matrix.

[0090] The target row is a row whose minimum inter-row element value is less than the preset diagnosis threshold.

[0091] S209, take the ratio of the number of target rows to the total number of rows in the capacity characteristic coefficient matrix as the proportion coefficient.

[0092] The target row is a row whose minimum inter-row element value is less than the preset diagnosis threshold, that is, the row in which the element whose minimum inter-row element value is less than the preset diagnosis threshold is determined, and then the row is marked as the target row. Then calculate the proportion of the target row to the total number of rows to obtain the proportion coefficient.

[0093] S210, if the diagnosis coefficient is less than the preset diagnosis threshold and the proportion coefficient is greater than the preset proportion threshold, it is determined that the battery has a capacity jump.

[0094] The preset diagnostic threshold can be obtained from historical data. For example, the preset diagnostic threshold can be -0.8, meaning that when the diagnostic coefficient is less than the preset diagnostic threshold, it can be preliminarily considered that the battery has experienced a capacity drop. The preset proportion threshold can also be obtained from historical data. For example, the preset proportion threshold can be 82%. The proportion coefficient represents the percentage of the target row. In the capacity feature coefficient matrix, each row represents the capacity feature coefficients of multiple battery cells on the same day. The minimum value is selected as the extreme value of the data for that day. If at least one battery cell experiences a capacity drop, then in the data for each day, there will be at least one capacity feature coefficient less than the preset diagnostic threshold. Therefore, by statistically analyzing the proportion of the target, the percentage of days (dataset frames) with capacity drops in the most recent time period can be determined, i.e., the proportion coefficient. If the proportion coefficient is greater than the preset proportion threshold, it is determined that the battery has experienced a capacity drop, avoiding interference from individual erroneous data. For example, if all capacity characteristic data of other battery cells are 0 or 1, and the capacity characteristic coefficient of a certain battery cell is -10 on day 1, while the capacity characteristic coefficient is 0 on days 2-9, then the average capacity characteristic coefficient of that battery cell is -1. Theoretically, it can be determined that the capacity of that battery cell has dropped. However, by calculating the proportional coefficient, if the capacity characteristic coefficient is less than the preset diagnostic threshold only on one day, then the proportional coefficient corresponding to the target row is small (less than the preset proportional threshold), that is, the reliability of the diagnostic coefficient is low, and it is impossible to determine that the capacity of that battery cell has dropped, or that the battery has dropped in capacity.

[0095] In an optional embodiment of the present invention, after determining that the battery has experienced a capacity drop, the method further includes: determining that the battery cells corresponding to columns with average values ​​less than a preset diagnostic threshold have experienced a capacity drop. Specifically, after calculating the average value of the capacity characteristic coefficients corresponding to the battery cells, the average value is compared with the preset diagnostic threshold. If the average value is less than the preset diagnostic threshold, the battery cell is marked. For example, if the average values ​​of battery cells 1-4 are -0.93, -0.72, 0.03, and -0.85 respectively, and the preset diagnostic threshold is -0.8, then battery cells 1 and 4 are marked. When determining that the battery has experienced a capacity drop, the marked battery cells can be directly identified as those experiencing a battery drop fault, making the process simple and efficient.

[0096] For example, such as Figure 7 As shown, Figure 7 This plot shows the average capacity characteristic coefficients of 96 individual battery cells over a recent time period. A pre-set diagnostic threshold of -0.8 is used, and a warning line is set at the average value of -0.8. Figure 7The capacity characteristic coefficient mean of the battery monomer with serial number 89 is less than-0.8, that is, the battery monomer with serial number 89 is a battery monomer that has a battery diving fault, and other battery monomers are normally operated. Setting the early warning line can facilitate the staff to quickly and efficiently see from the graph whether there is a battery monomer that has a diving fault and the serial number of the battery monomer that has a diving fault.

[0097] In order to clearly illustrate the process of battery capacity diving identification, the virtual data of two battery monomers is used as an example:

[0098] Step 1, collect the voltage and current of the battery monomer of the vehicle on the day, represented by matrices V and I respectively, with shapes of n x m and n x 1, n is the frame number of the data set, m represents the number of battery monomers, and it is assumed that the battery includes two battery monomers, then m = 2;

[0099] Step 2, calculate the monomer voltage median V media in each row of V, and detrend the voltage in V to obtain the polarization difference voltage matrix V diff .

[0100]

[0101] Step 3, V diff is summed by column to obtain V dsum , the elements of I are added to obtain I sum , and the element square sum of I is calculated to obtain I sq_sum .

[0102] V dsum = (-1, 1), it is assumed that

[0103] then I sum = -1 + (-2) = -3, I square_sum = (-1) 2 + (-2) 2 = 5;

[0104] Step 4, calculate the current capacity characteristic coefficient matrix C diff of the battery monomer, and substitute the above parameters into the formula:

[0105]

[0106] that is

[0107] Step 5, query the matrix C of the past 13-day historical capacity characteristic coefficients diff , and stack to obtain the capacity characteristic coefficient matrix C, it is assumed that the capacity characteristic coefficients of the previous 13 days are all (-1, 0);

[0108]

[0109] Step 6, C calculates the mean value of each column, marks the monomer with a mean value less than-0.8, and then calculates the minimum value to obtain the diagnostic coefficient of the capacity jump;

[0110] The mean value of each column is calculated to obtain (-13 / 14, 0 / 14) (-0.92, 0), and since the first value (-0.929) is less than-0.8, it indicates monomer No. 1, so monomer No. 1 is marked first, and the diagnostic coefficient coef is-0.929

[0111] Step 7, C calculates the minimum value of each column, and then calculates the proportion of the number of elements less than-0.8: 13 / 14=92.9%, and the proportion coefficient rate is 92.9%.

[0112] Step 8, assuming that the diagnostic coefficient is preset to-0.8 and the proportion coefficient is preset to 82%, if coef<-0.8 and rate>82%, it is determined that the marked battery monomer has a capacity jump fault, that is, the battery has a capacity jump fault.

[0113] The battery capacity jump identification method of the embodiment of the application sets a plurality of parameters and formulas to calculate the capacity characteristic coefficient, which can make the capacity characteristic coefficient more accurate, and thus the identification result of the battery capacity jump is more accurate. According to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, the change trend of the capacity characteristic coefficient of each monomer can be analyzed to obtain the diagnostic coefficient and the proportion coefficient. The diagnostic coefficient indicates the possibility of the battery monomer occurring a capacity jump in the recent time period, that is, the possibility of the battery occurring a capacity jump in the recent time period, so whether a capacity jump occurs can be judged according to the diagnostic coefficient. The proportion coefficient indicates the reliability of the diagnostic coefficient, and the larger the proportion coefficient is, the more reliable the judgment result is, avoiding the interference of false data on the identification result and enhancing the stability of identifying the battery capacity jump.

[0114] Embodiment three

[0115] Figure 8 A structural schematic diagram of a battery capacity jump identification device provided for the third embodiment of the application. As shown in the figure, the battery capacity jump identification device comprises: Figure 8

[0116] The current voltage acquisition module 301 is configured to acquire the current and voltage of each battery monomer in the battery on the current date.

[0117] The current capacity characteristic coefficient calculation module 302 is configured to calculate the current capacity characteristic coefficient of the battery monomer according to the current and voltage.

[0118] ​The historical capacity characteristic coefficient acquisition module 303 is configured to acquire historical capacity characteristic coefficients of the battery monomer at multiple dates before the current date.

[0119] The judgment parameter determination module 304 is configured to determine a diagnosis coefficient and a proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, wherein the diagnosis coefficient represents a possibility of capacity diving of the battery monomer in a recent time period, and the proportion coefficient represents a reliability of the diagnosis coefficient.

[0120] The capacity diving judgment module 305 is configured to identify whether the battery has capacity diving based on the diagnosis coefficient and the proportion coefficient.

[0121] In an optional embodiment of the present application, the current capacity characteristic coefficient calculation module 302 comprises:

[0122] The voltage matrix composition sub-module is configured to compose a voltage matrix according to the voltage, wherein a row of the voltage matrix represents voltages of different frame data sets, and a column of the voltage matrix represents voltages of different battery monomers.

[0123] The current matrix composition sub-module is configured to compose a current matrix according to the current, wherein a row of the current matrix represents currents of different frame data sets, and a column of the current matrix represents currents of the battery monomer.

[0124] The current capacity characteristic coefficient calculation sub-module is configured to calculate the current capacity characteristic coefficient of the battery monomer by using the voltage matrix and the current matrix.

[0125] In an optional embodiment of the present application, the current capacity characteristic coefficient calculation sub-module comprises:

[0126] The trend processing unit is configured to perform detrending processing on the voltages of the voltage matrix to obtain a polarization difference voltage matrix.

[0127] The voltage sum matrix acquisition unit is configured to sum elements in the polarization difference voltage matrix by column to obtain a voltage sum matrix.

[0128] The element square sum calculation unit is configured to sum elements in the current matrix to obtain a current sum, and calculate an element square sum of the current matrix.

[0129] The current capacity characteristic coefficient calculation unit is configured to calculate the current capacity characteristic coefficient of the battery monomer according to the polarization difference voltage matrix, the current matrix, the voltage sum matrix, the current sum and the element square sum.

[0130] In an optional embodiment of the present application, the current capacity characteristic coefficient of the battery monomer is calculated by the following formula:

[0131]

[0132] wherein C diff is a matrix of current capacity characteristic coefficients of the battery cells, I is the current matrix, n is the number of frames of the data set, V diff is a matrix of polarization difference voltages, V dsum is a matrix of voltage sums, I sum is a current sum, I sq_sum is an element sum of squares.

[0133] In an optional embodiment of the present application, the determination parameter determining module 304 comprises:

[0134] A capacity characteristic coefficient matrix forming sub-module is configured to stack the current capacity characteristic coefficients and the historical capacity characteristic coefficients into a capacity characteristic coefficient matrix, wherein a row of the capacity characteristic coefficient matrix represents capacity characteristic coefficients of all the battery cells on different dates, and a column of the capacity characteristic coefficient matrix represents capacity characteristic coefficients of different battery cells on different dates.

[0135] A diagnosis coefficient calculating sub-module is configured to average the capacity characteristic coefficient matrix column by column, and take the minimum average value as a diagnosis coefficient.

[0136] A target row number calculating sub-module is configured to determine the number of target rows in the capacity characteristic coefficient matrix, wherein the target rows are rows in which the minimum value of elements in the rows is less than a preset diagnosis threshold.

[0137] A proportion coefficient calculating sub-module is configured to take the ratio of the number of the target rows to the total number of rows of the capacity characteristic coefficient matrix as a proportion coefficient.

[0138] In an optional embodiment of the present application, the capacity jump determination module 305 comprises:

[0139] A capacity jump determination sub-module is configured to determine that the battery has a capacity jump if the diagnosis coefficient is less than a preset diagnosis threshold and the proportion coefficient is greater than a preset proportion threshold.

[0140] In an optional embodiment of the present application, the battery capacity jump identification device further comprises:

[0141] A cell capacity jump determination module is configured to determine that a battery cell corresponding to a column in which the average value is less than a preset diagnosis threshold has a capacity jump.

[0142] The battery capacity diving identification device provided by the embodiment of the present application can execute the battery capacity diving identification method provided by any embodiment of the present application, has the function module and the beneficial effect corresponding to the execution method.

[0143] Embodiment four

[0144] Figure 9 A structural schematic diagram of an electronic device 40 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0145] As shown in Figure 9 The electronic device 40 includes at least one processor 41, and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., which is communicatively connected to the at least one processor 41, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0146] A plurality of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0147] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes various methods and processes described above, such as the battery capacity diving identification method.

[0148] In some embodiments, the battery capacity drop identification method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 48. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 40 via, e.g., ROM 42 and / or communication unit 49. When the computer program is loaded onto RAM 43 and executed by processor 41, one or more steps of the battery capacity drop identification method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the battery capacity drop identification method by other means, e.g., with the aid of firmware.

[0149] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0150] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0151] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0153] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0154] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0155] It should be understood that the various forms of flow shown above can be used to reorder, add or delete steps. For example, each step described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which is not limited herein.

[0156] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A battery capacity dive identification method, characterized by, The method comprises: obtaining current and voltage of each battery cell in the battery on a current date; calculating a current capacity characteristic coefficient of the battery cell according to the current and the voltage; obtaining historical capacity characteristic coefficients of the battery cell on multiple dates before the current date; determining a diagnostic coefficient and a proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, the diagnostic coefficient representing a possibility of the battery cell having a capacity drop in a recent time period, and the proportion coefficient representing a reliability of the diagnostic coefficient; identifying whether the battery has a capacity drop based on the diagnostic coefficient and the proportion coefficient; calculating a current capacity characteristic coefficient of the battery cell according to the current and the voltage, comprising: composing a voltage matrix according to the voltage, a row of the voltage matrix representing voltage of different frame data sets, and a column of the voltage matrix representing voltage of different battery cells; composing a current matrix according to the current, a row of the current matrix representing current of different frame data sets, and a column of the current matrix representing current of the battery cell; calculating the current capacity characteristic coefficient of the battery cell by using the voltage matrix and the current matrix; the calculating the current capacity characteristic coefficient of the battery cell by using the voltage matrix and the current matrix, comprising: de-trending the voltage of the voltage matrix to obtain a polarization difference voltage matrix; summing elements in the polarization difference voltage matrix by column to obtain a voltage sum value matrix; adding elements in the current matrix to obtain a current sum value, and calculating a square sum of elements in the current matrix; calculating the current capacity characteristic coefficient of the battery cell according to the polarization difference voltage matrix, the current matrix, the voltage sum value matrix, the current sum value and the square sum of elements; the current capacity characteristic coefficient of the battery cell is calculated by the following formula: ； wherein, is a matrix of current capacity characteristics of the battery cells, is the current matrix, is the number of frames of the data set, is the polarization difference voltage matrix, is the voltage sum matrix, is the current sum, is the element sum of squares.

2. The method of claim 1, wherein, the determining the diagnostic coefficient and the proportion coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficients, comprising: stacking the current capacity characteristic coefficient and the historical capacity characteristic coefficients into a capacity characteristic coefficient matrix, a row of the capacity characteristic coefficient matrix representing capacity characteristic coefficients of all the battery cells in different dates, and a column of the capacity characteristic coefficient matrix representing capacity characteristic coefficients of different battery cells in different dates; averaging the capacity characteristic coefficient matrix by column, and taking the smallest average value as the diagnostic coefficient; determining a number of target rows in the capacity characteristic coefficient matrix, the target rows being rows in which the minimum value of elements in the rows is less than a preset diagnostic threshold; taking a ratio of the number of the target rows to a total number of rows of the capacity characteristic coefficient matrix as the proportion coefficient.

3. The method of claim 1, wherein, the identifying whether the battery cell has a capacity drop based on the diagnostic coefficient and the proportion coefficient, comprising: if the diagnostic coefficient is less than a preset diagnostic threshold, and the proportion coefficient is greater than a preset proportion threshold, determining that the battery has a capacity drop.

4. The method of claim 2, wherein, after determining that the battery has a capacity drop, further comprising: determining that a battery cell corresponding to a column in which the average value is less than the preset diagnostic threshold has a capacity drop.

5. A battery capacity dive identification apparatus, characterized by, The method comprises: The current capacity characteristic coefficient calculation module is configured to calculate the current capacity characteristic coefficient of the battery monomer according to the current and the voltage. The history capacity characteristic coefficient acquisition module is configured to acquire a history capacity characteristic coefficient of the battery monomer at a plurality of dates before the current date. The judgment parameter determination module is configured to determine a diagnosis coefficient and a proportion coefficient according to the current capacity characteristic coefficient and the history capacity characteristic coefficient, the diagnosis coefficient representing a possibility of capacity drop of the battery monomer in a recent time period, and the proportion coefficient representing a reliability of the diagnosis coefficient. The capacity drop judgment module is configured to identify whether the battery has a capacity drop based on the diagnosis coefficient and the proportion coefficient. The current capacity characteristic coefficient calculation module includes: The voltage matrix composition submodule is configured to compose a voltage matrix according to the voltage, a row of the voltage matrix representing a voltage of different frame data sets, and a column of the voltage matrix representing a voltage of different battery monomers. The current matrix composition submodule is configured to compose a current matrix according to the current, a row of the current matrix representing a current of different frame data sets, and a column of the current matrix representing a current of the battery monomer. The current capacity characteristic coefficient calculation submodule is configured to calculate the current capacity characteristic coefficient of the battery monomer by using the voltage matrix and the current matrix. The battery capacity drop identification device is configured to perform the battery capacity drop identification method according to any one of claims 1-4. The electronic device includes:

6. An electronic device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the battery capacity drop identification method according to any one of claims 1-4. The computer readable storage medium stores computer instructions for enabling a processor to perform the battery capacity drop identification method according to any one of claims 1-4 when executed by the processor.

7. A computer-readable storage medium, characterized in that, ​

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