CN115754756A - Battery capacity diving identification method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a method and a device for identifying battery capacity diving, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring the current and voltage of each battery cell in the battery on the current date; the method comprises the steps of calculating a current capacity characteristic coefficient of a single battery according to current and voltage, determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and a historical capacity characteristic coefficient, and identifying whether the battery has capacity diving or not based on the diagnosis coefficient and the proportionality coefficient. The capacity characteristic coefficient represents the characteristics of the capacity of the single battery, the change trend of the capacity characteristic coefficient of each single battery can be analyzed according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient to obtain a diagnosis coefficient and a proportionality coefficient, the diagnosis coefficient represents the possibility of capacity diving of the single battery in the recent time period, the proportionality coefficient represents the reliability of the diagnosis coefficient, the stability of identifying the capacity diving of the battery is enhanced, the test cost is low, the method is simple, and the method is suitable for most working conditions.
Description
Technical Field
The invention relates to the technical field of battery detection, in particular to a battery capacity diving identification method and device, electronic equipment and a storage medium.
Background
Lithium batteries have the characteristics of light weight, low discharge rate and long service life, and are widely applied to various electrical devices, such as electric vehicles, avionics devices and the like.
When the battery system generates capacity diving, the performance of the battery is obviously attenuated, so that the system may have great potential safety hazard due to the fact that the individual monomer in the system generates the capacity diving, and therefore accurate identification of the capacity diving plays a very key role in battery safety early warning.
At present, a capacity trend identification method is mostly adopted in a method for researching capacity diving, and the method accurately estimates the State of health (SOH) of a battery system through a charging curve and then identifies the SOH based on the change trend of the SOH. The method is high in precision, can directly and quantitatively evaluate the capacity fading condition, but has high requirements on the quality of charging data, needs a large amount of test data for support, needs high test cost, is complex in algorithm design, and is difficult to apply to actual working conditions.
Disclosure of Invention
The invention provides a battery capacity diving identification method, which aims to solve the problems that the existing capacity diving identification method needs a large amount of test data for support, is complex in algorithm design and is difficult to apply to actual working conditions.
In a first aspect, the present invention provides a method for identifying battery capacity diving, including:
acquiring the current and voltage of each battery cell in the battery on the current date;
calculating the current capacity characteristic coefficient of the battery cell according to the current and the voltage;
acquiring historical capacity characteristic coefficients of the battery monomer on a plurality of dates before the current date;
determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, wherein the diagnosis coefficient represents the possibility of capacity diving of the single battery in the recent time period, and the proportionality coefficient represents the reliability of the diagnosis coefficient;
and identifying whether the battery generates capacity diving or not based on the diagnosis coefficient and the proportionality coefficient.
In a second aspect, the present invention provides a battery capacity diving identification device, comprising:
the current and voltage acquisition module is used for acquiring the current and voltage of each battery cell in the battery on the current date;
the current capacity characteristic coefficient calculation module is used for calculating the current capacity characteristic coefficient of the single battery according to the current and the voltage;
the historical capacity characteristic coefficient acquisition module is used for acquiring historical capacity characteristic coefficients of the battery monomer on a plurality of dates before the current date;
the judgment parameter determining module is used for determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, wherein the diagnosis coefficient represents the possibility of capacity diving of the single battery in the latest time period, and the proportionality coefficient represents the reliability of the diagnosis coefficient;
and the capacity diving judgment module is used for identifying whether the battery generates capacity diving or not based on the diagnosis coefficient and the proportionality coefficient.
In a third aspect, the present invention provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the battery capacity diving identification method according to the first aspect of the present invention.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to implement the battery capacity diving identification method according to the first aspect of the present invention when executed.
According to the battery capacity diving identification method, the current and the voltage of each battery monomer in the battery on the current date are obtained; calculating the current capacity characteristic coefficient of the single battery according to the current and the voltage, and acquiring historical capacity characteristic coefficients of the single battery at a plurality of dates before the current date; and determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, and identifying whether the battery has capacity diving or not based on the diagnosis coefficient and the proportionality coefficient. The capacity characteristic coefficient represents the characteristics of the capacity of the single battery, the change trend of the capacity characteristic coefficient of each single battery can be analyzed according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient to obtain a diagnosis coefficient and a proportionality coefficient, the diagnosis coefficient represents the possibility of capacity diving of the single battery in the latest time period, namely the possibility of capacity diving of the battery in the latest time period, and whether the capacity diving occurs or not can be judged according to the diagnosis coefficient; the proportional coefficient represents the reliability of the diagnosis coefficient, the larger the proportional coefficient is, the more reliable the judgment result is, and the stability of identifying the battery capacity diving can be enhanced. On the other hand, the capacity characteristic coefficient and the historical capacity characteristic coefficients of a plurality of dates are only needed to be adopted for judgment, a large amount of test data support is not needed, the test cost is low, the efficiency is high, the method is simple, and the method is suitable for most working conditions.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for identifying battery capacity diving according to an embodiment of the present invention;
fig. 2 is a flowchart of a battery capacity diving identification method according to a second embodiment of the present invention;
fig. 3 is a current data graph of a battery cell provided by a second embodiment of the invention in one day;
fig. 4 is a voltage heat diagram of different battery cells in one day according to a second embodiment of the present invention;
fig. 5 is a polarization difference voltage heat diagram of different battery cells in one day according to the second embodiment of the present invention;
fig. 6 illustrates current capacity characteristic coefficients of different battery cells at a current date according to a second embodiment of the present invention;
fig. 7 is a mean value graph of capacity characteristic coefficients of different battery cells in a recent time period according to a second embodiment of the present invention;
fig. 8 is a schematic structural diagram of a battery capacity diving recognition device according to a third embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Fig. 1 is a flowchart of a method for identifying battery capacity diving according to an embodiment of the present invention, where the embodiment is applicable to a situation that whether the battery has capacity diving is detected, and the method may be executed by a battery capacity diving identification device, where the battery capacity diving identification device may be implemented in hardware and/or software, and the battery capacity diving identification device may be configured in an electronic device, for example, may be disposed in a vehicle-mounted computer of a vehicle.
As shown in fig. 1, the battery capacity diving identification method includes:
s101, acquiring the current and the voltage of each battery cell in the battery on the current date.
The battery of this embodiment is a lithium battery, and one battery includes a plurality of battery cells, for example, the number of the battery cells may be 96, 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 may be different, because during the use of the battery, substances in the battery cells may change due to an electrochemical reaction, so that the internal resistance of each battery cell changes to different degrees, and the voltage of each battery cell may be different.
The battery of the embodiment can be applied to electric equipment such as an electric automobile and an automatic sweeper, a battery management system is generally arranged in the electric equipment, the battery management system can collect data of each battery cell and send the data to external equipment interacting with the battery cell, and then current and voltage of the battery cell on the current date can be acquired from the battery management system. The battery management system can collect the data of the battery monomers according to a preset collection period, a frame data set is obtained every time the data are collected, the frame data set comprises the voltage and the current of each battery monomer, and all the battery monomers are connected in series, so that the frame data set only comprises one current.
And S102, calculating the current capacity characteristic coefficient of the battery cell according to the current and the voltage.
In the same battery, under the condition of the same residual capacity, the voltage value changes due to the size of discharge current, the voltage is highest when no current exists, the current is positive when the current is discharged, and the voltage is lower when the discharge current is larger; the current is negative during charging, and the larger the charging current, the higher the voltage.
In the battery, if the capacity of a certain battery cell is much lower than that of other battery cells, that is, if the battery cell has capacity jump, the internal resistance of the certain battery cell is larger than that of the other battery cells, the voltage is higher during charging than that of the other battery cells, and is lower during discharging than that of the other battery cells.
Since the voltage and the current are associated with the battery capacity based on the above phenomena of the voltage, the current, and the capacity, the capacity characteristic coefficient of each battery cell may be extracted based on the voltage and the current, and the value of the capacity characteristic coefficient may be regulated to a fixed range in which the value of the capacity characteristic coefficient is positively or negatively associated with the possibility of occurrence of capacity flooding in the battery cell.
Specifically, the capacity characteristic coefficient may be calculated by setting a capacity characteristic coefficient calculation model into which a current and a voltage are input, the plurality of batteries corresponding to the plurality of capacity characteristic coefficients. For one single battery, the voltage and the current are fused into one capacity characteristic coefficient, comparison with the capacity characteristic coefficients of other dates is facilitated to obtain the change trend of the capacity characteristics, and the analysis of the change trend of the capacity characteristics can be simpler and easier.
The current capacity characteristic coefficient is calculated according to the current and the voltage, and the battery capacity is related to the current and the voltage, so that the current capacity characteristic coefficient represents the capacity characteristic of each battery cell at the current date.
S103, acquiring historical capacity characteristic coefficients of the battery cells on a plurality of dates before the current date.
Wherein a plurality of dates before the current date may be consecutive dates of a preset number of days, and the preset number of days may be a number between 10 and 20, for example, 13, in order to reduce the burden of increasing the test volume while securing the richness of data. Then the historical capacity characteristic coefficient of the battery cell 13 days before the current date is obtained, and the capacity characteristic coefficient of the battery cell within 2 weeks is obtained in total. The historical capacity characteristic coefficients of the battery cells on a plurality of dates before the current date are obtained, so that the change trend of the capacity characteristic coefficients of the battery cells in a period of time can be analyzed conveniently.
It should be noted that the historical capacity characteristic coefficient is also obtained through S101-S102, and after the current and voltage of the battery cell on the current day are obtained, the capacity characteristic data can be calculated and stored, so that the battery cell can be called directly in the future, and the data processing time is reduced. For the current date and a plurality of dates before the current date, the number of frames of the data set acquired on each date may be different, and the data accuracy is higher as the number of frames of the data set is larger, so the accuracy of the capacity characteristic coefficient of the battery cell on each date may also be different.
And S104, determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient.
The diagnosis coefficient represents the possibility of capacity diving of the battery monomer in the latest time period, the latest time period refers to the current date and a plurality of dates before the current date, and the diagnosis coefficient is the centralized embodiment of whether the capacity diving of the battery occurs in the latest time period. Since the occurrence of the capacity jump of at least one cell corresponds to the occurrence of the capacity jump of the entire battery, the diagnostic coefficient also indicates the possibility of the capacity jump of the battery in the latest time period.
The current capacity characteristic coefficient and the historical capacity characteristic coefficient both correspond to the same battery cell, so that the current capacity characteristic coefficient and the historical capacity characteristic coefficient are the same in column number. In the calculation of the diagnostic coefficient, the average value of the capacity characteristic coefficients of the battery cells in the latest time period can be calculated, the average value represents the concentrated expression of the variation trend of the capacity characteristic coefficients of each battery cell in the latest time period, if the value of the capacity characteristic coefficient is positively correlated with the possibility of the occurrence of the capacity jump 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 the occurrence of the capacity jump of the battery cell, the minimum average value is taken as the diagnostic coefficient, and the diagnostic coefficient is the diagnostic coefficient of whether the capacity jump occurs in the whole battery.
For example, if the 1 st battery cell has a corresponding capacity feature coefficient corresponding to the element in the 1 st column in both the current capacity feature coefficient and the historical capacity feature coefficient, the element in the 1 st column may be taken out from the current capacity feature coefficient and the historical capacity feature coefficient respectively as the capacity feature coefficient of the 1 st battery cell, and the average value may be calculated.
And the proportional coefficient represents the reliability of the diagnosis coefficient, the higher the reliability of the diagnosis coefficient is, the more reliable the judgment result is, and the reliability of the battery capacity diving identification result is judged according to the proportional coefficient. The scaling factor may be a scaling to target data in all of the volume diving feature coefficients, where the target data is the volume diving feature coefficient that is less than a preset diagnostic threshold.
And S105, identifying whether the battery has capacity diving or not based on the diagnosis coefficient and the proportionality coefficient.
A preset diagnostic threshold range may be set that indicates a capacity kick in the battery when the diagnostic factor falls within the preset diagnostic threshold range.
A preset proportion threshold value can be set, if the target data proportion exceeds the preset proportion, it is confirmed that more battery monomers have capacity diving, the reliability of the battery capacity diving is considered to be high, and the reliability of the judgment result can be guaranteed.
According to the battery capacity diving identification method, the current and the voltage of each battery monomer in the battery on the current date are obtained; calculating the current capacity characteristic coefficient of the single battery according to the current and the voltage, and acquiring historical capacity characteristic coefficients of the single battery at a plurality of dates before the current date; and determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, and identifying whether the battery has capacity diving or not based on the diagnosis coefficient and the proportionality coefficient. The capacity characteristic coefficient represents the characteristic of the capacity of the single battery, the change trend of the capacity characteristic coefficient of each single battery can be analyzed according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient to obtain a diagnosis coefficient and a proportionality coefficient, the diagnosis coefficient represents the possibility of capacity diving of the single battery in the recent time period, namely the possibility of capacity diving of the single battery in the recent time period, and whether the capacity diving occurs can be judged according to the diagnosis coefficient; the proportional coefficient represents the reliability of the diagnosis coefficient, the larger the proportional coefficient is, the more reliable the judgment result is, and the stability of identifying the battery capacity diving can be enhanced. On the other hand, the capacity characteristic coefficient and the historical capacity characteristic coefficients of a plurality of dates are only needed to be adopted for judgment, a large amount of test data support is not needed, the test cost is low, the efficiency is high, the method is simple, and the method is suitable for most working conditions.
Example two
Fig. 2 is a flowchart of a battery capacity diving identification method according to a second embodiment of the present invention, which is optimized based on the first embodiment of the present invention, and as shown in fig. 2, the battery capacity diving identification method includes:
s201, obtaining the current and the voltage of each battery cell in the battery at the current date.
Generally, the current and the voltage of the battery cell can be obtained through a battery management system of the electric device where the battery is located, and the battery management system can collect and send data of the battery cell to an external device interacting with the battery cell, for example, a battery capacity identification diving device, and then the current and the 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 monomers in a preset collection period, a frame data set is obtained every time the data are collected, the frame data set comprises the voltage and the current of each battery monomer, and all the battery monomers are connected in series, so that the frame data set only comprises one current.
Illustratively, when the number of the battery cells is 96, as shown in fig. 3, fig. 3 is a current curve diagram of the battery cells in one day, and the currents of the 96 battery cells are the same, wherein the abscissa is the collection time, the collection time is from 0 to 24 points in one day, and the ordinate is the current value. As shown in fig. 4, fig. 4 is a thermal diagram of voltage of different battery cells in a day, the collection time is from 0 point to 24 points in the day, in fig. 4, the color depth of the lines is different, the voltage values represented by the different color depth are different, specifically, the voltage value corresponding to each color depth is shown in the right histogram in fig. 4, the thermal diagram corresponds to a three-dimensional diagram, and the corresponding three dimensions are the collection time, the serial number of the battery cell, and the voltage.
And S202, forming a voltage matrix according to the voltages.
In the voltage matrix, voltages are arranged in sequence according to the frame number of the data set, the rows of the voltage matrix represent the voltages of different frame data sets, and the columns of the voltage matrix represent the voltages of different battery cells. The voltage matrix is a matrix of rows and columns, for example, the 1 st row and 3 rd column elements in the voltage matrix represent the voltage of the 3 rd cell in the 1 st frame data set.
And S203, forming a current matrix according to the current.
In the current matrix, the currents are sequentially arranged according to the frame number of the data set, the rows of the current matrix represent the currents of different data sets of the data set, and the columns of the current matrix represent the currents of the single batteries. The current matrix is a multi-row single column matrix, for example, then in the current matrix, the 5 th row element represents the current of all the cells in the 5 th frame data set.
And S204, calculating the current capacity characteristic coefficient of the single battery by adopting the voltage matrix and the current matrix.
The current capacity characteristic coefficient is calculated according to the current and the voltage, and the battery capacity is related to the current and the voltage, so that the current capacity characteristic coefficient represents the capacity characteristic of each battery cell at the current date.
In an example of this embodiment, calculating a current capacity characteristic coefficient of a battery cell by using a voltage matrix and a current matrix specifically includes: performing de-trending processing on the voltage of the voltage matrix to obtain a polarization difference voltage matrix, and summing elements in the polarization difference voltage matrix according to columns to obtain a voltage sum value matrix; adding elements in the current matrix to obtain a current sum value, and calculating the element square sum of the current matrix; and 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 element square sum.
Exemplarily, when the number of the battery cells is 96, the voltage of each battery cell in fig. 4 is subjected to detrending processing, so as to obtain the polarization difference voltage of each battery cell, as shown in fig. 5, fig. 5 is a thermal diagram of the polarization difference voltage of different battery cells in one day, in fig. 5, the color depth of the line is different, the indicated polarization difference voltage is different in magnitude, and the values of the polarization difference voltage corresponding to different color depth are shown in the right-hand bar diagram in fig. 5, wherein the smaller the numerical value is, the darker the color is, the larger the numerical value is, the lighter the color is, the color is from-0.15 to 0.05, and the color is from dark to light.
Detrending is a de-linear trend that focuses analysis on fluctuations in trend data by de-trending from the data, which generally represents a systematic increase or decrease in the data. For example, sensor drift may cause systematic shifts. Although the trend may be meaningful, after the linear trend is removed, the influence of the offset generated when the sensor acquires the voltage data on the later calculation is eliminated, so that the capacity characteristic coefficient in the subsequent step can more accurately represent the capacity diving characteristic of the battery cell.
Specifically, the detrending the voltage of the voltage matrix to obtain a polarization difference voltage matrix may include: and calculating a voltage 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 a polarization difference voltage matrix. Namely, the voltages in the data set of one frame are respectively subjected to trend removing processing to obtain the polarization difference voltage of each single battery.
After the de-trending process is performed on the voltages in the voltage matrix, the average polarization difference voltage of the same frame data set (the same row) in the polarization difference voltage matrix is equal to or very close to 0. Specifically, the current capacity characteristic coefficient of the battery cell is calculated by the following formula:
wherein, C diff Is a matrix of current capacity characteristic coefficients of the battery cells, I is a current matrix, n is the number of frames of the data set, V diff Is a polarization difference voltage matrix, V dsum As a voltage and value matrix, I sum Is the sum of the currents, I sq_sum Is element is plainThe formula is as follows.
Where n is the number of frames in the data set, i.e., n may be the number of rows in the current matrix or the number of rows in the voltage matrix. The current capacity characteristic coefficient of each cell is generally in the range of-1, 1.
Illustratively, the number of battery cells is 96, and the calculated matrix C diff Then it is a matrix of 1 × 96, where each column represents the current capacity feature coefficient of the battery cell with the serial number corresponding to the number of columns, as shown in fig. 6, fig. 6 is the current capacity feature coefficient of 96 battery cells at the current date, and the current capacity feature coefficient of the battery cell with the serial number of 1 corresponds to the matrix C diff Data of column 1 in, which is-0.4; the current capacity characterization coefficient for cell number 90 corresponds to the data in column 90 of the matrix, which is-0.38.
The matrix of the current capacity characteristic coefficient is a single-row matrix, and elements in the matrix of the current capacity characteristic coefficient sequentially correspond to the current capacity characteristic coefficient of each battery cell. For one single battery, the voltage and the current are fused into one capacity characteristic coefficient, comparison with the capacity characteristic coefficients of other dates is facilitated to obtain the change trend of the capacity characteristics, and the analysis of the change trend of the capacity characteristics can be simpler and easier. In addition, various parameters and formulas are set for calculating the capacity characteristic coefficient, so that the identification of the battery capacity diving is more accurate.
S205, obtaining historical capacity characteristic coefficients of the battery cells on a plurality of dates before the current date.
The plurality of dates before the current date can be continuous dates of preset days, and historical capacity characteristic coefficients of the battery cells on the plurality of dates before the current date are obtained, so that the change trend of the capacity characteristic coefficients of the battery cells in a period of time can be analyzed conveniently.
S206, stacking the current capacity characteristic coefficient and the historical capacity characteristic coefficient into a capacity characteristic coefficient matrix.
The rows of the capacity characteristic coefficient matrix represent the capacity characteristic coefficients of all the battery cells in different dates, and the columns of the capacity characteristic coefficient matrix represent the capacity characteristic coefficients of different battery cells in different dates. That is, the capacity characteristic data of all the battery cells on the 1 st date are sequentially arranged on the 1 st row, the capacity characteristic data of all the battery cells on the 2 nd date are sequentially arranged on the 2 nd row, and so on.
For example, when the number of the battery cells is 96, the latest time period is 14 days, and the calculated capacity feature coefficient matrix is a 14 × 96 matrix, where each row represents the capacity feature coefficient of each battery cell in one day, and each column represents the capacity feature coefficient of the battery cell with the serial number corresponding to the number of columns, for example, the data in the 4 th row and the 5 th column may represent the capacity feature coefficient of the battery cell with the serial number 5 on the 4 th day.
And S207, averaging the capacity characteristic coefficient matrix according to columns, and taking the minimum average value as a diagnosis coefficient.
In the capacity characteristic coefficient matrix, a column of data represents all capacity characteristic coefficients of a single battery in the latest time period, and the capacity characteristic coefficient matrix is averaged according to the column, so that the centralized representation of the capacity characteristic coefficients of all the single batteries in the latest time period can be obtained.
Illustratively, when the number of the battery cells is 96, averaging the capacity characteristic coefficient matrix according to columns to obtain a 1 × 96 average matrix, where each column number represents an average value of the capacity characteristic coefficients of the battery cells of the serial numbers corresponding to the column number in the latest time period, as shown in fig. 7, fig. 7 is an average value graph of the capacity characteristic coefficients of the 96 battery cells in the latest time period, where the capacity characteristic coefficient average value of the battery cell of the serial number 1 is data of the 1 st column in the average matrix, which is-0.42; the mean value of the capacity characteristic coefficients of the battery cell with the serial number 89 is the data of the 89 th column in the mean matrix, which is-0.944. As can be seen from fig. 7, since the average value of the capacity characteristic coefficients of the battery cell with the serial number 89 is the smallest, the average value is taken as the diagnostic coefficient of the whole battery, that is, the diagnostic coefficient of the battery is-0.944.
And S208, determining the number of target rows in the capacity characteristic coefficient matrix.
And the minimum value of the elements between the target line rows is smaller than the preset diagnosis threshold value.
And S209, taking the ratio of the number of the target rows to the total number of the rows of the capacity characteristic coefficient matrix as a proportionality coefficient.
The target row is a row in which the minimum value of the inter-row elements is smaller than a preset diagnosis threshold value, namely, the row in which the minimum value of the inter-row elements is smaller than the preset diagnosis threshold value is determined, then the row in which the inter-row elements are smaller than the preset diagnosis threshold value is marked, and then the proportion of the target row to the total row number is calculated to obtain the example coefficient.
And S210, if the diagnosis coefficient is smaller than a preset diagnosis threshold value and the proportionality coefficient is larger than a preset proportionality threshold value, determining that the battery has capacity to jump.
The preset diagnostic threshold may be obtained from historical data, for example, the preset diagnostic threshold may be-0.8, i.e., when the diagnostic coefficient is less than the preset diagnostic threshold, it may be preliminarily assumed that a capacity kick of the battery has occurred. The preset proportion threshold may also be obtained according to historical data, for example, the preset proportion threshold may be 82%, for the proportion coefficient, the proportion of the target row is counted, in the capacity characteristic coefficient matrix, a row of data represents the capacity characteristic coefficients of a plurality of battery cells in the same day, a minimum value among the capacity characteristic coefficients is selected, and the minimum value may be used as an extreme value of the data of the day, and if at least one battery cell has capacity diving, at least one capacity characteristic coefficient smaller than the preset diagnosis threshold exists in the data of each day. Therefore, the ratio of the targets is counted, the ratio of the days (the number of data set frames) for generating the capacity diving in the recent time period can be determined, namely the proportionality coefficient, if the proportionality coefficient is larger than a preset proportionality threshold value, the capacity diving of the battery is determined, and the interference caused by individual error data can be avoided. For example, if all the capacity characteristic data of other battery cells are 0 or 1, the capacity characteristic coefficient of a certain battery cell on the 1 st day is-10, and the capacity characteristic coefficient on the 2 nd to 9 th days is 0, the average value of the capacity characteristic coefficients of the battery cell is-1, and it can be theoretically determined that the battery cell has capacity diving, but by calculating the proportionality coefficient, only if the capacity characteristic coefficient is smaller than the preset diagnosis threshold value in one day, the proportionality coefficient corresponding to the target row is smaller (smaller than the preset proportionality threshold value), that is, the reliability of the diagnosis coefficient is low, it cannot be determined that the battery cell has capacity diving, that is, it cannot be determined that the battery cell has capacity diving.
In an optional embodiment of the present invention, after determining that the battery has capacity to jump, further comprising: and determining the battery monomer generation capacity diving corresponding to the row with the average value smaller than the preset diagnosis threshold value. Specifically, after calculating the mean value of the capacity characteristic coefficients corresponding to the battery cells, the mean value is compared with a preset diagnosis threshold value, and if the mean value is smaller than the preset diagnosis threshold value, the battery cells are marked, for example, the mean values of No. 1-4 battery cells are respectively-0.93, -0.72, 0.03, -0.85, and the preset diagnosis threshold value is-0.8, and the No. 1 and No. 4 battery cells are marked. When the occurrence capacity of the battery is determined to be in a water-jumping state, the marked battery monomer can be directly determined to be the battery monomer with the battery water-jumping fault, and the process is simple and efficient.
For example, as shown in fig. 7, fig. 7 is a mean value graph of the capacity characteristic coefficients of 96 battery cells in the latest time period, the preset diagnosis threshold value is-0.8, and an early warning line is set at the mean value of-0.8, so that it is seen from fig. 7 that the mean value of the capacity characteristic coefficients of the battery cell with the serial number 89 is less than-0.8, that is, the battery cell with the serial number 89 is a battery cell with a battery diving fault, and the other battery cells operate normally. The arrangement of the early warning line can facilitate workers to quickly and efficiently see whether the battery cell with the fault and the serial number of the battery cell with the fault and the water jump exists in the graph.
To clearly illustrate the process of battery capacity diving identification, virtual data of 2 battery cells are used for illustration:
V dsum = (-1, 1), assuming
Then I sum ＝-1+(-2)＝-3，I square_sum ＝(-1) 2 +(-2) 2 ＝5；
namely, it is
Step 5, inquiring a matrix C of the past 13-day history capacity characteristic coefficients diff Stacking to obtain a capacity characteristic coefficient matrix C, and assuming that the capacity characteristic coefficients of the previous 13 days are all (-1, 0);
averaging to give (-13/14, 0/14) (-0.92, 0), since the first value (-0.929) is less than-0.8, which represents monomer No. 1, monomer No. 1 is labeled first, and the diagnostic coefficient coef is-0.929
Step 7, C, calculating the minimum value according to the sequence, and then calculating the number of the elements less than-0.8 to account for the ratio: 13/14=92.9%, and the proportionality coefficient rate is 92.9%.
And 8, assuming that the preset diagnosis coefficient is-0.8, the preset proportionality coefficient is 82%, coef is < -0.8 and rate is greater than 82%, determining that the marked battery cell has the capacity diving fault, namely the battery has the capacity diving fault.
According to the battery capacity diving identification method, various parameters and formulas are set to calculate the capacity characteristic coefficient, so that the capacity characteristic coefficient is more accurate, and the identification result of the battery capacity diving 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 single body can be analyzed to obtain a diagnosis coefficient and a proportionality coefficient, the diagnosis coefficient represents the possibility of capacity diving of the battery single body in the recent time period, namely the possibility of capacity diving of the battery in the recent time period, and whether the capacity diving occurs can be judged according to the diagnosis coefficient; the proportional coefficient represents the reliability of the diagnosis coefficient, the larger the proportional coefficient is, the more reliable the judgment result is, the interference of error data to the identification result is avoided, and the stability of identifying the battery capacity diving is enhanced.
EXAMPLE III
Fig. 8 is a schematic structural diagram of a battery capacity diving recognition device according to a third embodiment of the present invention. As shown in fig. 8, the battery capacity diving recognition apparatus includes:
a current and voltage obtaining module 301, configured to obtain a current and a voltage of each battery cell in the battery at a current date;
a current capacity characteristic coefficient calculation module 302, configured to calculate a current capacity characteristic coefficient of the battery cell according to the current and the voltage;
a historical capacity characteristic coefficient obtaining module 303, configured to obtain historical capacity characteristic coefficients of the battery cell on multiple dates before a current date;
a judgment parameter determining module 304, configured to determine a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, where the diagnosis coefficient indicates a possibility of occurrence of capacity diving of the battery cell in a recent time period, and the proportionality coefficient indicates a reliability of the diagnosis coefficient;
and a capacity diving judgment module 305 for identifying whether the battery has capacity diving based on the diagnosis coefficient and the proportionality coefficient.
In an optional embodiment of the present invention, the current capacity characteristic coefficient calculating module 302 includes:
the voltage matrix composition submodule is used for forming a voltage matrix according to the voltages, the rows of the voltage matrix represent the voltages of different frame data sets, and the columns of the voltage matrix represent the voltages of different battery monomers;
the current matrix composition submodule is used for forming a current matrix according to the current, the rows of the current matrix represent the current of different frame data sets, and the columns of the current matrix represent the current of the battery monomer;
and the current capacity characteristic coefficient calculation submodule is used for calculating the current capacity characteristic coefficient of the single battery by adopting the voltage matrix and the current matrix.
In an optional embodiment of the present invention, the current capacity feature coefficient calculating sub-module includes:
the trend processing unit is used for carrying out trend removing processing on the voltage of the voltage matrix to obtain a polarization difference voltage matrix;
the voltage sum value matrix acquisition unit is used for summing elements in the polarization difference voltage matrix according to columns to obtain a voltage sum value matrix;
the element square sum calculation unit is used for adding elements in the current matrix to obtain a current sum value and calculating the element square sum of the current matrix;
and the current capacity characteristic coefficient calculating unit is used for 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 element square sum.
In an optional embodiment of the present invention, the current capacity characteristic coefficient of the battery cell is calculated by the following formula:
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 the polarization difference voltage matrix, V dsum For said voltage and value matrix, I sum Is the sum of the currents, I sq_sum Is the sum of the squares of the elements.
In an optional embodiment of the present invention, the determining parameter determining module 304 includes:
a capacity characteristic coefficient matrix composition submodule, configured to stack the current capacity characteristic coefficient and the historical capacity characteristic coefficient into a capacity characteristic coefficient matrix, where rows of the capacity characteristic coefficient matrix represent capacity characteristic coefficients of all the battery cells on different dates, and columns of the capacity characteristic coefficient matrix represent capacity characteristic coefficients of different battery cells on different dates;
the diagnostic coefficient calculation submodule is used for solving the mean value of the capacity characteristic coefficient matrix according to columns and taking the minimum mean value as a diagnostic coefficient;
the target line number operator module is used for determining the number of target lines in the capacity characteristic coefficient matrix, and the minimum value of elements between the target lines is smaller than a preset diagnosis threshold value;
and the scaling coefficient calculation submodule is used for taking the ratio of the number of the target rows to the total number of the rows of the capacity characteristic coefficient matrix as a scaling coefficient.
In an optional embodiment of the present invention, the capacity diving judgment module 305 includes:
and the capacity diving judgment submodule is used for determining that the battery generates capacity diving if the diagnosis coefficient is smaller than a preset diagnosis threshold value and the proportionality coefficient is larger than a preset proportionality threshold value.
In an optional embodiment of the present invention, the battery capacity diving identification apparatus further includes:
and the single cell capacity diving determination module is used for determining the capacity diving of the battery cell corresponding to the row of which the average value is smaller than the preset diagnosis threshold value.
The battery capacity diving identification device provided by the embodiment of the invention can execute the battery capacity diving identification method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 9 shows a schematic block diagram of an electronic device 40 that may be used to implement embodiments of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 9, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from a storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
A number 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, and the like; a storage unit 48 such as a magnetic disk, optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the battery capacity diving identification method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the battery capacity diving identification method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the battery capacity diving identification method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may 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 may 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, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
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 a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; 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 may be received in any form, including acoustic, speech, or tactile input.
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: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.
The computing system may 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 called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A battery capacity diving identification method is characterized by comprising the following steps:
acquiring the current and voltage of each battery cell in the battery on the current date;
calculating the current capacity characteristic coefficient of the battery cell according to the current and the voltage;
acquiring historical capacity characteristic coefficients of the battery monomer on a plurality of dates before the current date;
determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, wherein the diagnosis coefficient represents the possibility of capacity diving of the single battery in the recent time period, and the proportionality coefficient represents the reliability of the diagnosis coefficient;
identifying whether a capacity kick of the battery occurs based on the diagnostic coefficient and the scaling coefficient.
2. The method of claim 1, wherein calculating a present capacity characterization coefficient for the battery cell from the current and the voltage comprises:
forming a voltage matrix according to the voltages, wherein rows of the voltage matrix represent voltages of different frame data sets, and columns of the voltage matrix represent voltages of different battery cells;
forming a current matrix according to the currents, wherein rows of the current matrix represent currents of different frame data sets, and columns of the current matrix represent currents of the battery cells;
and calculating the current capacity characteristic coefficient of the single battery by adopting the voltage matrix and the current matrix.
3. The method of claim 2, wherein the calculating the present capacity characterization coefficient of the battery cell using the voltage matrix and the current matrix comprises:
performing detrending processing on the voltage of the voltage matrix to obtain a polarization difference voltage matrix;
summing elements in the polarization difference voltage matrix according to columns to obtain a voltage sum value matrix;
adding elements in the current matrix to obtain a current sum value, and calculating the element square sum of the current matrix;
and 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 element square sum.
4. The method of claim 3, wherein the current capacity characterization coefficient of the battery cell is calculated by the following formula:
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 said polarization difference voltage matrix, V dsum For said voltage and value matrix, I sum Is the sum of the currents, I sq_sum Is the sum of the squares of the elements.
5. The method of any of claims 1 to 4, wherein determining a diagnostic coefficient and a scaling coefficient based on the current capacity characterization coefficient and the historical capacity characterization coefficient comprises:
stacking the current capacity characteristic coefficients and the historical capacity characteristic coefficients into a capacity characteristic coefficient matrix, wherein rows of the capacity characteristic coefficient matrix represent capacity characteristic coefficients of all the battery monomers in different dates, and columns of the capacity characteristic coefficient matrix represent capacity characteristic coefficients of different battery monomers in different dates;
averaging the capacity characteristic coefficient matrix according to columns, and taking the minimum average as a diagnosis coefficient;
determining the number of target rows in the capacity characteristic coefficient matrix, wherein the minimum value of elements between rows of the target rows is smaller than a preset diagnosis threshold value;
and taking the ratio of the number of the target rows to the total number of the rows of the capacity characteristic coefficient matrix as a proportionality coefficient.
6. The method of any of claims 1 to 4, wherein identifying whether a capacity kick occurs in the battery cell based on the diagnostic coefficient and the scaling coefficient comprises:
and if the diagnosis coefficient is smaller than a preset diagnosis threshold value and the proportionality coefficient is larger than a preset proportionality threshold value, determining that the battery has capacity diving.
7. The method of claim 5, after determining that a capacity kick has occurred in the battery, further comprising:
and determining the occurrence capacity of the battery monomer corresponding to the row with the average value smaller than the preset diagnosis threshold value.
8. A battery capacity diving identification device, comprising:
the current and voltage acquisition module is used for acquiring the current and voltage of each battery cell in the battery on the current date;
the current capacity characteristic coefficient calculation module is used for calculating the current capacity characteristic coefficient of the single battery according to the current and the voltage;
the historical capacity characteristic coefficient acquisition module is used for acquiring historical capacity characteristic coefficients of the battery monomer on a plurality of dates before the current date;
the judging parameter determining module is used for determining a diagnosis coefficient and a proportionality coefficient according to the current capacity characteristic coefficient and the historical capacity characteristic coefficient, the diagnosis coefficient represents the possibility of capacity diving of the single battery in the recent time period, and the proportionality coefficient represents the reliability of the diagnosis coefficient;
and the capacity diving judgment module is used for identifying whether the battery generates capacity diving or not based on the diagnosis coefficient and the proportionality coefficient.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the battery capacity diving identification method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a processor to implement the battery capacity diving identification method of any one of claims 1-7 when executed.
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