Voltage monitoring method and device, electronic equipment and vehicle

Abstract

The invention relates to a voltage monitoring method and device, electronic equipment and a vehicle. The method comprises the following steps: acquiring cell voltages of a plurality of cells in a to-be-monitored target power battery; determining a first battery cell with an abnormal battery cell voltage from the plurality of battery cells according to the battery cell voltages of the plurality of battery cells; correcting the battery cell voltage of the first battery cell to obtain a corrected target battery cell voltage; determining a monitoring result of the target power battery according to the target battery cell voltage and the battery cell voltage of the second battery cell; the second battery cell is other battery cells except the first battery cell in the plurality of battery cells, and the monitoring result represents whether the voltage consistency of the target power battery is abnormal or not.

Description

Technical Field

[0001] The present disclosure relates to the field of battery technologies, and in particular, to a voltage monitoring method, apparatus, electronic device, and vehicle. Background Art

[0002] As a core component of a vehicle, the stability and safety of a power battery are crucial to the development of the new energy vehicle industry. Good voltage consistency can ensure that the monomer voltage of the power battery remains relatively stable during charge and discharge, and improve the overall performance of the power battery.

[0003] Currently, the method for monitoring voltage consistency is usually to sample and identify the characteristic parameters of the battery. However, this method cannot distinguish the misjudgment of consistency caused by abnormal sampling. Summary of the Invention

[0004] To solve the above problems, the present disclosure provides a voltage monitoring method, apparatus, electronic device, and vehicle.

[0005] According to a first aspect of an embodiment of the present disclosure, there is provided a voltage monitoring method, the method including: obtaining the cell voltages of a plurality of cells in a target power battery to be monitored; determining, from the plurality of cells, a first cell with an abnormal cell voltage according to the cell voltages of the plurality of cells; correcting the cell voltage of the first cell to obtain a corrected target cell voltage; determining a monitoring result of the target power battery according to the target cell voltage and the cell voltage of a second cell; the second cell is the other cells in the plurality of cells except the first cell, and the monitoring result indicates whether the voltage consistency of the target power battery is abnormal.

[0006] Optionally, the determining, from the plurality of cells, a first cell with an abnormal cell voltage according to the cell voltages of the plurality of cells includes: determining the voltage difference between every two adjacent cells according to the cell voltages of the plurality of cells; taking the adjacent cells corresponding to the voltage difference with the largest absolute value as candidate cells; obtaining a first voltage average value of the candidate cells and a second voltage average value of a third cell; the third cell includes the other cells in the plurality of cells except the candidate cells; determining, from the plurality of cells, a first cell with an abnormal cell voltage according to the first voltage average value and the second voltage average value.

[0007] Optionally, the determining, from the plurality of battery cells, a first battery cell with abnormal cell voltage according to the first voltage average value and the second voltage average value includes: determining a voltage range of the plurality of battery cell voltages according to the cell voltages of the plurality of battery cells; determining whether the candidate battery cell satisfies a preset voltage abnormality condition according to the voltage range, the voltage difference with the largest absolute value, the first voltage average value, and the second voltage average value; and when the candidate battery cell satisfies the preset voltage abnormality condition, using the candidate battery cell as the first battery cell.

[0008] Optionally, the preset voltage abnormality condition includes: the voltage range is greater than a preset range threshold; the ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio; and the difference between the first voltage average value and the second voltage average value is not within a preset difference interval.

[0009] Optionally, the correcting the cell voltage of the first battery cell to obtain a corrected target cell voltage includes: determining a median of the cell voltage of the first battery cell and the cell voltage of the second battery cell; and correcting the cell voltage of the first battery cell according to the median to obtain the corrected target cell voltage.

[0010] Optionally, the determining a monitoring result of the target power battery according to the target cell voltage and the cell voltage of the second battery cell includes: establishing a voltage data set including the target cell voltage and the cell voltage of the second battery cell, where the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times; obtaining cell temperature values of the plurality of battery cells at each monitoring time; establishing a data matrix corresponding to each monitoring time, where variables of the data matrix include the minimum value of the difference between each sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the difference between the extreme value of the sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the standard deviation of the sub-voltage data other than the extreme value of the sub-voltage data in the sub-voltage data set, and the maximum value of the difference between the temperature value of each battery cell and the median of the cell temperature values of the plurality of battery cells; calculating a monitoring statistic based on a weighted moving average algorithm according to the data matrices of multiple monitoring times; and determining the monitoring result of the target power battery according to the monitoring statistic.

[0011] Optionally, before establishing the data matrix corresponding to each monitoring time, the method further includes: performing data transformation on each sub-voltage data set based on the Box-Cox transformation method.

[0012] Optionally, determining the monitoring result of the target power battery according to the monitoring statistic includes: when the monitoring statistic is greater than a preset statistic threshold, determining that the monitoring result of the target power battery is abnormal voltage consistency.

[0013] According to a second aspect of the embodiments of the present disclosure, there is provided a voltage monitoring device, the device includes: An acquisition module, configured to acquire the cell voltages of a plurality of cells in a target power battery to be monitored; a first determination module, configured to determine a first cell with an abnormal cell voltage from the plurality of cells according to the cell voltages of the plurality of cells; a correction module, configured to correct the cell voltage of the first cell to obtain a corrected target cell voltage; a second determination module, configured to determine a monitoring result of the target power battery according to the target cell voltage and the cell voltage of a second cell; the second cell is other cells in the plurality of cells except the first cell, and the monitoring result characterizes whether the voltage consistency of the target power battery is abnormal.

[0014] Optionally, the first determination module is further configured to determine the voltage difference between every two adjacent cells according to the cell voltages of the plurality of cells; use the adjacent cells corresponding to the voltage difference with the largest absolute value as candidate cells; acquire a first voltage average value of the candidate cells and a second voltage average value of a third cell; the third cell includes other cells in the plurality of cells except the candidate cells; determine a first cell with an abnormal cell voltage from the plurality of cells according to the first voltage average value and the second voltage average value.

[0015] Optionally, the first determination module is further configured to determine the voltage range of the cell voltages of the plurality of cells according to the cell voltages of the plurality of cells; determine whether the candidate cells meet a preset voltage abnormality condition according to the voltage range, the voltage difference with the largest absolute value, the first voltage average value, and the second voltage average value; when the candidate cells meet the preset voltage abnormality condition, use the candidate cells as the first cell.

[0016] Optionally, the preset voltage abnormality condition includes: the voltage range is greater than a preset range threshold; the ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio; and, the difference between the first voltage average value and the second voltage average value is not within a preset difference interval.

[0017] Optionally, the correction module is further configured to determine the median of the cell voltage of the first cell and the cell voltage of the second cell; correct the cell voltage of the first cell according to the median to obtain a corrected target cell voltage.

[0018] Optionally, the second determination module is further configured to establish a voltage data set including the target cell voltage and the cell voltages of the second cell, where the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times; the obtaining module is further configured to obtain the cell temperature values of the multiple cells at each of the monitoring times; the second determination module is further configured to establish a data matrix corresponding to each of the monitoring times, where the variables of the data matrix include the minimum value among the differences between each sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the difference between the extreme value of the sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the standard deviation of the other sub-voltage data in the sub-voltage data set except the extreme value of the sub-voltage data, and the maximum value among the differences between the temperature value of each cell and the median of the cell temperature values of the multiple cells; based on the data matrices of multiple monitoring times, calculate a monitoring statistic based on a weighted moving average algorithm; and determine a monitoring result of the target power battery according to the monitoring statistic.

[0019] Optionally, the second determination module is further configured to perform data transformation on each of the sub-voltage data sets based on the Box-Cox transformation method.

[0020] Optionally, the second determination module is further configured to determine that the monitoring result of the target power battery is abnormal voltage consistency when the monitoring statistic is greater than a preset statistic threshold.

[0021] According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including: a memory storing a computer program thereon; a processor configured to execute the computer program in the memory to implement the steps of the method described in the first aspect of the present disclosure.

[0022] According to a fourth aspect of the embodiments of the present disclosure, there is provided a vehicle including the electronic device described in the third aspect of the present disclosure.

[0023] According to the above technical solution, it is possible to obtain the cell voltages of multiple cells in a target power battery to be monitored, and determine a first cell with abnormal cell voltage from the multiple cells according to the cell voltages of the multiple cells, correct the cell voltage of the first cell to obtain a corrected target cell voltage, and determine a voltage consistency monitoring result of the target power battery according to the target cell voltage and the cell voltages of the second cell. In this way, by determining and correcting the cell with abnormal voltage, the influence of sampling anomalies on the voltage consistency discrimination result is reduced, and the accuracy of voltage consistency anomaly discrimination is improved. At the same time, determining the monitoring result according to the corrected cell voltage can enhance data stability and ensure the effectiveness of anomaly identification.

[0024] Other features and advantages of the present disclosure will be described in detail in the following detailed description section. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the present disclosure, and constitute a part of the specification. Together with the following detailed description, they are used to explain the present disclosure, but do not constitute a limitation to the present disclosure. In the drawings: Figure 1 is a flowchart of a voltage monitoring method shown according to an exemplary embodiment.

[0026] Figure 2 is a flowchart of another voltage monitoring method shown according to an exemplary embodiment.

[0027] Figure 3 is a block diagram of a voltage monitoring device shown according to an exemplary embodiment.

[0028] Figure 4 is a block diagram of an electronic device provided according to an exemplary embodiment of the present disclosure.

[0029] Figure 5 is a block diagram of a vehicle provided according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The following will describe in detail the specific embodiments of the present disclosure with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for the purpose of illustration and explanation, and are not intended to limit the present disclosure.

[0031] In the following description, terms such as "first" and "second" are only used for the purpose of distinguishing descriptions, and should not be construed as indicating or implying relative importance, nor as indicating or implying an order.

[0032] In the related art, as a core component of a vehicle, the stability and safety of a power battery are crucial for the development of the new energy vehicle industry. Good voltage consistency can ensure that the monomer voltage of the power battery remains relatively stable during charging and discharging, improving the overall performance of the power battery. Currently, the common method for monitoring voltage consistency is to sample and identify the characteristic parameters of the battery. However, this method cannot distinguish the misjudgment of consistency caused by sampling failures.

[0033] To solve the above problems, the present disclosure provides a voltage monitoring method, apparatus, electronic device, and vehicle, which can obtain the cell voltages of multiple cells in a target power battery to be monitored, and determine a first cell with an abnormal cell voltage from the multiple cells according to the cell voltages of the multiple cells, and correct the cell voltage of the first cell to obtain a corrected target cell voltage, and determine a voltage consistency monitoring result of the target power battery according to the target cell voltage and the cell voltage of a second cell. In this way, by determining and correcting the cell with abnormal voltage, the influence of sampling anomalies on the voltage consistency discrimination result is reduced, and the accuracy of voltage consistency anomaly discrimination is improved. At the same time, determining the monitoring result according to the corrected cell voltage can enhance data stability and ensure the effectiveness of anomaly identification.

[0034] The following describes the present disclosure in conjunction with specific embodiments.

[0035] Figure 1 is a flowchart of a voltage monitoring method shown according to an exemplary embodiment, as Figure 1 shown, the method may include: In step S101, obtain the cell voltages of multiple cells in a target power battery to be monitored.

[0036] Among them, a cell is the basic unit that constitutes a power battery and has the function of storing and releasing electrical energy. A power battery can be composed of multiple interconnected cells and a BMS (Battery Management System). Exemplarily, the cell voltage can be obtained through the BMS, and data preprocessing is performed on the cell voltage. The data preprocessing methods include, but are not limited to, methods such as null value deletion, mean correction, adjacent value filling, and outlier deletion. Specific implementation manners can refer to relevant methods in related technologies and will not be elaborated here.

[0037] In step S102, determine a first cell with an abnormal cell voltage from the multiple cells according to the cell voltages of the multiple cells.

[0038] In step S103, correct the cell voltage of the first cell to obtain a corrected target cell voltage.

[0039] In a possible implementation manner, the median of the cell voltage of the first cell and the cell voltage of a second cell can be determined, and the cell voltage of the first cell is corrected according to the median to obtain a corrected target cell voltage.

[0040] Among them, the second cell is other cells in the multiple cells except the first cell. Exemplarily, the voltages of m first cells , , ……, and the voltages of the n second battery cells , ， ……, arrange them in ascending order to obtain the median of these voltage values , and then replace the voltages of the m first battery cells with the median to obtain the corrected target battery cell voltage.

[0041] In step S104, based on the target battery cell voltage and the battery cell voltages of the second battery cells, determine the monitoring result of the target power battery.

[0042] Among them, the monitoring result characterizes whether the voltage consistency of the target power battery is abnormal.

[0043] By using the above method, it is possible to obtain the battery cell voltages of multiple battery cells in the target power battery to be monitored, and based on the battery cell voltages of the multiple battery cells, determine the first battery cells with abnormal battery cell voltages from the multiple battery cells, correct the battery cell voltages of the first battery cells to obtain the corrected target battery cell voltage, and based on the target battery cell voltage and the battery cell voltages of the second battery cells, determine the voltage consistency monitoring result of the target power battery. In this way, by determining and correcting the battery cells with abnormal voltages, the influence of sampling anomalies on the voltage consistency discrimination result is reduced, and the accuracy of voltage consistency anomaly discrimination is improved. At the same time, determining the monitoring result based on the corrected battery cell voltage can enhance data stability and ensure the effectiveness of anomaly identification.

[0044] In some embodiments, the above step S102 may include: S1021. Determine the voltage difference between every two adjacent battery cells based on the battery cell voltages of the multiple battery cells.

[0045] Exemplarily, the battery cell voltages of the n battery cells can be expressed as , , ……, , and the voltage difference can be calculated by the following formula:

[0046] where is the voltage difference between the (n - 1)-th adjacent battery cells, is the battery cell voltage of the (n - 1)-th battery cell, is the battery cell voltage of the n-th battery cell.

[0047] S1022. Take the adjacent battery cells corresponding to the voltage difference with the largest absolute value as the candidate battery cells.

[0048] Exemplarily, the calculated voltage differences , , ……, Take the absolute values respectively to obtain the voltage difference with the largest absolute value, denoted as , for example, the voltage difference has the largest absolute value, then take the k-th battery cell and the (k + 1)-th battery cell as candidate battery cells.

[0049] S1023. Obtain the first voltage mean value of the candidate battery cell and the second voltage mean value of the third battery cell.

[0050] Wherein, the third battery cell includes other battery cells among the multiple battery cells except the candidate battery cells. For example, if the k-th battery cell and the (k + 1)-th battery cell are candidate battery cells, take the average value of the voltage value of the k-th battery cell and the voltage value of the (k + 1)-th battery cell as the first voltage mean value , correspondingly, take the other battery cells among the n battery cells except the k-th battery cell and the (k + 1)-th battery cell as the third battery cell to obtain the second voltage mean value of the third battery cell .

[0051] S1024. Determine the first battery cell with abnormal cell voltage from the multiple battery cells according to the first voltage mean value and the second voltage mean value.

[0052] In some embodiments, the voltage range of the multiple battery cells can be determined according to the cell voltages of the multiple battery cells, and whether the candidate battery cells meet the preset voltage abnormality condition can be determined according to the voltage range, the voltage difference with the largest absolute value, the first voltage mean value and the second voltage mean value, and when the candidate battery cells meet the preset voltage abnormality condition, take the candidate battery cells as the first battery cell.

[0053] Wherein, the voltage maximum value , , ……, can be obtained from the cell voltages of the n battery cells and the voltage minimum value , and the difference between the maximum value and the minimum value is determined as the voltage range, denoted as .

[0054] In some embodiments, the preset voltage abnormality condition may include: the voltage range is greater than a preset range threshold; the ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio; and, the difference between the first voltage mean value and the second voltage mean value is not within a preset difference interval.

[0055] Wherein, the preset range threshold can be , the theoretical voltage lower limit and the theoretical voltage upper limit corresponding to the cell voltage can be obtained by pre-testing, and the difference between the theoretical voltage upper limit and the theoretical voltage lower limit is used as the preset range threshold. The preset ratio can be λ, and the preset difference interval can be (- , ). Exemplarily, when simultaneously satisfying , , and , the candidate cells k and k + 1 are determined as the first cell. In the formula, is the voltage range of n cells, is the preset range threshold, is the voltage difference with the largest absolute value, λ is the preset ratio, is the first voltage mean of the candidate cell k and the candidate cell k + 1, is the second voltage mean of the third cells except the k-th cell and the (k + 1)-th cell, represents the absolute value of the difference between the first voltage mean and the second voltage mean, is the endpoint of the preset difference interval.

[0056] The above technical solution can screen the sampling anomaly phenomenon by combining the voltage range, the ratio of the voltage difference with the largest absolute value to the voltage range, and the difference between the first voltage mean and the second voltage mean. Subsequently, the sampling anomaly data is corrected, and the misjudgment phenomenon of voltage consistency caused by sampling anomaly can be excluded, thereby improving the monitoring accuracy of voltage consistency.

[0057] In some embodiments, the above step S104 may include: S1401. Establish a voltage data set including the voltage of the target cell and the voltage of the second cell.

[0058] Wherein, the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times. The multiple monitoring times may be multiple data frames within the monitoring time period, and the number of data frames may be determined according to actual requirements.

[0059] S1402. Obtain the cell temperature values of multiple cells at each of the monitoring times.

[0060] Wherein, the cell temperature value can be collected through the BMS system.

[0061] S1403. Establish a data matrix corresponding to each of the monitoring times.

[0062] Wherein, the data matrix may include vectors , , and , which can be expressed as ( ). The vector is the minimum value among the differences between each sub-voltage data in the sub-voltage dataset and the median of the sub-voltage data. The deviation degree between the sub-voltage data and the median can be measured by the minimum value of these differences. The larger the minimum value of these differences, the higher the deviation degree between the sub-voltage data and the median; the vector is the difference between the extreme value of the sub-voltage data in the sub-voltage dataset and the median of the sub-voltage data. The extreme value of the sub-voltage data can be the minimum value of the sub-voltage data or the maximum value of the sub-voltage data. For example, in the case where the extreme value of the sub-voltage data is the minimum value of the sub-voltage data, the dispersion degree of the sub-voltage data can be measured by the difference between the extreme value and the median. The larger the difference between the extreme value of the sub-voltage data and the median of the sub-voltage data, the higher the dispersion degree of the data in the sub-voltage dataset; the vector is the standard deviation of the other sub-voltage data in the voltage dataset except the extreme value of the sub-voltage data. The extreme value of the sub-voltage data can be the minimum value of the sub-voltage data or the maximum value of the sub-voltage data. For example, in the case where the extreme value of the sub-voltage data is the minimum value of the sub-voltage data, the dispersion degree of the sub-voltage data can be measured by this standard deviation. The larger the standard deviation, the higher the dispersion degree of the data in the sub-voltage dataset. At the same time, calculating the standard deviation after removing the data extreme value can also reduce errors and improve the calculation accuracy; the vector is the maximum value among the differences between the temperature value of each battery cell and the median of the temperature values of multiple battery cells. The dispersion degree of the battery cell temperature values can be measured by the maximum value of these differences. The larger the maximum value of these differences, the higher the dispersion degree of the battery cell temperature values.

[0063] S1404. According to the data matrix at multiple monitoring times, based on the weighted moving average algorithm, calculate the monitoring statistic.

[0064] S1405. According to the monitoring statistic, determine the monitoring result of the target power battery.

[0065] In the above technical solution, by adopting the weighted moving average algorithm to perform weighted averaging on the monitoring data corresponding to multiple monitoring times within the monitoring time period, it can more effectively identify the small changes in the monitoring data, ensure the effectiveness of anomaly identification. At the same time, different weights are assigned to the monitoring data at multiple monitoring times within the monitoring time period according to the time law, so that the finally calculated monitoring result is more in line with the actual law, and further improves the accuracy of voltage consistency anomaly discrimination.

[0066] In a possible implementation manner, the above step S1404 may include: S1. Construct a data matrix corresponding data observation matrix .

[0067] Exemplarily, the data observation matrix can be constructed by the following formula:

[0068] where, is the data observation matrix corresponding to the th monitoring time, is the data matrix corresponding to the th monitoring time, is the transposed matrix of , is the data matrix . In this embodiment, the value of can be the number of battery cells in the target battery.

[0069] S2. Calculate the data prediction matrix corresponding to each monitoring time through the data observation matrices corresponding to multiple monitoring times.

[0070] Exemplarily, the data prediction matrix can be calculated by the following formula:

[0071] where, is the data prediction matrix corresponding to the th monitoring time, is the data observation matrix corresponding to the th monitoring time, is the prediction coefficient obtained through experimental determination, and the value range of is . Exemplarily, when , the data prediction matrix corresponding to the 1st monitoring time, when , the data prediction matrix corresponding to the 2nd monitoring time, and so on. The data prediction matrix corresponding to the th monitoring time is calculated.

[0072] S3. Calculate the coefficient matrix corresponding to the data prediction matrix ]>.

[0073] Exemplarily, the coefficient matrix can be calculated by the following formula: ]>.

[0074] where, is the coefficient matrix corresponding to , is the prediction coefficient obtained according to experimental measurement, is the dimension of the observation vector. In this embodiment, the value of can be the number of monitoring times.

[0075] S4. Calculate the initial statistic at the th monitoring time.

[0076] Exemplarily, the initial statistic can be calculated by the following formula:

[0077] where, is the initial statistic at the th monitoring time, is the data prediction matrix at the th monitoring time, is the corresponding coefficient matrix, is the transpose matrix of, is the inverse matrix of.

[0078] S5. Based on the mean filtering method, remove the noise from the initial statistic to obtain the monitoring statistic .

[0079] It should be noted that the mean filtering method can refer to the mean filtering method in the prior art and will not be elaborated here.

[0080] In some embodiments, the above step S1405 may include: when the monitoring statistic is greater than a preset statistic threshold, determining that the monitoring result of the target power battery is abnormal in voltage consistency.

[0081] wherein, the preset statistic threshold can be obtained according to experimental measurement. For example, through experimental measurement, the preset statistic threshold can be set to H. For example, when the monitoring statistic satisfies , determining that the monitoring result is abnormal in voltage consistency.

[0082] In some embodiments, before establishing the data matrix corresponding to each monitoring time, the data of each sub-voltage data set can also be transformed based on the Box-Cox transformation method, which can compress the range of voltage data, reduce the risk of data overfitting, enhance data stability, and improve the subsequent monitoring accuracy.

[0083] Exemplarily, the Box-Cox transformation method can be represented by the following formula:

[0084] Among them, is the voltage data to be subjected to data transformation, including the target cell voltage and the cell voltage of the second cell, is the voltage value after the data transformation of the target cell voltage and the cell voltage of the second cell, is the transformation parameter obtained through multiple tests and determinations.

[0085] Figure 2 is a flowchart of another voltage monitoring method shown according to an exemplary embodiment, as Figure 2 shown, this method may include: S201. Obtain the cell voltages of multiple cells in the target power battery to be monitored.

[0086] Among them, the cell voltage can be obtained through the BMS.

[0087] S202. Determine the voltage difference between every two adjacent cells according to the cell voltages of the multiple cells.

[0088] Among them, the voltage difference can be calculated through the following formula:

[0089] Among them, is the voltage difference between the (n - 1)th adjacent cells, is the cell voltage of the (n - 1)th cell, is the cell voltage of the nth cell.

[0090] S203. Take the adjacent cells corresponding to the voltage difference with the largest absolute value as the candidate cells.

[0091] Exemplarily, if the absolute value of the voltage difference is the largest, then take the kth cell and the (k + 1)th cell as the candidate cells.

[0092] S204. Obtain the first voltage mean value of the candidate cells and the second voltage mean value of the third cell.

[0093] Among them, the third cell includes the other cells in the multiple cells except the candidate cells.

[0094] S205. Determine the voltage range of the multiple cell voltages according to the cell voltages of the multiple cells.

[0095] Among them, the difference between the maximum value and the minimum value of the multiple cell voltages can be determined as the voltage range.

[0096] S206. Determine whether the candidate battery cell meets the preset voltage anomaly condition according to the voltage range, the voltage difference with the largest absolute value, the first voltage mean value, and the second voltage mean value.

[0097] Among them, the preset voltage anomaly condition may include: the voltage range is greater than a preset range threshold, and the preset range threshold may be ; the ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio, and the preset ratio may be λ; and, the difference between the first voltage mean value and the second voltage mean value is not within a preset difference interval, and the preset difference interval may be (-ε, ε).

[0098] S207. When the candidate battery cell meets the preset voltage anomaly condition, determine the candidate battery cell as the first battery cell with abnormal cell voltage.

[0099] S208. Correct the cell voltage of the first battery cell to obtain the corrected target cell voltage.

[0100] Among them, the median of the cell voltage of the first battery cell and the cell voltage of the second battery cell can be determined, and the voltage of the first battery cell is replaced with the median to obtain the corrected target cell voltage.

[0101] S209. Establish a voltage data set including the target cell voltage and the cell voltage of the second battery cell.

[0102] Among them, the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times.

[0103] S210. Based on the Box-Cox transformation method, perform data transformation on each sub-voltage data set.

[0104] S211. Obtain the cell temperature values of multiple battery cells at each monitoring time.

[0105] Among them, the cell temperature value can be collected through the BMS system.

[0106] S212. Establish a data matrix corresponding to each monitoring time.

[0107] Among them, the data matrix may include vectors 、 、 and , and can be expressed as ( ). The vector is the minimum value among the differences between each sub-voltage data in the sub-voltage data set after data transformation and the median of the sub-voltage data; the vector is the difference between the extreme value of the sub-voltage data and the median of the sub-voltage data in the sub-voltage dataset after data transformation, and the extreme value of the sub-voltage data can be the minimum value of the sub-voltage data; vector is the standard deviation of the other sub-voltage data except the extreme value of the sub-voltage data in the voltage dataset after data transformation, and the extreme value of the sub-voltage data can be the minimum value of the sub-voltage data; vector is the maximum value among the differences between the temperature value of each battery cell and the median of the temperature values of multiple battery cells.

[0108] S213. Based on the weighted moving average algorithm, calculate the monitoring statistic according to the data matrix at multiple monitoring times.

[0109] Among them, a data observation matrix corresponding to multiple monitoring times can be constructed through the data matrix, and the data prediction matrix at each monitoring time can be calculated through the data observation matrices at multiple monitoring times. The initial statistic can be obtained according to the data prediction matrix, and the initial statistic is denoised based on the mean filtering method to obtain the monitoring statistic.

[0110] S214. When the monitoring statistic is greater than the preset statistic threshold, determine that the monitoring result of the target power battery is abnormal in voltage consistency.

[0111] Among them, the preset statistic threshold can be obtained through experimental determination, and the preset statistic threshold can be H.

[0112] By adopting the above solution, it is possible to obtain the cell voltages of multiple cells in the target power battery to be monitored, determine the voltage range difference of the multiple cell voltages and the voltage difference between every two adjacent cells according to the cell voltages of the multiple cells, determine the first cell with abnormal cell voltage from the multiple cells according to the voltage range difference and the voltage difference, correct the cell voltage of the first cell to obtain the corrected target cell voltage, and obtain the transformed voltage data set through Box-Cox data transformation based on the target cell voltage and the cell voltage of the second cell, and determine the voltage consistency monitoring result of the target power battery based on the weighted moving average algorithm by combining the sub-voltage data sets corresponding to multiple monitoring moments in the voltage data set with the cell temperature values of the multiple cells corresponding to each monitoring moment obtained. In this way, by determining and correcting the cells with abnormal voltages, the influence of sampling anomalies on the voltage consistency discrimination result is reduced. Performing data transformation based on the Box-Cox transformation method can compress the range of voltage data, reduce the risk of data overfitting, enhance data stability, and improve the subsequent monitoring accuracy. At the same time, performing weighted averaging on the monitoring data corresponding to multiple monitoring moments within the monitoring time period can more effectively identify the small changes in the monitoring data, ensure the effectiveness of anomaly identification, and assigning different weights to the monitoring data at multiple monitoring moments within the monitoring time period according to the time law can also make the finally calculated monitoring result more in line with the actual law, further improving the accuracy of voltage consistency anomaly discrimination.

[0113] It should be noted that the relevant descriptions of the steps in the above Figure 2 illustrated embodiments can refer to the descriptions of the relevant steps in the foregoing embodiments, and will not be repeated here.

[0114] In addition, for the above method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence. For example, the above steps S201 to S210 and step S211 are not limited to the step sequence shown in the current embodiment. It is also possible to execute step S211 first, and then execute steps S201 to S210, or execute steps S201 to S210 and step S211 simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0115] Figure 3 is a block diagram of a voltage monitoring device 300 shown according to an exemplary embodiment. Referring to Figure 3 this, the device includes: An acquisition module 301 is configured to acquire the cell voltages of multiple cells in a target power battery to be monitored; a first determination module 302 is configured to determine, from the multiple cells, a first cell with an abnormal cell voltage according to the cell voltages of the multiple cells; a correction module 303 is configured to correct the cell voltage of the first cell to obtain a corrected target cell voltage; a second determination module 304 is configured to determine a monitoring result of the target power battery according to the target cell voltage and the cell voltage of a second cell, where the second cell is another cell among the multiple cells except the first cell, and the monitoring result indicates whether the voltage consistency of the target power battery is abnormal.

[0116] Optionally, the first determination module 302 is further configured to determine a voltage difference between every two adjacent cells according to the cell voltages of the multiple cells; use the adjacent cells corresponding to the voltage difference with the largest absolute value as candidate cells; acquire a first voltage average value of the candidate cells and a second voltage average value of a third cell, where the third cell includes the other cells among the multiple cells except the candidate cells; and determine, from the multiple cells, a first cell with an abnormal cell voltage according to the first voltage average value and the second voltage average value.

[0117] Optionally, the first determination module 302 is further configured to determine a voltage range of the cell voltages of the multiple cells according to the cell voltages of the multiple cells; determine whether the candidate cells meet a preset voltage abnormality condition according to the voltage range, the voltage difference with the largest absolute value, the first voltage average value, and the second voltage average value; and use the candidate cells as the first cells when the candidate cells meet the preset voltage abnormality condition.

[0118] Optionally, the preset voltage abnormality condition includes: the voltage range is greater than a preset range threshold; the ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio; and the difference between the first voltage average value and the second voltage average value is not within a preset difference interval.

[0119] Optionally, the correction module 303 is further configured to determine a median of the cell voltage of the first cell and the cell voltage of a second cell; and correct the cell voltage of the first cell according to the median to obtain a corrected target cell voltage.

[0120] Optionally, the second determination module 304 is further configured to establish a voltage data set including the target cell voltage and the cell voltages of the second cells, where the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times; the acquisition module 301 is further configured to acquire the cell temperature values of the multiple cells at each of the monitoring times; the second determination module 304 is further configured to establish a data matrix corresponding to each of the monitoring times, where the variables of the data matrix include the minimum value of the difference between each sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the difference between the extreme value of the sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the standard deviation of the other sub-voltage data except the extreme value of the sub-voltage data in the sub-voltage data set, and the maximum value of the difference between the temperature value of each cell and the median of the cell temperature values of the multiple cells; based on the data matrices of multiple monitoring times, calculate a monitoring statistic based on a weighted moving average algorithm; and determine the monitoring result of the target power battery according to the monitoring statistic.

[0121] Optionally, the second determination module 304 is further configured to perform data transformation on each of the sub-voltage data sets based on the Box-Cox transformation method.

[0122] Optionally, the second determination module 304 is further configured to determine that the monitoring result of the target power battery is abnormal voltage consistency when the monitoring statistic is greater than a preset statistic threshold.

[0123] By using the above device, it is possible to obtain the cell voltages of multiple cells in the target power battery to be monitored, determine the voltage range difference of the multiple cell voltages and the voltage difference between every two adjacent cells according to the cell voltages of the multiple cells, determine the first cell with abnormal cell voltage from the multiple cells according to the voltage range difference and the voltage difference, correct the cell voltage of the first cell to obtain the corrected target cell voltage, and obtain the transformed voltage data set through Box-Cox data transformation based on the target cell voltage and the cell voltage of the second cell. Then, based on the sub-voltage data sets corresponding to multiple monitoring moments in the voltage data set and in combination with the cell temperature values of the multiple cells corresponding to each monitoring moment obtained, the voltage consistency monitoring result of the target power battery is determined based on the weighted moving average algorithm. In this way, by determining and correcting the cells with abnormal voltages, the influence of sampling anomalies on the voltage consistency discrimination result is reduced. Performing data transformation based on the Box-Cox transformation method can compress the range of voltage data, reduce the risk of data overfitting, enhance data stability, and improve the subsequent monitoring accuracy. At the same time, performing weighted averaging on the monitoring data corresponding to multiple monitoring moments within the monitoring time period can more effectively identify the minor changes in the monitoring data, ensure the effectiveness of anomaly identification, and assigning different weights to the monitoring data at multiple monitoring moments within the monitoring time period according to the time law can also make the finally calculated monitoring result more in line with the actual law, further improving the accuracy of voltage consistency anomaly discrimination.

[0124] Regarding the device in the above embodiment, the specific manners in which each module performs operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0125] Figure 4 is a block diagram of an electronic device 400 provided according to an exemplary embodiment of the present disclosure. As Figure 4 shown, the electronic device 400 may include: a processor 401, a memory 402. The electronic device 400 may further include one or more of a multimedia component 403, an input / output (I / O) interface 404, and a communication component 405.

[0126] Among them, the processor 401 is used to control the overall operation of the electronic device 400 to complete all or part of the steps in the above voltage monitoring method. The memory 402 is used to store various types of data to support the operation of the electronic device 400. These data may include, for example, instructions for any application or method operating on the electronic device 400, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc. The multimedia component 403 may include a screen and an audio component. Among them, the screen may be a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 402 or sent through the communication component 405. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 404 provides an interface between the processor 401 and other interface modules, and the above other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 405 is used for wired or wireless communication between the electronic device 400 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 405 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0127] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above voltage monitoring method.

[0128] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above voltage monitoring method are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 402 including program instructions, and the above program instructions may be executed by the processor 401 of the electronic device 400 to complete the above voltage monitoring method.

[0129] Figure 5 FIG. 500 is a block diagram of a vehicle 500 according to an exemplary embodiment of the present disclosure, and the vehicle 500 includes the above-mentioned electronic device 400.

[0130] The preferred embodiments of the present disclosure have been described in detail above in conjunction with the accompanying drawings. However, the present disclosure is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0131] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present disclosure will not separately describe various possible combination methods.

[0132] Furthermore, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

Claims

1. A voltage monitoring method, characterized in that, The method includes: Obtaining the cell voltages of multiple cells in a target power battery to be monitored; Determining a first cell with an abnormal cell voltage from the multiple cells according to the cell voltages of the multiple cells; Correcting the cell voltage of the first cell to obtain a corrected target cell voltage; Determining a monitoring result of the target power battery according to the target cell voltage and the cell voltages of second cells; the second cells are other cells in the multiple cells except the first cell, and the monitoring result characterizes whether the voltage consistency of the target power battery is abnormal.

2. The method according to claim 1, wherein The determining a first cell with an abnormal cell voltage from the multiple cells according to the cell voltages of the multiple cells includes: Determining the voltage difference between every two adjacent cells according to the cell voltages of the multiple cells; Taking the adjacent cells corresponding to the voltage difference with the largest absolute value as candidate cells; Obtaining a first voltage average value of the candidate cells and a second voltage average value of third cells; the third cells include other cells in the multiple cells except the candidate cells; Determining a first cell with an abnormal cell voltage from the multiple cells according to the first voltage average value and the second voltage average value.

3. The method according to claim 2, wherein The determining a first cell with an abnormal cell voltage from the multiple cells according to the first voltage average value and the second voltage average value includes: Determining the voltage range of the cell voltages of the multiple cells according to the cell voltages of the multiple cells; Determining whether the candidate cells meet a preset voltage abnormality condition according to the voltage range, the voltage difference with the largest absolute value, the first voltage average value, and the second voltage average value; When the candidate cells meet the preset voltage abnormality condition, taking the candidate cells as the first cells.

4. The method according to claim 3, characterized in that The preset voltage abnormality condition includes: The voltage range is greater than a preset range threshold; The ratio of the voltage difference with the largest absolute value to the voltage range is greater than or equal to a preset ratio; and The difference between the first voltage average value and the second voltage average value is not within a preset difference interval.

5. The method according to claim 1, wherein The correcting the cell voltage of the first cell to obtain a corrected target cell voltage includes: Determining the median of the cell voltage of the first cell and the cell voltages of second cells; Correcting the cell voltage of the first cell according to the median to obtain a corrected target cell voltage.

6. The method according to any one of claims 1 to 5, characterized in that: The determining a monitoring result of the target power battery according to the target cell voltage and the cell voltages of second cells includes: Establishing a voltage data set including the target cell voltage and the cell voltages of second cells, where the voltage data set includes sub-voltage data sets corresponding to multiple monitoring times; Obtaining the cell temperature values of the multiple cells at each of the monitoring times; Establish a data matrix corresponding to each of the monitoring times. The variables of the data matrix include the minimum value among the differences between each sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the difference between the extreme value of the sub-voltage data in the sub-voltage data set and the median of the sub-voltage data, the standard deviation of the other sub-voltage data in the sub-voltage data set except the extreme value of the sub-voltage data, and the maximum value among the differences between the temperature value of each battery cell and the median of the temperature values of multiple battery cells; Based on the data matrices at multiple monitoring times, calculate a monitoring statistic using a weighted moving average algorithm; Determine the monitoring result of the target power battery according to the monitoring statistic; 7. The method according to claim 6, wherein Before establishing the data matrix corresponding to each of the monitoring times, the method further includes: Perform data transformation on each of the sub-voltage data sets based on the Box-Cox transformation method; 8. The method according to claim 6, characterized in that The determining the monitoring result of the target power battery according to the monitoring statistic includes: When the monitoring statistic is greater than a preset statistic threshold, determine that the monitoring result of the target power battery is abnormal voltage consistency; 9. A voltage monitoring device, characterized in that, The device includes: An acquisition module, configured to acquire the cell voltages of multiple battery cells in a target power battery to be monitored; A first determination module, configured to determine a first battery cell with abnormal cell voltage from the multiple battery cells according to the cell voltages of the multiple battery cells; A correction module, configured to correct the cell voltage of the first battery cell to obtain a corrected target cell voltage; A second determination module, configured to determine the monitoring result of the target power battery according to the target cell voltage and the cell voltage of a second battery cell; the second battery cell is the other battery cells among the multiple battery cells except the first battery cell, and the monitoring result indicates whether the voltage consistency of the target power battery is abnormal; 10. An electronic device, characterized in that, Includes: A memory, on which a computer program is stored; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1-8; 11. A vehicle, characterized in that, Includes the electronic device according to claim 10;

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