Battery cell detection method and battery cell detection device

Abstract

The invention provides a battery cell detection method and a battery cell detection device, and belongs to the field of batteries. The method comprises the following steps: for each battery cell in M battery cells, determining a detection factor of the battery cell, m is a positive integer greater than or equal to 2; determining an abnormal battery cell from the M battery cells based on the detection factors of the M battery cells; the detection factor of the battery cell is determined according to the observation value and the reference value of the electrical property of the battery cell, and accurate identification of the abnormal battery cell is realized.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and more specifically to a cell testing method and a cell testing device. Background Technology

[0002] Typically, internally short-circuited battery cells continuously accumulate heat during charge-discharge cycles. Once they reach the final stage of thermal runaway, they can trigger a chain reaction of exothermic reactions, leading to a fire and explosion of the battery pack. Therefore, it is crucial to identify and isolate high-risk cells before thermal runaway is triggered. However, there is currently no effective solution for identifying abnormal battery cells. Summary of the Invention

[0003] The embodiments of the present invention provide a battery cell detection method and a battery cell detection device, which can effectively and accurately identify abnormal battery cells.

[0004] In a first aspect, embodiments of the present invention provide a battery cell detection method, comprising: for each of M battery cells, determining a detection factor for the battery cell; M being a positive integer greater than or equal to 2; identifying abnormal battery cells from the M battery cells based on the detection factors of the M battery cells; the detection factor of the battery cell being determined by observing and referencing the electrical performance of the battery cell.

[0005] In one embodiment, the observed value of the battery cell is determined based on the actual electrical performance value of the battery cell, and the reference value is determined based on the average electrical performance value of the battery cell group to which the battery cell belongs; the detection factor represents the difference between the actual electrical performance value of the battery cell and the average electrical performance value of the battery cell group to which the battery cell belongs;

[0006] Based on the detection factors of the M cells, abnormal cells are identified from the M cells.

[0007] In one embodiment, the detection factor includes a deviation, and the electrical performance value includes open-circuit voltage;

[0008] The observed value for each cell is determined based on a first deviation value and a second deviation value, and the reference value is determined based on the first deviation value; the first deviation value represents the deviation between the open-circuit voltage of the cell in the Oth measurement and the average open-circuit voltage of the cell group to which the cell belongs, and the second deviation value represents the deviation between the open-circuit voltage of the cell in the Pth measurement and the average open-circuit voltage of the cell group to which the cell belongs; O and P are both positive integers, and the values ​​of O and P are different.

[0009] Based on the characteristic that normal cells tend to have similar deviations, while abnormal cells deviate significantly, the deviation between the observed value and the reference value can be used as a basis for judging whether a cell is abnormal. This method can be called the voltage drop observation deviation method. By using the voltage drop observation deviation method, interference caused by the inherent differences in open-circuit voltage among cells in the group can be eliminated, and abnormal cells (such as short circuits) can be accurately separated.

[0010] Furthermore, by using relative deviations (such as the first deviation value and the second deviation value) as a measure (quantitative index) of single-point fluctuations in the battery cell, the influence of the median absolute value can be eliminated, thereby reducing the differences between groups and improving the comparability of data between groups.

[0011] Furthermore, the reference value for the battery cell is determined based on a relative deviation (such as a first deviation value), and the observed value for the battery cell is determined based on at least two relative deviations (such as a first deviation value and a second deviation value), which can further enhance the accuracy of detection. Moreover, compared to related technologies where inherent differences (non-abnormalities) in the battery cell are confused with true anomalies, and where differences in the production characteristics of the battery cell are misjudged as abnormalities, resulting in an excessively high rejection rate for normal battery cells, this embodiment of the invention, through a multi-dimensional error separation mechanism, can achieve both inherent difference compensation and true anomaly identification by comparing the observed value (values ​​determined by multiple relative deviations) with the reference value.

[0012] In one embodiment, determining abnormal cells from the M cells based on detection factors of the M cells includes:

[0013] If the deviation of the first cell among the M cells is greater than or equal to a first threshold, the first cell is determined to be an abnormal cell.

[0014] Based on this, if the deviation is greater than or equal to the first threshold, it means that the relative deviation of the first cell fluctuates greatly between multiple measurements. Therefore, the first cell can be identified as an abnormal cell based on the characteristic of the consistency of the relative deviation of the cells.

[0015] In one embodiment, the absolute value of the deviation between the observed value and the reference value of the battery cell is greater than or equal to the absolute value of the difference between the first deviation value and the second deviation value.

[0016] Compared to related technologies where minute short-circuit signals are difficult to capture, this invention amplifies the changes in relative deviations between multiple measurements (such as the difference between a first deviation value and a second deviation value). This increases the intensity of the minute signal, allowing for precise determination of whether the battery cell is abnormal through the amplified deviation.

[0017] In one embodiment, the absolute value of the observation is greater than or equal to the absolute value of the second deviation value.

[0018] Instead of directly using the second deviation value as the observed value, this embodiment of the invention can amplify the absolute value of the observed value, making it greater than or equal to the absolute value of the second deviation value. This amplifies the deviation used to measure whether a battery cell is abnormal, thereby improving the accuracy of identifying abnormal battery cells.

[0019] In one embodiment, the observed value is the Euclidean distance between the first deviation value and the second deviation value, or the observed value is the Manhattan distance between the first deviation value and the second deviation value.

[0020] Compared to related technologies that rely on single-dimensional analysis (such as absolute voltage value), which struggles to capture minute short-circuit signals and lacks sensitivity to abnormal signals like short circuits, resulting in low accuracy in identifying faulty battery cells, this invention achieves multi-dimensional signal amplification by synthesizing the relative voltage deviations (such as first and second deviation values) in a spatial dimension. Thus, through vector synthesis, the intensity of minute abnormal signals can be enhanced.

[0021] The observed value is the Euclidean distance between the first deviation value and the second deviation value, which can also be understood / replaced as: the distance between the first deviation value and the second deviation value is calculated using the Euclidean algorithm.

[0022] In one embodiment, the average open-circuit voltage includes the median open-circuit voltage or the average open-circuit voltage.

[0023] In one embodiment, the M cells belong to N cell groups, where N is a positive integer greater than or equal to 2.

[0024] In this invention, relative deviation is used as a measure (quantitative index) of single-point fluctuation of the battery cell. By removing the influence of the median absolute value, the differences between groups can be reduced and the comparability of data between groups can be improved.

[0025] In one embodiment, the Pth measurement is one or more measurements adjacent to the Oth measurement, and / or the Oth measurement includes one or more measurements.

[0026] In this way, more dimensions of relative deviation can be used to amplify the changes between relative deviations, improve the amplification effect, and thus improve the detection accuracy of abnormal cells.

[0027] In one embodiment, cell risk data can be output in real time. On the one hand, the risk data can prompt the interception of high-risk cells before leaving the factory, reducing the probability of thermal runaway in the battery pack from the source. On the other hand, the risk data can drive the optimization of key processes, creating a closed loop for improving battery quality.

[0028] In one embodiment, the algorithm of the present invention can be embedded in a production system to achieve a response time within seconds. Furthermore, the ID-level location of abnormal battery cells can replace the batch sampling inspection mode of related technologies.

[0029] Secondly, embodiments of the present invention provide a cell testing method, including:

[0030] For each of the M battery cells, the deviation between the observed value and the reference value of the battery cell is obtained; M is a positive integer greater than or equal to 2; the deviation is obtained by amplifying the relative deviation of multiple measurements of the battery cell;

[0031] Based on the deviation of the M cells, abnormal cells are identified from the M cells.

[0032] Thirdly, the present invention provides a battery cell testing device, comprising: a processor configured to perform a method of any of the above-described aspects.

[0033] Optionally, the device may further include the memory and / or the communication interface.

[0034] The communication interface is coupled to the processor and is used for inputting and / or outputting information.

[0035] The memory is used to store computer programs, and the processor is configured to perform a method of any of the above-described designs, which can be implemented as: a method for executing a computer program stored in the memory to perform any of the above-described designs.

[0036] Alternatively, the processor can be a hardware-implemented circuit, such as an artificial intelligence (AI) processor, to improve operating speed. This invention does not limit the specific implementation of the processor.

[0037] Optionally, the cell testing device can be a complete device or a module within the device, such as a chip.

[0038] Fourthly, embodiments of the present invention provide a computer-readable storage medium including computer instructions that, when executed on a device, cause the device to perform the method in any possible design of any of the above aspects.

[0039] Fifthly, embodiments of the present invention provide a computer program product that, when run on a device, causes the device to perform the method in any possible design of any of the above aspects.

[0040] Sixthly, embodiments of the present invention provide a circuit system including processing circuitry configured to perform the methods in any possible design of any of the above aspects. The processing circuitry may be implemented as a corresponding circuit component, such as one or more processors. Alternatively, it may be implemented as a processor and a memory. Yet another example is a processor and a transceiver.

[0041] In a seventh aspect, embodiments of the present invention provide a chip system including at least one processor and at least one interface circuit, the at least one interface circuit being used to perform transceiver functions and send instructions to at least one processor, wherein when at least one processor executes instructions, at least one processor performs the method as described in the first aspect and any of the designs therein.

[0042] Eighthly, embodiments of the present invention provide a battery cell testing device, including a functional module, unit, or means for performing the method as described in any possible design of any aspect of the present invention above. This module may be implemented in software or hardware, or in a combination of both. It may include a processing unit and a communication unit, without limitation.

[0043] The beneficial effects of the embodiments of the present invention are as follows:

[0044] In embodiments of the present invention, a detection factor for the battery cell is determined based on observed and reference values ​​of the battery cell's electrical performance, so as to determine whether the battery cell is abnormal by using the detection factor of electrons, thereby achieving accurate identification of abnormal battery cells and ensuring the identification accuracy of abnormal battery cells. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram illustrating the factors affecting self-discharge provided by an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a scenario for the cell testing method provided in an embodiment of the present invention;

[0048] Figure 3 This is a schematic flowchart of the battery cell testing method provided in an embodiment of the present invention;

[0049] Figures 4-15 This is a schematic diagram of a scenario for the cell testing method provided in an embodiment of the present invention;

[0050] Figures 16-17This is a schematic diagram of the battery cell testing device provided in an embodiment of the present invention;

[0051] Figure 18 This is a schematic diagram of the chip system provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Furthermore, it should be understood that the specific embodiments described herein are only for illustration and explanation of the present invention and are not intended to limit the present invention.

[0053] The data involved in the embodiments of this invention can be processed in some ways. For example, for data with the same units, standardization is not necessary, or standardization such as Z-score can be performed.

[0054] In related technologies, performance-related defects account for up to 68.5% of cell testing, with 90% being differential pressure defects. Failure mode analysis and disassembly results indicate that short circuits are the primary cause. The development path for identifying such problems in related technologies is: K-value screening → Key Lithium-ion Battery Safety Signals (KLSS) solution. However, defective cells still manage to enter the market.

[0055] Taking the K-value screening method as an example, this method has the following problems: (1) It is difficult to extract weak signals. The self-discharge characterization signal itself has the characteristics of low amplitude and poor signal-to-noise ratio. It needs to be left to stand for a long time to accumulate to the detectable threshold. This is fundamentally contradictory to the fast production cycle. (2) It is susceptible to noise interference. In the industrial environment, factors such as sampling circuit noise and temperature and humidity disturbances will further degrade the signal quality, causing the misjudgment rate of the K-value method to increase, which seriously affects the judgment result. It can be seen that the performance of the cell detection method in the relevant technology is not good, and it is urgent to propose a new cell detection scheme to optimize the identification ability of risky cells.

[0056] In view of this, embodiments of the present invention provide a cell detection method. This method integrates inter-group differences not considered by a single K value, as well as individual cell attribute differences not considered by KLSS, making the detection of outliers more accurate and precise.

[0057] In this embodiment of the invention, the main influencing factors of cell self-discharge can be obtained based on fault tree analysis (FTA). See also... Figure 1The diagram shown illustrates the factors influencing self-discharge (or the factors affecting self-discharge). These factors include: environmental factors, intrinsic property factors, and detection factors. These three types of factors can respectively cause inter-group differences, intra-group differences, and abnormal fluctuations in cell data.

[0058] Among them, such as Figure 2 In (a) and (b) of the data, if the abnormal fluctuations are small, they are easily masked by fluctuations in other factors, making it impossible to distinguish abnormal cells from the basic cell data, resulting in defective cells being discharged. Considering this, in this embodiment of the invention, abnormal fluctuations can be highlighted (amplified) and inter-group and intra-group differences can be eliminated to identify abnormal cells. The following is a detailed description of the solution in this embodiment of the invention:

[0059] Figure 3 An exemplary flow diagram of a battery cell testing method according to an embodiment of the present invention is shown. This method can be executed by a battery cell testing device or a chip in a battery cell testing device. Figure 3 The method may include the following steps:

[0060] S101. For each of the M cells, determine the detection factor for that cell.

[0061] Where M is a positive integer greater than or equal to 2. The detection factor of the battery cell is determined by comparing the observed values ​​of the cell's electrical performance with reference values.

[0062] In some embodiments, the observed value of a battery cell is determined based on its actual electrical performance value, while the reference value is determined based on the average electrical performance value of the cell group to which it belongs. The detection factor represents the difference between the actual electrical performance value of the battery cell and the average electrical performance value of the cell group. Determining the observed value of a battery cell based on its actual electrical performance value determines its actual characteristics, while the reference value is determined based on the average electrical performance value of the cell group. Determining the difference between the observed value and the reference value determines the deviation of the battery cell's actual characteristics. This deviation is then used to determine whether the battery cell is abnormal, thereby achieving accurate identification of abnormal batteries and ensuring the accuracy of abnormal battery cell recognition.

[0063] Optionally, the above-mentioned detection factors include deviation, which may also be referred to as observation deviation. The above-mentioned electrical performance values ​​include open-circuit voltage.

[0064] The observed value for each cell is determined based on a first deviation value and a second deviation value, and the reference value is determined based on the first deviation value; the first deviation value represents the deviation between the open-circuit voltage of the cell in the Oth measurement and the average open-circuit voltage of the cell group to which the cell belongs, and the second deviation value represents the deviation between the open-circuit voltage of the cell in the Pth measurement and the average open-circuit voltage of the cell group to which the cell belongs; O and P are both positive integers, and the values ​​of O and P are different.

[0065] The first deviation value is used to assess the degree of deviation of the cell's open-circuit voltage from the average open-circuit voltage in the Oth measurement. The second deviation value is used to assess the degree of deviation of the cell's open-circuit voltage from the average open-circuit voltage in the Pth measurement. The first and second deviation values ​​can be referred to as average deviation or relative deviation. For example, ... Figure 4 An example of relative deviation is shown.

[0066] Optionally, the average open-circuit voltage includes the median open-circuit voltage (e.g., denoted as Med) or the average open-circuit voltage (e.g., denoted as Mean). This embodiment of the invention does not limit the specific implementation of the average open-circuit voltage. The median open-circuit voltage is the median of the open-circuit voltages of all cells in the group. The average open-circuit voltage is the average of the open-circuit voltages of all cells in the group. When the average open-circuit voltage is the median open-circuit voltage, the relative deviation can be called the median deviation or the median relative deviation, without limitation on the name. When the average open-circuit voltage is the average open-circuit voltage, the relative deviation can be called the mean deviation or the mean relative deviation. The following will use the median open-circuit voltage as an example, and this will be stated uniformly without further elaboration. The median can also be called the geometric median.

[0067] An implementation where the relative deviation is the median deviation can be called the median deviation method. An implementation where the relative deviation is the mean deviation can be called the mean deviation method. For example, the mean deviation method is suitable for normally distributed data environments.

[0068] For example, taking cell A1 in cell group A as an example, in the first measurement, the open-circuit voltage of cell A1 (denoted as OCV1) and the open-circuit voltage of all cells in cell group A are measured, and the median open-circuit voltage is calculated based on the open-circuit voltages of all cells in cell group A. The deviation between the open-circuit voltage of cell A and the median open-circuit voltage (OCV1-Med1) is taken as the first deviation value of cell A1 (denoted as median deviation MD1).

[0069] In the third measurement, the open-circuit voltage of cell A1 (denoted as OCV3) and the open-circuit voltage of all cells in cell group A are measured, and the median open-circuit voltage is calculated based on the open-circuit voltages of all cells in cell group A. The deviation between the open-circuit voltage OCV3 of cell A and the median open-circuit voltage (OCV3-Med3) is taken as the second deviation value of cell A1 (denoted as median deviation MD3). The determination of the median deviation of other cells in the M cells can refer to this process and will not be repeated here.

[0070] Besides being denoted as MD, the relative deviations obtained from different measurements can also be denoted by other symbols. For example, the relative deviation obtained from the first measurement can be denoted as ΔX1, the relative deviation obtained from the second measurement can be denoted as ΔX2, and so on, without restriction.

[0071] In this embodiment of the invention, the observed deviation can characterize the fluctuation between relative deviations in multiple measurements. As one possible implementation, the median deviation MD1 is used as a reference value, and the median deviation MD3 is used as the observed value to obtain the observed deviation: MD3-MD1. Based on this observed deviation, abnormal risk cells can be distinguished. Figure 5 An example of the observed deviation of a battery cell is shown. Taking cell B as an example, Figure 5 The observed deviation shown characterizes the fluctuation of the relative deviation of cell B in the first and third measurements.

[0072] Based on production stability and the reliability of the testing system, the relative deviations of normal battery cells show consistency (small fluctuations) across multiple measurements, while the relative deviations of abnormal battery cells fluctuate greatly. Since the observed deviation can characterize the fluctuations between relative deviations in multiple measurements, abnormal battery cells can be identified based on the observed deviation. Figure 6 An example of identifying abnormal battery cells based on observed deviations is shown. For example... Figure 6 If cell X shows large fluctuations in relative deviations across three measurements, while the other cells show smaller fluctuations, then cell X is determined to be an abnormal cell, and the other cells are normal cells.

[0073] The above example uses a reference value and an observed value as the first deviation value and the second deviation value, respectively, and the observed deviation is the difference between the first deviation value and the second deviation value. In this embodiment of the invention, to improve the distinguishability between data, the observed deviation is amplified so that the absolute value of the observed deviation is greater than or equal to the absolute value of the difference between the first deviation value and the second deviation value. Amplifying the observed deviation means amplifying the change (difference) in the relative deviation of multiple measurements.

[0074] The following are some ways to amplify the observed deviation:

[0075] Method 1: The observed value is the Euclidean distance between the first deviation value and the second deviation value. That is, for the relative deviations obtained from multiple measurements, the Euclidean distance is taken and used as the observed value. The Euclidean distance can also be called the multi-factor Euclidean distance or the multi-dimensional Euclidean distance.

[0076] In this embodiment of the invention, the formula for calculating Euclidean distance is:

[0077] Continuing with the example above, if we take the relative deviation MD1 obtained from the first measurement as the first deviation value and the relative deviation MD3 obtained from the third measurement as the second deviation value, then the Euclidean distance between the two can be calculated. And This is an observation value. This observation value can also be called the Euclidean distance composite quantity.

[0078] In this embodiment of the invention, it is possible to calculate The deviation from the first deviation value MD1 yields the amplified observation deviation. It is evident that, compared to directly subtracting the second deviation value MD3 from the first deviation value MD1, [the method is more efficient]. The absolute value of the difference between the observed deviation and the first deviation value MD1 is larger. In other words, the difference between the relative deviations is amplified. Thus, based on the amplified observed deviation, abnormal cells can be identified more accurately.

[0079] For example, Figure 7 (a) shows the observation deviation before magnification. Figure 7 (b) shows an example of the magnified observation deviation. Figure 8 (a) in the image shows the observation deviation before magnification in another example. Figure 8 (b) in the example shows the magnified observation deviation. Figure 9 (a) in the image shows the observation deviation before magnification in another example. Figure 9 (b) in the example shows the magnified observation deviation.

[0080] Method 2: The observed value is the Manhattan distance between the first deviation value and the second deviation value. That is, for the relative deviation obtained from multiple measurements, the Manhattan distance is taken and used as the observed value.

[0081] In this embodiment of the invention, the Manhattan distance is calculated as: |MD1|+|MD2|…+|MDi|.

[0082] Continuing with the example above, if we take the relative deviation MD1 obtained from the first measurement as the first deviation value and the relative deviation MD3 obtained from the third measurement as the second deviation value, then we can calculate the Manhattan distance |MD1|+|MD3|, and take |MD1|+|MD3| as the observed value. This observed value can also be called the composite Manhattan distance.

[0083] In this embodiment of the invention, the deviation between |MD1|+|MD3| and the first deviation value MD1 can be calculated to obtain the amplified observed deviation. Compared to directly subtracting the first deviation value MD1 from the second deviation value MD3, the absolute value of the difference between |MD1|+|MD3| and the first deviation value MD1 is larger. That is, the difference between relative deviations is amplified.

[0084] The Manhattan distance implementation reduces computational complexity and is suitable for scenarios with high real-time requirements and low power consumption.

[0085] As can be seen, compared to directly using the second deviation value as the observed value, both Method 1 and Method 2 can amplify the absolute value of the observed value, making the absolute value of the observed value greater than or equal to the absolute value of the second deviation value.

[0086] The above examples using Euclidean distance and Manhattan distance illustrate methods for amplifying observation deviation. In other embodiments, other methods can be used to amplify observation deviation. For example, exponential or logarithmic methods can be used. This embodiment of the invention does not limit the specific amplification method.

[0087] S102. Based on the detection factors of M cells, identify abnormal cells from the M cells.

[0088] In some embodiments, if the detection factor of the first cell among the M cells is greater than or equal to a first threshold, the first cell is determined to be an abnormal cell.

[0089] As mentioned above, based on the characteristic of the relative deviation consistency of battery cells, in this embodiment of the invention, when a battery cell is detected to have a large fluctuation in relative deviation between multiple measurements, the battery cell is determined to be an abnormal battery cell. Since the observed deviation can characterize the fluctuation between relative deviations in multiple measurements, when a battery cell is detected to have a large observed deviation, the battery cell can be determined to be an abnormal battery cell.

[0090] As one possible implementation, a first threshold can be set, and if the observed deviation of the first cell among the M cells is greater than or equal to the first threshold, the first cell can be determined to be an abnormal cell.

[0091] The solution in this invention is based on the characteristic that the deviation of normal cells tends to converge, while the deviation of abnormal cells deviates significantly. Therefore, the deviation between the observed value and the reference value can be used as a basis for determining whether a cell is abnormal. This method can be called the voltage drop observation deviation method. By using this method, interference caused by the inherent differences in open-circuit voltage among cells within a group can be eliminated, enabling accurate separation of abnormal cells (such as those with short circuits).

[0092] Furthermore, by using relative deviations (such as the first deviation value and the second deviation value) as a measure (quantitative index) of single-point fluctuations in the battery cell, the influence of the median absolute value can be eliminated, thereby reducing the differences between groups and improving the comparability of data between groups.

[0093] Furthermore, the reference value of the battery cell is determined based on a relative deviation (such as a first deviation value), and the observed value of the battery cell is determined based on at least two relative deviations (such as a first deviation value and a second deviation value), which can further enhance the accuracy of the detection.

[0094] In some embodiments, the M cells belong to N cell groups, where N is a positive integer greater than or equal to 2. In conjunction with the above, in this embodiment of the invention, the average open-circuit voltage can be used as a reference point to calculate the relative deviation between the open-circuit voltage of each cell and the average open-circuit voltage. That is, in abnormal cell detection, the specific magnitude of the average open-circuit voltage is not considered; instead, the abnormal cell is determined based on the magnitude of the relative deviation.

[0095] One possible implementation involves measuring the open-circuit voltage of each cell and using this open-circuit voltage as sample data, then grouping the sample data. Grouping the sample data by the cell's tray number reduces interference from factors such as time, temperature, and batch. In other examples, grouping can also be based on process data (e.g., time / temperature / batch). This embodiment of the invention does not limit the specific basis for grouping.

[0096] Next, the median of each group of sample data can be calculated. For example, each group of sample data can be sorted, and the median value can be taken as the median. For instance, if n is the number of battery cells in each group, and n is an odd number, the median is: X (n+1) / 2 That is, the (n+1) / 2th sample data in the above sorting is taken as the median. When n is even, the median is: (X n / 2 +X n / 2+1 ) / 2. For example, Figure 10 (a) shows the OCV values ​​for each group in group AC. It can be seen that the median OCV values ​​differ significantly between different groups, making inter-group comparability limited. For example, Figure 11 (a) also shows that there are significant differences in the median OCV among different groups.

[0097] Next, the relative deviation of each group of sample data can be calculated, where the relative deviation MD is: OCV j–Med, where Med is the OCV value in the corresponding group.

[0098] For example, Figure 10 (b) shows the relative deviation of each OCV group in group AC.

[0099] For example, Figure 11 (b) in the figure also shows the relative deviations of the OCVs of different groups. Compared to Figure 11 (a) in the middle, Figure 11 In (b) of the dataset, the differences between groups have decreased. This shows that obtaining the relative bias can create a new sample dataset.

[0100] Compared to directly comparing cell voltage data from different batches / environments (temperature, time) in related technologies, distinguishing data anomalies caused by inter-group environmental interference is difficult, and inconsistent benchmarks can easily lead to misjudgments. As can be seen, the embodiments of this invention provide dynamic benchmark reconstruction capabilities. By dynamically generating a data benchmark through the intra-group median, and using relative deviation as a measure (quantitative index) of single-point fluctuations in the cell, the influence of the absolute value of the median can be eliminated, reducing inter-group differences and removing the problem of excessive benchmark differences caused by batch / environmental interference, thus improving the comparability of inter-group data. For example, the comparability of cross-group data can be improved to the same order of magnitude.

[0101] The method described above, which uses the relative deviation of the median as a measure (quantitative index) of the single-point fluctuation of a battery cell, can be called the univariate median method. The method using the relative deviation of the mean as a measure (quantitative index) of the single-point fluctuation of a battery cell can be called the univariate mean method. Both methods can be collectively referred to as the univariate averaging method. The univariate averaging method can reduce inter-group differences.

[0102] In this embodiment of the invention, the median absolute deviation (MAD) can be used to compare the discriminative power of related techniques with the scheme of this embodiment. Discriminative power characterizes the ability to distinguish outlier data; generally, the higher the discriminative power, the stronger the ability to detect outlier data. The method of this embodiment can be called the DMP method.

[0103] like Figure 12 Tables (a)-(c) show the K value, KLSS, and the discriminative power of the DMP method in this embodiment of the invention, respectively. Figure 12 The data in (a)-(c), along with other test data, can be obtained as shown in Table 1 in some examples:

[0104] Table 1

[0105] Outlier K values K median K MAD K bias / MAD 0.083202 0.08207 0.08137 0.0224 Outlier KLSS KLSS median KLSS MAD KLSS bias / MAD 1.034 0.994252 0.999 0.0350 Outlier DMP DMP median DMP MAD DMP bias / MAD 0.000845 0.080825 0.0814 0.9896

[0106] From Table 1, the ratio of DMP deviation / MAD to KLSS deviation / MAD is approximately 28. The ratio of DMP deviation / MAD to K deviation / MAD is approximately 44. Therefore, the discrimination capability of the embodiment of the present invention is approximately 28 times that of the KLSS method. The discrimination capability of the embodiment of the present invention is approximately 44 times that of the K-value method. Thus, the stronger the ability of the embodiment of the present invention to detect abnormal data from sample data, the stronger its ability to detect abnormal battery cells.

[0107] Figure 13 The diagram illustrates the data distribution of the K-value method and the DMP method according to an embodiment of the present invention. It can be seen that in the K-value method, the distinction between abnormal data (yellow dots) and normal data (blue dots) is not high. However, in the DMP method of this embodiment, abnormal data (yellow dots) can be effectively distinguished from normal data (blue dots), which helps in detecting abnormal battery cells. Figure 14 This illustrates another data distribution diagram of the K-value method and the DMP method of this invention.

[0108] Figure 15 The diagram illustrates the data distribution of the KLSS method and the DMP method according to an embodiment of the present invention. The yellow dots above the red line represent the data of detected abnormal cells. It can be seen that... Figure 15 In the three figures of (a) above, the KLSS method shows no abnormal data (yellow dots) on the red line, meaning no abnormal cells were detected. However, in the DMP method of this embodiment, as shown... Figure 15 (b) in the diagram can effectively detect abnormal data (yellow dots), thereby identifying abnormal battery cells.

[0109] The above mainly uses MD1 as the first deviation value and MD3 as the second deviation value as an example. In some embodiments, the Pth measurement refers to one or more measurements adjacent to the Oth measurement. That is, the second deviation value can be a relative deviation obtained from multiple measurements. Optionally, the first deviation value can also be a relative deviation obtained from multiple measurements. Several example schemes are given in Table 2 below. Here, Sq() represents the square root, and ABS() represents taking the absolute value.

[0110] Table 2

[0111]

[0112]

[0113] As shown in Table 2, Scheme 1 represents: "the Euclidean distance between the relative deviation MD1 of the first measurement and the relative deviation MD2 of the second measurement" minus "the relative deviation MD1 of the first measurement". In this example, the first deviation value is MD1, and the second deviation value is MD2.

[0114] Scheme 2 means: "The Euclidean distance between the relative deviation MD1 of the first measurement and the relative deviation MD3 of the third measurement" minus "the relative deviation MD1 of the first measurement". In this example, the first deviation value is MD1, and the second deviation value is MD3.

[0115] Scheme 3 represents: "the Euclidean distance between the relative deviations MD1 of the first measurement, MD2 of the second measurement, and MD3 of the third measurement" minus "the relative deviation MD1 of the first measurement". In this example, the first deviation is MD1, and the second deviations are MD2 and MD3.

[0116] The above is just an example, and the specific implementation is not limited. For example, the formula can also be: Sq(MD3*MD3+MD2*MD2+MD1*MD1)-ABS(MD2*MD2+MD1*MD1). In this example, the first deviation value is MD1 and MD2, and the second deviation value is MD3.

[0117] This invention also provides a cell detection method, which differs from methods where observed values ​​are determined based on a first deviation value and a second deviation value, and a reference value is determined based on the first deviation value. In this method, the specific implementation of the observed and reference values ​​is not limited, as long as the difference between the relative deviations can be amplified to identify abnormal cells based on the amplified difference. The method includes: for each of M cells, obtaining the deviation between the observed value and the reference value for that cell; and determining abnormal cells from the M cells based on the deviations of the M cells. M is a positive integer greater than or equal to 2; the deviation is obtained by amplifying the relative deviations of multiple measurements of the cell.

[0118] For example, as shown in Scheme 4 of Table 4, Scheme 4 means: "the Euclidean distance between the relative deviation MD1 of the first measurement and the relative deviation MD3 of the third measurement" minus "the Euclidean distance between the relative deviation MD1 of the first measurement and the relative deviation MD3 of the second measurement".

[0119] In this embodiment of the invention, the battery cell testing device (or alternatively described as an electronic device) can be divided into functional modules according to the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the unit division in this embodiment of the invention is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0120] For example, an embodiment of the present invention provides a battery cell testing device 500. For example... Figure 16As shown, the cell detection device 500 may include a processor 510. Optionally, the device may also include a memory 520. Exemplarily, the cell detection device is a computing device.

[0121] Processor 510 may include one or more processing units, such as application processor (AP), modem processor, graphics processing unit (GPU), image signal processor (ISP), controller, video codec, digital signal processor (DSP), baseband processor, and / or neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0122] The controller can generate operation control signals based on the instruction opcode and timing signals to complete the control of instruction fetching and execution.

[0123] The processor 510 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 510 is a cache memory. This memory can store instructions or data that the processor 510 has just used or that are used repeatedly. If the processor 510 needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces the waiting time of the processor 510, and thus improves the efficiency of the system.

[0124] In some embodiments, processor 510 may include one or more interfaces. These one or more interfaces may be used to connect processor 510 to memory 520.

[0125] The memory 520 can be used to store computer executable program code, which includes instructions. The memory 520 may include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as an image playback function), etc. The data storage area may store data created during the use of the battery cell testing device 500, etc. The processor 510 executes various functional applications and data processing of the battery cell testing device 500 by running instructions stored in the memory 520 and / or instructions stored in memory located within the processor.

[0126] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the cell testing device. In other embodiments of the present invention, the cell testing device may include more or fewer components than those described above, or combine some components, or split some components, or arrange different components. These components may be implemented in hardware, software, or a combination of software and hardware.

[0127] This invention provides another battery cell testing device 2200, such as... Figure 17 As shown, the cell testing device 2200 can be used to implement the methods described in the above method embodiments. For example, the cell testing device 2200 may specifically include a processing unit 2201.

[0128] The processing unit 2201 is used to support the cell testing device 2200 in performing its functions. Figures 1-15 The processing function described in any one of the following.

[0129] Optionally, the cell testing device 2200 may further include a communication unit for supporting the cell testing device 2200 in performing the steps of communicating between the cell testing device and other devices in the embodiments of the present invention.

[0130] Optionally, the cell testing device 2200 may further include a storage unit 2203, which stores programs or instructions. When the processing unit 2201 executes the program or instructions, the cell testing device 2200 can perform the method shown in the above-described method embodiments.

[0131] The technical effects of the cell testing device 2200 can be referred to the technical effects of the method shown in the above method embodiments, and will not be repeated here. The processing unit 2201 involved in the cell testing device 2200 can be implemented by a processor or processor-related circuit components, and can be a processor or processing module. The communication unit can be implemented by a transceiver or transceiver-related circuit components, and can be a transceiver or transceiver module.

[0132] This invention also provides a chip system, such as... Figure 18As shown, the chip system includes at least one processor 2301 and at least one interface circuit 2302. The processor 2301 and the interface circuit 2302 are interconnected via lines. For example, the interface circuit 2302 can be used to receive signals from other devices. As another example, the interface circuit 2302 can be used to send signals to other devices (e.g., the processor 2301). Exemplarily, the interface circuit 2302 can read instructions stored in memory and send those instructions to the processor 2301. When the instructions are executed by the processor 2301, the chip system can perform the various steps executed by the cell detection device in the above embodiments. Of course, the chip system may also include other discrete devices, and this embodiment of the invention does not specifically limit this.

[0133] Optionally, the chip system may contain one or more processors. These processors can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0134] Optionally, the chip system may contain one or more memories. These memories may be integrated with the processor or separated from it; this invention is not limiting. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed on different chips. This invention does not specifically limit the type of memory or the arrangement of the memory and processor.

[0135] For example, the chip system may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0136] In some embodiments, the present invention also provides a vehicle. The vehicle may include the aforementioned battery cell detection device. Furthermore, an on-board terminal in the vehicle can implement the functions of the aforementioned battery cell detection device.

[0137] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor or by instructions in software form. The method steps disclosed in the embodiments of the present invention can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0138] The embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method of detecting a battery cell, the method comprising: The method comprises: For each of M battery cells, determining a detection factor of the battery cell; M is a positive integer greater than or equal to 2; Based on the detection factors of the M battery cells, determining an abnormal battery cell from the M battery cells; the detection factor of the battery cell is determined by an observation value of an electrical performance of the battery cell and a reference value.

2. The method of claim 1, wherein, The observation value of the battery cell is determined based on an actual electrical performance value of the battery cell, and the reference value is determined based on an average electrical performance value of a battery cell group in which the battery cell is located; the detection factor represents a difference between the actual electrical performance value of the battery cell and the average electrical performance value of the battery cell group in which the battery cell is located.

3. The method of claim 2, wherein, The detection factor includes a deviation amount, and the electrical performance value includes an open circuit voltage; The observation value of each battery cell is determined based on a first deviation value and a second deviation value, and the reference value is determined based on the first deviation value; the first deviation value represents a deviation between an open circuit voltage of the battery cell measured at the Oth time and an average open circuit voltage of the battery cell group in which the battery cell is located, and the second deviation value represents a deviation between an open circuit voltage of the battery cell measured at the Pth time and an average open circuit voltage of the battery cell group in which the battery cell is located; O and P are positive integers, and O and P have different values.

4. The method of claim 3, wherein, Based on the detection factors of the M battery cells, determining an abnormal battery cell from the M battery cells, comprises: In a case where the deviation amount of a first battery cell in the M battery cells is greater than or equal to a first threshold value, determining that the first battery cell is an abnormal battery cell.

5. The method according to claim 3 or 4, characterized in that, An absolute value of the deviation amount between the observation value and the reference value of the battery cell is greater than or equal to an absolute value of a difference between the first deviation value and the second deviation value.

6. The method of claim 5, wherein, An absolute value of the observation value is greater than or equal to an absolute value of the second deviation value.

7. The method according to any one of claims 3-6, characterized in that, The observation value is a Euclidean distance of the first deviation value and the second deviation value, or the observation value is a Manhattan distance of the first deviation value and the second deviation value.

8. The method according to any one of claims 3 to 7, characterized in that, The Pth measurement is one or more measurements adjacent to the Oth measurement, and / or the Oth measurement includes one or more measurements.

9. The method according to any one of claims 1 to 8, characterized in that, The M battery cells respectively belong to N battery cell groups, and N is a positive integer greater than or equal to 2.

10. An electric cell detection device, characterized by, The method comprises: A memory having a computer program stored thereon; A processor configured to execute the computer program in the memory to implement the method according to any one of claims 1 to 9.

11. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method according to any one of claims 1 to 9.

12. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the method according to any one of claims 1 to 9. The computer program is executed by the processor to implement the method according to any one of claims 1 to 9.

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