Abnormal cell identification method, system, and device

Abstract

This application provides a method, system, and device for identifying abnormal battery cells, relating to the field of abnormal battery cell identification technology. The method includes determining at least one impedance testing frequency based on the relaxation time distribution characteristics of a newly acquired battery cell; performing impedance testing on the battery cell under test according to the at least one impedance testing frequency to obtain an impedance value corresponding to each impedance testing frequency; determining a target impedance of the battery cell under test based on the impedance value corresponding to each impedance testing frequency; and determining whether the battery cell under test is abnormal based on the target impedance of the battery cell under test. This application can identify abnormal battery cells before thermal runaway, improving the accuracy of abnormal battery cell identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of abnormal cell identification, and in particular to an abnormal cell identification method, system and device. BACKGROUND

[0002] In the energy storage industry, the thermal runaway of the battery cell of the energy storage battery is a key factor affecting the safety of the energy storage system. In actual scenarios, there are many reasons causing the thermal runaway of the battery cell. The current industry mainly focuses on the thermal runaway phenomenon of the battery cell itself, such as issuing a temperature warning when the temperature of the battery cell is too high, so as to take appropriate cooling measures. However, when the temperature of the battery cell is too high, it indicates that the battery cell has already appeared abnormal before the temperature rises. Therefore, it is necessary to monitor the abnormal trend of the battery cell before the thermal runaway of the battery.

[0003] In the related art, the direct electrical characterization of the abnormal battery cell is the abnormal internal resistance. Currently, the method of dV / dI is usually used to calculate the internal resistance of the battery cell, that is, the voltage and current of the battery cell are collected, the current size is changed after a certain time interval (such as 30s), and then the voltage and current of the battery cell are collected again. The voltage difference and the current difference between the two times of collection can obtain the internal resistance of the battery cell (the formula is R=(U1-U2) / (I1-I2)).

[0004] However, the number of battery cells in the energy storage battery container is very large, and there is a delay in data collection and communication. The voltage and current of each battery cell cannot be collected synchronously at a certain time interval. The voltage changes very quickly during the current jump process (such as I1 jumps to I2). Due to the delay in collecting the voltage, there is an error in the value. In addition, the method of dV / dI calculates the total internal resistance of the battery cell under a single working condition. The total internal resistance contains a large number of irrelevant impedance components, such as contact impedance and ohmic impedance caused by aluminum bar welding, which are irrelevant to the abnormal battery cell. These irrelevant impedance components exist in each battery cell and the contact impedance of different battery cells is different. The internal resistance of the battery cell is in the order of milliohms. The error caused by the asynchronous data collection and the irrelevant interference impedance components will cause the deviation of the finally calculated internal resistance to be large and the precision to be low, thereby causing the accuracy of the abnormal battery cell identification to be low and the misjudgment rate to be high. SUMMARY

[0005] The embodiments of the present application provide an abnormal battery cell identification method, system and device to solve the problem of low accuracy of abnormal battery cell identification and high misjudgment rate caused by the error of asynchronous data collection and irrelevant impedance components in the prior art.

[0006] In a first aspect, the embodiments of the present application provide an abnormal battery cell identification method, comprising: determining at least one impedance test frequency according to the relaxation time distribution characteristics of the new battery cell; Based on the at least one impedance test frequency, the impedance of the cell under test is tested to obtain the impedance value corresponding to each impedance test frequency. The target impedance of the cell under test is determined based on the impedance value corresponding to each impedance test frequency. Based on the target impedance of the cell under test, determine whether the cell under test is abnormal.

[0007] In one possible implementation, the relaxation time distribution feature includes multiple independent peaks; The step of determining at least one impedance test frequency based on the obtained relaxation time distribution characteristics of the new battery cell includes: According to a preset first mapping relationship, at least one impedance test frequency is determined; the first mapping relationship is obtained by fitting the data of the target peak in advance, and is used to represent the correspondence between the number of tests and the relaxation time and the impedance test frequency, and the relaxation time corresponding to the number of tests is within the target relaxation time range covered by the target peak. The target peak is one or more peaks among the plurality of independent peaks whose relaxation time is greater than a preset value.

[0008] In one possible implementation, the target peak is the peak with the largest area corresponding to the longest relaxation time among the plurality of independent peaks.

[0009] In one possible implementation, the expression for the first mapping relationship is: ; in, The impedance test frequency is represented by a, b, and c, which are fitting parameters for the test frequency determined based on the data from the target peak. n represents the number of tests. This represents the relaxation time corresponding to the nth test.

[0010] In one possible implementation, determining the target impedance of the cell under test based on the impedance value corresponding to each impedance test frequency includes: The impedance values ​​corresponding to each impedance test frequency in the at least one impedance test frequency are added together to obtain the total impedance value within the corresponding frequency range. The total impedance value is used as the target impedance of the cell under test.

[0011] In one possible implementation, the step of performing impedance testing on the cell under test according to the at least one impedance test frequency to obtain the impedance value corresponding to each impedance test frequency includes: Within a preset time period after the cell under test is discharged to the cutoff voltage, an AC current of the corresponding impedance test frequency is input to the cell under test, and the impedance value at each impedance test frequency is collected.

[0012] In one possible implementation, before performing impedance testing on the cell under test according to the at least one impedance test frequency, the method further includes: Determine whether the state of charge of the battery cell under test is less than the preset state of charge when the discharge is cut off; If the charge state is less than the preset state of charge, then the impedance test is performed on the cell under test according to the at least one impedance test frequency.

[0013] In one possible implementation, before determining at least one impedance test frequency based on the relaxation time distribution characteristics of the acquired new battery cell, the method further includes: Obtain the impedance spectrum data of the new battery cell in the target frequency band; The relaxation time distribution characteristics of the new battery cell are obtained by performing relaxation time distribution analysis on the impedance spectrum data. The new battery cell and the battery cell under test have the same capacity, specifications and type.

[0014] Secondly, embodiments of this application provide an abnormal battery cell identification system, including: The battery module management layer includes multiple first control units, each of which manages a battery module, and each battery module includes multiple battery cells. The battery cluster management layer includes multiple second control units, each second control unit manages a battery cluster, and each battery cluster includes multiple battery modules; The battery container management layer includes multiple third control units, each of which manages one battery container, and each battery container includes multiple battery clusters. The cloud service layer is used to analyze the cell data of all battery containers in the battery station; The first control unit is used to determine at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell; to perform impedance testing on each battery cell under test in the battery module according to the at least one impedance test frequency, and to obtain the impedance value corresponding to each impedance test frequency; to determine the target impedance of each battery cell under test according to the impedance value corresponding to each impedance test frequency; and to send the target impedance of each battery cell under test to the corresponding second control unit in the battery cluster management layer. The second control unit is used to perform anomaly analysis on all cells in the battery cluster based on the target impedance of each cell under test; The third control unit is used to perform anomaly analysis on battery cells belonging to the same batch in the corresponding battery container; the battery cells in the same batch are those put into use at the same time.

[0015] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the abnormal battery cell identification method described in any of the first aspects above. In this embodiment, by determining at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell, a correlation between the electrochemical characteristics of the battery cell and the test frequency is established. Since the relaxation time distribution characteristics reflect the response patterns of different electrochemical processes inside the battery cell (such as short relaxation time corresponding to contact impedance and ohmic impedance, and long relaxation time corresponding to diffusion impedance and polarization impedance sensitive to battery cell bulging), the impedance components that are abnormally sensitive to the battery cell can be accurately found based on the relaxation time distribution characteristics of the new battery cell. Therefore, at least one impedance test frequency determined based on this characteristic can capture the core impedance changes caused by battery cell abnormalities. Then, impedance testing is performed on the cell under test at at least one impedance testing frequency. The target impedance of the cell under test is determined based on the impedance value corresponding to each impedance testing frequency. Finally, the abnormality of the cell under test is determined based on the target impedance. By performing impedance testing on the cell at different frequencies through the logic of "at least one impedance testing frequency → impedance testing on the cell under test → impedance value corresponding to each testing frequency → target impedance", the impedance values ​​of the cell at different frequencies can be obtained, covering multiple sensitive impedance components that affect the cell's abnormality. The final target impedance is a comprehensive quantitative result of the cell at different testing frequencies, which can effectively eliminate interference from irrelevant impedance components such as contact impedance and ohmic impedance. Compared with the traditional method of calculating the total impedance under a single operating condition based only on dV / dI, this method greatly improves the accuracy of cell impedance calculation, thereby improving the accuracy of abnormal cell identification.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the abnormal battery cell identification method provided in this application; Figure 2 This is a flowchart illustrating an abnormal battery cell identification method provided in another embodiment of this application; Figure 3 This is an impedance spectrum data diagram of a new battery cell provided in an embodiment of this application; Figure 4 This is a relaxation time distribution characteristic diagram of a new battery cell provided in an embodiment of this application; Figure 5 This application provides a comparison diagram of the relaxation time distribution characteristics of normal and bulging battery cells according to an embodiment of the present application; Figure 6 This is a schematic diagram of the abnormal battery cell identification device provided in one embodiment of this application; Figure 7 This is a schematic diagram of the abnormal battery cell identification system provided in one embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0019] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0020] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0021] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0022] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0025] Currently, research on batteries in the energy storage industry mainly focuses on the thermal runaway phenomenon of battery cells themselves. However, temperature monitoring cannot detect cell anomalies in a timely manner. Therefore, it is necessary to monitor cell anomaly trends before battery thermal runaway occurs. The direct electrical characterization of cell anomalies is abnormal internal resistance. Currently, the dV / dI method is commonly used to calculate the cell internal resistance. This involves collecting the cell's voltage and current, and then collecting the cell's voltage and current again after a certain time interval (e.g., 30 seconds). The difference between the two voltage measurements and the difference between the current measurements can be used to obtain the cell's internal resistance (formula: R = (U1 - U2) / (I1 - I2)).

[0026] However, energy storage battery containers contain a large number of cells, and data acquisition and communication are subject to delays. It is impossible to synchronously acquire the voltage and current of each cell at certain time intervals. During current transitions (such as when I1 jumps to I2), the voltage changes very rapidly. Due to the delay in voltage acquisition, errors occur in the numerical values. Furthermore, the dV / dI method calculates the total internal resistance of the cell under a single operating condition. This total internal resistance includes a large number of interference impedance components unrelated to cell anomalies, such as contact impedance caused by aluminum foil welding and ohmic impedance. These unrelated impedance components exist in every cell, and the contact impedances of different cells are different. Since the internal resistance of a cell is at the milliohm level, the errors caused by the asynchronous data acquisition mentioned above, as well as the unrelated interference impedance components such as contact impedance, will lead to large deviations and low accuracy in the final calculated internal resistance, resulting in low accuracy and high false positive rate in identifying abnormal cells.

[0027] To address the aforementioned technical problems, this application proposes the following technical concept: Current industry research on battery safety primarily focuses on thermal runaway, with insufficient attention paid to the mechanical properties of batteries (such as the expansion force generated by battery bulging). Comparative testing of the expansion force generated by battery bulging and thermal runaway warnings reveals that expansion force analysis triggers cell anomaly warnings earlier than temperature changes. In other words, the core precursor to cell thermal runaway is battery bulging, and the direct electrical characteristic of battery bulging is abnormal internal resistance. Therefore, this application takes the premise that cell thermal runaway is caused by abnormal cell bulging leading to abnormal internal resistance as its starting point, identifying abnormal cells and providing early warnings before thermal runaway. Furthermore, since the impedance changes caused by abnormal cell bulging involve polarization impedance, diffusion impedance, or interface impedance, these impedances, along with contact impedance, are all different impedance components of the cell. The relaxation time distribution characteristics of the cell can reflect the impedance components corresponding to different relaxation times. Therefore, based on the relaxation time distribution characteristics of the cell, the impedance components sensitive to cell abnormalities (battery bulging) can be accurately identified. Then, the impedance test frequency determined by this characteristic can capture the core impedance changes caused by cell abnormalities (such as cell bulging), thereby eliminating interference from irrelevant impedance components such as contact impedance and ohmic impedance. Compared with the traditional method of calculating the total impedance under a single operating condition based only on dV / dI, this method greatly improves the accuracy of cell impedance calculation and can accurately identify abnormal cells before thermal runaway, thus improving the accuracy of abnormal cell identification.

[0028] The abnormal cell identification method provided in this application is described below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart illustrating an embodiment of the abnormal battery cell identification method provided in this application.

[0030] like Figure 1 As shown, the method provided in this embodiment may include the following steps: Step S101: Determine at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell.

[0031] In this step, the relaxation time distribution characteristics of the battery cell include the cell impedance corresponding to different relaxation times. This reflects the different impedance components inside the cell corresponding to different relaxation times (relaxation time reflects the response speed of different electrochemical processes inside the cell, and different electrochemical processes correspond to different impedance components). Specifically, there are multiple independent electrochemical processes inside the battery cell (such as ion migration, charge transfer, electrode interface reaction, electrolyte diffusion, etc.). These processes have different reaction rates, and the corresponding relaxation times are also different. For fast processes (such as rapid ion migration in the electrolyte), the corresponding relaxation time is short; for slow processes (such as ion diffusion inside the electrode material, interface structure changes caused by cell bulging), the corresponding relaxation time is long. Therefore, based on the relaxation time distribution characteristics, the core impedance components (such as interface impedance, diffusion impedance, etc.) related to cell abnormalities (battery bulging) can be found, thereby eliminating interfering impedance components such as contact impedance and ohmic impedance. Then, the relaxation time corresponding to the core impedance components related to cell bulging abnormalities is determined, and then at least one impedance test frequency is determined by using the inverse relationship between frequency and relaxation time.

[0032] It should be noted that frequency f and relaxation time It is an inverse relationship This is the mathematical basis for determining the test frequency through relaxation time. If an abnormal process (such as abnormal cell bulging) corresponds to a long relaxation time (slow process), then a low-frequency test is needed to capture this abnormal impedance component, because only low-frequency signals can match the response speed of a slow process.

[0033] In one possible implementation, the number of impedance test frequencies can be one. For example, after finding the core impedance components (such as interface impedance and diffusion impedance) related to cell abnormalities (battery bulging) based on the relaxation time distribution characteristics, the target relaxation time corresponding to the peak impedance is selected, and then the impedance test frequency corresponding to the target relaxation time is determined by using the inverse relationship between frequency and relaxation time.

[0034] In another possible implementation, there can be multiple impedance test frequencies. For example, after finding the core impedance component related to cell abnormality (battery bulging) based on the relaxation time distribution characteristics, multiple target relaxation times can be arbitrarily selected within the relaxation time range corresponding to the core impedance component, thereby obtaining multiple impedance test frequencies.

[0035] It should be noted that since a battery container contains many battery clusters, and a battery cluster contains hundreds of cells, it would take a lot of time and effort to perform relaxation time distribution analysis on each individual cell when testing each cell. Therefore, in order to improve the efficiency of cell anomaly identification, this application first obtains the relaxation time distribution characteristics of the new cell. After determining at least one impedance test frequency based on the relaxation time distribution characteristics of the new cell, all subsequent cells to be tested that are of the same type, capacity and specifications as the new cell can be subjected to impedance testing using this impedance test frequency.

[0036] Step S102: According to the at least one impedance test frequency, perform impedance testing on the cell under test to obtain the impedance value corresponding to each impedance test frequency.

[0037] In this step, when performing impedance testing on the battery cell under test, the current injection method can be used. That is, an AC current of the corresponding impedance test frequency is injected into the battery cell under test, and the voltage value of the battery cell under test is collected when AC current of different frequencies is injected, and then the impedance value of the battery cell under test at each test frequency is calculated.

[0038] It should be noted that the detailed implementation process of the injection current method can be found in relevant technologies, and will not be described in detail here.

[0039] Step S103: Determine the target impedance of the cell under test based on the impedance value corresponding to each impedance test frequency.

[0040] In one possible implementation, if the impedance test frequency selected in the above steps is one, then the impedance value corresponding to that impedance test frequency is taken as the target impedance of the cell under test.

[0041] In another possible implementation, if multiple impedance test frequencies are selected in the above steps, the impedance values ​​corresponding to each of the multiple impedance test frequencies are summed, and the total impedance value obtained by summing is used as the target impedance of the cell under test.

[0042] It should be noted that by summing the impedances at multiple frequencies, the impedances of different electrochemical processes within the cell or different stages of the same electrochemical process can be covered, thereby improving the accuracy of identifying abnormal cells.

[0043] Step S104: Determine whether the battery cell under test is abnormal based on the target impedance of the battery cell under test.

[0044] In one possible implementation, after obtaining the target impedance of the cell under test, the target impedance of the cell under test can be compared with historical impedance data. The historical impedance data is obtained by performing impedance testing on the cell under test under the same test conditions some time ago (e.g., one year ago). If the target impedance of the cell under test obtained now exceeds the historical impedance, and the excess resistance is greater than a certain threshold, the cell under test is determined to be abnormal.

[0045] In this embodiment, by determining at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell, a correlation between the electrochemical characteristics of the battery cell and the test frequency is established. Since the relaxation time distribution characteristics reflect the response patterns of different electrochemical processes inside the battery cell (e.g., short relaxation time corresponds to contact impedance and ohmic impedance, while long relaxation time corresponds to diffusion impedance and polarization impedance that are sensitive to battery cell bulging), the impedance components that are abnormally sensitive to the battery cell can be accurately identified based on the relaxation time distribution characteristics of the new battery cell. Therefore, at least one impedance test frequency determined based on this characteristic can capture the core impedance changes caused by battery cell abnormalities. Then, impedance testing is performed on the cell under test at at least one impedance testing frequency. The target impedance of the cell under test is determined based on the impedance value corresponding to each impedance testing frequency. Finally, the abnormality of the cell under test is determined based on the target impedance. By performing impedance testing on the cell at different frequencies through the logic of "at least one impedance testing frequency → impedance testing on the cell under test → impedance value corresponding to each testing frequency → target impedance", the impedance values ​​of the cell at different frequencies can be obtained, covering multiple sensitive impedance components that affect the cell's abnormality. The final target impedance is a comprehensive quantitative result of the cell at different testing frequencies, which can effectively eliminate interference from irrelevant impedance components such as contact impedance and ohmic impedance. Compared with the traditional method of calculating the total impedance under a single operating condition based only on dV / dI, this method greatly improves the accuracy of cell impedance calculation, thereby improving the accuracy of abnormal cell identification.

[0046] Figure 2 This is a schematic flowchart of an embodiment of the abnormal battery cell identification method provided in this application. Figure 1 Based on the illustrated embodiment, the specific implementation process of the abnormal battery cell identification method is further described in detail.

[0047] like Figure 2 As shown, the abnormal battery cell identification method provided in this embodiment may include the following steps: Step S201: Obtain the impedance spectrum data of the new battery cell in the target frequency band.

[0048] Step S202: Perform relaxation time distribution analysis on the impedance spectrum data to obtain the relaxation time distribution characteristics of the new battery cell; wherein the new battery cell and the battery cell under test have the same capacity, specifications and type.

[0049] It should be noted that since a battery container contains many battery clusters, and a battery cluster contains hundreds of cells, it would take a lot of time and effort to perform relaxation time distribution analysis on each individual cell when testing each cell. Therefore, in order to improve the efficiency of cell anomaly identification, this application first obtains the relaxation time distribution characteristics of the new cell. After determining at least one impedance test frequency based on the relaxation time distribution characteristics of the new cell, all subsequent cells to be tested that are of the same type, capacity and specifications as the new cell can be subjected to impedance testing using this impedance test frequency.

[0050] In step S201, an impedance spectrum analyzer can be used to perform a full-band impedance spectrum test on the new battery cell, thereby obtaining the impedance spectrum data of the new battery cell in the target frequency band.

[0051] For example, such as Figure 3 The impedance spectrum data of the new battery cell shown is illustrated, with the horizontal axis representing the real impedance Re(Z) and the vertical axis representing the imaginary impedance Im(Z), both in milliohms (mΩ). It covers impedance values ​​at different frequencies and charge / discharge rates within the target frequency band (10mHz~1kHz). Figure 3 The blue dots represent the impedance values ​​at different frequencies when the charge / discharge rate is 0.5C, and the red dots represent the impedance values ​​at different frequencies when the charge / discharge rate is 0.25C.

[0052] In step S202, the impedance spectrum data of the acquired new battery cell is analyzed for relaxation time distribution. The core of this analysis is to decompose the complex total impedance signal inside the battery cell into impedance signals corresponding to single relaxation times. Different relaxation times correspond to different impedance components. Each decomposed signal corresponds to an independent electrochemical process, which is reflected as a peak on the relaxation time distribution feature map. The position of the peak (i.e., the horizontal axis relaxation time τ) corresponds to a specific electrochemical process (for example, the peak corresponding to a long relaxation time corresponds to a slow reaction process inside the battery cell, such as changes in the electrode interface structure caused by bulging; the peak corresponding to a short relaxation time corresponds to a fast reaction process inside the battery cell). The height / area of ​​the peak (reflected in the impedance value on the vertical axis) corresponds to the contribution ratio of the electrochemical process to the total impedance of the battery cell. The larger the peak area / height, the greater the impact of the impedance component of this process on the total impedance.

[0053] Typically, the relaxation time distribution of a battery cell exhibits multiple peaks, with the longest relaxation time corresponding to the largest peak area. This indicates that this portion of the impedance component constitutes the largest proportion and is the main source of the cell's total impedance. Therefore, this impedance component is most sensitive to cell abnormalities (and is also the core target for identifying cell bulging abnormalities). For example, the relaxation time distribution characteristics of a battery cell are as follows: Figure 4As shown, the horizontal axis represents the relaxation time τ, in seconds (s), and the vertical axis represents the impedance value, in milliohms (mΩ). The blue line represents the relaxation time distribution at a charge / discharge rate of 0.5C, and the red line represents the relaxation time distribution at a charge / discharge rate of 0.25C. The relaxation time distribution of a normal battery cell will exhibit multiple independent peaks, such as... Figure 4 As shown, it includes five independent peaks ( 、 、 、 as well as The five relaxation times correspond to five peaks. The larger the peak area or the higher the peak value, the higher the proportion of the impedance component corresponding to that part.

[0054] In one possible implementation, the relaxation time distribution of a normal battery cell can be compared with that of a bulging battery cell to identify the core impedance component associated with the cell bulging anomaly. For example, such as... Figure 5 As shown in the diagram, the red line represents the relaxation time distribution characteristic of the bulging battery cell, and the black line represents the relaxation time distribution characteristic of the normal battery cell. It can be seen from this relaxation time distribution characteristic graph that the peak value of the bulging battery cell is higher than that of the normal battery cell, indicating that the internal resistance of the bulging battery cell is higher than that of the normal battery cell. Furthermore, the longer the relaxation time, the higher the peak value of the bulging battery cell is compared to that of the normal battery cell. For example, the relaxation time... The peak value of the corresponding bulging cell is slightly higher than that of the normal cell, and the relaxation time is... The peak height and area of ​​the corresponding bulging cell are significantly larger than those of a normal cell, and the relaxation time is also larger. The corresponding peak height, area, and relaxation time of the bulging battery cell The peak height and area of ​​the corresponding normal cell are the most different. Therefore, the peak height / area corresponding to the long relaxation time of the bulging cell is significantly larger than that of the normal cell, proving that cell bulging will cause the impedance value corresponding to a specific relaxation time (longer relaxation time) to increase. The impedance components with the longest relaxation time (interface impedance, polarization impedance) are most sensitive to cell bulging abnormalities. This rule is the core basis for the selection of the impedance test frequency in the subsequent test.

[0055] Step S203: Fit the data of the target peak in the relaxation time distribution feature to obtain a first mapping relationship; and determine at least one impedance test frequency based on the first mapping relationship; the first mapping relationship is used to represent the correspondence between the number of tests and the relaxation time and the impedance test frequency, and the relaxation time corresponding to the number of tests is within the target relaxation time range covered by the target peak; wherein, the target peak is one or more peaks among the multiple independent peaks in the relaxation time distribution feature whose relaxation time is greater than a preset value.

[0056] In this step, the relaxation time distribution characteristics include multiple independent peaks, and as shown in the figure... Figure 5 As shown, by comparing the relaxation time distribution characteristics of normal cells and bulging cells, it can be seen that the height / area of ​​the peak corresponding to a longer relaxation time is significantly larger than that of a normal cell. This is because cell bulging leads to a larger electrode spacing and poorer interface contact inside the cell, which slows down the slow process of ion migration / charge transfer, thus lengthening the relaxation time. Ultimately, this manifests as an increase in the impedance value corresponding to this electrochemical process (higher peak and larger area). Therefore, the change in relaxation time is the underlying cause of the abnormal impedance of bulging cells. By setting a relaxation time threshold (preset value), selecting a target peak with a relaxation time greater than the preset value, and determining the impedance test frequency based on the data of the target peak, this impedance test frequency can accurately capture the abnormal impedance components (i.e., the impedance components corresponding to the target peak), thereby accurately identifying bulging cells.

[0057] In one possible implementation, the preset value of the relaxation time can be determined based on the actual situation, for example, such as... Figure 5 As shown, a comparison with the relaxation time distribution characteristics of a normal cell reveals that the height / area of ​​the two right peaks corresponding to the bulging cell is significantly higher than that of the normal cell. Therefore, the two right peaks can be selected as target peaks, and the preset value of the relaxation time can be determined based on the starting point of the middle peak ( To determine, select a relaxation time greater than 1. The two peaks (i.e.) and The corresponding peak) is used as the target peak. Similarly, the preset value of the relaxation time can also be based on the starting point of the maximum peak ( To determine this, a relaxation time greater than 10 ... One of the peaks is taken as the target peak (i.e. the peak with the largest area / height corresponding to the longest relaxation time).

[0058] In one possible implementation, since the peak with the largest height / area corresponding to the longest relaxation time best reflects the impedance change caused by the cell bulging abnormality, this embodiment uses the peak with the largest area / height corresponding to the longest relaxation time among multiple independent peaks as the target peak (e.g., Figure 5 The peak corresponding to 8.8s in the middle). Then, the data of the target peak is fitted, such as Figure 5 As shown, the data of the target peak is the target relaxation time range covered by the target peak ( ~ The first mapping relationship is obtained by fitting each relaxation time and the corresponding impedance value within the range of ) and the first mapping relationship is obtained so that the mapping relationship can represent the mapping from the number of tests to the relaxation time and the impedance test frequency (the relaxation time and the impedance test frequency are inversely proportional). That is, when the number of tests takes different values, the relaxation time determined according to the first mapping relationship falls within the target relaxation time range.

[0059] In one possible implementation, since the target peak corresponding to the longest relaxation time is distributed exponentially, the expression for the fitted first mapping relationship can be: ;in, The impedance test frequency is represented by a, b, and c, which are fitting parameters for the test frequency determined based on the data of the target peak (the fitting parameters are different for different types, capacities, and specifications of cells). n represents the number of tests, which can be set to any one or more integers such as 1, 2, 3, etc. This represents the relaxation time corresponding to the nth test. The relaxation time is calculated using this expression for different values ​​of n (e.g., 1, 2, 3...). All fall within the range covered by the target peak, that is, within the target relaxation time range. ~ (within) For example, such as Figure 5 As shown, when the battery cell needs to undergo 5 impedance frequency tests, the values ​​of n are 1, 2, 3, 4, and 5 respectively. Substituting these values ​​into the above expression yields the relaxation time corresponding to the first test. and impedance test frequency ( f 1) Relaxation time corresponding to the second test and impedance test frequency ( f 2) Relaxation time corresponding to the 3rd test and impedance test frequency ( f 3) Relaxation time corresponding to the 4th test and impedance test frequency ( f 4) Relaxation time corresponding to the 5th test and impedance test frequency ( f 5).

[0060] In the above expression, Utilizing the inverse relationship between the response speed (relaxation time) of the electrochemical process within the battery cell and the testing frequency, slow processes such as electrode interface structure changes caused by bulging and slow ion diffusion have long relaxation times, requiring low frequencies to capture. This relationship allows us to obtain the relaxation time sensitive to bulging as described above. This is converted into an impedance test frequency, and then the impedance test frequency... It can accurately detect impedance changes caused by abnormalities in the battery cell (such as bulging).

[0061] In the above expression, since the maximum peak in the relaxation time distribution characteristics (corresponding to the impedance component with the longest relaxation time and the most sensitive to bulging) is distributed in an exponential form (e.g., Figure 4 Among the five peaks of the battery cell, Figure 5 Of the three peaks shown, the one with the longest relaxation time has the largest peak area / height; therefore, the expression uses... The exponential form of the impedance test frequency is such that the calculated impedance test frequency is distributed exponentially when the number of tests n takes different values. That is, the frequency intervals in the low frequency band are dense (focusing on covering the abnormally sensitive area corresponding to the long relaxation time), so as to achieve accurate coverage of the abnormally sensitive area.

[0062] It should be noted that the exponential form of the distribution in the above expression can be replaced according to the actual situation. For example, ... Replace with For different types, capacities, and specifications of battery cells, specific parameters can be fitted through sample tests; subsequent battery cells of the same type can be directly reused without repeated full-band spectrum testing, reducing engineering application costs.

[0063] In another possible implementation, the above expression can be used to convert the impedance test frequency corresponding to different test numbers into a fixed value table. The values ​​are distributed in an exponential form, which is convenient to call when performing impedance tests on other cells of the same type, specification and capacity, thereby improving the impedance test efficiency.

[0064] In this embodiment, since the bulging anomaly of a large-capacity energy storage battery mainly involves slow processes such as changes in the electrode interface and slowed ion diffusion (corresponding to long relaxation times), this embodiment can accurately capture the impedance changes caused by the bulging anomaly by selecting the impedance test frequency (low frequency band) corresponding to the longest relaxation time. This allows the bulging anomaly of the battery cell to be detected before thermal runaway, improving the accuracy of abnormal battery cell identification. Furthermore, by designing the above expression, the abnormal sensitive frequency range of large-capacity battery cells can be matched more accurately, solving the problem that high-frequency testing cannot detect large-capacity battery cell anomalies in traditional technologies.

[0065] Step S204: Determine whether the state of charge of the battery cell under test at the discharge cutoff is less than the preset state of charge.

[0066] Step S205: If the charge state is less than the preset state of charge, then according to the at least one impedance test frequency, the battery cell under test is subjected to impedance test to obtain the impedance value corresponding to each impedance test frequency.

[0067] It should be noted that the value of the preset state of charge can be determined according to the actual situation, for example, it can be set to 5%.

[0068] In steps S204 and S205, since the state of charge of the battery significantly affects the charge transfer impedance and diffusion impedance in the low-frequency band, and in order to find the impedance components related to cell bulging abnormalities such as diffusion impedance, this application needs to perform impedance frequency testing on the cell in the low-frequency band. Under the condition of low state of charge (such as state of charge below 5%), the impedance value of the cell is relatively large and the rebound voltage value is relatively large. Using at least one impedance test frequency in the low-frequency band to perform impedance testing makes it easier to capture the diffusion impedance changes related to cell bulging abnormalities, thereby reducing the interference of state of charge on low-frequency impedance characteristics and improving the accuracy of abnormality identification.

[0069] In one possible implementation, the step of performing impedance testing on the cell under test according to the at least one impedance test frequency to obtain the impedance value corresponding to each impedance test frequency includes: inputting an AC current of the corresponding impedance test frequency to the cell under test within a preset time period after the cell under test is discharged to the cutoff voltage, and collecting the impedance value at each impedance test frequency.

[0070] In this embodiment, the preset time period can be determined according to the actual situation of the battery cell under test. For example, the preset time period can be set to 0.5 to 1.5 hours, and the impedance frequency test is performed on the battery cell within 0.5 to 1.5 hours after it has been discharged to the cutoff voltage. Usually, the battery cell is in a static state after being discharged to the cutoff voltage for a period of time. This static state can ensure that the battery condition will not change suddenly during the impedance test, so that the battery is in a stable state during the impedance test, thereby ensuring the accuracy of the impedance test results.

[0071] In this embodiment, when performing impedance testing on the battery cell under test, the current injection method can be used, that is, injecting an AC current of the corresponding impedance test frequency into the battery cell under test, and collecting the voltage value of the battery cell under test when injecting AC current of different frequencies, and then calculating the impedance value of the battery cell under test at each test frequency.

[0072] Step S206: Add the impedance values ​​corresponding to each impedance test frequency in the at least one impedance test frequency to obtain the total impedance value within the corresponding frequency range; use the total impedance value as the target impedance of the cell under test.

[0073] In this step, you can either add up the impedance values ​​corresponding to all the impedance test frequencies, or you can select a few impedance test frequencies and add them together.

[0074] For example, when five impedance frequency tests need to be performed on the battery cell, the values ​​of n are 1, 2, 3, 4, and 5, respectively. Substituting these values ​​into the expression of the first mapping relationship, the impedance test frequency corresponding to the first test can be obtained. f 1) The impedance test frequency corresponding to the second test (f 2) The impedance test frequency corresponding to the third test ( f 3) The impedance test frequency corresponding to the 4th test ( f 4) and the impedance test frequency corresponding to the 5th test ( f 5) Impedance tests were performed on the battery cell under test using five different impedance test frequencies, yielding five impedance values: R1, R2, R3, R4, and R5. These five impedance values ​​can be added together to obtain... f 5~ f The total impedance value for this frequency range; alternatively, R3, R4, and R5 can be added together to obtain... f 5~ f Total impedance value in the 3 frequency band.

[0075] In one possible implementation, the formula for summing multi-frequency impedances could be... ;in, This represents the total impedance value within the corresponding frequency range. This represents the impedance measurement value corresponding to a certain impedance test frequency (calculated from the expression of the first mapping relationship), and i represents the initial test number. In this summation formula, i can be flexibly set, and the summation range can be adjusted by setting the value of i. For example, i=3 means that the summation starts from the 3rd frequency and calculates the sum of impedances from the 3rd frequency to the nth frequency.

[0076] It should be noted that, as shown in the expression for the first mapping relationship above, the larger the value of n, the longer the corresponding relaxation time. The larger the value, the higher the corresponding impedance test frequency. The smaller the value, the more pronounced the impedance difference between bulging and normal cells is, typically within the target relaxation time range covered by the target peak, where the impedance difference is more significant over longer relaxation times (lower frequency bands). For example, when performing five impedance frequency tests on a cell, such as... Figure 5 As shown, n takes values ​​of 1, 2, 3, 4, and 5, corresponding to five relaxation times ( ~ In ), the relaxation time is greater than In the region where impedance differences are more pronounced, the difference between bulging cells (red line) and normal cells (black line) is more significant. Therefore, when summing impedances, the summation range can be adjusted by setting the value of i, eliminating portions with smaller impedance differences. For example, when i=3, the calculation... f 3. f 4 and f 5. The sum of the impedances corresponding to these three frequencies, excluding... and The corresponding impedance frequency test results, with no significant impedance difference, can thus differentiate between bulging and normal battery cells. ~ The relaxation time range is within which the total impedance difference is amplified, improving the accuracy of abnormal cell identification.

[0077] It should be noted that in practical scenarios, the values ​​of n and i need to be determined based on the relaxation time distribution characteristics of the battery cell. The above examples are for illustrative purposes only and do not constitute a limitation on this application.

[0078] In this step, the impedance values ​​corresponding to at least one impedance test frequency are summed, which is equivalent to calculating the approximate area of ​​the target peak, thus obtaining the total impedance value within the corresponding frequency range. Summing the impedance values ​​measured at multiple frequencies can cover the impedance of different electrochemical processes within the cell or different stages of the same electrochemical process, amplifying the impact of bulging anomalies on cell impedance changes, thereby improving the accuracy of abnormal cell identification. Furthermore, by flexibly adjusting the summation range, impedance components that are insensitive to bulging anomalies can be eliminated, ensuring that the calculated total impedance value accurately reflects the increase in internal resistance caused by bulging anomalies, further improving the accuracy of cell anomaly identification.

[0079] Step S207: Determine whether the battery cell under test is abnormal based on the target impedance of the battery cell under test.

[0080] In one possible implementation, after obtaining the target impedance of the cell under test, the target impedance of the cell under test can be compared with historical impedance data. The historical impedance data is obtained by performing impedance testing on the cell under test under the same test conditions some time ago (e.g., one year ago). If the target impedance of the cell under test obtained now exceeds the historical impedance, and the excess resistance is greater than a certain threshold, the cell under test is determined to be abnormal.

[0081] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0082] Figure 6 This is a schematic diagram of the abnormal battery cell identification device provided in one embodiment of this application.

[0083] like Figure 6 As shown, the abnormal battery cell identification device provided in this embodiment may include: The frequency determination module 601 is used to determine at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell. Impedance testing module 602 is used to perform impedance testing on the cell under test according to the at least one impedance testing frequency, and obtain the impedance value corresponding to each impedance testing frequency. Impedance calculation module 603 is used to determine the target impedance of the battery cell under test based on the impedance value corresponding to each impedance test frequency. The anomaly identification module 604 is used to determine whether the battery cell under test is abnormal based on the target impedance of the battery cell under test.

[0084] In one possible implementation, the relaxation time distribution feature includes multiple independent peaks; the frequency determination module 601 is specifically used to determine at least one impedance test frequency according to a preset first mapping relationship; the first mapping relationship is obtained by fitting the data of the target peak in advance, and is used to represent the correspondence between the number of tests and the relaxation time and the impedance test frequency, and the relaxation time corresponding to the number of tests is within the target relaxation time range covered by the target peak; wherein, the target peak is one or more peaks among the multiple independent peaks whose relaxation time is greater than a preset value.

[0085] In one possible implementation, the target peak is the peak with the largest area corresponding to the longest relaxation time among the plurality of independent peaks.

[0086] In one possible implementation, the expression for the first mapping relationship is: ; in, The impedance test frequency is represented by a, b, and c, which are fitting parameters for the test frequency determined based on the data from the target peak. n represents the number of tests. This represents the relaxation time corresponding to the nth test.

[0087] In one possible implementation, the impedance calculation module 603 is specifically used to add the impedance values ​​corresponding to each impedance test frequency in the at least one impedance test frequency to obtain the total impedance value within the corresponding frequency range; and to use the total impedance value as the target impedance of the cell under test.

[0088] In one possible implementation, the impedance testing module 602 is specifically used to input an AC current of the corresponding impedance testing frequency into the battery cell under test within a preset time period after the battery cell under test has discharged to the cutoff voltage, and to collect the impedance value at each impedance testing frequency.

[0089] In one possible implementation, the impedance testing module 602 is further configured to determine whether the state of charge of the cell under test at the discharge cutoff is less than a preset state of charge before performing an impedance test on the cell under test according to the at least one impedance testing frequency; if it is less than the preset state of charge, then perform an impedance test on the cell under test according to the at least one impedance testing frequency.

[0090] In one possible implementation, the data analysis module 605 is used to acquire impedance spectrum data of the new battery cell in a target frequency band before determining at least one impedance test frequency based on the acquired relaxation time distribution characteristics of the new battery cell; perform relaxation time distribution analysis on the impedance spectrum data to obtain the relaxation time distribution characteristics of the new battery cell; wherein the new battery cell and the battery cell under test have the same capacity, specifications and type.

[0091] It should be noted that the detailed implementation process of the above-mentioned device embodiments can be found in the relevant method embodiments section, and will not be repeated here.

[0092] Figure 7 This is a schematic diagram of the abnormal battery cell identification system provided in one embodiment of this application.

[0093] like Figure 7 As shown, the system provided in this embodiment includes: a battery module management layer, a battery cluster management layer, a battery container management layer, and a cloud service layer; The battery module management layer includes multiple first control units, each of which manages a battery module, and each battery module includes multiple battery cells. The battery cluster management layer includes multiple second control units, each second control unit manages a battery cluster, and each battery cluster includes multiple battery modules; The battery container management layer includes multiple third control units, each of which manages one battery container, and each battery container includes multiple battery clusters. The cloud service layer is used to analyze the cell data of all battery containers in the battery station; The first control unit is used to determine at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell; to perform impedance testing on each battery cell under test in the battery module according to the at least one impedance test frequency, and to obtain the impedance value corresponding to each impedance test frequency; to determine the target impedance of each battery cell under test according to the impedance value corresponding to each impedance test frequency; and to send the target impedance of each battery cell under test to the corresponding second control unit in the battery cluster management layer. The second control unit is used to perform anomaly analysis on all cells in the battery cluster based on the target impedance of each cell under test; The third control unit is used to perform anomaly analysis on battery cells belonging to the same batch in the corresponding battery container; the battery cells in the same batch are those put into use at the same time.

[0094] In this embodiment, as Figure 7As shown, the battery module management layer includes multiple first control units (Microcontroller Units, MCUs). Each control unit MCU can manage one battery module, and each battery module includes multiple cells (e.g., 104 cells). For example, the control unit MCU41 determines at least one impedance test frequency based on the relaxation time distribution characteristics of the acquired new cells. Based on the at least one impedance test frequency, it performs impedance testing on each of the 104 cells in the battery module to obtain the impedance value corresponding to each impedance test frequency. Based on the impedance value corresponding to each impedance test frequency, it determines the target impedance of each cell to be tested, thus obtaining 104 target impedances. Then, the 104 target impedances are sent to the corresponding second control unit (e.g., control unit MCU31) in the battery cluster management layer.

[0095] The battery cluster management layer includes multiple second control units (such as MCU31 and MCU32), each second control unit managing one battery cluster, and each battery cluster including multiple battery modules; for example Figure 7 As described above, four battery modules constitute a battery cluster. Similarly, MCU42, MCU43, and MCU44 each send 104 target impedances to MCU31, thus MCU31 in the battery cluster management layer obtains the target impedance data of a battery cluster (416 cells) composed of four battery modules. The second control unit (such as MCU31) performs anomaly analysis on the target impedance data of the 416 cells, identifying abnormal cells among them. When the number of abnormal cells in a battery cluster reaches the target number (such as 104), a corresponding alarm message is generated. Based on the alarm message, relevant personnel replace these 104 abnormal cells. The new 104 cells belong to the same batch of cells, and the relevant data of the same batch of cells is reported to the corresponding third control unit (such as MCU21) in the battery container management layer.

[0096] In one possible implementation, the second control unit in the battery cluster management layer identifies abnormal cells in a battery cluster (416 cells) based on 416 target impedance data. This can be achieved by: constructing a standard impedance distribution model for all cells in the battery cluster (i.e., a distribution model of standard impedance data for normal cells of the same specification and capacity under the same impedance test conditions); then, based on the target impedance data of the 416 cells in the current battery cluster, calculating the relative impedance deviation rate of each cell under test relative to the standard impedance data; and determining that the cell under test is abnormal when the relative impedance deviation rate is greater than a certain threshold.

[0097] The battery container management layer also includes multiple third control units, each managing one battery container. Each battery container comprises multiple battery clusters (e.g., 12 battery clusters constitute one battery container, and each battery container has 4992 cells). The third control units are used to perform anomaly analysis on cells belonging to the same batch within the corresponding battery container; the cells in the same batch are those put into use at the same time. Figure 7 As shown, after receiving the relevant impedance data of the same batch of cells reported by each of the second control units in the battery cluster management layer, the third control unit (such as MCU21) performs anomaly analysis on the same batch of cells. For example, after receiving the initial impedance data of 104 newly replaced cells in the same batch reported by MCU31, MCU21 constructs an initial impedance distribution model for each of the cells in that batch. As the cells are used, the current impedance data of the 104 cells in that batch is periodically acquired, and then the current impedance distribution model of the cells in that batch is determined. Based on the initial impedance distribution model and the current impedance distribution model of the 104 cells in that batch, the impedance deviation is calculated. If the impedance deviation is too large, the batch of cells is determined to be abnormal.

[0098] The cloud service layer is used to analyze, process, and send alarm information for the battery cells in all battery containers in the battery station.

[0099] Understandably, in order to reduce the computational load on the control units in the battery cluster management layer and the battery container management layer, data processing processes such as the construction of impedance distribution models can be completed in the cloud service layer.

[0100] It should be noted that the construction method of the above impedance distribution model, and the detailed calculation process of the relative impedance deviation rate and impedance deviation amount, can be found in relevant technologies. Parts not described in detail in this embodiment can be referred to the descriptions in the aforementioned method embodiments, and will not be repeated here.

[0101] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 8 As shown, the device 800 of this embodiment includes a processor 810 and a memory 820, wherein the memory 820 stores a computer program 821 that can run on the processor 810. When the processor 810 executes the computer program 821, it implements the steps in any of the above method embodiments. Alternatively, when the processor 810 executes the computer program 821, it implements the functions of each module / unit in the above device embodiments.

[0102] For example, computer program 821 may be divided into one or more modules / units, one or more of which are stored in memory 820 and executed by processor 810 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 821 in device 800.

[0103] Those skilled in the art will understand that Figure 8 This is merely an example of a device and does not constitute a limitation on the device. It may include more or fewer components than shown, or combinations of certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0104] The processor 810 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0105] The memory 820 can be an internal storage unit of the device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. The memory 820 can also include both internal and external storage units. The memory 820 is used to store computer programs and other programs and data required by the device. The memory 820 can also be used to temporarily store data that has been output or will be output.

[0106] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0107] An embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the abnormal battery cell identification method in the above-described method embodiments.

[0108] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the abnormal battery cell identification method in the above-described method embodiments.

[0109] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0110] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0111] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for identifying abnormal battery cells, characterized in that, include: Based on the relaxation time distribution characteristics of the new battery cell, at least one impedance test frequency is determined. Based on the at least one impedance test frequency, the impedance of the cell under test is tested to obtain the impedance value corresponding to each impedance test frequency. The target impedance of the cell under test is determined based on the impedance value corresponding to each impedance test frequency. Based on the target impedance of the cell under test, determine whether the cell under test is abnormal.

2. The abnormal cell identification method according to claim 1, characterized in that, The relaxation time distribution features include multiple independent peaks; The step of determining at least one impedance test frequency based on the obtained relaxation time distribution characteristics of the new battery cell includes: According to a preset first mapping relationship, at least one impedance test frequency is determined; the first mapping relationship is obtained by fitting the data of the target peak in advance, and is used to represent the correspondence between the number of tests and the relaxation time and the impedance test frequency, and the relaxation time corresponding to the number of tests is within the target relaxation time range covered by the target peak. The target peak is one or more peaks among the plurality of independent peaks whose relaxation time is greater than a preset value.

3. The abnormal cell identification method according to claim 2, characterized in that, The target peak is the peak with the largest area corresponding to the longest relaxation time among the multiple independent peaks.

4. The abnormal cell identification method according to claim 2, characterized in that, The expression for the first mapping relationship is: ; in, The impedance test frequency is represented by a, b, and c, which are fitting parameters for the test frequency determined based on the data from the target peak. n represents the number of tests. This represents the relaxation time corresponding to the nth test.

5. The abnormal cell identification method according to claim 2, characterized in that, Determining the target impedance of the battery cell under test based on the impedance value corresponding to each impedance test frequency includes: The impedance values ​​corresponding to each impedance test frequency in the at least one impedance test frequency are added together to obtain the total impedance value within the corresponding frequency range. The total impedance value is used as the target impedance of the cell under test.

6. The abnormal cell identification method according to claim 1, characterized in that, The step of performing impedance testing on the cell under test according to the at least one impedance test frequency to obtain the impedance value corresponding to each impedance test frequency includes: Within a preset time period after the cell under test is discharged to the cutoff voltage, an AC current of the corresponding impedance test frequency is input to the cell under test, and the impedance value at each impedance test frequency is collected.

7. The abnormal cell identification method according to claim 1, characterized in that, Before performing impedance testing on the cell under test according to the at least one impedance test frequency, the method further includes: Determine whether the state of charge of the battery cell under test is less than the preset state of charge when the discharge is cut off; If the charge state is less than the preset state of charge, then the impedance test is performed on the cell under test according to the at least one impedance test frequency.

8. The abnormal cell identification method according to any one of claims 1 to 7, characterized in that, Before determining at least one impedance test frequency based on the obtained relaxation time distribution characteristics of the new battery cell, the method further includes: Obtain the impedance spectrum data of the new battery cell in the target frequency band; The relaxation time distribution characteristics of the new battery cell are obtained by performing relaxation time distribution analysis on the impedance spectrum data. The new battery cell and the battery cell under test have the same capacity, specifications and type.

9. An abnormal battery cell identification system, characterized in that, include: The battery module management layer includes multiple first control units, each of which manages a battery module, and each battery module includes multiple battery cells. The battery cluster management layer includes multiple second control units, each second control unit manages a battery cluster, and each battery cluster includes multiple battery modules; The battery container management layer includes multiple third control units, each of which manages one battery container, and each battery container includes multiple battery clusters. The cloud service layer is used to analyze the cell data of all battery containers in the battery station; The first control unit is used to determine at least one impedance test frequency based on the relaxation time distribution characteristics of the new battery cell; to perform impedance testing on each battery cell under test in the battery module according to the at least one impedance test frequency, and to obtain the impedance value corresponding to each impedance test frequency; to determine the target impedance of each battery cell under test according to the impedance value corresponding to each impedance test frequency; and to send the target impedance of each battery cell under test to the corresponding second control unit in the battery cluster management layer. The second control unit is used to perform anomaly analysis on all cells in the battery cluster based on the target impedance of each cell under test; The third control unit is used to perform anomaly analysis on battery cells belonging to the same batch in the corresponding battery container; the battery cells in the same batch are those put into use at the same time.

10. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the abnormal cell identification method as described in any one of claims 1 to 8.

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