Battery cell impedance detection method and equipment of power battery and storage medium

Abstract

The invention relates to the technical field of power batteries, and discloses a power battery cell impedance detection method, and the method comprises the steps: obtaining a plurality of current values of each cell and a voltage value corresponding to each current value; dividing a plurality of current value intervals in the value range of the plurality of current values; calculating the number of voltage values corresponding to each current value interval; any two current value intervals form a candidate interval pair, and the evaluation score of the candidate interval pair is determined according to the current values of the candidate interval pair and the number of the corresponding voltage values; determining the candidate interval pair with the highest evaluation score in the at least two pairs of candidate interval pairs of each battery cell as a target current value interval pair of each battery cell; determining the impedance of each battery cell according to the current value of the target current value interval pair of each battery cell and a median voltage value in the corresponding plurality of voltage values; and determining the battery cell with abnormal impedance according to the impedance of each battery cell. In this way, the reliability of the battery cell impedance anomaly detection of the power battery is improved.

Description

Methods, equipment and storage media for detecting cell impedance of power batteries Technical Field

[0001] This application relates to the field of power battery technology, specifically to a method, device, and storage medium for detecting the cell impedance of a power battery. Background Technology

[0002] A Battery Management System (BMS) is an electronic system that manages rechargeable batteries or battery packs. It mainly uses real-time collected data on key parameters such as individual cell voltage, total current, and temperature to achieve functions such as SOC (State of Charge) estimation, active balancing, and thermal runaway protection.

[0003] The sampling accuracy and dynamic response speed of cell-level voltage signals directly affect the accuracy of BMS (Battery Management System) decisions. Currently, in power batteries composed of multiple cells, the voltages of multiple cells are acquired serially, which is inconsistent with the current acquisition time, resulting in asynchronous acquisition of current and voltage signals. Under conditions of rapid acceleration and deceleration, fast charging start-up and shutdown, and other drastic current changes, the voltage change lags significantly behind the current change. This causes a timing mismatch in the BMS's current and voltage acquisition, leading to a mismatch between the calculated voltage drop and the current change. This results in a serious impedance calculation deviation, reducing the accuracy of impedance calculation for power battery cells and thus reducing the reliability of cell impedance anomaly detection. Summary of the Invention

[0004] In view of the above problems, this application provides a method, device and storage medium for detecting the cell impedance of a power battery, which improves the reliability of detecting abnormal cell impedance of a power battery.

[0005] According to one aspect of the embodiments of this application, a method for detecting the cell impedance of a power battery is provided. The method includes: acquiring multiple current values ​​and voltage values ​​corresponding to each current value for each cell during operation of the power battery; for each cell, dividing the range of multiple current values ​​into multiple current value intervals, such that each current value interval corresponds to multiple voltage values; calculating the number of voltage values ​​corresponding to each current value interval for each cell; for at least two pairs of candidate intervals in each cell, determining an evaluation score for each candidate interval pair based on the current value and the number of corresponding voltage values ​​of the candidate interval pair, wherein each candidate interval pair consists of two current value intervals, and the difference between the current values ​​of the candidate interval pairs and the number of corresponding voltage values ​​are positively correlated with the evaluation score; determining the candidate interval pair with the highest evaluation score among the at least two pairs of candidate interval pairs for each cell as the target current value interval pair for each cell; determining the impedance of each cell based on the current value of the target current value interval pair for each cell and the median voltage value among the multiple corresponding voltage values; and identifying cells with abnormal impedance in the power battery based on the impedance of each cell.

[0006] In one alternative approach, for at least two pairs of candidate intervals in each cell, the evaluation score of the candidate interval pair is determined based on the number of current values ​​and corresponding voltage values ​​of the candidate interval pair. This further includes: for any two current value intervals forming a candidate interval pair in each cell, calculating the absolute value of the difference between the center current values ​​of the candidate interval pair to obtain a first center current difference; determining the maximum value among the number of voltage values ​​corresponding to the candidate interval pair; and determining the evaluation score of the candidate interval pair based on the first center current difference and the maximum value.

[0007] In one optional approach, the impedance of each cell is determined based on the current value of the target current value interval pair and the median voltage value among the corresponding multiple voltage values. This further includes: for each cell, determining the center current value of each current value interval in the target current value interval pair and the median voltage value among the corresponding multiple voltage values; calculating the difference between the center current values ​​of the two current value intervals to obtain a second center current difference; calculating the difference between the median voltage values ​​of the two current value intervals to obtain a median voltage difference; and dividing the median voltage difference by the second center current difference to obtain the impedance of each cell.

[0008] In one alternative approach, for each cell, multiple current value intervals are divided within the range of multiple current values, so that each current value interval corresponds to multiple voltage values. This further includes: for each cell, starting from the minimum value among the multiple current values ​​and ending at the maximum value, multiple current value intervals are divided according to a preset step size, so that each current value interval corresponds to multiple voltage values ​​corresponding to the current values ​​located within the current value interval.

[0009] In one optional approach, acquiring multiple current values ​​and corresponding voltage values ​​for each cell of the power battery during operation further includes: acquiring operating data for each cell at multiple time points to obtain multiple operating data, wherein each operating data includes a current value, a voltage value, and a state of charge value; for each cell, selecting operating data whose state of charge value is within a preset stable range from the multiple operating data to obtain multiple current values ​​and corresponding voltage values ​​for each current value.

[0010] In one optional approach, for each cell, operating data with state of charge values ​​within a preset stable range are selected from multiple operating data to obtain multiple current values ​​and a voltage value corresponding to each current value. This further includes: for each cell, selecting operating data with state of charge values ​​within a preset stable range from multiple operating data; filtering operating data with abnormal current values ​​and abnormal voltage values ​​from the operating data with state of charge values ​​within the preset stable range to obtain multiple current values ​​and a voltage value corresponding to each current value.

[0011] In one optional approach, determining the cells with impedance abnormalities in the power battery based on the impedance of each cell further includes: sorting the impedances of multiple cells in ascending order; determining the impedance of the cell corresponding to the first quartile and the impedance of the cell corresponding to the third quartile among the sorted cells; subtracting the impedance of the cell corresponding to the first quartile from the impedance of the cell corresponding to the third quartile to obtain the quartile impedance difference; determining a lower limit for impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the first quartile; determining an upper limit for impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the third quartile; and identifying cells in the power battery whose impedance is less than the lower limit for impedance abnormality or greater than the upper limit for impedance abnormality as cells with impedance abnormalities in the power battery.

[0012] In one optional embodiment, the method further includes: determining the impedance of the cell corresponding to the median from the impedances of the sorted multiple cells; obtaining an amplification factor; multiplying the impedance of the cell corresponding to the median by the amplification factor to obtain an abnormality threshold; identifying cells in the power battery whose impedance is less than the lower limit of impedance abnormality or greater than the upper limit of impedance abnormality as cells with impedance abnormality in the power battery, further including: identifying cells in the power battery whose impedance is less than the minimum value between the lower limit of impedance abnormality and the abnormality threshold or greater than the maximum value between the upper limit of impedance abnormality and the abnormality threshold as cells with impedance abnormality in the power battery.

[0013] According to another aspect of the embodiments of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the cell impedance detection method for a power battery provided in any of the above embodiments.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the cell impedance detection method for power batteries provided in any of the above embodiments.

[0015] In this embodiment of the application, for each battery cell, multiple current value intervals are divided within the range of multiple current values, so that each current value interval corresponds to multiple voltage values. Then, the evaluation score of the candidate interval pair is determined by the number of current values ​​and corresponding voltage values ​​of the candidate interval pair formed by two current value intervals, so that the evaluation scores of at least two pairs of candidate interval pairs for each battery cell can be obtained. After that, by determining the candidate interval pair with the highest evaluation score in each battery cell as the target current value interval pair, the impedance of each battery cell can be determined by the data of the target current value interval pair for each battery cell. Since the difference between current values ​​and the number of voltage values ​​in candidate interval pairs are both positively correlated with the evaluation score, selecting the candidate interval pair with the highest evaluation score allows us to choose two current value intervals from multiple current value intervals that have a large difference between the two center current values ​​and a large number of corresponding voltage values. In other words, we select two current value intervals with strong voltage response and stable current values ​​to calculate the impedance of each cell. This allows us to identify cells with impedance anomalies based on each cell. This reduces the impact of voltage lag compared to current on impedance calculation without increasing hardware costs, improves the stability of impedance calculation, and accurately identifies cells with impedance anomalies, thus improving the reliability of impedance anomaly detection at the cell level in power batteries. Furthermore, the median voltage value among multiple voltage values ​​in a current value interval is the steady-state voltage of that current value interval. Determining the impedance of each cell by using the current value of the target current value interval pair and the median voltage value among the corresponding multiple voltage values ​​can further improve the accuracy of the impedance of each cell, thereby improving the reliability of impedance anomaly detection in power batteries.

[0016] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same components throughout the drawings. In the drawings: Figure 1 shows a flowchart illustrating a cell impedance detection method for a power battery according to an embodiment of this application; Figure 2 shows a structural schematic diagram of a cell impedance detection device for a power battery according to an embodiment of this application; Figure 3 shows a structural schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0019] A Battery Management System (BMS) is an electronic system that manages rechargeable batteries or battery packs. As the core of energy control in electric vehicles, the BMS primarily uses real-time collected data on key parameters such as cell voltage, total current, and temperature to perform functions such as State of Charge (SOC) estimation, active balancing, and thermal runaway protection.

[0020] The sampling accuracy and dynamic response speed of the cell-level voltage signal directly affect the accuracy of BMS decision-making. Currently, in power batteries composed of multiple cells, the voltages of multiple cells are acquired serially, which is inconsistent with the current acquisition time. This results in asynchronous acquisition of current and voltage signals, leading to significant errors in the cell impedance calculation. Consequently, the accuracy of impedance anomaly detection is reduced when based on the calculated cell impedance.

[0021] For example, suppose a power battery consists of four cells, each with an internal resistance of 1mΩ. During a rapid acceleration, the current jumps instantaneously from 0A to 200A. Ideally, when the current suddenly increases by 200A, the voltage of each cell should drop instantaneously by ΔV: ΔV = I × R = 200A × 0.001Ω = 0.2V. However, in reality, the current sampled from t=0 to 100s is 200A. Assuming the voltage of each cell before the jump is 3.20V, then at t=1s, the voltage of cell 1 is 3.18V, at t=2s, the voltage of cell 2 is 3.14V, at t=3s, the voltage of cell 3 is 3.12V, at t=4s, the voltage of cell 4 is 3.10V, and at t=100s, the voltages of cells 1, 2, 3, and 4 are all 3.00V. When calculating the cell impedance using current and voltage data collected at t=4s, the calculated impedance values ​​for cells 1, 2, 3, and 4 were 0.1mΩ, 0.2mΩ, 0.3mΩ, and 0.4mΩ, respectively, all significantly different from the cell's internal ohmic resistance of 1mΩ. Only when using current and voltage data collected at t=100s were the calculated impedance values ​​for cells 1, 2, 3, and 4 all reached 1mΩ. This indicates that under conditions of rapid acceleration / deceleration and fast charging / stopping, where current changes drastically, voltage changes significantly lag behind current changes. This causes a timing mismatch in the BMS's current and voltage data collection, resulting in a mismatch between the calculated voltage drop and current changes. This leads to a severe bias in impedance calculation, reducing the accuracy of battery cell impedance calculations and consequently decreasing the reliability of battery cell impedance anomaly detection.

[0022] Existing solutions integrate impedance measurement functionality into the cell management chip, achieving full-time impedance monitoring via daisy-chain communication. While this approach improves data real-time performance, it requires refactoring the BMS hardware architecture and embedding dedicated ICs, increasing manufacturing costs. Therefore, there is an urgent need to design a solution that can accurately calculate the cell impedance of power batteries without increasing hardware costs, thereby improving the accuracy of power battery cell impedance and ultimately enhancing the reliability of power battery cell impedance anomaly detection.

[0023] Since cell impedance is calculated as the ratio of voltage change to current change, the voltage change lags behind the current change, leading to a mismatch between voltage drop and current transformation. Therefore, the voltage collected by the power battery during operation can be mapped to the current range where the current exists, resulting in multiple current ranges, each corresponding to multiple voltages. The longer the cell current is maintained within a current range, the more voltages are collected, and the greater the number of corresponding voltages. By selecting a current range with a larger number of voltages and calculating the cell impedance based on the median voltage within that range, we are essentially selecting a steady-state range where the current has been maintained for a certain period. The cell impedance is then calculated based on the steady-state voltage within this range. The steady-state voltage within the steady-state range has fully responded to the stable current and tends to stabilize. Calculating the impedance of each cell using the current and steady-state voltage from two steady-state ranges requires no additional hardware cost and improves the accuracy of each cell's impedance. Therefore, identifying cells with impedance anomalies based on the calculated impedance improves the reliability of power battery cell impedance anomaly detection.

[0024] The battery cell impedance detection method provided in this application is applicable to scenarios such as electric vehicle repair, battery inspection, used car sales, and old battery recycling. For example, during electric vehicle repair, impedance detection is performed on the battery cells to replace cells with abnormal impedance. Similarly, during old battery recycling, impedance anomaly detection is performed on the battery cells to assess the recycling value of the old battery and to replace cells with abnormal impedance in subsequent replacements. This application uses an electric vehicle repair scenario as an example for illustration and does not constitute a limitation.

[0025] Figure 1 shows a schematic flowchart of the cell impedance detection method for power batteries provided in an embodiment of this application. This method is executed by electronic devices such as computers, servers, and tablet computers, where the tablet computer can be an automotive diagnostic device. This embodiment uses an automotive diagnostic device as an example for illustration and is not intended to be limiting.

[0026] As shown in Figure 1, the method includes the following steps: Step 100: Obtain multiple current values ​​of each cell of the power battery and the voltage value corresponding to each current value during operation.

[0027] The power battery is a rechargeable battery pack, which includes multiple cells connected in series.

[0028] In this embodiment, during electric vehicle repair, the vehicle diagnostic equipment is connected to the vehicle's BMS via the vehicle's diagnostic interface (e.g., OBD [On-Board Diagnostics] interface). The vehicle is then started and operated under simulated real-world driving conditions. During operation, the BMS collects battery operating data at a fixed frequency and sends this data to the vehicle diagnostic equipment. For example, when the BMS collects battery operating data at a frequency of 10Hz for 5 minutes, the vehicle diagnostic equipment can acquire 3000 data points.

[0029] Operational data can be stored using time as an index. Each data point includes current, voltage, and state of charge (SOC) values. The current value is the total current of the battery, the voltage value is the voltage of each cell, and the SOC represents the current charge level of the battery. For example, assuming the battery has three cells, at time t1, with a current of 8A, the SOC is 50%. The voltages corresponding to this current are 3.2V for cell 1, 3.21V for cell 2, and 3.19V for cell 3. At time t2, with a current of -5A, the SOC is 51%. The voltages corresponding to this current are 3.205V for cell 1, 3.201V for cell 2, and 3.15V for cell 3.

[0030] When automotive diagnostic equipment acquires multiple operational data collected by the BMS, it can also obtain multiple current values ​​and corresponding voltage values ​​for each battery cell. For example, when the current value 1 of battery cell 1 is 8A, the corresponding voltage value 1 is 3.2V; when the current value 2 of battery cell 1 is -5A, the corresponding voltage value 2 is 3.205V; when the current value 3 of battery cell 2 is 8A, the corresponding voltage value 3 is 3.21V; when the current value 3 of battery cell 2 is -5A, the corresponding voltage value 3 is 3.201V.

[0031] When the state of charge (SOC) of a power battery is in an extreme range, such as SOC < 30% or SOC > 90%, the voltage curve of the cell changes with SOC very steeply (polarization phenomenon). In this case, a small change in SOC can lead to a huge voltage difference, which masks the voltage drop caused by the change in current, thereby interfering with the impedance calculation and reducing the accuracy of the impedance.

[0032] Therefore, based on the obtained operating data of the power battery, in order to improve the accuracy of cell impedance calculation, it is necessary to eliminate the interference of operating data corresponding to the SOC in the extreme range on the impedance calculation. Preferably, step 100 includes the following steps: Step 110: Obtain operating data of each cell in the power battery at multiple time points to obtain multiple operating data, wherein each operating data includes current value, voltage value and state of charge value.

[0033] Step 120: For each cell, select the operating data whose state of charge value is within the preset stable range from multiple operating data to obtain multiple current values ​​and the voltage value corresponding to each current value.

[0034] The preset stable range is SOC ∈ [30%, 90%). Within this range, the cell voltage is minimally affected by SOC, ensuring that voltage drop is primarily caused by current changes and improving the accuracy of cell impedance calculations.

[0035] By retaining operating data whose state of charge (SOC) values ​​fall within a preset stable range from multiple operating data sets, it is possible to select operating data that falls within a relatively flat range of the voltage-SOC characteristic curve and exclude operating data that falls within the extreme range of SOC. This effectively isolates voltage noise caused by SOC changes and ensures that the voltage difference used for subsequent impedance calculations mainly reflects the voltage drop caused by current changes, thereby improving the accuracy of cell impedance calculation results.

[0036] In some embodiments, after selecting operating data whose state of charge (SCC) values ​​are within a preset stable range from multiple operating data for each cell, operating data with abnormal current values ​​and abnormal voltage values ​​can be filtered out from the operating data whose SCC values ​​are within the preset stable range to obtain multiple current values ​​and the voltage value corresponding to each current value.

[0037] Among these, abnormal current operating data can refer to operating data with a current value of 0A. Abnormal voltage operating data can refer to operating data with a voltage value deviating from the cell's safe voltage. For example, when the cell's safe voltage is 3.5V, abnormal voltage operating data would be operating data greater than 3.5V, such as 10V.

[0038] By filtering out the abnormal operating data mentioned above, noise interference can be reduced and the accuracy of cell impedance calculation results can be improved.

[0039] Step 200: For each cell, divide the range of multiple current values ​​into multiple current value intervals so that each current value interval corresponds to multiple voltage values.

[0040] Here, the current value interval refers to multiple discrete intervals obtained by discretizing the continuous current value. The range of multiple current values ​​refers to the range from the minimum value to the maximum value of the current value.

[0041] Specifically, for each cell, starting from the smallest current value and ending at the largest current value, multiple current value intervals can be divided according to a preset step size so that each current value interval corresponds to the voltage value corresponding to multiple current values ​​located within the current value interval.

[0042] The preset step size can be set to 10A. For example, assuming that the range of multiple current values ​​is from -100A to 200A, and the preset step size is 10A, then 30 current value intervals can be obtained.

[0043] In this way, each current value can be accurately mapped to its corresponding current value range, ensuring that the multiple voltage values ​​corresponding to each current value range are correct. For example, suppose that the current value 1 of cell 1 is -136A at 10s, and the voltage value 1 corresponding to current value 1 is 3.1V; the current value 2 is 110A at 60s, and the voltage value 2 corresponding to current value 2 is 3V; and the current value 3 is -134A at 100s, and the voltage value 3 corresponding to current value 3 is 3.12V. Then, the voltage values ​​corresponding to the current value range [-140A, -130A) are voltage value 1 (3.1V) and voltage value 3 (3.12V), and the voltage value corresponding to the current value range [110A, 120A) is voltage value 2 (3V).

[0044] In some embodiments, K-means clustering can be used to divide the range of multiple current values ​​into multiple current value intervals.

[0045] Specifically, assuming the current value ranges from -100A to 200A, and the preset number of intervals K is 5, five current values ​​can be selected within this range as initial cluster centers, for example: -70A, 0A, 50A, 120A, and 180A. Multiple current values ​​are acquired (the number of current values ​​is much greater than 5), and the distance between each current value and the cluster center is calculated. Each current value is then assigned to the nearest cluster center. For example, current values ​​-88A and -82A are assigned to center -70A, and 92A is assigned to center 120A. Afterward, the average value of all current values ​​in each cluster is recalculated, and the new cluster center is updated to the average value. For example, the cluster containing -88A and -82A has its new cluster center updated to -85A, and the cluster containing 92A has its new cluster center updated to 90A due to the addition of other current values. Next, the above allocation and update operations are repeated until the cluster centers no longer change, and finally five stable cluster centers (e.g., -85A, -15A, 25A, 90A, 160A) and their corresponding multiple current values ​​are obtained. The voltage values ​​corresponding to these current values ​​are synchronously assigned to each cluster, thereby obtaining five current value ranges and their corresponding multiple voltage values.

[0046] In some embodiments, mean-shift clustering can be used to divide the range of multiple current values ​​into multiple current value intervals.

[0047] Specifically, assume the current values ​​include -88A, -82A, 92A, 85A, 95A, and 90A, with a bandwidth of 30A and a convergence threshold of 0.1A. First, determine the interval corresponding to each current value, centered on each current value. For example, the interval corresponding to the current value -88A is [-118A, -58A]. Assuming the current values ​​corresponding to the interval [-118A, -58A] are -88A, -82A, -85A, and -90A, the calculated interval average is -86.25A. Then, the current value -88A is moved to -86.25A. The moving distance is 1.75A, which is greater than the convergence threshold of 0.1A. Therefore, centered on the current value -86.25A, the interval is updated to [-116.25A, -56.25A]. At this point, the current values ​​corresponding to the new interval [-116.25A, -56.25A] are still -88A, -82A, -85A, and -90A, and the calculated interval average is still -86.25A. The shift distance is 0A, which is less than the convergence threshold of 0.1A. Therefore, the density peak value corresponding to the current value -88A is -86.25A.

[0048] After obtaining the density peak value corresponding to each current value, current values ​​with similar density peak values ​​are merged. For example, the merged density peak value 1 is -86.25A, and its corresponding current values ​​include -88A, -82A, -85A, and -90A; density peak value 2 is 93.5A, and its corresponding current values ​​include 92A and 95A. The voltage values ​​corresponding to these current values ​​are then synchronously assigned to the corresponding density peak values, resulting in multiple current value ranges and their corresponding voltage values.

[0049] Step 300: Calculate the number of voltage values ​​corresponding to each current value range for each cell.

[0050] The number of voltage values ​​corresponding to each current value range refers to the total number of voltage values ​​corresponding to that current value range. The number of voltage values ​​corresponding to each current value range reflects the richness of voltage data within that current value range. The more voltage values ​​there are, the longer the current value of the battery cell is maintained within that current value range, the more stable the voltage within that current value range, and the more stable and reliable the median voltage value among the multiple voltage values ​​within that current value range.

[0051] For example, if the number of voltage values ​​corresponding to the current value range [50A, 60A) is 100, and the number of voltage values ​​corresponding to the current value range [150A, 160A) is 10, then the voltage data for the current value range [50A, 60A) is more reliable.

[0052] Step 400: For each cell, there are at least two pairs of candidate intervals. The evaluation score of the candidate interval pairs is determined based on the current value and the number of corresponding voltage values ​​of the candidate interval pairs. Each pair of candidate intervals consists of any two current value intervals. The difference between the current values ​​of the candidate interval pairs and the number of corresponding voltage values ​​are positively correlated with the evaluation score.

[0053] Specifically, assuming the current value range includes 10 current value ranges, it is possible to form only 2 pairs, 6 pairs of candidate ranges, or 45 pairs of candidate ranges.

[0054] For example, when the current value range includes three current value ranges, such as current value range 1, current value range 2 and current value range 3, three pairs of candidate range pairs can be obtained: (current value range 1, current value range 2), (current value range 1, current value range 3) and (current value range 2, current value range 3).

[0055] The current value of a candidate interval pair can be the center current value of each current value interval, or the average current value among multiple current values ​​corresponding to each current value interval.

[0056] The impedance of the battery cell is calculated by dividing the voltage difference by the current difference. The current difference should be large enough to make the voltage difference a strong response signal, so that it is not overridden by noise or other interference signals, thus reducing the impact of noise interference and ensuring the accuracy of the voltage difference. The larger the gap between current value intervals, the greater the difference between the current values ​​within the current value intervals. Therefore, the difference between the current values ​​within the current value intervals is positively correlated with the evaluation score of the candidate interval pairs. Furthermore, as shown in step 300, the more voltage values ​​corresponding to the current value intervals, the more stable the median voltage value among the multiple voltage values ​​in the current value intervals. Therefore, the number of voltage values ​​corresponding to the candidate interval pairs is positively correlated with the evaluation score of the candidate interval pairs.

[0057] In this way, the evaluation score of the candidate interval pair is determined by the number of current values ​​and corresponding voltage values ​​of the candidate interval pair. By selecting the candidate interval pair with the higher evaluation score, two current value intervals with a larger difference in current value and a larger number of voltage values ​​can be selected from multiple candidate interval pairs.

[0058] Specifically, step 400 includes the following steps: Step 410: For any two current value intervals in each cell that form a candidate interval pair, calculate the absolute value of the difference between the center current values ​​of the candidate interval pair to obtain the first center current difference.

[0059] Assuming candidate intervals A: current value interval 1 [-10A, 0A) corresponds to 600 voltage values, and current value interval 2 [110A, 120A) corresponds to 8 voltage values, then the center current value I of current value interval 1 is... m The current value is -5A, and the center current value I in current range 2 is... n The absolute value of the difference between the center current values ​​of the candidate interval pair and the current value is 115A: |I m -I n |=|-5-115|=120A.

[0060] Step 420: Determine the maximum value among the number of voltage values ​​corresponding to the candidate interval pairs.

[0061] When the number of voltage values ​​corresponding to current value interval 1 is 600 and the number of voltage values ​​corresponding to current value interval 2 is 8, then the maximum number of voltage values ​​corresponding to candidate interval A is 600. This ensures that at least one of the current value intervals has a sufficiently large number of voltage values.

[0062] Step 430: Determine the evaluation score of the candidate interval pair based on the first center current difference and the maximum value.

[0063] The evaluation score, a function of the sum of current and voltage values, measures the stability and signal strength of candidate interval pairs. Specifically, the evaluation score of a candidate interval pair can be calculated using the following formula. : , among which, I m and I n These represent the center current values ​​of the two current value intervals of the candidate interval pair. and This represents the number of voltage values ​​corresponding to the two current value intervals of a candidate interval pair. It is a logarithmic function.

[0064] When the absolute value of the difference between the candidate interval and the center current value of A is 120A, and the maximum number of corresponding voltage values ​​is 600, the evaluation score of the candidate interval for A can be calculated as 333.6.

[0065] In one alternative approach, the evaluation score can be a linear weighted function of the current difference and the voltage magnitude, specifically: ,in, and For normalized or monotonic functions, and These are the weights of the first center current difference and the maximum value, respectively. .

[0066] In another alternative approach, the score is evaluated as a product function with constraints or penalty terms, specifically: ,in, This is for punishment purposes. Simultaneously, a constraint min( , )>Nmin, where Nmin is the lower limit of the number of samples, representing the minimum number of voltage values ​​that each current value interval in the candidate interval pair must contain, which can avoid the imbalance of the number of voltage values ​​in the candidate interval pair.

[0067] Optionally, the above-mentioned synthesis function can adopt linear weighting, product, logarithmic or other monotonic transformation forms. The weights can be adaptively adjusted according to the operating conditions or data distribution. Other constraints (minimum current difference threshold) or penalty terms can also be introduced to improve robustness.

[0068] By calculating the evaluation score of each candidate interval pair, the optimal current value interval pair can be automatically and accurately found based on the evaluation score of the candidate interval pairs, without the need for manual setting of thresholds or time windows, exhibiting strong adaptability and robustness. In addition, by selecting the optimal current value interval pair, the steady-state interval with significant current changes and a certain sampling length is automatically locked, thereby reducing the impact of errors caused by voltage sampling lag without changing the hardware.

[0069] Step 500: Determine the candidate interval pair with the highest evaluation score from at least two candidate interval pairs for each cell as the target current value interval pair for each cell.

[0070] Assuming candidate interval pair B is: current value interval 3 [0A, 10A), the number of corresponding voltage values ​​is 550, current value interval 4 [110A, 120A), the number of corresponding voltage values ​​is 8, the evaluation score of candidate interval pair B is calculated to be 301.4.

[0071] Assuming candidate interval pair C is: current value interval 5 [-150A, -140A), corresponding to 5 voltage values, and current value interval 6 [110A, 120A), corresponding to 8 voltage values, the evaluation score of candidate interval pair B is calculated to be 247.

[0072] Among candidate interval pairs A, B, and C of cell 1, candidate interval pair A has the highest evaluation score. It is a candidate interval pair with a large difference in center current value and a large number of voltage values, which can ensure that the target current value interval pair has both strong signal and stability. Therefore, candidate interval pair A is determined as the target current value interval pair of cell 1.

[0073] In some embodiments, a genetic algorithm can be used to determine the target current value range pair for each battery cell. Specifically, assuming there are 10 current value ranges, 6 candidate range pairs are randomly formed, and the evaluation scores of these 6 candidate range pairs are calculated. Then, the candidate range pair corresponding to the lowest evaluation score is eliminated, and the candidate range pair corresponding to the higher evaluation score is selected for exchange, generating new candidate range pairs, and the evaluation score of the new candidate range pairs is calculated. Then, based on the evaluation scores of the new candidate range pairs, candidate range pairs are eliminated and new candidate range pairs are generated... until the highest evaluation score in the new candidate range pairs no longer changes, and the new candidate range pair corresponding to the highest evaluation score is determined as the target current value range pair.

[0074] In other embodiments, a particle swarm optimization algorithm can be used to determine the target current value interval pair for each cell. Specifically, assuming there are 10 current value intervals, four candidate interval pairs are randomly formed, and the evaluation scores of these four candidate interval pairs are calculated. Then, the candidate interval pair corresponding to the highest evaluation score is determined, and using the candidate interval pair corresponding to the highest evaluation score as the target, the remaining three candidate interval pairs are perturbed in the discrete index space to obtain three new candidate interval pairs. These three new candidate interval pairs are combined with the candidate interval pair corresponding to the highest evaluation score as a new candidate interval pair, and the evaluation score of the new candidate interval pair is calculated. Then, the candidate interval pair corresponding to the highest evaluation score is determined, and so on, until the candidate interval pair corresponding to the highest evaluation score is determined to be the same candidate interval pair three times consecutively. The candidate interval pair corresponding to the highest evaluation score is then determined as the target current value interval pair.

[0075] In other embodiments, a greedy algorithm can be used to determine the target current value interval pair for each cell. Specifically, with the evaluation score as the optimization objective, intervals with larger current differences are preferentially selected, and these intervals are combined with current value intervals with a large number of voltage values ​​in their neighborhoods to form multiple candidate interval pairs. Then, the evaluation score of each candidate interval pair is calculated, and the candidate interval pair with the highest evaluation score is selected as the target current value interval pair.

[0076] Step 600: Determine the impedance of each cell based on the current value of the target current value range for each cell and the median voltage value among the corresponding multiple voltage values.

[0077] The target current value interval can be the center current value of each current value interval, or the average current value among multiple current values ​​corresponding to each current value interval.

[0078] Specifically, step 600 includes the following steps: Step 610: For each cell, determine the center current value of each current value interval in the target current value interval pair and the median voltage value among the corresponding multiple voltage values.

[0079] When candidate interval pair A is the target current value interval pair of cell 1, this step determines the center current values ​​of current value interval 1 and current value interval 2 as -5A and 115A, respectively.

[0080] The median voltage value refers to the median of all voltage values ​​within a current value interval. After determining the target current value interval pair, the multiple voltage values ​​within each of the two current value intervals can be sorted in ascending order, and then the median voltage value among the multiple voltage values ​​in each of the two current value intervals can be determined. For example, the median voltage values ​​for current value interval 1 and current value interval 2 can be determined to be 3.2V and 3.32V, respectively.

[0081] Step 620: Calculate the difference between the center current values ​​of the two current value intervals to obtain the second center current difference.

[0082] When the center current values ​​of the two current value intervals of the target current value interval are -5A and 115A respectively, the difference of the second center current is: 115A-(-5A)=120A.

[0083] Step 630: Calculate the difference between the median voltage values ​​of the two current value intervals to obtain the median voltage difference.

[0084] When the median voltage values ​​of the multiple voltage values ​​corresponding to the two current value intervals of the target current value interval are 3.2V and 3.32V respectively, the difference between the median voltage values ​​is: 3.32V-3.2V=0.12V.

[0085] Step 640: Divide the difference in median voltage by the difference in second center current to obtain the impedance of each cell.

[0086] For example, when the difference in the median voltage of cell 1 is 0.12V and the difference in the second center current is 120A, the impedance of cell 1 can be calculated to be approximately 1mΩ.

[0087] The median voltage of multiple voltage values ​​in each current range is the steady-state voltage of that current range. The impedance of each cell is determined by the center current value and the median voltage value of the target current range. In other words, the impedance of each cell is calculated by the steady-state voltage under steady-state current, which improves the accuracy of the impedance of each cell.

[0088] Step 700: Identify the cells with abnormal impedance in the power battery based on the impedance of each cell.

[0089] There may be one or more cells with abnormal impedance. After calculating the impedance of each cell in the power battery, the cells with abnormal impedance can be identified based on the impedance of each cell, so that the cells with abnormal impedance can be repaired or replaced.

[0090] Most existing detection technologies can only identify changes in the overall impedance of a power battery, making it difficult to identify specific faulty cells and thus difficult to repair or replace them. In this embodiment, each cell of the power battery can be numbered, allowing for subsequent location of each cell based on its assigned number.

[0091] Specifically, after calculating the impedance of each cell in the power battery, anomaly detection algorithms can be used to identify cells with abnormal impedance from multiple cells based on the time series or batch distribution of the cell impedance. Anomaly detection algorithms can be the 3σ criterion, Grubbs test, tree-based models (such as random forests), or equivalent replacements thereof. After identifying the cells with abnormal impedance, their numbers can be output, reducing fault location accuracy to the cell level. This allows maintenance personnel to directly locate the cells with abnormal impedance based on their numbers and promptly repair or replace them.

[0092] In this embodiment, after identifying a cell with abnormal impedance, an impedance detection report can be output. This report includes not only the cell's serial number but also repair recommendations for each cell, such as a recommendation to replace it. Thus, based on the impedance detection report, maintenance personnel can quickly locate the cell with abnormal impedance according to its serial number without disassembling the battery pack and quickly repair it according to the repair recommendations.

[0093] Preferably, the battery cells with abnormal impedance in the power battery can be identified by the following steps: Step 710: Sort the impedances of multiple battery cells in ascending order.

[0094] Step 720: Determine the impedance of the cell corresponding to the first quartile and the impedance of the cell corresponding to the third quartile among the sorted cells.

[0095] Step 730: Subtract the impedance of the cell corresponding to the first quartile from the impedance of the cell corresponding to the third quartile to obtain the quartile impedance difference.

[0096] Step 740: Determine the lower limit of impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the first quartile.

[0097] Step 750: Determine the upper limit of impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the third quartile.

[0098] Step 760: Identify cells in the power battery whose impedance is less than the lower limit of impedance abnormality or greater than the upper limit of impedance abnormality as cells with impedance abnormality in the power battery.

[0099] Assume the impedances of the five cells in the power battery are 1.09mΩ (cell 1), 0.91mΩ (cell 2), 1.05mΩ (cell 3), 1.10mΩ (cell 4), and 1.07mΩ (cell 5). The sorted impedances are 0.91mΩ (cell 2), 1.05mΩ (cell 3), 1.07mΩ (cell 5), 1.09mΩ (cell 1), and 1.10mΩ (cell 4). From the sorted impedances, we know that the impedance of the cell corresponding to the first quartile is 1.05mΩ (cell 3), and the impedance of the cell corresponding to the third quartile is 1.09mΩ (cell 1). The calculated quartile impedance difference is 0.04mΩ.

[0100] The lower limit of impedance anomaly can be obtained by subtracting 1.5 × the interquartile impedance difference from the impedance of the cell corresponding to the first quartile. For example, when the impedance of the cell corresponding to the first quartile is 1.05mΩ and the interquartile impedance difference is 0.04mΩ, the calculated lower limit of impedance anomaly is 0.99mΩ.

[0101] The upper limit of impedance anomaly can be obtained by adding 1.5 × the interquartile impedance difference to the impedance of the cell corresponding to the third quartile. For example, when the impedance of the cell corresponding to the third quartile is 1.09mΩ and the interquartile impedance difference is 0.04mΩ, the calculated upper limit of impedance anomaly is 1.15mΩ.

[0102] In this way, by determining whether the impedance of each cell is less than the lower limit of impedance abnormality or greater than the upper limit of impedance abnormality, the cell with impedance abnormality can be identified. For example, since the impedance of cell 2 among the above 5 cells is 0.91mΩ, which is less than the lower limit of impedance abnormality, cell 2 is identified as the cell with impedance abnormality.

[0103] In some cases, when the State of Harmony (SOH) of the power battery is high, the impedance of each cell is relatively close. Due to data transmission or calculation errors, the impedance of some cells with normal impedance deviates from that of most cells. In this situation, steps 710-760 will identify the cells with normal impedance as cells with abnormal impedance, reducing the reliability of impedance anomaly detection.

[0104] To avoid this problem, the impedance of the cell corresponding to the median can be determined from the impedances of the sorted cells. Then, an amplification factor can be obtained, and the impedance of the cell corresponding to the median can be multiplied by this amplification factor to obtain the abnormal threshold. In this way, the cells in the power battery whose impedance is less than the minimum value between the lower limit of impedance abnormality and the abnormal threshold can be identified as cells with impedance abnormalities in the power battery. Alternatively, the cells in the power battery whose impedance is greater than the maximum value between the upper limit of impedance abnormality and the abnormal threshold can be identified as cells with impedance abnormalities in the power battery.

[0105] The amplification factor is determined as follows: Based on historical impedance data, a pre-defined range of coefficient values ​​is established, and a fixed step size is used for iteration to identify multiple candidate coefficients. Then, for each candidate coefficient, it is evaluated on a validation set obtained from the historical impedance data to obtain the accuracy and false alarm rate. Accuracy is the proportion of cells successfully identified as having impedance anomalies, and false alarm rate is the proportion of cells incorrectly identified as having impedance-healthy cells. Finally, a candidate coefficient that balances accuracy and false alarm rate is selected as the amplification factor. For example, the amplification factor can be set to 1.5.

[0106] For example, when the lower limit of impedance abnormality is 0.96mΩ, the upper limit of impedance abnormality is 1.04mΩ, and the abnormal threshold is 1.5mΩ, the minimum value between the lower limit of impedance abnormality and the abnormal threshold is 0.96mΩ, and the maximum value between the upper limit of impedance abnormality and the abnormal threshold is 1.5mΩ. In this way, cells with impedance less than 0.96mΩ or greater than 1.5mΩ among multiple cells are identified as cells with impedance abnormalities.

[0107] In this way, when the impedance dispersion of all cells is low, cells with normal impedance can be avoided from being identified as cells with abnormal impedance, cells with abnormal impedance can be accurately identified, and the reliability of impedance anomaly detection can be improved.

[0108] Through steps 710-760, instead of judging the cells with abnormal impedance in the power battery based on a fixed threshold, the system can adaptively determine the cells with abnormal impedance from multiple cells in the power battery under the current state by adapting to the overall impedance level of the power battery. This reduces the fault location accuracy to the cell level and improves the maintenance efficiency of the power battery.

[0109] In this embodiment of the application, for each battery cell, multiple current value intervals are divided within the range of multiple current values, so that each current value interval corresponds to multiple voltage values. Then, the evaluation score of the candidate interval pair is determined by the number of current values ​​and corresponding voltage values ​​of the candidate interval pair formed by two current value intervals, so that the evaluation scores of at least two pairs of candidate interval pairs for each battery cell can be obtained. After that, by determining the candidate interval pair with the highest evaluation score in each battery cell as the target current value interval pair, the impedance of each battery cell can be determined by the data of the target current value interval pair for each battery cell. Since the difference between current values ​​and the number of voltage values ​​in candidate interval pairs are both positively correlated with the evaluation score, selecting the candidate interval pair with the highest evaluation score allows us to choose two current value intervals from multiple current value intervals that have a large difference between the two center current values ​​and a large number of corresponding voltage values. In other words, we select two current value intervals with strong voltage response and stable current values ​​to calculate the impedance of each cell. This allows us to identify cells with impedance anomalies based on each cell. This reduces the impact of voltage lag compared to current on impedance calculation without increasing hardware costs, improves the stability of impedance calculation, and accurately identifies cells with impedance anomalies, thus improving the reliability of impedance anomaly detection at the cell level in power batteries. Furthermore, the median voltage value among multiple voltage values ​​in a current value interval is the steady-state voltage of that current value interval. Determining the impedance of each cell by using the current value of the target current value interval pair and the median voltage value among the corresponding multiple voltage values ​​can further improve the accuracy of the impedance of each cell, thereby improving the reliability of impedance anomaly detection in power batteries.

[0110] Figure 2 shows a schematic diagram of the structure of the cell impedance detection device for a power battery provided in an embodiment of this application. As shown in Figure 2, the cell impedance detection device 10 for the power battery includes: an acquisition module 11, a division module 12, a calculation module 13, a first determination module 14, a second determination module 15, a third determination module 16, and a fourth determination module 17. The acquisition module 11 is used to acquire multiple current values ​​and the voltage value corresponding to each current value of each cell during the operation of the power battery; the division module 12 is used to divide multiple current value intervals within the range of multiple current values ​​for each cell, so that each current value interval corresponds to multiple voltage values; the calculation module 13 is used to calculate the number of voltage values ​​corresponding to each current value interval of each cell; the first determination module 14 is used to determine the evaluation score of at least two pairs of candidate intervals in each cell based on the number of current values ​​and corresponding voltage values ​​of the candidate interval pairs, wherein each pair of... The candidate interval pair consists of two current value intervals, and the difference between the current values ​​of the candidate interval pair and the number of corresponding voltage values ​​are positively correlated with the evaluation score; the second determining module 15 is used to determine the candidate interval pair with the highest evaluation score among the at least two candidate interval pairs of each cell as the target current value interval pair of each cell; the third determining module 16 is used to determine the impedance of each cell based on the current value of the target current value interval pair of each cell and the median voltage value among the corresponding multiple voltage values; the fourth determining module 17 is used to determine the cells with abnormal impedance in the power battery based on the impedance of each cell.

[0111] The specific implementation process and beneficial effects of the device embodiments in this application can be referred to the embodiment shown in Figure 1 above, and will not be repeated here.

[0112] Figure 3 shows a schematic diagram of the structure of the electronic device provided in the embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0113] As shown in Figure 3, the electronic device 800 may include a processor 802 and a memory 804.

[0114] The memory 804 is used to store the computer program 806. The memory 804 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive. The computer program 806 may include computer-executable instructions.

[0115] The processor 802 is used to execute the computer program 806 to implement the above-described embodiment of the cell impedance detection method for power batteries.

[0116] The processor 802 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The electronic device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0117] This application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described embodiment of the cell impedance detection method for power batteries.

[0118] This application provides a computer program that can be executed by a processor to implement the above-described method for detecting the cell impedance of a power battery.

[0119] This application provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described embodiment of the cell impedance detection method for power batteries.

[0120] In the several embodiments provided in this application, any function, if implemented as a software functional module / unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, part or all of the technical solutions of this application can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or other electronic device) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0122] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In claims enumerating several means, several units or modules of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting the cell impedance of a power battery, characterized in that, The method includes: acquiring multiple current values ​​and corresponding voltage values ​​for each cell of the power battery during operation; dividing each cell into multiple current value intervals within the range of the multiple current values, so that each current value interval corresponds to multiple voltage values; calculating the number of voltage values ​​corresponding to each current value interval for each cell; determining an evaluation score for at least two pairs of candidate intervals in each cell based on the number of current values ​​and corresponding voltage values ​​of the candidate interval pairs, wherein each pair of candidate intervals consists of two current value intervals, and the difference between the current values ​​and the number of corresponding voltage values ​​of the candidate interval pairs are positively correlated with the evaluation score; determining the candidate interval pair with the highest evaluation score among the at least two pairs of candidate intervals for each cell as the target current value interval pair for each cell; determining the impedance of each cell based on the median voltage value among the current values ​​and corresponding multiple voltage values ​​of the target current value interval pair for each cell; and identifying cells with abnormal impedance in the power battery based on the impedance of each cell.

2. The method according to claim 1, characterized in that, The step of determining the evaluation score of at least two candidate interval pairs in each of the battery cells based on the number of current values ​​and corresponding voltage values ​​of the candidate interval pairs further includes: for any two current value intervals forming a candidate interval pair in each of the battery cells, calculating the absolute value of the difference between the center current values ​​of the candidate interval pairs to obtain a first center current difference; determining the maximum value among the number of voltage values ​​corresponding to the candidate interval pairs; and determining the evaluation score of the candidate interval pairs based on the first center current difference and the maximum value.

3. The method according to claim 1, characterized in that, The step of determining the impedance of each battery cell based on the current value of the target current value interval pair and the median voltage value among the corresponding plurality of voltage values ​​further includes: for each battery cell, determining the center current value of each current value interval in the target current value interval pair and the median voltage value among the corresponding plurality of voltage values; calculating the difference between the center current values ​​of two current value intervals to obtain a second center current difference; calculating the difference between the median voltage values ​​of two current value intervals to obtain a median voltage difference; and dividing the median voltage difference by the second center current difference to obtain the impedance of each battery cell.

4. The method according to claim 1, characterized in that, The step of dividing each of the multiple current values ​​into multiple current value intervals within the range of multiple current values, so that each current value interval corresponds to multiple voltage values, further includes: for each of the multiple current values, starting from the minimum value and ending at the maximum value among the multiple current values, dividing multiple current value intervals according to a preset step size, so that each current value interval corresponds to multiple voltage values ​​corresponding to the current values ​​located in the current value interval.

5. The method according to claim 1, characterized in that, The step of acquiring multiple current values ​​and corresponding voltage values ​​for each cell of the power battery during operation further includes: acquiring operating data for each cell of the power battery at multiple time points to obtain multiple operating data, wherein each operating data includes a current value, a voltage value, and a state of charge value; for each cell, selecting operating data whose state of charge value is within a preset stable range from the multiple operating data to obtain the multiple current values ​​and corresponding voltage values ​​for each current value.

6. The method according to claim 5, characterized in that, The step of selecting operating data whose state of charge (SOC) values ​​are within a preset stable range from multiple operating data for each cell, and obtaining the multiple current values ​​and the voltage value corresponding to each current value, further includes: selecting operating data whose SOC values ​​are within a preset stable range from multiple operating data for each cell; filtering operating data with abnormal current values ​​and abnormal voltage values ​​from the operating data whose SOC values ​​are within the preset stable range, and obtaining the multiple current values ​​and the voltage value corresponding to each current value.

7. The method according to claim 1, characterized in that, The step of determining the impedance abnormality of a cell in the power battery based on the impedance of each of the cells further includes: sorting the impedances of the multiple cells in ascending order; determining the impedance of the cell corresponding to the first quartile and the impedance of the cell corresponding to the third quartile among the sorted multiple cells; subtracting the impedance of the cell corresponding to the first quartile from the impedance of the cell corresponding to the third quartile to obtain the quartile impedance difference; determining a lower limit value for impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the first quartile; determining an upper limit value for impedance abnormality based on the quartile impedance difference and the impedance of the cell corresponding to the third quartile; and identifying cells in the power battery whose impedance is less than the lower limit value or greater than the upper limit value for impedance abnormality as cells with impedance abnormality in the power battery.

8. The method according to claim 7, characterized in that, The method further includes: determining the impedance of the cell corresponding to the median from the impedances of the sorted plurality of cells; obtaining an amplification factor; multiplying the impedance of the cell corresponding to the median by the amplification factor to obtain an abnormality threshold; the step of determining the cells in the power battery whose impedance is less than the lower limit of impedance abnormality or greater than the upper limit of impedance abnormality as cells with impedance abnormality in the power battery further includes: determining the cells in the power battery whose impedance is less than the minimum value between the lower limit of impedance abnormality and the abnormality threshold or greater than the maximum value between the upper limit of impedance abnormality and the abnormality threshold as cells with impedance abnormality in the power battery.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the cell impedance detection method for a power battery according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the cell impedance detection method of the power battery according to any one of claims 1 to 8.

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