Battery EIS measurement method based on internal short circuit sensitive frequency band optimization

Abstract

The invention relates to the technical field of battery measurement, and discloses a battery EIS measurement method based on internal short circuit sensitive frequency band optimization, which is used for solving the problem that a real sensitive frequency band is changed during battery measurement, and comprises the following steps: acquiring sensitive frequency band influence parameters of a battery corresponding to each internal short circuit sensitive frequency band, calculating to obtain a frequency band influence index according to the sensitive frequency band influence parameter, judging whether the internal short-circuit sensitive frequency band needs to be adjusted according to the frequency band influence index, and if the internal short-circuit sensitive frequency band needs to be adjusted, adjusting the starting frequency and the ending frequency of the internal short-circuit sensitive frequency band according to the frequency band influence index. And traversing all the internal short-circuit sensitive frequency bands in the internal short-circuit sensitive frequency band set to obtain an adjusted internal short-circuit sensitive frequency band set, thereby effectively improving the accuracy of internal short-circuit judgment.

Description

Technical Field

[0001] This invention relates to the field of battery measurement technology, and more specifically to a battery EIS measurement method optimized based on the internal short-circuit sensitive frequency band. Background Technology

[0002] With the widespread application of high-energy-density batteries in energy storage systems, electric vehicles, and portable devices, accurate assessment of the battery's internal safety status has become crucial for ensuring stable system operation. Among these, minor internal short circuits, as a highly concealed potential risk that can evolve into serious safety incidents, directly impact the level of battery safety management through early detection. Electrochemical impedance spectroscopy (EIS), which reflects the electrochemical reaction kinetics and transport behavior of batteries, is widely used to study the internal state of batteries and is currently one of the important methods for identifying internal anomalies.

[0003] Current EIS testing typically applies excitation signals over a wide frequency range, calculating complex impedances at different frequencies and analyzing their changing trends to identify potential internal short-circuit characteristics. To improve detection sensitivity, the industry generally adopts a "sensitive frequency band identification" approach, which involves selecting specific frequency ranges that significantly affect internal short circuits and focusing on impedance changes within this frequency range during subsequent diagnostics. A common practice is to obtain impedance differences between normal and abnormal states under a single operating condition, using threshold filtering to identify several frequency points that are highly sensitive to internal short circuits, and then constructing a sensitive frequency band based on these points for subsequent anomaly identification.

[0004] However, the above-mentioned technologies have at least the following technical problems: However, in practical applications, batteries undergo various state changes during operation, and their impedance behavior differs under different operating conditions. This can lead to instability in the sensitive frequency band obtained by existing methods during actual detection. When the sensitive frequency band cannot accurately reflect the impedance sensitivity under the current test condition, the accuracy of subsequent internal short-circuit detection based on that frequency band may decrease, thereby affecting the early identification capability of minor anomalies. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, the present invention provides a battery EIS measurement method based on optimization of the internal short-circuit sensitive frequency band, so as to solve the problems existing in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A battery EIS measurement method based on optimization of the internal short-circuit sensitive frequency band includes the following steps: Step 1: Construct normal state samples and several samples with different internal short-circuit resistance values ​​for the target battery model, and perform conventional electrochemical impedance spectroscopy (EIS) testing to obtain impedance data corresponding to the normal state and impedance data corresponding to different internal short-circuit states; Step 2: According to the preset test frequency, for each different internal short-circuit state, obtain the impedance data corresponding to the normal state and the impedance data corresponding to the internal short-circuit state at each test frequency, and calculate the relative deviation value between the two at the same test frequency point to obtain the relative deviation value of all test frequency points. The difference is calculated, and the internal short-circuit sensitive frequency band is obtained based on the relative deviation of all test frequency points. The internal short-circuit sensitive frequency bands under different internal short-circuit states are summarized to obtain the internal short-circuit sensitive frequency band set; Step 3: Obtain the sensitive frequency band influence parameters of the battery corresponding to each internal short-circuit sensitive frequency band. The sensitive frequency band influence parameters include the measured value of the battery shell surface temperature, the measured value of the battery shell outer surface shape variable, and the measured value of the cell clamping structure clamping force. The frequency band influence index is calculated based on the sensitive frequency band influence parameters, and the internal short-circuit sensitive frequency band is determined to be adjusted based on the frequency band influence index; Step 4: If it is determined that the internal short-circuit sensitive frequency band needs to be adjusted, then according to the frequency band influence index... The segment influence index adjusts the start and end frequencies of the internal short-circuit sensitive frequency bands to obtain the adjusted internal short-circuit sensitive frequency bands. This process iterates through all internal short-circuit sensitive frequency bands in the set to obtain the adjusted internal short-circuit sensitive frequency band set. Step 5: Obtain the representative sensitivity of each internal short-circuit sensitive frequency band in the adjusted internal short-circuit sensitive frequency band set, and construct a frequency weighting function based on the representative sensitivity of the internal short-circuit sensitive frequency bands. Step 6: Obtain the minimum and maximum frequencies of the adjusted internal short-circuit sensitive frequency band set, determine the clock frequency and sequence length of the pseudo-random binary sequence based on the minimum and maximum frequencies, and then... Step 7: Generate a pseudo-random excitation signal using a pseudo-random binary sequence; Step 8: Optimize and adjust the pseudo-random excitation signal according to the frequency weighting function to obtain the optimized pseudo-random excitation signal; Step 9: Apply a small amplitude current disturbance to the battery using the optimized pseudo-random excitation signal, collect the battery's response signal, and perform frequency domain analysis on the optimized pseudo-random excitation signal and the response signal to calculate the complex impedance at each test frequency point; Step 10: Calculate the difference between the complex impedance at each frequency point and the reference impedance corresponding to the normal state to obtain the impedance difference at each frequency point, and determine whether the current battery has an internal short circuit based on the impedance difference at each frequency point.

[0007] Preferably, the steps for obtaining the internal short-circuit sensitive frequency band are as follows: For the same frequency point, obtain the impedance value in the normal state and the impedance value in the internal short-circuit state; calculate the absolute difference between the impedance value in the internal short-circuit state and the impedance value in the normal state, and divide it by the absolute value of the impedance value in the normal state to obtain the relative deviation value of the frequency point, which is recorded as the sensitivity of the frequency point; obtain the sensitivity of all frequency points, compare the sensitivity with the sensitivity threshold, and filter out all frequency points that are greater than or equal to the sensitivity threshold; group the frequency points whose adjacent frequency difference is less than a preset frequency interval into the same frequency point set; for each frequency point set, obtain the minimum frequency and the maximum frequency in the frequency point set, and form the internal short-circuit sensitive frequency band by the minimum frequency and the maximum frequency.

[0008] Preferably, the steps for obtaining the frequency band influence index are as follows: obtaining the measured value of the battery casing surface temperature, and calculating the surface thermal disturbance coefficient based on the measured value of the battery casing surface temperature; obtaining the measured value of the battery casing outer surface shape variable, and calculating the electrode breathing coupling coefficient based on the measured value of the battery casing outer surface shape variable; obtaining the measured value of the cell clamping structure clamping force, and calculating the clamping force offset coefficient based on the measured value of the cell clamping structure clamping force; normalizing the surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient, and calculating the frequency band influence index based on the normalized surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient.

[0009] Preferably, the step of obtaining the surface thermal disturbance coefficient is as follows: Temperature measurements are obtained from multiple temperature sampling points on the outer surface of the battery casing. The temperature measurements include the surface temperature of the battery casing surface at different spatial locations at the same sampling time, forming a surface temperature matrix. For any two spatially adjacent temperature sampling points in the surface temperature matrix, the absolute value of their temperature difference is calculated. The absolute values ​​of the temperature differences of all adjacent temperature sampling points are accumulated and summed to obtain the total spatial temperature difference at the current time. The total spatial temperature difference is divided by the number of temperature sampling points to obtain the dynamic thermal gradient value at the current time. Within a preset time window, the dynamic thermal gradient values ​​at all sampling times are obtained. The maximum and minimum values ​​of the dynamic thermal gradient values ​​within the preset time window are selected, and the difference between the maximum and minimum values ​​is calculated to obtain the thermal disturbance amplitude. The average dynamic thermal gradient value is calculated by averaging the dynamic thermal gradient values ​​at all sampling times within the preset time window. The thermal disturbance amplitude is divided by the average dynamic thermal gradient value to obtain the surface thermal disturbance coefficient.

[0010] Preferably, the step of obtaining the electrode breathing coupling coefficient is as follows: within a preset time window, the deformation measurement values ​​of multiple deformation measurement points arranged on the outer surface of the battery casing at each sampling time are obtained to form a deformation measurement matrix; for the deformation measurement matrix, at each sampling time, the deformation measurement values ​​of all measurement points at that sampling time are arithmetically averaged to obtain the equivalent deformation value at that sampling time; within the preset time window, the equivalent deformation values ​​at all sampling times are obtained, and the maximum and minimum values ​​of the equivalent deformation values ​​within the time window are selected respectively, and the difference between the maximum and minimum values ​​is calculated to obtain the battery breathing amplitude; the mean of the equivalent deformation values ​​at all sampling times within the preset time window is calculated to obtain the reference deformation; the battery breathing amplitude is divided by the reference deformation to obtain the electrode breathing coupling coefficient.

[0011] Preferably, the step of obtaining the clamping force offset coefficient is as follows: at each sampling moment within a preset time window, the clamping force measurement values ​​of multiple clamping force measurement points arranged in the cell clamping structure are obtained to form a clamping force measurement matrix; the clamping force measurement values ​​of all sampling moments and all clamping force measurement points in the clamping force measurement matrix are averaged to obtain a clamping force steady-state reference value; for each clamping force measurement value in the clamping force measurement matrix, the absolute value of the difference between the measurement value and the clamping force steady-state reference value is calculated, and the sum of all the absolute values ​​of the difference is divided by the total number of clamping force measurement values ​​to obtain the clamping force disturbance; the clamping force disturbance is divided by the clamping force steady-state reference value to obtain the clamping force offset coefficient.

[0012] Preferably, the step of determining whether the internal short-circuit sensitive frequency band needs to be adjusted based on the frequency band influence index is as follows: compare the frequency band influence index with the frequency band influence threshold; if the frequency band influence index is greater than or equal to the frequency band influence threshold, it is determined that the internal short-circuit sensitive frequency band needs to be adjusted; if the frequency band influence index is less than the frequency band influence threshold, it is determined that the internal short-circuit sensitive frequency band does not need to be adjusted.

[0013] Preferably, the step of obtaining the adjusted internal short-circuit sensitive frequency band set is as follows: For each sensitive frequency band in the internal short-circuit sensitive frequency band set, obtain the start frequency and end frequency of the sensitive frequency band, denoted as the initial start frequency and initial end frequency, and simultaneously obtain the frequency band influence index and the corresponding frequency band influence threshold of the sensitive frequency band; for each internal short-circuit sensitive frequency band, divide the frequency band influence index by the frequency band influence threshold to obtain the adjustment coefficient; when the frequency band influence index is greater than or equal to the frequency band influence threshold, obtain the preset frequency step size, subtract the product of the adjustment coefficient and the preset frequency step size from the initial start frequency to obtain the actual start frequency; add the adjustment coefficient and the preset frequency step size to the initial end frequency. The product is used to obtain the actual termination frequency. When the frequency band influence index is less than the frequency band influence threshold, the product of the initial starting frequency plus 1 minus the adjustment coefficient and the preset frequency step size is used to obtain the actual starting frequency. The product of the initial termination frequency minus 1 minus the adjustment coefficient and the preset frequency step size is used to obtain the actual termination frequency. When the starting frequency adjustment direction is different from the termination frequency adjustment direction, that is, when the actual starting frequency is greater than or equal to the actual termination frequency during the expansion or contraction process, the sensitive frequency band is marked as an invalid frequency band and removed from the sensitive frequency band set. The adjusted starting and termination frequencies of all sensitive frequency bands are recombined in the original frequency band order to obtain the adjusted internal short-circuit sensitive frequency band set.

[0014] Preferably, the step of obtaining the frequency weighting function is as follows: Normalize the representative sensitivity of each internal short-circuit sensitive frequency band to obtain the normalized representative sensitivity of each internal short-circuit sensitive frequency band, and calculate the reciprocal of each normalized representative sensitivity. Add the normalized representative sensitivity to the corresponding reciprocal to obtain the segmented weighting coefficient of the internal short-circuit sensitive frequency band. For each test frequency point within the test frequency range, determine whether the test frequency point falls within the frequency range of any internal short-circuit sensitive frequency band. If it falls within a certain internal short-circuit sensitive frequency band, set the weight of the test frequency point to the segmented weighting coefficient corresponding to that internal short-circuit sensitive frequency band. If it does not belong to any sensitive frequency band, set the weight of the test frequency point to the minimum value among all segmented weighting coefficients. Obtain the frequency value and its corresponding weight for each test frequency point, construct a "frequency-weight" mapping pair between the test frequency point and its weight, and arrange all mapping pairs in ascending order of frequency value. Use all the arranged "frequency-weight" mapping pairs as input to construct a frequency weighting function covering the test frequency range.

[0015] Preferably, the step of determining whether the current battery has an internal short circuit based on the impedance difference at each frequency point is as follows: compare the impedance difference at each frequency point with an impedance threshold, and filter out the number of frequency points whose impedance difference is greater than or equal to the impedance threshold, which is recorded as the number of internal short circuit frequency points. If the number of internal short circuit frequency points is greater than or equal to the frequency point quantity threshold, it is determined that the current battery has an internal short circuit. If the number of internal short circuit frequency points is less than the frequency point quantity threshold, it is determined that the current battery does not have an internal short circuit.

[0016] The technical effects and advantages of this invention are as follows: The system obtains the sensitive frequency band influence parameters of the battery corresponding to each internal short-circuit sensitive frequency band, calculates the frequency band influence index based on the sensitive frequency band influence parameters, and determines whether the internal short-circuit sensitive frequency band needs to be adjusted based on the frequency band influence index. If it is determined that the internal short-circuit sensitive frequency band needs to be adjusted, the start frequency and end frequency of the internal short-circuit sensitive frequency band are adjusted according to the frequency band influence index to obtain the adjusted internal short-circuit sensitive frequency band. By traversing all internal short-circuit sensitive frequency bands in the set of internal short-circuit sensitive frequency bands, the adjusted set of internal short-circuit sensitive frequency bands is obtained, which effectively improves the accuracy of internal short-circuit judgment. Attached Figure Description

[0017] Figure 1 A flowchart of a battery EIS measurement method optimized for internal short-circuit sensitive frequency bands provided in this application embodiment. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] This invention provides a battery EIS measurement method optimized based on the internal short-circuit sensitive frequency band, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct normal state samples and several samples with different internal short-circuit resistance values ​​for the target battery model. Perform conventional electrochemical impedance spectroscopy tests within a preset wide frequency range to obtain impedance data corresponding to the normal state and impedance data corresponding to different internal short-circuit states. It should be noted that the "conventional electrochemical impedance spectroscopy test" described in this embodiment is existing technology. It involves applying a small-amplitude AC perturbation signal to the battery within a preset frequency range and acquiring the voltage and current response signals at each test frequency. The complex impedance value at the corresponding frequency point is then calculated through frequency domain analysis. This testing process yields impedance data consisting of multiple frequency points and their corresponding impedance values, which can be used to characterize the battery's electrochemical and transport characteristics at different frequencies. Since this testing method and its impedance acquisition process are publicly available and widely used in battery performance analysis and impedance spectroscopy research, this embodiment will not elaborate further on it.

[0020] Step 2: According to the preset test frequency, for each different internal short circuit state, obtain the impedance data corresponding to the normal state and the impedance data corresponding to the internal short circuit state at each test frequency, and calculate the relative deviation value between the two for the same test frequency point. Obtain the relative deviation value of all test frequency points, and obtain the internal short circuit sensitive frequency band based on the relative deviation value of all test frequency points. It should be noted that a single internal short circuit state may have multiple internal short circuit sensitive frequency bands. The internal short circuit sensitive frequency bands of each different internal short circuit state are summarized to obtain the internal short circuit sensitive frequency band set. In this embodiment, it should be specifically explained that the steps for obtaining the internal short-circuit sensitive frequency band are as follows: For the same frequency point, obtain the impedance value under normal state and the impedance value under internal short circuit state. Calculate the absolute difference between the impedance value under internal short circuit state and the impedance value under normal state, and divide it by the absolute value of the impedance value under normal state to obtain the relative deviation value of that frequency point, which is recorded as the sensitivity of the frequency point. The sensitivity of all frequency points is obtained, and the sensitivity is compared with the sensitivity threshold. All frequency points that are greater than or equal to the sensitivity threshold are filtered out, and frequency points whose adjacent frequency difference is less than the preset frequency interval are grouped into the same frequency point set. It should be noted that the sensitivity threshold is determined by taking the statistical quantile value (e.g., median or 75th percentile value) of the sensitivity at all frequency points based on the overall deviation distribution of impedance data in the normal state and different internal short-circuit states in step 1 within a preset wide frequency range.

[0021] For each set of frequency points, obtain the minimum and maximum frequencies in the set, and construct the internal short-circuit sensitive frequency band from the minimum and maximum frequencies.

[0022] Step 3: Obtain the sensitive frequency band influence parameters of the battery corresponding to each internal short circuit sensitive frequency band. The sensitive frequency band influence parameters include the measured value of the battery casing surface temperature, the measured value of the battery casing outer surface shape change, and the measured value of the cell clamping force. Calculate the frequency band influence index based on the sensitive frequency band influence parameters, and determine whether the internal short circuit sensitive frequency band needs to be adjusted based on the frequency band influence index. In this embodiment, it should be specifically explained that the steps for obtaining the frequency band impact index are as follows: Obtain the surface temperature measurement value of the battery casing, and calculate the surface thermal disturbance coefficient based on the surface temperature measurement value of the battery casing. Obtain the measured values ​​of the external surface type variables of the battery casing, and calculate the electrode breathing coupling coefficient based on the measured values ​​of the external surface type variables of the battery casing. Obtain the measured value of the clamping force of the battery cell clamping structure, and calculate the clamping force offset coefficient based on the measured value of the clamping force of the battery cell clamping structure; The surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient are normalized. Specifically, in this embodiment, vector normalization can also be used to normalize these coefficients. Specifically, the three influence coefficients are combined into a three-dimensional vector. The norm value is obtained by calculating the square root of the sum of the squares of each component of this vector. Each influence coefficient is then divided by this norm value to achieve normalization. The purpose of this vector normalization is to ensure that the surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient have a uniform order of magnitude and scale standard when calculating the frequency band influence index. This avoids calculation bias or local amplification effects that may be introduced when the original numerical ranges of different parameters differ significantly, ensuring the stability and reliability of the frequency band influence index calculation process. Since vector normalization is an existing technology, its mathematical calculation process has been publicly disclosed and widely used in fields such as multi-parameter evaluation, signal analysis, and pattern recognition. Therefore, this embodiment will not elaborate on its specific algorithm steps. The frequency band influence index is calculated based on the normalized surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient. The specific steps for obtaining the index are as follows: ; In the formula, Expressed as the frequency band impact index, This represents the normalized surface thermal perturbation coefficient. The greater the non-uniformity of the temperature distribution on the battery casing surface, the more significant the impact of the corresponding thermal perturbation on the sensitive frequency band. When the surface thermal perturbation coefficient increases, it indicates that the battery exhibits strong spatial temperature gradient fluctuations within a preset time window, which may lead to a shift in the frequency response of related electrochemical processes, and the frequency band influence index increases accordingly. This represents the normalized electrode breathing coupling coefficient. The more significant the periodic deformation of the battery casing during charge-discharge cycles, the stronger the mechanical perturbation experienced by its internal electrode structure, making it more susceptible to shifts in the frequency response characteristics of specific electrochemical processes. As the electrode breathing coupling coefficient increases, it indicates a more pronounced change in the magnitude of casing expansion and contraction relative to the steady-state deformation level, resulting in a greater impact on perturbations in sensitive frequency bands, and consequently, a higher frequency band influence index. This represents the normalized clamping force offset coefficient. The more pronounced the clamping force fluctuations during cell clamping, the stronger the disturbance to the internal electrode stacking state and contact interface pressure, thus making it more prone to changes in the frequency response characteristics of electrochemical processes related to internal short circuits. When the clamping force offset coefficient increases, it indicates that the clamping force offset at different measurement points within the time window is more significant compared to the steady-state clamping force level, leading to increased sensitivity of the sensitive frequency band to mechanical disturbances, and consequently, a larger frequency band influence index. , , These are expressed as the normalized surface thermal disturbance coefficient, the normalized electrode breathing coupling coefficient, and the normalized clamping force offset coefficient, and... , , , Obtained through the analytic hierarchy process, for example , , The values ​​can be 0.3, 0.4, or 0.3. The Analytic Hierarchy Process (AHP) is a commonly used multi-index decision-making method. This method constructs a hierarchical model containing a target layer, a criterion layer, and an index layer. It compares the importance of different influencing factors in pairs, forming a judgment matrix. By obtaining the eigenvectors of the judgment matrix and performing consistency checks, it obtains weight coefficients reflecting the relative importance of each influencing factor. The AHP can obtain stable, reasonable, and mathematically based weight allocation results based on the relative relationships between influencing factors without requiring external empirical parameters. Since the AHP is existing technology, its construction process and calculation steps have been publicly disclosed and widely used in decision analysis, system evaluation, and signal processing. Therefore, this embodiment will not elaborate on its specific mathematical derivation process.

[0023] In this embodiment, it should be specifically explained that the steps for obtaining the surface thermal disturbance coefficient are as follows: The temperature measurement values ​​of multiple temperature sampling points on the outer surface of the battery casing are obtained. The temperature measurement values ​​include the surface temperature of the battery casing surface at different spatial locations at the same sampling time, forming a surface temperature matrix to characterize the current heat distribution state. It should be noted that the temperature measurements at multiple temperature sampling points on the outer surface of the battery casing can be obtained through multiple thermocouples or infrared point-type temperature sensors.

[0024] It should be noted that multiple temperature sensors can be evenly distributed on the outer surface of the battery casing according to a preset spatial spacing to obtain the surface temperature of different spatial locations of the casing.

[0025] For any two spatially adjacent temperature sampling points in the surface temperature matrix, calculate the absolute value of their temperature difference, and sum the absolute values ​​of the temperature differences of all adjacent temperature sampling points to obtain the total spatial temperature difference at the current moment. Divide the total spatial temperature difference by the number of temperature sampling points to obtain the dynamic thermal gradient value at the current moment, which is used to characterize the degree of non-uniformity of the temperature distribution on the surface of the battery casing in space. Within a preset time window, the dynamic thermal gradient values ​​at all sampling times are obtained. The maximum and minimum values ​​of the dynamic thermal gradient values ​​within the preset time window are selected, and the difference between the maximum and minimum values ​​is calculated to obtain the thermal disturbance amplitude. The average dynamic thermal gradient value is calculated by averaging the dynamic thermal gradient values ​​at all sampling times within the preset time window. The thermal disturbance amplitude is divided by the average dynamic thermal gradient value to obtain the surface thermal disturbance coefficient, which is used as a thermal disturbance quantification index for evaluating the frequency band influence index.

[0026] In this embodiment, it should be specifically explained that the steps for obtaining the electrode breathing coupling coefficient are as follows: Within a preset time window, the deformation measurement values ​​of multiple deformation measurement points placed on the outer surface of the battery casing at each sampling time are obtained. These values ​​can be obtained by displacement sensors or strain gauges, forming a deformation measurement matrix to characterize the changes of the casing over time during the battery's breathing process. It should be noted that the rows of the deformation measurement matrix correspond to different sampling times, and the columns correspond to different deformation measurement points. It should be noted that multiple displacement measurement units can be arranged on the outer surface of the battery casing at a preset spacing to record the casing deformation at each location at the sampling time.

[0027] For the deformation measurement matrix, at each sampling time, the arithmetic mean of the deformation measurement values ​​of all measurement points at that sampling time is taken to obtain the equivalent deformation value at that sampling time; the equivalent deformation values ​​of all sampling times are obtained within a preset time window, and the maximum and minimum values ​​of the equivalent deformation values ​​within the time window are selected respectively. The difference between the maximum and minimum values ​​is calculated to obtain the battery breathing amplitude. The mean value of the equivalent deformation at all sampling times within the preset time window is calculated to obtain the baseline deformation, which is used to characterize the average shell deformation level of the battery within the window. Dividing the battery breathing amplitude by the reference deformation yields the electrode breathing coupling coefficient, which characterizes the degree of structural disturbance induced by the internal electrodes during charging and discharging. The electrode breathing coupling coefficient increases with the increase of the casing deformation amplitude.

[0028] In this embodiment, it should be specifically explained that the step of obtaining the clamping force offset coefficient is as follows: At each sampling moment within a preset time window, the clamping force measurement values ​​of multiple clamping force measurement points deployed in the battery cell clamping structure are acquired to form a clamping force measurement matrix for characterizing the stress state of the battery cell. This matrix can be acquired through a pressure sensor or a strain-type force-bearing element. It should be noted that the clamping force measurement matrix in this embodiment is a two-dimensional matrix formed by arranging the clamping force measurement values ​​obtained according to the sampling period within a preset time window in chronological order and measurement point number. Its rows represent different sampling times, and its columns represent clamping force measurement points at different spatial locations.

[0029] It should be noted that multiple pressure sensors can be installed at different locations on the cell clamping structure according to a preset layout strategy to collect clamping force measurement values ​​at each location of the clamping structure.

[0030] The average value of the clamping force measured at all sampling times and all clamping force measurement points in the clamping force measurement matrix is ​​calculated to obtain the steady-state reference value of the clamping force, which is used to characterize the average clamping force level of the entire battery cell within a preset time window. For each clamping force measurement value in the clamping force measurement matrix, calculate the absolute value of the difference between the measurement value and the steady-state reference value of the clamping force, and sum all the absolute values ​​of the difference and divide by the total number of clamping force measurements to obtain the clamping force disturbance, which is used to characterize the average offset of all measurement points in the time and space dimensions. Dividing the clamping force disturbance by the clamping force steady-state reference value yields the clamping force offset coefficient, which serves as a quantitative index for clamping force disturbance used to evaluate the frequency band impact index. The clamping force offset coefficient increases with the increase of the clamping force disturbance.

[0031] In this embodiment, it should be specifically explained that the step of determining whether the short-circuit sensitive frequency band needs to be adjusted based on the frequency band impact index is as follows: The frequency band impact index is compared with the frequency band impact threshold. If the frequency band impact index is greater than or equal to the frequency band impact threshold, the internal short-circuit sensitive frequency band is determined to require adjustment; if the frequency band impact index is less than the frequency band impact threshold, the internal short-circuit sensitive frequency band is determined not to require adjustment. The frequency band impact threshold is obtained through an adaptive threshold method, a commonly used method that dynamically generates a judgment threshold based on data characteristics. This method is based on the historical data distribution, trend, or statistical characteristics within the current evaluation period. By calculating quantifiable indicators such as the mean, amplitude of change, degree of fluctuation, or distribution characteristics of the data, it automatically generates a judgment threshold that adapts to the current data state, thereby avoiding the failure problem that may occur with fixed thresholds under different operating conditions. Since the adaptive threshold method is existing technology, its specific calculation process will not be described in detail in this embodiment.

[0032] Step 4: If it is determined that the internal short-circuit sensitive frequency band needs to be adjusted, the start frequency and end frequency of the internal short-circuit sensitive frequency band are adjusted according to the frequency band influence index to obtain the adjusted internal short-circuit sensitive frequency band. Traverse all internal short-circuit sensitive frequency bands in the internal short-circuit sensitive frequency band set to obtain the adjusted internal short-circuit sensitive frequency band set. In this embodiment, it should be specifically explained that the steps for obtaining the adjusted set of internal short-circuit sensitive frequency bands are as follows: For each sensitive frequency band in the set of sensitive frequency bands for internal short circuits, obtain the start frequency and end frequency of the sensitive frequency band, and record them as the initial start frequency and initial end frequency. At the same time, obtain the frequency band influence index and the corresponding frequency band influence threshold of the sensitive frequency band. For each internal short-circuit sensitive frequency band, the frequency band influence index is divided by the frequency band influence threshold to obtain the adjustment coefficient. When the frequency band influence index is greater than or equal to the frequency band influence threshold, the adjustment coefficient is greater than or equal to 1. When the frequency band influence index is less than the frequency band influence threshold, the adjustment coefficient is less than 1. This is used to determine the magnitude of the sensitive frequency band boundary adjustment. When the band influence index is greater than or equal to the band influence threshold, it indicates that the sensitive band needs to be extended. A preset frequency step size is obtained. The product of the adjustment coefficient and the preset frequency step size is subtracted from the initial starting frequency to obtain the actual starting frequency. The product of the adjustment coefficient and the preset frequency step size is added to the initial ending frequency to obtain the actual ending frequency. This allows the coverage of the sensitive band to expand proportionally to both ends as the band influence index increases. It should be noted that the preset frequency step size in this embodiment is used to adjust the start and end frequencies of the sensitive frequency band. Its value can be set according to the minimum frequency interval of the test frequency set used for impedance testing in step 2. Specifically, the preset test frequencies in step 2 are usually arranged according to a fixed frequency interval. This can be achieved by sorting all test frequencies from smallest to largest, calculating the difference between any two adjacent test frequencies, and selecting the minimum difference as the frequency resolution of the test frequency. By using this minimum frequency interval as the preset frequency step size or an integer multiple thereof, it can be ensured that the adjusted sensitive frequency band boundary still falls within the measurable range of the test frequency, avoiding situations where actual measurement is impossible or the frequency band is crossed, thereby ensuring that the frequency band boundary adjustment has feasibility and physical significance.

[0033] When the band influence index is less than the band influence threshold, it indicates that the coverage of the current sensitive band is too large and the boundary needs to be shrunken. The actual starting frequency is obtained by adding 1 to the initial starting frequency, subtracting the adjustment coefficient, and multiplying it with the preset frequency step size. The actual ending frequency is obtained by subtracting 1 to the initial ending frequency, subtracting the adjustment coefficient, and multiplying it with the preset frequency step size. This makes the band influence index smaller, and the left and right boundaries of the sensitive band shrink inward proportionally. When the direction of the initial frequency adjustment is different from the direction of the final frequency adjustment, that is, when the actual starting frequency is greater than or equal to the actual ending frequency during the expansion or contraction process, the sensitive frequency band is marked as an invalid frequency band and removed from the sensitive frequency band set to avoid frequency reversal or negative bandwidth. The adjusted start and end frequencies of all sensitive frequency bands are recombined in the original frequency band order to obtain the adjusted set of internal short-circuit sensitive frequency bands.

[0034] Step 5: Obtain the representative sensitivity of each internal short-circuit sensitive frequency band in the adjusted internal short-circuit sensitive frequency band set. It should be noted that the representative sensitivity is the average value of the sensitivity of all frequency points in the sensitive frequency band. Construct the frequency weighting function based on the representative sensitivity of the internal short-circuit sensitive frequency band. In this embodiment, it should be specifically explained that the steps for obtaining the frequency weighting function are as follows: The representative sensitivity of each internal short-circuit sensitive frequency band is normalized to obtain the normalized representative sensitivity of each internal short-circuit sensitive frequency band. The reciprocal of each normalized representative sensitivity is calculated. The normalized representative sensitivity is added to the corresponding reciprocal to obtain the segmented weighting coefficient of the internal short-circuit sensitive frequency band. For each test frequency point within the test frequency range, determine whether the test frequency point falls within the frequency range of any internal short-circuit sensitive frequency band. If it falls within an internal short-circuit sensitive frequency band, set the weight of the test frequency point to the segmented weighting coefficient corresponding to that internal short-circuit sensitive frequency band. If it does not belong to any sensitive frequency band, set the weight of the test frequency point to the minimum value among all segmented weighting coefficients. Obtain the frequency value and its corresponding weight for each test frequency point, form a "frequency-weight" mapping pair between the test frequency points and their weights, and arrange all mapping pairs in ascending order of frequency value; Using all the arranged "frequency-weight" mapping pairs as input, a frequency weighting function covering the test frequency range is constructed. The frequency weighting function consists of each test frequency point and its corresponding weight, and is used to perform segmented weighting of the power spectrum of the pseudo-random excitation signal.

[0035] Step 6: Obtain the minimum and maximum frequencies of the adjusted internal short-circuit sensitive frequency band set, determine the clock frequency and sequence length of the pseudo-random binary sequence based on the minimum and maximum frequencies, and generate a pseudo-random excitation signal through the pseudo-random binary sequence. It should be noted that determining the clock frequency and sequence length of the pseudo-random binary sequence based on the minimum and maximum frequencies, and generating a pseudo-random excitation signal through the pseudo-random binary sequence, is existing technology, and this embodiment will not provide a detailed description of its specific steps.

[0036] A pseudo-random binary sequence is a binary sequence generated by a linear shift register under the control of a specific feedback polynomial. Its values ​​include only logic high and logic low levels. The sequence statistically satisfies the characteristics of zero mean, strong randomness, and controllable periodicity.

[0037] The pseudo-random excitation signal refers to the current disturbance signal generated based on the pseudo-random binary sequence. By mapping the binary output of the pseudo-random binary sequence into a small amplitude current pulse that can be applied to the battery terminal, a broadband excitation covering both sensitive and non-sensitive frequency bands can be formed in the frequency domain, so that the battery can respond simultaneously on multiple frequency components, thereby satisfying the frequency excitation conditions required for online impedance calculation.

[0038] By selecting the clock frequency and sequence length of the pseudo-random binary sequence based on the minimum and maximum frequencies of the set of sensitive internal short-circuit frequencies, the pseudo-random excitation signal generated by the sequence can cover the entire sensitive frequency range in the frequency domain. This ensures that the impedance characteristics within the sensitive frequency band can be effectively excited and observed, avoiding the loss of response at key frequency points due to insufficient excitation bandwidth, and improving the accuracy and completeness of impedance measurement in the sensitive frequency band.

[0039] Step 7: Optimize and adjust the pseudo-random excitation signal according to the frequency weighting function to obtain the optimized pseudo-random excitation signal; In this embodiment, it should be specifically explained that the steps for obtaining the optimized pseudo-random excitation signal are as follows: The pseudo-random excitation signal is subjected to a fast Fourier transform to obtain the spectral amplitude and spectral phase of the pseudo-random excitation signal at each test frequency point, which are used as input data for spectrum optimization. For each test frequency point within the test frequency range, obtain the weight corresponding to that test frequency point in the frequency weighting function, and multiply the spectral amplitude of that test frequency point by the corresponding weight to obtain the weighted spectral amplitude. The spectral phase of each test frequency point remains unchanged. The weighted and adjusted spectral amplitudes of all test frequency points are recombined with the original spectral phases in ascending order of frequency to form the spectral data of the optimized pseudo-random excitation signal. An inverse fast Fourier transform is performed on the optimized spectral data to convert the frequency domain data into a time domain excitation waveform, resulting in an optimized pseudo-random excitation signal to be applied to the battery.

[0040] It should be specifically noted that Fast Fourier Transform (FFT) refers to an algorithmic processing method that converts a time-domain pseudo-random excitation signal into frequency-domain data. By segmenting and rearranging the discrete time-domain sampling sequence and performing butterfly operations, the spectral amplitude and spectral phase of the signal at each test frequency point can be efficiently calculated to obtain the frequency domain characteristics of the signal. The spectral amplitude and spectral phase obtained through FFT are existing technologies, and this embodiment will not describe their specific steps in detail.

[0041] The inverse fast Fourier transform (IFFT) is an algorithmic processing method that converts frequency domain data after spectrum optimization back into a time-domain excitation waveform. By performing an inverse butterfly operation on the spectral amplitude and spectral phase of each test frequency point, the time-domain excitation signal corresponding to the input frequency domain data can be recovered. This signal is used to generate the final time-domain current disturbance signal that can be applied to the battery terminals. The conversion of frequency domain data into a time-domain excitation waveform through IFFT is an existing technology, and this embodiment will not describe its specific steps in detail.

[0042] Step 8: Apply a small amplitude current disturbance to the battery using the optimized pseudo-random excitation signal, collect the battery's response signal, which includes voltage response signal and current response signal, and perform frequency domain analysis on the optimized pseudo-random excitation signal and response signal to calculate the complex impedance at each test frequency point; It should be noted that performing frequency domain analysis on the optimized pseudo-random excitation signal and response signal to calculate the complex impedance at each test frequency point is existing technology, and this embodiment will not provide a detailed description of its specific steps.

[0043] Step 9: Calculate the difference between the complex impedance at each frequency point and the reference impedance corresponding to the normal state to obtain the impedance difference at each frequency point. Determine whether the current battery has an internal short circuit based on the impedance difference at each frequency point.

[0044] In this embodiment, it should be specifically explained that the step of determining whether the current battery has an internal short circuit based on the impedance difference at each frequency point is as follows: The impedance difference at each frequency point is compared with the impedance threshold. The number of frequency points with an impedance difference greater than or equal to the impedance threshold is selected and recorded as the number of internal short-circuit frequency points. If the number of internal short-circuit frequency points is greater than or equal to the frequency point number threshold, it is determined that the current battery has an internal short circuit. If the number of internal short-circuit frequency points is less than the frequency point number threshold, it is determined that the current battery does not have an internal short circuit. It should be noted that the impedance threshold is determined by calculating the statistical upper bound of the impedance difference in the normal state (such as the mean plus the standard deviation) based on the difference distribution of the impedance data in the normal state at each test frequency point in step 1. The frequency point number threshold is determined by selecting the statistical quantile value (such as the median or the 75th percentile value) of the distribution based on the distribution of the number of typical sensitive frequency points in all internal short-circuit sensitive frequency bands obtained in step 2.

[0045] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0046] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A battery EIS measurement method optimized based on internal short-circuit sensitive frequency band, characterized in that, Includes the following steps; Step 1: Construct normal state samples and several samples with different internal short-circuit resistance values ​​for the target battery model, perform conventional electrochemical impedance spectroscopy tests, and obtain impedance data corresponding to the normal state and impedance data corresponding to different internal short-circuit states. Step 2: According to the preset test frequency, for each different internal short circuit state, obtain the impedance data corresponding to the normal state and the impedance data corresponding to the internal short circuit state at each test frequency, and calculate the relative deviation value of the two for the same test frequency point. Obtain the relative deviation value of all test frequency points, and obtain the internal short circuit sensitive frequency band based on the relative deviation value of all test frequency points. Summarize the internal short circuit sensitive frequency bands of each different internal short circuit state to obtain the internal short circuit sensitive frequency band set. Step 3: Obtain the sensitive frequency band influence parameters of the battery corresponding to each internal short circuit sensitive frequency band. The sensitive frequency band influence parameters include the measured value of the battery casing surface temperature, the measured value of the battery casing outer surface shape change, and the measured value of the cell clamping force. Calculate the frequency band influence index based on the sensitive frequency band influence parameters, and determine whether the internal short circuit sensitive frequency band needs to be adjusted based on the frequency band influence index. Step 4: If it is determined that the internal short-circuit sensitive frequency band needs to be adjusted, the start frequency and end frequency of the internal short-circuit sensitive frequency band are adjusted according to the frequency band influence index to obtain the adjusted internal short-circuit sensitive frequency band. Traverse all internal short-circuit sensitive frequency bands in the internal short-circuit sensitive frequency band set to obtain the adjusted internal short-circuit sensitive frequency band set. Step 5: Obtain the representative sensitivity of each internal short-circuit sensitive frequency band in the adjusted internal short-circuit sensitive frequency band set, and construct the frequency weighting function based on the representative sensitivity of the internal short-circuit sensitive frequency bands; Step 6: Obtain the minimum and maximum frequencies of the adjusted internal short-circuit sensitive frequency band set, determine the clock frequency and sequence length of the pseudo-random binary sequence based on the minimum and maximum frequencies, and generate a pseudo-random excitation signal through the pseudo-random binary sequence. Step 7: Optimize and adjust the pseudo-random excitation signal according to the frequency weighting function to obtain the optimized pseudo-random excitation signal; Step 8: Apply a small amplitude current disturbance to the battery using the optimized pseudo-random excitation signal, collect the battery's response signal, and perform frequency domain analysis on the optimized pseudo-random excitation signal and response signal to calculate the complex impedance at each test frequency point; Step 9: Calculate the difference between the complex impedance at each frequency point and the reference impedance corresponding to the normal state to obtain the impedance difference at each frequency point. Determine whether the current battery has an internal short circuit based on the impedance difference at each frequency point.

2. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 1, characterized in that: The steps for obtaining the internal short-circuit sensitive frequency band are as follows: For the same frequency point, obtain the impedance value under normal state and the impedance value under internal short circuit state. Calculate the absolute difference between the impedance value under internal short circuit state and the impedance value under normal state, and divide it by the absolute value of the impedance value under normal state to obtain the relative deviation value of that frequency point, which is recorded as the sensitivity of the frequency point. The sensitivity of all frequency points is obtained, and the sensitivity is compared with the sensitivity threshold. All frequency points that are greater than or equal to the sensitivity threshold are filtered out, and frequency points whose adjacent frequency difference is less than the preset frequency interval are grouped into the same frequency point set. For each set of frequency points, obtain the minimum and maximum frequencies in the set, and construct the internal short-circuit sensitive frequency band from the minimum and maximum frequencies.

3. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 1, characterized in that, The steps for obtaining the frequency band impact index are as follows: Obtain the surface temperature measurement value of the battery casing, and calculate the surface thermal disturbance coefficient based on the surface temperature measurement value of the battery casing. Obtain the measured values ​​of the external surface type variables of the battery casing, and calculate the electrode breathing coupling coefficient based on the measured values ​​of the external surface type variables of the battery casing. Obtain the measured value of the clamping force of the battery cell clamping structure, and calculate the clamping force offset coefficient based on the measured value of the clamping force of the battery cell clamping structure; The surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient are normalized, and the frequency band influence index is calculated based on the normalized surface thermal disturbance coefficient, electrode breathing coupling coefficient, and clamping force offset coefficient.

4. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 3, characterized in that, The steps for obtaining the surface thermal disturbance coefficient are as follows: The temperature measurement values ​​of multiple temperature sampling points on the outer surface of the battery casing are obtained. The temperature measurement values ​​include the surface temperature of the battery casing surface at different spatial locations at the same sampling time, forming a surface temperature matrix. For any two spatially adjacent temperature sampling points in the surface temperature matrix, calculate the absolute value of their temperature difference, and sum the absolute values ​​of the temperature differences of all adjacent temperature sampling points to obtain the total spatial temperature difference at the current moment. Divide the total spatial temperature difference by the number of temperature sampling points to obtain the dynamic thermal gradient value at the current moment; Within a preset time window, the dynamic thermal gradient values ​​at all sampling times are obtained. The maximum and minimum values ​​of the dynamic thermal gradient values ​​within the preset time window are selected, and the difference between the maximum and minimum values ​​is calculated to obtain the thermal disturbance amplitude. The average dynamic thermal gradient value is calculated by averaging the dynamic thermal gradient values ​​at all sampling times within the preset time window. The surface thermal disturbance coefficient is obtained by dividing the thermal disturbance amplitude by the average dynamic thermal gradient value.

5. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 3, characterized in that: The steps for obtaining the electrode breathing coupling coefficient are as follows: Within a preset time window, the deformation measurement values ​​of multiple deformation measurement points deployed on the outer surface of the battery casing at each sampling time are obtained to form a deformation measurement matrix; For the deformation measurement matrix, at each sampling time, the arithmetic mean of the deformation measurement values ​​of all measurement points at that sampling time is taken to obtain the equivalent deformation value at that sampling time; The equivalent deformation values ​​of all sampling times are obtained within a preset time window. The maximum and minimum values ​​of the equivalent deformation values ​​within the time window are selected respectively. The difference between the maximum and minimum values ​​is calculated to obtain the battery breathing amplitude. The mean value of the equivalent deformation variables at all sampling times within the preset time window is calculated to obtain the baseline deformation variable; Divide the battery breathing amplitude by the reference deformation to obtain the electrode breathing coupling coefficient.

6. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 3, characterized in that: The steps for obtaining the clamping force offset coefficient are as follows: At each sampling moment within a preset time window, the clamping force measurement values ​​of multiple clamping force measurement points deployed in the cell clamping structure are acquired to form a clamping force measurement matrix. The average value of the clamping force is calculated by averaging the clamping force measurements at all sampling times and all clamping force measurement points in the clamping force measurement matrix to obtain the steady-state reference value of the clamping force. For each clamping force measurement value in the clamping force measurement matrix, calculate the absolute value of the difference between the measurement value and the steady-state reference value of the clamping force, and sum all the absolute values ​​of the difference and divide by the total number of clamping force measurements to obtain the clamping force disturbance. Divide the clamping force disturbance by the steady-state reference value of the clamping force to obtain the clamping force offset coefficient.

7. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 1, characterized in that: The steps for determining whether short-circuit sensitive frequency bands need adjustment based on the frequency band impact index are as follows: The frequency band impact index is compared with the frequency band impact threshold. If the frequency band impact index is greater than or equal to the frequency band impact threshold, it is determined that the internal short-circuit sensitive frequency band needs to be adjusted; if the frequency band impact index is less than the frequency band impact threshold, it is determined that the internal short-circuit sensitive frequency band does not need to be adjusted.

8. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 7, characterized in that: The steps for obtaining the adjusted set of internal short-circuit sensitive frequency bands are as follows: For each sensitive frequency band in the set of sensitive frequency bands for internal short circuits, obtain the start frequency and end frequency of the sensitive frequency band, and record them as the initial start frequency and initial end frequency. At the same time, obtain the frequency band influence index and the corresponding frequency band influence threshold of the sensitive frequency band. For each internal short-circuit sensitive frequency band, the frequency band influence index is divided by the frequency band influence threshold to obtain the adjustment coefficient; When the frequency band influence index is greater than or equal to the frequency band influence threshold, obtain the preset frequency step size, subtract the product of the adjustment coefficient and the preset frequency step size from the initial starting frequency to obtain the actual starting frequency; add the product of the adjustment coefficient and the preset frequency step size to the initial ending frequency to obtain the actual ending frequency. When the frequency band influence index is less than the frequency band influence threshold, the product of the initial starting frequency plus 1 minus the adjustment coefficient and the preset frequency step size is used to obtain the actual starting frequency; the product of the initial ending frequency minus 1 minus the adjustment coefficient and the preset frequency step size is used to obtain the actual ending frequency. When the direction of the initial frequency adjustment is different from the direction of the final frequency adjustment, that is, when the actual initial frequency is greater than or equal to the actual final frequency during the expansion or contraction process, the sensitive frequency band is marked as an invalid frequency band and removed from the set of sensitive frequency bands. The adjusted start and end frequencies of all sensitive frequency bands are recombined in the original frequency band order to obtain the adjusted set of internal short-circuit sensitive frequency bands.

9. The battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 1, characterized in that: The steps for obtaining the frequency weighting function are as follows: The representative sensitivity of each internal short-circuit sensitive frequency band is normalized to obtain the normalized representative sensitivity of each internal short-circuit sensitive frequency band. The reciprocal of each normalized representative sensitivity is calculated. The normalized representative sensitivity is added to the corresponding reciprocal to obtain the segmented weighting coefficient of the internal short-circuit sensitive frequency band. For each test frequency point within the test frequency range, determine whether the test frequency point falls within the frequency range of any internal short-circuit sensitive frequency band. If it falls within an internal short-circuit sensitive frequency band, set the weight of the test frequency point to the segmented weighting coefficient corresponding to that internal short-circuit sensitive frequency band. If it does not belong to any sensitive frequency band, set the weight of the test frequency point to the minimum value among all segmented weighting coefficients. Obtain the frequency value and its corresponding weight for each test frequency point, form a "frequency-weight" mapping pair between the test frequency points and their weights, and arrange all mapping pairs in ascending order of frequency value; Using all the sorted "frequency-weight" mapping pairs as input, construct a frequency weighting function that covers the test frequency range.

10. A battery EIS measurement method based on internal short-circuit sensitive frequency band optimization according to claim 1, characterized in that: The step of determining whether the current battery has an internal short circuit based on the impedance difference at each frequency point is as follows: The impedance difference at each frequency point is compared with the impedance threshold. The number of frequency points with an impedance difference greater than or equal to the impedance threshold is selected and recorded as the number of internal short circuit frequency points. If the number of internal short circuit frequency points is greater than or equal to the frequency point quantity threshold, it is determined that the current battery has an internal short circuit. If the number of internal short circuit frequency points is less than the frequency point quantity threshold, it is determined that the current battery does not have an internal short circuit.

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