Inspection method and system for consistency of single storage battery pack of electric power system

Abstract

The invention provides a method and a system for inspecting the consistency of single storage battery packs of a power system, and the method comprises the steps: carrying out the AC impedance test of a to-be-tested battery, and obtaining the ohm internal resistance value of the battery through fitting analysis; calculating the local reachable density of the battery ohm internal resistance value composition sample by using a local abnormal factor algorithm; and determining a local abnormal factor according to the determined local reachable density, and outputting a result, thereby performing consistency evaluation on the sample. According to the method, the technical problems that the current available capacity and residual life of the storage battery cannot be accurately evaluated by adopting time domain data, regular capacity checking operation is still needed, and the accuracy and effectiveness of storage battery evaluation are low are solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy detection and evaluation, and in particular to a method and system for inspecting the consistency of battery cells in a power system. Background Art

[0002] The battery pack is the power source of the substation and is responsible for providing electric energy for the operation of the power system. As the core component of the DC system, its stable and reliable operation provides safety protection for the operation of the substation. The maintenance of the battery pack has always been a problem that troubles the operation and maintenance personnel due to the large maintenance workload and low degree of automation, especially the reliance on mobile maintenance equipment for manual capacity verification during capacity verification, the existence of many and heavy equipment, numerous wiring, complex operations, and high risks.

[0003] At present, in the process of digital operation and maintenance of batteries, the floating charge voltage, floating charge current, and internal resistance collected in time series are used to monitor and evaluate batteries, replacing repetitive and tedious manual labor. It not only saves a lot of human resources and reduces costs for grassroots operation and maintenance units, but also has a higher degree of precision in intelligent systems, which has become the driving force for the rapid development of grassroots units. However, it is difficult to achieve a comprehensive evaluation of the battery status with the floating charge voltage, floating charge current, internal resistance and other data collected in time series. By introducing the frequency domain-based electrochemical impedance spectroscopy measurement, the evaluation dimension of the battery will be greatly enriched. However, these historical data lack algorithm-based data analysis and effective and convenient equipment operation and maintenance experience guidance, and cannot provide effective guidance for the current and future DC power supply equipment operation and inspection work, resulting in the increasingly prominent contradiction between heavy statistical reports and the lack of operation and maintenance personnel. Therefore, it is of great research significance to develop a remote nuclear capacity health monitoring and analysis algorithm for lead-acid batteries based on electrochemical impedance spectroscopy, floating charge voltage, floating charge current, and internal resistance.

[0004] The Chinese invention patent application document "Energy Storage Battery SOC Determination Method and System Based on Electrochemical Impedance Spectroscopy" with publication number CN117572258A, the existing method includes: using forward AC sinusoidal excitation sweep measurement to obtain the electrochemical impedance spectrum in the frequency domain under forward excitation; using reverse AC sinusoidal excitation sweep measurement to obtain the electrochemical impedance spectrum in the frequency domain under reverse excitation; comparing the results of the electrochemical impedance spectrum in the frequency domain under forward excitation and the electrochemical impedance spectrum in the frequency domain under reverse excitation, and screening out the influencing factors through Pearson correlation calculation; establishing a mathematical model to express the relationship between the influencing factors and the SOC of the energy storage battery; inputting the energy storage battery data collected in real time into the mathematical model to obtain the corresponding SOC. However, in the aforementioned prior art, the electrochemical impedance spectrum characteristics of lead-acid batteries are different from those of energy storage batteries, especially lithium-ion batteries. At the same time, the deterioration of a single battery is often earlier than the deterioration of the entire group. At the same time, the deterioration of a single battery will also accelerate the deterioration of the entire group of batteries. Therefore, the health evaluation of a single battery is often more effective.

[0005] Traditional battery health status assessment mainly relies on the floating charge voltage, floating charge current, and internal resistance collected in time series. Currently, it is impossible to accurately assess the current available capacity and remaining life of the battery relying solely on these time domain data. Regular capacity verification is still required to determine the available capacity of the battery.

[0006] The existing invention patent application document "A method for estimating the health status of a fuel cell based on electrochemical impedance spectroscopy" with publication number CN118630264A, the existing method includes: conducting an electrochemical impedance spectroscopy experiment on the fuel cell and recording the response data; drawing an impedance spectrum diagram, fitting an improved Randles equivalent circuit model according to the impedance spectrum diagram, and completing parameter identification of the model based on the nonlinear least squares method; using the polarization resistance and electrochemical active surface area of ​​the fuel cell to quantify the health status evaluation index. Among them, the local anomaly factor LOF algorithm is applied to the SOH value of the fuel cell stack for anomaly detection. The data set H = {h1, h2, ..., hn} is composed of the SOH values ​​h1~hn of n fuel cell stacks. For the SOH value hi of the ith fuel cell stack, the Euclidean distance with its k nearest neighboring fuel cell stacks is calculated, and then the local density is calculated. For each fuel cell stack SOH value hi, the ratio of the local density of its neighboring fuel cell stacks to its own local density is calculated to measure the degree of abnormality of the fuel cell stack. The larger the ratio, the greater the difference between the fuel cell stack and its neighboring fuel cell stacks. The ratio of each local density is summed and then divided by the size of the neighborhood set |Nk(hi)| to obtain the local anomaly factor. By analyzing the SOH values ​​of different fuel cell stacks with the local anomaly factor LOF algorithm and giving timely feedback, it is possible to achieve timely detection and processing of abnormal fuel cell stacks. However, the existing scheme focuses on application to fuel cells, which have huge differences in electrochemical properties from lead-acid batteries. In addition, fuel cells are primary batteries that use hydrogen and other gases as the main principle to generate electricity, while lead-acid batteries are secondary batteries that are mainly charged by an external power source and discharged under appropriate conditions. The definitions of battery health status for the two types of batteries are also completely different, and the existing scheme is difficult to be directly used to evaluate lead-acid batteries.

[0007] In summary, the existing technology has the technical problem that the current available capacity and remaining life of the battery cannot be accurately evaluated by using time domain data, and regular capacity verification operations are still required, resulting in low accuracy and effectiveness of battery evaluation. Summary of the invention

[0008] The technical problem to be solved by the present invention is: how to solve the technical problem that the existing technology cannot accurately evaluate the current available capacity and remaining life of the battery using time domain data, and still needs to rely on regular capacity verification operations, resulting in low accuracy and effectiveness of battery evaluation.

[0009] The present invention adopts the following technical solution to solve the above technical problem: A method for inspecting the consistency of battery pack monomers in a power system comprises:

[0010] S1. Perform AC impedance test on the battery cell to be tested, and obtain the solution resistance Rs, charge transfer resistance Rct, double layer capacitance Cdl, and Warburg impedance W of the battery cell to be tested through fitting analysis, and obtain SOH based on electrochemical impedance spectroscopy. EIS Values ​​are used as test samples;

[0011] S2. Calculate the local reachable density of the test sample using the local anomaly factor algorithm;

[0012] S3. Determine the value of the local anomaly factor according to the local reachable density;

[0013] S4. According to the value of the local abnormal factor, the consistency evaluation of the test sample is performed to obtain the consistency evaluation result of the battery cell to be tested.

[0014] The present invention innovatively introduces the concept of electrochemical impedance spectroscopy, expands the dimension of battery monitoring data to multiple frequency domains, and completes the battery health assessment by extracting feature quantities related to battery consistency based on the electrochemical impedance spectroscopy, which can significantly improve the accuracy and effectiveness of battery assessment.

[0015] In a more specific technical solution, in S1, an equivalent circuit of the battery cell to be tested is established, and a fitting analysis is performed using ZView software to obtain the solution resistance Rs, charge transfer resistance Rct, double layer capacitance Cdl, and Warburg impedance W of the battery cell to be tested, and the SOH based on the electrochemical impedance spectroscopy is obtained by formula (1): EIS value as the test sample.

[0016]

[0017] Rs0-initial battery cell solution resistance Rs;

[0018] Rct0-initial battery charge transfer resistance Rct;

[0019] Cdl0-initial battery double layer capacitance Cdl;

[0020] W0-initial battery Warburg impedance W;

[0021] - Proportional adjustment parameters

[0022] In a more specific technical solution, S2 includes:

[0023] S21, performing distance calculation on the detection sample;

[0024] S22. Determine the first point SOH EIS (i) The number of samples of all points within the kth distance is obtained, and the kth distance neighborhood N is obtained. k (SOH EIS (i) ), denoted as |N k (SOH EIS (i) )|;

[0025] S23, determine the detection sample and its own k-th distance neighborhood N k (SOH EIS (i) ) is the reachable distance between samples;

[0026] S24. Calculate the local reachable density based on the detection samples and the reachable distance.

[0027] The present invention determines the health status of the tested battery pack by obtaining a reference electrochemical impedance spectrum and a test electrochemical impedance spectrum of the tested battery and calculating the deviation between the electrochemical impedance spectrum and the reference electrochemical impedance spectrum, thereby helping to improve the good power supply and safe and reliable operation of the battery.

[0028] In a more specific technical solution, in S21, the distance calculation is performed using the following logic:

[0029]

[0030] Where, d k (SOH EIS (i) ) indicates the first point (SOH EIS (i) )’s kth distance, SOH EIS (k＝t) Indicates the distance from the first point SOH EIS (i) The k-th farthest data point.

[0031] In a more specific technical solution, in S22, the kth distance neighborhood N k (SOH EIS (i) ) satisfies the following constraints:

[0032] |N k (SOH EIS (i) )|≥k.

[0033] In a more specific technical solution, step S23 uses the following logic to calculate the reachable distance:

[0034] SOHEIS d t (SOH EIS (i) ,SOH EIS (j) )=max(dk(SOH EIS (i) ),‖SOH EIS (i) -SOH EIS (j) ‖)=max(‖

[0035] SOH EIS (i) -SOH EIS (k＝k) ‖,‖SOH EIS (i) -SOH EIS (j) ‖) (2)

[0036] In the formula, the first point SOH EIS (i) With its own domain object SOH EIS (j) The reachable distance is defined as SOH EIS d t (SOH EIS (i) ,SOH EIS (j) ).

[0037] In a more specific technical solution, the following logic is used to determine the point SOH EIS (i) With the point SOH EIS (j) The kth distance of is:

[0038] ‖SOH EIS (i) -SOH EIS (k＝k) ‖

[0039] Among them, ‖SOH EIS (i) -SOH EIS (j) ‖ indicates point SOH EIS (i) With the point SOH EIS (j) The distance between.

[0040] The present invention uses dynamic electrochemical AC impedance as the basis for evaluating the consistency of battery cells between modules. The ohmic internal resistance, charge transfer internal resistance, and Warburg impedance of solid phase diffusion in the electrochemical AC impedance spectrum can reflect the dynamic working conditions of the battery, and reflect the change in the internal resistance of the battery cells and the aging of the battery. By arranging wiring units for the battery cells between battery modules, the battery cells between modules can be tracked and detected.

[0041] In a more specific technical solution, in S24, the local reachable density lrd is calculated using the following logic: k (SOH EIS (i) ):

[0042]

[0043] In the formula, the denominator is the first point SOH EIS (i) The k distances of all sample points in the neighborhood to the first point SOH EIS (i) The average reachable distance.

[0044] In a more specific technical solution, in S3, the following logic is used to express the local abnormality factor lof k (SOH EIS (i) ):

[0045]

[0046] In the formula, the molecular part is SOH EIS (i) The mean of the local reachability density of all samples in the k-distance neighborhood, and the denominator is SOH EIS (i) The local reachable density of .

[0047] The present invention not only uses the dynamic parameters of electrochemical impedance spectroscopy to evaluate the consistency of battery cells between battery modules, but also uses the LOF algorithm to achieve a mathematical description of the degree of abnormality of the differences between cells, making the consistency evaluation more accurate and more adaptable. It can intuitively reflect the differences in battery parameters and also expand the application field of the LOF algorithm.

[0048] The testing process of the present invention does not require large and expensive charging and discharging equipment, but only requires an electrochemical workstation to apply a small amount of current disturbance to the battery, which is not only low-cost but also can avoid safety risks.

[0049] In a more specific technical solution, a power system battery pack single cell consistency inspection system includes:

[0050] The fitting analysis module is used to perform an AC impedance test on the battery cell to be tested, and obtain the ohmic internal resistance value of the battery cell to be tested through fitting analysis as a test sample;

[0051] A reachable density calculation module is used to calculate the local reachable density of the detection sample using a local anomaly factor algorithm, and the reachable density calculation module is connected to the fitting analysis module;

[0052] An abnormal factor processing module is used to determine the value of the local abnormal factor according to the local reachable density, and the abnormal factor processing module is connected to the reachable density calculation module;

[0053] The consistency evaluation module is used to perform consistency evaluation on the test sample according to the value of the local abnormal factor, and obtain the consistency evaluation result of the battery cell to be tested. The consistency evaluation module is connected to the abnormal factor processing module.

[0054] Compared with the prior art, the present invention has the following advantages:

[0055] The present invention innovatively introduces the concept of electrochemical impedance spectroscopy, expands the dimension of battery monitoring data to multiple frequency domains, and completes the battery health assessment by extracting feature quantities related to battery consistency based on the electrochemical impedance spectroscopy, which can significantly improve the accuracy and effectiveness of battery assessment.

[0056] The present invention determines the health status of the tested battery pack by obtaining a reference electrochemical impedance spectrum and a test electrochemical impedance spectrum of the tested battery and calculating the deviation between the electrochemical impedance spectrum and the reference electrochemical impedance spectrum, thereby helping to improve the good power supply and safe and reliable operation of the battery.

[0057] The present invention uses dynamic electrochemical AC impedance as the basis for evaluating the consistency of battery cells between modules. The ohmic internal resistance, charge transfer internal resistance, and Warburg impedance of solid phase diffusion in the electrochemical AC impedance spectrum can reflect the dynamic working conditions of the battery, and reflect the change in the internal resistance of the battery cells and the aging of the battery. By arranging wiring units for the battery cells between battery modules, the battery cells between modules can be tracked and detected.

[0058] The present invention not only uses the dynamic parameters of electrochemical impedance spectroscopy to evaluate the consistency of battery cells between battery modules, but also uses the LOF algorithm to achieve a mathematical description of the degree of abnormality of the differences between cells, making the consistency evaluation more accurate and more adaptable. It can intuitively reflect the differences in battery parameters and also expand the application field of the LOF algorithm.

[0059] The testing process of the present invention does not require large and expensive charging and discharging equipment, but only requires an electrochemical workstation to apply a small amount of current disturbance to the battery, which is not only low-cost but also can avoid safety risks.

[0060] The present invention solves the technical problem in the prior art that the current available capacity and remaining life of the battery cannot be accurately evaluated by using time domain data, and that regular capacity verification operations are still required, resulting in low accuracy and effectiveness of battery evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of basic steps of a method for inspecting the consistency of battery cells in a power system according to Embodiment 1 of the present invention;

[0062] Figure 2 This is a schematic diagram of the specific steps for calculating the local achievable density of a sample of battery ohmic internal resistance composition according to Example 1 of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] like Figure 1 As shown, the present invention provides a method for inspecting the consistency of a battery pack in a power system, which also includes the following basic steps:

[0066] S1. Perform an AC impedance test on the battery cell to be tested, and obtain the ohmic internal resistance value of the battery cell through fitting analysis, and use it as a test sample;

[0067] In this embodiment, before performing an AC impedance spectrum test on the battery to be tested, the battery to be tested is allowed to stand still to obtain a battery cell to be tested with a stable internal state.

[0068] In the process of fitting and analyzing the ohmic internal resistance of the battery, the corresponding equivalent circuit is established and fitted using ZView software. The solution resistance Rs, charge transfer resistance Rct, double layer capacitance Cdl, and Warburg impedance W of the battery cell to be tested are obtained by fitting and analyzing using ZView software. The SOH based on the electrochemical impedance spectroscopy is obtained by formula (1). EIS value as the test sample.

[0069]

[0070] S2. Calculate the local reachable density of the battery ohmic internal resistance composition sample using the local anomaly factor algorithm;

[0071] like Figure 2As shown, in this embodiment, the step S2 of calculating the local reachable density of the battery ohmic internal resistance composition sample further includes the following specific steps:

[0072] S21, performing distance calculation on the sample composed of ohm internal resistance values;

[0073] In this embodiment, in the process of calculating the distance of the sample composed of ohmic internal resistance values, the following formula (2) is used for calculation:

[0074]

[0075] Among them, d k (SOH EIS (i) ) indicates point (SOH EIS (i) )’s kth distance, SOH EIS (k＝t) Indicates distance from SOH EIS (i) The kth most distant data point, excluding point SOH EIS (i) .

[0076] S22. Determine SOH EIS (i) The number of samples of all points within the kth distance, that is, the kth distance neighborhood N k (SOH EIS (i) ), denoted as |N k (SOH EIS (i) )|, and|N k (SOH EIS (i) )|≥k;

[0077] S23. Determine the sample and its N k (SOH EIS (i) ) is the reachable distance of the samples in ;

[0078] In this embodiment, the sample is determined and its Nk(SOH EIS In the process of calculating the reachable distance of the sample in (i), the following formula (3) is used for calculation:

[0079] SOH EIS d t (SOH EIS (i) ,SOH EIS (j) )=max(dk(SOH EIS (i) ),‖SOH EIS(i) -SOH EIS (j) ‖)=max(‖

[0080] SOH EIS (i) -SOH EIS (k＝k) ‖,‖SOH EIS (i) -SOH EIS (j) ‖) (3)

[0081] Among them, sample SOH EIS (i) With its domain object SOH EIS (j) The reachable distance is defined as SOH EIS d t (SOH EIS (i) ,SOH EIS (j) ), SOH EIS (i) With SOH EIS (j) The kth distance of is ‖SOH EIS (i) -SOH EIS (k＝k) ‖, SOH EIS (i) and SOH EIS (j) The distance between them is ‖SOH EIS (i) -SOH EIS (j) ‖.

[0082] S24, according to the sample and its N k (SOH EIS (i) ) to calculate the local reachability density.

[0083] In this embodiment, the local reachable density of the sample is expressed as lrd k (SOH EIS (i) ) indicates that the formula is as follows:

[0084]

[0085] Among them, the denominator is SOH EIS (i) All sample points in the k-distance neighborhood to SOH EIS (i)The average reachable distance.

[0086] S3, determining the value of the local anomaly factor according to the local reachable density;

[0087] In this embodiment, the local outlier factor is lof k (SOH EIS (i) ) indicates that the formula is as follows:

[0088]

[0089] Among them, the molecular part is SOH EIS (i) The mean of the local reachability density of all samples in the k-distance neighborhood, and the denominator is SOH EIS (i) The local reachable density of .

[0090] S4. Evaluate the consistency of the test samples according to the local abnormal factor values, so as to obtain the consistency results of the battery cells.

[0091] In this embodiment, when the value of the local abnormality factor of the sample is less than or equal to 1, the sample is a normal point and the battery cell meets the consistency requirement; otherwise, the sample is an abnormal point and the battery cell does not meet the consistency requirement.

[0092] In summary, the present invention innovatively introduces the concept of electrochemical impedance spectroscopy, expands the dimension of battery monitoring data to multiple frequency domains, and completes the battery health assessment by extracting feature quantities related to battery consistency based on the electrochemical impedance spectroscopy, which can significantly improve the accuracy and effectiveness of battery evaluation.

[0093] The present invention determines the health status of the tested battery pack by obtaining a reference electrochemical impedance spectrum and a test electrochemical impedance spectrum of the tested battery and calculating the deviation between the electrochemical impedance spectrum and the reference electrochemical impedance spectrum, thereby helping to improve the good power supply and safe and reliable operation of the battery.

[0094] The present invention uses dynamic electrochemical AC impedance as the basis for evaluating the consistency of battery cells between modules. The ohmic internal resistance, charge transfer internal resistance, and Warburg impedance of solid phase diffusion in the electrochemical AC impedance spectrum can reflect the dynamic working conditions of the battery, and reflect the change in the internal resistance of the battery cells and the aging of the battery. By arranging wiring units for the battery cells between battery modules, the battery cells between modules can be tracked and detected.

[0095] The present invention not only uses the dynamic parameters of electrochemical impedance spectroscopy to evaluate the consistency of battery cells between battery modules, but also uses the LOF algorithm to achieve a mathematical description of the degree of abnormality of the differences between cells, making the consistency evaluation more accurate and more adaptable. It can intuitively reflect the differences in battery parameters and also expand the application field of the LOF algorithm.

[0096] The testing process of the present invention does not require large and expensive charging and discharging equipment, but only requires an electrochemical workstation to apply a small amount of current disturbance to the battery, which is not only low-cost but also can avoid safety risks.

[0097] The present invention solves the technical problem in the prior art that the current available capacity and remaining life of the battery cannot be accurately evaluated by using time domain data, and that regular capacity verification operations are still required, resulting in low accuracy and effectiveness of battery evaluation.

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for inspecting the consistency of battery cells in a power system, characterized in that: The method comprises: S1, performing an AC impedance test on the battery cell to be tested, and obtaining the ohmic internal resistance value of the battery cell to be tested through fitting analysis to serve as a test sample; S2. Calculate the local reachable density of the detection sample using a local anomaly factor algorithm; S3. Determine the value of the local anomaly factor according to the local reachable density; S4. According to the value of the local abnormal factor, the consistency evaluation of the test sample is performed to obtain the consistency evaluation result of the battery cell to be tested.

2. A method for inspecting the consistency of battery cells in a power system according to claim 1, characterized in that: In S1, the equivalent circuit of the battery cell to be tested is established, and the fitting analysis is performed using ZView software to obtain the solution resistance Rs, charge transfer resistance Rct, double layer capacitance Cdl, and Warburg impedance W of the battery cell to be tested, and the SOH based on the electrochemical impedance spectroscopy is obtained by formula (1). EIS value as the test sample.

3. A method for inspecting the consistency of battery cells in a power system according to claim 1, characterized in that: The S2 includes: S21, performing distance calculation on the detection sample; S22, determine the first point SOH EIS (i) The number of samples of all points within the kth distance is obtained, and the kth distance neighborhood N is obtained. k (SOH EIS (i) ), denoted as |N k (SOH EIS (i) )|; S23, determining the kth distance neighborhood N between the detection sample and itself k (SOH EIS (i) ) is the reachable distance between samples; S24. Obtain the local reachable density according to the detection sample and the reachable distance.

4. A method for inspecting the consistency of battery cells in a power system according to claim 3, characterized in that: In S21, the distance calculation is performed using the following logic: Where, d k (SOH EIS (i) ) represents the first point (SOH EIS (i) )’s kth distance, SOH EIS (k＝t) Indicates the distance from the first point SOH EIS (i) The k-th farthest data point.

5. A method for inspecting the consistency of battery cells in a power system according to claim 3, characterized in that: In S22, the kth distance neighborhood N k (SOH EIS (i) ) satisfies the following constraints: |N k (SOH EIS (i) )|≥k。 6. A method for inspecting the consistency of battery cells in a power system according to claim 3, characterized in that: In step S23, the reachable distance is calculated using the following logic: SOH EIS d t (SOH EIS (i) ,SOH EIS (j) )=max(dk(SOH EIS (i) ),‖SOH EIS (i) -SOH EIS (j) ‖)=max(‖ SOH EIS (i) -SOH EIS (k＝k) ‖,‖SOH EIS (i) -SOH EIS (j) ‖) (3) In the formula, the first point SOH EIS (i) With its own domain object SOH EIS (j) The reachable distance is defined as SOH EIS d t (SOH EIS (i) ,SOH EIS (j) ).

7. A method for inspecting the consistency of battery cells in a power system according to claim 6, characterized in that: The point SOH is determined using the following logic: EIS (i) With the point SOH EIS (j) The k-th distance is: ‖SOH EIS (i) -SOH EIS (k＝k) ‖ Among them, ‖SOH EIS (i) -SOH EIS (j) ‖ indicates the point SOH EIS (i) With the point SOH EIS (j) The distance between.

8. A method for inspecting the consistency of battery cells in a power system according to claim 3, characterized in that: In S24, the local reachable density lrd is calculated using the following logic: k (SOH EIS (i) ): In the formula, the denominator is the first point SOH EIS (i) The k distances of all sample points in the neighborhood to the first point SOH EIS (i) The average reachable distance.

9. A method for inspecting the consistency of battery cells in a power system according to claim 1, characterized in that: In S3, the local abnormality factor lof is expressed using the following logic: k (SOH EIS (i) ): In the formula, the molecular part is SOH EIS (i) The mean of the local reachability density of all samples in the k-distance neighborhood, and the denominator is SOH EIS (i) The local reachable density of .

10. A power system battery pack single cell consistency inspection system, characterized in that: The system comprises: The fitting analysis module is used to perform AC impedance test on the battery cell to be tested. The solution resistance Rs, charge transfer resistance Rct, double layer capacitance Cdl, and Warburg impedance W of the battery cell to be tested are obtained through fitting analysis. The SOH based on the electrochemical impedance spectroscopy is obtained by formula (1). EIS Values ​​are used as test samples; A reachable density calculation module, used to calculate the local reachable density of the detection sample using a local anomaly factor algorithm, the reachable density calculation module being connected to the fitting analysis module; An abnormal factor processing module, used for determining the value of the local abnormal factor according to the local reachable density, the abnormal factor processing module is connected to the reachable density calculation module; The consistency evaluation module is used to perform consistency evaluation on the test sample according to the value of the local abnormal factor to obtain the consistency evaluation result of the battery cell to be tested. The consistency evaluation module is connected to the abnormal factor processing module.

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