Abnormal voltage filtering method and device, electronic equipment and target energy storage system

Abstract

The invention relates to the technical field of batteries, in particular to an abnormal voltage filtering method and device, electronic equipment and a target energy storage system. Acquiring current voltage data corresponding to each battery cell in the target energy storage system; identifying the current voltage data, and determining abnormal voltage data in the current voltage data; determining an abnormal duration corresponding to the abnormal voltage data; and if the abnormal duration is smaller than or equal to the first preset duration, the abnormal voltage data is filtered, so that abnormal jump of the single voltage can be reduced, and the situation that abnormal voltage frequently appears to bring adverse effects to the stability of the target energy storage system and bring shutdown risks and income loss to customers is avoided.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to an abnormal voltage filtering method, device, electronic equipment and a target energy storage system. Background Art

[0002] As the global demand for clean energy grows, large-scale energy storage systems have developed rapidly as a key technical means to balance energy supply and demand and improve grid stability and reliability. In large-scale energy storage systems, the battery management system (BMS) plays a vital role. It is responsible for monitoring and managing the status of each battery cell in the battery pack to ensure the safe and efficient operation of the entire energy storage system.

[0003] However, large-scale energy storage systems currently face a severe challenge in their operation, namely, abnormal cell voltage jumps. This abnormal jump is manifested as a sudden and large change in the cell voltage in a short period of time, and the change range exceeds the voltage fluctuation range under normal working conditions. In most cases, in-depth research and actual operating experience have found that these abnormal voltages are not caused by physical or chemical performance problems of the cell itself, but by external factors.

[0004] On the one hand, electromagnetic interference is one of the important reasons for abnormal cell voltage jumps. On the other hand, communication abnormalities can also cause similar problems. These abnormal voltages caused by non-cell problems such as electromagnetic interference or communication abnormalities make it extremely difficult for the BMS to accurately judge the cell status.

[0005] In summary, effectively solving abnormal cell voltage jumps caused by non-cell problems and improving the accuracy of BMS's judgment on cell status have become one of the key issues that need to be urgently addressed in the current development of large-scale energy storage system technology. Summary of the invention

[0006] In view of this, the present invention provides an abnormal voltage filtering (subject name) to solve the problem of abnormal voltage filtering.

[0007] In a first aspect, the present invention provides an abnormal voltage filtering method, the method comprising:

[0008] Obtain the current voltage data corresponding to each battery cell in the target energy storage system;

[0009] Identify each current voltage data, and determine abnormal voltage data in each current voltage data;

[0010] Determine the duration of the abnormality corresponding to the abnormal voltage data;

[0011] If the duration of the abnormality is less than or equal to the first preset duration, the abnormal voltage data is filtered.

[0012] The abnormal voltage filtering method provided in the embodiment of the present application obtains the current voltage data corresponding to each battery cell in the target energy storage system; identifies each current voltage data, determines the abnormal voltage data in each current voltage data, and ensures the accuracy of the determined abnormal voltage data. Determine the abnormal duration corresponding to the abnormal voltage data; if the abnormal duration is less than or equal to the first preset duration, filter the abnormal voltage data. This can reduce the abnormal jump of single cell voltage and avoid the frequent occurrence of abnormal voltage, which will have an adverse effect on the stability of the target energy storage system and bring downtime risks and revenue losses to customers.

[0013] In an optional implementation manner, identifying each current voltage data and determining abnormal voltage data in each current voltage data includes:

[0014] Perform statistical calculations on each current voltage data to determine the preset voltage data range corresponding to the target energy storage system;

[0015] Compare each current voltage data with a preset voltage data range;

[0016] If the current voltage data is not within the preset voltage data range, the current voltage data is determined as abnormal voltage data.

[0017] The abnormal voltage filtering method provided in the embodiment of the present application performs statistical calculations on each current voltage data to determine the preset voltage data range corresponding to the target energy storage system, thereby ensuring that the preset voltage data range corresponding to the determined target energy storage system is related to the current voltage data corresponding to each battery cell in the target storage system. Then, each current voltage data is compared with the preset voltage data range; if the current voltage data is not within the preset voltage data range, the current voltage data is determined as abnormal voltage data, thereby ensuring the accuracy of the current voltage data being determined as abnormal voltage data.

[0018] In an optional implementation, statistical calculations are performed on each current voltage data to determine a preset voltage data range corresponding to the target energy storage system, including:

[0019] Calculate the mean voltage data and the standard deviation voltage data corresponding to each current voltage data;

[0020] Based on the mean voltage data and the standard deviation voltage data, a preset voltage data range corresponding to the target energy storage system is determined.

[0021] The abnormal voltage filtering method provided in the embodiment of the present application calculates the mean voltage data and the standard deviation voltage data corresponding to each current voltage data; based on the mean voltage data and the standard deviation voltage data, determines the preset voltage data range corresponding to the target energy storage system, thereby ensuring the accuracy of the preset voltage data range corresponding to the determined target energy storage system.

[0022] In an optional implementation, determining a preset voltage data range corresponding to the target energy storage system based on the mean voltage data and the standard deviation voltage data includes:

[0023] Obtain the current state of charge data and current health status data corresponding to the target energy storage system;

[0024] Calculate the target value based on the current state of charge data and the current health status data;

[0025] The difference between the mean voltage data and the product of the target value and the standard deviation voltage data is taken as the minimum value in the preset voltage data range;

[0026] The sum of the mean voltage data plus the product of the target value and the standard deviation voltage data is taken as the maximum value in the preset voltage data range.

[0027] The abnormal voltage filtering method provided in the embodiment of the present application obtains the current state of charge data and the current health state data corresponding to the target energy storage system; based on the current state of charge data and the current health state data, the target value is calculated, ensuring that the determined target value is related to the current state of charge data and the current health state data corresponding to the target energy storage system, and ensuring the accuracy of the calculated target value. The difference between the mean voltage data and the product of the target value and the standard deviation voltage data is taken as the minimum value in the preset voltage data range; the sum of the mean voltage data plus the product of the target value and the standard deviation voltage data is taken as the maximum value in the preset voltage data range, ensuring the accuracy of the determined preset voltage data range.

[0028] In an optional implementation, if the duration of the abnormality is within a first preset duration, filtering the abnormal voltage data includes:

[0029] If the duration of the abnormality is within the first preset duration, multiple cell clusters generated after clustering the cells are obtained.

[0030] Determine the target cell cluster to which the target cell corresponding to the abnormal voltage data belongs;

[0031] The abnormal voltage data is filtered based on the current voltage data corresponding to each battery cell in the target battery cell cluster.

[0032] The abnormal voltage filtering method provided in the embodiment of the present application, if the duration of the abnormality is within the first preset duration, obtains multiple cell clusters generated after clustering each cell. Then, the target cell cluster to which the target cell corresponding to the abnormal voltage data belongs is determined; the abnormal voltage data is filtered based on the current voltage data corresponding to each cell in the target cell cluster, thereby ensuring the rationality of filtering the abnormal voltage data and avoiding unreasonable filtering of the abnormal voltage data, which may lead to abnormal jumps in single cell voltage.

[0033] In an optional implementation, the process of generating multiple cell clusters after clustering the cells includes:

[0034] Obtain the historical voltage data of each battery cell within a preset historical period;

[0035] Calculate the first-order derivative data and the second-order derivative data corresponding to each historical voltage data corresponding to each battery cell;

[0036] Based on multiple preset clustering algorithms, clustering is performed on the historical voltage data, first-order derivative data, and second-order derivative data corresponding to each battery cell, and the silhouette coefficient corresponding to each preset clustering algorithm is calculated;

[0037] Determining a target clustering algorithm from among various preset clustering algorithms based on the silhouette coefficient;

[0038] Based on the preset optimization algorithm, determine the optimal parameter combination corresponding to the target clustering algorithm;

[0039] Based on the optimal parameter combination in the target clustering algorithm, each battery cell is clustered to generate multiple battery cell clusters.

[0040] The abnormal voltage filtering method provided in the embodiment of the present application obtains the historical voltage data of each battery cell within a preset historical time length, calculates the first-order derivative data and the second-order derivative data corresponding to each historical voltage data corresponding to each battery cell, and these features can reflect the change trend and stability of the battery cell voltage, so that the battery cells can be clustered based on multiple features. Then, based on multiple preset clustering algorithms, the historical voltage data, first-order derivative data and second-order derivative data corresponding to each battery cell are clustered, and the contour coefficient corresponding to each preset clustering algorithm is calculated, so that the accuracy of the calculated contour coefficient corresponding to each preset clustering algorithm is guaranteed, so that each preset clustering algorithm can be evaluated based on the contour coefficient corresponding to each preset clustering algorithm. Based on the contour coefficient, the target clustering algorithm is determined from each preset clustering algorithm, and the accuracy of the determined target clustering algorithm is guaranteed. Based on the preset optimization algorithm, the optimal parameter combination corresponding to the target clustering algorithm is determined, and the accuracy of the optimal parameter combination corresponding to the determined target clustering algorithm is guaranteed. Then, based on the optimal parameter combination in the target clustering algorithm, each battery cell is clustered to generate multiple battery cell clusters, and the accuracy of the generated multiple battery cell clusters is guaranteed.

[0041] In an optional embodiment, the method further includes:

[0042] If the duration of the abnormality is greater than the first preset duration and less than the second preset duration, it is determined that the current voltage data is normal, and the output current voltage data is normal; the second preset duration is greater than the first preset duration;

[0043] If the duration of the abnormality is greater than the second preset duration, and / or the abnormal voltage data is not within the preset allowable abnormal range, it is determined that a fault occurs in the target energy storage system, and a fault prompt message is output.

[0044] The abnormal voltage filtering method provided in the embodiment of the present application determines that the current voltage data is normal and outputs the current voltage data as normal if the duration of the abnormality is greater than the first preset duration and less than the second preset duration, thereby ensuring the accuracy of the result that the current voltage data is normal. If the duration of the abnormality is greater than the second preset duration and / or the abnormal voltage data is not within the preset allowable abnormal range, it is determined that the target energy storage system has a fault and outputs a fault prompt message. This ensures that the user can receive the fault prompt message in a timely manner to avoid serious impact on the target storage system.

[0045] In a second aspect, the present invention provides an abnormal voltage filtering device, the device comprising:

[0046] An acquisition module is used to obtain current voltage data corresponding to each battery cell in the target energy storage system;

[0047] A first determination module is used to identify each current voltage data and determine abnormal voltage data in each current voltage data;

[0048] A second determination module is used to determine the duration of the abnormality corresponding to the abnormal voltage data;

[0049] The filtering module is used to filter the abnormal voltage data if the duration of the abnormality is less than or equal to a first preset duration.

[0050] The abnormal voltage filtering device provided in the embodiment of the present application obtains the current voltage data corresponding to each battery cell in the target energy storage system; identifies each current voltage data, determines the abnormal voltage data in each current voltage data, and ensures the accuracy of the determined abnormal voltage data. Determine the duration of the abnormality corresponding to the abnormal voltage data; if the duration of the abnormality is less than or equal to the first preset duration, filter the abnormal voltage data. This can reduce the abnormal jump of the single cell voltage and avoid the frequent occurrence of abnormal voltage, which will have an adverse effect on the stability of the target energy storage system and bring downtime risks and revenue losses to customers.

[0051] In a third aspect, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the abnormal voltage filtering method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0052] In a fourth aspect, the present invention provides a target energy storage system, comprising: an electronic device and an energy storage system body, wherein the electronic device is used to execute the abnormal voltage filtering method of the first aspect or any corresponding embodiment thereof.

[0053] In a fifth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the abnormal voltage filtering method of the first aspect or any corresponding embodiment thereof.

[0054] In a sixth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the abnormal voltage filtering method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 is a flow chart of an abnormal voltage filtering method according to an embodiment of the present invention;

[0057] Figure 2 is a flow chart of another abnormal voltage filtering method according to an embodiment of the present invention;

[0058] Figure 3 is a flow chart of another abnormal voltage filtering method according to an embodiment of the present invention;

[0059] Figure 4 is a schematic diagram of clustering 120 battery cells to generate multiple battery cell clusters according to an embodiment of the present invention;

[0060] Figure 5 is a structural block diagram of an abnormal voltage filtering device according to an embodiment of the present invention;

[0061] Figure 6 is a structural block diagram of another abnormal voltage filtering device according to an embodiment of the present invention;

[0062] Figure 7 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment 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 below in conjunction with the drawings in 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0064] As the global demand for clean energy grows, large-scale energy storage systems have developed rapidly as a key technical means to balance energy supply and demand and improve grid stability and reliability. In large-scale energy storage systems, the battery management system (BMS) plays a vital role. It is responsible for monitoring and managing the status of each battery cell in the battery pack to ensure the safe and efficient operation of the entire energy storage system.

[0065] However, large-scale energy storage systems currently face a severe challenge in their operation, namely, abnormal cell voltage jumps. This abnormal jump is manifested as a sudden and large change in the cell voltage in a short period of time, and the change range exceeds the voltage fluctuation range under normal working conditions. In most cases, in-depth research and actual operating experience have found that these abnormal voltages are not caused by physical or chemical performance problems of the cell itself, but by external factors.

[0066] On the one hand, electromagnetic interference is one of the important reasons for abnormal cell voltage jumps. On the other hand, communication abnormalities can also cause similar problems. These abnormal voltages caused by non-cell problems such as electromagnetic interference or communication abnormalities make it extremely difficult for the BMS to accurately judge the cell status.

[0067] In summary, effectively solving abnormal cell voltage jumps caused by non-cell problems and improving the accuracy of BMS's judgment on cell status have become one of the key issues that need to be urgently addressed in the current development of large-scale energy storage system technology.

[0068] It should be noted that the method for abnormal voltage filtering provided in the embodiment of the present application may be executed by a device for abnormal voltage filtering, and the device for abnormal voltage filtering may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware, wherein the electronic device may be a control device in a storage system, or may be an electronic device independent of the storage system but connected to the storage system for communication. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0069] According to an embodiment of the present invention, an abnormal voltage filtering method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0070] In this embodiment, an abnormal voltage filtering method is provided, which can be used in the above-mentioned electronic equipment. Figure 1 is a flow chart of an abnormal voltage filtering method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0071] Step S101, obtaining current voltage data corresponding to each battery cell in the target energy storage system.

[0072] Specifically, the electronic device may detect current voltage data corresponding to each battery cell in the target energy storage system based on a voltage sensor.

[0073] Step S102: identifying each current voltage data, and determining abnormal voltage data in each current voltage data.

[0074] Specifically, the electronic device can identify each current voltage data and calculate the mean voltage data corresponding to each current voltage data. Then, the electronic device compares each current voltage data with the mean voltage data, and if the absolute value of the difference between the current voltage data and the mean voltage data is greater than a preset difference threshold, the current voltage data is determined to be abnormal voltage data.

[0075] If no abnormal voltage data is detected, the electronic device outputs that the current voltage data is normal.

[0076] This step will be described in detail below.

[0077] Step S103, determining the abnormal duration corresponding to the abnormal voltage data.

[0078] Specifically, the electronic device can determine the target battery cell corresponding to the abnormal voltage data, and then continuously monitor the target current voltage data corresponding to the target battery cell, and then detect whether the target current voltage data obtained each time is abnormal, so as to determine the duration of the abnormality corresponding to the abnormal voltage data.

[0079] Step S104: If the duration of the abnormality is less than or equal to the first preset duration, filtering the abnormal voltage data.

[0080] Specifically, the electronic device may receive the first preset duration input by the user, or may receive the first preset duration sent by other devices, and may also determine the first preset duration according to the attribute information corresponding to the target energy storage system. Then, the electronic device compares the abnormal duration with the first preset duration, and if the abnormal duration is less than or equal to the first preset duration, the abnormal voltage data is filtered.

[0081] This step will be described in detail below.

[0082] The abnormal voltage filtering method provided in the embodiment of the present application obtains the current voltage data corresponding to each battery cell in the target energy storage system; identifies each current voltage data, determines the abnormal voltage data in each current voltage data, and ensures the accuracy of the determined abnormal voltage data. Determine the abnormal duration corresponding to the abnormal voltage data; if the abnormal duration is less than or equal to the first preset duration, filter the abnormal voltage data. This can reduce the abnormal jump of single cell voltage and avoid the frequent occurrence of abnormal voltage, which will have an adverse effect on the stability of the target energy storage system and bring downtime risks and revenue losses to customers.

[0083] In this embodiment, an abnormal voltage filtering method is provided, which can be used in the above-mentioned electronic equipment. Figure 2 is a flow chart of an abnormal voltage filtering method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0084] Step S201, obtaining current voltage data corresponding to each battery cell in the target energy storage system.

[0085] For details about this step, please refer to the above description of step S101, which will not be elaborated here.

[0086] Step S202: identifying each current voltage data, and determining abnormal voltage data in each current voltage data.

[0087] Specifically, the above step S202 may include the following steps:

[0088] Step S2021, performing statistical calculations on each current voltage data to determine a preset voltage data range corresponding to the target energy storage system.

[0089] Specifically, the above step S2021 may include the following steps:

[0090] Step a1, calculating the mean voltage data and the standard deviation voltage data corresponding to each current voltage data.

[0091] Specifically, the electronic device may calculate mean voltage data and standard deviation voltage data corresponding to each current voltage data.

[0092] Step a2: determining a preset voltage data range corresponding to the target energy storage system based on the mean voltage data and the standard deviation voltage data.

[0093] Specifically, the above step a2 may include the following steps:

[0094] Step a21, obtaining current state of charge data and current health status data corresponding to the target energy storage system.

[0095] Specifically, the electronic device may perform integral calculation on the charge and discharge current corresponding to the target energy storage system within a period of time to estimate the current state of charge data corresponding to the target energy storage system.

[0096] Exemplarily, the electronic device may calculate using the following formula:

[0097]

[0098] Among them, SOC(t) is the current state of charge data corresponding to the target energy storage system, SOC(t0) is the initial state of charge data corresponding to the target energy storage system, C is the rated capacity of the target energy storage system, and I(τ) is the current value at time τ| (charging current is positive and discharging current is negative).

[0099] Specifically, the electronic device can calculate the current health status data corresponding to the target energy storage system based on a preset equivalent circuit model.

[0100] The preset equivalent circuit model may be any one of the equivalent circuit models such as the Rint model, the Thevenin model, the PNGV model, etc.

[0101] Optionally, as the target energy storage system is used and ages, its internal resistance will gradually increase. By monitoring the change of the internal resistance of the target energy storage system in real time and comparing it with the internal resistance of the target energy storage system in a new state, the SOH can be calculated. The formula is Among them, R new is the internal resistance of the target energy storage system in its new state, R aged is the internal resistance of the target energy storage system currently measured.

[0102] Optionally, the electronic device can also estimate the SOH based on the capacity decay of the target energy storage system. The capacity of the target energy storage system will gradually decrease with the increase of usage times and time. The SOH is calculated by regularly measuring the actual capacity of the target energy storage system (such as using a standard charge and discharge test method) and comparing it with the rated capacity of the target energy storage system. The formula is Among them, Cactual is the target energy storage system capacity actually measured at present, C rated is the rated capacity of the target energy storage system. The SOH estimation methods based on internal resistance change and capacity attenuation can be combined to comprehensively consider multiple factors and improve the accuracy of SOH estimation. For example, by using the weighted summation method, SOH new =βSOH R +(1-β)SOH C , where SOH R It is the SOH value calculated based on the change of internal resistance. C It is the SOH value calculated based on capacity attenuation, and β is the weighting coefficient, which is determined according to the importance of the internal resistance and capacity on the health status of the target energy storage system.

[0103] Step a22, calculating the target value based on the current state of charge data and the current health state data.

[0104] Specifically, the electronic device can calculate the target value based on the current state of charge data and the current health state data using the following formula:

[0105] n=f(SOC,SOH)=a1+a2*SOC+a3*SOH+a4*SOC^2+a5*SOC*SOH+a6*SOC^3+a7*SOC^2*SOH;

[0106] Among them, SOC represents the current state of charge data corresponding to the target energy storage system, ranging from 0 to 100; SOH represents the current health state data corresponding to the target energy storage system, ranging from 80 to 100. a1-a7 are the corresponding coefficients. For example, a1=9.3257, a2=-0.2334, a3=-0.0536, a4=0.0019, a5=0.0021, a6=3.50e-06, a7=-2.07e-05.

[0107] Usually SOC and SOH are both expressed in percentages. The above relationship uses numerical values ​​rather than percentages. For example, SOH 80-100 represents 80%-100%, and the same applies to SOC.

[0108] The following table lists the calculation results of n at different SOCs between 80% and 100% SOH:

[0109]

[0110]

[0111] Step a23, taking the difference obtained by subtracting the product of the target value and the standard deviation voltage data from the mean voltage data as the minimum value in the preset voltage data range.

[0112] Specifically, the electronic device may use the difference obtained by subtracting the product of the target value and the standard deviation voltage data from the mean voltage data as the minimum value in the preset voltage data range.

[0113] Step a24, taking the sum of the mean voltage data plus the product of the target value and the standard deviation voltage data as the maximum value in the preset voltage data range.

[0114] Specifically, the electronic device may take the sum of the mean voltage data plus the product of the target value and the standard deviation voltage data as the maximum value in the preset voltage data range.

[0115] Exemplarily, the preset voltage data range is (μ-nσ, μ+nσ), where μ is the mean voltage data, σ is the standard deviation voltage data, and n is the target value.

[0116] Step S2022, comparing each current voltage data with a preset voltage data range.

[0117] Specifically, the electronic device may compare each current voltage data with a minimum value and a maximum value in a preset voltage data range.

[0118] If the current voltage data is greater than the minimum value in the preset voltage data range and less than the maximum value in the preset voltage data range, it is determined that the current voltage data is within the preset voltage data range; if the current voltage data is less than the minimum value in the preset voltage data range, or greater than the maximum value in the preset voltage data range, it is determined that the current voltage data is not within the preset voltage data range.

[0119] Step S2023: if the current voltage data is not within the preset voltage data range, the current voltage data is determined as abnormal voltage data.

[0120] Specifically, if the current voltage data is not within the preset voltage data range, the electronic device determines the current voltage data as abnormal voltage data.

[0121] Step S203, determining the abnormal duration corresponding to the abnormal voltage data.

[0122] For details about this step, please refer to the above description of step S103, which will not be elaborated here.

[0123] Step S204: If the duration of the abnormality is less than or equal to the first preset duration, filtering the abnormal voltage data.

[0124] For details about this step, please refer to the above description of step S104, which will not be elaborated here.

[0125] The abnormal voltage filtering method provided in the embodiment of the present application calculates the mean voltage data and the standard deviation voltage data corresponding to each current voltage data. The current state of charge data and the current health state data corresponding to the target energy storage system are obtained; based on the current state of charge data and the current health state data, the target value is calculated to ensure that the determined target value is related to the current state of charge data and the current health state data corresponding to the target energy storage system, and the accuracy of the calculated target value is ensured. The difference between the mean voltage data and the product of the target value and the standard deviation voltage data is taken as the minimum value in the preset voltage data range; the sum of the mean voltage data plus the product of the target value and the standard deviation voltage data is taken as the maximum value in the preset voltage data range, which ensures the accuracy of the determined preset voltage data range, and ensures that the determined preset voltage data range corresponding to the target energy storage system is related to the current voltage data corresponding to each battery cell in the target storage system. Then, each current voltage data is compared with the preset voltage data range; if the current voltage data is not within the preset voltage data range, the current voltage data is determined as abnormal voltage data, which ensures the accuracy of the determined current voltage data as abnormal voltage data.

[0126] In this embodiment, an abnormal voltage filtering method is provided, which can be used in the above-mentioned electronic equipment. Figure 3 is a flow chart of an abnormal voltage filtering method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0127] Step S301, obtaining the current voltage data corresponding to each battery cell in the target energy storage system. For this step, please refer to the above description of step S201, which will not be repeated here.

[0128] Step S302: identifying each current voltage data, and determining abnormal voltage data in each current voltage data.

[0129] For details about this step, please refer to the above description of step S202, which will not be elaborated here.

[0130] Step S303, determining the abnormal duration corresponding to the abnormal voltage data.

[0131] For details about this step, please refer to the above description of step S203, which will not be elaborated here.

[0132] Step S304: If the duration of the abnormality is less than or equal to the first preset duration, filtering the abnormal voltage data.

[0133] Specifically, the above step S304 may include the following steps:

[0134] Step S3041: if the duration of the abnormality is within a first preset duration, a plurality of cell clusters generated after the cell clustering is obtained.

[0135] Specifically, if the duration of the abnormality is within a first preset duration, the electronic device may obtain, from the storage space, a plurality of cell clusters generated after the cell clustering.

[0136] Specifically, the process of generating multiple cell clusters after clustering the cells in step S3041 may include the following steps:

[0137] Step b1, obtaining historical voltage data of each battery cell within a preset historical time period.

[0138] Specifically, the electronic device can obtain the historical voltage data of each battery cell within a preset historical period from the storage space. Then, the electronic device can perform necessary cleaning and preprocessing on the historical voltage data, such as removing outliers (obviously erroneous data points that may be caused by sensor failure or short-term interference) and filling missing values ​​(using appropriate interpolation methods, such as linear interpolation or neighboring data average interpolation).

[0139] Step b2, calculating the first-order derivative data and the second-order derivative data corresponding to each historical voltage data corresponding to each battery cell.

[0140] Specifically, the electronic device uses a preset numerical differential method to calculate the first-order derivative and the second-order derivative corresponding to each historical voltage data. The preset numerical differential method may include a forward differential method, a backward differential method, and a central differential method. For example, the formula for calculating the first-order derivative using the central differential method is: (where Δt is the time interval), the formula for calculating the second-order derivative is

[0141] By calculating the first-order derivative, electronic devices can obtain the rate of change of each cell voltage over time, reflecting the charge and discharge rate of each cell or the speed of voltage change. The second-order derivative further reveals the trend of the voltage change rate. For example, a positive second-order derivative may indicate that the voltage change rate is increasing, that is, the state change of the cell is accelerating; a negative second-order derivative may indicate that the voltage change rate is decreasing, and the state change tends to stabilize or slow down. These derivative data provide more information about the dynamic characteristics of the cell for subsequent cluster analysis.

[0142] Step b3: Based on a plurality of preset clustering algorithms, clustering processing is performed on the historical voltage data, first-order derivative data and second-order derivative data corresponding to each battery cell, and the silhouette coefficient corresponding to each preset clustering algorithm is calculated.

[0143] Among them, the preset clustering algorithms may include K-means algorithm, hierarchical clustering algorithm, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, etc.

[0144] Specifically, for each preset clustering algorithm, the electronic device uses the historical voltage data, first-order derivative data and second-order derivative data of each battery cell as input features for clustering processing.

[0145] After clustering is completed, the electronic device can calculate the silhouette coefficient corresponding to each preset clustering algorithm. The silhouette coefficient is an indicator used to evaluate the clustering effect, and its value range is between -1 and 1. The steps for calculating the silhouette coefficient are as follows:

[0146] For each battery cell, the electronic device calculates the average distance a(i) from it to other battery cells in its cluster, which indicates how compact the battery cell is within the cluster.

[0147] In addition, the electronic device calculates the minimum value b(i) of the average distances from the battery cell to all the battery cells in other clusters, which represents the degree of separation of the battery cell from other clusters.

[0148] Then, the electronic device calculates the silhouette coefficient corresponding to each preset clustering algorithm based on the minimum value of the average distance between each battery cell and other battery cells in its cluster and the average distance between the battery cell and all battery cells in other clusters.

[0149] The specific formula is as follows: For the entire data set, the silhouette coefficient is the average of the silhouette coefficients of all data points. By calculating the silhouette coefficient, the clustering effect of each preset clustering algorithm on the cell data can be evaluated. The closer the silhouette coefficient is to 1, the better the clustering effect is, the data points are tightly clustered within the cluster and the clusters are clearly separated; the silhouette coefficient close to 0 indicates that the clustering effect is average, there may be overlap between clusters or dispersion within the cluster; the silhouette coefficient close to -1 indicates that the clustering effect is poor, and the data points may be misclassified.

[0150] Step b4: determining a target clustering algorithm from various preset clustering algorithms based on the silhouette coefficient.

[0151] Specifically, the electronic device may compare the silhouette coefficients corresponding to the preset clustering algorithms, and select the preset clustering algorithm with the highest silhouette coefficient as the target clustering algorithm.

[0152] For example, after calculating the silhouette coefficients of K-means, hierarchical clustering and DBSCAN algorithms, it is found that the silhouette coefficient of K-means algorithm is the highest, which is 0.75, while the silhouette coefficient of hierarchical clustering algorithm is 0.60, and the silhouette coefficient of DBSCAN algorithm is 0.55, so K-means algorithm is determined as the target clustering algorithm. The purpose of selecting the target clustering algorithm is to obtain the best clustering effect so as to classify and analyze the battery cells more accurately.

[0153] Step b5: Determine the optimal parameter combination corresponding to the target clustering algorithm based on a preset optimization algorithm.

[0154] Specifically, the electronic device may generate a plurality of initial particles, each of which represents an initial parameter combination corresponding to the target clustering algorithm.

[0155] Each initial particle has a position vector (indicating the parameter value) and a velocity vector, which determines the moving direction and speed of the initial particle in the parameter space. At the same time, an individual optimal position (pbest) is set for each initial particle, recording the best parameter value found so far for the initial particle. A global optimal position (gbest) is set to record the best parameter value found so far for the entire initial particle group.

[0156] Then, the electronic device calculates the fitness value of each initial particle (i.e., the value of the clustering effect evaluation index) according to the objective function, and updates the individual optimal position and the global optimal position according to the fitness value. Then, according to the speed update formula of the initial particle (where is the speed of the particle at time, is the inertia weight, and is the learning factor, and is a random number between to, is the position of the particle at time) and the position update formula, the speed and position of the initial particle are updated, so that the initial particle moves to a better solution in the parameter space, and the initial particle is updated to obtain an updated particle. This cycle is repeated. Finally, the electronic device calculates the fitness value corresponding to each updated particle, and then determines the target particle according to the fitness value corresponding to each updated particle, thereby determining the optimal parameter combination corresponding to the target clustering algorithm according to the target particle.

[0157] Step b6: clustering the battery cells based on the optimal parameter combination in the target clustering algorithm to generate multiple battery cell clusters.

[0158] Specifically, the electronic device clusters the battery cells based on the optimal parameter combination in the target clustering algorithm to generate a plurality of battery cell clusters.

[0159] For example, Figure 4 As shown, it is a schematic diagram of clustering 120 battery cells to generate multiple battery cell clusters.

[0160] It should be noted that clustering of each battery cell can be independent of filtering of abnormal voltage data, that is, clustering of each battery cell can be performed first. Then, filtering of abnormal voltage data can be performed. Clustering of each battery cell can also be performed when it is detected that the duration of abnormality is less than or equal to the first preset duration.

[0161] Optionally, the electronic device can perform clustering operations on each battery cell at regular intervals. For example, once a day or a week, after one clustering, before the next clustering, the battery cell cluster information corresponding to each battery cell remains unchanged until it is updated to the latest "battery cell cluster" information after the next clustering. In particular, in the early stage of the target energy storage system being put into operation, before the first clustering of each battery cell, all batteries are assigned to the same "battery cell cluster" by default.

[0162] Step S3042, determining the target cell cluster to which the target cell corresponding to the abnormal voltage data belongs.

[0163] Specifically, the electronic device may determine the target cell cluster to which the target cell corresponding to the abnormal voltage data belongs according to the location of the target cell corresponding to the abnormal voltage data.

[0164] Step S3043 , filtering the abnormal voltage data based on the current voltage data corresponding to each cell in the target cell cluster.

[0165] Optionally, the electronic device may calculate an average value of current voltage data corresponding to each cell in the target cell cluster, and then replace the abnormal voltage data with the average value of current voltage data corresponding to each cell in the target cell cluster, thereby filtering the abnormal voltage data.

[0166] Optionally, the electronic device may calculate the median of the current voltage data corresponding to each cell in the target cell cluster, and then replace the abnormal voltage data with the median of the current voltage data corresponding to each cell in the target cell cluster, thereby filtering the abnormal voltage data.

[0167] Step S305: if the duration of the abnormality is greater than the first preset duration and less than the second preset duration, it is determined that the current voltage data is normal, and the current voltage data is output as normal.

[0168] The second preset duration is greater than the first preset duration.

[0169] Specifically, if the duration of the abnormality is greater than the first preset duration and less than the second preset duration, the electronic device considers that it is not an accidental abnormality caused by electromagnetic interference or the like, but real data, and will not perform filtering processing, and determines that the current voltage data is normal and the output is normal.

[0170] Step S306: If the duration of the abnormality is greater than a second preset duration, and / or the abnormal voltage data is not within a preset allowable abnormal range, it is determined that a fault occurs in the target energy storage system, and a fault prompt message is output.

[0171] Specifically, the electronic device compares the duration of the abnormality with the second preset duration, and compares the abnormal voltage data with the maximum and minimum values ​​in the preset allowable abnormal range. If the duration of the abnormality is greater than the second preset duration, and / or the abnormal voltage data is not within the preset allowable abnormal range, the electronic device determines that the target energy storage system has a fault and outputs a fault prompt message.

[0172] The maximum value in the preset allowable abnormal range is greater than the maximum value in the preset voltage data range, and the minimum value in the preset allowable abnormal range is less than the minimum value in the preset voltage data range.

[0173] The abnormal voltage filtering method provided in the embodiment of the present application, if the duration of the abnormality is within the first preset duration, then multiple cell clusters generated after clustering each cell are obtained. Then, the target cell cluster to which the target cell corresponding to the abnormal voltage data belongs is determined; the abnormal voltage data is filtered based on the current voltage data corresponding to each cell in the target cell cluster, thereby ensuring the rationality of filtering the abnormal voltage data and avoiding unreasonable filtering of the abnormal voltage data, which leads to abnormal jumps in single cell voltage. Among them, the process of generating multiple cell clusters after clustering each cell includes obtaining the historical voltage data of each cell within the preset historical duration. Then, the first-order derivative data and the second-order derivative data corresponding to each historical voltage data corresponding to each cell are calculated. These features can reflect the changing trend and stability of the cell voltage, so that the cells can be clustered based on multiple features. Then, based on multiple preset clustering algorithms, the historical voltage data, first-order derivative data and second-order derivative data corresponding to each battery cell are clustered, and the contour coefficient corresponding to each preset clustering algorithm is calculated, so as to ensure the accuracy of the calculated contour coefficient corresponding to each preset clustering algorithm, so that each preset clustering algorithm can be evaluated based on the contour coefficient corresponding to each preset clustering algorithm. Based on the contour coefficient, the target clustering algorithm is determined from each preset clustering algorithm, so as to ensure the accuracy of the determined target clustering algorithm. Based on the preset optimization algorithm, the optimal parameter combination corresponding to the target clustering algorithm is determined, so as to ensure the accuracy of the optimal parameter combination corresponding to the determined target clustering algorithm. Then, based on the optimal parameter combination in the target clustering algorithm, each battery cell is clustered to generate multiple battery cell clusters, so as to ensure the accuracy of the generated multiple battery cell clusters. Thus, it is ensured that each battery cell cluster includes similar batteries. .

[0174] In addition, if the duration of the abnormality is greater than the first preset duration and less than the second preset duration, the current voltage data is determined to be normal, and the current voltage data is output to be normal, ensuring the accuracy of the result that the current voltage data is determined to be normal. If the duration of the abnormality is greater than the second preset duration, and / or the abnormal voltage data is not within the preset allowable abnormal range, it is determined that the target energy storage system has a fault, and a fault prompt message is output. This ensures that the user can receive the fault prompt message in a timely manner, avoiding serious impact on the target storage system.

[0175] In this embodiment, an abnormal voltage filtering device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0176] This embodiment provides an abnormal voltage filtering device, such as Figure 5 As shown, including:

[0177] An acquisition module 401 is used to acquire current voltage data corresponding to each battery cell in a target energy storage system;

[0178] A first determination module 402 is used to identify each current voltage data and determine abnormal voltage data in each current voltage data;

[0179] The second determination module 403 is used to determine the abnormal duration corresponding to the abnormal voltage data;

[0180] The filtering module 404 is configured to filter the abnormal voltage data if the duration of the abnormality is less than or equal to a first preset duration.

[0181] In some optional implementations, the first determination module 402 is specifically used to perform statistical calculations on each current voltage data to determine a preset voltage data range corresponding to the target energy storage system; compare each current voltage data with the preset voltage data range; if the current voltage data is not within the preset voltage data range, the current voltage data is determined as abnormal voltage data.

[0182] In some optional implementations, the first determination module 402 is specifically used to calculate mean voltage data and standard deviation voltage data corresponding to each current voltage data; based on the mean voltage data and standard deviation voltage data, determine a preset voltage data range corresponding to the target energy storage system.

[0183] In some optional embodiments, the first determination module 402 is specifically used to obtain current state of charge data and current health status data corresponding to the target energy storage system; calculate the target value based on the current state of charge data and the current health status data; take the difference between the mean voltage data and the product of the target value and the standard deviation voltage data as the minimum value in the preset voltage data range; take the sum of the mean voltage data plus the product of the target value and the standard deviation voltage data as the maximum value in the preset voltage data range.

[0184] In some optional embodiments, the filtering module 404 is specifically used to obtain multiple battery cell clusters generated after clustering the battery cells if the duration of the abnormality is within a first preset duration, and determine the target battery cell cluster to which the target battery cell corresponding to the abnormal voltage data belongs; and filter the abnormal voltage data based on the current voltage data corresponding to each battery cell in the target battery cell cluster.

[0185] In some optional embodiments, the filtering module 404 is specifically used to obtain historical voltage data of each battery cell within a preset historical time length; calculate the first-order derivative data and second-order derivative data corresponding to each historical voltage data corresponding to each battery cell; based on multiple preset clustering algorithms, cluster the historical voltage data, first-order derivative data and second-order derivative data corresponding to each battery cell, and calculate the contour coefficient corresponding to each preset clustering algorithm; determine a target clustering algorithm from each preset clustering algorithm based on the contour coefficient; based on the preset optimization algorithm, determine the optimal parameter combination corresponding to the target clustering algorithm; based on the optimal parameter combination in the target clustering algorithm, cluster each battery cell to generate multiple battery cell clusters.

[0186] In some optional implementations, the abnormal voltage filtering device, such as Figure 6 As shown, it also includes:

[0187] The output module 405 is used to determine that the current voltage data is normal and output that the current voltage data is normal if the duration of the abnormality is greater than the first preset duration and less than the second preset duration; the second preset duration is greater than the first preset duration; if the duration of the abnormality is greater than the second preset duration, and / or the abnormal voltage data is not within the preset allowable abnormal range, it is determined that a fault occurs in the target energy storage system and a fault prompt message is output.

[0188] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0189] The abnormal voltage filtering device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0190] The embodiment of the present invention also provides an electronic device having the above Figure 5 and Figure 6 The abnormal voltage filtering device is shown.

[0191] See also Figure 7 , Figure 7 is a schematic diagram of the structure of an electronic device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the electronic device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0192] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0193] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0194] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0195] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0196] The electronic device further comprises a communication interface 30 for the electronic device to communicate with other devices or a communication network.

[0197] The present invention provides a target energy storage system, comprising: an electronic device and an energy storage system body, wherein the electronic device is used to execute the abnormal voltage filtering method of the first aspect or any corresponding embodiment thereof.

[0198] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0199] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0200] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An abnormal voltage filtering method, characterized in that: The method comprises: Obtain the current voltage data corresponding to each battery cell in the target energy storage system; Identify each of the current voltage data, and determine abnormal voltage data in each of the current voltage data; Determine the abnormal duration corresponding to the abnormal voltage data; If the abnormal duration is less than or equal to a first preset duration, filtering is performed on the abnormal voltage data.

2. The method according to claim 1, characterized in that The identifying each of the current voltage data and determining abnormal voltage data in each of the current voltage data includes: Performing statistical calculations on each of the current voltage data to determine a preset voltage data range corresponding to the target energy storage system; Comparing each of the current voltage data with the preset voltage data range; If the current voltage data is not within the preset voltage data range, the current voltage data is determined as the abnormal voltage data.

3. The method according to claim 2, characterized in that The performing statistical calculation on each of the current voltage data to determine a preset voltage data range corresponding to the target energy storage system includes: Calculate the mean voltage data and the standard deviation voltage data corresponding to each of the current voltage data; Based on the mean voltage data and the standard deviation voltage data, a preset voltage data range corresponding to the target energy storage system is determined.

4. The method according to claim 3, characterized in that The determining, based on the mean voltage data and the standard deviation voltage data, a preset voltage data range corresponding to the target energy storage system includes: Obtaining current state of charge data and current health status data corresponding to the target energy storage system; Calculating a target value based on the current state of charge data and the current health state data; The difference between the mean voltage data and the product of the target value and the standard deviation voltage data is taken as the minimum value in the preset voltage data range; The sum of the mean voltage data plus the product of the target value and the standard deviation voltage data is taken as the maximum value in the preset voltage data range.

5. The method according to claim 1, characterized in that If the duration of the abnormality is within a first preset duration, filtering the abnormal voltage data includes: If the duration of the abnormality is within a first preset duration, a plurality of cell clusters generated after the cell clustering is obtained, Determine a target cell cluster to which the target cell corresponding to the abnormal voltage data belongs; The abnormal voltage data is filtered based on the current voltage data corresponding to each of the cells in the target cell cluster.

6. The method according to claim 5, characterized in that The process of generating a plurality of cell clusters after clustering the cells comprises: Obtaining historical voltage data of each of the battery cells within a preset historical time period; Calculating first-order derivative data and second-order derivative data corresponding to each of the historical voltage data corresponding to each of the battery cells; Based on a plurality of preset clustering algorithms, clustering is performed on the historical voltage data, the first-order derivative data, and the second-order derivative data corresponding to each of the battery cells, and a silhouette coefficient corresponding to each of the preset clustering algorithms is calculated; Determining a target clustering algorithm from the preset clustering algorithms based on the silhouette coefficient; Based on a preset optimization algorithm, determining an optimal parameter combination corresponding to the target clustering algorithm; Based on the optimal parameter combination in the target clustering algorithm, the battery cells are clustered to generate a plurality of battery cell clusters.

7. The method according to claim 1, characterized in that The method further comprises: If the duration of the abnormality is greater than the first preset duration and less than the second preset duration, it is determined that the current voltage data is normal, and the current voltage data is output to be normal; the second preset duration is greater than the first preset duration; If the duration of the abnormality is greater than the second preset duration, and / or the abnormal voltage data is not within the preset allowable abnormal range, it is determined that a fault occurs in the target energy storage system, and a fault prompt message is output.

8. An abnormal voltage filtering device, characterized in that: The device comprises: An acquisition module is used to obtain current voltage data corresponding to each battery cell in the target energy storage system; A first determining module, used for identifying each of the current voltage data, and determining abnormal voltage data in each of the current voltage data; A second determination module is used to determine the abnormal duration corresponding to the abnormal voltage data; The filtering module is used to filter the abnormal voltage data if the duration of the abnormality is less than or equal to a first preset duration.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the abnormal voltage filtering method according to any one of claims 1 to 7 by executing the computer instructions.

10. A target energy storage system, characterized in that: include: An electronic device and an energy storage system body, wherein the electronic device is used to execute the abnormal voltage filtering method according to any one of claims 1 to 7.

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