A method and device for diagnosing battery status in an energy storage power station

Abstract

The present application belongs to the technical field of electrochemical energy storage power stations, and discloses a method and device for diagnosing the battery status in an energy storage power station. By obtaining the operating data of all single cells within a preset time interval, extracting the voltage extreme value in the operating data, determining the extreme value moment corresponding to the voltage extreme value, calculating the voltage average value of all single cells at the extreme value moment, and calculating the voltage difference between the real-time voltage value of the single cell and the voltage average value based on the voltage average value; determining the abnormal cells according to the voltage difference and the preset threshold value, and obtaining an abnormal cell set; performing a Lagerbus distribution check on the operating data, obtaining the abnormal value probability of the single cell, determining the problem cells according to the abnormal value probability, and obtaining a problem cell set; comprehensively evaluating the abnormal cell set and the problem cell set, and determining the final abnormal cell. It is possible to timely analyze the health status of the battery from the battery operating data, and ensure the safe and efficient operation of the energy storage power station.

Description

Technical Field

[0001] The present application relates to the technical field of electrochemical energy storage power stations, and in particular to a method and device for diagnosing the status of batteries in an energy storage power station. Background Art

[0002] With the shortage of non-renewable energy sources like coal and oil, and the increasing prominence of social issues such as climate change, more and more countries are actively taking measures, investing significant human and material resources in the research and development of new energy sources like wind power and solar power. Simultaneously, the electrochemical energy storage industry has attracted unprecedented attention. While this industry is growing, it also faces challenges. While lithium-ion batteries continue to advance, despite continuous improvements in manufacturing processes and packaging, they can still fail due to operational errors and aging. Once a battery failure occurs, if not promptly addressed, it can severely impact the performance of the entire battery pack, leading to irreversible damage. Therefore, timely and accurate diagnosis of battery failures is crucial. Each energy storage power station contains tens of thousands of energy storage cells, generating a vast amount of operational data such as temperature, voltage, and current. Therefore, it is crucial to develop a method to derive the battery's health status from this operational data. Summary of the Invention

[0003] To this end, the embodiments of the present application provide a method for diagnosing the battery status in an energy storage power station, which enables timely analysis of the battery health status from battery operating data and ensures the safe and efficient operation of the energy storage power station.

[0004] In a first aspect, the present application provides a method for diagnosing the status of batteries in an energy storage power station.

[0005] This application is achieved through the following technical solutions:

[0006] A method for diagnosing battery status in an energy storage power station, the method comprising:

[0007] Obtaining operating data of all single battery cells within a preset time interval, extracting voltage extremes from the operating data, determining an extreme value moment corresponding to the voltage extreme, calculating an average voltage value of all single battery cells at the extreme value moment, and calculating a voltage difference between real-time voltage values ​​of all single battery cells and the voltage average value based on the voltage average value;

[0008] Determine abnormal cells based on the voltage difference and a preset threshold value, and obtain an abnormal cell set;

[0009] Performing a Lagerbus distribution check on the operating data to obtain outlier probabilities of all the single battery cells, determining problematic battery cells based on the outlier probabilities, and obtaining a set of problematic battery cells;

[0010] The abnormal battery cell set and the problem battery cell set are comprehensively evaluated to determine the final abnormal battery cell.

[0011] In a preferred example of the present application, the steps of obtaining operating data of all single battery cells within a preset time interval, extracting voltage extremes from the operating data, determining extreme time points corresponding to the voltage extremes, calculating an average voltage value of all single battery cells at the extreme time points, and calculating a voltage difference between the real-time voltage values ​​of all single battery cells and the average voltage value based on the average voltage value include:

[0012] Extract the first voltage extreme value in the operating data Determine the first voltage extreme value At the corresponding first extreme moment k1, the first voltage average value of all single cells at the first extreme moment k1 is calculated. Calculate the real-time voltage value of all single cells and the first voltage average value A first voltage difference ΔV1 between

[0013] Extracting the second voltage extreme value from the operating data Determine the second voltage extreme value At the corresponding second extreme moment k2, the second voltage average value of all single cells at the second extreme moment k2 is calculated. Calculate the real-time voltage value of all single cells and the second voltage average value A second voltage difference ΔV2 is generated between them.

[0014] In a preferred example of the present application, it can be further configured that the step of determining abnormal cells based on the voltage difference and a preset threshold value to obtain an abnormal cell set includes:

[0015] If ΔV1>0 and ΔV2<0, and ABS(ΔV1)+ABS(ΔV2)≥C1 is satisfied at the same time, the cell is determined to be a short-board cell, and a short-board cell set is obtained based on the short-board cell, where C1 is the short-board threshold and ABS is the absolute value;

[0016] If ΔV1>0 and ΔV2<0, and C1>ABS(ΔV1)+ABS(ΔV2)≥C2 is satisfied, the cell is determined to be a short board trend cell, and a short board trend cell set is obtained based on the short board trend cell, C2 is the short board trend threshold, and ABS is the absolute value;

[0017] If ΔV1>0 and ΔV2>0, and ABS(ΔV1)+ABS(ΔV2)≥C3 is satisfied, the battery cell is determined to be an uneven battery cell, and an uneven battery cell set is obtained based on the uneven battery cell, where C3 is the uneven threshold and ABS is the absolute value;

[0018] If ΔV1<0 and ΔV2<0, and ABS(ΔV1)+ABS(ΔV2)≥C4 is satisfied, the cell is determined to be a bottom uneven cell, and a bottom uneven cell set is obtained based on the bottom uneven cell, where C4 is the bottom uneven threshold and ABS is the absolute value;

[0019] The abnormal battery cell set includes a short board battery cell set, a short board trend battery cell set, an upper uneven battery cell set and a lower uneven battery cell set.

[0020] In a preferred example of the present application, it can be further configured that after the step of obtaining the battery data of all single cells within a preset time interval, the following further steps are included:

[0021] The operating data is filtered according to a preset voltage range, and battery data that meets the preset voltage range is retained.

[0022] In a preferred example of the present application, it can be further set that the preset voltage range is:

[0023] The real-time voltage value of a single cell is greater than 3.5V or the real-time voltage value of a single cell is less than 3.0V.

[0024] In a preferred example of the present application, it can be further configured that the operation data is subjected to a Lagerbus distribution check to obtain the outlier probability of all single battery cells, and the problem battery cells are determined according to the outlier probability. The step of obtaining the problem battery cell set includes:

[0025] Performing a Lagabus check on the operating data, calculating a Lagabus distribution determination threshold, and obtaining a first threshold corresponding to a 99.5% abnormal probability and a second threshold corresponding to a 95% abnormal probability;

[0026] Normalizing the operating data to obtain a normalized value for each battery cell, comparing the normalized value with the first and second thresholds, and determining that the battery cell is a seriously problematic battery cell if the normalized value is greater than the first threshold;

[0027] If the normalized value is less than or equal to the first threshold and greater than the second threshold, the battery cell is determined to be a weak problem battery cell.

[0028] In a preferred example of the present application, it can be further configured that the short board threshold, the short board trend threshold, the upper unevenness threshold and the lower unevenness threshold are all set according to battery characteristics and charge and discharge rates.

[0029] In a second aspect, the present application provides a device for diagnosing the battery status in an energy storage power station.

[0030] This application is achieved through the following technical solutions:

[0031] A device for diagnosing battery status in an energy storage power station, the device comprising:

[0032] A first abnormality determination module is configured to obtain operating data of all single battery cells within a preset time interval, extract voltage extremes from the operating data, determine the extreme value moments corresponding to the voltage extremes, calculate the average voltage values ​​of all single battery cells at the extreme value moments, and calculate the voltage difference between the real-time voltage values ​​of all single battery cells and the voltage average value based on the voltage average value; determine abnormal battery cells based on the voltage difference and a preset threshold value to obtain an abnormal battery cell set;

[0033] A second abnormality determination module is configured to perform a Lagerbus distribution check on the operating data to obtain abnormal value probabilities of all the single battery cells, determine the problem battery cells based on the abnormal value probabilities, and obtain a set of problem battery cells;

[0034] The comprehensive determination module is used to comprehensively evaluate the abnormal battery cell set and the problem battery cell set to determine the final abnormal battery cell.

[0035] In a third aspect, the present application provides a computer device.

[0036] This application is achieved through the following technical solutions:

[0037] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements the steps of any one of the above-mentioned methods for diagnosing the battery status in an energy storage power station.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium.

[0039] This application is achieved through the following technical solutions:

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the above-mentioned methods for diagnosing the battery status in an energy storage power station.

[0041] In summary, compared with the prior art, the technical solution provided by the embodiment of the present application brings at least the following beneficial effects: by obtaining the operating data of all single cells within a preset time interval, extracting the voltage extreme value in the operating data, determining the extreme value moment corresponding to the voltage extreme value, calculating the voltage average value of all single cells at the extreme value moment, and calculating the voltage difference between the real-time voltage value of the single cell and the voltage average value based on the voltage average value; determining abnormal cells according to the voltage difference and the preset threshold value, and obtaining an abnormal cell set; performing a Lagrange distribution check on the operating data to obtain the abnormal value probability of the single cell, determining the problem cell according to the abnormal value probability, and obtaining a problem cell set; comprehensively evaluating the abnormal cell set and the problem cell set to determine the final abnormal cell. At the energy storage energy management system level, by collecting the operating data of the storage battery, the two methods of determining the abnormal cell by the voltage difference and the Grubbs criterion check model are used to screen out the single cells with poor health status in the battery system, which can guide the operation and maintenance of the energy storage power station, determine the abnormal cells and take corresponding measures in time, and ensure the efficient and safe operation of the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A flowchart of a method for diagnosing the battery status in an energy storage power station provided by an exemplary embodiment of the present application;

[0043] Figure 2 A schematic structural diagram of a battery status diagnosis system in an energy storage power station provided as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0044] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make non-creative modifications to the present embodiment as needed, but as long as they are within the scope of the claims of the present application, they are protected by the patent law.

[0045] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] In addition, the term "and / or" in this application is simply a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this application, unless otherwise specified, generally indicates that the related objects are in an "or" relationship.

[0047] In this application, the terms "first", "second", etc. are used to distinguish identical or similar items with substantially the same effects and functions. It should be understood that there is no logical or temporal dependency between "first", "second", and "nth", nor is there any limitation on the quantity and execution order.

[0048] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0049] In one embodiment of the present application, a method for diagnosing the battery status in an energy storage power station is provided, such as Figure 1 As shown, the main steps are described as follows:

[0050] S10: Obtain operating data of all single battery cells within a preset time interval, extract voltage extremes in the operating data, determine the extreme moment corresponding to the voltage extremes, calculate the voltage average of all single battery cells at the extreme moment, and calculate the voltage difference between the real-time voltage values ​​of all single battery cells and the voltage average based on the voltage average.

[0051] The battery management system of the energy storage power station is used to obtain the battery cell operation data within n hours of a certain day when battery status diagnosis is required. n is a natural number and can be selected as 1 hour, 2 hours or 3 hours, and can be set according to demand. The operation data of the single battery cells in the energy storage power station include: the voltage data, current data, temperature data, SOC data of the single battery cells, and the voltage data, current data, temperature data, SOC data of the battery pack. In this embodiment, the voltage data of the single battery cells are selected for analysis and diagnosis. The voltage extreme values ​​in the voltage data in the operation data are extracted, including the highest voltage value and the lowest voltage value within a preset time interval, and the voltage average value of all single battery cells at the corresponding moments of the two voltage extreme values ​​is calculated. The voltage difference between the real-time voltage value of all single battery cells and the voltage average value is calculated based on the voltage average value.

[0052] In some embodiments, after obtaining the operating data of all battery cells within a preset time interval, the operating data needs to be screened, and the operating data that meets the preset voltage range is retained according to the preset voltage range.

[0053] Specifically, when filtering data, it is determined whether there is a single cell voltage value that meets V in the operating data at time k. k >3.5V or Vk <3.0V, if at time k there is a single cell voltage value that satisfies V k >3.5V or V k <3.0V, then retain the operating data of the single cell at time k; if there is no single cell voltage value that meets V k >3.5V or V k If the voltage is <3.0V, the operating data of the single cell at time k will be eliminated. Preliminary screening of the operating data can ensure that the retained data conforms to the normal distribution, ensuring the accuracy of subsequent diagnosis. At the same time, it can simplify the data, reduce the diagnosis volume, and improve diagnostic efficiency.

[0054] In some embodiments, the specific steps of calculating the voltage difference are:

[0055] Extract the first voltage extreme value from the operating data The first voltage extreme value is the highest voltage value, and the first voltage extreme value is determined Corresponding to the first extreme moment k1, obtain the voltage values ​​of m single cells at the first extreme moment k1 And calculate the average first voltage of m single cells And calculate the voltage value of m single cells at the first extreme moment k1 Respectively with the first voltage average The difference between ΔV k1 ,

[0056] in is the voltage value of the first single cell at the first extreme moment k1 With the first voltage average The difference between is the voltage value of the second single cell at the first extreme moment k1 With the first voltage average The difference between is the voltage value of the mth single cell at the first extreme moment k1 With the first voltage average The difference between

[0057] Extract the second voltage extreme value from the operating data The second voltage extreme value is the lowest voltage value, and the second voltage extreme value is further determined At the corresponding second extreme moment k2, the voltage values ​​of m single cells at the second extreme moment k2 are obtained. And calculate the average second voltage of m single cells And calculate the voltage value of m single cells at the second extreme moment k2 and the second voltage average value The second voltage difference ΔV between k2 , in is the voltage value of the first single cell at the second extreme moment k2 and the second voltage average value The difference between is the voltage value of the second single cell at the first extreme moment k1 and the second voltage average value The difference between is the voltage value of the mth single cell at the first extreme moment k1 and the second voltage average value The difference between

[0058]

[0059] S20: Determine abnormal cells based on the voltage difference and a preset threshold value to obtain an abnormal cell set.

[0060] Based on the voltage difference calculated above, the voltage difference is compared with the preset threshold according to pre-set rules to identify abnormal cells with poor health. Each single cell is assigned a unique cell number during production. This cell number corresponds to each cell, and can be used to locate various information such as the cell's operating data and storage location. The battery numbers of all abnormal cells with poor health are aggregated to form the abnormal cell set.

[0061] Specifically, when the first voltage difference ΔV k1 Greater than 0, the second voltage difference ΔV k2 Less than 0, and at the same time satisfying the first voltage difference ΔV k1 The absolute value of the second voltage difference ΔV k2 When the absolute value of the combination is greater than or equal to the short board threshold C1, that is, if ΔV k1 >0 and ΔV k2 <0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C1, the single cell is determined to be a short-board cell, and the cell number corresponding to the single cell is added to the short-board cell set. Specifically, the short-board cell set can be represented as: DB = {a, a∈DB}, where a is the battery number of the corresponding single cell.

[0062] When the first voltage difference ΔV k1 Greater than 0, the second voltage difference ΔV k2 Less than 0, and at the same time satisfying the first voltage difference ΔV k1 The absolute value of the second voltage difference ΔV k2 When the absolute value of the combination is greater than or equal to the short board trend threshold C2 and less than the short board threshold C1, that is, if ΔV k1 >0 and ΔV k2 <0, and C1>ABS(ΔV k1 )+ABS(ΔV k2 )≥C2, the cell is determined to be a short-board trend cell, and the battery number corresponding to the cell is added to the short-board trend cell set. The short-board trend cell set can be expressed as: QS={b,b∈QS}, where b is the battery number of the corresponding cell.

[0063] When the first voltage difference ΔV k1 Greater than 0, the second voltage difference ΔV k2 Greater than 0, and at the same time satisfying the first voltage difference ΔV k1 The absolute value of the second voltage difference ΔV k2 When the sum of the absolute values ​​of is greater than or equal to the upper misalignment threshold C3, that is, ΔV k1 >0 and ΔV k2 >0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C3, the single cell is determined to be an uneven cell, and the battery number corresponding to the cell is added to the uneven cell set. The uneven cell set can be expressed as: UP = {c, c∈UP}, where c is the battery number of the corresponding single cell.

[0064] When the first voltage difference ΔV k1 Less than 0, the second voltage difference ΔV k2 Less than 0, and at the same time satisfying the first voltage difference ΔV k1 The absolute value of the second voltage difference ΔV k2 When the sum of the absolute values ​​of is greater than or equal to the lower uneven threshold C4, that is, ΔV k1 <0 and ΔV k2 <0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C4, the single cell is determined to be a down-mismatched cell, and the battery number corresponding to the single cell is added to the down-mismatched cell set. The down-mismatched cell set can be represented as: Down={d,d∈Down}, where d represents the battery number of the corresponding single cell.

[0065] The short board threshold, short board trend threshold, upper unevenness threshold, and lower unevenness threshold are all set by engineers based on the characteristics of the battery cell and the charge and discharge rate. For example, lithium-ion batteries, sodium-ion batteries, nickel-cadmium batteries, and polymer batteries require different settings due to the different battery cell types. Furthermore, for the same battery cell type, different thresholds may be required when charging and discharging at different charge and discharge currents (e.g., 1C, 2C, 5C, or 10C), or when charging and discharging at different temperatures (e.g., 25°C, 0°C, or -10°C). For example, when the ternary lithium-ion battery is charged and discharged at 0.5C at 25°C, the short board threshold can be set to 100mV, when charged and discharged at 1.0C, the short board threshold can be set to 150mV, and when charged and discharged at 2.0C, the short board threshold can be set to 200mV; accordingly, the short board trend threshold is 50mV greater than the short board threshold; the upper uneven threshold is set to half of the short board threshold; and the lower uneven threshold is set to half of the short board threshold.

[0066] The short board battery cell set DB, the short board trend battery cell set QS, the upper uneven battery cell set UP and the lower uneven battery cell set Down together constitute the abnormal battery cell set.

[0067] S30: Perform a Lagerbus distribution check on the operating data to obtain the outlier probability of all single cells, determine the problem cells based on the outlier probability, and obtain a set of problem cells.

[0068] It should be noted that the Grubbs distribution verification method is more rigorous and has a clear probability meaning, making it a better judgment criterion. For the m single cells in the battery cluster of the energy storage power station, assuming that the voltage value of these m single cells is V i Satisfies the normal distribution, then for the voltage values ​​of m single cells at a certain moment V1, V2, ..., V m It can be considered as a set of sample data that satisfies the normal distribution. According to Grubbs criterion, for a single cell voltage value V i When the following formula is satisfied:

[0069] in is the average voltage of m single cells, σ m is the voltage variance of m single cells, T(α,m) is the threshold; T i = is the Lagbus calculated value. The voltage value of the single cell is considered abnormal, indicating that the single cell may have a problem and is a problem cell. The cell numbers of all problem cells are counted to obtain a problem cell set.

[0070] Specifically, perform a Lagrange check on the operation data, calculate the determination threshold of the Lagrange distribution, and obtain the first threshold f1 corresponding to an abnormal probability of 99.5% and the second threshold f2 corresponding to an abnormal probability of 95%.

[0071] Normalize the above operation data to obtain the normalized value h of each single cell. i , and for the normalized value h of each single cell i Compare it with the first threshold f1 and the second threshold f2. If the normalized value h of a certain single cell i is greater than the first threshold f1, that is, when h i > f1, then determine that the single cell is a severely problematic cell, and put the battery number corresponding to the single cell into the severely problematic cell set. The severely problematic cell set can be expressed as: S1 = {e, e ∈ S1}, where e represents the battery number of the single cell.

[0072] If the normalized value of a certain single cell is less than or equal to the first threshold f1 and greater than the second threshold f2, that is, f1 ≤ h i < f2, then determine that the problematic cell is a weakly problematic cell, and put the battery number corresponding to the single cell into the weakly problematic cell set. The weakly problematic cell set can be expressed as: S2 = {f, f ∈ S2}, where f represents the battery number of the single cell. In some embodiments, the value of f1 is 3.8171 and the value of f2 is 3.2685.

[0073] Among them, normalization is performed using the N method, with μ = 0 and σ = 1. The sklearn.preprocessing.scale function is used for normalization. The operation data is subtracted from its mean value by row or column, and then divided by the variance. Finally, the result is that for each attribute / each column, all data is concentrated near 0, and the element matrix with a standard value of 1. The calculation formula of the N method is: x * =(X - X mean ) / X std , where X is the element, X mean is the average value of all elements, X std is the element standard deviation, and x * [[ID= 31]]is the normalized value.

[0074] S40: Evaluate by combining the abnormal cell set and the problematic cell set to determine the final abnormal cells.

[0075] Perform a comprehensive determination on the abnormal cell set and the problematic cell set determined by the above voltage difference, and comprehensively screen out the cells with relatively poor health status in the battery management system through two methods to guide the operation and maintenance work of the energy storage power station.

[0076] By comprehensively screening the battery cells with poor health status in the battery management system through two methods, the accuracy of the judgment can be improved.

[0077] Specifically, the comprehensive judgment method is as follows: the single cells belonging to the short-board cell set DB and the weak problem cell set S2 or the single cells belonging to the strong problem cell set S1 are judged as the final short-board cells and included in the final short-board cell set, which is recorded as DB2 = {j|j∈(DB∩S2)∪S1}; the single cells that simultaneously belong to the lower uneven cell set Down, belong to the weak problem cell set S2, but do not belong to the final short-board cell set DB2 are recorded as set A, which is recorded as The single cells that belong to both the upper uneven cell set UP and the weak problem cell set S2, but do not belong to the final short board cell set DB2, are recorded as set B. Further, the single cells belonging to both set A and set B are determined as the final short board trend cell set QS2, QS2 = {j|j∈A&j∈B}; the single cells belonging to set A but not belonging to the final short board trend cell QS2 are determined as the final uneven cells The single cells that belong to set B but not to the final short board trend cell set QS2 are recorded as the final uneven cells UP2. Combining the two methods to screen out single cells in poor health status can effectively improve the accuracy of analysis and judgment.

[0078] Abnormal cells are also categorized as final short board cells, final short board trend cells, final top uneven cells, and final bottom uneven cells. This helps differentiate the degree of abnormality and address it accordingly. Cells categorized as final top uneven cells or final bottom uneven cells can be restored to normal by adjusting battery parameters and do not require replacement. Cells categorized as final short board trend cells require continuous parameter monitoring after parameter adjustment to monitor changes in the cell. If normal is restored, normal use is permitted; if the cell's performance deteriorates, replacement is required. Cells categorized as short board cells require timely replacement to prevent them from impacting the overall battery pack's performance. Adopting different treatment methods for different cell types can improve the efficiency of energy storage power plants, maintain the consistency of the battery packs within them, and ensure maximum efficiency.

[0079] The present application also provides a device for diagnosing the battery status in an energy storage power station, such as Figure 2 As shown, the diagnostic device includes:

[0080] The first abnormality determination module 1 is used to obtain the operating data of all single battery cells within a preset time interval, extract the voltage extreme value in the operating data, determine the extreme value time corresponding to the above voltage extreme value, calculate the voltage average value of all single battery cells at the extreme value time, and calculate the voltage difference between the real-time voltage value of all single battery cells and the voltage average value based on the voltage average value; determine the abnormal battery cells based on the voltage difference and the preset threshold value to obtain the abnormal battery cell set;

[0081] The second abnormality determination module 2 is used to perform a Lagerbus distribution check on the above-mentioned operating data to obtain the abnormal value probability of all single cells, determine the problem cells according to the abnormal value probability, and obtain a set of problem cells;

[0082] Comprehensive judgment module 3 is used to comprehensively evaluate the abnormal battery cell set and the problem battery cell set to determine the final abnormal battery cell.

[0083] The first abnormality determination module 1 further includes a difference calculation unit 11 for extracting the first voltage extreme value in the operation data. Determine the first voltage extreme value At the corresponding first extreme moment k1, the first voltage average value of all single cells at the first extreme moment k1 is calculated. Calculate the real-time voltage value of all single cells and the first voltage average A first voltage difference ΔV1 between

[0084] Extract the second voltage extreme value from the operating data Determine the second voltage extreme At the corresponding second extreme moment k2, the second voltage average value of all single cells at the second extreme moment k2 is calculated. Calculate the real-time voltage value of all single cells and the second voltage average value A second voltage difference ΔV2 is generated between them.

[0085] In one embodiment, a computer device is provided, which may be a server.

[0086] The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium contains an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When executed by the processor, the computer program implements any of the above-described methods for diagnosing the battery status in an energy storage power station.

[0087] In one embodiment, a computer-readable storage medium is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement any of the above-mentioned methods for diagnosing the battery status in an energy storage power station.

[0088] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0089] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, the division of the above-mentioned functional units and modules is only used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system described in this application is divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for diagnosing battery status in an energy storage power station, characterized in that: The method comprises: Obtaining operating data of all single battery cells within a preset time interval, extracting voltage extremes from the operating data, determining an extreme value moment corresponding to the voltage extreme, calculating an average voltage value of all single battery cells at the extreme value moment, and calculating a voltage difference between real-time voltage values ​​of all single battery cells and the voltage average value based on the voltage average value; The first voltage extreme value in the operating data is extracted Determine the first voltage extreme value At the corresponding first extreme moment k1, the first voltage average value of all single cells at the first extreme moment k1 is calculated. Calculate the real-time voltage value of all single cells and the first voltage average value The first voltage difference ΔV between k1 ; Extracting the second voltage extreme value from the operating data Determine the second voltage extreme value At the corresponding second extreme moment k2, the second voltage average value of all single cells at the second extreme moment k2 is calculated. Calculate the real-time voltage value of all single cells and the second voltage average value The second voltage difference ΔV between k2 ; Determine abnormal cells based on the voltage difference and a preset threshold value, and obtain an abnormal cell set; Among them, if ΔV k1 >0 and ΔV k2 <0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C1, the cell is determined to be a short-board cell, and a short-board cell set is obtained based on the short-board cell, where C1 is the short-board threshold and ABS is the absolute value; If ΔV k1 >0 and ΔV k2 <0, and C1>ABS(ΔV k1 )+ABS(ΔV k2 )≥C2, the cell is determined to be a short board trend cell, and a short board trend cell set is obtained based on the short board trend cell, where C2 is the short board trend threshold and ABS is the absolute value; If ΔV k1 >0 and ΔV k2 >0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C3, the battery cell is determined to be an uneven battery cell, and an uneven battery cell set is obtained based on the uneven battery cell, where C3 is an uneven threshold and ABS is an absolute value; If ΔV k1 <0 and ΔV k2 <0, and ABS(ΔV k1 )+ABS(ΔV k2 )≥C4, the cell is determined to be a bottom uneven cell, and a bottom uneven cell set is obtained based on the bottom uneven cell, where C4 is a bottom uneven threshold and ABS is an absolute value; The abnormal battery cell set includes a short board battery cell set, a short board trend battery cell set, an upper uneven battery cell set and a lower uneven battery cell set; Performing a Lagerbus distribution check on the operating data to obtain outlier probabilities of all the single battery cells, determining problematic battery cells based on the outlier probabilities, and obtaining a set of problematic battery cells; The abnormal battery cell set and the problem battery cell set are comprehensively evaluated to determine the final abnormal battery cell.

2. The method for diagnosing battery status in an energy storage power station according to claim 1, characterized in that: After the step of obtaining the operating data of all single cells within a preset time interval, the method further includes: The operating data is screened according to a preset voltage range, and operating data that meets the preset voltage range is retained.

3. The method for diagnosing battery status in an energy storage power station according to claim 2, characterized in that: The preset voltage range is: The real-time voltage value of a single cell is greater than 3.5V or the real-time voltage value of a single cell is less than 3.0V.

4. The method for diagnosing battery status in an energy storage power station according to claim 1, characterized in that: The steps of performing a Lagerbus distribution check on the operating data to obtain outlier probabilities of all single cells, and determining problematic cells based on the outlier probabilities to obtain a set of problematic cells include: Performing a Lagabus check on the operating data, calculating a Lagabus distribution determination threshold, and obtaining a first threshold corresponding to a 99.5% abnormal probability and a second threshold corresponding to a 95% abnormal probability; Normalizing the operating data to obtain a normalized value for each battery cell, comparing the normalized value with the first and second thresholds, and determining that the battery cell is a seriously problematic battery cell if the normalized value is greater than the first threshold; If the normalized value is less than or equal to the first threshold and greater than the second threshold, the battery cell is determined to be a weak problem battery cell.

5. The method for diagnosing battery status in an energy storage power station according to claim 1, characterized in that: The short board threshold, the short board trend threshold, the upper unevenness threshold, and the lower unevenness threshold are all set according to battery characteristics and charge and discharge rates.

6. A diagnostic device for battery status in an energy storage power station, characterized in that: For performing the diagnostic method according to claim 1, the apparatus comprises: A first abnormality determination module is configured to obtain operating data of all single battery cells within a preset time interval, extract voltage extremes from the operating data, determine the extreme value moments corresponding to the voltage extremes, calculate the average voltage values ​​of all single battery cells at the extreme value moments, and calculate the voltage difference between the real-time voltage values ​​of all single battery cells and the voltage average value based on the voltage average value; determine abnormal battery cells based on the voltage difference and a preset threshold value to obtain an abnormal battery cell set; A second abnormality determination module is configured to perform a Lagerbus distribution check on the operating data to obtain abnormal value probabilities of all the single battery cells, determine the problem battery cells based on the abnormal value probabilities, and obtain a set of problem battery cells; The comprehensive determination module is used to comprehensively evaluate the abnormal battery cell set and the problem battery cell set to determine the final abnormal battery cell.

7. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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