Energy storage battery fault online diagnosis and positioning method, electronic equipment and medium

Abstract

The present invention discloses a method, electronic device, and medium for online diagnosis and location of energy storage battery faults, relating to the field of battery fault technology. The method includes sequentially numbering m energy storage battery modules in a lithium-ion energy storage battery system and obtaining a data set corresponding to each numbered energy storage battery module; calculating a feature set corresponding to each data set; the feature set includes similarity, data distribution correlation coefficient, maximum current change rate, maximum voltage change rate, maximum SOC change rate, and maximum temperature change rate; and, based on the feature set and data set corresponding to each numbered energy storage battery module, determining the fault status of the numbered energy storage battery module in order of numbering. The present invention can achieve the purpose of online location and diagnosis of energy storage battery faults.

Description

Technical Field

[0001] The present invention relates to the technical field of battery failure, and in particular to an online diagnosis and positioning method for energy storage battery failure, electronic equipment, and a medium. Background Art

[0002] Currently, lithium-ion batteries have become one of the most widely used energy storage technologies due to their high energy density, high energy conversion efficiency, and long cycle life. However, existing lithium-ion battery systems cannot guarantee their safety, especially in the large-scale application of lithium-ion batteries in the energy storage field. Large lithium-ion energy storage battery systems are densely packed with lithium-ion batteries in large numbers. Therefore, the imbalance between individual lithium-ion batteries during the charging and discharging process is also a major cause of safety accidents in lithium-ion energy storage battery systems. The large amount of heat released by lithium-ion batteries from charge and material transport, as well as various chemical and electrochemical reactions, tends to accumulate within the battery. If the lithium-ion battery is operated in an internal environment with insufficient heat transfer, such as insulation or high temperature, the lithium-ion battery temperature will rise significantly, which may form "hot spots" within the lithium-ion battery and ultimately face the risk of thermal runaway. Therefore, safety issues have become a bottleneck for the large-scale application of lithium-ion batteries and a technical challenge that must be overcome.

[0003] To reduce the property losses and casualties caused by lithium-ion energy storage battery safety accidents, timely diagnosis and early warning of lithium-ion battery failures are particularly important. Therefore, researchers have conducted a series of studies on lithium-ion battery fault diagnosis, including monitoring and judging the safety status of lithium-ion batteries, promptly detecting abnormal conditions within lithium-ion batteries, and issuing early warnings of possible safety incidents. For example, when a lithium-ion battery cell exhibits an abnormality, such as high temperature, fire, or even explosion, the lithium-ion battery cell can promptly alarm and isolate it within a certain range, without affecting surrounding lithium-ion batteries, thus preventing a chain reaction that could lead to the expansion of the accident.

[0004] The types of faults currently involved in fault diagnosis research include electrical faults, defect faults, and thermal runaway faults. Electrical faults include overcharging, overdischarging, short circuits, and forced discharge. Defect faults include puncture faults and extrusion faults. Puncture faults can cause the battery to short-circuit at the puncture point. The short-circuit area forms a localized hot zone due to the large amount of Joule heat. When the temperature of the hot zone exceeds the critical point, thermal runaway will occur, resulting in smoke, fire, or even explosion. Extrusion faults are similar to puncture faults in that they both cause localized internal short circuits and may lead to thermal runaway. However, extrusion faults do not necessarily damage the battery casing.

[0005] In addition, existing technical methods focus on fault diagnosis of lithium-ion energy storage batteries before use, while there is little research on online real-time diagnostic technical methods during use; in addition, in fault diagnosis, various types of fault diagnosis are performed separately, such as single electrical fault diagnosis, single defect fault diagnosis, etc.; finally, most current diagnostic technologies cannot accurately locate the diagnosed faults, such as hazardous gas diagnosis. Summary of the Invention

[0006] The purpose of the present invention is to provide an energy storage battery fault online diagnosis and positioning method, electronic equipment and medium, which can achieve the purpose of online positioning and diagnosis of energy storage battery fault status.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] In a first aspect, the present invention provides a method for online diagnosis and location of energy storage battery faults, comprising:

[0009] Sequentially numbering m energy storage battery modules in a lithium-ion energy storage battery system and obtaining a data set corresponding to each numbered energy storage battery module; the data set includes image data, electrical quantity data, and temperature; the electrical quantity data includes current, terminal voltage, internal resistance, and SOC change rate of the energy storage battery module;

[0010] Calculating a feature set corresponding to each of the data sets; the feature set includes similarity, data distribution correlation coefficient, maximum current change rate, maximum voltage change rate, maximum SOC change rate, and maximum temperature change rate;

[0011] According to the feature set and data set corresponding to each numbered energy storage battery module, the fault status of the numbered energy storage battery module is determined in sequence according to the numbering sequence;

[0012] Wherein, for any of the numbered energy storage battery modules, the fault status judgment process is as follows:

[0013] An instruction for determining whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet a first condition, thereby obtaining a first judgment result; the energy storage battery module numbered i is any energy storage battery module after the number; and the first condition is that the similarity is greater than a minimum similarity when determining that the image is defect-free and the data distribution correlation coefficient is greater than a minimum data distribution correlation coefficient when determining that the image is defect-free;

[0014] If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, then it is determined that the energy storage battery module numbered i has no defect fault, and it is determined whether the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, to obtain a second judgment result; the second condition is that the maximum current change rate is less than the maximum current change rate allowed when the energy storage battery module is operating normally, the maximum voltage change rate is less than the maximum voltage change rate allowed when the energy storage battery module is operating normally, and the maximum SOC change rate is less than the maximum SOC change rate allowed when the energy storage battery module is operating normally;

[0015] If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, then it is determined that the energy storage battery module numbered i has no short circuit and no capacity rapid decay fault, and it is determined whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, to obtain a third judgment result; the third condition is that the temperature is less than the maximum operating temperature allowed for the energy storage battery module during normal operation and the maximum temperature change rate is less than the maximum temperature change rate allowed for the energy storage battery module during normal operation;

[0016] If the third judgment result indicates that the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, it is determined that the energy storage battery module numbered i does not have a thermal runaway fault, and the first operation is performed;

[0017] The first operation is to update the energy storage battery module numbered i to the energy storage battery module numbered i+1, and determine whether the updated energy storage battery module numbered i is the energy storage battery module numbered m+1. If so, the operation is stopped. If not, the operation returns to determine whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, and obtain a first judgment result.

[0018] Optionally, before executing the step of determining whether the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition and obtaining the third determination result, the method further includes:

[0019] Determine whether the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet a fourth condition, and obtain a fourth judgment result; the fourth condition is that the terminal voltage is less than the maximum terminal voltage allowed when the energy storage battery module is operating normally and the internal resistance is less than the maximum internal resistance allowed when the energy storage battery module is operating normally;

[0020] If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet the fourth condition, it is determined that the energy storage battery module numbered i has no overcharge or over-discharge fault, and the process jumps to the step of determining whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition to obtain a third judgment result;

[0021] If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i do not meet the fourth condition, it is determined that the energy storage battery module numbered i has an overcharge or over-discharge fault, and the first operation and the second operation are executed; the second operation is to output an instruction to cut off the energy storage battery module numbered i and an alarm instruction.

[0022] Optionally, it also includes:

[0023] If the third judgment result indicates that the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i do not meet the third condition, then it is determined whether the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed when the energy storage battery module is operating normally, to obtain a fifth judgment result;

[0024] If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed during normal operation of the energy storage battery module, then a second operation and the first operation are output; the second operation is an operation of outputting an instruction to remove the energy storage battery module numbered i and an alarm instruction;

[0025] If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is less than the maximum temperature change rate allowed when the energy storage battery module is operating normally, then it is determined whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain a sixth judgment result;

[0026] If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, then determining that the temperature of the energy storage battery module numbered i is too high, outputting an instruction to cut off the energy storage battery module numbered i, and returning to the step of determining whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain the sixth judgment result;

[0027] If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is lower than the maximum operating temperature allowed when the energy storage battery module is operating normally, an instruction to put the energy storage battery module numbered i back into use is output, and the first operation is executed.

[0028] Optionally, it also includes:

[0029] If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i do not meet the first condition, then the energy storage battery module numbered i is determined to be defective and faulty, and the first operation and the fourth operation are performed; the fourth operation is to output an instruction to remove the energy storage battery module numbered i and an alarm instruction, and to store the image data corresponding to the energy storage battery module numbered i in a database;

[0030] If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i do not meet the second condition, it is determined that the energy storage battery module numbered i has a short circuit fault or a rapid capacity decay fault, and the first operation and the second operation are performed;

[0031] The second operation is to output an instruction to cut off the energy storage battery module numbered i and an alarm instruction.

[0032] Optionally, calculating the feature set corresponding to each data set specifically includes:

[0033] The following steps are performed for each of the data sets:

[0034] Processing the image data to obtain similarity and data distribution correlation coefficient;

[0035] Processing the electrical quantity data to obtain a maximum current change rate, a maximum voltage change rate, and a maximum SOC change rate;

[0036] The temperature is processed to obtain a maximum temperature change rate.

[0037] Optionally, the processing of the image data to obtain similarity and data distribution correlation coefficient specifically includes:

[0038] Preprocessing the image data;

[0039] The preprocessed image data is input into the defect recognition model to obtain image defect information, and the similarity and data distribution correlation coefficient are calculated based on the image defect information.

[0040] Optionally, the processing of the electrical quantity data to obtain the maximum current change rate, the maximum voltage change rate, and the maximum SOC change rate specifically includes:

[0041] Based on the electrical quantity data, draw a current change curve, a terminal voltage change curve, and an SOC change rate curve within a set time period;

[0042] determining a maximum current change rate according to the current change curve;

[0043] determining a maximum voltage change rate according to the terminal voltage change curve;

[0044] According to the SOC change rate curve, a maximum SOC change rate is determined.

[0045] Optionally, processing the temperature to obtain a maximum temperature change rate specifically includes:

[0046] According to the temperature, a temperature change curve within a set time period is drawn;

[0047] According to the temperature change curve, a maximum temperature change rate is determined.

[0048] In a second aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the method for online diagnosis and positioning of energy storage battery faults according to the first aspect.

[0049] In a third aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for online diagnosis and positioning of energy storage battery faults as described in the first aspect.

[0050] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0051] The present invention performs cross-cycle online fault diagnosis on the electrical quantity state, image state and thermal runaway condition of the lithium-ion energy storage battery during use, thereby achieving the purpose of accurately locating and diagnosing the fault state of the energy storage battery online. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 Schematic diagram of the process of online diagnosis and positioning method for energy storage battery faults of the present invention;

[0054] Figure 2 This is a schematic diagram of the data processing flow of the energy storage battery module of the present invention;

[0055] Figure 3 This is a schematic diagram of the energy storage battery module fault judgment process of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] Example 1

[0059] The diagnosis object described in the embodiment of the present invention is a lithium-ion energy storage battery system. The lithium-ion energy storage battery system includes m groups of energy storage battery modules. The energy storage battery module is the smallest diagnosis unit in the embodiment of the present invention.

[0060] like Figure 1 As shown, an embodiment of the present invention provides an online diagnosis and positioning method for energy storage battery faults, including:

[0061] Step 100: Sequentially number the m energy storage battery modules in the lithium-ion energy storage battery system and obtain a data set corresponding to each numbered energy storage battery module; the data set includes image data, electrical quantity data, and temperature; the electrical quantity data includes the current, terminal voltage, internal resistance, and SOC change rate of the energy storage battery module.

[0062] As a preferred implementation, step 100 described in the embodiment of the present invention specifically includes:

[0063] Step 101: number m groups of energy storage battery modules (ESBM) in a lithium-ion energy storage battery system in sequence as #1, #2...#i...#m, i∈[1,...,m], and store them in a data acquisition and processing system.

[0064] Step 102: The data acquisition and processing system collects each set of ESBM image data, electrical quantity data, and temperature T in sequence from #1, #2 to #m. Each set of data forms a data set, and finally forms m sets of data sets. The number of each data set corresponds to the ESBM number. The ESBM electrical quantity data includes ESBM current I, terminal voltage V, internal resistance R, and State of Charge (SOC) change rate v. SOC .

[0065] Step 200: Calculate a feature set corresponding to each of the data sets; the feature set includes similarity, data distribution correlation coefficient, maximum current change rate, maximum voltage change rate, maximum SOC change rate and maximum temperature change rate.

[0066] As a preferred implementation, step 200 described in the embodiment of the present invention is as follows: Figure 2 As shown, specifically including:

[0067] The following steps are performed for each of the data sets:

[0068] Step 201: Process the image data to obtain similarity and data distribution correlation coefficient.

[0069] Step 202: Process the electrical quantity data to obtain a maximum current change rate, a maximum voltage change rate, and a maximum SOC change rate.

[0070] Step 203: Process the temperature to obtain a maximum temperature change rate.

[0071] Furthermore, step 201 specifically includes:

[0072] Step A: Preprocess the image data.

[0073] In the embodiment of the present invention, image preprocessing technology is used to further improve the quality of image data, mainly including image smoothing and denoising, filtering, image contrast enhancement, etc.

[0074] Step B: Input the preprocessed image data into the defect recognition model to obtain image defect information, and calculate the similarity α based on the image defect information i and data distribution correlation coefficient β i .

[0075] Among them, the minimum similarity for judging that the image is defect-free is set to α low , the minimum data distribution correlation coefficient β for judging that the image is defect-free low .

[0076] Furthermore, step 202 specifically includes:

[0077] According to the electrical quantity data, the current change curve, terminal voltage change curve and SOC change rate curve within the set time period Δt1 are drawn, and the maximum current change rate v within the set time period Δt1 is determined according to the current change curve. Ii , determine the maximum voltage change rate v within the set time period Δt1 according to the terminal voltage change curve Vi , determine the maximum SOC change rate v within the set time period Δt1 according to the SOC change rate curve SOCi .

[0078] Set the maximum terminal voltage allowed when the energy storage battery module is working normally to V max , the maximum allowable internal resistance is R max The maximum allowable current change rate is v I max The maximum allowable voltage change rate is v V max The maximum allowed SOC change rate is v SOC max .

[0079] Furthermore, step 203 specifically includes:

[0080] According to the temperature, a temperature change curve within the set time period Δt2 is drawn, and according to the temperature change curve, the maximum temperature change rate v within the set time period Δt2 is determined. Ti .

[0081] Set the maximum operating temperature allowed for the energy storage battery module to be T max The maximum allowable temperature change rate is v T max .

[0082] Step 300: According to the feature set and data set corresponding to each numbered energy storage battery module, the fault status of the numbered energy storage battery module is determined in sequence according to the numbering order.

[0083] Wherein, for any of the numbered energy storage battery modules, the fault status judgment process is as follows:

[0084] An instruction for determining whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet a first condition to obtain a first judgment result; the energy storage battery module numbered i is any energy storage battery module after the number; the first condition is that the similarity is greater than the minimum similarity when determining that the image is defect-free and the data distribution correlation coefficient is greater than the minimum data distribution correlation coefficient when determining that the image is defect-free.

[0085] If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, it is determined that the energy storage battery module numbered i has no defect fault, and it is determined whether the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, thereby obtaining a second judgment result; the second condition is that the maximum current change rate is less than the maximum current change rate allowed when the energy storage battery module is operating normally, the maximum voltage change rate is less than the maximum voltage change rate allowed when the energy storage battery module is operating normally, and the maximum SOC change rate is less than the maximum SOC change rate allowed when the energy storage battery module is operating normally.

[0086] If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, it is determined that the energy storage battery module numbered i has no short circuit and no capacity rapid decay fault, and it is determined whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, thereby obtaining a third judgment result; the third condition is that the temperature is less than the maximum operating temperature allowed for the energy storage battery module during normal operation and the maximum temperature change rate is less than the maximum temperature change rate allowed for normal operation of the energy storage battery module.

[0087] If the third judgment result indicates that the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, it is determined that the energy storage battery module numbered i has no thermal runaway fault, and the first operation is performed.

[0088] The first operation is to update the energy storage battery module numbered i to the energy storage battery module numbered i+1, and determine whether the updated energy storage battery module numbered i is the energy storage battery module numbered m+1. If so, the operation is stopped. If not, the operation returns to determine whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, and obtain a first judgment result.

[0089] Furthermore, before executing the step of determining whether the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition and obtaining the third determination result, the method further includes:

[0090] Determine whether the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet a fourth condition to obtain a fourth judgment result; the fourth condition is that the terminal voltage is less than the maximum terminal voltage allowed when the energy storage battery module is operating normally and the internal resistance is less than the maximum internal resistance allowed when the energy storage battery module is operating normally.

[0091] If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet the fourth condition, it is determined that the energy storage battery module numbered i has no overcharge or over-discharge fault, and the process jumps to the step of determining whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition to obtain the third judgment result.

[0092] If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i do not meet the fourth condition, it is determined that the energy storage battery module numbered i has an overcharge or over-discharge fault, and the first operation and the second operation are executed; the second operation is to output an instruction to cut off the energy storage battery module numbered i and an alarm instruction.

[0093] Furthermore, the method for online diagnosis and location of energy storage battery faults provided by the embodiment of the present invention further includes:

[0094] If the third judgment result indicates that the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i do not meet the third condition, then it is determined whether the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed when the energy storage battery module is operating normally, to obtain a fifth judgment result.

[0095] If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed during normal operation of the energy storage battery module, the second operation and the first operation are output; the second operation is to output an instruction to cut off the energy storage battery module numbered i and an alarm instruction.

[0096] If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is less than the maximum temperature change rate allowed when the energy storage battery module is operating normally, then it is determined whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain a sixth judgment result.

[0097] If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, it is determined that the temperature of the energy storage battery module numbered i is too high, and an instruction to cut off the energy storage battery module numbered i is output. The process returns to the step of determining whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain the sixth judgment result.

[0098] If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is lower than the maximum operating temperature allowed when the energy storage battery module is operating normally, an instruction to put the energy storage battery module numbered i back into use is output, and the first operation is executed.

[0099] Furthermore, the positioning method provided by the embodiment of the present invention further includes:

[0100] If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i do not meet the first condition, the energy storage battery module numbered i is determined to be defective and faulty, and the first operation and the fourth operation are performed; the fourth operation is to output an instruction to remove the energy storage battery module numbered i and an alarm instruction, and to store the image data corresponding to the energy storage battery module numbered i in a database.

[0101] If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i do not meet the second condition, it is determined that the energy storage battery module numbered i has a short circuit fault or a rapid capacity decay fault, and the first operation and the second operation are executed; the second operation is to output an instruction to remove the energy storage battery module numbered i and an alarm instruction.

[0102] An example:

[0103] like Figure 3 As shown, the fault condition of each ESBM group is diagnosed online in sequence from group #1 to group #m. The specific steps are as follows.

[0104] Input the initial value i=1.

[0105] 4.1 Determine whether the condition i≤m is met.

[0106] 4.1.1 If not satisfied, this diagnosis ends and jumps to step: Diagnose the next lithium-ion energy storage battery system.

[0107] 4.1.2 If satisfied, skip to step 4.2.

[0108] 4.2 Determine whether there is a defect or fault. Judgment condition α i >α low ∩β i >β low Is it satisfied?

[0109] 4.2.1 If not satisfied, skip to step 4.6.

[0110] 4.2.2 If satisfied, it is determined that group #i ESBM has no defect faults and jump to step 4.3.

[0111] 4.3 Determine whether there is a short circuit fault or rapid capacity decay. Ii <v I max ∩v Vi <v V max ∩v SOCi <v SOC max Is it satisfied?

[0112] If 4.3.1 is not satisfied, skip to step 4.6.

[0113] 4.3.2 If the conditions are met, determine that there is no short-circuit fault or rapid capacity decay in group #i ESBM, and jump to step 4.4.

[0114] 4.4 Determine if there is an overcharge or overdischarge fault. Determine if there is an overcharge or overdischarge fault. i <V max ∩R i <Rmax Is it satisfied?

[0115] 4.4.1 If not satisfied, skip to step 4.6.

[0116] 4.4.2 If the conditions are met, it is determined that there is no overcharge or overdischarge fault in group #i ESBM, and the process goes to step 4.5.

[0117] 4.5 Determine whether there is thermal runaway. Determination condition T i <T max ∩v Ti <v T max Is it satisfied?

[0118] 4.5.1 If it is not satisfied (i.e., if v Ti ≥v T max If the error is not found, skip to step 4.6.

[0119] 4.5.2 If not satisfied (i.e., T i ≥T max ∩v Ti <v T max If the temperature of group #i ESBM is too high, the group #i ESBM is temporarily removed and the process goes to step 4.5.4.

[0120] 4.5.3 If satisfied, determine that group #i ESBM has no faults and jump to step 4.7.

[0121] 4.5.4 Judgment Condition T i <T max Is it satisfied?

[0122] If 4.5.4.1 is not satisfied, skip to step 4.5.2.

[0123] 4.5.4.2 If satisfied, put group #i of ESBM back into use and skip to step 4.7.

[0124] 4.6 Determine that group #i ESBM is faulty, and then remove group #i ESBM. At the same time, the early warning system issues an early warning, and the fault data is stored in the database to achieve the purpose of expanding the database. Jump to step 4.7.

[0125] 4.7 Execute operation i=i+1, then jump to step 4.1 to perform the next set of ESBM fault diagnosis.

[0126] The embodiment of the present invention performs cross-cycle online fault diagnosis on the electrical quantity state, image state, and thermal runaway condition of the lithium-ion energy storage battery during use, and accurately locates the fault with the help of computer storage functions. In addition, the addition of the "automatic reclosing" concept (steps 4.5.2-4.5.4) greatly improves the accuracy of fault diagnosis.

[0127] The embodiments of the present invention can realize online real-time fault diagnosis of lithium-ion energy storage battery modules with high accuracy; the technical structure is simple, the calculation speed is fast, and the efficiency is high.

[0128] The defect failures mentioned in the embodiments of the present invention include: scratches, holes, bulges, bubbles, punctures, foreign matter, hidden cracks, defects, and decarbonization; electrical failures include: overcharging, over-discharging, short circuits, and rapid capacity decay; thermal runaway failures refer to: a situation where the temperature increases sharply in a short period of time.

[0129] Example 2

[0130] An embodiment of the present invention provides an electronic device including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the online diagnosis and positioning method for energy storage battery faults of embodiment 1.

[0131] Optionally, the above-mentioned electronic device may be a server.

[0132] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for online diagnosis and location of energy storage battery faults of the first embodiment is implemented.

[0133] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0134] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for online diagnosis and positioning of energy storage battery faults, characterized in that: include: Sequentially numbering m energy storage battery modules in a lithium-ion energy storage battery system and obtaining a data set corresponding to each numbered energy storage battery module; the data set includes image data, electrical quantity data, and temperature; the electrical quantity data includes current, terminal voltage, internal resistance, and SOC change rate of the energy storage battery module; Calculating a feature set corresponding to each of the data sets; the feature set includes similarity, data distribution correlation coefficient, maximum current change rate, maximum voltage change rate, maximum SOC change rate, and maximum temperature change rate; According to the feature set and data set corresponding to each numbered energy storage battery module, the fault status of the numbered energy storage battery module is determined in sequence according to the numbering sequence; Wherein, for any of the numbered energy storage battery modules, the fault status judgment process is as follows: An instruction for determining whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet a first condition, thereby obtaining a first judgment result; the energy storage battery module numbered i is any energy storage battery module after the number; and the first condition is that the similarity is greater than a minimum similarity when determining that the image is defect-free and the data distribution correlation coefficient is greater than a minimum data distribution correlation coefficient when determining that the image is defect-free; If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, then it is determined that the energy storage battery module numbered i has no defect fault, and it is determined whether the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, to obtain a second judgment result; the second condition is that the maximum current change rate is less than the maximum current change rate allowed when the energy storage battery module is operating normally, the maximum voltage change rate is less than the maximum voltage change rate allowed when the energy storage battery module is operating normally, and the maximum SOC change rate is less than the maximum SOC change rate allowed when the energy storage battery module is operating normally; If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i meet the second condition, then it is determined that the energy storage battery module numbered i has no short circuit and no capacity rapid decay fault, and it is determined whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, to obtain a third judgment result; the third condition is that the temperature is less than the maximum operating temperature allowed for the energy storage battery module during normal operation and the maximum temperature change rate is less than the maximum temperature change rate allowed for the energy storage battery module during normal operation; If the third judgment result indicates that the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition, it is determined that the energy storage battery module numbered i does not have a thermal runaway fault, and the first operation is performed; The first operation is to update the energy storage battery module numbered i to the energy storage battery module numbered i+1, and determine whether the updated energy storage battery module numbered i is the energy storage battery module numbered m+1. If so, the operation is stopped. If not, the operation returns to determine whether the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i meet the first condition, and obtain a first judgment result.

2. The method for online diagnosis and positioning of energy storage battery faults according to claim 1, characterized in that: Before executing the step of determining whether the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition and obtaining the third determination result, the method further includes: Determine whether the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet a fourth condition, and obtain a fourth judgment result; the fourth condition is that the terminal voltage is less than the maximum terminal voltage allowed when the energy storage battery module is operating normally and the internal resistance is less than the maximum internal resistance allowed when the energy storage battery module is operating normally; If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i meet the fourth condition, it is determined that the energy storage battery module numbered i has no overcharge or over-discharge fault, and the process jumps to the step of determining whether the temperature and maximum temperature change rate corresponding to the energy storage battery module numbered i meet the third condition to obtain a third judgment result; If the fourth judgment result indicates that the terminal voltage and internal resistance corresponding to the energy storage battery module numbered i do not meet the fourth condition, it is determined that the energy storage battery module numbered i has an overcharge or over-discharge fault, and the first operation and the second operation are executed; the second operation is to output an instruction to cut off the energy storage battery module numbered i and an alarm instruction.

3. The method for online diagnosis and positioning of energy storage battery faults according to claim 1, characterized in that: Also includes: If the third judgment result indicates that the temperature and the maximum temperature change rate corresponding to the energy storage battery module numbered i do not meet the third condition, then it is determined whether the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed when the energy storage battery module is operating normally, to obtain a fifth judgment result; If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is greater than or equal to the maximum temperature change rate allowed during normal operation of the energy storage battery module, then a second operation and the first operation are output; the second operation is an operation of outputting an instruction to remove the energy storage battery module numbered i and an alarm instruction; If the fifth judgment result indicates that the maximum temperature change rate corresponding to the energy storage battery module numbered i is less than the maximum temperature change rate allowed when the energy storage battery module is operating normally, then it is determined whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain a sixth judgment result; If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, then determining that the temperature of the energy storage battery module numbered i is too high, outputting an instruction to cut off the energy storage battery module numbered i, and returning to the step of determining whether the temperature corresponding to the energy storage battery module numbered i is greater than or equal to the maximum operating temperature allowed when the energy storage battery module is operating normally, to obtain the sixth judgment result; If the sixth judgment result indicates that the temperature corresponding to the energy storage battery module numbered i is lower than the maximum operating temperature allowed when the energy storage battery module is operating normally, an instruction to put the energy storage battery module numbered i back into use is output, and the first operation is executed.

4. The method for online diagnosis and positioning of energy storage battery faults according to claim 1, characterized in that: Also includes: If the first judgment result indicates that the similarity and data distribution correlation coefficient corresponding to the energy storage battery module numbered i do not meet the first condition, then the energy storage battery module numbered i is determined to be defective and faulty, and the first operation and the fourth operation are performed; the fourth operation is to output an instruction to remove the energy storage battery module numbered i and an alarm instruction, and to store the image data corresponding to the energy storage battery module numbered i in a database; If the second judgment result indicates that the maximum current change rate, maximum voltage change rate, and maximum SOC change rate corresponding to the energy storage battery module numbered i do not meet the second condition, it is determined that the energy storage battery module numbered i has a short circuit fault or a rapid capacity decay fault, and the first operation and the second operation are executed; the second operation is to output an instruction to remove the energy storage battery module numbered i and an alarm instruction.

5. The method for online diagnosis and positioning of energy storage battery faults according to claim 1, characterized in that: The calculating of the feature set corresponding to each of the data sets specifically includes: The following steps are performed for each of the data sets: Processing the image data to obtain similarity and data distribution correlation coefficient; Processing the electrical quantity data to obtain a maximum current change rate, a maximum voltage change rate, and a maximum SOC change rate; The temperature is processed to obtain a maximum temperature change rate.

6. The method for online diagnosis and positioning of energy storage battery faults according to claim 5, characterized in that: The processing of the image data to obtain similarity and data distribution correlation coefficient specifically includes: Preprocessing the image data; The preprocessed image data is input into the defect recognition model to obtain image defect information, and the similarity and data distribution correlation coefficient are calculated based on the image defect information.

7. The method for online diagnosis and positioning of energy storage battery faults according to claim 5, characterized in that: The processing of the electrical quantity data to obtain the maximum current change rate, the maximum voltage change rate, and the maximum SOC change rate specifically includes: Based on the electrical quantity data, draw a current change curve, a terminal voltage change curve, and an SOC change rate curve within a set time period; determining a maximum current change rate according to the current change curve; determining a maximum voltage change rate according to the terminal voltage change curve; According to the SOC change rate curve, a maximum SOC change rate is determined.

8. The method for online diagnosis and positioning of energy storage battery faults according to claim 5, characterized in that: The processing of the temperature to obtain the maximum temperature change rate specifically includes: According to the temperature, a temperature change curve within a set time period is drawn; According to the temperature change curve, a maximum temperature change rate is determined.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method for online diagnosis and positioning of energy storage battery faults according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements the energy storage battery fault online diagnosis and positioning method according to any one of claims 1 to 7.

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