Electrochemical energy storage system fault diagnosis method and system and electronic equipment

Abstract

The invention relates to an electrochemical energy storage system fault diagnosis method and system and electronic equipment, and the method comprises the steps: obtaining a plurality of real-time operation parameters of an electrochemical energy storage system, and determining a first type of parameters and a second type of parameters in the real-time operation parameters; when it is determined that the first type of parameters exist, the first type of parameters are diagnosed through a threshold diagnosis method, and the fault type of the energy storage system is determined; when it is determined that the second type of parameters exist, the second type of parameters are diagnosed through a parameter fusion diagnosis method, and the parameter fusion diagnosis method comprises the steps that feature fusion is conducted on the second type of parameters to obtain comprehensive parameters, and the fault type of the energy storage system is judged according to the comprehensive parameters and a preset fault threshold value.

Description

Technical Field

[0001] The present application relates to the field of fault diagnosis, and in particular to a method, system and electronic equipment for fault diagnosis of an electrochemical energy storage system. Background Art

[0002] The safe operation of electrochemical energy storage systems is related to the economic benefits and personnel safety of energy storage power stations. With the rapid development of energy storage technology, how to ensure the safety and reliability of energy storage systems has become a hot topic in current research.

[0003] Most of the existing energy storage system fault diagnosis methods rely on traditional detection methods, which judge a single parameter or several parameters through simple formula calculations, which cannot fully reflect the actual state of the system, and the diagnostic accuracy and efficiency are limited. Therefore, it is of great significance to develop an efficient and accurate energy storage system fault diagnosis method.

[0004] The comprehensiveness and diagnostic accuracy of existing energy storage system fault diagnosis methods need to be improved. Summary of the invention

[0005] The embodiments of the present application provide an electrochemical energy storage system fault diagnosis method, system and electronic device to at least solve the problem that the comprehensiveness and diagnostic accuracy of the energy storage system fault diagnosis method in the related art need to be improved.

[0006] In a first aspect, an embodiment of the present application provides a method for diagnosing a fault in an electrochemical energy storage system, comprising:

[0007] Acquire a plurality of real-time operating parameters of the electrochemical energy storage system, and determine a first category parameter and a second category parameter from the plurality of real-time operating parameters;

[0008] When the first category parameters are determined, diagnose the first category parameters by a threshold diagnosis method to determine the fault type of the energy storage system;

[0009] When the second category parameters are determined, the second category parameters are diagnosed by a parameter fusion diagnosis method, wherein the parameter fusion diagnosis method includes performing feature fusion on the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

[0010] In one embodiment, the second category parameters include different types of real-time operating parameters, and feature fusion is performed on the second category parameters to obtain comprehensive parameters, including:

[0011] Perform time synchronization on multiple input real-time operating parameters;

[0012] Standardize the real-time operating parameters after time synchronization;

[0013] Screening the real-time operating parameters after standardization;

[0014] The principal component analysis or independent component analysis method is used to perform feature fusion on the screened real-time operating parameters to obtain comprehensive parameters.

[0015] In one embodiment, the parameter fusion diagnosis method is implemented by a machine learning model, and the model includes a dynamic adaptive module.

[0016] The dynamic adaptive module is used to automatically adjust the parameters of the model according to historical operating parameters and real-time operating parameters.

[0017] In one embodiment, the first category parameters include a single type of real-time operating parameters, and the threshold diagnosis method includes:

[0018] The fault type of the energy storage system is directly determined according to the input first category parameter and the preset diagnosis threshold corresponding to the first category parameter.

[0019] In one embodiment, the method further comprises:

[0020] Determine a fault grade according to the fault type and a preset fault grade threshold value corresponding to the fault type;

[0021] The fault type and the fault level are fed back to the operation and maintenance personnel.

[0022] In one embodiment, the method further comprises:

[0023] Obtaining corresponding operation and maintenance suggestions from a preset operation and maintenance database according to the fault type and the fault level;

[0024] An operation and maintenance work order is generated according to the fault type, fault level and the operation and maintenance suggestion, so that the operation and maintenance personnel can perform maintenance according to the operation and maintenance work order.

[0025] In one embodiment, after obtaining a plurality of real-time operating parameters of the electrochemical energy storage system, the method further includes:

[0026] The multiple real-time operating parameters are screened and normalized.

[0027] In a second aspect, an embodiment of the present application provides an electrochemical energy storage system fault diagnosis system, comprising:

[0028] An acquisition module: used for acquiring a plurality of real-time operating parameters of the electrochemical energy storage system, and determining a first category parameter and a second category parameter from the plurality of real-time operating parameters;

[0029] A threshold diagnosis module: used for diagnosing the first category parameters by a threshold diagnosis method to determine the fault type of the energy storage system when the first category parameters are determined;

[0030] Parameter fusion diagnosis module: used to diagnose the second category parameters through a parameter fusion diagnosis method when the second category parameters are determined, the parameter fusion diagnosis method includes fusing the features of the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

[0031] In a third aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electrochemical energy storage system fault diagnosis method as described in the first aspect above is implemented.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the electrochemical energy storage system fault diagnosis method as described in the first aspect above.

[0033] The electrochemical energy storage system fault diagnosis method, system and electronic device provided in the embodiments of the present application have at least the following technical effects.

[0034] This application monitors multiple operating parameters of the energy storage system in real time, adopts different diagnostic methods for different parameters, and combines the parameter fusion diagnostic method to fuse multiple operating parameters into a comprehensive parameter that can fully reflect the operating status and performance of the energy storage system, thereby improving the comprehensiveness, accuracy and reliability of fault diagnosis.

[0035] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 is a flow chart of a method for diagnosing a fault in an electrochemical energy storage system provided in an embodiment of the present application;

[0038] Figure 2 is a structural schematic diagram of a fault diagnosis system for an electrochemical energy storage system according to an exemplary embodiment;

[0039] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0041] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0042] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0043] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0044] In a first aspect, the present application provides a method for diagnosing a fault in an electrochemical energy storage system. Figure 1 is a flow chart of a method for diagnosing a fault in an electrochemical energy storage system provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0045] Step S101, obtaining a plurality of real-time operating parameters of the electrochemical energy storage system, and determining a first category parameter and a second category parameter from the plurality of real-time operating parameters.

[0046] Optionally, the operating parameters of the electrochemical energy storage system are collected in real time through the battery management system, energy storage inverter and multi-dimensional sensors, mainly through the battery management system, energy storage inverter interface to open the collection of battery cells, battery clusters, battery compartment telesignaling and telemetry data. The operating parameters include but are not limited to voltage, current, temperature, sound, gas, stress, internal resistance and other parameters. The first category parameters are preset single category operating parameters, and the second category parameters are different categories of operating parameters. For example, the input parameters are time, cluster current of the battery cluster, battery charge and battery cell voltage, then the input parameters as a whole are the second category operating parameters, and the cluster current of the battery cluster and the battery cell voltage are the first category parameters respectively.

[0047] In one example, after obtaining multiple real-time operating parameters of the electrochemical energy storage system, the method further includes: screening and normalizing the multiple real-time operating parameters. Optionally, the real-time operating parameters are screened, cleaned and normalized to filter out interfering data such as problematic data and missing data to improve data quality.

[0048] Step S102: when it is determined that there are first category parameters, the first category parameters are diagnosed by a threshold diagnosis method to determine the fault type of the energy storage system.

[0049] Optionally, the health of key components and systems such as the battery body, battery cluster, battery compartment, circuit breaker, and insulating components in the electrochemical energy storage system is determined by a threshold diagnosis method. The threshold diagnosis method mainly involves operating parameters such as battery voltage, current, temperature, and charge and discharge rate. The main thresholds set include but are not limited to voltage thresholds (upper voltage threshold, lower voltage threshold), current thresholds (charging current threshold is generally 10% to 20% of the rated current of the battery, discharge current threshold), upper temperature threshold, and charge and discharge rate threshold. When the real-time operating parameters are within the threshold range, it is a normal operating state; when the real-time operating parameters are not within the threshold range, there is a fault problem, and the type of fault is determined based on the real-time operating parameters.

[0050] In one example, the first category parameters include a single type of real-time operating parameters, and the threshold diagnosis method includes: directly determining the fault type of the energy storage system according to the input first category parameters and a preset diagnosis threshold corresponding to the first category parameters.

[0051] Optionally, the first category parameters include but are not limited to the voltage, temperature, and current of a single cell; the voltage, current, power, and health status of a battery cluster. A threshold corresponding to each parameter is preset. When the operating parameter is within the threshold range, it is a normal operating state; when the operating parameter is not within the threshold range, there is a fault problem, and the type of fault is determined according to the operating parameter. Table 1 is a threshold diagnosis fault condition table according to an exemplary embodiment.

[0052] Table 1

[0053]

[0054]

[0055] As shown in Table 1, the fault type is determined according to the single operating parameter of the battery component and the preset threshold, wherein the fault type is presented in the form of component-abnormal item-fault item. The fault items involved in this method are not limited to those listed in Table 1, which is only an example.

[0056] Referring to Table 1, the method further includes: determining a fault level according to the fault type and a preset fault classification threshold corresponding to the fault type, and feeding back the fault type and the fault level to the operation and maintenance personnel.

[0057] Optionally, each fault item is set with a corresponding fault level. For example, if the battery cell current is greater than a preset current threshold, it is a level 2 fault. If the battery cell temperature is less than a preset temperature threshold, it is a normal state; if the battery cell temperature is greater than a preset temperature threshold and less than a level 1 temperature fault threshold, it is a level 2 fault; if the battery cell temperature is greater than or equal to a level 1 temperature fault threshold, it is a level 1 fault. The preset diagnostic threshold and preset fault classification threshold corresponding to each fault can be set according to the actual application scenario.

[0058] Step S103, when it is determined that there are second category parameters, the second category parameters are diagnosed by a parameter fusion diagnosis method, the parameter fusion diagnosis method includes fusing features of the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

[0059] Optionally, the second category parameters include different types of real-time operating parameters, such as time, current, and voltage as a group of second category parameters; time, cluster current, battery power, and single cell voltage as a group of second category parameters. Table 2 is a parameter fusion diagnosis fault condition table according to an exemplary embodiment.

[0060] Table 2

[0061]

[0062] Optionally, the parameter fusion diagnosis method is implemented through a machine learning model, and the real-time collected operating parameters are input into the trained machine learning model to determine whether the energy storage system has risks such as abnormal failure, internal short circuit, thermal runaway, etc. according to the input feature vector. Among them, the diagnosis types involved in this method are not limited to those listed in Table 2, which is only an example.

[0063] In one example, the parameter fusion diagnosis method is implemented by a machine learning model, which includes a dynamic adaptive module, and the dynamic adaptive module is used to automatically adjust the parameters of the model according to historical operating parameters and real-time operating parameters.

[0064] Optionally, the parameter fusion diagnosis method is implemented by a machine learning model, which is trained based on historical operating parameters and corresponding operating conditions. A dynamic adaptive mechanism is introduced on the basis of the machine learning algorithm, so as to automatically adjust the parameters and structure of the model according to the real-time operating conditions and historical fault data of the electrochemical energy storage system, so as to improve the adaptability and accuracy of the model diagnosis.

[0065] In one example, the second category parameters include different types of real-time operating parameters. In step S103, feature fusion is performed on the second category parameters to obtain comprehensive parameters, including:

[0066] Step S1031, time synchronization is performed on the input multiple real-time operating parameters. Optionally, since different sensors may sample data at different time points, it is necessary to time synchronize the data at different time points to ensure that all data correspond to the same system state.

[0067] Step S1032, standardize the real-time operating parameters after time synchronization. Optionally, for operating data with different units and dimensions, standardization is required to eliminate the impact of unit differences on subsequent analysis. Data standardization is usually achieved through data mean and standard deviation.

[0068] Step S1033, screen the normalized real-time operating parameters. Optionally, feature selection is required before parameter fusion to remove operating parameters that have little contribution to diagnosis or are highly correlated with other features. This can be achieved by using feature selection methods such as statistical tests, information gain, and mutual information.

[0069] Step S1034, perform feature fusion on the screened real-time operating parameters by principal component analysis or independent component analysis to obtain comprehensive parameters. Optionally, principal component analysis and independent component analysis can make full use of the correlation between operating parameters to extract more representative features.

[0070] After data fusion, the fused comprehensive parameters can also be evaluated based on historical data to ensure that the comprehensive parameters can accurately reflect the state of the energy storage system. Through steps S1031 to S1034, multiple parameters are integrated into a comprehensive vector to reflect the operating status of the energy storage system. In this way, the integrity and accuracy of parameter information are ensured, while data redundancy and conflict are reduced, thereby comprehensively and accurately reflecting the operating status of the energy storage system, and improving the accuracy and reliability of the fault diagnosis method in the energy storage system.

[0071] As an example, referring to Table 2, it is assumed that the second category parameters are time, battery cluster current, battery power, and battery cell voltage, and the diagnosis type is an internal short circuit fault. The model fuses the features of the input parameters to obtain comprehensive parameters, including: obtaining the remaining charging power after each full charge, and obtaining the leakage power between the two charges by the difference in the remaining charging power of the battery cell after the two charges, that is, the power consumed by the internal short circuit. After obtaining the leakage power, the internal short circuit current is obtained according to the difference between the two charging end times. The level of the internal short circuit fault is determined according to the threshold of the corresponding level of the internal short circuit current. The preset fault threshold of the internal short circuit fault is based on the industrial standard (GB / T31484-2015) that the power retention rate of a fully charged battery cell after standing for 28 days should be above 85%, that is, the self-discharge rate of a normal battery cannot exceed C / 4480, where C is the capacitance.

[0072] An internal short circuit in an energy storage battery refers to a direct contact between two or more electrodes inside the energy storage battery, which causes a large amount of current to be unable to flow along the expected path, thereby causing safety issues such as overheating, expansion, explosion, and even fire inside the battery. By monitoring and analyzing the equipment operation data and current operating status, the internal short circuit current of the battery is monitored. When an internal short circuit occurs in the battery, an early warning is issued to facilitate timely operation and maintenance, avoid affecting the efficient operation of the energy storage power station, and reduce the risk and probability of accidents.

[0073] In one example, the method further includes: determining a fault level according to the fault type and a preset fault grading threshold corresponding to the fault type, and feeding back the fault type and the fault level to the operation and maintenance personnel.

[0074] Optionally, the fault level can be divided into level one, level two, and level three faults. Level one faults are abnormal faults that seriously affect the operation of energy storage equipment, and the equipment needs to be stopped immediately and repairs arranged as soon as possible. Level two faults are abnormal faults with self-healing abnormalities, abnormal operation of the equipment, but no immediate shutdown is required. Level three faults are faults that cause minor safety or quality risks due to abnormal on-site environmental conditions, which can be eliminated after the external environmental conditions are restored. Fault levels can also be divided into colors to quickly distinguish the severity of the faults, for example, level one faults are red, level two faults are orange, and level three faults are yellow. Among them, the fault level division and color distinction in this example are only used as an example, and the specific fault levels and colors can be divided according to actual application scenarios and specific needs. In this way, fault information is fed back to the operation and maintenance personnel in a timely manner, so that the operation and maintenance personnel can carry out operation and maintenance in a timely manner, avoid affecting the efficient operation of the energy storage power station, and reduce the risk and probability of accidents.

[0075] As an example, the second category parameters are time, battery cluster current, battery power, battery cell voltage, and the diagnosis type is internal short circuit fault diagnosis. When performing fault diagnosis, the preset fault threshold (three-level fault threshold) of the internal short circuit fault is C / 4480, the second-level fault threshold is C / 1000, and the first-level fault threshold is C / 100. When the internal short circuit current is less than C / 4480, it indicates that the system is operating normally; when the internal short circuit current is greater than C / 4480 and less than C / 1000, it is a third-level fault, and a yellow warning is issued; when the internal short circuit current is greater than C / 1000 and less than C / 100, it is a second-level fault, and an orange warning is issued; when the internal short circuit current is greater than C / 1000, it is a first-level fault, and a red warning is issued.

[0076] In one example, the method further includes:

[0077] Step S401: Obtain corresponding operation and maintenance suggestions from a preset operation and maintenance database according to the fault type and fault level.

[0078] Step S402: Generate an operation and maintenance work order according to the fault type, fault level and operation and maintenance suggestion, so that the operation and maintenance personnel can perform maintenance according to the operation and maintenance work order.

[0079] Optionally, assuming that there is an internal short-circuit fault in the energy storage system, if it is a level 1 fault, it is recommended to shut down immediately and troubleshoot the problem, fill in the defect record, and fill in the maintenance plan. If it is a level 2 or 3 fault, reduce the power and troubleshoot the problem, including: check whether the appearance of the problem battery is intact without damage or expansion, and whether there is deformation, leakage, etc. Check the peripheral connections of the battery and tighten the positive and negative terminals of the battery. After confirming that there are no above problems, shut down and let it stand for a while, restart the machine and observe whether the problem is eliminated. If the problem still exists, contact the manufacturer for testing and replacement. In this way, it is convenient for operation and maintenance personnel to carry out operation and maintenance in a timely manner to avoid affecting the efficient operation of the energy storage power station and reduce the risk and probability of accidents.

[0080] In summary, the electrochemical energy storage system fault diagnosis method provided in the present application monitors the various operating parameters of the energy storage system in real time and adopts different diagnostic methods for different parameters. And combined with the parameter fusion diagnosis method, it comprehensively reflects the operating status and performance of the energy storage system, and improves the accuracy and reliability of fault diagnosis. At the same time, the diagnosis results are fed back to the operation and maintenance personnel in a timely manner, so that the operation and maintenance personnel can carry out operation and maintenance inspections in a timely manner, avoid affecting the efficient operation of the energy storage power station, and reduce the risk and probability of accidents. In addition, a dynamic adaptive machine learning algorithm is used to enable the model to automatically adjust parameters and structures according to the real-time operation of the system and historical fault data, thereby improving the adaptability and accuracy of model diagnosis.

[0081] In a second aspect, the present application provides an electrochemical energy storage system fault diagnosis system. Figure 2is a schematic diagram of the structure of a fault diagnosis system for an electrochemical energy storage system according to an exemplary embodiment. Figure 2 As shown, the system includes:

[0082] The acquisition module 100 is used to acquire a plurality of real-time operating parameters of the electrochemical energy storage system, and determine a first category parameter and a second category parameter from the plurality of real-time operating parameters.

[0083] The threshold diagnosis module 200 is used to diagnose the first category parameters by using a threshold diagnosis method when the first category parameters are determined to determine the fault type of the energy storage system.

[0084] Parameter fusion diagnosis module 300: used to diagnose the second category parameters through a parameter fusion diagnosis method when the second category parameters are determined. The parameter fusion diagnosis method includes fusing the features of the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

[0085] In one embodiment, the second category parameters include different types of real-time operating parameters, and the parameter fusion diagnosis module 300 includes:

[0086] Time synchronization is performed on multiple input real-time operating parameters.

[0087] Standardize the real-time operating parameters after time synchronization.

[0088] Screen the real-time operating parameters after standardization.

[0089] The principal component analysis or independent component analysis method is used to perform feature fusion on the screened real-time operating parameters to obtain comprehensive parameters.

[0090] In one embodiment, the parameter fusion diagnosis method is implemented by a machine learning model, which includes a dynamic adaptive module, and the dynamic adaptive module is used to automatically adjust the parameters of the model according to historical operating parameters and real-time operating parameters.

[0091] In one embodiment, the first category parameters include a single type of real-time operating parameters, and the threshold diagnosis module 200 includes: directly determining the fault type of the energy storage system according to the input first category parameters and the preset diagnosis threshold corresponding to the first category parameters.

[0092] In one embodiment, the system further includes: determining the fault level according to the fault type and a preset fault grading threshold corresponding to the fault type; and feeding back the fault type and fault level to the operation and maintenance personnel.

[0093] In one embodiment, the system further comprises:

[0094] Used to obtain corresponding operation and maintenance suggestions from the preset operation and maintenance database based on fault type and fault level.

[0095] Generate an operation and maintenance work order based on the fault type, fault level and operation and maintenance suggestions, so that the operation and maintenance personnel can perform repairs according to the operation and maintenance work order.

[0096] In one embodiment, after the acquisition module 100, the system further includes: a method for screening and normalizing a plurality of real-time operating parameters.

[0097] In summary, this application adopts different diagnostic methods for different parameters by real-time monitoring of various operating parameters of the energy storage system. And combined with the parameter fusion diagnosis method, it comprehensively reflects the operating status and performance of the energy storage system, and improves the accuracy and reliability of fault diagnosis. At the same time, the diagnosis results are fed back to the operation and maintenance personnel in a timely manner, so that the operation and maintenance personnel can carry out operation and maintenance inspections in a timely manner, avoid affecting the efficient operation of the energy storage power station, and reduce the risk and probability of accidents. In addition, a machine learning algorithm based on a dynamic adaptive mechanism is adopted, so that the model can automatically adjust parameters and structures according to the real-time operation of the system and historical fault data, thereby improving the adaptability and accuracy of model diagnosis.

[0098] In a third aspect, an embodiment of the present application provides an electronic device, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the electrochemical energy storage system fault diagnosis method provided in the first aspect is implemented. Figure 3 The electronic device 60 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0099] The electronic device 60 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 60 may include but are not limited to: at least one processor 61, at least one memory 62, and a bus 63 connecting different system components (including the memory 62 and the processor 61).

[0100] The bus 63 includes a data bus, an address bus, and a control bus.

[0101] The memory 62 may include a volatile memory, such as a random access memory (RAM) 621 and / or a cache memory 622 , and may further include a read-only memory (ROM) 623 .

[0102] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, such program modules 624 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0103] The processor 61 executes various functional applications and data processing by running the computer program stored in the memory 62, such as the electrochemical energy storage system fault diagnosis method provided in the first aspect of the present application.

[0104] The electronic device 60 may also communicate with one or more external devices 64 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 65. Furthermore, the model-generated device 60 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 66. As shown, the network adapter 66 communicates with other modules of the model-generated device 60 via a bus 63. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the model-generated device 60, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0105] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0106] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon, and when the program is executed by a processor, the electrochemical energy storage system fault diagnosis method provided in the first aspect is implemented.

[0107] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0108] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the electrochemical energy storage system fault diagnosis method provided in the first aspect.

[0109] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0110] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0111] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for diagnosing faults in an electrochemical energy storage system, characterized in that: include: Acquire a plurality of real-time operating parameters of the electrochemical energy storage system, and determine a first category parameter and a second category parameter from the plurality of real-time operating parameters; When the first category parameters are determined, diagnose the first category parameters by a threshold diagnosis method to determine the fault type of the energy storage system; When the second category parameters are determined, the second category parameters are diagnosed by a parameter fusion diagnosis method, wherein the parameter fusion diagnosis method includes performing feature fusion on the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

2. The electrochemical energy storage system fault diagnosis method according to claim 1, characterized in that: The second category parameters include different types of real-time operating parameters, and the second category parameters are subjected to feature fusion to obtain comprehensive parameters, including: Perform time synchronization on multiple input real-time operating parameters; Standardize the real-time operating parameters after time synchronization; Screening the real-time operating parameters after standardization; The principal component analysis or independent component analysis method is used to perform feature fusion on the screened real-time operating parameters to obtain comprehensive parameters.

3. The electrochemical energy storage system fault diagnosis method according to claim 2, characterized in that: The parameter fusion diagnosis method is implemented by a machine learning model, which includes a dynamic adaptive module. The dynamic adaptive module is used to automatically adjust the parameters of the model according to historical operating parameters and real-time operating parameters.

4. The electrochemical energy storage system fault diagnosis method according to claim 1, characterized in that: The first category parameters include a single type of real-time operating parameters, and the threshold diagnosis method includes: The fault type of the energy storage system is directly determined according to the input first category parameter and the preset diagnosis threshold corresponding to the first category parameter.

5. The electrochemical energy storage system fault diagnosis method according to claim 1, characterized in that: The method further comprises: Determine a fault grade according to the fault type and a preset fault grade threshold value corresponding to the fault type; The fault type and the fault level are fed back to the operation and maintenance personnel.

6. The electrochemical energy storage system fault diagnosis method according to claim 5, characterized in that: The method further comprises: Obtaining corresponding operation and maintenance suggestions from a preset operation and maintenance database according to the fault type and the fault level; An operation and maintenance work order is generated according to the fault type, fault level and the operation and maintenance suggestion, so that the operation and maintenance personnel can perform maintenance according to the operation and maintenance work order.

7. The electrochemical energy storage system fault diagnosis method according to claim 1, characterized in that: After obtaining a plurality of real-time operating parameters of the electrochemical energy storage system, the method further includes: The multiple real-time operating parameters are screened and normalized.

8. An electrochemical energy storage system fault diagnosis system, characterized in that: include: An acquisition module: used for acquiring a plurality of real-time operating parameters of the electrochemical energy storage system, and determining a first category parameter and a second category parameter from the plurality of real-time operating parameters; A threshold diagnosis module: used for diagnosing the first category parameters by a threshold diagnosis method to determine the fault type of the energy storage system when the first category parameters are determined; Parameter fusion diagnosis module: used to diagnose the second category parameters through a parameter fusion diagnosis method when the second category parameters are determined, the parameter fusion diagnosis method includes fusing the features of the second category parameters to obtain comprehensive parameters, and judging the fault type of the energy storage system according to the comprehensive parameters and a preset fault threshold.

9. An electronic device, characterized in that: include Memory, processor, and A computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electrochemical energy storage system fault diagnosis method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the electrochemical energy storage system fault diagnosis method according to any one of claims 1 to 7 is implemented.

Images