Battery fault detection method and apparatus, and device and computer-readable storage medium

Abstract

Provided in the present disclosure are a battery fault detection method and apparatus, and a device and a computer-readable storage medium. The method comprises: acquiring respective first detection data of a plurality of batteries, wherein the first detection data is determined by means of executing preset fault detection contents; on the basis of each of the fault detection contents, performing data analysis on a plurality of pieces of first detection data, in order to determine aggregated data for fault detection of the plurality of batteries; and on the basis of the aggregated data for fault detection for each of the fault detection contents, compiling statistics on abnormal data meeting a preset indicator condition, wherein the preset indicator condition is a rule that is used for determining, on the basis of the aggregated data for fault detection, whether a battery has a potential risk.

Description

Battery fault detection method and device, equipment and computer readable storage medium

[0001] Cross-reference to related applications

[0002] The present disclosure is based on a Chinese patent application No. 202411136695.6, filed on August 16, 2024, entitled "Battery fault detection method and device, equipment and computer readable storage medium", and claims priority to the Chinese patent application, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates to, but is not limited to, the technical field of batteries, and in particular to a battery fault detection method and device, equipment and computer readable storage medium. BACKGROUND

[0004] With the rapid development of emerging businesses such as new energy vehicles and electrochemical energy storage, the shipment volume of new energy batteries continues to grow, and the society has paid great attention to the battery status such as battery safety, state estimation and residual value evaluation of electric vehicles and energy storage stations.

[0005] At present, some battery manufacturers and battery application parties (such as vehicle manufacturers and energy storage parties) have deployed battery clouds for detecting the safety status of batteries, such as the Beiren new energy platform, which is responsible for collecting electric vehicle battery data from various battery application parties across the country. These battery cloud platforms are based on battery measurement data uploaded by electric vehicle or energy storage station batteries. With more and more new energy vehicles, how to efficiently identify potential risks of a large number of batteries and improve the ability to actively identify risks and resolve risks in advance is a major pain point in the current industry. SUMMARY

[0006] Embodiments of the present disclosure aim to provide a battery fault detection method and device, equipment and computer readable storage medium, which can quickly and accurately identify potential risks of multiple batteries, thereby improving the fault detection efficiency.

[0007] The technical solution of the present disclosure is implemented as follows:

[0008] The present disclosure provides a battery fault detection method, which comprises:

[0009] Obtaining first detection data of each of a plurality of batteries; the first detection data is determined by performing a preset fault detection content;

[0010] According to each fault detection content, performing data analysis on the plurality of first detection data to determine aggregated data of the fault detection of the plurality of batteries;

[0011] The abnormal data satisfying the preset index condition is counted based on the aggregated data of the fault detection of each fault detection content, and the preset index condition is used to determine a rule for determining whether the battery has hidden dangers based on the aggregated data of the fault detection.

[0012] It can be understood that the abnormal data corresponding to each fault detection content is counted based on the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries, and the potential risks of the plurality of batteries can be quickly and accurately identified based on the abnormal data subsequently, thereby improving the fault detection efficiency.

[0013] In the above scheme, the plurality of first detection data is subjected to data analysis according to each fault detection content, and the aggregated data of the fault detection of the plurality of batteries is determined, including: the plurality of first detection data of each fault detection content is subjected to fault detection index aggregation analysis, and the aggregated data of the fault detection of the plurality of batteries after being used for the first time is determined; and the aggregated data of the fault detection is the overall analysis data of the plurality of batteries.

[0014] It can be understood that the aggregated data of the fault detection of the plurality of batteries after being used for the first time is determined by subjecting the plurality of first detection data of each fault detection content to fault detection index aggregation analysis, and the aggregated data of the fault detection can be the overall analysis data of the plurality of batteries, so that the potential risks existing in the plurality of batteries can be quickly identified based on the overall analysis data of the plurality of batteries.

[0015] In the above scheme, the abnormal data satisfying the preset index condition is counted based on the aggregated data of the fault detection of each fault detection content, including: the abnormal data satisfying the preset abnormal threshold is determined based on the aggregated data of the fault detection of any fault detection content; and / or, the preset number of extreme value data is determined from the aggregated data of the fault detection of any fault detection content; wherein the preset index condition includes one or more of the following: the preset abnormal threshold and the preset number of extreme value data.

[0016] It can be understood that the abnormal data is determined based on the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries according to the preset index condition, and the potential risks of the plurality of batteries can be quickly and accurately identified based on the abnormal data subsequently, thereby improving the fault detection efficiency.

[0017] In the above scheme, the method further includes: obtaining a preset template corresponding to the fault detection content; and loading the preset template based on the aggregated data of the fault detection and the abnormal data corresponding to the preset fault detection content, to generate a fault detection report.

[0018] It can be understood that the preset template corresponding to the fault detection content is obtained; the preset template is loaded with data based on the aggregated data and the abnormal data of the fault detection, to generate a fault detection report, so as to facilitate subsequent visual display of the aggregated data and the abnormal data of the fault detection through the fault detection report, and facilitate the battery application party of the power consumption equipment using multiple batteries and / or the battery application party producing batteries to quickly locate potential risks of the multiple batteries.

[0019] In the above scheme, obtaining the first detection data of each of the multiple batteries includes: obtaining the measurement point data of the multiple batteries; and performing a fault detection task to detect the measurement point data of the multiple batteries according to a preset fault detection content, to determine the first detection data of each of the multiple batteries.

[0020] It can be understood that the measurement point data of the multiple batteries is detected according to the fault detection task to perform automatic fault detection of the preset fault detection content, to determine the first detection data of each of the multiple batteries. The automatic fault detection reduces the need for manual intervention, and improves the accuracy and efficiency of fault detection. At the same time, through remote monitoring and data analysis, remote management and maintenance of the multiple batteries can be realized, reducing the operation and maintenance cost and time cost.

[0021] In the above scheme, obtaining the measurement point data of the multiple batteries includes: obtaining the measurement point data of the multiple batteries at a first time; or reading the measurement point data of the multiple batteries according to an address where the measurement point data of the battery is stored.

[0022] It can be understood that obtaining the measurement point data of the battery at the first time can ensure the real-time and freshness of the data. This is particularly important for application scenarios that require real-time monitoring of battery status, such as battery management systems of electric vehicles and battery monitoring of energy storage power stations. Real-time data helps to discover problems and prevent faults in a timely manner, ensuring stable operation of the system. Reading the measurement point data of the battery through the storage address can ensure the integrity and traceability of the data.

[0023] In the above scheme, performing a fault detection task to detect the measurement point data of the multiple batteries according to a preset fault detection content, to determine the first detection data of each of the multiple batteries, includes: in a case where it is determined that the fault detection task is a task-type fault detection, obtaining, from the measurement point data of the multiple batteries, measurement point data of a target battery in a preset data time interval corresponding to the fault detection task; and performing one-time detection of the measurement point data of the target battery according to the preset fault detection content, to determine the first detection data of each of the target battery.

[0024] It can be understood that the target battery and the preset data time interval of the measurement point data of the target battery can be customized, thereby ensuring the flexibility and diversity of the fault detection task. The target battery can be understood as a battery that needs to be detected for faults.

[0025] In the above scheme, the fault detection task is executed, the detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries, and the first detection data of each of the plurality of batteries is determined. In the case where it is determined that the fault detection task is a periodic fault detection, the detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries obtained at a time according to the fault detection cycle frequency, and the first detection data is determined.

[0026] It can be understood that the periodic fault detection refers to the periodic fault detection of the fault detection content. The periodic fault detection task needs to be created only once. Subsequently, the execution of the fault detection task is automatically triggered according to the fault detection cycle frequency, thereby improving the battery fault detection efficiency.

[0027] In the above scheme, the fault detection task is executed, the detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries, and the first detection data of each of the plurality of batteries is determined. In the case where it is determined that the fault detection task is a periodic fault detection, the detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries obtained at a time according to the fault detection cycle frequency, and the first detection data is determined.

[0028] It can be understood that at least one of data analysis, data deduplication, data cleaning, data conversion and data aggregation is performed on the measurement point data of the plurality of batteries according to the fault detection task, which can remove invalid data and repeated data, so as to improve the detection efficiency in the process of performing the detection of the preset fault detection content on the to-be-measured point data and determining the first detection data of each of the plurality of batteries.

[0029] In the above scheme, the measurement point data of the plurality of batteries is obtained, including: in response to a triggering operation of a data entry displayed on the fault detection management interface, displaying a measurement point data interface; in response to a data selection operation in the measurement point data interface, importing the measurement point data of the plurality of batteries corresponding to the entity of the fault detection object; displaying data items of the measurement point data of the plurality of batteries; wherein the data items include at least one of the following: data identifier, import time, import state and import description.

[0030] It can be understood that in response to the triggering operation of the data entry, the measurement point data interface is displayed; in response to the data selection operation in the measurement point data interface, the fault detection object data corresponding to the entity of the fault detection object is imported; and the data items of the fault detection object data are displayed, which facilitates the subsequent establishment of the fault detection task.

[0031] In the above scheme, the method further includes: after the fault detection is completed, displaying a fault detection report; and / or, based on a preset subscription relationship, pushing the fault detection report.

[0032] It can be understood that the task progress display of the fault detection task displays the fault detection report after completing the fault detection; and the fault detection report is pushed based on the preset subscription relationship, so that the fault detection report of the battery data can be learned in a timely manner.

[0033] The battery fault detection device provided in the embodiments of the present disclosure comprises:

[0034] The acquisition unit is configured to acquire first detection data of each of the plurality of batteries, wherein the first detection data is determined by performing preset fault detection content.

[0035] The processing unit is configured to perform data analysis on the plurality of first detection data according to each fault detection content, and determine aggregated data of the fault detection of the plurality of batteries.

[0036] The processing unit is configured to count abnormal data satisfying a preset index condition based on the aggregated data of the fault detection of each fault detection content, wherein the preset index condition is used to determine a rule for determining whether the battery has a hidden danger based on the aggregated data of the fault detection.

[0037] It can be understood that the abnormal data corresponding to each fault detection content is counted according to the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries, and the potential risk of the plurality of batteries can be quickly and accurately identified according to the abnormal data, thereby improving the fault detection efficiency.

[0038] The battery fault detection device provided in the embodiments of the present disclosure comprises a processor and a memory, wherein:

[0039] The memory is configured to store executable instructions.

[0040] The processor is configured to execute the executable instructions stored in the memory, so as to implement the battery fault detection method.

[0041] The computer readable storage medium provided in the embodiments of the present disclosure stores executable instructions, and is used to cause the processor to execute the battery fault detection method.

[0042] The battery fault detection method and device, equipment and computer readable storage medium provided by the embodiments of the present disclosure comprise: obtaining first detection data of a plurality of batteries respectively; the first detection data is determined by performing preset fault detection content; performing data analysis on the plurality of first detection data according to each fault detection content to determine aggregated data of fault detection of the plurality of batteries; based on the aggregated data of fault detection of each fault detection content, abnormal data meeting a preset index condition is counted; the preset index condition is a rule for determining whether the batteries have hidden dangers based on the aggregated data of fault detection. In this way, the first detection data of the plurality of batteries is batch processed according to each fault detection content to obtain the aggregated data of fault detection of each fault detection content corresponding to the plurality of batteries, and the abnormal data corresponding to each fault detection content is counted from the aggregated data of fault detection, which can be used to quickly and accurately identify potential risks of the plurality of batteries, thereby improving the fault detection efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0043] The drawings incorporated into the specification and forming a part of the specification, show embodiments consistent with the present disclosure, and together with the specification, serve to explain the technical solutions of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those of ordinary skill in the art without creative effort based on these drawings.

[0044] The flowcharts shown in the drawings are only exemplary and do not necessarily include all contents and operations / steps, nor do they necessarily have to be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may be changed according to the actual situation.

[0045] FIG. 1 is an optional flowchart of a battery fault detection method according to an embodiment of the present disclosure;

[0046] FIG. 2 is an optional measurement point data interface diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0047] FIG. 3 is an optional fault detection task management interface diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0048] FIG. 4 is an optional fault detection engineering list diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0049] FIG. 5 is an optional first data item diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0050] Fig. 6 is an optional task progress diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0051] Fig. 7 is an optional battery fault detection method diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0052] Fig. 8 is an optional battery fault detection method diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0053] Fig. 9 is an optional fault detection engineering diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0054] Fig. 10 is an optional fault detection content diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0055] Fig. 11 is an optional fault detection report diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0056] Fig. 12 is an optional fault detection report production flowchart of a battery fault detection method according to an embodiment of the present disclosure;

[0057] Fig. 13 is an optional SOH overall distribution result diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0058] Fig. 14 is an optional online PDF rendering flowchart of a battery fault detection method according to an embodiment of the present disclosure;

[0059] Fig. 15 is an optional page structure data diagram of a battery fault detection method according to an embodiment of the present disclosure;

[0060] Fig. 16 is an optional overall flowchart of a battery fault detection business according to an embodiment of the present disclosure;

[0061] Fig. 17 is a structural diagram of a battery fault detection device according to an embodiment of the present disclosure;

[0062] Fig. 18 is a structural diagram of a battery fault detection device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the specific technical solutions of the present disclosure will be further described in detail below with reference to the drawings in the embodiments of the present disclosure. The following embodiments are used to illustrate the present disclosure, but not to limit the scope of the present disclosure.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used in the disclosure is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.

[0065] In the following description, reference is made to "some embodiments", "the embodiment", "the present embodiment" and the like, which describe a subset of all possible embodiments. It is to be understood that "some embodiments" can be the same subset or different subsets as each other and as reference to other embodiments, and that reference to "some embodiments" is made for the purpose of describing all possible embodiments without necessarily referring to the same subset of embodiments.

[0066] If the application file contains similar descriptions of "first / second", the following explanations are added. In the following description, the terms "first\second\third" referred to only distinguish similar objects, and do not represent a specific order of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence as allowed, so that the embodiments of the disclosure described herein can be implemented in an order other than that illustrated or described herein.

[0067] The use of power consumption equipment (such as new energy vehicles) by current battery application parties is increasing, and how to efficiently identify the potential risks of a large number of battery packs and improve the ability to actively identify risks and resolve risks in advance is a major pain point in the current industry.

[0068] The embodiments of the disclosure provide a battery fault detection method, which can effectively use a battery fault detection device to implement a batch fault detection task of measurement point data of a plurality of batteries, identify abnormal data in the batteries, and thus determine key safety hazards and capacity risks existing in the batteries, so as to achieve more efficient active maintenance of a large number of batteries.

[0069] FIG. 1 is an optional flowchart of a battery fault detection method according to an embodiment of the disclosure. As shown in FIG. 1, the battery fault detection method includes the following steps:

[0070] S101, obtaining first detection data of a plurality of batteries respectively; the first detection data is determined by performing a preset fault detection content.

[0071] In some embodiments of the disclosure, the plurality of batteries are from batteries on a plurality of power consumption equipment (for example, vehicles) of the same battery application party. The plurality of power consumption equipment can be the same model of power consumption equipment, or can be different models of power consumption equipment.

[0072] In some embodiments of the present disclosure, the first detection data of the battery can be understood as the result data obtained after the battery is periodically checked, tested and analyzed. The first detection data is used to evaluate the performance, health status, service life and potential safety hazards of the battery, which is not limited in the embodiments of the present disclosure.

[0073] In some embodiments of the present disclosure, the first detection data is the result obtained by performing preset fault detection content on the measurement point data of the battery. The preset fault detection content includes at least one of the following: fault detection key conclusion, fault detection data analysis, safety fault detection and health fault detection.

[0074] S102, according to each fault detection content, data analysis is performed on the plurality of first detection data to determine the aggregated data of the fault detection of the plurality of batteries.

[0075] In some embodiments of the present disclosure, the preset fault detection content includes each fault detection content. When the preset fault detection content is the fault detection key conclusion, each fault detection content includes at least one of the following: data analysis conclusion, safety fault detection conclusion, health fault detection conclusion and key attention information. When the preset fault detection content is the fault detection data analysis, each fault detection content includes at least one of the following: fault detection data display, fault detection data quality and battery image analysis. When the preset fault detection content is the safety fault detection, each fault detection content includes at least one of the following: battery safety fault detection information overview, serious fault warning information and battery fault analysis. When the preset fault detection content is the health fault detection, each fault detection content includes at least one of the following: battery health fault detection information overview, low-capacity battery pack information and fast-capacity-decay battery pack information.

[0076] In some embodiments of the present disclosure, according to each fault detection content, data analysis is performed on the plurality of first detection data to determine the aggregated data of the fault detection of the plurality of batteries. For example, when the fault detection content is the battery health fault detection information overview, the first detection data is the health degree of the battery. By performing data analysis on the health degrees of the plurality of first batteries, the overall distribution of the health degrees of the plurality of first batteries, i.e. the aggregated data of the fault detection, is determined. When the fault detection content is the low-capacity battery pack information, the first detection data is the health degree of the battery. By performing data analysis on the health degrees of the plurality of batteries, the information of the low-capacity battery in the plurality of batteries, i.e. the aggregated data of the fault detection, is determined.

[0077] In some embodiments of the present disclosure, the first detection data is analyzed according to each fault detection content to determine the aggregated data of the fault detection of the plurality of batteries. For example, when the fault detection content is battery health fault detection information overview, the first detection data is the health degree of the battery. The health degrees of the plurality of batteries are analyzed to determine the overall distribution of the health degrees of the plurality of batteries, i.e., the aggregated data of the fault detection. When the fault detection content is low-capacity battery pack information, the first detection data is the health degree of the battery. The health degrees of the plurality of batteries are analyzed to determine the information of the low-capacity batteries in the plurality of batteries, i.e., the aggregated data of the fault detection.

[0078] In S103, abnormal data satisfying a preset index condition is counted based on the aggregated data of the fault detection of each fault detection content. The preset index condition is a rule for determining whether the battery has a hidden danger based on the aggregated data of the fault detection.

[0079] In some embodiments of the present disclosure, the abnormal data satisfying the preset index condition is counted based on the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries. Subsequently, the potential risks of the plurality of batteries can be quickly and accurately identified according to the abnormal data, thereby improving the fault detection efficiency.

[0080] In some embodiments of the present disclosure, the normal data not satisfying the preset index condition is counted based on the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries. The normal data indicates that the battery does not have a hidden danger.

[0081] In some embodiments of the present disclosure, the battery fault detection method is applied to a battery energy cloud platform, i.e., a battery fault detection device or a battery fault detection equipment. For example, the battery fault detection equipment can be a server, and the present disclosure does not make specific limitations thereto.

[0082] In some embodiments of the present disclosure, the battery fault detection method can realize various deployment modes, supports public cloud deployment mode, and also supports private deployment mode of the power equipment side, thereby realizing effective fault detection on massive battery measurement point data, determining potential risks, life aging, and other problems and characteristic battery images that may exist in the battery running process, and further optimizing the subsequent battery design.

[0083] In some embodiments of the present disclosure, the first detection data of each of the plurality of batteries is acquired; the first detection data is determined by performing a preset fault detection content; the plurality of first detection data is analyzed according to each fault detection content to determine aggregated data of the fault detection of the plurality of batteries; and abnormal data meeting a preset index condition is counted based on the aggregated data of the fault detection of each fault detection content; and the preset index condition is a rule for determining whether the battery has a hidden danger based on the aggregated data of the fault detection. In this way, the abnormal data corresponding to each fault detection content is counted according to the aggregated data of the fault detection of each fault detection content corresponding to the plurality of batteries, and the potential risk of the plurality of batteries can be quickly and accurately identified according to the abnormal data, thereby improving the fault detection efficiency.

[0084] In some embodiments of the present disclosure, S102 can include: performing fault detection index aggregation analysis on the plurality of first detection data of each fault detection content to determine aggregated data of the fault detection of the plurality of batteries after the first time of use; and the aggregated data of the fault detection is overall analysis data of the plurality of batteries.

[0085] In some embodiments of the present disclosure, the preset fault detection content is a fault detection key conclusion, and each fault detection content includes at least one of the following: a data analysis conclusion, a safety fault detection conclusion, a health fault detection conclusion, and key attention information. When the fault detection content is a data analysis conclusion, the fault detection index includes at least one of the following: report basic information and report power equipment data information. When the fault detection content is a safety fault detection conclusion, the fault detection index includes at least one of the following: fault early warning basic information, fault type information, reporting alarm information on a battery management system (BMS) of a power equipment end, and reporting battery fault early warning information. When the fault detection content is a health fault detection conclusion, the fault detection index includes: health degree (SOH) estimation fault information.

[0086] In some embodiments of the present disclosure, when the preset fault detection content is fault detection data analysis, each fault detection content includes at least one of the following: fault detection data display, fault detection data quality, and battery portrait analysis. When the fault detection content is fault detection data display, the fault detection index includes at least one of the following: data quantity, number of electrical equipment, battery energy, and number of battery models. When the fault detection content is fault detection data quality, the fault detection index includes at least one of the following: data loss rate, and data missing rate distribution according to battery pack model. When the fault detection content is battery portrait analysis, the fault detection index includes at least one of the following: charging behavior analysis (fast / slow charging), charging behavior analysis (charging current distribution), charging behavior analysis (State of Charge (SOC) distribution before and after charging), different temperature charging behavior analysis, different temperature discharging behavior analysis, electrical equipment long standing behavior analysis, electrical equipment high SOC standing behavior analysis, and cumulative mileage distribution.

[0087] In some embodiments of the present disclosure, when the preset fault detection content is safety fault detection, each fault detection content includes at least one of the following: battery safety fault detection information overview, serious fault warning information, and battery fault analysis. When the fault detection content is battery safety fault detection information overview, the fault detection index includes at least one of the following: total number of warning faults, number of end-side problems, fault distribution according to battery cell model, and serious fault distribution according to battery cell model. When the fault detection content is serious fault warning information, the fault detection index includes at least one of the following: self-discharge type fault detection result, balancing type fault detection result, insulation anomaly type fault detection result, and component fault detection result. When the fault detection content is battery fault analysis, the fault detection index includes at least one of the following: fault density ranking of different equipment models, fault density ranking of different battery cell models, fault distribution according to different types, serious fault distribution according to different types, and fault cause and distribution proportion.

[0088] In some embodiments of the present disclosure, when the preset fault detection content is health fault detection, each fault detection content includes at least one of the following: battery health fault detection information overview, low-capacity battery information, and fast-capacity-decay battery information. When the fault detection content is the battery health fault detection information overview, the fault detection index includes at least one of the following: SOH overall distribution, and correspondence between driving mileage and SOH. When the fault detection content is low-capacity electrical equipment information, the fault detection index includes at least one of the following: PACK-level SOH information and CELL-level SOH information. When the fault detection content is fast-capacity-decay electrical equipment information, the fault detection index includes: PACK-level SOH decay rate. Wherein, CELL refers to a single battery, which is the basic unit of a battery system. The CELL-level SOH monitoring focuses more on the health assessment of the battery monomer level. PACK refers to a battery pack composed of multiple single batteries through series, parallel, or other ways, which is a common battery form in electrical equipment and other applications. The PACK-level SOH information refers to the health status of the entire battery pack, including the performance status of all single batteries in the battery pack, and is processed by algorithms and models for weighting or averaging to obtain the health status assessment of the entire battery pack.

[0089] For example, when the fault detection content is the battery health fault detection information overview, the first detection data is the health degree of the battery. By analyzing the health degrees of the multiple batteries, the overall distribution of the health degrees of the multiple batteries is determined, that is, the aggregated data of the fault detection.

[0090] In some embodiments of the present disclosure, S103 can include: determining, from the aggregated data of the fault detection of any fault detection content, abnormal data that meets a preset abnormal threshold; and / or,

[0091] In some embodiments of the present disclosure, from the aggregated data of the fault detection of any fault detection content, a preset number of extreme value data are determined; wherein, the preset index condition includes one or more of the following: meeting the preset abnormal threshold and the preset number of extreme value data.

[0092] In some embodiments of the present disclosure, based on the preset abnormal threshold, data in the aggregated data of the fault detection of any fault detection content within the preset abnormal threshold is counted as abnormal data. And / or, extreme value data in the aggregated data of the fault detection of any fault detection content is counted as abnormal data, and the extreme value data includes minimum value and / or maximum value.

[0093] For example, taking the battery health fault detection information overview as an example, the aggregated data of the fault detection of the fault detection content includes at least one of the SOH distribution interval probability of the plurality of batteries, the SOH mean and median of the plurality of batteries. Based on this, the preset abnormal threshold can be set to be less than the first SOH distribution interval probability, and the proportion of the electrical equipment less than the first SOH distribution interval probability is counted. The preset abnormal threshold can also be set to be greater than or equal to the first SOH distribution interval probability and less than the second SOH distribution interval probability, and the proportion of the electrical equipment in this range is counted.

[0094] In some embodiments of the present disclosure, the method further comprises: providing the aggregated data of the fault detection and the abnormal data to a battery application side of the electrical equipment using the plurality of batteries.

[0095] In the embodiments of the present disclosure, the aggregated data of the fault detection and the abnormal data are provided to the battery application side of the electrical equipment using the plurality of batteries, so that the battery application side can quickly locate which batteries have hidden dangers, so as to solve the hidden dangers.

[0096] In some embodiments of the present disclosure, the method further comprises: obtaining a preset template corresponding to the fault detection content; based on the aggregated data of the fault detection and the abnormal data corresponding to the preset fault detection content, loading data to the preset template to generate a fault detection report; the fault detection report displays one or more of the aggregated data of the fault detection, the abnormal data and the key conclusion; wherein the key conclusion is the key information extracted based on the aggregated data of the fault detection and the abnormal data of each fault detection content.

[0097] In some embodiments of the present disclosure, the preset template is generated based on the preset fault detection content.

[0098] In some embodiments of the present disclosure, the server can load one or more of the aggregated data of the fault detection and the abnormal data corresponding to the preset fault detection content into the preset template to generate the fault detection report.

[0099] In some embodiments of the present disclosure, the server extracts the key information, i.e., the key conclusion, based on the aggregated data of the fault detection and the abnormal data corresponding to the preset fault detection content, and loads one or more of the aggregated data of the fault detection, the abnormal data and the key conclusion corresponding to the preset fault detection content into the preset template through the preset template to generate the fault detection report.

[0100] In some embodiments of the present disclosure, the structure data of the page is obtained through a preset template; the structure data is parsed to obtain a plurality of component information and a plurality of configuration information; the plurality of component information is loaded and processed through the plurality of configuration information based on the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content, to generate page data; and the page data is processed by page to generate a fault detection report.

[0101] In some embodiments of the present disclosure, the plurality of component information includes at least two of the following components: a chart component, a template component, a content component, and a layout component. The plurality of configuration information includes at least two of the following configurations: a layout configuration, a node configuration, and a user configuration; the node configuration includes a node identifier, a determination rule configuration, a template component configuration, an index configuration, and an interaction rule configuration.

[0102] In some embodiments of the present disclosure, the preset template is prepared based on the preset fault detection content. Therefore, the aggregated data and the abnormal data of the fault detection generated by detecting the preset fault detection content can be filled into the preset template to generate a fault detection report. The fault detection report can reflect the fault detection results of a plurality of fault detection contents of the preset fault detection content.

[0103] It can be understood that a preset template corresponding to a fault detection task is obtained; the preset template is loaded with data based on the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content, to generate a fault detection report, which facilitates subsequent visual display of the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content through the fault detection report.

[0104] In some embodiments of the present disclosure, the preset template is loaded with data based on the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content, to generate a fault detection report, which can be implemented through S1, S2, and S3 as follows:

[0105] S1, the structure data of the page is obtained through a preset template; and the structure data is parsed to obtain a plurality of component information and a plurality of configuration information.

[0106] In some embodiments of the present disclosure, the server obtains the structure data of the page through a preset template, parses the structure data to obtain a plurality of component information and a plurality of configuration information.

[0107] In some embodiments of the present disclosure, the structure data is parsed and the component type is determined to obtain a plurality of component information. The structure data is parsed to obtain a plurality of configuration information.

[0108] S2, based on the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content, loading and processing the component information through the multiple configuration information to generate page data.

[0109] In some embodiments of the present disclosure, the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content are loaded into the chart component, the template component, the content component and the layout component through the index configuration, the determination rule configuration and the template component configuration in the multiple configuration information to obtain loading data; the loading data is processed to generate page data.

[0110] In some embodiments of the present disclosure, the server loads the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content into the chart component, the template component, the content component and the layout component through the index configuration and the template component configuration in the multiple configuration information to obtain loading data. The loading data is processed through the determination rule configuration to generate page data.

[0111] S3, performing paging processing on the page data to generate a fault detection report.

[0112] In some embodiments of the present disclosure, the server performs paging processing on the page data to obtain a fault detection report.

[0113] It can be understood that the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content are loaded through the preset template to obtain page data, the page data is processed by paging to generate a fault detection report, the calculated numerical form of the aggregated data and the abnormal data of the fault detection corresponding to the preset fault detection content are loaded and processed to generate a visual fault detection report, which is convenient for intuitively understanding the fault detection result.

[0114] In some embodiments of the present disclosure, after generating the fault detection report, the following operations are further performed: displaying the fault detection report after completing the fault detection; and / or, based on a preset subscription relationship, pushing the fault detection report.

[0115] In some embodiments of the present disclosure, the fault detection report is an output mode of the fault detection result, which supports automatic output of the fault detection report, and at the same time, the report content can also be edited online according to the needs of the battery application party (i.e. the customer) and / or the battery production party, so that the fault detection report can be pushed to the related customers, and can also be downloaded online by the battery application party and / or the battery production party. In this way, the battery application party and / or the battery production party can timely understand the fault detection situation of the battery data.

[0116] In some embodiments of the present disclosure, after the task progress display of the fault detection task displays that the fault detection is completed, according to a preset subscription relationship, a subscription client or a subscription client group that needs to receive the fault detection report is screened out, and the fault detection report can be sent to the subscription client by using a mail push, an SMS push, an instant message push or the like. For example, by using the mail push, a mail subject and a content of a body can be set, and the report can be included in the mail as an attachment or a download link. In this way, the fault detection report can be effectively pushed to the relevant client based on the preset subscription relationship, and the battery fault processing efficiency and response speed are improved.

[0117] In some embodiments of the present disclosure, the first detection data of each of the plurality of batteries is obtained by: obtaining the measurement point data of the plurality of batteries; and performing a fault detection task to perform detection of preset fault detection content on the measurement point data of the plurality of batteries to determine the first detection data of each of the plurality of batteries.

[0118] It can be understood that the first detection data of each of the plurality of batteries is determined by performing automated fault detection of the preset fault detection content on the measurement point data of the plurality of batteries according to the fault detection task. The automated fault detection reduces the need for manual intervention and improves the accuracy and efficiency of fault detection. At the same time, through remote monitoring and data analysis, remote management and maintenance of the plurality of batteries can be realized, and the operation and maintenance cost and time cost are reduced.

[0119] In some embodiments of the present disclosure, the first detection data of each of the plurality of batteries is obtained by: obtaining the measurement point data of the plurality of batteries; and performing a fault detection task to perform detection of preset fault detection content on the measurement point data of the plurality of batteries to determine the first detection data of each of the plurality of batteries.

[0120] It should be noted that the preset fault detection content includes a plurality of fault detection contents. Each fault detection content corresponds to at least one operation, so that detection of the preset fault detection content is performed on the measurement point data of each battery to obtain at least one feature value corresponding to each fault detection content. Further, the at least one feature value corresponding to the fault detection content of each battery is aggregated to obtain the first detection data of the fault detection content corresponding to each battery. Here, there are a plurality of batteries, and therefore a plurality of first detection data of the plurality of batteries, i.e., a plurality of first detection data, is obtained.

[0121] It should be noted that when the fault detection task is established, the indicators are configured, the fault detection content is configured, and the subscription information is configured. According to the configured indicators, the configured fault detection content, and the configured subscription information, a template file is finally generated. The template file is used for subsequent generation of a fault detection report.

[0122] In some embodiments of the present disclosure, the server performs aggregation of the fault detection indicators based on at least one feature value corresponding to each fault detection content and at least one historical feature value corresponding to each fault detection content stored in the server, to determine a plurality of fault detection results.

[0123] It should be noted that the historical feature value is generated by performing preset fault detection content processing on the measurement point data of the battery of the historical power consumption equipment, and thus stored in the server. When it is necessary to determine a plurality of fault detection results, the historical feature value is directly obtained to perform aggregation of the fault detection indicators.

[0124] In some embodiments of the present disclosure, the aggregation of the fault detection indicators is performed based on at least one feature value corresponding to each fault detection content, to determine a plurality of first detection data. For example, the preset fault detection content is health fault detection, the fault detection content included in the preset fault detection content is battery health fault detection information overview, and the fault detection indicator is SOH overall distribution. For the battery health fault detection information overview, the aggregation of the SOH overall distribution is performed on at least one health degree (i.e., feature value) corresponding to each of a plurality of first batteries, to determine first detection data of each of the plurality of first batteries, i.e., to obtain a plurality of first detection data. The battery health fault detection information overview belongs to a state estimation analysis.

[0125] In some embodiments of the present disclosure, the fault detection task is executed, the preset fault detection content is detected on the measurement point data of the plurality of batteries, and at least one feature value corresponding to each fault detection content is determined, including: according to the fault detection task, first data corresponding to each fault detection content in the preset fault detection content is obtained from the measurement point data of the plurality of batteries; and at least one feature processing is performed on the first data to obtain at least one feature value corresponding to each fault detection content.

[0126] In some embodiments of the present disclosure, according to the fault detection task, first data corresponding to each fault detection content in the preset fault detection content is queried from the measurement point data of the plurality of batteries, at least one feature processing is performed on the first data corresponding to each fault detection content, and at least one feature value corresponding to each fault detection content is obtained.

[0127] For example, the feature processing can be average value operation, maximum value operation, and minimum value operation, etc.

[0128] Exemplarily, the fault detection content accumulation definition 50+ fault detection content items correspond to more than 280+ feature values, and from the three dimensions of battery data, battery failure and battery health, different working condition scenarios, different business perspectives and different usage habits are creatively combined to analyze the performance, trend and cause analysis of the battery in each fault detection content.

[0129] In some embodiments of the present disclosure, the step of obtaining the measurement point data of the plurality of batteries comprises: displaying a measurement point data interface in response to a trigger operation of a data entry displayed on the fault detection management interface; and importing the measurement point data of the plurality of batteries corresponding to the entity of the fault detection object in response to a data selection operation in the measurement point data interface; and displaying data items of the measurement point data of the plurality of batteries; wherein the data items comprise at least one of the following: data identifier, import time, import state and import description.

[0130] Exemplarily, the measurement point data of the battery of the power consumption equipment can be detected and evaluated by the energy cloud management software deployed on the battery energy cloud platform. The home page of the energy cloud management software includes a plurality of submenus, which are data center, report center, fault management, battery virtual computed tomography (CT), energy storage battery virtual CT, state estimation, health management, battery fault detection, enterprise management and system setting. The fault detection management interface is displayed in response to a trigger operation of the energy storage battery virtual CT submenu.

[0131] In some embodiments of the present disclosure, after displaying the fault detection management interface, the fault detection management interface displays the entity selection control (specifically including the power consumption equipment entity control and the power consumption equipment identification code control), the data entry (i.e. the control for importing data) and the data items corresponding to the plurality of power consumption equipment respectively selected in response to the trigger operation of the fault detection management interface. The data items include: power consumption equipment identification code, creation time, tenant identifier (ID), power consumption equipment model, battery pack quantity, power consumption equipment production year, battery application party (i.e. customer) code and power consumption equipment category. The tenant refers to the customer renting the cloud platform storage space. The customer includes vehicle enterprises, energy storage parties, etc.

[0132] Exemplarily, the measurement point data interface is shown in FIG. 2, and the data items include at least one of the following: file name 21, import time 22, import state 23 and abnormal description 24. The file name 21 is the data identifier, and the abnormal description 24 is the import description.

[0133] In some embodiments of the present disclosure, before obtaining the measurement point data of the plurality of batteries, the battery energy cloud platform needs to be deployed on the server in a plurality of deployment modes, including public deployment mode and private deployment mode.

[0134] In some embodiments of the present disclosure, the public deployment mode is a method of deploying software or an application on a public cloud platform, which can be accessed through networking. According to the number of modules used on the public cloud platform, the use period is paid to obtain the use right of the public cloud platform. After obtaining the use right of the public cloud platform, the battery energy cloud platform is deployed according to the business needs. When deploying the battery energy cloud platform, there is no need to maintain the own server and hardware facilities, and the computing resources can be flexibly adjusted according to the actual needs, thereby improving the resource utilization.

[0135] In some embodiments of the present disclosure, the private deployment mode is a method of deploying software or an application on a local hardware device or a private cloud server. The private deployment mode needs to install a hardware server locally and build a cloud environment, and deploy the battery energy cloud platform based on the locally built cloud environment. In this deployment mode, the measurement point data of the battery is stored locally, which guarantees the independence and security of the data, can completely control the storage and processing process of the battery data, and reduces the risk of battery data leakage and security vulnerabilities.

[0136] In some embodiments of the present disclosure, the measurement point data of the plurality of batteries is obtained, including: obtaining the measurement point data of the plurality of batteries at a first time; or reading the measurement point data of the plurality of batteries according to an address for storing the measurement point data of the battery.

[0137] In some embodiments of the present disclosure, the deployment mode of the battery fault detection service can support public cloud deployment and private environment deployment. Therefore, the measurement point data of the plurality of batteries is obtained in two ways, including public and private. One is that the battery application party (i.e., the customer) transmits the battery measurement point data to the battery energy cloud platform at a first time (i.e., public), wherein the battery energy cloud platform is used to perform the fault detection of the battery, so that the battery energy cloud platform obtains the measurement point data of the plurality of batteries; the other is that the battery energy cloud platform reads the relevant battery measurement point data according to the folder address specified by the battery application party for storing the battery measurement point data (i.e., private). The first time can be a preset time for performing the fault detection of the battery.

[0138] In some embodiments of the present disclosure, the fault detection task is performed, the detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries, and the first detection data of the plurality of batteries is determined, including: obtaining target measurement point data corresponding to the preset fault detection content from the measurement point data of the plurality of batteries; performing the detection of the preset fault detection content on the target measurement point data of the plurality of batteries, and determining the first detection data of the plurality of batteries.

[0139] It should be noted that different preset fault detection contents correspond to different measurement point data, therefore, the target measurement point data corresponding to the preset fault detection content is obtained from the measurement point data of the plurality of batteries, and then the detection of the preset fault detection content of the target measurement point data is performed to obtain the first detection data of each of the plurality of batteries corresponding to the preset fault detection content. Thus, the accuracy of the detection data is ensured.

[0140] In some embodiments of the present disclosure, before performing the fault detection task, the fault detection task is determined, including: in response to a triggering operation of a fault detection project entry in a fault detection task management interface, displaying a fault detection project creation interface; the fault detection project creation page contains a plurality of selection controls; in response to a screening instruction of the plurality of selection controls of the fault detection project creation page, determining the fault detection task; wherein the fault detection task at least includes one of fault detection type, task data range, fault detection time parameter and management parameter.

[0141] In some embodiments of the present disclosure, the plurality of selection controls includes at least one of the following: a fault detection type control, a data selection control, a time selection control, and a management control.

[0142] In some embodiments of the present disclosure, in the fault detection task management interface, the server displays the fault detection project creation interface in response to the triggering operation of the fault detection project entry in the fault detection task management interface.

[0143] For example, the fault detection task management interface is shown in FIG. 3, the fault detection task management interface includes two submenus of fault detection project and fault detection task, the fault detection project enters the editing data page (i.e. the fault detection project creation interface) through the new project control (i.e. in response to the triggering operation of the fault detection project entry in the fault detection task management interface), and the editing data page includes controls corresponding to customer, template category, data time and project name respectively. Among them, the project name determines the type of the fault detection project. In FIG. 3, the customer is i7dp9rw7m472, the template category is word version, the data time is 2023-05-2023-08, and the project name is task type poster board 01. In addition to this, the data time in FIG. 3 can also set the fault detection period frequency, and the project name can be set as periodic poster board 02, etc.

[0144] It can be understood that in response to the triggering operation of the fault detection project entry in the fault detection task management interface, the fault detection project creation interface is displayed; in response to the screening instruction of the plurality of selection controls of the fault detection project creation page, the fault detection task is determined, and after the fault detection task is established, the fault detection task can be executed and a plurality of fault detection results can be obtained.

[0145] In some embodiments of the present disclosure, the plurality of selection controls comprises at least one of a fault detection type control, a data selection control, a time selection control, and a management control; in response to the screening instruction of the plurality of selection controls of the fault detection project creation page, determining the fault detection task comprises: in response to a first screening instruction of the fault detection type control, determining a first task type of the fault detection task; the first task type represents task-based fault detection; in response to a second screening instruction of the data selection control, determining the measurement point data of a target battery in the measurement point data of the plurality of batteries; in response to a third screening instruction of the time selection control, determining a data time interval corresponding to the measurement point data of the target battery; and in response to a fourth screening instruction of the management control, determining the fault detection task.

[0146] It can be understood that, through the first screening instruction of the fault detection type control, it is determined that the type of the fault detection task is task-based fault detection, and through the data selection control, the measurement point data of the target battery is determined, and through the time selection control, the data time interval is determined, so as to establish the fault detection task, facilitating subsequent execution of the task-based fault detection task.

[0147] In some embodiments of the present disclosure, task-based fault detection is mainly aimed at a batch of batteries, such as several thousand or tens of thousands of vehicles or several months of measurement point data of battery packs, and the performance of each dimension of the batteries in the batch of data is detected, and the task-based fault detection can be defined. After the fault detection is completed, a one-time fault detection project is formed, and after the fault detection project is run, a fault detection report is output and the detection is ended.

[0148] In some embodiments of the present disclosure, in response to the screening instruction of the plurality of selection controls of the fault detection project creation page, determining the fault detection task comprises: in response to a fifth screening instruction of the fault detection type control, determining a second task type of the fault detection task; the second task type represents periodic fault detection; in response to a sixth screening instruction of the data selection control, determining the measurement point data of a target battery in the measurement point data of the plurality of batteries; in response to a seventh screening instruction of the time selection control, determining a fault detection cycle frequency corresponding to the measurement point data of the target battery; and based on the fault detection cycle frequency, determining to start periodic fault detection; and in response to an eighth screening instruction of the management control, determining the fault detection task.

[0149] It can be understood that, through the fifth screening instruction of the fault detection type control, it is determined that the type of the fault detection task is periodic fault detection, and through the data selection control, the measurement point data of the target battery is determined, and through the time selection control, the fault detection cycle frequency is determined, so as to establish the fault detection task, facilitating subsequent execution of the periodic fault detection task. The periodic fault detection task only needs to be created once. Subsequently, the execution of the fault detection task is automatically triggered according to the fault detection cycle frequency.

[0150] In some embodiments of the present disclosure, the periodic fault detection is mainly for the battery energy cloud platform that has established business cooperation. The relevant battery measurement point data is uploaded to the battery energy cloud platform periodically, and the overall operation of the battery can be periodically detected. The power equipment belonging to the same battery application party can correspond to a periodic fault detection task. The periodic fault detection task sets the data range, fault detection frequency, etc. The battery energy cloud platform can determine the fault detection frequency, automatically activate the periodic fault detection project according to the period, for example, once a month. After the monthly fault detection project is completed, a corresponding fault detection report will also be output, and the relevant operation status of the battery in the past month is determined.

[0151] For example, after determining the fault detection task in response to the filtering instruction of the plurality of selection controls of the fault detection project creation page, the established project name and the corresponding fault detection state, creation time, corresponding task and operation are displayed in the fault detection project list. As shown in FIG. 4, the newly created project name is task type poster board 01, the fault detection state is to be executed, the creation time is 2024-05-28, the operation is to start execution, and the data is edited.

[0152] It can be understood that the fault detection project creation interface is displayed in response to the triggering operation of the fault detection project entry in the fault detection task management interface; in response to the filtering instruction of the plurality of selection controls of the fault detection project creation page, the fault detection task is determined, and after the fault detection task is established, the fault detection task can be executed and a plurality of fault detection results can be obtained.

[0153] In some embodiments of the present disclosure, the fault detection task is executed, the preset fault detection content is detected from the measurement point data of the plurality of batteries, and the first detection data of each of the plurality of batteries is determined. In the case where the fault detection task is determined to be a task type fault detection, the measurement point data of the target battery in the preset data time interval corresponding to the fault detection task is obtained from the measurement point data of the plurality of batteries. The one-time detection of the preset fault detection content is performed on the measurement point data of the target battery, and the first detection data of each of the target batteries is determined.

[0154] In some embodiments of the present disclosure, the task-based fault detection refers to one-time fault detection on fault detection content. For the task-based fault detection, a target battery and a preset data time interval of the target battery are set in advance, so that when the task-based fault detection is started, one-time detection of the preset fault detection content is performed based on the measurement point data of the target battery in the preset data time interval to determine the first detection data of each target battery. The target battery and the preset data time interval of the measurement point data of the target battery can be set by the user, thereby ensuring the flexibility and diversity of the fault detection task. The target battery can be understood as a battery that needs to be detected.

[0155] In some embodiments of the present disclosure, performing the fault detection task, performing detection of the preset fault detection content on the measurement point data of the plurality of batteries, and determining the first detection data of each of the plurality of batteries include: in a case where it is determined that the fault detection task is a periodic fault detection, performing detection of the preset fault detection content on the measurement point data of the plurality of batteries acquired at a time according to a fault detection cycle frequency, and determining the first detection data.

[0156] In some embodiments of the present disclosure, the periodic fault detection refers to periodic fault detection on fault detection content. The periodic fault detection task needs to be created only once, and subsequent execution of the fault detection task is automatically triggered according to a fault detection cycle frequency, thereby improving the efficiency of battery fault detection.

[0157] For example, at a first time, detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries to determine the first detection data. At a second time, detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries to determine the first detection data. At a third time, detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries to determine the first detection data, and so on.

[0158] In some embodiments of the present disclosure, performing the fault detection task, performing detection of the preset fault detection content on the measurement point data of the plurality of batteries, and determining the first detection data of each of the plurality of batteries include: performing the fault detection task, performing at least one of data analysis, data deduplication, data cleaning, data conversion, and data aggregation on the measurement point data of the plurality of batteries respectively to determine the to-be-detected point data; and performing detection of the preset fault detection content on the to-be-detected point data to determine the first detection data of each of the plurality of batteries.

[0159] In some embodiments of the present disclosure, at least one of data analysis, data deduplication, data cleaning, data conversion and data aggregation is performed on the measurement point data of the plurality of batteries according to the fault detection task to determine the to-be-measured point data; and the preset fault detection content is detected on the to-be-measured point data to determine the first detection data of each of the plurality of batteries. The data analysis, data deduplication, data cleaning, data conversion and data aggregation are performed to remove data irrelevant to the preset fault detection content.

[0160] In some embodiments of the present disclosure, the server performs detection of the preset fault detection content on the measurement point data of the plurality of batteries in different dimensions according to the fault detection task in response to a triggering operation of the fault detection task in the fault detection task management interface to determine a plurality of first detection data.

[0161] Different dimension data distribution;

[0162] Different dimension data loss;

[0163] Different dimension fast and slow charging behavior analysis;

[0164] Different dimension battery long-term storage behavior analysis;

[0165] Different dimension fault early warning feature analysis;

[0166] Different dimension serious fault early warning feature analysis;

[0167] Different dimension analysis of fault distribution / reason;

[0168] Different dimension battery life state estimation feature analysis;

[0169] Low-capacity battery pack feature analysis;

[0170] Decaying fast battery pack feature analysis.

[0171] In some embodiments of the present disclosure, in response to a triggering operation of the fault detection task in the fault detection task management interface, detection of the preset fault detection content is performed on the measurement point data of the plurality of batteries in different dimensions according to the fault detection task to obtain a plurality of feature values corresponding to different dimensions, the feature values are aggregated for fault detection indexes, and a plurality of first detection data are determined.

[0172] In some embodiments of the present disclosure, the battery fault detection method further includes: in response to a triggering operation of the fault detection management interface, displaying the first data item corresponding to each of the plurality of electrical equipment selected, and an entity selection control and a data entry of the fault detection object.

[0173] For example, the first data item is shown in FIG. 5, which includes the electrical equipment identification code, creation time, tenant ID, electrical equipment model, battery pack quantity, electrical equipment production year, battery application party (i.e. customer) code and electrical equipment type. The entity selection control of the fault detection object is the control corresponding to the electrical equipment entity and the electrical equipment identification code in FIG. 5 respectively; and the data entry is the imported data in FIG. 5.

[0174] In some embodiments of the present disclosure, the battery fault detection method further comprises: displaying the task progress of the fault detection task.

[0175] For example, after executing the fault detection task, the fault detection task progress is displayed, specifically the fault detection task has started to execute, the customer information, the data time and the progress bar. As shown in FIG. 6, after executing the fault detection task, the fault detection task progress is displayed, specifically the fault detection task has started to execute, the customer: i7dp9rw7m472, the data time: 2023-05-01-2023-08-31 and the progress 12%.

[0176] It can be understood that, in response to the triggering operation of the fault detection project entry in the fault detection task management interface, the fault detection project creation interface is displayed; in response to the screening instruction of the plurality of selection controls of the fault detection project creation page, the fault detection task is determined, and after the fault detection task is established, the fault detection task can be executed and a plurality of fault detection results can be obtained.

[0177] In some embodiments of the present disclosure, the entire fault detection service (i.e. battery fault detection method) includes fault detection project management, fault detection content management, fault detection plan management and fault detection result display. For example, as shown in FIG. 7, the fault detection service 7 includes fault detection project management 71, fault detection content management 72, fault detection plan management 73 and fault detection result display 74. Specifically, it also involves index management 75, fault detection report management 76, subscription and push configuration 77, task scheduling engine 78 and index query engine 79.

[0178] In some embodiments of the present disclosure, the battery fault detection (i.e., the battery fault detection method) is divided into three parts: fault detection engineering, fault detection content, and fault detection report. For example, as shown in FIG. 8, the battery fault detection includes ① fault detection engineering, ② fault detection content, and ③ fault detection report. Among them, the fault detection engineering: mainly for different battery application parties to define task type fault detection or periodic fault detection according to their business needs, and then set the data range related to fault detection, fault detection frequency, management of related fault detection tasks / engineering, etc. The fault detection content: that is, the specific content item of the supported battery fault detection, the preset fault detection content accumulates to define 50+ fault detection content items, more than 280+ characteristic values, and analyzes the performance, trend and reason of the battery in each dimension. The fault detection report: as the output mode of the fault detection result, supports outputting the fault detection report after each fault detection engineering is completed.

[0179] In some embodiments of the present disclosure, the content contained in ① fault detection engineering is shown in FIG. 9, ① fault detection engineering includes task type fault detection and periodic fault detection; wherein, the task type fault detection includes selecting tenant data, selecting data time span, and fault detection engineering management; the periodic fault detection includes selecting tenant data, selecting periodic frequency, and creating fault detection task; creating fault detection task further includes periodic fault detection engineering and fault detection engineering management.

[0180] It should be noted that the periodic fault detection or the task type fault detection is realized through a fault detection type control; the selection of tenant data is realized through a data selection control; the selection of periodic frequency or the selection of data time span is realized through a time selection control; and the fault detection engineering management is realized through a management control.

[0181] In some embodiments of the present disclosure, the content contained in ② fault detection content is shown in FIG. 10, ② fault detection content includes fault detection data analysis, battery safety fault detection, battery health fault detection, and fault detection key round. The fault detection data analysis includes fault detection data display, fault detection data quality, and battery portrait analysis. Among them, the fault detection data display includes measurement point data, battery pack capacity, battery cell number, battery pack number, and electric equipment / vehicle type number; the fault detection data quality includes data loss rate; the battery portrait analysis includes charging behavior analysis and battery long-term storage behavior analysis. The battery safety fault detection includes battery safety fault detection information overview, serious fault warning information, and battery fault analysis; wherein, the battery safety fault detection information overview includes cloud warning and vehicle end warning; the serious fault warning information includes serious fault information list and fault information details; the battery fault analysis includes multi-dimensional fault distribution analysis. The battery health fault detection includes battery life state overall information, low capacity battery information, and capacity attenuation fast battery information.

[0182] In some embodiments of the present disclosure, the content contained in the fault detection report is shown in FIG. 11, and the fault detection report includes report automatic output, report online editing, report automatic pushing and report downloading.

[0183] In some embodiments of the present disclosure, the fault detection report production flow chart is shown in FIG. 12, as follows:

[0184] S1, create a fault detection project.

[0185] In some embodiments of the present disclosure, creating a fault detection project includes: S1a, configuring an index; S1b, configuring fault detection content; S1c, configuring subscription information; and S1d, generating a template file. The template file is used for subsequent generation of a fault detection report.

[0186] In some embodiments of the present disclosure, creating a fault detection project includes establishing periodic fault detection and task-based fault detection. The periodic fault detection establishment includes: S1e, configuring fault detection project execution plan and S1f, executing plan scheduling.

[0187] It should be noted that the template file is a preset template.

[0188] S2, execute the fault detection project.

[0189] Executing the fault detection project includes: S2a, obtaining fault detection project configuration; S2b, obtaining execution plan; S2c, loading fault detection point data; S2d, executing fault detection algorithm and S2e, aggregating fault detection index.

[0190] S3, generate a fault detection report.

[0191] Generating a fault detection report includes: S3a, obtaining a template; S3b, obtaining an index; S3c, filling data; S3d, outputting a result PDF file (fault detection report).

[0192] S4, push the fault detection report.

[0193] Taking the "SOH overall distribution" part of the "battery health fault detection" included in the fault detection report as an example, the business value and implementation process of the battery fault detection business are further described, as shown in FIG. 13:

[0194] a) "SOH overall distribution" business value:

[0195] Business value 1: Present the SOH estimation result distribution of all battery packs involved in the battery fault detection project in the last month, for example, December 2023, and calculate the SOH distribution interval probability of all battery packs according to the SOH estimation result of each battery pack, and calculate the mean and median of the SOH of all battery packs for the battery application party to analyze and refer. The SOH distribution interval probability, the mean and the median of the SOH correspond to the aggregated data of the fault detection mentioned in the previous embodiment.

[0196] Business value 2: Identify the proportion of vehicles (i.e., electric devices) with low-capacity battery packs (SOH < 80%) and potential low-capacity battery packs (80% ≤ SOH < 85%) among all battery packs involved in the battery fault detection project, which can be used by the battery application party for targeted processing to improve the satisfaction of the battery application party.

[0197] b) "SOH overall distribution" business process:

[0198] Create a battery fault detection project, which defines information such as the number of related fault detection vehicles, models, data duration, fault detection type (periodic or task-based), etc. After the battery fault detection project is executed, read the relevant battery measurement point data and corresponding customer profile information, and perform cleaning, extraction, transformation, loading (Extract-Transform-Load, ETL), etc. to obtain the processed data.

[0199] The battery fault detection project calls the SOH estimation algorithm of the energy cloud platform, and through multiple SOH estimation algorithms and SOH comprehensive estimation algorithms, the SOH estimation result of each battery pack is derived, i.e., the first detection data.

[0200] After the SOH estimation algorithm is executed and the SOH estimation result of each battery pack is output, the battery fault detection project calls the fault detection index aggregation related algorithm to calculate the SOH value interval distribution probability, the mean (e.g., 97.21%) and the median (e.g., 97.63%) of the SOH, the number of low-capacity battery packs and potential low-capacity battery packs, and then calculate the proportion of vehicles with low-capacity battery packs (SOH < 80%) and potential low-capacity battery packs (80% ≤ SOH < 85%).

[0201] In some embodiments of the present disclosure, the online PDF rendering flowchart is shown in FIG. 14 as follows:

[0202] S11, PDF page loading.

[0203] In some embodiments of the present disclosure, the PDF page is loaded, i.e., a preset template is obtained.

[0204] S12, obtain page information.

[0205] In some embodiments of the present disclosure, the structural data of the PDF page, i.e., the page information, is obtained through the backend interface.

[0206] S13, data analysis.

[0207] In some embodiments of the present disclosure, the obtained page information is subjected to data analysis to obtain a plurality of component information.

[0208] S14, component type determination.

[0209] In some embodiments of the present disclosure, after data analysis, the component type is determined, and the component to be loaded is rendered according to the component type (including but not limited to the chart component 131, the template component 132, the content component 133, and the layout component 134 in FIG. 13).

[0210] S15, loading of index data.

[0211] In some embodiments of the present disclosure, the loaded component (i.e., the chart component 131 and the template component 132) loads the index data.

[0212] S16, loading of template script.

[0213] In some embodiments of the present disclosure, the loaded component (i.e., the template component 132) loads the template script.

[0214] S17, obtaining of determination condition.

[0215] In some embodiments of the present disclosure, the loaded component (i.e., the template component 132) obtains the determination condition and performs data loading and processing. After S15, S16, and S17 are executed, the page is generated.

[0216] S18, pagination processing.

[0217] In some embodiments of the present disclosure, the generated page is subjected to pagination processing to obtain the paginated page.

[0218] S19, rendering.

[0219] In some embodiments of the present disclosure, the paginated page is rendered to obtain the final PDF rendering effect. The structural data of the page is shown in FIG. 15, and the structural data of the page includes a PDF template 14, which includes a layout configuration 141, a node configuration 142, and a user configuration 143. The node configuration 142 and the user configuration 143 further include a node identifier 14a, a determination rule configuration 14b, a template component configuration 14c, an index configuration 14d, and an interaction rule configuration 14e.

[0220] 1) Layout configuration 141: describes the tree structure of the PDF template node group, containing the node identification required by the PDF template and its composition relationship.

[0221] 2) Node configuration 142: describes all node information constituting the PDF template, including node identification 14a, decision rule configuration 14b, template component configuration 14c, index configuration 14d, interaction rule configuration 14e, etc.

[0222] 3) Node identification 14a: has uniqueness, used to identify the node.

[0223] 4) Decision rule configuration 14b: according to the decision rule and the index data, system parameters (i.e. the aggregation data of the fault detection of the fault detection content mentioned above), the conclusion after the decision is output. Specifically, for the aggregation data of the fault detection of different fault detection contents, its decision rule is not the same, for example: it can be compared with the preset number of extreme value data according to the aggregation data of the fault detection, and the extreme value data can be the maximum value and the minimum value; It can also be compared with the preset abnormal threshold according to the aggregation data of the fault detection, and the setting of the preset abnormal threshold is mainly based on the experience value of the power battery industry.

[0224] For example, the index data of the data missing rate is 8%, and the decision rule of the data missing rate quality is: the data missing rate <5%, the data quality is good; the data missing rate is 5%~10%, the data quality is general; the data missing rate >10%, the data quality is poor. Thus, the final conclusion is that the data quality is general.

[0225] 5) Template component configuration 14c: contains component type identification and component attribute configuration (such as: style, title, component template, component parameter, etc.).

[0226] 6) Index configuration 14d: contains the index id list of the index data required by the node. According to the index id list, the required index data is loaded for system rendering.

[0227] 7) Interaction rule configuration 14e: contains data interaction (processing system data), display interaction (controlling the display behavior of the component, such as: display or hide), behavior interaction (controlling other components or browsers, such as: opening a pop-up window, component animation, etc.).

[0228] 8) User configuration 143: the content is the same as node configuration 142, except that this configuration is input by the user. User configuration 143 has higher priority and will overwrite the corresponding node configuration 142, thereby realizing the user's self-defined function.

[0229] In some embodiments of the present disclosure, the overall flowchart of the fault detection service is shown in FIG. 16, and the core processing nodes include data access 151, data processing 152, algorithm execution 153, and result aggregation 154.

[0230] 1) The user transmits the battery measurement point data to the data gateway through data import. The battery measurement point data is imported into the system, and according to the difference of the fault detection project, if it is a task-based fault detection, a batch of measurement point data of the power equipment for a period of time needs to be imported at one time; and for the periodic fault detection, the measurement point data of the battery is imported continuously through the cloud platform.

[0231] 2) After the data gateway performs data analysis and data deduplication on the imported data, the original data is written into the database.

[0232] 3) Through task scheduling, the original data is processed, mainly involving data cleaning, data ETL, and data aggregation.

[0233] 4) Through the operator engine, the data aggregated data is queried by the operator, and the process of querying data, executing the operator, and outputting features is executed for each operator to obtain the message queue (MQ) and the features, and the features are stored. Through the rule engine, the process of querying features, executing rules, and outputting results is executed to obtain the MQ and the results, and the results are stored.

[0234] In some embodiments of the present disclosure, in the algorithm execution, the measurement point data is queried by the operator, the measurement point data (i.e., query data) is executed by the operator, the features are output, and the features are stored. In the result analysis, the features of the current measurement point data and the features of the historical measurement point data are analyzed and aggregated, and the results are output.

[0235] 5) The application service processes the results in the library to generate battery fault warning results, battery state estimation results, and battery portrait results, etc.

[0236] In some embodiments of the present disclosure, in the result aggregation 154, the battery fault warning service, the battery state estimation service, and the battery portrait service are mainly performed. In the battery fault warning service, the fault warning result is generated by the stored result, and the fault warning result is stored; in the battery state estimation service, the state estimation result is generated by the stored result, and the state estimation result is stored; in the battery portrait service, the battery portrait result is generated by the stored result, and the battery portrait result is stored.

[0237] 6) According to the output result of each module, the result output and presentation are carried out according to the overall framework of the battery fault detection report, and a document in a preset format is formed, supporting related push services. The document in the preset format can include a PDF document, a Word document, etc.

[0238] In some embodiments of the present disclosure, a battery fault detection report is generated by the stored fault early warning result, the state estimation result and the battery image result.

[0239] It can be understood that the battery fault detection method provided by the embodiments of the present disclosure can flexibly adapt to the needs of various battery fault detection deployment scenarios, and is deployed on a battery energy cloud platform. Since the energy cloud platform has the ability to deploy across the cloud and has an efficient deployment process (for example, 2 days to complete deployment), it can thus unify the underlying cloud to implement the present disclosure. In addition, different battery cells, battery packs and battery application data (i.e. battery measurement point data) can be accessed to quickly obtain data, quickly support business data and data assetization, and efficiently develop and smoothly expand data, thereby achieving battery fault detection. It should be noted that the present disclosure provides the energy cloud platform in a componentized manner, which can reduce repeated development work and highly reuse to improve efficiency.

[0240] The embodiments of the present disclosure provide a battery fault detection device, and FIG. 17 is a structural schematic diagram of a battery fault detection device provided by an embodiment of the present disclosure. As shown in FIG. 17, the battery fault detection device 1600 includes an acquisition unit 1601 and a processing unit 1602, wherein,

[0241] The acquisition unit 1601 is configured to acquire first detection data of a plurality of batteries respectively; the first detection data is determined by executing a preset fault detection content;

[0242] The processing unit 1602 is configured to perform data analysis on the plurality of first detection data according to each fault detection content, and determine aggregated data of the fault detection of the plurality of batteries;

[0243] The processing unit 1602 is configured to count abnormal data satisfying a preset index condition based on the aggregated data of the fault detection of each fault detection content; the preset index condition is a rule for determining whether the battery has a hidden danger based on the aggregated data of the fault detection.

[0244] In some embodiments of the present disclosure, the processing unit 1602 is further configured to perform fault detection index aggregation analysis on the plurality of first detection data of each fault detection content, and determine aggregated data of the fault detection after a first time of use of the plurality of first batteries; the aggregated data of the fault detection is overall analysis data of the plurality of first batteries.

[0245] In some embodiments of the present disclosure, the processing unit 1602 is further configured to determine, from the aggregated data of the fault detection of any fault detection content, abnormal data satisfying a preset abnormal threshold; and / or, determine a preset number of extreme value data from the aggregated data of the fault detection of any fault detection content; wherein the preset index condition comprises one or more of the following: the preset abnormal threshold and the preset number of extreme value data.

[0246] In some embodiments of the present disclosure, the obtaining unit 1601 is further configured to obtain a preset template corresponding to the fault detection task; and the processing unit 1602 is further configured to perform data loading on the preset template based on the aggregated data of the fault detection and the abnormal data corresponding to the preset fault detection content, to generate a fault detection report.

[0247] In some embodiments of the present disclosure, the obtaining unit 1601 is further configured to obtain the measurement point data of the plurality of batteries; and perform the fault detection task to perform detection of the preset fault detection content on the measurement point data of the plurality of batteries, to determine the first detection data of each of the plurality of batteries.

[0248] In some embodiments of the present disclosure, the obtaining unit 1601 is further configured to obtain the measurement point data of the plurality of batteries at a first time; or, read the measurement point data of the plurality of batteries according to an address where the measurement point data of the battery is stored.

[0249] In some embodiments of the present disclosure, the processing unit 1602 is further configured to, in a case where it is determined that the fault detection task is a task-type fault detection, obtain, from the measurement point data of the plurality of batteries, measurement point data of a target battery in a preset data time interval corresponding to the fault detection task.

[0250] perform one-time detection of the preset fault detection content on the measurement point data of the target battery, to determine first detection data of the target battery.

[0251] In some embodiments of the present disclosure, the processing unit 1602 is further configured to, in a case where it is determined that the fault detection task is a periodic fault detection, perform detection of the preset fault detection content on the measurement point data of the plurality of batteries obtained at a fault detection cycle frequency, to determine the first detection data.

[0252] In some embodiments of the present disclosure, the processing unit 1602 is further configured to perform the fault detection task to perform at least one of data analysis, data deduplication, data cleaning, data conversion and data aggregation on the measurement point data of the plurality of batteries, respectively, to determine to-be-measured point data.

[0253] perform detection of the preset fault detection content on the to-be-measured point data, to determine the first detection data of each of the plurality of batteries.

[0254] In some embodiments of the present disclosure, the acquisition unit 1601 is further configured to display a measurement point data interface in response to a trigger operation of a data entry displayed by the fault detection management interface; in response to a data selection operation in the measurement point data interface, import measurement point data of a plurality of batteries corresponding to an entity of the fault detection object; and display data items of the measurement point data of the plurality of batteries; wherein the data items include at least one of the following: a data identifier, an import time, an import state, and an import description.

[0255] In some embodiments of the present disclosure, the processing unit 1602 is further configured to display a fault detection report after completing the fault detection; and / or push the fault detection report based on a preset subscription relationship.

[0256] Embodiments of the present disclosure further provide another battery fault detection device. FIG. 18 is a structural schematic diagram of a battery fault detection device according to an embodiment of the present disclosure. As shown in FIG. 18, the battery fault detection device 1700 includes a processor 1701 and a memory 1702 configured to store a computer program capable of running on the processor.

[0257] When the processor 1701 is configured to run the computer program, the processor 1701 performs the method steps in the foregoing embodiments.

[0258] Of course, in actual applications, as shown in FIG. 17, various components in the battery fault detection device 1700 are coupled together through a bus system 1703. It can be understood that the bus system 1703 is used to realize the connection and communication between the components. The bus system 1703 includes not only a data bus, but also a power bus, a control bus, and a status signal bus. However, for the purpose of clarity, all kinds of buses are marked as the bus system 1703 in FIG. 17.

[0259] In embodiments of the present disclosure, the processor 1701 can be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, or a microprocessor. It can be understood that, for different devices, the electronic device used to realize the function of the processor can also be other devices, and the embodiments of the present disclosure are not limited in this regard.

[0260] The embodiment of the present disclosure provides a computer readable storage medium, which stores a computer program, and is used for realizing the battery fault detection method of any one of the above embodiments when the computer program is executed by a processor.

[0261] Exemplarily, the program instructions corresponding to the battery fault detection method in the embodiment can be stored on a storage medium such as an optical disc, a hard disk, a U disk, and the like. When the program instructions corresponding to the battery fault detection method in the storage medium are read by an electronic device or executed, the battery fault detection method of any one of the above embodiments can be realized.

[0262] The integrated unit, if implemented in the form of a software function module and not sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiment can essentially or contribute to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the embodiment method.

[0263] Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium. When the program is executed, the steps of the above method embodiments are executed. The foregoing storage medium includes a mobile storage device, a read only memory (Read Only Memory, ROM), a magnetic disc or an optical disc, and various media that can store program codes.

[0264] The modules described as separate components above can or can not be physically separated, and the components displayed as modules can or can not be physical modules; they can be located in one place or distributed on multiple network units; and part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0265] In addition, each functional module in each embodiment of the present disclosure can be integrated in one processing unit, or each module can be a single unit, or two or more modules can be integrated in one unit; the integrated module can be realized in the form of hardware or in the form of hardware plus software function unit. It should be understood that "one embodiment" or "an embodiment" mentioned in the present disclosure means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present disclosure. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner.

[0266] It should be understood that in various embodiments of the present disclosure, the size of the serial number of each step / process does not mean the order of execution, and the execution order of each step / process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure. The serial number of the above embodiments of the present disclosure is only for description, not representing the pros and cons of the embodiments.

[0267] It should be noted that in the application, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or further includes elements inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or apparatus including the element.

[0268] In several embodiments provided by the present disclosure, it should be understood that the disclosed system, device and method can be implemented in other ways. The above-described device embodiments are only schematic, for example, the division of units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the various components shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0269] The disclosed methods in several method embodiments provided by the embodiments of the present disclosure can be combined in any manner without conflict, to obtain new method embodiments.

[0270] The features disclosed in several product embodiments provided by the embodiments of the present disclosure can be combined arbitrarily without conflict to obtain new product embodiments.

[0271] The features disclosed in several method or device embodiments provided by the embodiments of the present disclosure can be combined arbitrarily without conflict to obtain new method embodiments or device embodiments.

[0272] The above merely describes the implementation of the embodiments of the present disclosure, but the protection scope of the embodiments of the present disclosure is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present disclosure, which should be covered within the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the embodiments of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A battery fault detection method, comprising: obtaining first detection data of each of a plurality of batteries; determining the first detection data by performing preset fault detection content; performing data analysis on the plurality of first detection data according to each fault detection content to determine aggregated data of fault detection of the plurality of batteries; based on the aggregated data of fault detection of each fault detection content, counting abnormal data meeting a preset index condition; the preset index condition is used to determine a rule for determining whether a battery has a hidden danger based on the aggregated data of fault detection.

2. The method of claim 1, wherein, the aggregated data of fault detection of the plurality of batteries is determined by performing data analysis on the plurality of first detection data according to each fault detection content, comprising: performing aggregated analysis of fault detection indicators on the plurality of first detection data of each fault detection content to determine aggregated data of fault detection of the plurality of batteries after a first time of use; the aggregated data of fault detection is overall analysis data of the plurality of batteries.

3. The method of claim 1 or 2, wherein, the aggregated data of fault detection of each fault detection content is counted based on the aggregated data of fault detection of each fault detection content, comprising: determining abnormal data meeting a preset abnormal threshold from the aggregated data of fault detection of any fault detection content; and / or, determining a preset number of extreme value data from the aggregated data of fault detection of any fault detection content; wherein the preset index condition includes one or more of the following: meeting a preset abnormal threshold and a preset number of extreme value data.

4. The method according to any one of claims 1 to 3, wherein, the method further comprises: obtaining a preset template corresponding to the fault detection content; based on the aggregated data of fault detection and the abnormal data corresponding to the preset fault detection content, loading data on the preset template to generate a fault detection report.

5. The method according to any one of claims 1 to 4, wherein, the first detection data of each of the plurality of batteries is obtained, comprising: obtaining measurement point data of the plurality of batteries; performing a fault detection task to detect the measurement point data of the plurality of batteries according to preset fault detection content to determine the first detection data of each of the plurality of batteries.

6. The method of claim 5, wherein, the measurement point data of the plurality of batteries is obtained, comprising: obtaining the measurement point data of the plurality of batteries at a first time; or reading the measurement point data of the plurality of batteries according to an address where the measurement point data of the plurality of batteries is stored.

7. The method of claim 5, wherein, the first detection data of each of the plurality of batteries is obtained, comprising: in a case where it is determined that the fault detection task is a task type fault detection, obtaining measurement point data of a target battery in a preset data time interval corresponding to the fault detection task from the measurement point data of the plurality of batteries; performing one-time detection of the preset fault detection content on the measurement point data of the target battery to determine the first detection data of each of the target battery.

8. The method of claim 5, wherein, the first detection data of each of the plurality of batteries is obtained, comprising: In a case where the fault detection task is determined to be a periodic fault detection, the preset fault detection content is detected according to a fault detection cycle frequency, and the first detection data is determined.

9. The method of claim 5, wherein, The method comprises: The method comprises: The method comprises:

10. The method of claim 5, wherein, The method comprises: The method comprises: The method comprises: The method comprises: The method comprises:

11. The method according to any one of claims 5 to 10, wherein, The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises:

13. The battery failure detection apparatus of claim 12, wherein, The method comprises: The method comprises:

14. The battery failure detection apparatus according to claim 12 or 13, wherein The method comprises:

15. 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16. The battery fault detection apparatus of any one of claims 12 to 15, wherein, The acquisition unit is further configured to acquire the measurement point data of the plurality of batteries; and the processing unit is further configured to perform a fault detection task, perform detection of preset fault detection content on the measurement point data of the plurality of batteries, and determine first detection data of the plurality of batteries respectively.

17. The battery failure detection apparatus of claim 16, wherein, The processing unit is specifically configured to, in a case where it is determined that the fault detection task is a task-type fault detection, acquire, from the measurement point data of the plurality of batteries, measurement point data of a target battery corresponding to a preset data time interval of the fault detection task. The processing unit is specifically configured to, in a case where it is determined that the fault detection task is a task-type fault detection, acquire, from the measurement point data of the plurality of batteries, measurement point data of a target battery corresponding to a preset data time interval of the fault detection task.

18. The battery failure detection apparatus of claim 16, wherein, The processing unit is specifically configured to, in a case where it is determined that the fault detection task is a periodic fault detection, perform detection of the preset fault detection content on the measurement point data of the plurality of batteries acquired at a timing according to a fault detection cycle frequency, and determine the first detection data. 19.A battery fault detection device, comprising: a memory configured to store executable instructions; a processor configured to implement the battery fault detection method according to any one of claims 1-11 when executing the executable instructions stored in the memory. 20.A computer readable storage medium, the storage medium storing executable instructions, the executable instructions being configured to cause a processor to implement the battery fault detection method according to any one of claims 1-11 when executed.

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