Method and device for detecting fracture of connecting assembly of energy storage system and energy storage system

Abstract

The invention relates to a method and device for detecting breakage of a connecting assembly of an energy storage system and the energy storage system. The method is applied to energy storage edge ends. The method comprises the steps that operation state parameters of at least one secondary battery connected with a connecting assembly are obtained, parameter discrete characteristic values between the operation state parameters are determined, and the parameter discrete characteristic values represent the discrete degree of the operation state parameters of the secondary batteries; according to the size relation between the parameter discrete characteristic value and a preset discrete characteristic value threshold value, fracture detection is conducted on the connecting assembly, and an edge end fracture detection result is obtained; the operation state parameters and the edge end fracture detection result are sent to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection assembly according to the cloud fracture detection result and the edge end fracture detection result, and the cloud fracture detection result is obtained through prediction according to the operation state parameters. By adopting the method, the detection effect of fracture detection on the connecting assembly of the energy storage system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage battery management systems, and in particular to a method and device for detecting fracture of connection components of an energy storage system, and an energy storage system. Background Art

[0002] With the continuous development of science and technology, energy storage systems have been widely used in many fields. Energy storage systems are composed of multiple energy storage modules that achieve electrical connection and signal exchange with conductive components such as nickel sheets, copper sheets, or nickel-plated copper sheets through CCS (Cell Contact System, integrated busbar), thus forming a complete system that can stably achieve energy storage and release. Among them, CCS is a key connection component in the energy storage system, and its reliability is crucial to the safe operation of the energy storage system.

[0003] Currently, fracture detection of connected components in energy storage systems is ineffective due to the limited recognition capabilities of the naked eye and the difficulty for operators to match the scale of the energy storage system. This can lead to detection lags, errors, and prolonged testing. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device and energy storage system for detecting the fracture of the connection components of the energy storage system to improve the detection effect of the fracture detection of the connection components of the energy storage system in order to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for detecting a break in a connection assembly of an energy storage system, which is applied to an energy storage edge. The connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery. The method for detecting a break in a connection assembly of an energy storage system includes:

[0006] Acquiring the operating status parameters of the at least one secondary battery collected and uploaded by the collecting component;

[0007] Determining a parameter discrete characteristic value between each operating state parameter, wherein the parameter discrete characteristic value represents a degree of discreteness of each operating state parameter of the secondary battery;

[0008] Performing a fracture detection on the connection component according to a magnitude relationship between the discrete eigenvalue of the parameter and a preset discrete eigenvalue threshold to obtain an edge fracture detection result;

[0009] The operating status parameters and the edge fracture detection results are sent to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection component based on the cloud fracture detection result and the edge fracture detection result, wherein the cloud fracture detection result is predicted based on each of the operating status parameters.

[0010] In a second aspect, the present application provides a method for detecting a break in a connection component of an energy storage system, which is applied to an energy storage cloud. The connection component includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery. The method for detecting a break in a connection component of an energy storage system includes:

[0011] Acquire an edge fracture detection result sent by the energy storage edge and an operating status parameter of the at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters;

[0012] predicting a parameter deviation characteristic value of each of the secondary batteries according to the operating state parameters, wherein the parameter deviation characteristic value represents a degree of deviation of the operating state parameter of any of the secondary batteries relative to a reference operating state parameter;

[0013] Performing a fracture detection on the connection component based on a magnitude relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-based fracture detection result;

[0014] By comparing the edge fracture detection result and the cloud fracture detection result, the fracture detection result of the connection component is obtained.

[0015] In a third aspect, the present application further provides a connection assembly fracture detection device for an energy storage system, which is applied to an energy storage edge, wherein the connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery; the connection assembly fracture detection device for the energy storage system includes:

[0016] a first acquisition module, configured to acquire the operating status parameters of the at least one secondary battery collected and uploaded by the acquisition component;

[0017] a determination module, determining a parameter discrete characteristic value between each operating state parameter, wherein the parameter discrete characteristic value represents a degree of discreteness of each operating state parameter of the secondary battery;

[0018] A first detection module is configured to perform a fracture detection on the connection component based on a magnitude relationship between the discrete eigenvalue of the parameter and a preset discrete eigenvalue threshold, thereby obtaining an edge fracture detection result;

[0019] A sending module is used to send the operating status parameters and the edge fracture detection results to the energy storage cloud, so that the energy storage cloud can generate a fracture detection result of the connection component based on the cloud fracture detection results and the edge fracture detection results, wherein the cloud fracture detection result is predicted based on each of the operating status parameters.

[0020] In a fourth aspect, the present application further provides a connection component fracture detection device for an energy storage system, which is applied to an energy storage cloud. The connection component includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery. The connection component fracture detection device for the energy storage system includes:

[0021] a second acquisition module, configured to acquire an edge fracture detection result sent by the energy storage edge and an operating status parameter of the at least one secondary battery collected and uploaded by the acquisition component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters;

[0022] a prediction module, configured to predict a parameter deviation characteristic value of each of the secondary batteries based on the operating state parameter, wherein the parameter deviation characteristic value represents a degree of deviation of the operating state parameter of any of the secondary batteries relative to a reference operating state parameter;

[0023] A second detection module is configured to perform a fracture detection on the connection component based on a magnitude relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold, and obtain a fracture detection result on the cloud;

[0024] The comparison module is used to obtain the fracture detection result of the connection component by comparing the edge fracture detection result with the cloud fracture detection result.

[0025] In a fifth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Obtaining the operating status parameters of the at least one secondary battery collected and uploaded by the collection component; determining a parameter discrete characteristic value between each operating status parameter, wherein the parameter discrete characteristic value represents the degree of discreteness of the operating status parameter of each secondary battery; performing a fracture detection on the connection component according to a size relationship between the parameter discrete characteristic value and a preset discrete characteristic value threshold value to obtain an edge fracture detection result; sending the operating status parameters and the edge fracture detection result to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection component according to the cloud fracture detection result and the edge fracture detection result, wherein the cloud fracture detection result is predicted based on each of the operating status parameters; or,

[0027] Obtain the edge fracture detection result sent by the energy storage edge and the operating status parameters of the at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters; predict the parameter deviation characteristic value of each of the secondary batteries based on the operating status parameters, and the parameter deviation characteristic value represents the degree of deviation of the operating status parameter of any of the secondary batteries relative to the benchmark operating status parameters; perform fracture detection on the connection component based on the size relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-based fracture detection result; obtain the fracture detection result of the connection component by comparing the edge fracture detection result with the cloud-based fracture detection result.

[0028] In a sixth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0029] Obtaining the operating status parameters of the at least one secondary battery collected and uploaded by the collection component; determining a parameter discrete characteristic value between each operating status parameter, wherein the parameter discrete characteristic value represents the degree of discreteness of the operating status parameter of each secondary battery; performing a fracture detection on the connection component according to a size relationship between the parameter discrete characteristic value and a preset discrete characteristic value threshold value to obtain an edge fracture detection result; sending the operating status parameters and the edge fracture detection result to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection component according to the cloud fracture detection result and the edge fracture detection result, wherein the cloud fracture detection result is predicted based on each of the operating status parameters; or,

[0030] Obtain the edge fracture detection result sent by the energy storage edge and the operating status parameters of the at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters; predict the parameter deviation characteristic value of each of the secondary batteries based on the operating status parameters, and the parameter deviation characteristic value represents the degree of deviation of the operating status parameter of any of the secondary batteries relative to the benchmark operating status parameters; perform fracture detection on the connection component based on the size relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-based fracture detection result; obtain the fracture detection result of the connection component by comparing the edge fracture detection result with the cloud-based fracture detection result.

[0031] In a seventh aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0032] Obtaining the operating status parameters of the at least one secondary battery collected and uploaded by the collection component; determining a parameter discrete characteristic value between each operating status parameter, wherein the parameter discrete characteristic value represents the degree of discreteness of the operating status parameter of each secondary battery; performing a fracture detection on the connection component according to a size relationship between the parameter discrete characteristic value and a preset discrete characteristic value threshold value to obtain an edge fracture detection result; sending the operating status parameters and the edge fracture detection result to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection component according to the cloud fracture detection result and the edge fracture detection result, wherein the cloud fracture detection result is predicted based on each of the operating status parameters; or,

[0033] Obtain the edge fracture detection result sent by the energy storage edge and the operating status parameters of the at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters; predict the parameter deviation characteristic value of each of the secondary batteries based on the operating status parameters, and the parameter deviation characteristic value represents the degree of deviation of the operating status parameter of any of the secondary batteries relative to the benchmark operating status parameters; perform fracture detection on the connection component based on the size relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-based fracture detection result; obtain the fracture detection result of the connection component by comparing the edge fracture detection result with the cloud-based fracture detection result.

[0034] The above-mentioned method, device and energy storage system for detecting the fracture of the connection component of the energy storage system, the energy storage edge first collects the operating status parameters of at least one secondary battery connected to the connection component, and then analyzes the discrete degree of the operating status parameters of different secondary batteries based on each operating status parameter to obtain the discrete characteristic value of the parameter, and then uses the discrete characteristic value of the parameter as an indicator, combined with the preset discrete characteristic value threshold, to perform fracture detection on the connection component, thereby obtaining the edge fracture detection result of the energy storage edge for detecting the fracture of the connection component, which can achieve the purpose of preliminarily detecting the fracture abnormality of the connection component by analyzing the discrete degree of the operating status parameters at the energy storage edge. Furthermore, the energy storage edge sends the operating status parameters and the edge fracture detection result together to the energy storage cloud, so that the energy storage cloud can predict the cloud fracture detection result based on each operating status parameter, which can achieve the purpose of obtaining the fracture abnormality of the connection component by predicting the operating status parameters at the energy storage cloud, and finally By comparing the edge fracture detection results with the cloud fracture detection results, the fracture detection results of the connected components can be obtained. Since the edge is close to the connected components, it can capture the operating status parameters in real time and perform feature analysis to achieve rapid early warning of the fracture of the connected components. The energy storage cloud has powerful computing power, and can accurately predict the fracture of the connected components through the operating status parameters uploaded by the energy storage edge. Finally, the fracture detection results of the connected components are obtained by comparing and verifying the results of both ends. That is, the purpose of collaborative energy storage edge and energy storage cloud to complete the automatic detection of the fracture of the connected components is achieved, rather than relying on manual identification by the naked eye of the operator. Therefore, it overcomes the technical defects of limited naked eye recognition ability and the difficulty of the operator's detection efficiency to match the scale of the energy storage system, which leads to detection lags, detection errors, or detection time being too long. Therefore, the detection effect of fracture detection of the connected components of the energy storage system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 1 is a flow chart of a method for detecting fracture of a connection component of an energy storage system according to one embodiment;

[0037] Figure 2 is a schematic flow chart of a method for detecting fracture of a connection component of an energy storage system in another embodiment;

[0038] Figure 3Schematic diagram of a process flow for detecting fracture results of a connection component in one embodiment;

[0039] Figure 4 1 is a structural block diagram of a device for detecting fracture of a connection assembly of an energy storage system applied to an energy storage edge in one embodiment;

[0040] Figure 5 This is a structural block diagram of a device for detecting fracture of a connection component of an energy storage system applied to an energy storage cloud in one embodiment;

[0041] Figure 6 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0043] First, it should be understood that the CCS (Cell Contact System, integrated busbar), as an integrated connection component in the battery module, is used to achieve electrical connection and signal interaction between energy storage modules. It can also be called the connection component of the energy storage system, or the wiring harness board assembly. Among them, nickel sheets, copper sheets, or nickel-plated copper sheets are key components of the CCS for achieving electrical connection and signal interaction. Specifically, nickel sheets, copper sheets, or nickel-plated copper sheets can all be called conductive components. If a conductive component breaks, it may cause circuit interruption (the secondary battery cell cannot participate in charging and discharging, reducing the capacity of the battery module), local overheating (arcing or local high temperature may be caused at the broken conductive component, thereby accelerating the aging of the insulation material), and safety risks (thermal runaway in extreme cases). Therefore, the reliability of the connection component is crucial to the safe operation of the energy storage system. Fracture detection of the connection component of the energy storage system is to detect the fracture of conductive components such as nickel sheets, copper sheets, or nickel-plated copper sheets in the connection component.

[0044] At present, the fracture detection of conductive components is usually carried out based on the naked eye identification of operators. For example, cracks, deformation or corrosion on the surface of nickel sheets are detected by high-power microscopes or optical sensors. For example, if the pressure difference or temperature difference of the secondary battery is too large during the offline or integrated testing of the battery pack, the battery pack is opened to manually determine whether there is any fracture abnormality in the nickel sheet of the CCS. However, no matter which detection method is used, it needs to rely on the naked eye of the operator, and the naked eye identification ability of the operator is limited. In addition, the efficiency of manual detection is low and difficult to match the scale of the energy storage system, which makes it easy to have detection lags, detection errors or detection time-consuming situations. Therefore, the current detection method for fracture detection of conductive components urgently needs to be optimized. That is, there is an urgent need for a method for detecting fracture of connection components of energy storage systems that can improve the detection effect of fracture detection of connection components of energy storage systems.

[0045] In one embodiment, Figure 1As shown, a connection component fracture detection method of an energy storage system is provided. In this embodiment, the method is applied to an energy storage edge terminal. The energy storage edge terminal refers to a computing, control and communication unit deployed locally or in a nearby location of an energy storage device in an energy storage system. It is the layer closest to the physical device in the overall energy storage system architecture and can also be referred to as an "energy storage edge terminal". Specifically, it can include an embedded edge controller, an industrial-grade edge computing gateway, an intelligent sensor or an actuator, etc. The energy storage edge terminal is provided with a first acquisition module, a determination module, a first detection module and a sending module. The first acquisition module is configured to acquire running state parameters of at least one secondary battery collected and uploaded by a collection component. The determination module is configured to determine parameter discrete characteristic values between the running state parameters. The parameter discrete characteristic values represent the discrete degree of the running state of each secondary battery. The first detection module is configured to perform fracture detection on the connection component according to the size relationship between the parameter discrete characteristic values and a preset discrete characteristic value threshold, to obtain an edge terminal fracture detection result. The sending module is configured to send the running state parameters and the edge terminal fracture detection result to an energy storage cloud end, so that the energy storage cloud end generates a fracture detection result of the connection component according to a cloud end fracture detection result and the edge terminal fracture detection result. The cloud end fracture detection result is predicted according to the running state parameters. For example, in an implementable manner, the main function of the first acquisition module is to collect the running state parameters of the energy storage device. It can be deployed in an intelligent sensor or a data collection card of the energy storage edge terminal. The main function of the determination module is to process and analyze the collected running state parameters. It can be deployed in an embedded edge controller or an industrial-grade edge computing gateway of the energy storage edge terminal. The main function of the first detection module is to perform fracture detection on the connection component. It can also be deployed in an embedded edge controller or an industrial-grade edge computing gateway of the energy storage edge terminal. The main function of the sending module is to interact with the energy storage cloud end. It can be configured with a communication module such as a 4G / 5G module, a Wi-Fi module or an Ethernet module in the energy storage edge terminal. It can also be deployed in an industrial-grade edge computing gateway of the energy storage edge terminal. The industrial-grade edge computing gateway has a communication function.Further, through the information interaction among the first acquisition module, the determination module, the first detection module and the sending module, the purpose of preliminarily detecting the fracture abnormality of the connecting assembly by analyzing the discrete degree of the operating state parameters at the energy storage edge can be realized. Further, the energy storage edge sends the operating state parameters and the edge fracture detection result to the energy storage cloud end together, so that the cloud end fracture detection result is predicted based on each operating state parameter. That is, the purpose of obtaining the fracture abnormality of the connecting assembly by predicting the operating state parameters at the energy storage cloud end can be realized. Finally, by comparing the edge fracture detection result and the cloud end fracture detection result, the fracture detection result of the connecting assembly can be obtained, so that the purpose of realizing the automatic detection of the fracture of the connecting assembly by the energy storage edge and the energy storage cloud end is realized. Therefore, the fracture detection of the conductive part can be accurately and efficiently completed, and the detection effect of the fracture detection of the connecting assembly of the energy storage system can be improved. In the embodiment, the method comprises the following steps:

[0046] In step 202, the operating state parameters of at least one secondary battery collected and uploaded by the collection component are acquired.

[0047] It should be noted that the connection component is the energy storage module CCS, which is the core integrated module of the energy storage module and is directly embedded in the interior of the energy storage module. It can be understood that the energy storage module is the basic energy unit of the energy storage system, and a complete energy storage system is formed through multi-level integration. The energy storage module can be specifically composed of multiple secondary batteries through a certain connection method, wherein the secondary battery refers to a battery that can be recharged repeatedly, such as a lithium-ion battery; the connection component includes a conductive component and a collection component, wherein the collection component is used to collect the operating status parameters of the battery in the energy storage module, thereby providing a decision basis for the BMS (Battery Management System, Battery Management System), which can be specifically FPC (Flexible Printed Circuit, Flexible Printed Circuit Board), FFC (Flexible Flat Cable, Flexible Flat Cable) or FDC (Flexible Die Cutting, flexible die-cut circuit board), etc. The conductive components are used to realize high-voltage series and parallel connection of battery cells and model transmission to ensure stable current flow. Specifically, they can be aluminum-copper bars, nickel sheets, copper sheets or nickel-plated copper sheets, etc. The acquisition components and conductive components can be connected to the conductive components through welding or hot pressing processes, so as to transmit battery cell signals to the BMS; it can be understood that the battery management system is used to manage and maintain the secondary batteries of each unit, monitor the operating status of the secondary batteries, and prevent the secondary batteries from overcharging and over-discharging. Specifically, the battery management system includes BMU (Battery Management Unit), BCU (Battery Control Unit) and SCU (Storage Control Unit). BMU is used to collect the operating status parameters of the secondary battery and execute balancing control instructions, and send them to the battery BCU through the CAN communication protocol. BCU is used for battery data processing and status estimation, and for safety protection, as well as through Modbus The data is sent to the SCU via the TCP communication protocol. The SCU can perform numerical calculations, performance analysis, alarm processing, and record storage on the real-time battery data, clean the edge data, and send the cleaned voltage, current, temperature and other data to the energy storage cloud via the MQTT protocol. In addition, the connection component may also include structural support components and insulation protection components. The structural support component is used to fix the battery cell and the collection component and the conductive component to ensure the structural strength and space utilization of the connection component. Specifically, it can be an injection-molded bracket or a blister isolation plate. The insulation protection component is used to prevent the battery cell from short-circuiting and overheating to improve system safety. Specifically, it can be a hot-pressed insulation film or a fuse.For example, in one practicable embodiment, the energy storage module is a secondary battery module, specifically a lithium-ion battery module. The connecting component is a nickel sheet. The collection component includes a sampling line. The nickel sheet is directly connected to the lithium-ion battery cell tab in the lithium-ion battery module by welding to ensure current conduction. The connection assembly integrates a busbar, sampling line, and connector. The busbar is connected to the nickel sheet to achieve series and parallel connection between multiple battery cells. The sampling line contacts the battery cell or nickel sheet through a specific structure, thereby collecting operating status parameters of the lithium-ion battery.

[0048] It should be noted that the number of secondary batteries electrically connected to the conductive component is not specifically limited in this embodiment, and can be 12, 24, 48, 13, 26, etc.; for example, in one practicable manner, the conductive component is electrically connected to 52 secondary batteries connected in series in a battery module; the operating state parameter represents the physical or electrical quantity of the current working state of the secondary battery, and can specifically include voltage, temperature, current, and SOC (State of Charge); the acquisition component can be an independent acquisition module deployed in the connection component, such as a voltage acquisition chip, a temperature sensor, or a current sensor, or it can be an integrated acquisition module, such as a sampling harness and a sampling board integrated in the connection component, which can realize the simultaneous acquisition of the voltage and temperature of the secondary battery.

[0049] As an example, step 202 includes: acquiring operating status parameters of at least one secondary battery uploaded by a collection component, wherein the operating status parameters are collected by the collection component.

[0050] Step 204 : determining a parameter discrete characteristic value between each operating state parameter, wherein the parameter discrete characteristic value represents the degree of discreteness of the operating state parameter of each secondary battery.

[0051] It should be noted that the parameter discrete eigenvalue characterizes the degree of discreteness of the operating state parameters of each secondary battery, which may specifically be a temperature discrete eigenvalue, a voltage discrete eigenvalue, a current discrete eigenvalue, etc.; specifically, the parameter discrete eigenvalue corresponding to each operating state parameter can be calculated through the parameter eigenvalue of each operating state parameter, wherein the parameter eigenvalue characterizes the quantitative index of the operating state parameter of a single secondary battery in a specific dimension. For example, assuming that the parameter discrete eigenvalue is a temperature discrete eigenvalue, the parameter eigenvalue may be the actual operating temperature value, temperature change rate, or temperature difference between the temperature and the ambient temperature of a single secondary battery; for example, in one practicable manner, assuming that there are n secondary batteries in total, and the collected operating state parameters are the actual operating temperature values ​​of n secondary batteries, which are respectively recorded as , then the average temperature of n secondary batteries is , The expression is as follows:

[0052]

[0053] Further, the square of the difference between the actual operating temperature value and the average temperature value of each secondary battery is calculated, denoted as Further, the average value of the square of the temperature difference is calculated , The expression of the average value of the square of the temperature difference is as follows:

[0054]

[0055] wherein, is the average value of the square of the temperature difference, is the actual operating temperature value, is the average temperature value; finally, the temperature dispersion characteristic value S is calculated by the average value of the square of the temperature difference The expression of the temperature dispersion characteristic value S is as follows:

[0056]

[0057] wherein, S can be used to measure the dispersion degree of the actual operating temperature value of the n secondary batteries relative to the average temperature. It can be understood that the greater S is, the greater the dispersion degree of the temperature is, and the smaller S is, the smaller the dispersion degree of the temperature is.

[0058] As an example, step 204 comprises: obtaining parameter characteristic values of the operating state parameters by fusing the operating state parameters, and taking the parameter characteristic values as the parameter dispersion characteristic values between the operating state parameters.

[0059] Step 206, according to the size relationship between the parameter dispersion characteristic value and the preset dispersion characteristic value threshold, the fracture detection of the connecting assembly is carried out to obtain the edge-end fracture detection result.

[0060] It should be noted that by comparing the size relationship between the parameter dispersion characteristic value and the preset dispersion characteristic value threshold, the fracture condition of the connecting assembly can be determined, specifically, the fracture condition of the connecting sheet such as nickel sheet, copper sheet or plated nickel copper sheet in the connecting assembly can be determined; the edge-end fracture detection result represents the fracture detection result of the connecting assembly by the energy storage edge-end; the edge-end fracture detection result includes the edge-end fracture detection result of the connecting assembly with fracture anomaly and the edge-end fracture detection result of the connecting assembly without fracture anomaly, wherein the preset dispersion characteristic threshold can be obtained by self-setting according to the demand.

[0061] As an example, in step 206, if the parameter discrete feature value is greater than the preset discrete feature value threshold, an edge fracture detection result that the connection assembly has a fracture anomaly is obtained, and if the parameter discrete feature value is less than or equal to the preset discrete feature value threshold, an edge fracture detection result that the connection assembly does not have a fracture anomaly is obtained.

[0062] In step 208, the running state parameters and the edge fracture detection result are sent to the energy storage cloud end, so that the energy storage cloud end generates a fracture detection result of the connection assembly according to a cloud end fracture detection result and the edge fracture detection result, wherein the cloud end fracture detection result is predicted according to each running state parameter.

[0063] It should be noted that the energy storage edge end has certain limitations in detecting the fracture condition of the connection assembly, which is specifically reflected in that: 1) the energy storage edge end can only collect local parameters, and it is difficult to obtain global data, so it is impossible to detect the fracture of the connection assembly from the global working condition; 2) the real-time detection requirement is not met, and the algorithm deployed by the energy storage edge end mostly uses a lightweight model, and the recognition accuracy for complex scenes is insufficient; therefore, after obtaining the edge fracture detection result, the energy storage cloud end is further coordinated to generate the fracture detection result of the connection assembly.

[0064] It should be noted that the energy storage cloud end is a remote server cluster or platform for energy storage system management and data analysis based on cloud computing technology, which can be a centralized management platform of a large energy storage power station or an energy storage cloud platform, etc.; the cloud end fracture detection result represents the fracture detection result of the connection assembly by the energy storage cloud end, wherein the cloud end fracture detection result includes a cloud end fracture detection result that the connection assembly has a fracture anomaly and an edge fracture detection result that the connection assembly does not have a fracture anomaly; for example, in an implementable manner, if the edge fracture detection result that the connection assembly has a fracture anomaly is obtained, and the cloud end fracture detection result that the connection assembly has a fracture anomaly is obtained, it is determined that the fracture detection result that the connection assembly has a fracture anomaly is obtained, that is, the connection assembly is in an abnormal working state, which can be a nickel sheet fracture or a nickel sheet anomaly, etc.

[0065] As an example, in step 208, the running state parameters and the edge fracture detection result are sent to the energy storage cloud end, so that the energy storage cloud end generates a fracture detection result of the connection assembly according to a cloud end fracture detection result and the edge fracture detection result, wherein the cloud end fracture detection result is predicted according to each running state parameter.

[0066] In one possible implementation, the energy storage cloud is a cloud platform built on the IoT framework, providing full lifecycle data management, which may include: 1) data acquisition: receiving real-time data uploaded by the SCU; 2) data cleaning: eliminating outliers in the data to ensure data quality; 3) data storage: establishing a time series database to support the backtracking of historical data; 4) data visualization: displaying the operating status of the secondary battery through a visual monitoring platform to assist in operation and maintenance decision-making; the cloud platform provides reliable data support for subsequent big data analysis and model training. Specifically, the cloud platform is deployed with a cloud processor, and the main functions of the cloud processor include: 1) digital twin modeling: building a battery voltage or temperature prediction model based on historical data to simulate the actual operating status of the secondary battery; 2) residual analysis: comparing predicted data with actual data to identify abnormal operating fluctuations; 3) fault diagnosis: locating faults such as CCS nickel sheet breakage through algorithms, and providing specific maintenance suggestions.

[0067] In the above-mentioned method for detecting the fracture of a connection component of an energy storage system, the energy storage edge first collects the operating status parameters of at least one secondary battery connected to the connection component, and then analyzes the discrete degree of the operating status parameters of different secondary batteries based on each operating status parameter to obtain the discrete characteristic value of the parameter, and then uses the discrete characteristic value of the parameter as an indicator, combined with a preset discrete characteristic value threshold, to perform fracture detection on the connection component, thereby obtaining the edge fracture detection result of the energy storage edge performing fracture detection on the connection component, which can achieve the purpose of preliminarily detecting the fracture abnormality of the connection component by analyzing the discrete degree of the operating status parameters at the energy storage edge. Furthermore, the energy storage edge sends the operating status parameters and the edge fracture detection result together to the energy storage cloud, so that the energy storage cloud can predict the cloud fracture detection result based on each operating status parameter, which can achieve the purpose of obtaining the fracture abnormality of the connection component by predicting the operating status parameters at the energy storage cloud, and finally by comparing the edge. The fracture detection results and the cloud-based fracture detection results can be used to obtain the fracture detection results of the connected components. Since the edge end is close to the connected components, it can capture the operating status parameters in real time and perform feature analysis to achieve rapid early warning of the fracture of the connected components. The energy storage cloud has powerful computing power, and can accurately predict the fracture of the connected components through the operating status parameters uploaded by the energy storage edge end. Finally, the fracture detection results of the connected components are obtained by comparing and verifying the results on both ends. That is, the purpose of collaborative energy storage edge end and energy storage cloud end to complete the automatic detection of the fracture of the connected components is achieved, rather than relying on manual identification by the naked eye of the operator. Therefore, it overcomes the technical defects of limited naked eye recognition ability and the difficulty of the operator's detection efficiency to match the scale of the energy storage system, which leads to detection lags, detection errors, or detection time being too long. Therefore, the detection effect of fracture detection of the connected components of the energy storage system is improved.

[0068] In one embodiment, the parameter discrete feature value comprises a first parameter discrete feature value; the parameter discrete feature values between the operating state parameters are determined, comprising

[0069] The first parameter feature value commonly corresponding to the operating state parameters is determined, and the second parameter feature value commonly corresponding to the operating state parameters is determined, wherein the first parameter feature value and the second parameter feature value are parameter feature values of different dimensions; the first parameter discrete feature value is obtained by fusing the first parameter feature value and the second parameter feature value.

[0070] It should be noted that, since the single parameter discrete feature value is easy to be disturbed by noise, the multi-parameter discrete feature value can be used as the detection basis for detecting the fracture of the connecting assembly, wherein the first parameter discrete feature value represents the degree of deviation of the operating state parameters from the normal range in the data distribution, and can be a numerical value calculated based on the standard Z-Score algorithm, the first parameter feature value can be a sample mean, and the second parameter feature value can be a sample standard deviation, wherein the first parameter feature value and the second parameter feature value are parameter feature values of different dimensions, i.e., the parameter feature values reflecting the dispersion degree of the operating state parameters from different dimensions.

[0071] As an example, the first parameter feature value commonly corresponding to the operating state parameters is calculated by a first preset expression, wherein the first preset expression is as follows:

[0072]

[0073] wherein, is the first parameter feature value, which can be a sample mean, is the i th secondary battery, is the number of secondary batteries, is the operating state parameter of the i th secondary battery; the second parameter feature value commonly corresponding to the operating state parameters is calculated by a second preset expression, wherein the second preset expression is as follows:

[0074]

[0075] wherein, is the second parameter feature value, which can be a sample standard deviation, is the first parameter feature value, which can be a sample mean, is the i th secondary battery, is the number of secondary batteries, is an operation state parameter of the i th secondary battery; the first parameter discrete characteristic value is obtained by fusing the first parameter characteristic value and the second parameter characteristic value through a third preset expression, and the third preset expression is as follows:

[0076]

[0077] wherein, is the first parameter discrete characteristic value, is the second parameter characteristic value, and specifically can be a sample standard deviation, is an operation state parameter of the i th secondary battery. In this way, by determining the first parameter characteristic value and the second parameter characteristic value of each operation state parameter in different dimensions, and fusing the first parameter characteristic value and the second parameter characteristic value to obtain the first parameter discrete characteristic value, the dispersion degree between each operation state parameter can be accurately and comprehensively evaluated, so that a more reliable basis can be provided for the fracture detection of the connection assembly of the energy storage edge, and thus a foundation is laid for improving the detection effect of the fracture detection of the connection assembly of the energy storage system.

[0078] In one embodiment, the parameter discrete characteristic value includes a second parameter discrete characteristic value; determining the parameter discrete characteristic value between each operation state parameter includes:

[0079] According to the size relationship between each operation state parameter, an operation state characteristic parameter is selected from each operation state parameter; and a second parameter discrete characteristic value is obtained by fusing each operation state parameter and the operation state characteristic parameter.

[0080] It should be noted that the second parameter discrete characteristic value represents the fluctuation dispersion of each operation state parameter around the operation state characteristic parameter, and specifically can be a value calculated based on a robust Z-Score algorithm; and the operation state characteristic parameter represents an operation state parameter with a specific characteristic, and specifically can be a median.

[0081] As an example, each operation state parameter is arranged in descending order to form an operation state parameter sequence, and an operation state parameter located at the middle position of the operation state parameter sequence is selected as an operation state characteristic parameter; a second parameter discrete characteristic value is obtained by fusing each operation state parameter and the operation state characteristic parameter through a fourth preset expression, and the fourth expression is as follows:

[0082]

[0083] wherein, is the second parameter discrete characteristic value, is the operation state characteristic parameter, and specifically can be a median, is the running state parameter of the i th secondary battery, c is a consistency constant, and can be specifically taken as 1.4826, Specifically, it refers to the median absolute deviation value. In this way, first, the size relationship between each running state parameter is compared one by one, and the running state characteristic parameter is selected from each running state parameter. Finally, the second parameter discrete feature value is obtained by sequentially fusing the running state characteristic and each running state parameter. Therefore, the subtle change of the connecting assembly in the running process can be accurately captured, so that a more reliable basis can be further provided for the fracture detection of the connecting assembly of the energy storage edge. Therefore, it lays a foundation for further improving the detection effect of the fracture detection of the connecting assembly of the energy storage system.

[0084] In one embodiment, the parameter discrete feature value includes a first parameter discrete feature value and a second parameter discrete feature value, and the preset discrete feature value threshold includes a first preset discrete feature value threshold and a second preset discrete feature value threshold. According to the size relationship between the parameter discrete feature value and the preset discrete feature value threshold, the fracture detection of the connecting assembly is performed to obtain an edge fracture detection result, including:

[0085] detecting the size relationship between the first parameter discrete feature value and the first preset discrete feature value threshold, and detecting the size relationship between the second parameter discrete feature value and the second preset discrete feature value threshold;

[0086] If the first parameter discrete feature value is greater than the first preset discrete feature value threshold, and the second parameter discrete feature value is greater than the second preset discrete feature value threshold, then the edge fracture detection result is generated according to the first parameter discrete feature value and the second parameter discrete feature value.

[0087] If the first parameter discrete feature value is less than or equal to the first preset discrete feature value threshold, and / or the second parameter discrete feature value is less than or equal to the second preset discrete feature value threshold, then the first preset component state information is taken as the edge fracture detection result, wherein the first preset component state information represents that the connecting assembly of the energy storage edge is in a normal working state.

[0088] It should be noted that in the case of fracture detection of the connecting assembly by the first parameter discrete feature value and the second parameter discrete feature value of the multiple parameters, the discrete feature value threshold can be set for the first parameter discrete feature value and the second parameter discrete feature value, and the size relationship between the two groups of parameter discrete feature values and the discrete feature value threshold is determined as the decision basis for deciding whether the connecting assembly is fractured. The first preset discrete feature value threshold and the second preset discrete feature value threshold can be the same or different.

[0089] As an example, the size relationship between the first parameter discrete characteristic value and the first preset discrete characteristic value threshold is compared, and the size relationship between the second parameter discrete characteristic value and the second preset discrete characteristic value threshold is compared; if the first parameter discrete characteristic value is greater than the first preset discrete characteristic value threshold, and the second parameter discrete characteristic value is greater than the second preset discrete characteristic value threshold, then according to the first parameter discrete characteristic value and the second parameter discrete characteristic value, the target secondary battery that appears abnormal is located, and the edge-end fracture detection result that the connection assembly connected by the target secondary battery is in an abnormal working state is generated; if the first parameter discrete characteristic value is less than or equal to the first preset discrete characteristic value threshold, the first preset component state information is taken as the edge-end fracture detection result, or if the second parameter discrete characteristic value is less than or equal to the second preset discrete characteristic value threshold, the first preset component state information is taken as the edge-end fracture detection result, or if the first parameter discrete characteristic value is less than or equal to the first preset discrete characteristic value threshold, and the second parameter discrete characteristic value is less than or equal to the second preset discrete characteristic value threshold, the first preset component state information is taken as the edge-end fracture detection result.

[0090] In an implementable manner, assuming that the size relationship between the parameter discrete characteristic value and the preset discrete characteristic value threshold is used to express the fracture detection of the connection assembly as follows:

[0091]

[0092] wherein, the first parameter discrete characteristic value is the second parameter discrete characteristic value is the first preset discrete characteristic value threshold can be specifically in the range of [ 0.02V ， 0.05V ] that is, the first preset discrete characteristic value threshold can be any specific value in the value range of greater than or equal to 0.02V to less than or equal to 0.05V, the second preset discrete characteristic value threshold can be specifically in the range of [ 3 ℃ ， 5 ℃ ] that is, the second preset discrete characteristic value threshold can be any specific value in the value range of greater than or equal to 3℃ to less than or equal to 5℃, indicating that the edge-end fracture detection result that the energy storage edge-end determines that the connection assembly is in an abnormal working state is generated.

[0093] In the embodiment, in the process of fracture detection of the connecting assembly, the size relationship between the first parameter discrete characteristic value and the second parameter discrete characteristic value and the respective corresponding preset discrete characteristic value threshold is used as a decision basis for deciding whether the connecting assembly is fractured, so that the fracture characteristics of the connecting assembly can be more accurately and comprehensively captured, and the misjudgment or omission of a single parameter determination can be reduced, thereby laying a foundation for further improving the detection effect of fracture detection of the connecting assembly of the energy storage system.

[0094] In one embodiment, the first parameter discrete characteristic value is a voltage discrete characteristic value, and the second parameter discrete characteristic value is a temperature discrete characteristic value; and the edge fracture detection result is generated according to the first parameter discrete characteristic value and the second parameter discrete characteristic value, including:

[0095] According to the operating state parameter corresponding to the voltage discrete characteristic value, a first target secondary battery with voltage anomaly is selected from the secondary batteries, and according to the operating state parameter corresponding to the temperature discrete characteristic value, a second target secondary battery with temperature anomaly is selected from the secondary batteries; at least one first abnormal position point with voltage anomaly on the connecting assembly is located according to the connection relationship between the first target secondary battery and the connecting assembly; at least one second abnormal position point with temperature anomaly on the connecting assembly is located according to the heat conduction path of the second target secondary battery; a target abnormal position point on the connecting assembly is obtained by fusing the first abnormal position points and the second abnormal position points; and the edge fracture detection result is generated according to the first position information of the target abnormal position point.

[0096] It should be noted that the voltage discrete characteristic value represents the discrete degree index of the secondary battery in the running process relative to the normal distribution, and it can be understood that in the case of abnormality of the connecting assembly, the voltage fluctuation will be intensified, thereby causing the voltage discrete characteristic value to increase; the temperature discrete characteristic value represents the discrete degree of the secondary battery in time, and it can be understood that in the case of abnormality of the connecting assembly, the local resistance will increase, thereby causing the temperature distribution to be abnormal; the operating state parameter corresponding to the voltage discrete characteristic value can be a real-time sampling value of the voltage, and the operating state parameter corresponding to the temperature discrete characteristic value can be a real-time sampling value of the temperature; the heat conduction path refers to the physical path of heat transfer from the heat source to the surrounding environment through the transfer assembly; the first target secondary battery represents the secondary battery with voltage anomaly, the second target secondary battery represents the secondary battery with temperature anomaly, and the target abnormal position represents the high-confidence abnormal position on the connecting assembly. In the process of fracture detection of the connecting assembly, the energy storage edge can not only detect whether the connecting assembly is fractured, but also determine the actual position of the connecting assembly in the case of fracture of the connecting assembly.

[0097] As an example, by comparing the operating status parameters corresponding to the discrete eigenvalues ​​of voltage, a first target secondary battery with abnormal voltage is selected from each secondary battery, and by comparing the operating status parameters corresponding to the discrete eigenvalues ​​of temperature, a second target secondary battery with abnormal temperature is selected from each secondary battery; based on the connection relationship between the first target secondary battery and the connecting component, at least one first abnormal position point of the voltage abnormality is located on the connecting component; based on the heat conduction path of the second target secondary battery, a heat conduction path model of the second target secondary battery is constructed, and the second abnormal position point of the second target secondary battery is predicted through the heat conduction path model; the abnormal position points that overlap between each first abnormal position point and each second abnormal position point are used as target abnormal position points on the connecting component; and the first position information of the target abnormal position point is encapsulated as an edge fracture detection result.

[0098] In one practicable manner, assuming that the conductive component is a nickel sheet, there are 13 secondary batteries, numbered 1, 2, 3...12 and 13, wherein, by comparing the real-time sampled voltage values ​​of No. 13 batteries in pairs, the secondary battery with the largest voltage fluctuation is obtained, that is, the first target secondary battery with abnormal voltage is obtained, and the first target secondary batteries are No. 1, No. 5, No. 7 and No. 8. Then, by comparing the real-time sampled temperature values ​​of No. 13 batteries in pairs, the secondary battery with the largest temperature fluctuation is obtained, that is, the second target secondary battery with abnormal temperature is obtained. The target secondary battery is No. 2, No. 5, No. 9 and No. 12. The first abnormal location point is the connection between the No. 1, No. 5, No. 7 and No. 8 secondary batteries and the nickel sheet. The second abnormal location point is the connection between the No. 2, No. 5, No. 9 and No. 12 secondary batteries and the nickel sheet. The abnormal location point that overlaps between the first abnormal location point and the second abnormal location point (the connection between the nickel sheet and the No. 5 secondary battery) is used as the target abnormal location point. Therefore, the edge fracture detection result is that the connection between the nickel sheet and the No. 5 secondary battery is fractured.

[0099] In this embodiment, when it is determined that a connection component is broken, the first target secondary battery with voltage abnormality and the second target secondary battery with temperature abnormality are located respectively through discrete eigenvalues ​​of different parameters, and then the target abnormal position points on the connection component are screened from the first abnormal position points of the first target secondary battery and the second abnormal position points of the second target secondary battery. Finally, based on the first position information of the target abnormal position points, the edge fracture detection result is generated, which can achieve the purpose of accurately locating the fracture position on the connection component. Therefore, it lays the foundation for further improving the detection effect of fracture detection on the connection components of the energy storage system.

[0100] In one embodiment, obtaining a target abnormal position point on a connected component by fusing each first abnormal position point with each second abnormal position point includes:

[0101] Determine at least one candidate abnormal location point that has an intersection between each first abnormal location point and each second abnormal location point; prioritize each candidate abnormal location point according to the second location information of each candidate abnormal location point to obtain a priority ranking result; and select a target abnormal location point from each candidate abnormal location point according to the priority ranking result.

[0102] It should be noted that the candidate abnormal location point represents the location point where the first abnormal location point and the second abnormal location point overlap or are adjacent in space. It can be understood that when there are multiple candidate abnormal location points, the target abnormal location point can be selected from each candidate abnormal location point based on the priority relationship between the positions of different candidate abnormal location points. For example, in one feasible method, since the head and tail positions are stress concentration areas and have poor heat dissipation conditions, the head and tail positions are given a higher priority; the priority sorting result represents the result of sorting each candidate abnormal location point according to the mapping relationship between position and risk level.

[0103] As an example, at least one abnormal location point that intersects each first abnormal location point and each second abnormal location point is used as a candidate abnormal location point; based on the second location information of each candidate abnormal location point, the risk level of each candidate abnormal location point is queried, and the candidate abnormal location points are sorted in descending order according to their risk levels to obtain a priority sorting result; the candidate abnormal location point with the highest priority in the priority sorting result is used as the target abnormal location point. In this way, in the process of determining the target abnormal location point, at least one candidate abnormal location point is first determined, and then the priority of each candidate abnormal location point is sorted to obtain a priority sorting result. Finally, based on the priority sorting result, the target abnormal location point is selected from each candidate abnormal location point, thereby avoiding interference with the detection result due to a large number of redundant abnormal points, while focusing on the potential fracture location with the highest risk. Therefore, it further lays the foundation for improving the detection effect of fracture detection of the connection components of the energy storage system.

[0104] In one embodiment, priority ranking is performed on each candidate abnormal location point based on the second location information of each candidate abnormal location point to obtain a priority ranking result, including:

[0105] Based on the second position information of each candidate abnormal location point, the voltage abnormality characteristic value and the temperature abnormality characteristic value of each candidate abnormal location point are determined; the voltage abnormality weight value and the temperature abnormality weight value of each candidate abnormal location point are determined; by fusing the voltage abnormality weight value, the temperature abnormality weight value, the voltage abnormality characteristic value and the temperature abnormality characteristic value, the comprehensive abnormality characteristic value of each candidate abnormal location point is obtained; according to the comprehensive abnormality characteristic value, the candidate abnormal location points are prioritized to obtain a priority ranking result.

[0106] It should be noted that the voltage anomaly eigenvalue represents an indicator describing the deviation of the voltage at the candidate anomaly location from the normal state, and the temperature anomaly eigenvalue represents an indicator describing the deviation of the temperature at the candidate anomaly location from the normal state. Since different types of anomalies have different degrees of influence on the fracture of connected components, weights can be configured for different anomaly eigenvalues, and a comprehensive anomaly eigenvalue can be obtained through eigenvalue fusion and quantification, where the comprehensive anomaly eigenvalue is used for sorting priority.

[0107] As an example, the voltage anomaly characteristic value and the temperature anomaly characteristic value of each candidate abnormal location point are determined based on the second location information of each candidate abnormal location point; a voltage anomaly weight value and a temperature anomaly weight value are assigned to each candidate abnormal location point; the voltage anomaly weight value, the temperature anomaly weight value, the voltage anomaly characteristic value, and the temperature anomaly characteristic value are input into a preset expression to calculate the comprehensive anomaly characteristic value of each candidate abnormal location point, wherein the preset expression is as follows:

[0108]

[0109] in, is the comprehensive abnormal characteristic value, is the voltage anomaly weight value, is the voltage abnormal characteristic value, is the temperature anomaly weight value, The temperature anomaly characteristic value is used; the candidate anomaly locations are prioritized in descending order of their combined anomaly characteristic values ​​to obtain a priority ranking result. By assigning corresponding weights to different anomaly characteristic values ​​and prioritizing them based on the fused combined anomaly characteristic value, the priority ranking is ensured to be more consistent with actual fault patterns, thus laying the foundation for improving the accuracy of selecting target anomaly locations.

[0110] In one embodiment, Figure 2As shown, a method for detecting the fracture of a connection component of an energy storage system is provided. This embodiment takes the application of this method to an energy storage cloud as an example, wherein the energy storage cloud refers to a cloud platform based on cloud computing technology for remote monitoring, data management, analysis and decision-making, and intelligent operation and maintenance of the energy storage system. The energy storage cloud includes but is not limited to personal computers and laptops. A connection component fracture detection system for the energy storage system is deployed on the energy storage cloud, wherein the connection component fracture detection system includes a second acquisition module, a prediction module, a second detection module and a comparison module, wherein the second acquisition module is used to obtain the edge fracture detection result sent by the energy storage edge and the operating status parameter of at least one secondary battery collected and uploaded by the acquisition component, wherein the edge fracture detection result is obtained according to the detection of each operating status parameter, and the prediction module is used to predict the parameter deviation characteristic value of each secondary battery according to the operating status parameter, and the parameter deviation characteristic value represents the operating status parameter of any secondary battery relative to the benchmark operating status parameter. The deviation degree of the number, the second detection module is used to perform fracture detection on the connection component according to the size relationship between the parameter deviation characteristic value and the preset deviation characteristic value threshold, and obtain the cloud fracture detection result, and the comparison module is used to obtain the fracture detection result of the connection component by comparing the edge fracture detection result and the cloud fracture detection result; and then through the information interaction between the second acquisition module, the prediction module, the second detection module and the comparison module, it is possible to achieve the purpose of predicting the operating status parameters on the energy storage cloud to obtain the fracture abnormality of the connection component, and finally the energy storage cloud can obtain the fracture detection result of the connection component by comparing the edge fracture detection result and the cloud fracture detection result, thereby achieving the purpose of collaborative energy storage edge and energy storage cloud to complete the automatic detection of the fracture of the connection component, so that the fracture detection of the conductive component can be completed accurately and efficiently, so that the detection effect of the fracture detection of the connection component of the energy storage system can be improved. In this embodiment, the method includes the following steps:

[0111] Step 302 : Acquire the edge rupture detection result sent by the energy storage edge and the operating status parameters of at least one secondary battery collected and uploaded by the collection component, wherein the edge rupture detection result is obtained based on the detection of the operating status parameters.

[0112] It should be noted that the steps of obtaining the edge fracture detection result based on the detection of various operating status parameters at the energy storage edge can refer to the above embodiment, and will not be repeated in this embodiment.

[0113] As an example, step 302 includes: obtaining an edge fracture detection result sent by the energy storage edge, and obtaining an operating status parameter of at least one secondary battery collected and uploaded by a collection component.

[0114] At step 304, according to the operating state parameters, a parameter deviation characteristic value of each secondary battery is predicted, and the parameter deviation characteristic value represents a deviation degree of the operating state parameter of any secondary battery relative to a reference operating state parameter.

[0115] It should be noted that the parameter deviation characteristic value represents a deviation degree of the operating parameter of each secondary battery relative to the reference operating state parameter, and can be a voltage deviation characteristic value, a temperature deviation characteristic value, and a current deviation characteristic value. The reference operating state parameter refers to a standard operating parameter value of the secondary battery under ideal working conditions, and is used as a basis for evaluating the operating state parameters of each secondary battery. Specifically, the parameter deviation characteristic value corresponding to each operating state parameter can be calculated by each operating state parameter.

[0116] As an example, step 304 includes obtaining a parameter characteristic value of each operating state parameter by fusing each operating state parameter, and taking the parameter characteristic value as the parameter deviation characteristic value between each operating state parameter.

[0117] At step 306, according to the size relationship between the parameter deviation characteristic value and the preset deviation characteristic value threshold, a fracture detection of the connecting assembly is performed to obtain a cloud fracture detection result.

[0118] As an example, in the case where the parameter deviation characteristic value is greater than the preset deviation characteristic value threshold, a cloud fracture detection result that the connecting assembly has a fracture anomaly is obtained, and in the case where the parameter deviation characteristic value is less than or equal to the preset deviation characteristic value threshold, a cloud fracture detection result that the connecting assembly does not have a fracture anomaly is obtained.

[0119] At step 308, by comparing the edge fracture detection result and the cloud fracture detection result, a fracture detection result of the connecting assembly is obtained.

[0120] It should be noted that in the process of fracture detection of the connecting assembly by the energy storage edge and the energy storage cloud, since the energy storage cloud can not only collect local parameters, but also obtain global data, the fracture detection of the connecting assembly can be performed from the global working condition, so that the limitations of the fracture detection of the connecting assembly by the energy storage edge can be overcome, and an objective fracture detection result of the fracture condition of the connecting assembly can be obtained.

[0121] As an example, step 308 includes: if the cloud fracture detection result and the edge fracture detection result both detect that the connecting assembly has a fracture anomaly, a fracture detection result that the connecting assembly has a fracture anomaly is obtained, and if the cloud fracture detection result does not detect that the connecting assembly has an anomaly, and the edge fracture detection result detects that the connecting assembly has an anomaly, a fracture detection result that the connecting assembly does not have a fracture anomaly is obtained.

[0122] As another example, step 308 includes: if both the cloud-side fracture detection result and the edge-side fracture detection result detect that the connection component has a fracture abnormality, then a fracture detection result indicating that the connection component has a fracture abnormality is obtained; if the edge-side fracture detection result does not detect that the connection component has an abnormality, and the cloud-side fracture detection result detects that the connection component has an abnormality, then a fracture detection result indicating that the connection component does not have a fracture abnormality is obtained.

[0123] As another example, step 308 includes: if both the cloud-side fracture detection result and the edge-side fracture detection result detect that the connection component has a fracture abnormality, then a fracture detection result indicating that the connection component has a fracture abnormality is obtained; if neither the edge-side fracture detection result nor the cloud-side fracture detection result detects that the connection component has an abnormality, then a fracture detection result indicating that the connection component does not have a fracture abnormality is obtained.

[0124] In one practicable manner, referring to Figure 3 , Figure 3 This is a flow chart of the fracture detection results of connected components. A fracture detection result indicating a fracture abnormality in the connected component is obtained only when both the cloud-side fracture detection results and the edge-side fracture detection results detect a fracture abnormality in the connected component. Otherwise, a fracture detection result indicating no fracture abnormality in the connected component is obtained.

[0125] The connection component fracture detection method of the energy storage system described above, the energy storage cloud first collects the edge fracture detection result sent by the energy storage edge and the operating state parameters of at least one secondary battery connected by the connection component, and then predicts the parameter deviation characteristic value of each secondary battery based on each operating state parameter, so as to obtain the purpose of feeding back the deviation degree of the operating state parameter of any secondary battery relative to the reference operating state parameter to the parameter deviation characteristic value, and then comparing the size relationship between the parameter deviation characteristic value and the preset deviation characteristic value threshold, the fracture detection of the connection component is carried out, and the cloud fracture detection result is obtained. The fracture detection result of the connection component is obtained by comparing the edge fracture detection result and the cloud fracture detection result; since the energy storage cloud is close to the connection component, the operating state parameters can be captured in real time and feature analysis can be performed to realize the rapid early warning of the fracture condition of the connection component. The energy storage cloud has strong computing power, so the fracture condition of the connection component can be accurately predicted by the operating state parameters uploaded by the energy storage edge. Finally, the fracture detection result of the connection component is obtained by comparing and verifying the double-end results, that is, the purpose of realizing the automatic detection of the fracture condition of the connection component by the energy storage edge and the energy storage cloud is achieved, instead of relying on the naked eye of the operator for manual identification. Therefore, the detection effect of the fracture detection of the connection component of the energy storage system is improved.

[0126] In one embodiment, the parameter deviation characteristic value includes a voltage deviation characteristic value; according to the operating state parameters, the parameter deviation characteristic value of each secondary battery is predicted, including:

[0127] The concentration polarization voltage value, diffusion polarization voltage value, concentration polarization resistance value, ohmic resistance value, diffusion polarization resistance value, concentration polarization capacitance value, diffusion polarization capacitance value and actual operating current value of each secondary battery are extracted from the operating state parameters; the concentration polarization voltage characteristic value of each secondary battery is predicted according to the concentration polarization voltage value, concentration polarization resistance value, concentration polarization capacitance value and actual operating current value, and the diffusion polarization voltage characteristic value of each secondary battery is predicted according to the diffusion polarization voltage value, diffusion polarization resistance value, diffusion polarization capacitance value and actual operating current value, wherein the concentration polarization voltage characteristic value represents the change of the concentration polarization voltage value with the actual operating current value, and the diffusion polarization voltage characteristic value represents the change of the diffusion polarization voltage value with the actual operating current value; the voltage deviation characteristic value of each secondary battery is obtained by fusing the concentration polarization voltage characteristic value, diffusion polarization voltage characteristic value, ohmic resistance value and actual operating current value.

[0128] It should be noted that since a single parameter deviation eigenvalue is also susceptible to noise interference, a multi-parameter deviation eigenvalue can be used as the basis for detecting fractures in connected components. The first parameter deviation eigenvalue can specifically be the voltage value predicted by a voltage prediction model constructed based on a second-order equivalent circuit, and the least squares estimation method with forgetting factor and the extended Kalman filter algorithm are considered.

[0129] As an example, the concentration polarization voltage value, diffusion polarization voltage value, concentration polarization resistance value, ohmic resistance value, diffusion polarization resistance value, concentration polarization capacitance value, diffusion polarization capacitance value, and actual operating current value of each secondary battery are extracted from the operating state parameters; and the concentration polarization voltage characteristic value of the secondary battery corresponding to the concentration polarization voltage value, concentration polarization resistance value, concentration polarization capacitance value, ohmic resistance value, and actual operating current value is calculated using a fifth preset expression, where the fifth preset expression is as follows:

[0130]

[0131] in, is the characteristic value of concentration polarization voltage, is the concentration polarization voltage value, is the concentration polarization resistance value, is the concentration polarization capacitance value, is the actual operating current value; and the diffusion polarization voltage characteristic value of each secondary battery corresponding to the diffusion polarization voltage value, the diffusion polarization resistance value, the diffusion polarization capacitance value, and the actual operating current value is calculated by the sixth preset expression, wherein the sixth preset expression is as follows:

[0132]

[0133] in, is the characteristic value of diffusion polarization voltage, is the diffusion polarization voltage value, is the diffusion polarization resistance value, is the diffusion polarization capacitance value, is the actual operating current value, wherein the concentration polarization voltage characteristic value represents the change of the concentration polarization voltage value with the actual operating current value, and the diffusion polarization voltage characteristic value represents the change of the diffusion polarization voltage value with the actual operating current value; the voltage deviation characteristic value corresponding to the concentration polarization voltage characteristic value, the diffusion polarization voltage characteristic value, the ohmic resistance value, and the actual operating current value is calculated by the seventh preset expression, wherein the seventh preset expression is as follows:

[0134]

[0135] in, is the characteristic value of concentration polarization voltage, is a diffusion polarization voltage characteristic value, is a voltage deviation characteristic value, is an actual operating current value, is an ohmic resistance value. In this way, by establishing a battery voltage prediction model based on a second-order equivalent circuit and considering the least square estimation method with a forgetting factor and the extended Kalman filtering algorithm, the deviation degree of any operating state parameter relative to the reference operating state parameter can be accurately and comprehensively evaluated, so that a more reliable basis can be provided for the energy storage cloud to detect the fracture of the connecting component, thereby laying a foundation for improving the detection effect of detecting the fracture of the connecting component of the energy storage system.

[0136] In one embodiment, the parameter deviation characteristic value includes a temperature deviation characteristic value; according to the operating state parameter, the parameter deviation characteristic value of each secondary battery is predicted, including:

[0137] The heat capacity value, the thermal resistance value, the actual operating temperature value and the ambient temperature value of each secondary battery are extracted from the operating state parameter; and the temperature deviation characteristic value of each secondary battery is predicted according to the heat capacity value, the thermal resistance value, the actual operating temperature value and the ambient temperature value.

[0138] It should be noted that, on the basis of the equivalent circuit model, a thermodynamic model is established to predict the cell temperature, thereby determining the temperature deviation characteristic value of each secondary battery.

[0139] As an example, the heat capacity value, the thermal resistance value, the actual operating temperature value and the ambient temperature value of each secondary battery are extracted from the operating state parameter; and the temperature deviation characteristic value corresponding to the heat capacity value, the thermal resistance value, the actual operating temperature value and the ambient temperature value is calculated through an eighth preset expression, wherein the eighth preset expression is as follows:

[0140]

[0141] wherein, is the heat capacity value of the secondary battery, is the thermal resistance value of the secondary battery, T is the actual operating temperature value, is the ambient temperature value, is a concentration polarization voltage characteristic value, is a diffusion polarization voltage characteristic value, is an ohmic resistance value. In this way, the deviation degree of any operating state parameter relative to the reference operating state parameter can be accurately and comprehensively evaluated through the thermodynamic model, so that a more reliable basis can be provided for the energy storage cloud to detect the fracture of the connecting component, thereby laying a foundation for improving the detection effect of detecting the fracture of the connecting component of the energy storage system.

[0142] In one embodiment, the parameter deviation characteristic value includes a voltage deviation characteristic value and a temperature deviation characteristic value, the preset deviation characteristic value threshold includes a preset voltage deviation characteristic value threshold and a preset temperature deviation characteristic value threshold; according to the size relationship between the parameter deviation characteristic value and the preset deviation characteristic value threshold, the connection assembly is subjected to fracture detection to obtain a cloud fracture detection result, including:

[0143] The actual operating voltage value of each secondary battery is extracted from the operating state parameter; the voltage relative deviation value is obtained by fusing each actual operating voltage value and the voltage deviation characteristic value, and the temperature relative deviation value is obtained by fusing each actual operating temperature value and the temperature deviation characteristic value; the size relationship between the voltage relative deviation value and the preset voltage deviation characteristic value threshold is detected, and the size relationship between the temperature relative deviation value and the preset temperature deviation characteristic value threshold is detected; if the voltage relative deviation value is greater than the preset voltage deviation characteristic value threshold, and the temperature relative deviation value is less than the preset temperature deviation characteristic value threshold, then the cloud fracture detection result is generated according to the voltage deviation characteristic value and the temperature deviation characteristic value; if the voltage relative deviation value is less than or equal to the preset voltage deviation characteristic value threshold, and / or, the temperature relative deviation value is greater than or equal to the preset temperature deviation characteristic value threshold, then the second preset component state information is taken as the cloud fracture detection result, wherein the second preset component state information represents that the energy storage cloud determines that the connection assembly is in a normal working state.

[0144] It should be noted that in the case of fracture detection of the connection assembly by the first parameter deviation characteristic value and the second parameter deviation characteristic value of the multiple parameters, a specific determination condition can be set to determine the cloud fracture detection result based on the determination condition.

[0145] As an example, the actual operating voltage value of each secondary battery is extracted from the operating state parameter; the voltage relative deviation value corresponding to each actual operating voltage value and the voltage deviation characteristic value is calculated by a ninth preset expression, wherein the ninth preset expression is as follows:

[0146]

[0147] wherein, the voltage relative deviation value, the voltage deviation characteristic value, the actual operating voltage value; the actual operating temperature value and the temperature deviation characteristic value are calculated by a tenth preset expression, wherein the tenth preset expression is specifically as follows:

[0148]

[0149] wherein, the temperature relative deviation value, the temperature deviation characteristic value, is a preset voltage deviation characteristic value threshold, and the preset temperature deviation characteristic value threshold can be specifically , represents a voltage residual error threshold, and the preset voltage deviation characteristic value threshold can be specifically [ 0.05V ， 0 . 1 V ] that is, the preset voltage deviation characteristic value threshold can be any specific value in the value range of greater than or equal to 0.05V to less than or equal to 0.1V, and the preset temperature deviation characteristic value threshold can be specifically , represents a temperature residual error threshold, and the preset temperature deviation characteristic value threshold can be specifically [ 2 ℃ ， 3 ℃ ] that is, the preset temperature deviation characteristic value threshold can be any specific value in the value range of greater than or equal to 2℃ to less than or equal to 3℃; if the voltage relative deviation value is less than or equal to the preset voltage deviation characteristic value threshold, and / or, the temperature relative deviation value is greater than or equal to the preset temperature deviation characteristic value threshold, the second preset component state information is taken as the cloud fracture detection result, wherein the second preset component state information represents that the energy storage cloud determines that the connecting component is in a normal working state.

[0150] In one embodiment, the cloud fracture detection result is generated according to the voltage deviation characteristic value and the temperature deviation characteristic value, including:

[0151] According to the voltage deviation characteristic value, a voltage deviation average characteristic value commonly corresponding to each secondary battery is determined; a boundary secondary battery is selected from each secondary battery, and a first actual operating voltage value of the boundary secondary battery and a second actual operating voltage value of a reference secondary battery adjacent to the boundary secondary battery are extracted; by fusing the voltage deviation average characteristic value, the first actual operating voltage value and the second actual operating voltage value, a voltage deviation correlation characteristic value between the boundary secondary battery and the reference secondary battery is obtained; in the case that the first actual operating voltage value is less than a preset actual operating voltage value threshold, and the voltage deviation correlation characteristic value is less than a preset correlation characteristic value threshold, the cloud fracture detection result is generated according to the voltage deviation characteristic value and the temperature deviation characteristic value.

[0152] As an example, the voltage deviation characteristic values are averaged to obtain a voltage deviation average characteristic value corresponding to each secondary battery; a boundary secondary battery is selected from the secondary batteries, and a first actual operating voltage value of the boundary secondary battery and a second actual operating voltage value of a reference secondary battery adjacent to the boundary secondary battery are extracted; and a voltage deviation correlation characteristic value between the boundary secondary battery and the reference secondary battery is calculated through an eleventh preset expression, wherein the eleventh preset expression is as follows:

[0153]

[0154] wherein, is the voltage deviation correlation characteristic value, is the first actual operating voltage value of the boundary secondary battery, is the second actual operating voltage value of the reference secondary battery, is the voltage deviation average characteristic value; in a case where the first actual operating voltage value is less than a preset actual operating voltage value threshold and the voltage deviation correlation characteristic value is less than a preset correlation characteristic value threshold, a cloud end fracture detection result is generated according to the voltage deviation characteristic value and the temperature deviation characteristic value, wherein the preset actual operating voltage value threshold can be 0.3, that is, , and the preset correlation characteristic value threshold can be represented as , and the specific interpretation can be a pressure difference threshold, for example, in an implementable manner, in a case where the energy storage battery module is in a multi-string parallel connection, the preset correlation characteristic value threshold [ 0.03V ， 0.1V ] , that is, the preset correlation characteristic value threshold can be any specific value in a value range greater than or equal to 0.03V to less than or equal to 0.1V.

[0155] In an implementable manner, for the temperature deviation characteristic value, the determination condition of determining the temperature sampling fracture is as follows:

[0156]

[0157] wherein, is the temperature relative deviation value, is the temperature deviation characteristic value, is the actual operating temperature value, is the average temperature value, is a preset temperature deviation characteristic value threshold, wherein the preset temperature deviation characteristic value threshold [ 2 ℃ ， 3 ℃ ] , that is, the preset temperature deviation characteristic value threshold can be any specific value in a value range greater than or equal to 2℃ to less than or equal to 3℃.

[0158] It should be understood that although each step in the flowchart involved in the above embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowchart involved in the above embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately or alternately executed with at least part of other steps or steps or stages in other steps.

[0159] Based on the same inventive concept, the embodiments of the present application also provide a connection component fracture detection device of an energy storage system for implementing the above-mentioned connection component fracture detection method of an energy storage system. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more connection component fracture detection device embodiments of the energy storage system provided below can refer to the limitations of the connection component fracture detection method of the energy storage system described above, and will not be repeated here.

[0160] In one exemplary embodiment, as shown in Figure 3 A connection component fracture detection device of an energy storage system is provided, applied to an energy storage edge, the connection component includes a conductive component and a collection component, the conductive component is electrically connected to at least one secondary battery; the connection component fracture detection device of the energy storage system includes: a first acquisition module 401, a determination module 402, a first detection module 403 and a sending module 404, wherein:

[0161] The first acquisition module 401 is configured to acquire the operating state parameters of the at least one secondary battery collected and uploaded by the collection component;

[0162] The determination module 402 is configured to determine parameter discrete characteristic values between the operating state parameters, wherein the parameter discrete characteristic values represent the discrete degrees of the operating state parameters of the secondary batteries.

[0163] The first detection module 403 is configured to perform fracture detection on the connection component according to the size relationship between the parameter discrete characteristic values and a preset discrete characteristic value threshold, and obtain an edge fracture detection result.

[0164] The sending module 404 is configured to send the operating state parameters and the edge fracture detection result to an energy storage cloud end, so that the energy storage cloud end generates a connection component fracture detection result according to a cloud end fracture detection result and the edge fracture detection result, wherein the cloud end fracture detection result is predicted according to the operating state parameters.

[0165] In one exemplary embodiment, as shown in Figure 4 A connection component fracture detection device of an energy storage system is provided, applied to an energy storage cloud, the connection component including a conductive component and a collection component, the conductive component being electrically connected to at least one secondary battery; the connection component fracture detection device of the energy storage system includes: a second acquisition module 501, a prediction module 502, a second detection module 503, and a comparison module 504, wherein:

[0166] The second acquisition module 501 is configured to acquire an edge fracture detection result sent by an energy storage edge and an operating state parameter of at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is detected according to each operating state parameter.

[0167] The prediction module 502 is configured to predict a parameter deviation characteristic value of each secondary battery according to the operating state parameter, the parameter deviation characteristic value representing a deviation degree of the operating state parameter of any secondary battery relative to a reference operating state parameter.

[0168] The second detection module 503 is configured to perform fracture detection on the connection component according to a size relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold, to obtain a cloud fracture detection result.

[0169] The comparison module 504 is configured to obtain a fracture detection result of the connection component by comparing the edge fracture detection result and the cloud fracture detection result.

[0170] Each module in the above connection component fracture detection device of the energy storage system can be realized by software, hardware, or a combination thereof, in whole or in part. Each module described above can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0171] In one exemplary embodiment, an energy storage system is provided, which includes an energy storage system management terminal, and an internal structure diagram of the energy storage system management terminal can be as shown in Figure 5The management and control terminal of the energy storage system includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the management and control terminal of the energy storage system is used to provide computing and control capabilities. The memory of the management and control terminal of the energy storage system includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The input / output interface of the management and control terminal of the energy storage system is used to exchange information between the processor and external devices. The communication interface of the management and control terminal of the energy storage system is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement a connection component fracture detection method of an energy storage system. Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the management and control terminal of the energy storage system to which the scheme of the present application is applied. The specific management and control terminal of the energy storage system can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0172] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0173] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0174] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for detecting fracture of a connection component of an energy storage system, characterized in that: Applied to the energy storage edge, the connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery; The method for detecting fracture of a connection component of the energy storage system comprises: Acquiring the operating status parameters of the at least one secondary battery collected and uploaded by the collecting component; Determining a parameter discrete characteristic value between each operating state parameter, wherein the parameter discrete characteristic value represents a degree of discreteness of each operating state parameter of the secondary battery; Performing a fracture detection on the connection component according to a magnitude relationship between the discrete eigenvalue of the parameter and a preset discrete eigenvalue threshold to obtain an edge fracture detection result; The operating status parameters and the edge fracture detection results are sent to the energy storage cloud, so that the energy storage cloud generates a fracture detection result of the connection component based on the cloud fracture detection result and the edge fracture detection result, wherein the cloud fracture detection result is predicted based on each of the operating status parameters.

2. The method for detecting fracture of a connection component of an energy storage system according to claim 1, characterized in that: The parameter discrete eigenvalues ​​include first parameter discrete eigenvalues; The determining of discrete characteristic values ​​of parameters between the operating state parameters includes: Determining a first parameter characteristic value commonly corresponding to each of the operating status parameters, and determining a second parameter characteristic value commonly corresponding to each of the operating status parameters, wherein the first parameter characteristic value and the second parameter characteristic value are parameter characteristic values ​​of different dimensions; The first parameter discrete eigenvalue is obtained by fusing the first parameter eigenvalue and the second parameter eigenvalue.

3. The method for detecting fracture of a connection component of an energy storage system according to claim 1, characterized in that: The parameter discrete eigenvalues ​​include second parameter discrete eigenvalues; The determining of discrete characteristic values ​​of parameters between the operating state parameters includes: Selecting an operating state characteristic parameter from each of the operating state parameters according to a magnitude relationship between the operating state parameters; By fusing each of the operating state parameters and the operating state characteristic parameters, a discrete characteristic value of the second parameter is obtained.

4. The method for detecting fracture of a connection component of an energy storage system according to claim 1, characterized in that: The parameter discrete eigenvalues ​​include a first parameter discrete eigenvalue and a second parameter discrete eigenvalue, and the preset discrete eigenvalue thresholds include a first preset discrete eigenvalue threshold and a second preset discrete eigenvalue threshold; performing fracture detection on the connection component based on a magnitude relationship between the parameter discrete eigenvalues ​​and the preset discrete eigenvalue thresholds to obtain an edge fracture detection result includes: Detecting a magnitude relationship between the first parameter discrete eigenvalue and the first preset discrete eigenvalue threshold, and detecting a magnitude relationship between the second parameter discrete eigenvalue and the second preset discrete eigenvalue threshold; If the first parameter discrete eigenvalue is greater than the first preset discrete eigenvalue threshold, and the second parameter discrete eigenvalue is greater than the second preset discrete eigenvalue threshold, generating the edge fracture detection result according to the first parameter discrete eigenvalue and the second parameter discrete eigenvalue; If the discrete eigenvalue of the first parameter is less than or equal to the first preset discrete eigenvalue threshold, and / or the discrete eigenvalue of the second parameter is less than or equal to the second preset discrete eigenvalue threshold, the first preset component status information is used as the edge fracture detection result, wherein the first preset component status information represents that the energy storage edge determines that the connection component is in normal working state.

5. The method for detecting fracture of a connection component of an energy storage system according to claim 4, characterized in that: The first parameter discrete characteristic value is a voltage discrete characteristic value, and the second parameter discrete characteristic value is a temperature discrete characteristic value; Generating the edge fracture detection result according to the first parameter discrete eigenvalue and the second parameter discrete eigenvalue includes: selecting a first target secondary battery with abnormal voltage from the secondary batteries according to the operating state parameter corresponding to the discrete voltage characteristic value, and selecting a second target secondary battery with abnormal temperature from the secondary batteries according to the operating state parameter corresponding to the discrete temperature characteristic value; Locating at least one first abnormal location point of voltage abnormality on the connecting assembly based on a connection relationship between the first target secondary battery and the connecting assembly; locating at least one second abnormal location point of temperature abnormality on the connection component according to the heat conduction path of the second target secondary battery; Obtaining a target abnormal position point on the connected component by fusing each of the first abnormal position points and each of the second abnormal position points; The edge fracture detection result is generated according to the first position information of the target abnormal position point.

6. The method for detecting fracture of a connection component of an energy storage system according to claim 5, characterized in that: The step of obtaining a target abnormal position point on the connection component by fusing each of the first abnormal position points and each of the second abnormal position points includes: Determine at least one candidate abnormal location point having an intersection between each of the first abnormal location points and each of the second abnormal location points; Prioritizing each of the candidate abnormal location points according to the second location information of each candidate abnormal location point to obtain a priority ranking result; According to the priority sorting result, a target abnormal location point is selected from each of the candidate abnormal location points.

7. The method for detecting fracture of a connection component of an energy storage system according to claim 6, characterized in that: The step of prioritizing each of the candidate abnormal location points according to the second location information of each of the candidate abnormal location points to obtain a priority ranking result includes: Determining a voltage anomaly characteristic value and a temperature anomaly characteristic value of each candidate abnormal location point according to the second location information of each candidate abnormal location point; Determining a voltage anomaly weight value and a temperature anomaly weight value for each of the candidate anomaly locations; Obtaining a comprehensive abnormality feature value of each candidate abnormal location point by fusing the voltage abnormality weight value, the temperature abnormality weight value, the voltage abnormality feature value, and the temperature abnormality feature value; According to the comprehensive abnormal feature value, the candidate abnormal location points are prioritized to obtain a priority ranking result.

8. A method for detecting fracture of a connection component of an energy storage system, characterized in that: Applied to energy storage cloud, the connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery; The method for detecting fracture of a connection component of the energy storage system comprises: Acquire an edge fracture detection result sent by the energy storage edge and an operating status parameter of the at least one secondary battery collected and uploaded by the collection component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters; predicting a parameter deviation characteristic value of each of the secondary batteries according to the operating state parameters, wherein the parameter deviation characteristic value represents a degree of deviation of the operating state parameter of any of the secondary batteries relative to a reference operating state parameter; Performing a fracture detection on the connection component based on a magnitude relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-based fracture detection result; By comparing the edge fracture detection result and the cloud fracture detection result, the fracture detection result of the connection component is obtained.

9. The method for detecting fracture of a connection component of an energy storage system according to claim 8, characterized in that: The parameter deviation characteristic value includes a voltage deviation characteristic value; and predicting the parameter deviation characteristic value of each secondary battery according to the operating state parameter includes: Extracting the concentration polarization voltage value, diffusion polarization voltage value, concentration polarization resistance value, ohmic resistance value, diffusion polarization resistance value, concentration polarization capacitance value, diffusion polarization capacitance value and actual operating current value of each of the secondary batteries from the operating state parameters; Predicting a concentration polarization voltage characteristic value of each of the secondary batteries based on the concentration polarization voltage value, the concentration polarization resistance value, the concentration polarization capacitance value, and the actual operating current value, and predicting a diffusion polarization voltage characteristic value of each of the secondary batteries based on the diffusion polarization voltage value, the diffusion polarization resistance value, the diffusion polarization capacitance value, and the actual operating current value, wherein the concentration polarization voltage characteristic value represents a change in the concentration polarization voltage value with the actual operating current value, and the diffusion polarization voltage characteristic value represents a change in the diffusion polarization voltage value with the actual operating current value; The voltage deviation characteristic value of each of the secondary batteries is obtained by fusing the concentration polarization voltage characteristic value, the diffusion polarization voltage characteristic value, the ohmic resistance value, and the actual operating current value.

10. The method for detecting fracture of a connection component of an energy storage system according to claim 8, characterized in that: The parameter deviation characteristic value includes a temperature deviation characteristic value; and predicting the parameter deviation characteristic value of each secondary battery according to the operating state parameter includes: Extracting the thermal capacity value, thermal resistance value, actual operating temperature value and ambient temperature value of each of the secondary batteries from the operating state parameters; A temperature deviation characteristic value of each of the secondary batteries is predicted based on the heat capacity value, the thermal resistance value, the actual operating temperature value, and the ambient temperature value.

11. The method for detecting fracture of a connection component of an energy storage system according to claim 8, characterized in that: The parameter deviation characteristic value includes a voltage deviation characteristic value and a temperature deviation characteristic value, and the preset deviation characteristic value threshold includes a preset voltage deviation characteristic value threshold and a preset temperature deviation characteristic value threshold; The performing fracture detection on the connection component according to the magnitude relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold to obtain a cloud-side fracture detection result includes: extracting the actual operating voltage value of each of the secondary batteries from the operating state parameters; Obtaining a relative voltage deviation value by fusing each actual operating voltage value and the voltage deviation characteristic value, and obtaining a relative temperature deviation value by fusing each actual operating temperature value and the temperature deviation characteristic value; Detecting a magnitude relationship between the voltage relative deviation value and the preset voltage deviation characteristic value threshold, and detecting a magnitude relationship between the temperature relative deviation value and the preset temperature deviation characteristic value threshold; If the voltage relative deviation value is greater than the preset voltage deviation characteristic value threshold, and the temperature relative deviation value is less than the preset temperature deviation characteristic value threshold, generating the cloud fracture detection result according to the voltage deviation characteristic value and the temperature deviation characteristic value; If the voltage relative deviation value is less than or equal to the preset voltage deviation characteristic value threshold, and / or the temperature relative deviation value is greater than or equal to the preset temperature deviation characteristic value threshold, the second preset component status information is used as the cloud fracture detection result, wherein the second preset component status information indicates that the energy storage cloud determines that the connection component is in normal working condition.

12. The method for detecting fracture of a connection component of an energy storage system according to claim 11, characterized in that: The generating the cloud fracture detection result according to the voltage deviation characteristic value and the temperature deviation characteristic value includes: determining, according to the voltage deviation characteristic value, an average voltage deviation characteristic value corresponding to each of the secondary batteries; Selecting a boundary secondary battery from each of the secondary batteries, and extracting a first actual operating voltage value of the boundary secondary battery and a second actual operating voltage value of a reference secondary battery adjacent to the boundary secondary battery; Obtaining a voltage deviation correlation characteristic value between the boundary secondary battery and the reference secondary battery by fusing the voltage deviation average characteristic value, the first actual operating voltage value, and the second actual operating voltage value; When it is detected that the first actual operating voltage value is less than the preset actual operating voltage value threshold and the voltage deviation associated characteristic value is less than the preset associated characteristic value threshold, the cloud break detection result is generated according to the voltage deviation characteristic value and the temperature deviation characteristic value.

13. A device for detecting fracture of a connection component of an energy storage system, characterized in that: Applied to the energy storage edge, the connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery; The connection component fracture detection device of the energy storage system includes: a first acquisition module, configured to acquire the operating status parameters of the at least one secondary battery collected and uploaded by the acquisition component; a determination module, determining a parameter discrete characteristic value between each operating state parameter, wherein the parameter discrete characteristic value represents a degree of discreteness of each operating state parameter of the secondary battery; A first detection module is configured to perform a fracture detection on the connection component based on a magnitude relationship between the discrete eigenvalue of the parameter and a preset discrete eigenvalue threshold, thereby obtaining an edge fracture detection result; A sending module is used to send the operating status parameters and the edge fracture detection results to the energy storage cloud, so that the energy storage cloud can generate a fracture detection result of the connection component based on the cloud fracture detection results and the edge fracture detection results, wherein the cloud fracture detection result is predicted based on each of the operating status parameters.

14. A device for detecting fracture of a connection component of an energy storage system, characterized in that: Applied to energy storage cloud, the connection assembly includes a conductive component and a collection component, and the conductive component is electrically connected to at least one secondary battery; The connection component fracture detection device of the energy storage system includes: a second acquisition module, configured to acquire an edge fracture detection result sent by the energy storage edge and an operating status parameter of the at least one secondary battery collected and uploaded by the acquisition component, wherein the edge fracture detection result is obtained based on the detection of each of the operating status parameters; a prediction module, configured to predict a parameter deviation characteristic value of each of the secondary batteries based on the operating state parameter, wherein the parameter deviation characteristic value represents a degree of deviation of the operating state parameter of any of the secondary batteries relative to a reference operating state parameter; A second detection module is configured to perform a fracture detection on the connection component based on a magnitude relationship between the parameter deviation characteristic value and a preset deviation characteristic value threshold, and obtain a fracture detection result on the cloud; The comparison module is used to obtain the fracture detection result of the connection component by comparing the edge fracture detection result with the cloud fracture detection result.

15. An energy storage system comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method for detecting fracture of a connection component of an energy storage system according to any one of claims 1 to 7 or 8 to 12 are implemented.

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