Method for evaluating short circuit risk in battery of energy storage power station

Abstract

The invention provides an energy storage power station battery internal short circuit risk assessment method, and belongs to the technical field of energy storage power station lithium battery safety diagnosis, and the method comprises the steps: collecting real-time battery data based on a battery management system, a collection gateway or an EMS server; based on the trained battery internal short circuit model, judging whether the real-time battery data is abnormal or not, if the real-time battery data is abnormal, estimating a characteristic parameter evolution rate, and determining a characteristic parameter evolution rate boundary and a characteristic parameter boundary; and quantitatively evaluating the internal short circuit risk of the battery based on the real-time distance between the weighted characteristic parameter evolution rate and the characteristic parameter evolution rate boundary and the real-time distance between the weighted real-time battery data and the characteristic parameter boundary, and outputting an evaluated value of the internal short circuit risk. According to the scheme, the internal short circuit risk can be accurately evaluated.

Description

Technical Field

[0001] The present application relates to the technical field of safety diagnosis of lithium batteries in energy storage power stations, and in particular to a method for assessing the risk of short circuits within batteries in energy storage power stations. Background Art

[0002] Lithium-ion batteries have inherent safety risks that cannot be ignored. Mechanical abuse, thermal abuse, electrical abuse, etc. may cause thermal runaway of lithium-ion batteries, which can lead to chain reactions in surrounding batteries, thermal runaway of energy storage power stations, and even explosions.

[0003] The current monitoring and early warning systems of energy storage power stations are insufficient in early warning capabilities. The BMS battery monitoring system, EMS energy management system, fire protection system, temperature control system, and electronic control system of energy storage power stations are mainly used for emergency response in the middle and late stages of thermal runaway. The early warning measures are not yet perfect, and most of them are thermal runaway fire alarms. At this time, the battery system is already in a high-risk state. Even if an alarm signal is issued, it cannot completely guarantee the avoidance of fire, that is, the effect of early warning cannot be achieved.

[0004] Therefore, how to accurately assess the internal short circuit risk becomes an issue that needs to be resolved. Summary of the invention

[0005] The purpose of this application is to provide a method for assessing the risk of short circuits in batteries of energy storage power stations, which can solve the problem of insufficient early warning capability of monitoring and early warning systems of energy storage power stations in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for assessing the risk of short circuit in a battery of an energy storage power station, which is implemented based on a battery safety early warning system of an energy storage power station. The method includes:

[0007] Collect real-time battery data based on battery management system, collection gateway or EMS server;

[0008] Based on the trained battery internal short-circuit model, determine whether the real-time battery data is abnormal. If the real-time battery data is abnormal, estimate the characteristic parameter evolution rate, and determine the characteristic parameter evolution rate boundary and characteristic parameter boundary;

[0009] Based on the real-time distance between the characteristic parameter evolution rate after setting the weight and the characteristic parameter evolution rate boundary, as well as the real-time distance between the real-time battery data after setting the weight and the characteristic parameter boundary, the internal short circuit risk of the battery is quantitatively evaluated, and the evaluation value of the internal short circuit risk is output.

[0010] In a possible implementation manner of the first aspect, the real-time battery data includes: voltage, current, state of charge, temperature, health, and internal resistance of the battery.

[0011] In a possible implementation manner of the first aspect, judging whether real-time battery data is abnormal based on a trained battery internal short circuit model includes:

[0012] The error boundary is determined based on the trained battery internal short-circuit model, and the difference between the historical battery data and the real-time battery data is calculated; and it is determined whether the difference exceeds the error boundary.

[0013] In a possible implementation manner of the first aspect, determining a characteristic parameter evolution rate boundary and a characteristic parameter boundary includes:

[0014] Based on the trained battery internal short-circuit model, the charge and discharge data and test data of the battery before leaving the factory are integrated to determine the characteristic parameter evolution rate boundary and characteristic parameter boundary.

[0015] In a possible implementation manner of the first aspect, the method further includes:

[0016] The abnormal real-time battery data is corrected and the correction value of the abnormal real-time battery data is output.

[0017] In a possible implementation manner of the first aspect, the method further includes:

[0018] According to the percentage of internal short circuit risk, the level of internal short circuit risk is divided into high risk, medium risk and low risk; the assessment value of the internal short circuit risk is rated to determine the risk level of the assessment value of the internal short circuit risk.

[0019] In a possible implementation manner of the first aspect, the method further includes:

[0020] According to the characteristic parameter evolution rate and real-time battery data, the battery internal short-circuit model is trained and optimized.

[0021] In a possible implementation manner of the first aspect, the system includes: a battery management system, a collection gateway, a communication switching device, an EMS server, a BSS server, and a BSS client;

[0022] Battery management system, used to collect real-time battery data;

[0023] Collection gateway, used to collect real-time battery data collected by the battery management system;

[0024] Communication switching equipment, used to forward real-time battery data to the EMS server;

[0025] The EMS server is used to obtain the real-time battery data forwarded by the communication switching device, and store and display the real-time battery data;

[0026] The BSS server is used to obtain the real-time battery data collected by the battery management system in real time based on the communication exchange device, or to obtain the real-time battery data based on the EMS server, or to obtain the real-time battery data collected by the collection gateway based on the communication exchange device;

[0027] BSS client is used to access BSS server based on Ethernet and perform data viewing, display, management and setting operations in WEB mode.

[0028] In a possible implementation manner of the first aspect, the battery management system includes: a battery management unit, a battery cluster management unit, and a battery array management unit;

[0029] The battery management unit is used to sample the voltage and temperature of the battery cells and use differential UART daisy chain for communication and control; the battery cluster management unit is used to collect and monitor the battery cluster status, SOC estimation, fault diagnosis, charge and discharge control and balance control, main power circuit on-off control and battery thermal management;

[0030] The battery array management unit is used to communicate with the EMS server and PCS, manage the battery SOC protection logic, manage the coordinated operation of the battery system, and manage the information collection of the battery cluster management unit.

[0031] In a possible implementation manner of the first aspect, the communication switching device includes: a switch and a core switch, and the switch and the core switch are used to forward real-time battery data.

[0032] In a possible implementation of the first aspect, the BSS server is deployed with: BSS data processing software, BSS core algorithm and WEB server software, which are used for data sending and receiving, data cleaning, data calculation, algorithm training, internal short circuit risk calculation and providing WEB browser access.

[0033] In a third aspect, an embodiment of the present application provides a short circuit risk assessment device for a battery in an energy storage power station, which is implemented based on the short circuit risk assessment method for a battery in an energy storage power station according to any one of the first aspects, and the device includes:

[0034] A collection unit, used to collect real-time battery data based on a battery management system, a collection gateway or an EMS server;

[0035] A processing unit, used to determine whether the real-time battery data is abnormal based on the trained battery internal short-circuit model, and if the real-time battery data is abnormal, estimate the characteristic parameter evolution rate, and determine the characteristic parameter evolution rate boundary based on the trained battery internal short-circuit model;

[0036] The evaluation unit is used to quantitatively evaluate the internal short circuit risk of the battery based on the characteristic parameter evolution rate or real-time battery data after setting the weight, and the real-time distance from the characteristic parameter evolution rate boundary, and output the evaluation value of the internal short circuit risk.

[0037] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for assessing the risk of short circuit in a battery of an energy storage power station according to any one of the first aspects above is implemented.

[0038] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for assessing the risk of short circuit in a battery of an energy storage power station according to any one of the first aspects above is implemented.

[0039] In a sixth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the method for assessing the risk of short circuit in a battery of an energy storage power station according to any one of the first aspects above.

[0040] The present application scheme first collects real-time battery data through a battery management system, a collection gateway or an EMS server, and then estimates the characteristic parameter evolution rate when the real-time battery data is abnormal based on the battery internal short-circuit model, and determines the characteristic parameter evolution rate boundary and the characteristic parameter boundary. Finally, based on the real-time distance between the characteristic parameter evolution rate and its boundary, and the real-time distance between the real-time battery data and the characteristic parameter boundary, the internal short-circuit risk of the battery is quantitatively evaluated.

[0041] The present application scheme uses an internal short-circuit model to estimate the evolution rate of characteristic parameters, determine boundaries, and calculate real-time distances, so as to accurately assess the risk of internal short circuits and provide early warning for the safety of batteries in energy storage power stations. It has strong ease of use and practicality.

[0042] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 creative labor.

[0044] Figure 1 It is a schematic diagram of the steps of the method for assessing the risk of short circuit in a battery of an energy storage power station provided in an embodiment of the present application;

[0045] Figure 2 It is a schematic diagram of the overall structure of the energy storage power station battery safety early warning system provided in the embodiment of the present application;

[0046] Figure 3 It is a specific structural diagram of the energy storage power station battery safety early warning system provided in the embodiment of the present application;

[0047] Figure 4 It is a flow chart of a method for assessing the risk of short circuit in a battery of an energy storage power station provided in an embodiment of the present application;

[0048] Figure 5 It is a structural schematic diagram of a short circuit risk assessment device for a battery in an energy storage power station provided in an embodiment of the present application;

[0049] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0051] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or photovoltaic components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, photovoltaic components and / or combinations thereof.

[0052] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0053] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0055] In addition, in the description of the present application, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0056] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0057] Lithium-ion batteries themselves have potential safety hazards that cannot be ignored. One of the characteristics of large-scale energy storage power stations is that there are many batteries and they are arranged relatively densely. The number of internal single batteries can reach tens of thousands. The concentrated distribution of batteries in the energy storage compartment will increase the safety risks of batteries. Lithium-ion batteries may suffer from thermal runaway due to mechanical abuse, thermal abuse, electrical abuse, etc., which may cause a chain reaction in the surrounding batteries, resulting in thermal runaway of the energy storage power station, or even explosion. Once a thermal runaway fire or explosion occurs in a lithium-ion battery energy storage power station, the consequences will be disastrous.

[0058] The current monitoring and early warning systems of energy storage power stations are insufficient in early warning capabilities. The BMS battery monitoring system, EMS energy management system, fire protection system, temperature control system, and electronic control system of energy storage power stations are mainly used for emergency response in the middle and late stages of thermal runaway. The early warning measures are not yet perfect, and most of them are thermal runaway fire alarms. At this time, the battery system is already in a high-risk state. Even if an alarm signal is issued, it cannot completely guarantee the avoidance of fire, that is, the effect of early warning cannot be achieved. The core of solving the safety risk problem of energy storage power stations is to conduct early fault detection, that is, to detect fault signals in a timely manner in the early stage of the fault and issue early warning information to avoid the fault from further developing into thermal runaway. Fire hazards can be eliminated through early warning.

[0059] There are many technical means to detect the early internal short circuit fault of lithium battery, which are based on models, statistical data, artificial intelligence, fusion method, etc. The present invention belongs to the lithium battery internal short circuit fault detection method based on fusion method.

[0060] When lithium batteries are fast charged at low temperatures, lithium is easily deposited at the negative electrode. Multiple fast charges will cause lithium to accumulate in places where impurities are present at the negative electrode and pass through the SEI membrane to form lithium dendrites. When the lithium dendrites grow to a certain extent, they will break through the diaphragm and cause an internal short circuit. According to this electrochemical principle, the early stage of thermal runaway of batteries is often accompanied by changes in multiple state parameters such as voltage, internal resistance, temperature, characteristic gas, and characteristic sound. Using these characteristic parameters as fault signals for early safety warning of batteries is an effective technical means to improve the safety of energy storage systems.

[0061] Therefore, how to accurately assess the internal short circuit risk becomes an issue that needs to be resolved.

[0062] In response to the above-mentioned defects, an embodiment of the present application provides a method for assessing the risk of internal short circuit in batteries of an energy storage power station. First, real-time battery data is collected through a battery management system, a collection gateway or an EMS server. Then, based on the battery internal short circuit model, when the real-time battery data is judged to be abnormal, the characteristic parameter evolution rate is estimated, and the characteristic parameter evolution rate boundary and the characteristic parameter boundary are determined. Finally, based on the real-time distance between the characteristic parameter evolution rate and its boundary, and the real-time distance between the real-time battery data and the characteristic parameter boundary, the internal short circuit risk of the battery is quantitatively assessed.

[0063] The present application scheme uses an internal short-circuit model to estimate the evolution rate of characteristic parameters, determine boundaries, and calculate real-time distances, so as to accurately assess the risk of internal short circuits and provide early warning for the safety of batteries in energy storage power stations. It has strong ease of use and practicality.

[0064] The specific process implemented by this application is introduced below through specific embodiments.

[0065] See also Figure 1 , Figure 1 Schematic diagram of the steps of the short circuit risk assessment method for the energy storage power station battery provided in the embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0066] S101, collecting real-time battery data based on a battery management system, a collection gateway or an EMS server.

[0067] According to one embodiment of the present application, real-time battery data includes: battery voltage, current, state of charge, temperature, health, and internal resistance.

[0068] S102, based on the trained battery internal short-circuit model, determine whether the real-time battery data is abnormal. If the real-time battery data is abnormal, estimate the characteristic parameter evolution rate, and determine the characteristic parameter evolution rate boundary and the characteristic parameter boundary.

[0069] Among them, the battery internal short circuit model is a battery electrochemical-thermal-aging model, a battery internal mechanism model. In one embodiment, if there is no abnormality in the real-time battery data, it indicates that the battery has not short-circuited. If there is an abnormality in the real-time battery data, the changes in characteristic parameters such as SOC and internal resistance are estimated, and the characteristic parameter evolution rate is estimated based on the internal short circuit test.

[0070] The characteristic parameter evolution rate is the rate of change of the characteristic parameters before the battery has an internal short circuit. The characteristic parameter evolution rate = the rate of change of the characteristic parameters = |(real-time characteristic parameters - historical characteristic parameters)| / unit time. For example, the temperature evolution rate T b =(25-20)℃ / 1h=5℃ / h.

[0071] According to one embodiment of the present application, based on a trained battery internal short-circuit model, determining whether real-time battery data is abnormal includes: determining an error boundary based on the trained battery internal short-circuit model, calculating the difference between historical battery data and real-time battery data; and determining whether the difference exceeds the error boundary.

[0072] The historical battery data is some known data when the battery is operating normally. In one embodiment, the U and T models of the battery (including the historical battery data U and T) are subtracted from the measured U and T, and the difference is recorded as ΔU and ΔT. The error boundaries of U and T are determined by the internal short-circuit model, and it is further determined whether ΔU and ΔT exceed the error boundaries.

[0073] According to one embodiment of the present application, determining the characteristic parameter evolution rate boundary and the characteristic parameter boundary includes: based on a trained battery internal short circuit model, comprehensively analyzing the battery's pre-factory charge and discharge data and test data, determining the characteristic parameter evolution rate boundary and the characteristic parameter boundary.

[0074] In some embodiments, the characteristic parameter evolution rate boundary is the maximum value of the characteristic parameter evolution rate, such as the temperature evolution rate boundary is ΔT b =3℃ / h, the internal resistance evolution rate boundary is ΔR b , SOC evolution rate boundary is ΔR b The closer the characteristic parameter evolution rate is to the boundary, the greater the risk of internal short circuit. The characteristic parameter boundary is the maximum value of the characteristic parameter. For example, the boundary of internal resistance R is R max , the closer the characteristic parameter is to the boundary, the greater the risk of internal short circuit.

[0075] According to an embodiment of the present application, the method further includes: correcting abnormal real-time battery data and outputting a correction value of the abnormal real-time battery data.

[0076] According to one embodiment of the present application, the method further includes: training a battery internal short-circuit model based on the characteristic parameter evolution rate and real-time battery data, and optimizing the battery internal short-circuit model.

[0077] In some embodiments, the internal short-circuit model is trained and optimized based on the evolution rate and real-time data of characteristic parameters such as SOC and internal resistance. In terms of model accuracy and thermal runaway judgment, the measured data set of the battery plays a vital role in the optimization of the model and the boundary setting of characteristic parameters.

[0078] The acquired data sets include: basic data of battery cell manufacturers and actual battery test data. Based on the basic data, data collection specifications are formulated to obtain certain standardized data. The basic battery electrochemical model will be deeply learned in the application and optimized for the model.

[0079] S103, based on the real-time distance between the characteristic parameter evolution rate after setting the weight and the characteristic parameter evolution rate boundary, and the real-time distance between the real-time battery data after setting the weight and the characteristic parameter boundary, quantitatively evaluate the internal short circuit risk of the battery, and output the evaluation value of the internal short circuit risk.

[0080] In some embodiments, a weight is set for each characteristic parameter evolution rate and real-time battery data, and the evaluation value of the internal short circuit risk is comprehensively calculated. For example, the weight of the temperature evolution rate is 10%, the weight of the internal resistance evolution rate is 50%, etc.; the weight of the temperature is 20%, the weight of the internal resistance is 30%, etc.

[0081] In some embodiments, the temperature evolution rate T after setting the weight b and the temperature evolution rate boundary ΔT b The real-time distance is |T b -ΔT b |, set the weighted internal resistance R and internal resistance boundary R max The real-time distance is |RR max |. Based on |T b -ΔT b |,|RR max |Equal real-time distance, calculate the assessment value of internal short circuit risk, for example, 80%. The closer the distance, the greater the internal short circuit risk; the farther the distance, the smaller the internal short circuit risk.

[0082] According to one embodiment of the present application, the method also includes: dividing the level of internal short circuit risk into high risk, medium risk and low risk according to the percentage of internal short circuit risk; rating the assessment value of the internal short circuit risk to determine the risk level of the assessment value of the internal short circuit risk.

[0083] In one embodiment, if the internal short circuit risk is [0% to 40%], the risk level of the internal short circuit risk is low risk; if the internal short circuit risk is (40% to 80%], the risk level of the internal short circuit risk is medium risk; if the internal short circuit risk is (80% to 100%], the risk level of the internal short circuit risk is high risk. The risk level is determined by judging to which interval the evaluation value of the internal short circuit risk belongs. For example, if the evaluation value of the internal short circuit risk is 90%, the risk level is high risk.

[0084] When the risk of internal short circuit is high or medium, the lithium battery is in the early stage of thermal runaway. Before the lithium dendrites completely break through the diaphragm and cause internal short circuit, the battery safety of the energy storage power station is warned in advance, reminding the operation and maintenance personnel to perform maintenance, thereby avoiding accidents.

[0085] See also Figure 2 , Figure 2 Schematic diagram of the overall structure of the energy storage power station battery safety warning system provided in the embodiment of the present application. Figure 2 As shown, the system, namely BSS (Battery Safety System), includes: BMS (Battery Management System), acquisition gateway, communication switching equipment, EMS (Energy Management System), BSS server and BSS client.

[0086] The battery management system is used to collect real-time battery data, the collection gateway is used to collect real-time battery data collected by the battery management system, and the communication exchange device is used to forward the real-time battery data to the EMS server. The EMS server is used to obtain the real-time battery data forwarded by the communication exchange device, and store and display the real-time battery data.

[0087] In some embodiments, the EMS obtains real-time battery data collected by the BMS, such as voltage, SOC, temperature and other data, stores and displays them in the EMS system, and provides a data access interface for the BSS system.

[0088] The BSS server is used to obtain real-time battery data collected by the battery management system based on the communication exchange device, or to obtain real-time battery data based on the EMS server, or to obtain real-time battery data collected by the acquisition gateway based on the communication exchange device. The BSS client is used to access the BSS server based on Ethernet, and uses the WEB (World Wide Web) mode to perform data viewing, display, management and setting operations.

[0089] In some embodiments, there are three ways for the BSS server to obtain data: one is to directly obtain the real-time data of the BMS through the switch, the second is to obtain real-time data through the EMS, and the third is to obtain real-time data collected by the collection gateway through the switch.

[0090] In some embodiments, the BSS client accesses the BSS server through Ethernet or other means to access the BSS system, and performs operations such as data viewing, display, management, and setting in WEB mode for use by staff.

[0091] In some embodiments, the battery safety warning system is connected to a lithium battery, such as a lithium iron phosphate battery. A lithium battery is composed of cells, which are connected in series to form a battery pack. A battery pack plus a BMU (Battery Management Unit) constitutes a battery module, and a battery module plus a distribution box and a switch box constitute a battery cluster. Cell-level data can be collected through the BMS.

[0092] In some embodiments, a battery early warning diagnosis platform based on B / S architecture, which develops pre-diagnosis models, algorithms and functions for lithium batteries, enables remote information browsing and data query, while simplifying the client computer load and reducing the cost and workload of system maintenance and upgrades.

[0093] According to one embodiment of the present application, the battery management system includes: a battery management unit, a battery cluster management unit and a battery array management unit. The battery management unit is used to sample the voltage and temperature of the battery cell and use a differential UART (Universal Asynchronous Receiver / Transmitter) daisy chain for communication and control.

[0094] The battery cluster management unit is used to collect and monitor the battery cluster status, SOC estimation, fault diagnosis, charge and discharge control and balance control, main power circuit on-off control and battery thermal management. The battery array management unit is used to communicate with the EMS server and PCS (Power Conversion System, energy storage converter), manage the battery SOC (State of Charge) protection logic, manage the coordinated operation of the battery system, and manage the information collection of the battery cluster management unit.

[0095] In some embodiments, the BMS is composed of three levels of BMS, including: BMU, BCMU (Battery Cluster Unit), and BAMU (Battery Array Management Unit). The battery management unit (BMU) has functions such as cell voltage and temperature sampling, active and passive balancing, etc. It adopts a software-free design and realizes communication and control functions through a differential UART daisy chain.

[0096] In some embodiments, the BCMU has functions such as battery cluster status (such as current, voltage, insulation resistance, etc.) collection and monitoring, SOC estimation, fault diagnosis, charge and discharge control and balance control, main power circuit on-off control, and battery thermal management. The BAMU communicates with the EMS and PCS, and has functions such as battery SOC protection logic management, battery system coordinated operation management, and BCMU information collection.

[0097] See also Figure 3 , Figure 3 Schematic diagram of the specific structure of the energy storage power station battery safety warning system provided by the embodiment of the present application. Figure 3 As shown, the communication switching equipment includes: a switch and a core switch, and the switch and the core switch are used to forward real-time battery data.

[0098] In some embodiments, the acquisition gateway obtains the real-time data of the collected lithium battery from the BMS system. The lower interface of the acquisition gateway has RS485, network port, and CAN (Controller Area Network) port, which are connected to the BMS. The upper interface of the acquisition gateway is the network port, which is connected to the switch. The acquisition gateway sends the voltage, SOC, temperature and other data obtained from the BMS to the BSS server through the switch.

[0099] In some embodiments, the BMS or the data collection gateway sends the collected real-time data to the BSS server or EMS through the switch or the core switch. The switch is an Ethernet communication switching device, the downstream port is an electrical port, and the upstream port is an electrical port or an optical port. The downstream port of the core switch is an electrical port or an optical port, and the upstream port is an electrical port or an optical port.

[0100] According to one embodiment of the present application, the BSS server is deployed with: BSS data processing software, BSS core algorithm and WEB server software, which are used for data sending and receiving, data cleaning, data calculation, algorithm training, internal short circuit risk calculation and providing WEB browser access and other operations.

[0101] See also Figure 4 , Figure 4 Schematic diagram of the process of the short circuit risk assessment method of the energy storage power station battery provided in the embodiment of the present application. Figure 4 As shown, the method comprises the following steps:

[0102] (1) Establishing an internal short-circuit model for lithium batteries: Establishing an internal short-circuit model based on alternative experimental data, the battery factory charge and discharge curve data obtained, and real-time battery voltage, current, temperature and other data.

[0103] (2) Data collection: Collect real-time data such as battery voltage U, current I, SOC, temperature T, SOH (State Of Health) through BMS, collection gateway or EMS.

[0104] (3) Determine whether the data is abnormal: Difference between the battery U and T models and the measured U and T, and record the difference as ΔU and ΔT. The U and T error boundaries are determined by the internal short-circuit model, and further determine whether ΔU and ΔT exceed the boundaries. If they do not exceed the normal error, there is no short circuit. If they exceed the normal error, further determine whether there is an internal short circuit risk.

[0105] (4) Estimate the evolution rate of characteristic parameters: Estimate the changes in characteristic parameters such as SOC and internal resistance, and estimate the evolution rate of characteristic parameters.

[0106] (5) Determine the evolution rate boundary and characteristic parameter boundary of the internal short circuit characteristic parameter: Based on the internal short circuit model, the charge and discharge data and test data before leaving the factory are combined to determine the evolution rate boundary and characteristic parameter boundary of the internal short circuit characteristic parameter. For example, the temperature evolution rate boundary is ΔT b , the internal resistance evolution rate boundary is ΔR b , SOC evolution rate boundary is ΔR b etc., the internal resistance boundary is R max .

[0107] (6) Quantitative calculation of internal short circuit risk: set weights for each characteristic parameter and its evolution rate, and perform comprehensive calculations. The real-time distance between the characteristic parameter evolution rate and the characteristic parameter evolution rate boundary, as well as the real-time distance between the characteristic parameter and the characteristic parameter boundary, are used to quantitatively represent the internal short circuit risk (ISR).

[0108] (7) Output diagnosis results: The diagnosis outputs the internal short circuit risk ISR and SOH correction value, and further corrects the SOC, SOH, etc. of the battery cell, battery module, and battery cluster.

[0109] In addition, internal short circuit risk rating: internal short circuit risk is divided into three levels: high risk, medium risk and low risk according to the percentage of internal short circuit risk.

[0110] In addition, the machine learning model is trained and optimized: the internal short-circuit model is trained and optimized according to the changes in characteristic parameters such as SOC and internal resistance and real-time data.

[0111] The method for assessing the internal short-circuit risk of a battery in an energy storage power station provided in an embodiment of the present application first collects real-time battery data through a battery management system, a collection gateway or an EMS server, and then estimates the characteristic parameter evolution rate when it is determined that the real-time battery data is abnormal based on a battery internal short-circuit model, and determines the characteristic parameter evolution rate boundary and the characteristic parameter boundary. Finally, based on the real-time distance between the characteristic parameter evolution rate and its boundary, and the real-time distance between the real-time battery data and the characteristic parameter boundary, the internal short-circuit risk of the battery is quantitatively assessed.

[0112] The present application scheme uses an internal short-circuit model to estimate the evolution rate of characteristic parameters, determine boundaries, and calculate real-time distances, so as to accurately assess the risk of internal short circuits and provide early warning for the safety of batteries in energy storage power stations. It has strong ease of use and practicality.

[0113] The present application scheme is mainly to detect the risk level of internal short circuit in the early stage of thermal runaway of lithium battery, before lithium dendrites completely break through the diaphragm and cause internal short circuit, so as to actively operate and maintain and avoid accidents.

[0114] This application scheme is based on the internal short-circuit model, which can accurately trace the changes in the internal mechanism of the battery. Through the SOH correction value and the digitization of the internal short-circuit risk for each battery cell, the future trend changes of the battery can be accurately predicted. Risks are detected in advance to buy enough time for active battery operation and maintenance to avoid accidents. Comprehensive monitoring and analysis are carried out, and multi-level diagnosis is performed from battery cells, battery boxes to battery clusters.

[0115] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] Corresponding to the method of the above embodiment, Figure 5 1 is a schematic diagram of the structure of the short circuit risk assessment device for the energy storage power station battery provided in the embodiment of the present application. For the convenience of explanation, only the parts related to the embodiment of the present application are shown.

[0117] Reference Figure 5 , the device comprises:

[0118] The collection unit 501 is used to collect real-time battery data based on the battery management system, the collection gateway or the EMS server;

[0119] The processing unit 502 is used to determine whether the real-time battery data is abnormal based on the trained battery internal short-circuit model. If the real-time battery data is abnormal, the characteristic parameter evolution rate is estimated, and the characteristic parameter evolution rate boundary is determined based on the trained battery internal short-circuit model.

[0120] The evaluation unit 503 is used to quantitatively evaluate the internal short circuit risk of the battery based on the characteristic parameter evolution rate or real-time battery data after setting the weight and the real-time distance from the characteristic parameter evolution rate boundary, and output an evaluation value of the internal short circuit risk.

[0121] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0122] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0123] See also Figure 6 , Figure 6 Schematic diagram of the structure of the electronic device 6 provided in the embodiment of the present application. Figure 6 As shown, the electronic device 6 includes: at least one processor 601 ( Figure 6 Only one is shown in the figure), a memory 603, and a computer program 602 stored in the memory 603 and executable on at least one processor 601, wherein the processor 601 implements the steps in the above method embodiment when executing the computer program 602.

[0124] The electronic device 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a mobile phone. The electronic device 6 may include, but is not limited to, a processor 601 and a memory 603. Those skilled in the art will appreciate that Figure 6 It is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0125] The processor 601 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware photovoltaic modules, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0126] In some embodiments, the memory 603 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 603 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital card (SecureDigital, SD), a flash card (Flash Card), etc. equipped on the electronic device 6. Further, the memory 603 may also include both an internal storage unit of the electronic device 6 and an external storage device. The memory 603 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as program codes of a computer program. The memory 603 may also be used to temporarily store data that has been output or is to be output.

[0127] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, when the present application implements all or part of the processes in the above-mentioned embodiment method, it can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps applied to the method embodiment can be implemented. Among them, the computer program includes a computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may at least include: any entity or device that can carry the computer program code to the computing device / electronic device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, a computer-readable storage medium cannot be an electric carrier signal and a telecommunication signal.

[0128] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0129] An embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device executes the steps in the above-mentioned method embodiments.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0132] In the embodiments provided in the present application, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. The device / electronic device embodiments described above are merely schematic, and the division of the above modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or photovoltaic components can be combined or integrated into another system, and some features can be ignored and not executed. Another point is that the indirect coupling, direct coupling or communication connection between each other shown or discussed can be an indirect coupling, direct coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0133] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for assessing the risk of short circuit in a battery of an energy storage power station, characterized in that: Based on the implementation of the energy storage power station battery safety early warning system, the method includes: Collect real-time battery data based on battery management system, collection gateway or EMS server; Based on the trained battery internal short-circuit model, determine whether the real-time battery data is abnormal. If the real-time battery data is abnormal, estimate the characteristic parameter evolution rate, and determine the characteristic parameter evolution rate boundary and the characteristic parameter boundary; Based on the real-time distance between the characteristic parameter evolution rate after setting the weight and the boundary of the characteristic parameter evolution rate, and the real-time distance between the real-time battery data after setting the weight and the boundary of the characteristic parameter, the internal short circuit risk of the battery is quantitatively evaluated, and an evaluation value of the internal short circuit risk is output.

2. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 1, characterized in that: The real-time battery data includes: battery voltage, current, state of charge, temperature, health and internal resistance.

3. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 2, characterized in that: Based on the trained battery internal short circuit model, judging whether the real-time battery data is abnormal includes: Determine an error boundary based on the trained battery internal short-circuit model, and calculate the difference between the historical battery data and the real-time battery data; It is determined whether the difference exceeds the error boundary.

4. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 1, characterized in that: Determine the characteristic parameter evolution rate boundary and characteristic parameter boundary, including: Based on the trained battery internal short-circuit model, the characteristic parameter evolution rate boundary and the characteristic parameter boundary are determined by integrating the charge and discharge data and test data of the battery before leaving the factory.

5. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 1, characterized in that: The method further comprises: The abnormal real-time battery data is corrected and the correction value of the abnormal real-time battery data is output.

6. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 1, characterized in that: The method further comprises: According to the percentage of internal short circuit risk, the level of internal short circuit risk is divided into high risk, medium risk and low risk; The assessment value of the internal short circuit risk is rated to determine the risk level of the assessment value of the internal short circuit risk.

7. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 6, characterized in that: The method further comprises: The battery internal short-circuit model is trained and optimized according to the characteristic parameter evolution rate and the real-time battery data.

8. The method for assessing the risk of internal short circuit in a battery of an energy storage power station according to any one of claims 1 to 7, characterized in that: The system includes: a battery management system, a collection gateway, a communication switching device, an EMS server, a BSS server and a BSS client; The battery management system is used to collect real-time battery data; The acquisition gateway is used to collect real-time battery data collected by the battery management system; The communication exchange device is used to forward the real-time battery data to the EMS server; The EMS server is used to obtain the real-time battery data forwarded by the communication switching device, and store and display the real-time battery data; The BSS server is used to obtain the real-time battery data collected by the battery management system in real time based on the communication switching device, or to obtain the real-time battery data based on the EMS server, or to obtain the real-time battery data collected by the collection gateway based on the communication switching device; The BSS client is used to access the BSS server based on Ethernet and perform data viewing, display, management and setting operations in WEB mode.

9. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 8, characterized in that: The battery management system comprises: a battery management unit, a battery cluster management unit and a battery array management unit; The battery management unit is used to sample the voltage and temperature of the battery cell and use a differential UART daisy chain for communication and control; The battery cluster management unit is used to collect and monitor the battery cluster status, SOC estimation, fault diagnosis, charge and discharge control and balance control, main power circuit on-off control and battery thermal management; The battery array management unit is used to communicate with the EMS server and the PCS, manage the battery SOC protection logic, manage the coordinated operation of the battery system, and manage the information collection of the battery cluster management unit.

10. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 8, characterized in that: The communication switching device includes: a switch and a core switch, and the switch and the core switch are used to forward the real-time battery data.

11. The method for assessing the risk of short circuit in a battery of an energy storage power station according to claim 8, characterized in that: The BSS server is deployed with: BSS data processing software, BSS core algorithm and WEB server software, which are used for data transmission and reception, data cleaning, data calculation, algorithm training, internal short circuit risk calculation and providing WEB browser access.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for assessing the risk of short circuit in a battery of an energy storage power station as described in any one of claims 1 to 11 is implemented.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for assessing the risk of short circuit in a battery of an energy storage power station according to any one of claims 1 to 11 is implemented.

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