Fire extinguishing performance testing device for energy storage system fire extinguishing system

Abstract

This invention provides a fire extinguishing performance testing device for fire protection systems used in energy storage systems, belonging to the field of fire extinguishing performance testing technology. It includes a basic characteristic parameter acquisition module, a test scheme intelligent generation module, a runaway test condition simulation module, a multi-dimensional parameter acquisition and monitoring module, and a fire extinguishing performance evaluation and analysis module. The invention constructs an individual characteristic parameter set for the tested energy storage system through the basic characteristic parameter acquisition module, enabling the test scheme intelligent generation module to generate targeted dynamic time-series test schemes. This achieves precise matching between the test scheme and the actual operating conditions of the energy storage system, as well as dynamic simulation of the entire thermal runaway process. Through the multi-dimensional parameter acquisition and monitoring module and the fire extinguishing performance evaluation and analysis module, it achieves a comprehensive evaluation of fire extinguishing efficiency, cooling performance, and decontamination effectiveness across three dimensions. Based on the distance to the evaluation nodes, it automatically outputs the optimal fire-fighting strategy, improving the intelligence level and engineering application value of the testing device.

Description

Technical Field

[0001] This invention belongs to the field of fire extinguishing performance testing technology, specifically relating to a fire extinguishing performance testing device for a fire protection system used in an energy storage system. Background Technology

[0002] As a key component of the new power system, the safety of energy storage systems is directly related to the stable operation of the power grid and public safety. Lithium-ion battery energy storage systems may experience thermal runaway under extreme conditions such as overcharging, heating, nail penetration, or external fire, leading to fires or even explosions. Therefore, conducting scientific and comprehensive testing and evaluation of the fire-fighting performance of energy storage systems is an important means to ensure their safe operation.

[0003] However, existing fire-fighting performance testing devices for energy storage systems have the following technical shortcomings:

[0004] First, existing technologies typically use fixed, preset environmental conditions and thermal runaway triggering parameters for testing. They cannot generate targeted test plans based on the environmental and characteristic parameters of the energy storage system under test during actual use. This results in a deviation between the test scenario and the actual operating conditions of the energy storage system, and the test results cannot truly reflect the fire extinguishing performance of the fire protection system in actual applications.

[0005] Second, existing technologies often only simulate static conditions at a single moment during testing, ignoring the dynamic changes in environmental parameters and the energy storage system's own operating parameters over time during thermal runaway. This makes it impossible to truly reproduce the complex conditions of the entire thermal runaway development process, resulting in significant differences between test results and actual fire extinguishing effects.

[0006] Third, existing technologies for assessing fire extinguishing performance typically focus only on a single indicator (such as the time to extinguish open flames), lacking a comprehensive quantitative assessment of the three dimensions of fire extinguishing efficiency, cooling performance, and decontamination effectiveness, making it difficult to fully measure the overall fire extinguishing capability of a fire protection system.

[0007] Fourth, existing technologies can only output test data and cannot automatically analyze and output the optimal fire-fighting strategy for different thermal runaway triggering methods based on multiple sets of test results. The intelligence level and engineering application value of the test device are low. Summary of the Invention

[0008] This invention provides a fire extinguishing performance testing device for a fire protection system used in an energy storage system, in order to solve at least one of the technical problems mentioned above.

[0009] To address the aforementioned technical problems, this invention discloses a fire extinguishing performance testing device for a fire protection system used in energy storage systems, comprising:

[0010] The basic characteristic parameter acquisition module is used to acquire the characteristic parameter set of the energy storage system under test. The characteristic parameter set of the energy storage system under test includes the battery cell level parameters, battery module level parameters, and energy storage system level parameters of the energy storage system under test.

[0011] The test plan intelligent generation module is used to generate reference cases for the energy storage system under test based on the characteristic parameter set of the energy storage system under test and the feature matrix library of historical thermal runaway energy storage systems. Based on the accident records of each reference case, it generates the test environment operating condition time series vector and test thermal runaway characteristic time series vector of the energy storage system under test under each thermal runaway triggering mode, and generates the test plan for the energy storage system under test.

[0012] The runaway test condition simulation module is used to simulate environmental conditions based on the test environment condition time series vector of the energy storage system under test under various thermal runaway triggering methods, simulate thermal runaway characteristics based on the test thermal runaway characteristic time series vector of the energy storage system under test under various thermal runaway triggering methods, and simulate fire-fighting conditions through the fire extinguishing system of the energy storage system under test.

[0013] The multi-dimensional parameter acquisition and monitoring module is used to collect fire-related parameters, cooling-related parameters, and decontamination-related parameters of the fire-extinguishing system of the tested energy storage system under different thermal runaway triggering methods and different fire-extinguishing parameters during the runaway test simulation process.

[0014] The fire extinguishing performance evaluation and analysis module is used to evaluate the fire extinguishing efficiency, cooling performance and decontamination effectiveness of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters based on fire extinguishing related parameters, cooling related parameters and decontamination related parameters, and to find the optimal fire protection strategy with comprehensive performance under the corresponding thermal runaway triggering mode.

[0015] Preferably, the basic feature parameter acquisition module includes:

[0016] The battery cell level parameter acquisition submodule is used to acquire the positive electrode material type, electrolyte composition, rated capacity, and energy density of the battery cell.

[0017] The battery module-level parameter acquisition submodule is used to acquire the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module.

[0018] The energy storage system-level parameter acquisition submodule is used to acquire the expected operating ambient temperature range of the energy storage system, the type of installation location of the energy storage system, and the expected charge / discharge rate characteristics of the energy storage system.

[0019] Preferably, the intelligent test plan generation module includes:

[0020] The feature vector construction submodule is used to construct the battery cell feature vector, battery module feature vector, and energy storage system feature vector of the energy storage system under test based on the feature parameter set of the energy storage system under test, and combine them into the feature matrix of the energy storage system under test in a fixed order;

[0021] The case comparison and filtering submodule is used to calculate the weighted cosine similarity between the feature matrix of the tested energy storage system and the feature matrices of each historical thermal runaway energy storage system in the historical thermal runaway energy storage system feature matrix library, and to filter out the system with the highest weighted cosine similarity based on each weighted cosine similarity. The historical cases corresponding to the characteristic matrix of a historical thermal runaway energy storage system are used as reference cases;

[0022] The test feature vector generation submodule is used to generate the test environment condition time-series vector and test thermal runaway feature time-series vector of the tested energy storage system under each thermal runaway triggering mode based on the accident records of each reference case in the historical thermal runaway energy storage system feature matrix library.

[0023] The test scheme generation submodule is used to generate test schemes based on the test environment operating condition time-series vector and the test thermal runaway characteristic time-series vector of the energy storage system under test under various thermal runaway triggering modes.

[0024] Preferably, the feature vector construction submodule includes:

[0025] The battery cell feature vector construction unit is used to construct the battery cell feature vector of the energy storage system under test based on the positive electrode material type, electrolyte composition, rated capacity and energy density of the battery cells in the feature parameter set of the energy storage system under test.

[0026] The battery module feature vector construction unit is used to construct the battery module feature vector of the energy storage system under test based on the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module in the feature parameter set of the energy storage system under test.

[0027] The energy storage system feature vector construction unit is used to construct the energy storage system feature vector of the energy storage system under test based on the expected operating environment temperature range, installation location type, and expected charge / discharge rate characteristics of the energy storage system in the feature parameter set of the energy storage system under test.

[0028] The energy storage system feature matrix construction unit is used to combine the feature vectors of the battery cells, battery modules, and energy storage system of the energy storage system under test in a fixed order to form the feature matrix of the energy storage system under test.

[0029] Preferably, the test feature vector generation submodule includes:

[0030] The accident record extraction subunit extracts thermal runaway accident records, historical environmental condition parameter sets during thermal runaway, historical thermal runaway characteristic parameter sets, and historical fire extinguishing performance evaluation values ​​after thermal runaway from the historical thermal runaway energy storage system feature matrix library for each reference case, and determines the corresponding thermal runaway triggering mode based on the thermal runaway accident records.

[0031] The time-series vector generation unit is used to generate time-series vectors of historical environmental conditions and historical thermal runaway characteristics for each reference case based on the historical environmental condition parameter set and the historical thermal runaway characteristic parameter set during the thermal runaway process of each reference case.

[0032] The weighted centroid calculation subunit calculates the test environment operating condition time vector and test thermal runaway characteristic time vector of the energy storage system under test under the corresponding thermal runaway triggering mode for all reference cases with the same thermal runaway triggering mode using the weighted centroid method.

[0033] Preferably, the runaway test condition simulation module includes:

[0034] The environmental condition simulation submodule is used to adjust the temperature, humidity and salt spray concentration inside the chamber according to the time sequence vector of the test environment condition of the energy storage system under test under various thermal runaway triggering modes.

[0035] The thermal runaway characteristic simulation submodule is used to execute corresponding triggering actions according to the time sequence vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes. The triggering actions include overcharge triggering, heating triggering, needle penetration triggering or external fire triggering.

[0036] The fire-fighting condition simulation submodule is used to regulate and execute the fire-fighting parameters of the tested energy storage system under different thermal runaway triggering modes.

[0037] Preferably, the thermal runaway characteristic simulation submodule includes:

[0038] The basic operating parameter simulation unit is used to control the basic operating parameters of the energy storage system under test according to the timing sequence of the energy storage system voltage, current, power, temperature, SOC and internal resistance in the timing vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes.

[0039] The overcharge trigger simulation unit is used to perform overcharge trigger simulation according to the timing sequence of the equivalent charging rate, equivalent BMS voltage protection threshold, and equivalent BMS temperature protection threshold in the test thermal runaway characteristic timing vector when the thermal runaway triggering mode is overcharge triggering.

[0040] The heating-triggered simulation unit is used to perform heating-triggered simulation according to the time sequence of the equivalent heating power density and the equivalent heat source temperature in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is heating-triggered.

[0041] The needle-triggered simulation unit is used to perform needle-triggered simulation according to the time sequence based on the equivalent needle-triggered degree time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering method is needle-triggered.

[0042] The external fire-triggered simulation unit is used to perform external fire-triggered simulation according to the time sequence based on the equivalent flame temperature time sequence and equivalent heat flux density time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is external fire-triggered.

[0043] Preferably, the multi-dimensional parameter acquisition and monitoring module includes:

[0044] The fire extinguishing-related parameter acquisition submodule is used to collect the fire extinguishing system of the tested energy storage system from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and different fire extinguishing parameters. The time sequence of the open flame status of the tested energy storage system, the time sequence of the flame area of ​​the tested energy storage system, the start time of the fire extinguishing system, and the open flame extinguishing time of the fire extinguishing system are all collected.

[0045] The cooling-related parameter acquisition submodule is used to acquire the temperature time sequence, peak temperature, cooling rate, and thermal runaway propagation speed of the tested energy storage system during the fire extinguishing process from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and fire extinguishing parameters.

[0046] The decontamination-related parameter acquisition submodule is used to collect the time sequence of gas concentration in the chamber, the time sequence of smoke diffusion range in the chamber, and the detection results of electrolyte leakage at the bottom of the chamber during the decontamination process of the fire extinguishing system of the tested energy storage system from the end of fire extinguishing to the end of monitoring under different thermal runaway triggering methods and different fire extinguishing parameters.

[0047] Preferably, the fire extinguishing performance evaluation and analysis module includes:

[0048] The evaluation value acquisition submodule is used to input the fire extinguishing related parameters, cooling related parameters, and decontamination related parameters collected by the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters into the pre-trained fire extinguishing efficiency evaluation model, cooling performance evaluation model, and decontamination efficiency evaluation model corresponding to different thermal runaway triggering modes, so as to obtain the fire extinguishing efficiency evaluation value, cooling performance evaluation value, and decontamination efficiency evaluation value of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters;

[0049] The performance space construction submodule is used to construct a three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods. It maps the fire extinguishing efficiency evaluation value, cooling performance evaluation value and decontamination efficiency evaluation value obtained from each test under different fire extinguishing parameters to the three-dimensional performance space coordinate system of the corresponding thermal runaway triggering method, forming the evaluation node for each test under different fire extinguishing parameters.

[0050] The fire protection strategy optimization submodule is used to calculate the distance from each evaluation node in the three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods to the origin of the corresponding three-dimensional performance space coordinate system after completing the tests of the fire protection system of all tested energy storage systems under different thermal runaway triggering methods and different fire protection parameters. The evaluation node with the largest distance is selected as the fire protection strategy with the best comprehensive performance under the thermal runaway triggering method.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] This invention constructs a set of individual characteristic parameters for the tested energy storage system through a basic characteristic parameter acquisition module, enabling the intelligent test scheme generation module to generate targeted dynamic time-series test schemes. This achieves precise matching between the test scheme and the actual operating conditions of the energy storage system, as well as dynamic simulation of the entire thermal runaway process. Through a multi-dimensional parameter acquisition and monitoring module and a fire extinguishing performance evaluation and analysis module, it achieves a comprehensive evaluation of fire extinguishing efficiency, cooling performance, and decontamination effectiveness in three dimensions, and automatically outputs the optimal fire-fighting strategy based on the distance of the evaluation nodes, thereby improving the intelligence level and engineering application value of the testing device. This invention solves the technical problems of traditional technologies, such as the inability to generate targeted test schemes based on the characteristics of the tested object, the inability to realistically reproduce the complex operating conditions of the entire thermal runaway development process, the single evaluation dimension, and the low level of intelligence and engineering application value. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a schematic diagram of a fire extinguishing performance testing device for a fire protection system used in an energy storage system according to the present invention. Detailed Implementation

[0055] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0056] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions and features of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If a combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0057] The present invention provides the following embodiments.

[0058] Example 1

[0059] This invention provides a fire extinguishing performance testing device for a fire protection system used in an energy storage system, such as... Figure 1 As shown, it includes:

[0060] The basic characteristic parameter acquisition module is used to acquire the characteristic parameter set of the energy storage system under test. The characteristic parameter set of the energy storage system under test includes the battery cell level parameters, battery module level parameters, and energy storage system level parameters of the energy storage system under test.

[0061] The test plan intelligent generation module is used to generate reference cases for the energy storage system under test based on the characteristic parameter set of the energy storage system under test and the feature matrix library of historical thermal runaway energy storage systems. Based on the accident records of each reference case, it generates the test environment operating condition time series vector and test thermal runaway characteristic time series vector of the energy storage system under test under each thermal runaway triggering mode, and generates the test plan for the energy storage system under test.

[0062] The runaway test condition simulation module is used to simulate environmental conditions based on the test environment condition time series vector of the energy storage system under test under various thermal runaway triggering methods, to simulate thermal runaway characteristics based on the test thermal runaway characteristic time series vector of the energy storage system under test under various thermal runaway triggering methods, and to simulate fire-fighting conditions through the fire extinguishing system of the energy storage system under test.

[0063] The multi-dimensional parameter acquisition and monitoring module is used to collect fire-related parameters, cooling-related parameters, and decontamination-related parameters of the fire-extinguishing system of the tested energy storage system under different thermal runaway triggering methods and different fire-extinguishing parameters during the runaway test simulation process.

[0064] The fire extinguishing performance evaluation and analysis module is used to evaluate the fire extinguishing efficiency, cooling performance and decontamination effectiveness of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters based on fire extinguishing related parameters, cooling related parameters and decontamination related parameters, and to find the optimal fire protection strategy with comprehensive performance under the corresponding thermal runaway triggering mode.

[0065] In this embodiment, the energy storage system under test includes at least one battery cluster, each battery cluster includes at least one battery module, and each battery module includes at least one battery cell (i.e., a battery cell).

[0066] In this embodiment, the basic characteristic parameter acquisition module communicates with the battery management system of the energy storage system under test through a data interface, and is used to read the battery cell level parameters, battery module level parameters and energy storage system level parameters of the energy storage system under test.

[0067] Battery cell level parameters include:

[0068] (1) Type of positive electrode material of battery cell Discrete numerical coding is used, with different codes corresponding to different cathode material types in individual battery cells. The coding rules are as follows: lithium iron phosphate is coded as 1, lithium nickel cobalt manganese oxide (ternary material) as 2, lithium nickel cobalt aluminum oxide as 3, lithium manganese oxide as 4, lithium titanate as 5, sodium ion layered oxide as 6, Prussian blue analogue as 7, oxide solid electrolyte as 8, sulfide solid electrolyte as 9, and polymer solid electrolyte as 10. If the tested energy storage system uses multiple cathode materials simultaneously (such as a hybrid cell design), then... Encoded as a collection of multiple encoded values, for example This indicates that both lithium iron phosphate and ternary materials are used.

[0069] (2) Electrolyte composition of battery cells Discrete numerical coding is used, and different electrolyte compositions in individual battery cells correspond to different codes. The coding rule is: containing... The liquid electrolyte is coded as 1, containing The coding for liquid electrolytes is 2, for gel electrolytes it is 3, for oxide solid electrolytes it is 4, for sulfide solid electrolytes it is 5, and for polymer solid electrolytes it is 6; if the energy storage system under test uses multiple electrolyte components simultaneously, then Encoded as a collection of multiple encoded values, for example This indicates that both liquid electrolyte and gel electrolyte are used simultaneously.

[0070] (3) Rated capacity of individual battery cells The unit is ampere-hour (Ah), and the measured value or nominal value should be used, for example... .

[0071] (4) Energy density of a single battery cell The unit is watt-hours per kilogram (Wh / kg), and the measured value or nominal value should be used, for example... .

[0072] In this embodiment, the battery module level parameters include:

[0073] Types of cooling structures for battery modules Composite feature encoding is used, which encodes the data into a four-dimensional binary vector. ,in Indicates whether liquid cooling plate is used (1 for yes, 0 for no). Indicates whether air-cooled aisle cooling is used (1 for yes, 0 for no). Indicates whether phase change material cooling is used (1 for yes, 0 for no). Indicates whether natural convection cooling is used (1 for yes, 0 for no); for example, when both liquid plate cooling and air-cooled aisle cooling are used simultaneously. .

[0074] Spacing between adjacent battery cells within a battery module The unit is millimeters (mm), and the measured value or design value is used, for example... .

[0075] Types of heat insulation measures for battery modules Composite feature encoding is used, which encodes the data into a five-dimensional binary vector. ,in Indicates whether an aerogel insulation pad is used (1 for yes, 0 for no). Indicates whether mica board is used (1 for yes, 0 for no). Indicates whether ceramic fiber paper is used (1 for yes, 0 for no). Indicates whether a fire-retardant coating is used (1 for yes, 0 for no). Indicates whether a composite insulation layer is used (1 for yes, 0 for no); for example, when ceramic fiber paper and fire-retardant coating are used simultaneously. .

[0076] Battery module pressure relief valve design parameters Composite feature encoding is used to encode a three-dimensional vector. ,in The number of pressure relief valves. Pressure to be released (unit: MPa). Encode the direction of pressure relief (1 for top pressure relief, 2 for side pressure relief, 3 for bottom pressure relief); for example... This indicates that there are two pressure relief valves, with an opening pressure of 0.5 MPa and pressure relief direction is from the top.

[0077] In this embodiment, the parameters at the energy storage system level include:

[0078] Expected operating temperature range of energy storage systems The unit is degrees Celsius (°C). These are the minimum and maximum values ​​of the expected operating environment temperature, for example... This indicates that it is expected to operate in an environment ranging from -20℃ to 45℃.

[0079] Types of installation locations for energy storage systems Discrete numerical coding is used; the coding rules are as follows: inland plains are coded as 1, coastal areas (within 5 kilometers of the coastline) are coded as 2, high-altitude areas (above 2000 meters) are coded as 3, offshore platforms are coded as 4, desert areas are coded as 5, and high-altitude cold areas are coded as 6; if the installation location has multiple attributes (such as being both a coastal area and a high-altitude area), then... Encoded as a collection of multiple encoded values, for example .

[0080] Expected charge / discharge rate characteristics of energy storage systems The unit is C, and the value is the design value or typical operating value, for example. This indicates a charge / discharge rate of 0.5C.

[0081] In this embodiment, the characteristic parameter set of the energy storage system under test is represented as follows: .

[0082] In this embodiment, the feature matrix library of historical thermal runaway energy storage systems stores... The characteristic matrix of a historical thermal runaway energy storage system.

[0083] In this embodiment, the reference case is the thermal runaway case of a historical thermal runaway energy storage system that is closest to the parameter set of the characteristic parameter set of the energy storage system under test, stored in the feature matrix library of historical thermal runaway energy storage systems.

[0084] In this embodiment, the test environment operating condition time sequence vector of the energy storage system under test under each thermal runaway triggering mode includes the temperature time sequence, humidity time sequence and salt spray concentration time sequence inside the chamber during the subsequent runaway test operating condition simulation process.

[0085] In this embodiment, the test thermal runaway characteristic time sequence vector of the energy storage system under each thermal runaway triggering mode includes the voltage time sequence, current time sequence, power time sequence, temperature time sequence, SOC time sequence, internal resistance time sequence, and equivalent heat source parameter time sequence corresponding to the thermal runaway triggering mode of the energy storage system under test during the subsequent runaway test simulation process.

[0086] This invention, by setting up a basic feature parameter acquisition module, collects the battery cell level parameters, battery module level parameters, and energy storage system level parameters of the energy storage system under test in a structured manner according to the physical hierarchy, constructing a feature parameter set that can completely characterize the individual features of the energy storage system under test. This feature parameter set serves as the data foundation for the intelligent test scheme generation module to perform similarity matching with a feature matrix library of historical thermal runaway energy storage systems. This enables the intelligent test scheme generation module to generate a test scheme for the current energy storage system under test based on accident records of historical energy storage systems that have experienced thermal runaway with similar feature parameters. This allows the test scenario to reflect the essential characteristics of the energy storage system under test during actual use, solving the technical problem that traditional technologies cannot generate targeted test schemes based on the characteristics of the tested object.

[0087] This invention forms a complete technical closed loop from characteristic parameter acquisition, historical case matching, test scheme generation, operational condition simulation to performance evaluation by setting up a test scheme intelligent generation module, a runaway test condition simulation module, a multi-dimensional parameter acquisition and monitoring module, and a fire extinguishing performance evaluation and analysis module. Specifically, the test scheme intelligent generation module generates test environment condition time-series vectors and test thermal runaway characteristic time-series vectors for the tested energy storage system under various thermal runaway triggering methods based on accident records of reference cases in a historical thermal runaway energy storage system characteristic matrix library. This enables the test scheme to simulate the complete dynamic process from before to after thermal runaway. The runaway test condition simulation module adjusts the chamber temperature, humidity, and salt spray concentration according to the time sequence based on the test environment condition time-series vector, and executes overcharge triggering, heating triggering, needle penetration triggering, or external fire triggering according to the time sequence based on the test thermal runaway characteristic time-series vector. This achieves accurate simulation of the dynamic changes of environmental parameters and the energy storage system's own operating parameters over time during thermal runaway, solving the technical problem that traditional technologies cannot realistically reproduce the complex operational conditions of the entire thermal runaway development process.

[0088] This invention achieves a comprehensive quantitative evaluation of three dimensions: fire extinguishing efficiency, cooling performance, and decontamination effectiveness, by setting up a multi-dimensional parameter acquisition and monitoring module and a fire extinguishing performance evaluation and analysis module. During runaway test simulations, the multi-dimensional parameter acquisition and monitoring module collects fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters of the tested energy storage system under different thermal runaway triggering methods and fire extinguishing parameters. Based on these multi-dimensional parameters, the fire extinguishing performance evaluation and analysis module outputs fire extinguishing efficiency evaluation values, cooling performance evaluation values, and decontamination effectiveness evaluation values, achieving a comprehensive quantitative evaluation of the fire protection system's overall fire extinguishing capability and solving the technical problem of traditional technologies having only one evaluation dimension.

[0089] This invention, by setting up a fire extinguishing performance evaluation and analysis module, realizes the output of optimization strategies based on multiple sets of test results. The fire extinguishing performance evaluation and analysis module maps the fire extinguishing efficiency evaluation values, cooling performance evaluation values, and decontamination efficiency evaluation values ​​obtained from multiple tests under different thermal runaway triggering methods and different fire extinguishing parameters to form evaluation nodes in a three-dimensional performance space coordinate system. By calculating the distance from each evaluation node to the origin, the evaluation node with the largest distance is selected as the optimal fire-fighting strategy for that thermal runaway triggering method, directly outputting the optimal fire-fighting strategy for different thermal runaway triggering methods. This enables the testing device not only to output test data but also to provide optimization guidance, solving the technical problems of low intelligence level and low engineering application value of traditional technologies.

[0090] Example 2

[0091] Based on Example 1, the basic feature parameter acquisition module includes:

[0092] The battery cell level parameter acquisition submodule is used to acquire the positive electrode material type, electrolyte composition, rated capacity, and energy density of the battery cell.

[0093] The battery module-level parameter acquisition submodule is used to acquire the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module.

[0094] The energy storage system-level parameter acquisition submodule is used to acquire the expected operating ambient temperature range of the energy storage system, the type of installation location of the energy storage system, and the expected charge / discharge rate characteristics of the energy storage system.

[0095] The beneficial effects of the above technical solution are as follows: By setting up a battery cell level parameter acquisition submodule, a battery module level parameter acquisition submodule, and an energy storage system level parameter acquisition submodule, the present invention realizes the orderly collection and structured organization of characteristic parameters of the tested energy storage system, providing comprehensive data support for the subsequent case comparison and screening submodule to accurately select the most similar historical cases from the historical thermal runaway energy storage system characteristic matrix library.

[0096] Example 3

[0097] Based on Example 1, the intelligent test plan generation module includes:

[0098] The feature vector construction submodule is used to construct the battery cell feature vector, battery module feature vector, and energy storage system feature vector of the energy storage system under test based on the feature parameter set of the energy storage system under test, and combine them into the feature matrix of the energy storage system under test in a fixed order;

[0099] The case comparison and filtering submodule is used to calculate the weighted cosine similarity between the feature matrix of the tested energy storage system and the feature matrices of each historical thermal runaway energy storage system in the historical thermal runaway energy storage system feature matrix library, and to filter out the system with the highest weighted cosine similarity based on each weighted cosine similarity. The historical cases corresponding to the characteristic matrix of a historical thermal runaway energy storage system are used as reference cases;

[0100] The test feature vector generation submodule is used to generate the test environment condition time-series vector and test thermal runaway feature time-series vector of the tested energy storage system under each thermal runaway triggering mode based on the accident records of each reference case in the historical thermal runaway energy storage system feature matrix library.

[0101] The test scheme generation submodule is used to generate test schemes based on the test environment operating condition time-series vector and the test thermal runaway characteristic time-series vector of the energy storage system under test under various thermal runaway triggering modes.

[0102] In this embodiment, the feature matrix library of historical thermal runaway energy storage systems Stored The feature matrix of each historical thermal runaway energy storage system includes the following information: the feature vector of each battery cell in the historical thermal runaway energy storage system. Characteristic vectors of battery modules in energy storage systems with historical thermal runaway Characteristic vector of energy storage systems with historical thermal runaway The characteristic matrix of the j-th historical thermal runaway energy storage system in the historical thermal runaway energy storage system characteristic matrix library is represented as follows: :

[0103] ;

[0104] in, Represents the feature vector of a single battery cell in the energy storage system that experienced thermal runaway in the j-th historical period; Represents the feature vector of the battery module in the energy storage system that experienced the j-th historical thermal runaway; The energy storage system feature vector represents the energy storage system that experienced thermal runaway in the j-th historical period.

[0105] ;in, These represent the cathode material type, electrolyte composition, rated capacity, and energy density of the battery cell in the energy storage system that experienced the j-th historical thermal runaway, respectively.

[0106] ;in, These represent the cooling structure type of the battery module in the energy storage system with the j-th historical thermal runaway, the spacing between adjacent battery cells in the battery module, the type of thermal insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module, respectively.

[0107] ;in, These represent the expected operating temperature range, installation location type, and expected charge / discharge rate characteristics of the energy storage system that experienced the j-th historical thermal runaway.

[0108] In this embodiment, the characteristic matrix of the energy storage system under test Compare with the historical feature matrices in the historical thermal runaway energy storage system feature matrix library. The similarity calculation formula is as follows:

[0109] ;

[0110] in: express The Middle Line number Column elements, express The Middle Line number Column elements; For the first Line number The weight coefficients corresponding to the elements of the column; For a valid value indicator function, when and Take when neither is missing Otherwise take ; For elements The modulus is taken as the absolute value for a scalar, the Euclidean norm for a vector, the total number of elements for a set, and the average value for an interval. for and The similarity function between two elements is defined as follows:

[0111] If the element type is a scalar numeric (e.g.) Then the similarity function ;

[0112] If the element type is a collection (such as...) If the similarity function is Jaccard similarity, then the similarity function will be used. ,express and The size of the intersection and and The ratio of the sizes of the union;

[0113] If the element type is a binary vector (e.g.) Then the similarity function ,in, express and Hamming distance between two elements Divide by the length of the binary vector The binary vector elements are hour The value is 4, and the binary vector elements are... hour The value is 5;

[0114] If the element type is a three-dimensional vector (e.g.) Then the similarity function ,express and Cosine similarity;

[0115] If the element type is a range (e.g.) Then the similarity function , This represents the maximum possible width of the temperature range. These represent the characteristic matrices of the energy storage system. The corresponding minimum and maximum values ​​of the expected operating environment temperature; Representing the historical feature matrix respectively The corresponding minimum and maximum values ​​of the expected operating environment temperature.

[0116] In this embodiment, a test plan is generated based on the test environment operating condition time-series vector and the test thermal runaway characteristic time-series vector of the energy storage system under test under various thermal runaway triggering modes, including:

[0117] Suppose the types of thermal runaway triggering methods that have appeared in the reference case are: kind( For each triggering method If at least one reference case has occurred under this triggering method, a test plan is generated. The test plan includes the thermal runaway triggering method and the corresponding test environment operating condition timing vector and test thermal runaway characteristic timing vector of the energy storage system under test.

[0118] The beneficial effects of the above technical solution are as follows: by setting up a feature vector construction submodule, a case comparison and screening submodule, a test feature vector generation submodule, and a test scheme generation submodule, intelligent test scheme generation based on historical cases is realized. The feature vector construction submodule transforms the feature parameter set of the energy storage system under test into a feature matrix. The case comparison and screening submodule selects the reference case most similar to the energy storage system under test from the feature matrix library of historical thermal runaway energy storage systems through weighted cosine similarity calculation. The test feature vector generation submodule generates test environment condition time-series vectors and test thermal runaway feature time-series vectors for the tested energy storage system under various thermal runaway triggering modes based on the actual thermal runaway accident records in the reference case (including the time-series changes of environmental operating parameters and the energy storage system's own operating parameters during thermal runaway). The reference case is a real energy storage system that has experienced thermal runaway, and its time-series data during the thermal runaway process reflects the actual evolution law of thermal runaway. Applying these time-series data to the test scheme of the tested energy storage system enables the test scheme to simulate the complete dynamic process from before to after thermal runaway, thereby significantly improving the consistency between the test results and the actual fire extinguishing effect. The test scheme generation submodule generates test schemes based on the test environment condition time-series vectors and test thermal runaway feature time-series vectors of the tested energy storage system under various thermal runaway triggering modes, providing complete parameter inputs for the execution of the subsequent runaway test condition simulation module.

[0119] Example 4

[0120] Based on Example 3, the feature vector construction submodule includes:

[0121] The battery cell feature vector construction unit is used to construct the battery cell feature vector of the energy storage system under test based on the positive electrode material type, electrolyte composition, rated capacity and energy density of the battery cells in the feature parameter set of the energy storage system under test.

[0122] The battery module feature vector construction unit is used to construct the battery module feature vector of the energy storage system under test based on the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module in the feature parameter set of the energy storage system under test.

[0123] The energy storage system feature vector construction unit is used to construct the energy storage system feature vector of the energy storage system under test based on the expected operating environment temperature range, installation location type, and expected charge / discharge rate characteristics of the energy storage system in the feature parameter set of the energy storage system under test.

[0124] The energy storage system feature matrix construction unit is used to combine the feature vectors of the battery cells, battery modules, and energy storage system of the energy storage system under test in a fixed order to form the feature matrix of the energy storage system under test.

[0125] In this embodiment, the feature vector of a single battery cell Defined as: ;

[0126] Battery module feature vector Defined as: ;

[0127] Energy storage system feature vector Defined as: .

[0128] In this embodiment, the energy storage system feature matrix Defined as stacking three feature vectors row by row:

[0129] ;

[0130] Among them, the energy storage system characteristic matrix Missing positions are filled with 1.

[0131] The beneficial effects of the above technical solution are as follows: By setting up a battery cell feature vector construction unit, a battery module feature vector construction unit, an energy storage system feature vector construction unit, and an energy storage system feature matrix construction unit, the present invention provides a directly calculable numerical basis for the weighted cosine similarity calculation of the case comparison and screening submodule.

[0132] Example 5

[0133] Based on Example 3, the test feature vector generation submodule includes:

[0134] The accident record extraction subunit extracts thermal runaway accident records, historical environmental condition parameter sets during thermal runaway, historical thermal runaway characteristic parameter sets, and historical fire extinguishing performance evaluation values ​​after thermal runaway from the historical thermal runaway energy storage system feature matrix library for each reference case, and determines the corresponding thermal runaway triggering mode based on the thermal runaway accident records.

[0135] The time-series vector generation unit is used to generate time-series vectors of historical environmental conditions and historical thermal runaway characteristics for each reference case based on the historical environmental condition parameter set and the historical thermal runaway characteristic parameter set during the thermal runaway process of each reference case.

[0136] The weighted centroid calculation subunit calculates the test environment operating condition time vector and test thermal runaway characteristic time vector of the energy storage system under test under the corresponding thermal runaway triggering mode for all reference cases with the same thermal runaway triggering mode using the weighted centroid method.

[0137] In this embodiment, the feature matrix library of historical thermal runaway energy storage systems stores, in addition to... In addition to the characteristic matrix of a historical thermal runaway energy storage system, it also stores:

[0138] (1) Records of thermal runaway accidents of historical cases corresponding to each historical thermal runaway energy storage system Specifically, these include: "overcharge protection failure leading to continuous charging," "charging voltage exceeding the upper limit," "uncontrolled charging current," "external short circuit causing overheating," "internal short circuit," "heating element malfunction," "mechanical puncture," "diaphragm rupture due to compression," "foreign object insertion," "ignition by external fire source," "spread of fire from surrounding areas," and "burning by open flame." Indicates the first A record of historical thermal runaway accidents.

[0139] (2) Thermal runaway accident record and corresponding thermal runaway triggering method The values ​​include overcharge trigger, heating trigger, needle puncture trigger, or external fire trigger;

[0140] If thermal runaway accident record The description of "overcharge protection failure leading to continuous charging", "charging voltage exceeding the upper limit" or "charging current runaway" indicates the thermal runaway triggering method. Triggered by overcharge;

[0141] If accident record If described as "external short circuit causing overheating," "internal short circuit triggering," or "heating element malfunction," then the thermal runaway triggering method is... Triggered by heating;

[0142] If accident record The thermal runaway triggering method is described as "mechanical puncture", "diaphragm rupture due to compression" or "foreign object insertion". Triggered by needle prick;

[0143] If accident record The thermal runaway triggering method is described as "ignition by an external fire source", "fire spreading from a surrounding area", or "burning by an open flame". Triggered by external fire;

[0144] in, Indicates the first The thermal runaway triggering method in historical cases.

[0145] (3) Set of historical environmental parameters during thermal runaway ,in , This is a time series (°C) of historical ambient temperature during the thermal runaway process. This is the historical relative humidity time series (%) during the thermal runaway process. This is a time series (%) of historical salt spray concentration during thermal runaway; each time series records data within the 300 seconds preceding thermal runaway at a fixed sampling frequency, such as 1 Hz; among which, Indicates the first A set of historical environmental operating condition parameters for each historical case.

[0146] (4) Set of historical thermal runaway characteristic parameters It contains time-series changes in the energy storage system's own operating parameters and external trigger parameters during the accident, and consists of two parts:

[0147] Part 1: Time-series sequences of basic operating parameters of energy storage systems during historical thermal runaway events, obtained directly from sensors at the historical thermal runaway accident sites, including: voltage time-series sequences of the energy storage system during thermal runaway. The unit is V (obtained from voltage data recorded by the BMS of the historical energy storage system before and during the accident); the current timing sequence of the energy storage system during thermal runaway. The unit is amperes (A) (obtained from current data recorded by the BMS of the historical energy storage system before and during the accident); the power time series sequence of the energy storage system during thermal runaway. , The unit is kW; the temperature time series of the energy storage system during thermal runaway. The unit is °C (obtained through thermocouples placed on the surface of the historical energy storage system); the SOC time series of the energy storage system during thermal runaway. The unit is % (obtained from the battery charge percentage recorded by the BMS of the historical energy storage system before and during the accident); the internal resistance time series of the energy storage system during thermal runaway. , The unit is mΩ;

[0148] in, Indicates the first A set of historical thermal runaway characteristic parameters from historical cases; , , , , , They represent the first The historical case corresponds to the following timing sequences during thermal runaway: voltage, current, power, temperature, SOC, and internal resistance of the energy storage system.

[0149] Part Two: Triggering Mechanisms and Equivalent Heat Source Parameter Time Series of Historical Thermal Runaway Energy Storage Systems

[0150] (1) If thermal runaway is triggered by... For overcharge triggering, the corresponding equivalent heat source parameter timing sequence includes the equivalent charging rate timing sequence (in C), the equivalent BMS voltage protection threshold timing sequence (in V), and the equivalent BMS temperature protection threshold timing sequence (in °C).

[0151] No. Equivalent charge rate timing sequence of thermal runaway processes in historical cases Calculation, where For the first The rated capacity of the energy storage system corresponding to each historical case, and the equivalent charging rate reflects the charging stress intensity that the corresponding energy storage system was subjected to when the accident occurred.

[0152] No. Equivalent BMS voltage protection threshold in thermal runaway processes of historical cases , For the first Voltage timing sequence of energy storage system during thermal runaway corresponding to historical cases The voltage value before the last significant voltage drop point before the thermal runaway is taken as the equivalent BMS voltage protection threshold value throughout the entire thermal runaway process. This constant;

[0153] No. Equivalent BMS temperature protection threshold in thermal runaway processes of historical cases , The value is the first Temperature time series of energy storage systems during thermal runaway corresponding to historical cases. The temperature value before the last significant temperature rise point before the thermal runaway is triggered, and the equivalent BMS temperature protection threshold during the entire thermal runaway process is the temperature value before the last significant temperature rise point before the thermal runaway is triggered.

[0154] Thermal runaway triggering methods The set of historical thermal runaway characteristic parameters corresponding to overcharge triggering. Represented as: .

[0155] If thermal runaway is triggered by... For heating triggering, the corresponding equivalent heat source parameter time series includes the equivalent heating power density time series (unit: W / cm²). 2 Equivalent heat source temperature time series (unit: °C);

[0156] No. Equivalent heating power density time series of thermal runaway processes in historical cases ;in, For the first The heat capacity (in J / ℃) of the energy storage system corresponding to each historical case. For the first The rate of temperature change of the energy storage system during thermal runaway accidents corresponding to these historical cases. For the first The heat dissipation coefficient (in W / ℃) of the energy storage system corresponding to each historical case. For the first The ambient temperature (in °C) corresponding to each historical case. The heating element area (in cm²) used in subsequent test simulations 2 );

[0157] No. Equivalent heat source temperature time series of thermal runaway processes in historical cases ,in, The thermal conductivity of the heating element material is expressed in W / (m·K). The thickness of the heating element is expressed in meters (m).

[0158] Thermal runaway triggering methods The set of historical thermal runaway characteristic parameters corresponding to the heating trigger. Represented as: .

[0159] (3) If thermal runaway is triggered by... For acupuncture-triggered, the corresponding equivalent heat source parameter time series includes the equivalent acupuncture degree time series (dimensionless).

[0160] Define voltage drop rate The unit is V / s. The faster the voltage drops, the more severe the needle puncture damage.

[0161] Define current spike coefficient , dimensionless, reflects the relative intensity of instantaneous current impact; express The maximum value in;

[0162] Define the rate of temperature rise The unit is ℃ / s. The faster the temperature rises, the more intense the internal short circuit heating is.

[0163] Define the internal resistance mutation coefficient Dimensionless, reflecting the relative change in internal resistance, where This corresponds to the moment when thermal runaway begins in the first second.

[0164] Then the first Equivalent needle penetration time series of thermal runaway processes in historical cases ;in These are the weighting coefficients corresponding to the voltage drop rate, current spike coefficient, temperature rise rate, and defined internal resistance abrupt change coefficient, respectively, satisfying... (In this embodiment, all values ​​are taken as 0.25); where, express The maximum value, express The maximum value, express The maximum value.

[0165] Thermal runaway triggering methods This refers to the set of historical thermal runaway characteristic parameters corresponding to the triggering of acupuncture. Represented as: .

[0166] (4) If thermal runaway is triggered by... For external fire triggering, the corresponding equivalent heat source parameter time series includes the equivalent flame temperature time series (in °C) and the equivalent heat flux density time series (in kW / m³). 2 );

[0167] No. Equivalent flame temperature time series of thermal runaway processes in historical cases The value is obtained through The video footage of fire scenes from historical accident sites was analyzed frame by frame to assign values ​​to the flame colors. For example, bright red flames correspond to 800-1000℃, orange-yellow to 1100-1200℃, and white to 1300-1500℃. Values ​​were assigned based on the dominant color of the flame in each frame, with different values ​​corresponding to different dominant colors. This process can be further refined by analyzing the flame colors within each frame. This conclusion was drawn from analyzing fire scene videos of historical accident cases.

[0168] No. Equivalent heat flux density time series of thermal runaway processes in historical cases ;in: The Stefan-Boltzmann constant has a value of [value missing]. ; The flame emissivity of the propane burner used in subsequent test simulations is dimensionless; in this embodiment, it is taken as 0.95.

[0169] Thermal runaway triggering methods The set of historical thermal runaway characteristic parameters corresponding to external fire triggering. Represented as: .

[0170] All triggering methods have specific parameter timing sequences that are related to Recorded with the same sampling frequency and time range.

[0171] (5) Historical fire extinguishing performance evaluation value after thermal runaway is completed Values arrive The decimal values ​​are used to assess the actual fire extinguishing effect, with 0 indicating complete failure and 1 indicating perfect fire extinguishing.

[0172] In this embodiment, the reference index set is set as follows: Each reference case is indicated by a subscript. The corresponding characteristic matrix is ​​denoted as . The historical thermal runaway accident record corresponding to the k-th reference case is represented as follows: Thermal runaway accident record The corresponding thermal runaway triggering method is represented as follows: The set of historical environmental parameters during thermal runaway is represented as follows: The set of historical thermal runaway characteristic parameters is represented as follows: The historical fire extinguishing performance evaluation value after thermal runaway is expressed as follows: .

[0173] In this embodiment, for the reference case The time-series vector of historical environmental operating condition parameters corresponding to the k-th reference case. ;in, , , The first Historical ambient temperature time series, historical relative humidity time series, and historical salt spray concentration time series of one reference case;

[0174] In this embodiment, the thermal runaway triggering method corresponding to the kth reference case is the time series vector of historical thermal runaway characteristic parameters corresponding to overcharge triggering. ;

[0175] In this embodiment, the thermal runaway triggering method corresponding to the kth reference case is the time series vector of historical thermal runaway characteristic parameters corresponding to the heating triggering. ;

[0176] In this embodiment, the thermal runaway triggering method corresponding to the kth reference case is the time series vector of historical thermal runaway characteristic parameters corresponding to the needle-triggered trigger. ;

[0177] In this embodiment, the thermal runaway triggering method corresponding to the kth reference case is the time series vector of historical thermal runaway characteristic parameters corresponding to external fire triggering. .

[0178] In this embodiment, for all reference cases with the same thermal runaway triggering method, the weighted centroid method is used to calculate the test environment condition time-series vector and the test thermal runaway characteristic time-series vector of the energy storage system under test corresponding to the thermal runaway triggering method, including:

[0179] For each thermal runaway triggering method Collect all reference cases For the case where the index set is... ;

[0180] like If not empty, then for each time point of the time series vector... The weighted centroid method was used to calculate the test environment operating condition time-series vector of the energy storage system under test in this triggering mode. and test thermal runaway characteristic time vector :

[0181] ;

[0182] Among them, weight ( For the first Fire extinguishing performance evaluation values ​​for one reference case. This indicates complete failure. (representing perfection), this weight makes the test scenario with the worst historical fire extinguishing performance account for a larger proportion in the center of mass, thus causing the final environmental conditions and thermal runaway characteristics to tilt towards the high-risk conditions;

[0183] like If the value is empty (i.e., the thermal runaway triggering method does not exist in the reference case), then no test plan will be generated for this triggering method, and this triggering method will not be included in the subsequent test plan generation.

[0184] Assuming there are 5 reference cases, their thermal runaway triggering methods are as follows: Reference Case 1: Overcharge triggering, fire extinguishing performance evaluation value Reference Case 2: Overcharge Trigger, Fire Extinguishing Performance Assessment Value Reference Case 3: Heating Trigger, Fire Extinguishing Performance Evaluation Value Reference Case 4: External fire triggering, fire extinguishing performance assessment value Reference Case 5: External fire triggering, fire extinguishing performance assessment value ;

[0185] For overcharge triggering, we collect case 1 and case 2, with weights respectively. , For each time point Test environment operating condition time series vector Test thermal runaway characteristic time-series vector Similarly, and Weighted summation; for heating-triggered events, only in Case 3, the weights are... ,but , For external fire triggers, cases 4 and 5 were collected, with weights... , The calculation methods for the test environment condition timing vector and the test thermal runaway characteristic timing vector are the same as those for overcharge triggering; for needle penetration triggering, if there is no reference case, no test plan corresponding to needle penetration triggering will be generated.

[0186] The final test plan includes three test schemes: overcharge trigger scheme, heating trigger scheme, and external fire trigger scheme. Each scheme includes the corresponding triggering method. and .

[0187] The beneficial effects of the above technical solution are as follows: By setting up an accident record extraction subunit, a time-series vector generation subunit, and a weighted centroid calculation subunit, the present invention realizes intelligent generation of test feature vectors based on historical cases of accident records. The accident record extraction subunit extracts thermal runaway accident records, historical environmental condition parameter sets, historical thermal runaway characteristic parameter sets, and historical fire extinguishing performance evaluation values ​​for each reference case from the historical thermal runaway energy storage system feature matrix library. The time-series vector generation unit organizes the above data into time-series vectors, so that the thermal runaway process of each reference case is preserved in a complete time-series form. The weighted centroid calculation subunit uses the weighted centroid method to weight and fuse the historical environmental condition parameter time-series vectors and historical thermal runaway characteristic parameter time-series vectors of each reference case under the same thermal runaway triggering mode according to the historical fire extinguishing performance evaluation values. The weight is the reciprocal of the historical fire extinguishing performance evaluation value. The lower the historical fire extinguishing performance evaluation value, the more dangerous the thermal runaway process or the more difficult the fire extinguishing of the reference case, and the greater its weight. This makes the generated test scheme automatically tilted towards high-risk conditions. The test environmental condition time-series vector and test thermal runaway characteristic time-series vector output by the weighted centroid calculation subunit provide parameter input for the test scheme generation subunit, while avoiding the technical problem that traditional test schemes are prone to omitting high-risk conditions.

[0188] Example 6

[0189] Based on Example 1, the runaway test condition simulation module includes:

[0190] The environmental condition simulation submodule is used to adjust the temperature, humidity and salt spray concentration inside the chamber according to the time sequence vector of the test environment condition of the energy storage system under test under various thermal runaway triggering modes.

[0191] The thermal runaway characteristic simulation submodule is used to execute corresponding triggering actions according to the time sequence vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes. The triggering actions include overcharge triggering, heating triggering, needle penetration triggering or external fire triggering.

[0192] The fire-fighting condition simulation submodule is used to regulate and execute the fire-fighting parameters of the tested energy storage system under different thermal runaway triggering modes.

[0193] In this embodiment, the environmental condition simulation submodule includes an environmental simulation chamber, which is based on the environmental condition time-series vector of the current test plan. Adjust cabin temperature according to time sequence ,humidity and salt spray concentration The control logic is as follows: at each time point... Adjust environmental parameters (including temperature, humidity, and salt spray concentration) to The corresponding value is maintained until the next time point; salt spray concentration adjustment is achieved through an atomizing device.

[0194] In this embodiment, the specific triggering action is related to the accident records of the reference cases. For example, if there is no needle puncture trigger in the accident records of all reference cases, the thermal runaway characteristic simulation submodule will not perform needle puncture trigger simulation.

[0195] The beneficial effects of the above technical solution are as follows: By setting up environmental condition simulation submodules, thermal runaway characteristic simulation submodules, and fire-fighting condition simulation submodules, the test scheme is executed in all dimensions. The environmental condition simulation submodule generates a time-series vector of the test environment conditions based on the test scheme's output subunits, and adjusts the cabin temperature, humidity, and salt spray concentration according to the time sequence. The thermal runaway characteristic simulation submodule generates a time-series vector of the test thermal runaway characteristics based on the test scheme's output subunits, and executes overcharge triggering, heating triggering, needle penetration triggering, or external fire triggering according to the time sequence. The fire-fighting condition simulation submodule regulates and executes the corresponding fire-fighting parameters of the tested energy storage system's fire-fighting system. The three submodules work collaboratively through a time synchronization mechanism, ensuring precise alignment of environmental conditions, thermal runaway characteristics, and fire-fighting actions in the time dimension, realistically reproducing the entire process of thermal runaway and fire-fighting in the energy storage system. This design provides a realistic test scenario for the subsequent multi-dimensional parameter acquisition and monitoring module to collect multi-dimensional parameters during the fire-fighting process, avoiding the technical problem of distorted test results caused by time asynchrony in traditional testing devices.

[0196] Example 7

[0197] Based on Example 6, the thermal runaway characteristic simulation submodule includes:

[0198] The basic operating parameter simulation unit is used to control the basic operating parameters of the energy storage system under test according to the timing vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes, including the voltage timing sequence, current timing sequence, power timing sequence, temperature timing sequence, SOC timing sequence and internal resistance timing sequence of the energy storage system under test.

[0199] The overcharge trigger simulation unit is used to perform overcharge trigger simulation according to the timing sequence of the equivalent charging rate, equivalent BMS voltage protection threshold, and equivalent BMS temperature protection threshold in the test thermal runaway characteristic timing vector when the thermal runaway triggering mode is overcharge triggering.

[0200] The heating-triggered simulation unit is used to perform heating-triggered simulation according to the time sequence of the equivalent heating power density and the equivalent heat source temperature in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is heating-triggered.

[0201] The needle-triggered simulation unit is used to perform needle-triggered simulation according to the time sequence based on the equivalent needle-triggered degree time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering method is needle-triggered.

[0202] The external fire-triggered simulation unit is used to perform external fire-triggered simulation according to the time sequence based on the equivalent flame temperature time sequence and equivalent heat flux density time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is external fire-triggered.

[0203] In this embodiment, the basic operating parameter simulation unit adjusts various basic operating parameters of the energy storage system under test in the following ways:

[0204] The voltage of the energy storage system under test during the simulation is achieved through a programmable DC power supply. The output of the programmable DC power supply is connected to the positive and negative terminals of the energy storage system under test, and the voltage is determined based on the timing vector of the thermal runaway characteristics during the test. The corresponding Time series, at each time point Set the output voltage value of the programmable DC power supply so that the voltage across the energy storage system under test is equal to... Consistent.

[0205] The current of the energy storage system under test during the simulation is achieved through a programmable electronic load. The programmable electronic load is connected to the output of the energy storage system under test, and the current is measured based on the timing vector of the thermal runaway characteristics. The corresponding Time series, at each time point Set the current absorption value of the programmable electronic load so that the current flowing through the energy storage system under test is equal to the current absorbed by the programmable electronic load. Consistent.

[0206] During the simulation, the power of the energy storage system under test is determined by the voltage of the energy storage system under test. and the current of the energy storage system under test Joint decision, During the simulation, the power timing is automatically achieved through the coordinated control of the voltage and current described above.

[0207] During the simulation, the temperature of the energy storage system under test is controlled in a closed loop using heating elements (such as silicone heating pads) embedded in the surface of the individual battery cells and temperature sensors. At each time point... The temperature sensor collects the current temperature of the tested energy storage system in real time, and compares it with the time-series vector of the thermal runaway characteristics. The corresponding In comparison, the power of the heating element is adjusted by a PID controller to make the temperature of the tested energy storage system similar to that of the controlled system. Consistent.

[0208] During the simulation, the State of Charge (SOC) of the energy storage system under test is achieved through a charge / discharge management system. The charge / discharge management system is based on the time-series vector of the thermal runaway characteristics tested. The corresponding The timing sequence, combined with the current voltage and current, automatically calculates and controls the charging and discharging strategy to ensure that the SOC value of the tested energy storage system is consistent with the actual value. Consistent.

[0209] During the simulation, the internal resistance of the energy storage system under test is monitored using a built-in AC injection method internal resistance monitoring module. At each time point... The internal resistance monitoring module injects a small-amplitude AC signal into the energy storage system under test, measures the response voltage and current, calculates the internal resistance value, and compares it with the timing vector of the thermal runaway characteristics. The corresponding Comparison and verification are performed. If the actual internal resistance deviates too much from the target internal resistance, it is corrected by adjusting the temperature or charging / discharging state to make the internal resistance of the tested energy storage system more consistent with the target internal resistance. Consistent.

[0210] In this embodiment, the execution of the overcharge-triggered simulation specifically includes: based on the test thermal runaway characteristic time vector... Equivalent charging rate Equivalent BMS voltage protection threshold and equivalent BMS temperature protection threshold Perform overcharge trigger simulation: at each time point Programmable charging device according to The timing sequence sets the charging current so that the charging rate is consistent with... Consistent; Temporarily set voltage protection threshold in BMS and temperature protection threshold Real-time monitoring of the voltage and temperature of the tested energy storage system; when the voltage exceeds... or temperature exceeds When the circuit is activated, the BMS immediately triggers a protection action to cut off the charging circuit; during the simulation, the voltage, temperature and protection trigger status are continuously recorded.

[0211] In this embodiment, the execution of the heating-triggered simulation specifically includes: the heating-triggered simulation unit performing the simulation based on the timing vector of the thermal runaway characteristics. Equivalent heating power density in and equivalent heat source temperature Perform heating-triggered simulation: Install a silicone heating pad on the surface of the battery cell of the energy storage system under test. The silicone heating pad is in close contact with the surface of the battery cell of the energy storage system under test, and the area of ​​the heating pad is [area missing]. At each point in time ,according to Calculation of heating power using time series: The actual output power of the heating element is adjusted by the power controller to match its output power. Consistent; at the same time, according to The surface temperature of the silicone heating element is controlled by a timing sequence. Closed-loop feedback control is achieved through thermocouples arranged on the surface of the silicone heating element, ensuring that the heating element temperature is synchronized with the surface temperature of the silicone heating element. Consistent.

[0212] In this embodiment, the execution of the needle-triggered simulation specifically includes: based on the test thermal runaway characteristic time vector... Equivalent acupuncture degree Perform needle puncture trigger simulation: A servo-controlled puncture mechanism is installed above the energy storage system under test, and the puncture needle is aligned with the predetermined puncture position; at each time point... The equivalent acupuncture degree The intensity of the puncture mechanism's action is mapped to, including, the puncture depth. ,in The preset maximum puncture depth and puncture force ,in The preset maximum puncture force and puncture speed ,in The preset maximum puncture speed; based on the mapped parameters, the servo controller drives the puncture mechanism to perform puncture actions according to a time sequence; when When the puncture mechanism does not move or remains in its initial position; when At that time, perform the maximum intensity puncture.

[0213] In this embodiment, the execution of the external fire-triggered simulation specifically includes: the external fire-triggered simulation unit performs the simulation based on the test thermal runaway characteristic time vector. Equivalent flame temperature and equivalent heat flux density Perform external fire-triggered simulation: Install a propane burner array in front of or to the side of the energy storage system under test, with the propane burner nozzles facing the energy storage system under test; at each time point... ,according to Dynamic flame temperature adjustment via timing sequence: The flame temperature output by the burner is controlled by adjusting the mixing ratio of propane and air, and closed-loop control is achieved through high-temperature thermocouples arranged in the flame zone; according to... Timing sequence adjustment of heat flux density: By adjusting the distance between the burner and the energy storage system, the number of burners, or the flame intensity, the heat flux density applied to the surface of the energy storage system under test is adjusted to match the... Consistent, monitored and fed back through heat flow sensors deployed on the surface of the energy storage system under test; when or When the time comes, turn off the burner.

[0214] The beneficial effects of the above technical solution are as follows: By setting up a basic operating parameter simulation unit, an overcharge trigger simulation unit, a heating trigger simulation unit, a needle puncture trigger simulation unit, and an external fire trigger simulation unit, the present invention achieves refined simulation of thermal runaway characteristics under different thermal runaway triggering modes.

[0215] Example 8

[0216] Based on Example 1, the multi-dimensional parameter acquisition and monitoring module includes:

[0217] The fire extinguishing-related parameter acquisition submodule is used to collect the fire extinguishing system of the tested energy storage system from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and different fire extinguishing parameters. The time sequence of the open flame status of the tested energy storage system, the time sequence of the flame area of ​​the tested energy storage system, the start time of the fire extinguishing system, and the open flame extinguishing time of the fire extinguishing system are all collected.

[0218] The cooling-related parameter acquisition submodule is used to acquire the temperature time sequence, peak temperature, cooling rate, and thermal runaway propagation speed of the tested energy storage system during the fire extinguishing process from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and fire extinguishing parameters.

[0219] The decontamination-related parameter acquisition submodule is used to collect the time sequence of gas concentration in the chamber, the time sequence of smoke diffusion range in the chamber, and the detection results of electrolyte leakage at the bottom of the chamber during the decontamination process of the fire extinguishing system of the tested energy storage system from the end of fire extinguishing to the end of monitoring under different thermal runaway triggering methods and different fire extinguishing parameters.

[0220] In this embodiment, the fire extinguishing-related parameter acquisition submodule is implemented through an image recording unit: eight high-speed cameras are arranged outside the observation windows around the test chamber, pointing at different angles of the energy storage system under test, to record the flame propagation process, and the open flame status time sequence of the energy storage system under test is output through image processing algorithms. (0 indicates no open flame, 1 indicates open flame) and the flame area time series of the tested energy storage system. (Unit: pixels or m) 2 Three panoramic monitoring devices are installed on the top of the cabin to record the overall fire development process inside the cabin. Through image processing algorithms, they automatically identify the activation time of the fire extinguishing system. (Time from thermal runaway triggering to the start of fire suppression system activation) and the time for the fire suppression system to extinguish open flames. (The time from the activation of the fire extinguishing device to the complete extinguishing of the flame).

[0221] In this embodiment, the cooling-related parameter acquisition submodule is implemented through a temperature monitoring unit and an electrical parameter monitoring unit: thermocouple probes are arranged on the positive electrode tab surface, negative electrode tab surface, center point of the battery cell shell, inner wall of the battery module shell, air inlet and outlet of the battery cluster, and top and bottom of the cabin of each battery cell in the energy storage system under test, to collect temperature data at each measuring point and output the temperature time series sequence of the energy storage system under test. (Average temperature of each battery cell's positive electrode tab surface, negative electrode tab surface, battery cell casing center point, battery module casing inner wall, battery cluster air inlet and outlet, and various measuring points at the top and bottom of the chamber); Five infrared thermal imagers are installed at the observation windows on the top and side walls of the test chamber, with their lenses aimed at the energy storage system under test, to collect two-dimensional temperature distribution data on the surface of the energy storage system under test, assisting in determining the peak temperature of the energy storage system under test. By analyzing temperature data, the cooling rate of the tested energy storage system is calculated. (Unit: °C / s) and the thermal runaway propagation speed of the tested energy storage system (Calculated by the time difference and spacing between adjacent thermocouples when their temperatures reach the threshold).

[0222] In this embodiment, the decontamination-related parameter acquisition submodule is implemented through a gas monitoring unit and an image recording unit: the Fourier transform infrared spectrometer sampling probe is installed at the exhaust port of the test chamber to detect the composition and concentration of the gas inside the chamber in real time and output the time series sequence of the gas concentration inside the chamber. The focus is on HF concentration; an electrochemical sensor array is installed at different heights within the chamber (0.5m, 1.5m, and 2.5m from the bottom) to detect the concentrations and spatial distribution of more than 20 characteristic gases, including HF, CO, CO2, and VOCs, in real time. Image processing analysis of the video captured by the panoramic monitoring equipment outputs a time-series sequence of the flue gas diffusion range within the chamber. (Unit: m) 2 The system detects electrolyte leakage by using level sensors and pH meters located at the bottom of the tank, and outputs the electrolyte leakage detection results. (0 indicates no leakage, 1 indicates leakage);

[0223] All monitoring data are aligned via a time synchronization unit, and the time synchronization error is controlled within a specified range. Within the range.

[0224] The beneficial effects of the above technical solution are as follows: By setting up sub-modules for collecting fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters, the present invention achieves accurate collection of multi-dimensional parameters during the fire extinguishing process, providing complete and synchronous multi-dimensional data support for the subsequent evaluation value acquisition sub-module.

[0225] Example 9

[0226] Based on Example 8, the fire extinguishing performance evaluation and analysis module includes:

[0227] The evaluation value acquisition submodule is used to input the fire extinguishing related parameters, cooling related parameters, and decontamination related parameters collected by the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters into the pre-trained fire extinguishing efficiency evaluation model, cooling performance evaluation model, and decontamination efficiency evaluation model corresponding to different thermal runaway triggering modes, so as to obtain the fire extinguishing efficiency evaluation value, cooling performance evaluation value, and decontamination efficiency evaluation value of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters;

[0228] The performance space construction submodule is used to construct a three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods. It maps the fire extinguishing efficiency evaluation value, cooling performance evaluation value and decontamination efficiency evaluation value obtained from each test under different fire extinguishing parameters to the three-dimensional performance space coordinate system of the corresponding thermal runaway triggering method, forming the evaluation node for each test under different fire extinguishing parameters.

[0229] The fire protection strategy optimization submodule is used to calculate the distance from each evaluation node in the three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods to the origin of the corresponding three-dimensional performance space coordinate system after completing the tests of the fire protection system of all tested energy storage systems under different thermal runaway triggering methods and different fire protection parameters. The evaluation node with the largest distance is selected as the fire protection strategy with the best comprehensive performance under the thermal runaway triggering method.

[0230] In this embodiment, the three-dimensional performance space coordinate system corresponds to different thermal runaway triggering methods. of Axis indicates triggering method The fire extinguishing efficiency dimension, with a range of values. ; Axis indicates triggering method The following is a range of values ​​for cooling performance. ; Axis indicates triggering method The following is a range of values ​​for the disinfection efficiency dimension. ;

[0231] For each thermal runaway triggering method The three evaluation values ​​obtained from each test (i.e., each fire suppression parameter configuration) will be used to evaluate the fire suppression parameters. This is mapped to an evaluation node in the performance space, with the evaluation node's coordinates as follows: ,in, Thermal runaway triggering method Next The fire extinguishing efficiency evaluation value of this test. Thermal runaway triggering method Next The cooling performance evaluation value of this test. Thermal runaway triggering method Next The evaluation value of the disinfection efficiency of this test.

[0232] In this embodiment, the pre-trained fire extinguishing efficiency evaluation models corresponding to different thermal runaway triggering methods include an overcharge-triggered fire extinguishing efficiency evaluation model, a heating-triggered fire extinguishing efficiency evaluation model, a needle-triggered fire extinguishing efficiency evaluation model, or an external fire-triggered fire extinguishing efficiency evaluation model.

[0233] The overcharge-triggered fire extinguishing efficiency assessment model is a neural network model trained using a large number of fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters collected under a large number of different fire extinguishing parameters (such as different extinguishing agent types, spray pressure, spray angle, flow rate, duration, etc.) under the overcharge triggering mode in the historical test database as inputs, and fire extinguishing efficiency assessment values, cooling performance assessment values, and decontamination effectiveness assessment values ​​evaluated by experts as outputs. The specific neural network training process is as follows: a large number of test samples with different fire extinguishing parameter configurations (including extinguishing agent type, spray pressure, spray angle, flow rate, duration, etc.) under the overcharge triggering mode are extracted from the historical test database. The input features of each sample are the open flame status time sequence, flame area time sequence, fire extinguishing system start time, and open flame extinguishing time collected by the fire extinguishing-related parameter acquisition submodule, the temperature time sequence, peak temperature, cooling rate, and thermal runaway propagation speed collected by the cooling-related parameter acquisition submodule, and the in-cabin gas concentration time sequence, in-cabin smoke diffusion range time sequence, and bottom electrolyte leakage detection results collected by the decontamination-related parameter acquisition submodule. After normalizing the input features, they are fed into a three-layer fully connected neural network with 64, 32, and 16 neurons in the hidden layers, using ReLU activation. The output layer consists of a single neuron with Sigmoid activation, and the output value is the fire extinguishing efficiency evaluation value under the specified fire extinguishing parameters. During network training, the fire extinguishing efficiency evaluation value assessed by experts based on actual fire extinguishing effects is used as the label. Mean squared error is used as the loss function, and gradient descent is performed using the Adam optimizer. The batch size is set to 32, the training epochs are 200, and the initial learning rate is 0.001, decreasing by 0.5 times every 50 epochs. After training, the model parameters are saved for obtaining evaluation values ​​during subsequent testing.

[0234] The heating-triggered fire extinguishing efficiency assessment model uses fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters collected under a large number of different fire extinguishing parameters in a historical test database as input, and fire extinguishing efficiency assessment values, cooling performance assessment values, and decontamination effectiveness assessment values ​​evaluated by experts as outputs. The model is trained using a neural network. The specific neural network training process is as follows: A large number of test samples with different fire extinguishing parameter configurations under the heating-triggered mode are extracted from the historical test database. The input features of each sample are the open flame status time series, flame area time series, fire extinguishing system activation time, open flame extinguishing time, temperature time series, peak temperature, cooling rate, thermal runaway propagation speed, cabin gas concentration time series, cabin smoke diffusion range time series, and bilge electrolyte leakage detection results collected by the multi-dimensional parameter acquisition and monitoring module under the heating-triggered mode. After normalizing the input features, they are input into a three-layer fully connected neural network with 64, 32, and 16 neurons in the hidden layers, using ReLU as the activation function, and a single Sigmoid neuron in the output layer, outputting the fire extinguishing efficiency assessment value. The training labels are expert-assessed fire extinguishing efficiency evaluation values. The loss function is mean squared error. The optimizer is Adam, the batch size is 32, the training epochs are 200, the initial learning rate is 0.001, and it decays to 0.5 times the original rate every 50 epochs. The model parameters are saved after training.

[0235] The needle-triggered fire extinguishing efficiency assessment model is a neural network model trained using a large number of fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters collected under a large number of different fire extinguishing parameters in the historical test database under the needle-triggered mode as input, and fire extinguishing efficiency assessment values, cooling performance assessment values, and decontamination effectiveness assessment values ​​evaluated by experts as output. The specific neural network training process is as follows: a large number of test samples with different fire extinguishing parameter configurations under the needle-triggered mode are extracted from the historical test database. The input features of each sample are the open flame status time sequence, flame area time sequence, fire extinguishing system start time, open flame extinguishing time, temperature time sequence, peak temperature, cooling rate, thermal runaway propagation speed, cabin gas concentration time sequence, cabin smoke diffusion range time sequence, and bilge electrolyte leakage detection results collected by the multi-dimensional parameter acquisition and monitoring module under the needle-triggered mode. The input features are normalized and fed into a three-layer fully connected neural network with 64, 32, and 16 neurons in the hidden layers, using ReLU activation. The output layer consists of a single sigmoid neuron, outputting the fire extinguishing efficiency evaluation value. The training labels are expert-assessed fire extinguishing efficiency evaluation values. The loss function is mean squared error, the optimizer is Adam, the batch size is 32, the training epochs are 200, the initial learning rate is 0.001, and it decays to 0.5 times the original rate every 50 epochs. The model parameters are saved after training.

[0236] The external fire-triggered fire extinguishing efficiency assessment model is a model obtained by training a neural network model with inputs of fire extinguishing-related parameters, cooling-related parameters, and decontamination-related parameters collected under a large number of different fire extinguishing parameters under the external fire triggering mode in the historical test database, and outputs fire extinguishing efficiency assessment values, cooling performance assessment values, and decontamination effectiveness assessment values ​​evaluated by experts. The specific neural network training process is as follows: a large number of test samples with different fire extinguishing parameter configurations under the external fire triggering mode are extracted from the historical test database. The input features of each sample are the open flame status time series, flame area time series, fire extinguishing system start time, open flame extinguishing time, temperature time series, peak temperature, cooling rate, thermal runaway propagation speed, cabin gas concentration time series, cabin smoke diffusion range time series, and bilge electrolyte leakage detection results collected by the multi-dimensional parameter acquisition and monitoring module under the external fire triggering mode. The input features are normalized and fed into a three-layer fully connected neural network with 64, 32, and 16 neurons in the hidden layers, using ReLU activation. The output layer consists of a single sigmoid neuron, outputting the fire extinguishing efficiency evaluation value. The training labels are expert-assessed fire extinguishing efficiency evaluation values. The loss function is mean squared error, the optimizer is Adam, the batch size is 32, the training epochs are 200, the initial learning rate is 0.001, and it decays to 0.5 times the original rate every 50 epochs. The model parameters are saved after training.

[0237] In this embodiment, the optimal fire-fighting strategy includes the fire-extinguishing parameters of the fire-extinguishing system of the tested energy storage system with the best overall performance under each thermal runaway triggering mode, that is, the fire-extinguishing parameters corresponding to the evaluation node with the largest distance.

[0238] The beneficial effects of the above technical solution are as follows: This invention achieves quantitative evaluation of the fire extinguishing performance of the fire protection system and outputs the optimal strategy by setting an evaluation value acquisition submodule, a performance space construction submodule, and a fire protection strategy optimization submodule. By calculating the distance from each evaluation node to the coordinate origin, and selecting the evaluation node with the largest distance as the comprehensive performance optimal fire protection strategy under the thermal runaway triggering mode, in this design, the coordinate origin represents the complete failure state. The farther the evaluation node is from the origin, the better the comprehensive performance in the three dimensions of fire extinguishing efficiency, cooling performance, and decontamination efficiency. Therefore, the node with the largest distance is the comprehensive performance optimal fire protection strategy. This strategy is directly output to the fire protection condition simulation submodule, enabling the testing device to automatically analyze and output the optimal fire protection strategy for different thermal runaway triggering modes based on multiple sets of test results.

[0239] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A device for testing the fire extinguishing performance of a fire extinguishing system for an energy storage system, characterized in that include: The basic characteristic parameter acquisition module is used to acquire the characteristic parameter set of the energy storage system under test. The characteristic parameter set of the energy storage system under test includes the battery cell level parameters, battery module level parameters, and energy storage system level parameters of the energy storage system under test. The test plan intelligent generation module is used to generate reference cases for the energy storage system under test based on the characteristic parameter set of the energy storage system under test and the feature matrix library of historical thermal runaway energy storage systems. Based on the accident records of each reference case, it generates the test environment operating condition time series vector and test thermal runaway characteristic time series vector of the energy storage system under test under each thermal runaway triggering mode, and generates the test plan for the energy storage system under test. The runaway test condition simulation module includes: The environmental condition simulation submodule is used to adjust the temperature, humidity and salt spray concentration inside the chamber according to the time sequence vector of the test environment condition of the energy storage system under test under various thermal runaway triggering modes. The thermal runaway characteristic simulation submodule is used to execute corresponding triggering actions according to the time sequence vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes. The triggering actions include overcharge triggering, heating triggering, needle penetration triggering or external fire triggering. The fire-fighting condition simulation submodule is used to regulate and execute the fire-fighting parameters of the tested energy storage system under different thermal runaway triggering modes. The multi-dimensional parameter acquisition and monitoring module is used to collect fire-related parameters, cooling-related parameters, and decontamination-related parameters of the fire-extinguishing system of the tested energy storage system under different thermal runaway triggering methods and different fire-extinguishing parameters during the runaway test simulation process. The fire extinguishing performance evaluation and analysis module is used to evaluate the fire extinguishing efficiency, cooling performance and decontamination effectiveness of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters based on fire extinguishing related parameters, cooling related parameters and decontamination related parameters, and to find the optimal fire protection strategy with comprehensive performance under the corresponding thermal runaway triggering mode.

2. The fire extinguishing performance testing device for a fire extinguishing system of an energy storage system according to claim 1, characterized in that: The basic feature parameter acquisition module includes: The battery cell level parameter acquisition submodule is used to acquire the positive electrode material type, electrolyte composition, rated capacity, and energy density of the battery cell. The battery module-level parameter acquisition submodule is used to acquire the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module. The energy storage system-level parameter acquisition submodule is used to acquire the expected operating ambient temperature range of the energy storage system, the type of installation location of the energy storage system, and the expected charge / discharge rate characteristics of the energy storage system.

3. The fire extinguishing performance testing device for a fire protection system for an energy storage system according to claim 1, characterized in that: The intelligent test plan generation module includes: The feature vector construction submodule is used to construct the battery cell feature vector, battery module feature vector, and energy storage system feature vector of the energy storage system under test based on the feature parameter set of the energy storage system under test, and combine them into the feature matrix of the energy storage system under test in a fixed order; The case comparison and filtering submodule is used to calculate the weighted cosine similarity between the feature matrix of the tested energy storage system and the feature matrices of each historical thermal runaway energy storage system in the historical thermal runaway energy storage system feature matrix library, and to filter out the historical cases corresponding to the K historical thermal runaway energy storage system feature matrices with the highest weighted cosine similarity based on each weighted cosine similarity, and use them as reference cases. The test feature vector generation submodule is used to generate the test environment condition time-series vector and test thermal runaway feature time-series vector of the tested energy storage system under each thermal runaway triggering mode based on the accident records of each reference case in the historical thermal runaway energy storage system feature matrix library. The test scheme generation submodule is used to generate test schemes based on the test environment operating condition time-series vector and the test thermal runaway characteristic time-series vector of the energy storage system under test under various thermal runaway triggering modes.

4. The device for testing the fire extinguishing performance of a fire extinguishing system for an energy storage system according to claim 3, characterized in that The feature vector construction submodule includes: The battery cell feature vector construction unit is used to construct the battery cell feature vector of the energy storage system under test based on the positive electrode material type, electrolyte composition, rated capacity and energy density of the battery cells in the feature parameter set of the energy storage system under test. The battery module feature vector construction unit is used to construct the battery module feature vector of the energy storage system under test based on the cooling structure type of the battery module, the spacing between adjacent battery cells in the battery module, the type of heat insulation measures of the battery module, and the design parameters of the pressure relief valve of the battery module in the feature parameter set of the energy storage system under test. The energy storage system feature vector construction unit is used to construct the energy storage system feature vector of the energy storage system under test based on the expected operating environment temperature range, installation location type, and expected charge / discharge rate characteristics of the energy storage system in the feature parameter set of the energy storage system under test. The energy storage system feature matrix construction unit is used to combine the feature vectors of the battery cells, battery modules, and energy storage system of the energy storage system under test in a fixed order to form the feature matrix of the energy storage system under test.

5. The device for testing the fire extinguishing performance of a fire extinguishing system for an energy storage system according to claim 3, characterized in that The test feature vector generation submodule includes: The accident record extraction subunit extracts thermal runaway accident records, historical environmental condition parameter sets during thermal runaway, historical thermal runaway characteristic parameter sets, and historical fire extinguishing performance evaluation values ​​after thermal runaway from the historical thermal runaway energy storage system feature matrix library for each reference case, and determines the corresponding thermal runaway triggering mode based on the thermal runaway accident records. The time-series vector generation unit is used to generate time-series vectors of historical environmental conditions and historical thermal runaway characteristics for each reference case based on the historical environmental condition parameter set and the historical thermal runaway characteristic parameter set during the thermal runaway process of each reference case. The weighted centroid calculation subunit calculates the test environment operating condition time vector and test thermal runaway characteristic time vector of the energy storage system under test under the corresponding thermal runaway triggering mode for all reference cases with the same thermal runaway triggering mode using the weighted centroid method.

6. The device of claim 1, wherein: The thermal runaway characteristic simulation submodule includes: The basic operating parameter simulation unit is used to control the basic operating parameters of the energy storage system under test according to the timing sequence of the energy storage system voltage, current, power, temperature, SOC and internal resistance in the timing vector of the test thermal runaway characteristics of the energy storage system under various thermal runaway triggering modes. The overcharge trigger simulation unit is used to perform overcharge trigger simulation according to the timing sequence of the equivalent charging rate, equivalent BMS voltage protection threshold, and equivalent BMS temperature protection threshold in the test thermal runaway characteristic timing vector when the thermal runaway triggering mode is overcharge triggering. The heating-triggered simulation unit is used to perform heating-triggered simulation according to the time sequence of the equivalent heating power density and the equivalent heat source temperature in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is heating-triggered. The needle-triggered simulation unit is used to perform needle-triggered simulation according to the time sequence based on the equivalent needle-triggered degree time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering method is needle-triggered. The external fire-triggered simulation unit is used to perform external fire-triggered simulation according to the time sequence based on the equivalent flame temperature time sequence and equivalent heat flux density time sequence in the test thermal runaway characteristic time sequence vector when the thermal runaway triggering mode is external fire-triggered.

7. The device of claim 1, wherein: The multi-dimensional parameter acquisition and monitoring module includes: The fire extinguishing-related parameter acquisition submodule is used to collect the fire extinguishing system of the tested energy storage system from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and different fire extinguishing parameters. The time sequence of the open flame status of the tested energy storage system, the time sequence of the flame area of ​​the tested energy storage system, the start time of the fire extinguishing system, and the open flame extinguishing time of the fire extinguishing system are all collected. The cooling-related parameter acquisition submodule is used to acquire the temperature time sequence, peak temperature, cooling rate, and thermal runaway propagation speed of the tested energy storage system during the fire extinguishing process from the start of thermal runaway triggering to the end of fire extinguishing under different thermal runaway triggering methods and fire extinguishing parameters. The decontamination-related parameter acquisition submodule is used to collect the time sequence of gas concentration in the chamber, the time sequence of smoke diffusion range in the chamber, and the detection results of electrolyte leakage at the bottom of the chamber during the decontamination process of the fire extinguishing system of the tested energy storage system from the end of fire extinguishing to the end of monitoring under different thermal runaway triggering methods and different fire extinguishing parameters.

8. The device for testing the fire extinguishing performance of a fire extinguishing system for an energy storage system according to claim 7, characterized in that The fire extinguishing performance evaluation and analysis module includes: The evaluation value acquisition submodule is used to input the fire extinguishing related parameters, cooling related parameters, and decontamination related parameters collected by the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters into the pre-trained fire extinguishing efficiency evaluation model, cooling performance evaluation model, and decontamination efficiency evaluation model corresponding to different thermal runaway triggering modes, so as to obtain the fire extinguishing efficiency evaluation value, cooling performance evaluation value, and decontamination efficiency evaluation value of the fire extinguishing system of the tested energy storage system under different thermal runaway triggering modes and different fire extinguishing parameters; The performance space construction submodule is used to construct a three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods. It maps the fire extinguishing efficiency evaluation value, cooling performance evaluation value and decontamination efficiency evaluation value obtained from each test under different fire extinguishing parameters to the three-dimensional performance space coordinate system of the corresponding thermal runaway triggering method, forming the evaluation node for each test under different fire extinguishing parameters. The fire protection strategy optimization submodule is used to calculate the distance from each evaluation node in the three-dimensional performance space coordinate system corresponding to different thermal runaway triggering methods to the origin of the corresponding three-dimensional performance space coordinate system after completing the tests of the fire protection system of all tested energy storage systems under different thermal runaway triggering methods and different fire protection parameters. The evaluation node with the largest distance is selected as the fire protection strategy with the best comprehensive performance under the thermal runaway triggering method.

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