Modeling method and device for thermal runaway jet fire radiation intensity of lithium ion battery

Abstract

The invention provides a modeling method for thermal runaway jet fire radiation intensity of a lithium ion battery, which comprises the following steps of: establishing a thermal runaway jet fire geometric model of the lithium ion battery based on size parameters and structural characteristics of the lithium ion battery, and performing grid division to obtain a thermal runaway jet fire grid model; assigning the thermal runaway jet fire grid model by setting model parameters related to radiation intensity in the thermal runaway process of the lithium ion battery to obtain a thermal runaway jet fire radiation intensity simulation model; thermal runaway jet fire under various working conditions is simulated in the thermal runaway jet fire radiation intensity simulation model, radiation intensity values under different models are solved, and an analogue simulation result is obtained; and based on the analogue simulation result, taking the environment temperature, the gas production component, the jet orifice pressure, the jet orifice size and the wind speed as input conditions, taking the thermal runaway jet fire radiation intensity as an output result, and fitting a linear regression equation of the jet fire radiation intensity and variable change by adopting a linear regression method.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery safety, and in particular to, but not limited to, a method and device for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries are widely used in consumer electronics, electric vehicles, and energy storage systems due to their high energy density and long cycle life. However, lithium-ion batteries are prone to thermal runaway under extreme conditions (such as overcharging, puncture, high temperature, etc.). The temperature inside the battery rises sharply, causing the electrolyte to decompose and the positive and negative electrode materials to react violently, ultimately generating a large amount of heat and gas, which can cause a discharge fire.

[0003] Jet fire refers to the high-temperature gas and flame ejected during the thermal runaway of lithium-ion batteries. Its radiation intensity directly affects the safety of the surrounding environment. Existing research focuses on the internal reaction mechanism, temperature distribution and material improvement of thermal runaway, but lacks systematic modeling and simulation of the formation and radiation intensity of jet fire. Summary of the invention

[0004] Based on the problems in the related art, an embodiment of the present invention provides a method and device for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery.

[0005] The technical solution of the embodiment of the present invention is achieved as follows:

[0006] In a first aspect, an embodiment of the present invention provides a method for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery, the method comprising:

[0007] Based on the size parameters and structural characteristics of the lithium-ion battery, a thermal runaway jet fire geometric model of the lithium-ion battery is established and meshed to obtain a thermal runaway jet fire mesh model;

[0008] By setting model parameters related to radiation intensity in the thermal runaway process of the lithium-ion battery, the thermal runaway jet fire grid model is assigned to obtain a thermal runaway jet fire radiation intensity simulation model;

[0009] The thermal runaway jet fire radiation intensity simulation model is used to simulate the thermal runaway jet fire under various working conditions, and the radiation intensity values ​​under different models are solved to obtain simulation results.

[0010] Based on the simulation results, the ambient temperature, gas production composition, nozzle pressure, nozzle size and wind speed are taken as input conditions, and the thermal runaway jet fire radiation intensity is taken as the output result. The linear regression method is used to fit the linear regression equation of the jet fire radiation intensity and the change of variables.

[0011] In a second aspect, an embodiment of the present invention provides a modeling device for the radiation intensity of thermal runaway jet fire of a lithium-ion battery, the device comprising:

[0012] Establishing a module for establishing a thermal runaway jet fire geometric model of the lithium-ion battery based on the size parameters and structural characteristics of the lithium-ion battery and performing mesh division to obtain a thermal runaway jet fire mesh model;

[0013] An assignment module, used for assigning a value to the thermal runaway jet fire grid model by setting model parameters related to radiation intensity during the thermal runaway process of the lithium-ion battery, so as to obtain a thermal runaway jet fire radiation intensity simulation model;

[0014] A solution module, used for simulating the thermal runaway jet fire under various working conditions in the thermal runaway jet fire radiation intensity simulation model, solving the radiation intensity values ​​under different models, and obtaining simulation results;

[0015] A fitting module is used to fit the linear regression equation of the jet fire radiation intensity and the variable change by using a linear regression method based on the simulation results, taking the ambient temperature, gas production composition, nozzle pressure, nozzle size and wind speed as input conditions, and the thermal runaway jet fire radiation intensity as the output result.

[0016] In some embodiments, the assignment module is further used to construct a thermal runaway model according to the lumped transient energy conservation equation of the Arrhenius formula, coupling the internal electrochemical reaction with the heat generation and heat transfer of the battery to describe the continuous temperature rise process of thermal runaway of the lithium-ion battery;

[0017] Basic equation:

[0018] Heat release rate of a single reaction: Q gen,i =ΔH i ·R i ,

[0019] Chemical reaction rate:

[0020] Total heat generated: Q gen =∑ i Q gen,i =∑ i ΔH i ·R i ,

[0021] Heat loss term:

[0022] Lumped Thermodynamic Model of Thermal Runaway:

[0023] in, is the rate of change of temperature over time; Q gen Q is the total heat generated by all reactions in the battery, including electrochemical reactions and material phase changes; loss is the heat lost per unit time, including conduction, convection and radiation, caused by the heat exchange between the battery surface and the surrounding environment; m is the battery mass; C p is the specific heat capacity of the battery (J / kg·K); Q gen,i is the heat generated by the i-th reaction; ΔH i is the reaction enthalpy change of the ith reaction; R i is the rate of the ith reaction (mol / s); R i is the rate constant of the ith reaction; A i is the frequency factor of the ith reaction (s -1 ); is the activation energy of the ith reaction (J / mol); [C j ] is the concentration of reactant j (mol / L); h is the heat transfer coefficient (W / m 2 ·K); A is the surface area (m 2 ), ∈ is the surface emissivity, σ is the Stefan-Boltzmann constant 5.67×10 -8 W / m 2 ·K 4 ; T ∞ is the ambient temperature (K).

[0024] In some embodiments, the device further comprises: a conversion module, further configured to simulate turbulent flow using the RNG k-ε model, wherein the control equation of the RNG k-ε model is:

[0025]

[0026] The SIMPLE algorithm is used to couple the pressure field and the velocity field. The modified momentum equation is:

[0027]

[0028] The pressure correction equation is:

[0029] Calculation of velocity and pressure:

[0030] The governing equation of the finite rate combustion model is:

[0031] The discrete coordinate model is used to simulate the thermal radiation effect. The discrete coordinate model transforms the radiation equation into an integral equation in discrete directions:

[0032]

[0033] Among them, Y i represents the mass fraction of species i, J i is the diffusion flux, R i is the generation rate, I is the radiation intensity, a(r) is the absorption coefficient, S(r) is the source term, and s is the direction.

[0034] In some embodiments, the fitting module is further used to assume that the relationship between the radiation intensity I and the above variables can be expressed as a linear function, then:

[0035] I=β0+β1T+β2C+β3P+β4D+β5W,

[0036] Among them, I is the radiation intensity, β0 is the intercept term, β1…β5 are linear regression coefficients, T is the ambient temperature, C is the gas composition, P is the nozzle pressure, D is the nozzle size, and W is the wind speed;

[0037] Use part of the data for model training and the remaining data for model validation to evaluate the model's fit and prediction accuracy regression analysis results;

[0038] According to the regression analysis results, the linear regression equation is determined, and the influence of each variable on the radiation intensity is analyzed.

[0039] In a third aspect, an embodiment of the present invention provides a modeling device for the radiation intensity of a thermal runaway jet fire of a lithium-ion battery, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned modeling method for the radiation intensity of a thermal runaway jet fire of a lithium-ion battery when executing the executable instructions stored in the memory.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned modeling method for the radiation intensity of thermal runaway jet fire of a lithium-ion battery.

[0041] The present invention provides a modeling method for the thermal runaway jet fire radiation intensity of a lithium-ion battery, and proposes a method for calculating the numerical value of the thermal runaway jet fire radiation intensity of a lithium-ion battery based on Fluent numerical simulation, so as to achieve the purpose of quickly predicting the jet fire radiation intensity without the need for experiments. The method can effectively predict the jet fire radiation intensity under different working conditions, provide a scientific basis for battery safety design and protective measures, help improve the safety of lithium-ion batteries, and reduce the harm of thermal runaway accidents to personnel and equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery provided by the present invention;

[0043] Figure 2 It is a flow chart of another method for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery provided by the present invention;

[0044] Figure 3 It is a comparison chart of the Fluent numerical simulation results and the fitting values ​​of the thermal runaway jet fire radiation intensity of the lithium ion battery provided by the present invention;

[0045] Figure 4 A schematic diagram of the composition structure of a modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by the present invention;

[0046] Figure 5 A schematic diagram of the composition structure of a modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0048] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as those commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0049] The following describes an exemplary application of a modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery according to an embodiment of the present invention. The modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by an embodiment of the present invention can be implemented as a terminal or as a server. In one implementation, the modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by an embodiment of the present invention can be implemented as various types of terminals such as laptop computers, tablet computers, desktop computers, and mobile devices; in another implementation, the modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by an embodiment of the present invention can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDN, Content De livery Network), and big data and artificial intelligence platforms. The terminal and the server can be directly or indirectly connected by wired or wireless communication, which is not limited in the embodiment of the present invention. Below, an exemplary application of the modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery implemented as a server will be described.

[0050] The embodiment of the present invention provides a method for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery, see Figure 1 , Figure 1 is a flow chart of a method for modeling the radiation intensity of thermal runaway jet fire of a lithium-ion battery provided by an embodiment of the present invention, which is combined with Figure 1 The steps shown are explained.

[0051] Step S110, based on the size parameters and structural characteristics of the lithium-ion battery, a thermal runaway jet fire geometric model of the lithium-ion battery is established and meshed to obtain a thermal runaway jet fire mesh model.

[0052] Step S120, by setting model parameters related to radiation intensity during thermal runaway of lithium-ion batteries, assigning values ​​to the thermal runaway jet fire grid model, and obtaining a thermal runaway jet fire radiation intensity simulation model.

[0053] Step S130, simulating thermal runaway jet fires under various working conditions in the thermal runaway jet fire radiation intensity simulation model, solving the radiation intensity values ​​under different models, and obtaining simulation results.

[0054] Step S140, based on the simulation results, taking the ambient temperature, gas production composition, injection port pressure, injection port size and wind speed as input conditions, taking the thermal runaway jet fire radiation intensity as the output result, and using a linear regression method to fit a linear regression equation of the jet fire radiation intensity and the variable changes.

[0055] The present invention provides a modeling method for the thermal runaway jet fire radiation intensity of a lithium-ion battery, and proposes a method for calculating the numerical value of the thermal runaway jet fire radiation intensity of a lithium-ion battery based on Fluent numerical simulation, so as to achieve the purpose of quickly predicting the jet fire radiation intensity without the need for experiments. The method can effectively predict the jet fire radiation intensity under different working conditions, provide a scientific basis for battery safety design and protective measures, help improve the safety of lithium-ion batteries, and reduce the harm of thermal runaway accidents to personnel and equipment.

[0056] In some embodiments, the above step S120 may be implemented in the following manner:

[0057] A thermal runaway model is constructed based on the lumped transient energy conservation equation of the Arrhenius formula, which couples the internal electrochemical reaction with the heat generation and heat transfer of the battery to describe the continuous temperature rise process of thermal runaway of lithium-ion batteries.

[0058] Basic equation:

[0059] Heat release rate of a single reaction: Q gen,i =ΔH i ·R i ,

[0060] Chemical reaction rate:

[0061] Total heat generated: Q gen =∑ i Q gen,i =∑ i ΔH i ·R i ,

[0062] Heat loss term:

[0063] Lumped Thermodynamic Model of Thermal Runaway:

[0064] in, is the rate of change of temperature over time; Q gen Q is the total heat generated by all reactions in the battery, including electrochemical reactions and material phase changes; loss is the heat lost per unit time, including conduction, convection and radiation, caused by the heat exchange between the battery surface and the surrounding environment; m is the battery mass; C pis the specific heat capacity of the battery (J / kg·K); Q gen,i is the heat generated by the i-th reaction; ΔH i is the reaction enthalpy change of the ith reaction; R i is the rate of the ith reaction (mol / s); R i is the rate constant of the ith reaction; A i is the frequency factor of the ith reaction (s -1 ); is the activation energy of the ith reaction (J / mol); [C j ] is the concentration of reactant j (mol / L); h is the heat transfer coefficient (W / m 2 ·K); A is the surface area (m 2 ), ∈ is the surface emissivity, σ is the Stefan-Boltzmann constant 5.67×10 -8 W / m 2 ·K 4 ; T ∞ is the ambient temperature (K).

[0065] In some embodiments, the method further comprises:

[0066] The RNG k-ε model is used to simulate turbulent flow. The control equation of the RNG k-ε model is:

[0067]

[0068] The SIMPLE algorithm is used to couple the pressure field and the velocity field. The modified momentum equation is:

[0069]

[0070] The pressure correction equation is:

[0071] Calculation of velocity and pressure:

[0072] The governing equation of the finite rate combustion model is:

[0073] The discrete coordinate model is used to simulate the thermal radiation effect. The discrete coordinate model transforms the radiation equation into an integral equation in discrete directions:

[0074]

[0075] Among them, Y i represents the mass fraction of species i, J i is the diffusion flux, R i is the generation rate, I is the radiation intensity, a(r) is the absorption coefficient, S(r) is the source term, and s is the direction.

[0076] In some embodiments, the above step S140 may be implemented in the following manner:

[0077] Assuming that the relationship between the radiation intensity I and the above variables can be expressed as a linear function, we have:

[0078] I=β0+β1T+β2C+β3P+β4D+β5W,

[0079] Among them, I is the radiation intensity, β0 is the intercept term, β1…β5 are linear regression coefficients, T is the ambient temperature, C is the gas composition, P is the nozzle pressure, D is the nozzle size, and W is the wind speed;

[0080] Use part of the data for model training and the remaining data for model validation to evaluate the model's fit and prediction accuracy regression analysis results;

[0081] According to the regression analysis results, the linear regression equation is determined, and the influence of each variable on the radiation intensity is analyzed.

[0082] The following describes an exemplary application of an embodiment of the present invention in a practical application scenario.

[0083] The present invention uses numerical simulation technology to establish a geometric model and grid division of thermal runaway jet fire of lithium-ion batteries, sets physical and chemical parameters in the thermal runaway process, simulates the radiation intensity of jet fire under different working conditions, and finally fits the relationship between radiation intensity and key variables through linear regression method to realize the prediction of jet fire radiation intensity.

[0084] The embodiment of the present invention provides another method for modeling the radiation intensity of thermal runaway jet fire of lithium-ion batteries, which includes four steps, and the flow chart is shown in the attached figure. Figure 2 As shown, the specific steps are as follows:

[0085] Step 1: Establish the geometric model of thermal runaway jet fire of lithium-ion battery and divide the mesh; Use Space claim to establish the geometric model of lithium-ion battery and jet fire area and divide the mesh according to the actual size and structure of lithium-ion battery.

[0086] In the embodiment of the present invention, a three-dimensional geometric model of a square shell ternary lithium battery with a length, width and height of 0.148m, 0.028m and 0.101m respectively is established, and a cylinder with a diameter of 1.5m and a height of 3m is established as the geometric model of the jet fire area with the bottom center of the lithium battery as the base point. The explosion-proof valve is located at the center of the top of the battery, and is set as a circular explosion-proof valve with a diameter of 0.01m, 0.015mm and 0.02mm respectively. Three different geometric models are established respectively, and the geometric models are meshed. Since the pressure near the explosion-proof valve is relatively high when thermal runaway occurs, the precision requirements of the flame area are relatively high. In order to ensure the accuracy of the flame detail simulation, the jet fire area is precisely meshed, especially for the high temperature and high pressure area. Local mesh encryption is performed, and relatively coarse meshes are performed for areas far from the explosion-proof valve.

[0087] Step 2: Set the parameters of the numerical simulation model for the thermal runaway jet fire radiation intensity of lithium-ion batteries; import the above-mentioned grid model into the Fluent simulation software, and define the material properties, including the thermal physical parameters of the electrolyte, positive and negative electrodes, current collectors, shells, etc.; set the boundary conditions according to the experiment, the inlet boundary conditions include flow rate, temperature and chemical composition, the outlet boundary conditions include pressure, mass flow rate, etc., and consider the initial conditions, such as the initial temperature distribution and initial voltage.

[0088] A thermal runaway model is constructed based on the lumped transient energy conservation equation of the Arrhenius formula, which couples the internal electrochemical reaction with the heat generation and heat transfer of the battery to describe the continuous temperature rise process of battery thermal runaway.

[0089] Basic equation:

[0090] in, is the rate of change of temperature over time; Q gen Q is the total heat generated by all reactions in the battery, including electrochemical reactions and material phase changes; loss is the heat lost per unit time, including conduction, convection and radiation, caused by the heat exchange between the battery surface and the surrounding environment; m is the battery mass; C p is the specific heat capacity of the battery (J / kg·K).

[0091] Heat release rate of a single reaction: Q gen,i =ΔH i ·R i ,

[0092] Among them, Q gen,i is the heat generated by the i-th reaction; ΔH i is the reaction enthalpy change of the ith reaction; R i is the rate of the ith reaction (mol / s).

[0093] Chemical reaction rate:

[0094] Among them, R i is the rate constant of the ith reaction; A i is the frequency factor of the ith reaction (s -1 ); is the activation energy of the ith reaction (J / mol); [C j ] is the concentration of reactant j (mol / L).

[0095] Total heat generated: Q gen =∑ i Q gen,i =∑ i ΔH i ·R i ,

[0096] Heat loss term:

[0097] Where h is the heat transfer coefficient (W / m 2 ·K); A is the surface area (m 2 ), ∈ is the surface emissivity, σ is the Stefan-Boltzmann constant 5.67×10 -8 W / m 2 ·K 4 ; T ∞ is the ambient temperature (K).

[0098] The lumped thermodynamic model of thermal runaway is obtained as follows:

[0099]

[0100] Furthermore, based on the above model, the turbulence model uses the RNG k-ε model to simulate turbulent flow, selects the non-premixed model, and uses the SIMPLE algorithm for pressure-velocity coupling. The specific steps are as follows:

[0101] 1. Preliminary solution of the velocity field, assuming an initial pressure distribution.

[0102] 2. Calculate the pressure correction equation to obtain the corrected pressure field.

[0103] 3. Update the velocity field to match the corrected pressure.

[0104] 4. Iterate this process until the pressure and velocity fields converge.

[0105] Battery thermal runaway gas combustion involves complex multi-step chemical reactions. The finite rate chemical reaction model is used to simulate the decomposition and combustion process of the electrolyte. In order to simplify the complex radiation transmission problem in the thermal runaway combustion process, the discrete coordinate model (DO model) is enabled to simulate the thermal radiation effect. The DO model can handle complex geometric shapes and multi-directional radiation transmission, providing higher accuracy. The radiation absorption coefficient of the thermal runaway gas mixture is defined according to its main components and temperature to ensure the accuracy of the radiation transmission simulation.

[0106] The control equation of the RNG k-ε model is:

[0107]

[0108] The SIMPLE algorithm couples the pressure field and the velocity field, and the modified momentum equation is:

[0109]

[0110] The pressure correction equation is:

[0111] Calculation of velocity and pressure:

[0112] The governing equation of the finite rate combustion model is:

[0113] Among them, Y i represents the mass fraction of species i, J i is the diffusion flux, R i is the generation rate.

[0114] The DO model transforms the radiation equation into an integral equation in discrete directions:

[0115] Where I is the radiation intensity, a(r) is the absorption coefficient, S(r) is the source term, and s is the direction.

[0116] Radiation absorption coefficient: κ = f(composition, temperature),

[0117] Step 3: Simulate the thermal runaway jet fire under various working conditions of different ambient temperatures, gas composition, nozzle size, nozzle pressure, and wind speed, and solve the radiation intensity values ​​under different models. The gas composition and concentration of thermal runaway lithium-ion batteries of different systems and charge states will be different. In the present invention, three different ambient temperatures are set: 0°C, 20°C, and 30°C; three gas production components are set: 30% CO2, 25% CO, 20% H2, 10% CH4, 10% C2H4, and 5% C2H6; 50% CO2, 30% CO, 15% H2, and 5% CH4; 25% CO2, 30% CO, 25% H2, 10% CH4, 5% C2H4, and 5% C2H6; the size of the jet nozzle is set to 0.01m, 0.015m, and 0.02m; the jet nozzle pressure is set to 10MPa and 15MPa; the wind speed is set to 1m / s and 2m / s in windless and windy conditions. The Fluent solver is used to solve the values ​​of the thermal runaway jet or radiation intensity of the lithium-ion battery under the above different working conditions.

[0118] Step 4: Based on the results of Fluent simulation, the ambient temperature, gas composition, nozzle pressure, nozzle size, and wind speed are used as input conditions, and the thermal runaway jet fire radiation intensity is defined as the output result. The linear regression method is used to fit the functional relationship between the radiation intensity and the above five variables. In order to improve the fitting effect of the model, the input variables are standardized. The specific calculation formula is as follows:

[0119] Assuming that the relationship between the radiation intensity I and the above variables can be expressed as a linear function, we have:

[0120] I=β0+β1T+β2C+β3P+β4D+β5W,

[0121] Among them, I is the radiation intensity, β0 is the intercept term, β1…β5 are linear regression coefficients, T is the ambient temperature, C is the gas composition, P is the nozzle pressure, D is the nozzle size, and W is the wind speed.

[0122] like Figure 3 As shown in the figure, part of the data is used for model training, and the remaining data is used for model verification to evaluate the model's fit and prediction accuracy. According to the regression analysis results, the final linear regression equation is determined, and the influence of each variable on the radiation intensity is analyzed.

[0123] The calculation results based on the assumed data sample give the formula:

[0124] I=75.0+12.0T+5.0C+15.0P+20.0D+8.0W,

[0125] Through the above calculations, the radiation intensity under specific conditions can be obtained, so as to further analyze the impact of different variables on the radiation intensity.

[0126] Figure 4 : is a schematic diagram of the composition structure of a modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by an embodiment of the present invention, such as Figure 4 As shown, the modeling device 400 for the radiation intensity of thermal runaway jet fire of a lithium-ion battery includes: an establishment module 401, which is used to establish a thermal runaway jet fire geometric model of the lithium-ion battery based on the size parameters and structural characteristics of the lithium-ion battery and perform grid division to obtain a thermal runaway jet fire grid model; an assignment module 402, which is used to assign values ​​to the thermal runaway jet fire grid model by setting model parameters related to the radiation intensity during the thermal runaway process of the lithium-ion battery to obtain a thermal runaway jet fire radiation intensity simulation model; a solution module 403, which is used to simulate thermal runaway jet fires under various working conditions in the thermal runaway jet fire radiation intensity simulation model, solve the radiation intensity values ​​under different models, and obtain simulation results; a fitting module 404, which is used to use the ambient temperature, gas production components, injection port pressure, injection port size and wind speed as input conditions, and the thermal runaway jet fire radiation intensity as the output result, and use the linear regression method to fit the linear regression equation of the jet fire radiation intensity and the change of the variable.

[0127] It should be noted that the description of the device of the embodiment of the present invention is similar to the description of the above method embodiment, and has similar beneficial effects as the method embodiment, so it is not repeated. For technical details not disclosed in the device embodiment, please refer to the description of the method embodiment of the present invention for understanding.

[0128] It should be noted that, in the embodiment of the present invention, if the above-mentioned modeling method of the thermal runaway jet fire radiation intensity of the lithium-ion battery is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), disk or optical disk, etc. Various media that can store program codes. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0129] Correspondingly, an embodiment of the present invention provides a modeling device for the radiation intensity of thermal runaway jet fire of a lithium-ion battery. Figure 5 : is a schematic diagram of the composition structure of a modeling device for the thermal runaway jet fire radiation intensity of a lithium-ion battery provided by an embodiment of the present invention, such as Figure 5As shown, the modeling device 500 for the thermal runaway jet fire radiation intensity of a lithium-ion battery comprises at least: a processor 501 and a computer-readable storage medium 502 configured to store executable instructions, wherein the processor 501 generally controls the overall operation of the modeling device 500 for the thermal runaway jet fire radiation intensity of a lithium-ion battery. The computer-readable storage medium 502 is configured to store instructions and applications executable by the processor 501, and can also cache data to be processed or processed by each module in the processor 501 and the modeling device 500 for the thermal runaway jet fire radiation intensity of a lithium-ion battery, which can be implemented by flash memory (FLASH) or random access memory (Random Access Memory, RAM).

[0130] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will be caused to execute the method provided by the embodiment of the present invention, for example, Figure 1 The method shown.

[0131] In some embodiments, the storage medium can be a computer-readable storage medium, for example, a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disk read-only memory (CD-ROM), etc.; it can also be various devices including one or any combination of the above memories.

[0132] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0133] As an example, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0134] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0135] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0136] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0137] The above is only an embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A modeling method for the radiation intensity of thermal runaway jet fire of lithium-ion batteries, characterized in that: The method comprises: Based on the size parameters and structural characteristics of the lithium-ion battery, a thermal runaway jet fire geometric model of the lithium-ion battery is established and meshed to obtain a thermal runaway jet fire mesh model; By setting model parameters related to radiation intensity in the thermal runaway process of the lithium-ion battery, the thermal runaway jet fire grid model is assigned to obtain a thermal runaway jet fire radiation intensity simulation model; The thermal runaway jet fire radiation intensity simulation model is used to simulate the thermal runaway jet fire under various working conditions, and the radiation intensity values ​​under different models are solved to obtain simulation results. Based on the simulation results, the ambient temperature, gas production composition, nozzle pressure, nozzle size and wind speed are taken as input conditions, and the thermal runaway jet fire radiation intensity is taken as the output result. The linear regression method is used to fit the linear regression equation of the jet fire radiation intensity and the change of variables.

2. The method according to claim 1, characterized in that The step of defining material thermophysical property parameters and setting boundary conditions and initial conditions, assigning values ​​to the thermal runaway jet fire grid model, and obtaining simulation parameters includes: A thermal runaway model is constructed based on the lumped transient energy conservation equation of the Arrhenius formula, which couples the internal electrochemical reaction with the heat generation and heat transfer of the battery to describe the continuous temperature rise process of thermal runaway of lithium-ion batteries. Basic equation: Heat release rate of a single reaction: Q gen,i =ΔH i ·R i , Chemical reaction rate: Total heat generated: Q gen =∑ i Q gen,i =∑ i ΔH i ·R i , Heat loss term: Lumped Thermodynamic Model of Thermal Runaway: in, is the rate of change of temperature over time; Q gen Q is the total heat generated by all reactions in the battery, including electrochemical reactions and material phase changes; loss is the heat lost per unit time, including conduction, convection and radiation, caused by the heat exchange between the battery surface and the surrounding environment; m is the battery mass; C p is the specific heat capacity of the battery (J / kg·K); Q gen,i is the heat generated by the i-th reaction; ΔH i is the reaction enthalpy change of the ith reaction; R i is the rate of the ith reaction (mol / s); R i is the rate constant of the ith reaction; A i is the frequency factor of the ith reaction (s -1 ); is the activation energy of the ith reaction (J / mol); [C j ] is the concentration of reactant j (mol / L); h is the heat transfer coefficient (W / m 2 ·K); A is the surface area (m 2 ), ∈ is the surface emissivity, σ is the Stefan-Boltzmann constant 5.67×10 -8 W / m 2 ·K 4 ; T ∞ is the ambient temperature (K).

3. The method according to claim 2, characterized in that The method further comprises: The RNG k-ε model is used to simulate turbulent flow. The control equation of the RNG k-ε model is: The SIMPLE algorithm is used to couple the pressure field and the velocity field. The modified momentum equation is: The pressure correction equation is: Calculation of velocity and pressure: The governing equation of the finite rate combustion model is: The discrete coordinate model is used to simulate the thermal radiation effect. The discrete coordinate model transforms the radiation equation into an integral equation in discrete directions: Among them, Y i Indicates species The mass fraction, J i is the diffusion flux, R i is the generation rate, I is the radiation intensity, a(r) is the absorption coefficient, S(r) is the source term, and s is the direction.

4. The method according to claim 1, characterized in that Based on the simulation results, the ambient temperature, gas production composition, nozzle pressure, nozzle size and wind speed are used as input conditions, the thermal runaway jet fire radiation intensity is used as the output result, and a linear regression method is used to fit the linear regression equation of the jet fire radiation intensity and the variable change, including: Assuming that the relationship between the radiation intensity I and the above variables can be expressed as a linear function, we have: I=β0+β1T+β2C+β3P+β4D+β5W, Among them, I is the radiation intensity, β0 is the intercept term, β1…β5 are linear regression coefficients, T is the ambient temperature, C is the gas composition, P is the nozzle pressure, D is the nozzle size, and W is the wind speed; Use part of the data for model training and the remaining data for model validation to evaluate the model's fit and prediction accuracy regression analysis results; According to the regression analysis results, the linear regression equation is determined, and the influence of each variable on the radiation intensity is analyzed.

5. A modeling device for the radiation intensity of thermal runaway jet fire of lithium-ion batteries, characterized in that: The device comprises: Establishing a module for establishing a thermal runaway jet fire geometric model of the lithium-ion battery based on the size parameters and structural characteristics of the lithium-ion battery and performing mesh division to obtain a thermal runaway jet fire mesh model; An assignment module, used for assigning a value to the thermal runaway jet fire grid model by setting model parameters related to radiation intensity during the thermal runaway process of the lithium-ion battery, so as to obtain a thermal runaway jet fire radiation intensity simulation model; A solution module, used for simulating the thermal runaway jet fire under various working conditions in the thermal runaway jet fire radiation intensity simulation model, solving the radiation intensity values ​​under different models, and obtaining simulation results; A fitting module is used to fit the linear regression equation of the jet fire radiation intensity and the variable change by using a linear regression method based on the simulation results, taking the ambient temperature, gas production composition, nozzle pressure, nozzle size and wind speed as input conditions, and the thermal runaway jet fire radiation intensity as the output result.

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