A digital simulation method for lithium battery fire dynamic evolution and smoke diffusion

Abstract

The application discloses a kind of lithium battery fire dynamic evolution and smoke diffusion digital simulation method, the present application relates to demand analysis technical field, including: complete lithium battery monomer, module, battery package cabin and the three-dimensional modeling of external environment, generate discrete grid;Electrochemical heating model is loaded to digital simulation platform, and the material set of electric core is obtained;The present application is by constructing the multi-physical field coupling model of electric core, module, battery package and external environment in simulation platform, under the dynamic calculation of thermal runaway trigger, flame spread path determination and smoke concentration variation trend, real-time output temperature field, velocity field and toxic gas concentration field, realize the prediction analysis to battery thermal disaster development whole process;The method significantly improves the visualization capability of lithium battery under abnormal working condition fire formation, diffusion and spread process, improves safety evaluation precision and fault prediction ability.

Description

Technical Field

[0001] This invention relates to the field of demand analysis technology, specifically to a digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires. Background Technology

[0002] Lithium-ion battery fire simulation systems often employ a single physical field or weak coupling method to analyze battery thermal behavior. Common methods include approximating the internal heating process of the battery with an equivalent heat source model, relying on a simplified heat conduction model to predict the temperature rise trend, or using an empirically driven gas release model to simulate the gas generation behavior after thermal runaway. However, such systems generally suffer from problems such as insufficient simulation accuracy, discontinuous physical processes, and difficulty in covering the complete fire evolution chain. Existing thermal runaway simulation methods often fail to simultaneously consider the coupling effect of electrochemical reactions, material decomposition reactions, and gas generation behavior within the cell, resulting in an inability to accurately reproduce the entire process from local abnormal temperature rise to thermal runaway triggering, flame spread, and smoke diffusion. Existing flue gas diffusion predictions typically operate independently of battery thermal runaway models, lacking a feedback mechanism between gas source intensity, temperature field, and flow field, making it difficult to handle the coordinated evolution of multi-scale structures, porous materials, and multiple gas components. To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires, comprising: S1. Complete the 3D modeling of lithium battery cells, modules, battery pack compartments and external environment, and generate discrete meshes; S2. Load the electrochemical heating model into the digital simulation platform to obtain the battery cell material set; S3. Monitor cell temperature rise, reaction status and environmental changes, perform thermal runaway simulation, and generate total heat release TOTQ from total heat source term; S4. Simulate the propagation of flames between cells based on the battery pack structure, and output the temperature field, velocity field and concentration field distribution; calculate the process of thermal runaway propagation range inside the battery pack, and obtain the thermal runaway propagation region. S5. Simulate the flue gas diffusion and toxic gas concentration field in the gas phase region to generate flue gas diffusion maps and toxic gas concentration maps.

[0005] In the digital simulation platform, refined finite volume meshes are established for lithium battery cells, battery modules, and battery pack compartments respectively. When inputting cell structural parameters, the electrode thickness, separator porosity, and number of electrode layers inside the cell are parametrically modeled in a hierarchical structure, and the material thermal property parameters are loaded into the multiphysics material library in the form of temperature dependence tables. A multidimensional lookup table structure including state-of-charge dependence curves, internal resistance change curves with temperature, and side reaction rate gain coefficients are set for electrochemical performance parameters.

[0006] The electrochemical heating model processing procedure includes: The cell heating is decomposed into two types of heat sources: reversible heating and irreversible heating. Electrochemical, material, and structural parameters are introduced into the cell grid domain. Reversible heating is calculated based on the entropy change coefficient of the active material, the distribution of the charged state, and the temperature dependence. Irreversible heating is based on ohmic loss, interfacial polarization, and side reaction heat to construct a comprehensive electrochemical impedance model including the current collector conductivity, coating porosity, ion mobility, electrolyte conductivity, interfacial resistance, and SEI film impedance change rate, thus obtaining the cell material set.

[0007] The thermal runaway simulation includes: After taking the thermal decomposition parameters DEG-E, electrolyte volatilization parameters VAP-K, positive electrode oxygen release kinetic parameters OXR-K, negative electrode side reaction kinetic parameters SBR-R, state of charge SOC-VAL, and internal temperature TMP-VAL of the battery cell material, the SEI film decomposition rate is calculated based on the temperature gradient, and the corresponding heat release and gas production are written into the energy equation and component transport equation. The exothermic contributions of the following processes are calculated sequentially according to the temperature range: the exothermic reaction of the negative electrode, the gas production from the electrolyte cracking, the oxygen release reaction of the positive electrode lattice, and the oxidation reaction of the current collector. The exothermic source terms and gas production source terms of each reaction are then superimposed into the coupled solution domain.

[0008] The thermal runaway simulation also includes: Based on the evolution of the current temperature TMP-VAL, local pressure PRS-VAL, and combustible gas concentration GAS-CON, the contribution ratio of each decomposition reaction is dynamically adjusted. Output total heat release from the heat source term: TOTQ; Among them, the heat release QSEI is calculated based on the SEI decomposition energy and decomposition rate, the heat release QELE from electrolyte pyrolysis is calculated based on the electrolyte volatility constant VAP-K, the heat release QNEG from the negative electrode side reaction is derived from the side reaction rate SBR-R, the heat release QPOS from the positive electrode lattice oxygen release is related to the oxygen release rate constant OXR-K, and α1 to α4 are dynamic coupling weights based on temperature TMP-VAL and state of charge SOC-VAL.

[0009] Simulating the propagation of a flame between battery cells includes: S411: After receiving the electrochemical state parameters current density CUR, open circuit voltage OCV, internal resistance parameter RES, and material thermal properties parameters specific heat capacity HCP and thermal conductivity HTC, an electrochemical heat source term is constructed in the solid region of the cell based on the energy equation, and the heat generated by ohmic internal resistance and polarization is written into the heat conservation equation. S412. When the temperature reaches the decomposition range, inject the exothermic source term and gas-producing source term output by the thermal runaway reaction model into the energy equation and component transport equation, respectively. S413. In the gas phase region of the battery pack, the velocity field and pressure field are solved based on the momentum conservation equation, and the gas generation rate is applied as a volume source term to the flow field solution domain. S414. Introduce corresponding component generation source terms into the diffusion equations for combustible gases, inert gases, and toxic gases to achieve continuous coupling of gas generation, convection transport, and diffusion processes. S415. Under the simultaneous iterative solution of the energy equation, momentum equation and component transport equation, realize the thermal behavior of the solid region and the flow behavior of the gas phase region, and output the temperature field, velocity field and concentration field distribution.

[0010] The process of thermal runaway propagation within the battery pack includes: S421. Within the solid structure region, the following parameters are used as input parameters: shell thermal conductivity (HCT), bracket equivalent thermal resistance (STR), insulation board thermal resistance (ITR), flame retardant layer shielding coefficient (SHF), structural contact thermal resistance (CTR), thermal runaway determination temperature threshold (TCR), and critical heat flux density threshold (QCR). S422. Based on the thermal conductivity parameters HCT, STR, ITR and the contact thermal resistance CTR, construct a solid thermal conduction path between the cell and the shell, bracket and insulation plate, and solve the heat flow distribution in each structural component. S423. Introduce a shielding factor SHF in the region containing the flame-retardant layer to reduce the incident heat flux density caused by thermal runaway of the battery cell, and calculate the equivalent net heat flux density QNF acting on the surface of adjacent battery cells. The net heat flux QNF is calculated in the following manner: Among them, the thermal conductivity heat flux density QCON is obtained from the thermal conductivity network, the radiation heat flux density QRAD is obtained based on the radiation heat transfer model, and the shielding reduction heat flux density QSHD is determined by the shielding coefficient SHF and the flame retardant layer coverage area. The coefficients β1, β2, and β3 are weight parameters set according to the structural material and surface condition. S424. Using the net heat flux QNF in the temperature field as the criterion, when the temperature of the surface of an adjacent cell exceeds the critical temperature TCR and the corresponding net heat flux QNF is greater than the critical heat flux QCR, the spatial location of the cell is marked as the thermal runaway propagation region.

[0011] The simulation of flue gas diffusion and toxic gas concentration field includes: S51. The mass release rate of flue gas generated in the fire zone, Q_sm(t), the generation rate of toxic gas, Q_tox,i(t), and the instantaneous velocity field VEL(t) and pressure field PRE(t) output by the fluid flow sub-model are used as input conditions for diffusion calculation. S52. Set the exposure threshold parameters for toxic gases, including the hydrogen concentration threshold C_H2_thr, the carbon monoxide concentration threshold C_CO_thr, the hydrogen fluoride concentration threshold C_HF_thr, and the short-term exposure limit STEL_i for each toxic component. S53. Based on the component transport equation, the mass fraction C_i of flue gas and each toxic gas is numerically discretized and solved. The concentration evolution of gas components in three-dimensional space is formed by convection term, molecular diffusion term and generation source term. S54. Calculate the turbulent eddy viscosity diffusion coefficient D_turb(k,ε) using the turbulent kinetic energy k(t) and turbulent dissipation rate ε(t) as inputs. S55. Calculate the vertical rise velocity of the flue gas based on the buoyancy driving term β(T-T_amb)g to obtain the flue gas diffusion map and toxic gas concentration map.

[0012] This invention provides a digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires. Compared with existing technologies, it has the following advantages: This invention constructs a multiphysics coupling model encompassing cells, modules, battery packs, and the external environment within a simulation platform. Under dynamic calculations of thermal runaway triggering, flame propagation path determination, and flue gas concentration trends, it outputs temperature, velocity, and toxic gas concentration fields in real time, enabling predictive analysis of the entire process of battery thermal disaster development. This method significantly enhances the visualization capabilities of lithium battery fire formation, diffusion, and propagation processes under abnormal operating conditions, thereby improving the accuracy of safety assessments and fault prediction capabilities. This invention uses the total heat release, gas generation, and temperature rise evolution trend output by the thermal runaway model, combined with air flow and toxic gas diffusion simulation, to form a temperature field, smoke diffusion map, and toxic gas concentration map that can be used for risk assessment. This mechanism effectively improves the real-time assessment capability in fire scenarios, enabling maintenance personnel to know the danger zone in advance and reduce the risk of personnel exposure and the probability of accident escalation. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 This application provides a digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires, including: S1. Complete the 3D modeling of lithium battery cells, modules, battery pack compartments and external environment, and generate discrete meshes; S2. Load the electrochemical heating model into the digital simulation platform to obtain the battery cell material set; S3. Monitor cell temperature rise, reaction status and environmental changes, perform thermal runaway simulation, and generate total heat release TOTQ from total heat source term; S4. Simulate the propagation of flames between cells based on the battery pack structure, and output the temperature field, velocity field and concentration field distribution; calculate the process of thermal runaway propagation range inside the battery pack, and obtain the thermal runaway propagation region. S5. Simulate the flue gas diffusion and toxic gas concentration field in the gas phase region to generate flue gas diffusion maps and toxic gas concentration maps.

[0016] In the digital simulation platform, refined finite volume meshes are established for lithium battery cells, battery modules, and battery pack compartments respectively. When inputting cell structural parameters, the electrode thickness, separator porosity, and number of electrode layers inside the cell are parametrically modeled in a hierarchical structure, and the material thermal property parameters are loaded into the multiphysics material library in the form of temperature dependence tables. A multidimensional lookup table structure including state-of-charge dependence curves, internal resistance change curves with temperature, and side reaction rate gain coefficients are set for electrochemical performance parameters.

[0017] The electrochemical heating model processing procedure includes: The cell heating is decomposed into two types of heat sources: reversible heating and irreversible heating. Electrochemical, material, and structural parameters are introduced into the cell grid domain. Reversible heating is calculated based on the entropy change coefficient of the active material, the distribution of the charged state, and the temperature dependence. Irreversible heating is based on ohmic loss, interfacial polarization, and side reaction heat to construct a comprehensive electrochemical impedance model including the current collector conductivity, coating porosity, ion mobility, electrolyte conductivity, interfacial resistance, and SEI film impedance change rate, thus obtaining the cell material set.

[0018] In this embodiment, the electrochemical heating model is constructed based on the decomposition and description of the heat generation mechanism of the battery cell under working and abnormal conditions. A set of battery cell materials reflecting the material, electrochemical behavior and structural characteristics is introduced into the battery cell grid domain to provide basic heat source terms for subsequent thermal runaway solutions.

[0019] After completing the 3D geometric modeling and mesh discretization of the battery cell, the simulation platform classifies the heat generation mechanism in the solid domain of the battery cell, decomposing the cell heating into two types of heat sources: reversible heating and irreversible heating. Among them, the reversible heating is calculated based on the reaction entropy change parameter of the active material of the battery cell, the spatial distribution of the state of charge and its dependence on temperature. The corresponding entropy change heat source is calculated in each mesh cell. By coupling the reaction entropy change coefficient with the local temperature field, the entropy change term can be synchronously corrected with the change of SOC and the temperature gradient.

[0020] Irreversible heating is solved using a multi-parameter impedance modeling approach based on the actual structural characteristics of the battery cell, incorporating ohmic internal resistance heat, interfacial polarization heat, and exothermic reactions into the impedance model. Specifically, the ohmic loss term determines the effective conductive channels using structural parameters such as the current collector conductivity and the material coating porosity; the polarization term establishes the electrochemical polarization impedance based on parameters such as the ion mobility of the active material, the electrolyte conductivity, and the interfacial resistance; and the side reaction term characterizes the aging and degradation behavior of the interfacial film with temperature and SOC evolution through the SEI film impedance change rate, thus forming a comprehensive electrochemical impedance model.

[0021] In the above impedance model, all kinds of impedance terms are stored in the form of parameter fields in the grid domain and are dynamically updated with the cell state parameters (including local temperature, SOC distribution, current density, etc.), thus obtaining a complete set of cell materials that reflects the internal resistance structure and thermal behavior of the cell at different working stages.

[0022] The thermal runaway simulation includes: After taking the thermal decomposition parameters DEG-E, electrolyte volatilization parameters VAP-K, positive electrode oxygen release kinetic parameters OXR-K, negative electrode side reaction kinetic parameters SBR-R, state of charge SOC-VAL, and internal temperature TMP-VAL of the battery cell material, the SEI film decomposition rate is calculated based on the temperature gradient, and the corresponding heat release and gas production are written into the energy equation and component transport equation. The exothermic contributions of the following processes are calculated sequentially according to the temperature range: the exothermic reaction of the negative electrode, the gas production from the electrolyte cracking, the oxygen release reaction of the positive electrode lattice, and the oxidation reaction of the current collector. The exothermic source terms and gas production source terms of each reaction are then superimposed into the coupled solution domain.

[0023] The thermal runaway simulation also includes: Based on the evolution of the current temperature TMP-VAL, local pressure PRS-VAL, and combustible gas concentration GAS-CON, the contribution ratio of each decomposition reaction is dynamically adjusted. Output total heat release from the heat source term: TOTQ; Among them, the heat release QSEI is calculated based on the SEI decomposition energy and decomposition rate, the heat release QELE from electrolyte pyrolysis is calculated based on the electrolyte volatility constant VAP-K, the heat release QNEG from the negative electrode side reaction is derived from the side reaction rate SBR-R, the heat release QPOS from the positive electrode lattice oxygen release is related to the oxygen release rate constant OXR-K, and α1 to α4 are dynamic coupling weights based on temperature TMP-VAL and state of charge SOC-VAL.

[0024] In this embodiment, the thermal runaway simulation is constructed based on the thermochemical parameters, electrochemical state parameters, and environmental state variables of the battery cell material. The entire thermal runaway reaction process is digitally characterized by solving the exothermic and gas-generating behaviors in stages. This process is completed within a thermally coupled solver, independent of external modules, ensuring the consistency of the multiphysics response.

[0025] The system reads thermal decomposition parameters DEG-E, electrolyte evaporation parameters VAP-K, positive electrode lattice oxygen release kinetic parameters OXR-K, and negative electrode side reaction kinetic parameters SBR-R from the material set, and maps these parameters to the cell grid cells in conjunction with real-time state of charge (SOC-VAL) and internal temperature (TMP-VAL). When entering the thermal runaway determination region, the system automatically calculates the SEI film decomposition rate in the corresponding region based on the cell temperature gradient, and generates heat release and gas production associated with the decomposition rate. The heat release is written into the solution matrix as a heat source term in the energy equation, and the gas production is injected into the gas phase domain as a generation source term in the component transport equation, achieving a coupled solution of thermal decomposition behavior.

[0026] When the cell temperature reaches different reaction ranges, the system sequentially calculates the contribution of various reactions according to a preset temperature segmentation strategy. These include the exothermic reaction of the negative electrode, the exothermic reaction and gas generated by the electrolyte cracking reaction, the heat and oxygen generated during the oxygen release stage of the positive electrode lattice, and the heat generated by the current collector oxidation reaction. The above exothermic source terms and gas generation source terms are accumulated and superimposed in the source term matrix to reflect the characteristics of multiple reaction coupling and superposition at different thermal runaway stages, and to evolve continuously in multiple reaction ranges.

[0027] To obtain the true development trend of thermal runaway behavior, this implementation further monitors the changes in the current temperature TMP-VAL, local pressure PRS-VAL, and combustible gas concentration GAS-CON in real time during the solution process, and uses these as adjustment parameters to dynamically adjust the contribution ratio of each decomposition reaction, so that the exothermic and gas generation processes can be automatically enhanced or weakened according to the actual state of the battery cell; when the pressure rises due to local gas generation or the accumulation of combustible gas reaches a certain level, the model automatically increases the weight of the high-temperature reaction term, so that the thermal runaway chain reaction can continue to advance, ensuring that the simulation results can reflect the suddenness and acceleration of thermal runaway.

[0028] After the above coupling solution is completed, the system integrates the heat released from all reaction stages to form a total heat release source term, and calculates the total heat release TOTQ as the final output.

[0029] Simulating the propagation of a flame between battery cells includes: S411: After receiving the electrochemical state parameters current density CUR, open circuit voltage OCV, internal resistance parameter RES, and material thermal properties parameters specific heat capacity HCP and thermal conductivity HTC, an electrochemical heat source term is constructed in the solid region of the cell based on the energy equation, and the heat generated by ohmic internal resistance and polarization is written into the heat conservation equation. S412. When the temperature reaches the decomposition range, inject the exothermic source term and gas-producing source term output by the thermal runaway reaction model into the energy equation and component transport equation, respectively. S413. In the gas phase region of the battery pack, the velocity field and pressure field are solved based on the momentum conservation equation, and the gas generation rate is applied as a volume source term to the flow field solution domain. S414. Introduce corresponding component generation source terms into the diffusion equations for combustible gases, inert gases, and toxic gases to achieve continuous coupling of gas generation, convection transport, and diffusion processes. S415. Under the simultaneous iterative solution of the energy equation, momentum equation and component transport equation, realize the thermal behavior of the solid region and the flow behavior of the gas phase region, and output the temperature field, velocity field and concentration field distribution.

[0030] In this embodiment, the simulation of flame propagation between battery cells is carried out based on a multiphysics field coupling solution framework of solid and gas phase regions. Through the continuous linkage between electrochemical heating, thermal runaway heat release, gas generation-driven flow field and gas diffusion model, a numerical simulation process that can reflect the actual fire evolution law is constructed.

[0031] The system receives electrochemical state parameters, including current density (CUR), open-circuit voltage (OCV), and internal resistance parameter (RES), and reads material thermophysical parameters, specific heat capacity (HCP) and thermal conductivity (HTC), and writes these parameters into the property matrix of the cell's solid-state grid cell. Within the solid region, a basic electrochemical heat source term is constructed based on the energy conservation equation. The heat generated by ohmic internal resistance and polarization is accumulated into the heat source matrix step by step over time, ensuring that the temperature rise of the cell under high load conditions is accurately reflected.

[0032] When the temperature of a certain battery cell reaches the preset thermal decomposition range, the solver automatically invokes the thermal runaway reaction model, injects the exothermic source term from the reaction model into the energy equation, and injects the gas production source term into the component transport equation, causing the dynamic behavior of local high temperature and high gas production to enter the stage of influencing the surrounding environment. At this time, the temperature field in the solid region will rise rapidly, and at the same time, gas production will drive changes in the gas phase region.

[0033] In the gas phase region of the battery pack, the velocity and pressure fields are solved based on the momentum conservation equation. The aforementioned gas generation rate is applied as a volume source term to the corresponding position in the flow field solution domain, enabling the gas generation to form a local high-pressure zone inside, which pushes the surrounding gas and thus becomes one of the important driving forces for flame propagation. The introduction of the gas generation volume source term directly affects the convective velocity field, enabling the flame plume to propagate along the cell gap direction.

[0034] To fully characterize the diffusion behavior of combustible, inert, and toxic gases in space, corresponding generation source terms for each type of gas are incorporated into the component transport equations. This allows them to undergo convective transport and molecular diffusion in complex space as the solution iterates. The generation rates of different components are jointly determined by the reaction model, the gas generation equations, and the combustion conditions, thus accurately describing the coupled process of local oxygen consumption and combustible gas accumulation during flame propagation.

[0035] The process of thermal runaway propagation within the battery pack includes: S421. Within the solid structure region, the following parameters are used as input parameters: shell thermal conductivity (HCT), bracket equivalent thermal resistance (STR), insulation board thermal resistance (ITR), flame retardant layer shielding coefficient (SHF), structural contact thermal resistance (CTR), thermal runaway determination temperature threshold (TCR), and critical heat flux density threshold (QCR). S422. Based on the thermal conductivity parameters HCT, STR, ITR and the contact thermal resistance CTR, construct a solid thermal conduction path between the cell and the shell, bracket and insulation plate, and solve the heat flow distribution in each structural component. S423. Introduce a shielding factor SHF in the region containing the flame-retardant layer to reduce the incident heat flux density caused by thermal runaway of the battery cell, and calculate the equivalent net heat flux density QNF acting on the surface of adjacent battery cells. The net heat flux QNF is calculated in the following manner: Among them, the thermal conductivity heat flux density QCON is obtained from the thermal conductivity network, the radiation heat flux density QRAD is obtained based on the radiation heat transfer model, and the shielding reduction heat flux density QSHD is determined by the shielding coefficient SHF and the flame retardant layer coverage area. The coefficients β1, β2, and β3 are weight parameters set according to the structural material and surface condition. S424. Using the net heat flux QNF in the temperature field as the criterion, when the temperature of the surface of an adjacent cell exceeds the critical temperature TCR and the corresponding net heat flux QNF is greater than the critical heat flux QCR, the spatial location of the cell is marked as the thermal runaway propagation region.

[0036] This invention constructs a multiphysics coupling model covering cells, modules, battery packs, and the external environment in a simulation platform. Under the dynamic calculation of thermal runaway triggering, flame propagation path judgment, and flue gas concentration change trend, it outputs temperature field, velocity field, and toxic gas concentration field in real time, realizing the prediction and analysis of the entire process of battery thermal disaster development. This method significantly improves the visualization capability of the formation, diffusion, and spread of lithium battery fires under abnormal operating conditions, and enhances the accuracy of safety assessment and fault prediction capability.

[0037] The simulation of flue gas diffusion and toxic gas concentration field includes: S51. The mass release rate of flue gas generated in the fire zone, Q_sm(t), the generation rate of toxic gas, Q_tox,i(t), and the instantaneous velocity field VEL(t) and pressure field PRE(t) output by the fluid flow sub-model are used as input conditions for diffusion calculation. S52. Set the exposure threshold parameters for toxic gases, including the hydrogen concentration threshold C_H2_thr, the carbon monoxide concentration threshold C_CO_thr, the hydrogen fluoride concentration threshold C_HF_thr, and the short-term exposure limit STEL_i for each toxic component. S53. Based on the component transport equation, the mass fraction C_i of flue gas and each toxic gas is numerically discretized and solved. The concentration evolution of gas components in three-dimensional space is formed by convection term, molecular diffusion term and generation source term. S54. Calculate the turbulent eddy viscosity diffusion coefficient D_turb(k,ε) using the turbulent kinetic energy k(t) and turbulent dissipation rate ε(t) as inputs. S55. Calculate the vertical rise velocity of the flue gas based on the buoyancy driving term β(T-T_amb)g to obtain the flue gas diffusion map and toxic gas concentration map.

[0038] In this embodiment, the simulation of smoke diffusion and toxic gas concentration field is based on the flow field solution framework of the gas phase region. By uniformly modeling the physical mechanisms such as smoke generation, toxic gas generation, air transport, turbulent diffusion and buoyancy upflow, the three-dimensional temporal distribution prediction of smoke and harmful gases during the fire process is realized.

[0039] In step S51, the flue gas mass release rate Q_sm(t) and the generation rate of various toxic gases Q_tox,i(t) are read from the combustion sub-model of the ignition zone, and the instantaneous velocity field VEL(t) and pressure field PRE(t) are obtained from the fluid flow sub-model. These are written into the component transport solution domain as the initial input conditions for diffusion calculation, so that the gas transport process can be consistent with the flow state during the flame propagation stage.

[0040] In step S52, exposure criteria for different toxic components are loaded, including the concentration thresholds C_thr of gases such as hydrogen, carbon monoxide, and hydrogen fluoride, as well as the corresponding short-term exposure limits STEL_i, and stored in the toxicity determination module for subsequent automatic assessment of personnel risk and regional exposure levels.

[0041] In step S53, the mass fractions C_i of flue gas and toxic gases are numerically discretized and solved based on the component transport equation. During the solution process, the convection term ∇ is... Molecular diffusion term∇ 2 C_i and the generation source term Q_tox,i(t) are simultaneously written into the discrete equation, so that the evolution of the concentration field of each component over time can accurately reflect the combined effect of flow, diffusion and chemical generation. The solver performs iterative convergence at each time step, thereby obtaining the dynamic distribution of flue gas and toxic components in the complex structural space.

[0042] Within each grid and time step, based on three physical processes—airflow (convection), concentration difference (molecular diffusion and turbulent diffusion), and ignition source generation (source term)—an algebraic equation system for component mass conservation is established and solved using the finite volume method, thereby obtaining the real-time mass fraction distribution of flue gas and each toxic gas in three-dimensional space. Specifically, the computational domain is divided into fine three-dimensional grids, and the time is divided into continuous steps. Within each time step, the component mass carried in and out as airflow passes through the grid boundaries is first calculated (convection term, using an upwind scheme to ensure stability). Then, the net migration of components between grids due to the concentration gradient is calculated. Next, the component mass is directly increased within the grid containing the ignition source based on the generation rate of S51 (source term). The contributions from all grids are combined into a large sparse linear equation system, which is solved using iterative solvers such as algebraic multigrid or conjugate gradient methods to obtain the new mass fraction of each component on each grid. Finally, the process is repeated to the next time step, thus simulating the continuous evolution of the concentration field over time.

[0043] In step S54, the turbulent kinetic energy k(t) and turbulent dissipation rate ε(t) output by the turbulence model are called to calculate the turbulent eddy viscosity diffusion coefficient D_turb, which is used to correct the effective diffusion coefficient of the component transport equation, so that the concentration field can maintain a higher diffusion capacity in the high turbulence region, thereby truly reflecting the behavior of large-scale rapid diffusion of smoke in fire scenarios.

[0044] In step S55, the buoyancy term β(T-T_amb)g is obtained based on the temperature field and written into the momentum equation to calculate the rising velocity of the flue gas plume, enabling the flue gas to automatically move upward in the high-temperature region. Subsequently, the height, diffusion radius, and downward fall region of the flue gas plume are calculated in three-dimensional space to form a visualized flue gas diffusion map. Finally, the concentration fields C_i(x,y,z,t) of each toxic gas and the flue gas temperature field T(x,y,z,t) are output as time series data to generate the flue gas diffusion map and the toxic gas concentration map.

[0045] This invention uses the total heat release, gas generation, and temperature rise evolution trend output by the thermal runaway model, combined with air flow and toxic gas diffusion simulation, to form a temperature field, smoke diffusion map, and toxic gas concentration map that can be used for risk assessment. This mechanism effectively improves the real-time assessment capability in fire scenarios, enabling maintenance personnel to know the danger zone in advance and reduce the risk of personnel exposure and the probability of accident escalation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired or wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0047] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0048] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A digital simulation method for the dynamic evolution and smoke diffusion of lithium battery fires, characterized in that, include: S1. Complete the 3D modeling of lithium battery cells, modules, and battery pack compartments, and generate discrete meshes; S2. Load the electrochemical heating model into the digital simulation platform to obtain the battery cell material set; S3. Monitor cell temperature rise, reaction status and environmental changes, perform thermal runaway simulation, and generate total heat release TOTQ from total heat source term; S4. Simulate the propagation of flames between cells based on the battery pack structure, and output the temperature field, velocity field and concentration field distribution; calculate the process of thermal runaway propagation range inside the battery pack, and obtain the thermal runaway propagation region. S5. Simulate the flue gas diffusion and toxic gas concentration field in the gas phase region to generate flue gas diffusion maps and toxic gas concentration maps.

2. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, Discrete meshes were established for lithium battery cells, battery modules, and battery pack compartments in the digital simulation platform. When inputting cell structure parameters, the electrode thickness, separator porosity, and number of electrode layers inside the cell were parametrically modeled in a hierarchical structure. The thermal property parameters of the materials were loaded into the multiphysics material library in the form of a temperature-dependent table. A multidimensional lookup table structure including state-of-charge curves, internal resistance curves as a function of temperature, and side reaction rate gain coefficients were set for the electrochemical performance parameters.

3. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, The electrochemical heating model processing procedure includes: The cell heating is decomposed into two types of heat sources: reversible heating and irreversible heating. Electrochemical, material, and structural parameters are introduced into the cell grid domain. Reversible heating is calculated based on the entropy change coefficient of the active material, the distribution of the charged state, and the temperature dependence. Irreversible heating is based on ohmic loss, interfacial polarization, and side reaction heat to construct a comprehensive electrochemical impedance model including the current collector conductivity, coating porosity, ion mobility, electrolyte conductivity, interfacial resistance, and SEI film impedance change rate, thus obtaining the cell material set.

4. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, The thermal runaway simulation includes: After taking the thermal decomposition parameters DEG-E, electrolyte volatilization parameters VAP-K, positive electrode oxygen release kinetic parameters OXR-K, negative electrode side reaction kinetic parameters SBR-R, state of charge SOC-VAL, and internal temperature TMP-VAL of the battery cell material, the SEI film decomposition rate is calculated based on the temperature gradient, and the corresponding heat release and gas production are written into the energy equation and component transport equation. The exothermic contributions of the following processes are calculated sequentially according to the temperature range: the exothermic reaction of the negative electrode, the gas production from the electrolyte cracking, the oxygen release reaction of the positive electrode lattice, and the oxidation reaction of the current collector. The exothermic source terms and gas production source terms of each reaction are then superimposed into the coupled solution domain.

5. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, The thermal runaway simulation also includes: Based on the evolution of the current temperature TMP-VAL, local pressure PRS-VAL, and combustible gas concentration GAS-CON, the contribution ratio of each decomposition reaction is dynamically adjusted. Output total heat release from the heat source term: TOTQ; Among them, the heat release QSEI is calculated based on the SEI decomposition energy and decomposition rate, the heat release QELE from electrolyte pyrolysis is calculated based on the electrolyte volatility constant VAP-K, the heat release QNEG from the negative electrode side reaction is derived from the side reaction rate SBR-R, the heat release QPOS from the positive electrode lattice oxygen release is related to the oxygen release rate constant OXR-K, and α1 to α4 are dynamic coupling weights based on temperature TMP-VAL and state of charge SOC-VAL.

6. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, Simulating the propagation of a flame between battery cells includes: S411: After receiving the electrochemical state parameters current density CUR, open circuit voltage OCV, internal resistance parameter RES, and material thermal properties parameters specific heat capacity HCP and thermal conductivity HTC, an electrochemical heat source term is constructed in the solid region of the cell based on the energy equation, and the heat generated by ohmic internal resistance and polarization is written into the heat conservation equation. S412. When the temperature reaches the decomposition range, inject the exothermic source term and gas-producing source term output by the thermal runaway reaction model into the energy equation and component transport equation, respectively. S413. In the gas phase region of the battery pack, the velocity field and pressure field are solved based on the momentum conservation equation, and the gas generation rate is applied as a volume source term to the flow field solution domain. S414. Introduce corresponding component generation source terms into the diffusion equations for combustible gases, inert gases, and toxic gases to achieve continuous coupling of gas generation, convection transport, and diffusion processes. S415. Under the simultaneous iterative solution of the energy equation, momentum equation and component transport equation, realize the thermal behavior of the solid region and the flow behavior of the gas phase region, and output the temperature field, velocity field and concentration field distribution.

7. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, The process of thermal runaway propagation within the battery pack includes: S421. Within the solid structure region, the following parameters are used as input parameters: shell thermal conductivity (HCT), bracket equivalent thermal resistance (STR), insulation board thermal resistance (ITR), flame retardant layer shielding coefficient (SHF), structural contact thermal resistance (CTR), thermal runaway determination temperature threshold (TCR), and critical heat flux density threshold (QCR). S422. Based on the thermal conductivity parameters HCT, STR, ITR and the contact thermal resistance CTR, construct a solid thermal conduction path between the cell and the shell, bracket and insulation plate, and solve the heat flow distribution in each structural component. S423. Introduce a shielding factor SHF in the region containing the flame-retardant layer to reduce the incident heat flux density caused by thermal runaway of the battery cell, and calculate the equivalent net heat flux density QNF acting on the surface of adjacent battery cells. The net heat flux QNF is calculated in the following manner: Among them, the thermal conductivity heat flux density QCON is obtained from the thermal conductivity network, the radiation heat flux density QRAD is obtained based on the radiation heat transfer model, and the shielding reduction heat flux density QSHD is determined by the shielding coefficient SHF and the flame retardant layer coverage area. The coefficients β1, β2, and β3 are weight parameters set according to the structural material and surface condition. S424. Using the net heat flux QNF in the temperature field as the criterion, when the temperature of the surface of an adjacent cell exceeds the critical temperature TCR and the corresponding net heat flux QNF is greater than the critical heat flux QCR, the spatial location of the cell is marked as the thermal runaway propagation region.

8. The digital simulation method for dynamic evolution and smoke diffusion of lithium battery fires according to claim 1, characterized in that, The simulation of flue gas diffusion and toxic gas concentration field includes: S51. The mass release rate of flue gas generated in the fire zone, Q_sm(t), the generation rate of toxic gas, Q_tox,i(t), and the instantaneous velocity field VEL(t) and pressure field PRE(t) output by the fluid flow sub-model are used as input conditions for diffusion calculation. S52. Set the exposure threshold parameters for toxic gases, including the hydrogen concentration threshold C_H2_thr, the carbon monoxide concentration threshold C_CO_thr, the hydrogen fluoride concentration threshold C_HF_thr, and the short-term exposure limit STEL_i for each toxic component. S53. Based on the component transport equation, the mass fraction C_i of flue gas and each toxic gas is numerically discretized and solved. The concentration evolution of gas components in three-dimensional space is formed by convection term, molecular diffusion term and generation source term. S54. Calculate the turbulent eddy viscosity diffusion coefficient D_turb(k,ε) using the turbulent kinetic energy k(t) and turbulent dissipation rate ε(t) as inputs. S55. Calculate the vertical rise velocity of the flue gas based on the buoyancy driving term β(T-T_amb)g to obtain the flue gas diffusion map and toxic gas concentration map.

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