Lithium battery energy storage cabin flue gas spreading modeling method and related device

Abstract

The invention provides a lithium battery energy storage cabin flue gas spreading modeling method and a related device, and belongs to the field of energy storage safety protection. The method comprises the following steps: establishing a three-dimensional lithium battery energy storage cabin model based on computational fluid mechanics and simulating operation to obtain an initial simulation result; acquiring real-time operation parameters of the energy storage cabin, comparing the real-time operation parameters with the initial simulation result, and correcting the model according to the deviation data to obtain a corrected model; and partitioning the corrected model according to the pressure gradient, and simulating flue gas spreading by using corresponding precision calculation rules and grid sizes for different regions to obtain a simulation result. According to the method, the three-dimensional CFD model coupled with the multi-physical field effect is constructed, and the model is corrected by combining the real-time operation parameters and the initial result deviation, so that accurate flue gas spreading simulation is realized, the problem of simulation deviation in the prior art is solved, and accurate simulation and real-time prediction of flue gas spreading of the lithium battery energy storage cabin are realized; and a reliable basis is provided for fire safety design and emergency decision making of the energy storage cabin.

Description

Technical Field

[0001] This invention belongs to the field of energy storage safety protection technology, specifically relating to a method and related device for modeling the spread of flue gas in a lithium battery energy storage chamber. Background Technology

[0002] With the continuous development of energy storage technology, lithium battery energy storage compartments, as an important energy storage device, have been widely used in power systems and other fields. However, lithium battery energy storage compartments pose a fire risk during operation. In the event of a fire, the spread of smoke can pose a serious challenge to personnel safety and firefighting efforts.

[0003] Accurately understanding the smoke propagation patterns within lithium battery energy storage compartments is crucial for ensuring personnel safety, implementing effective firefighting measures, and optimizing the fire safety design of these compartments. Therefore, modeling and studying the smoke propagation patterns within lithium battery energy storage compartments is of significant importance.

[0004] Currently, the main problem in modeling flue gas spread in lithium battery energy storage compartments is the inaccuracy of the models. Some commonly used modeling methods, such as models built using FDS software, are not precise enough in coupling complex physical processes, and the boundary conditions are often set as idealized situations, which deviate significantly from the actual scenario, resulting in simulation results that do not match the actual flue gas spread. Summary of the Invention

[0005] In view of this, the present invention provides a method and related apparatus for modeling the spread of smoke in lithium battery energy storage compartments. It aims to solve the problem of inaccurate models in the prior art by constructing a refined physical model and introducing real-time monitoring data correction, so as to achieve accurate simulation and real-time prediction of the spread of smoke in lithium battery energy storage compartments, and provide a reliable basis for fire safety design and emergency decision-making of energy storage compartments.

[0006] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for modeling the spread of flue gas in a lithium battery energy storage chamber, comprising the following steps:

[0008] A three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics was established and simulated to obtain initial simulation results. The three-dimensional lithium battery energy storage chamber model coupled the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway in the flue gas flow process.

[0009] The real-time operating parameters inside the energy storage compartment are obtained, and the real-time operating parameters are compared with the initial simulation results. Based on the deviation data obtained from the comparison, the three-dimensional lithium battery energy storage compartment model is corrected to obtain the corrected three-dimensional lithium battery energy storage compartment model.

[0010] The corrected three-dimensional lithium battery energy storage chamber model was divided into blocks according to the pressure gradient. For different block regions, the corresponding accuracy calculation rules and mesh size were used to simulate the flue gas spread, and the simulation results of the flue gas spread were obtained.

[0011] Furthermore, the process of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes:

[0012] The Arrhenius equation was used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data were obtained.

[0013] The thermal runaway reaction kinetics data are correlated with the energy and mass equations of the computational fluid dynamics model;

[0014] The energy equation, mass equation, and thermal runaway reaction kinetics data after correlation are coupled and solved to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

[0015] Furthermore, real-time operating parameters include at least temperature, smoke concentration, gas composition, and pressure.

[0016] Furthermore, in the step of refining the three-dimensional lithium battery energy storage chamber model, a data assimilation algorithm is used to achieve model refinement, including:

[0017] Extract the deviation data between real-time operating parameters and initial simulation results;

[0018] The deviation data is input into a preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage chamber model; the data assimilation algorithm is an algorithm that obtains the adjustment values ​​of the parameters of the three-dimensional lithium battery energy storage chamber model based on the deviation data inversion.

[0019] The corresponding parameters of the three-dimensional lithium battery energy storage chamber model are updated based on the adjustment values ​​to complete the model correction.

[0020] Furthermore, the corrected 3D lithium battery energy storage chamber model was divided into blocks according to the pressure gradient. For different blocks, corresponding accuracy calculation rules and mesh sizes were used to simulate flue gas propagation, including:

[0021] A full-domain pressure scan was performed on the corrected 3D lithium battery energy storage chamber model. Based on the scanned pressure values, high-risk and low-risk areas were divided. High-risk areas were those with pressure values ​​greater than a set pressure threshold, while low-risk areas were those with pressure values ​​less than or equal to the set pressure threshold.

[0022] For high-risk partitioned regions, double-precision calculation rules and a first-size computational grid are configured; for low-risk partitioned regions, single-precision calculation rules and a second-size computational grid are configured; the first size is smaller than the second size.

[0023] The simulation task of flue gas spread corresponding to high-risk and low-risk block areas is broken down into several independent computational subtasks.

[0024] By running each independent computational subtask synchronously in parallel computing mode and integrating the computational results of all subtasks, the simulation results of flue gas propagation are obtained.

[0025] Secondly, the present invention provides a lithium battery energy storage chamber flue gas spread modeling device, comprising:

[0026] The model building module is used to build a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics and run the simulation to obtain initial simulation results. The three-dimensional lithium battery energy storage chamber model couples the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway in the flue gas flow process.

[0027] The model correction module is used to obtain real-time operating parameters inside the energy storage compartment, compare the real-time operating parameters with the initial simulation results, and correct the three-dimensional lithium battery energy storage compartment model based on the deviation data obtained from the comparison, so as to obtain the corrected three-dimensional lithium battery energy storage compartment model.

[0028] The flue gas propagation simulation module is used to divide the corrected three-dimensional lithium battery energy storage chamber model into blocks according to the pressure gradient. For different block areas, corresponding accuracy calculation rules and mesh sizes are used to simulate flue gas propagation and obtain the flue gas propagation simulation results.

[0029] Furthermore, in the model building module, the step of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes:

[0030] The Arrhenius equation was used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data were obtained.

[0031] The thermal runaway reaction kinetics data are correlated with the energy and mass equations of the computational fluid dynamics model;

[0032] The energy equation, mass equation, and thermal runaway reaction kinetics data after correlation are coupled and solved to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

[0033] Furthermore, in the model correction module, a data assimilation algorithm is used to implement model correction, including:

[0034] Extract the deviation data between real-time operating parameters and initial simulation results;

[0035] The deviation data is input into a preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage chamber model; the data assimilation algorithm is an algorithm that obtains the adjustment values ​​of the parameters of the three-dimensional lithium battery energy storage chamber model based on the deviation data inversion.

[0036] The corresponding parameters of the three-dimensional lithium battery energy storage chamber model are updated based on the adjustment values ​​to complete the model correction.

[0037] Thirdly, the present invention provides a computer device, the device including a processor and a memory:

[0038] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0039] The processor executes, according to the instructions of the computer program, a method for modeling the spread of flue gas in a lithium battery energy storage compartment, as described in the first aspect.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for modeling the spread of flue gas in a lithium battery energy storage chamber as described in the first aspect.

[0041] In summary, this invention provides a method and related apparatus for modeling the spread of flue gas in a lithium battery energy storage chamber. The method includes the following steps: establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics and performing simulation to obtain initial simulation results; coupling the three-dimensional lithium battery energy storage chamber model with the multi-physics field effects of heat transfer, mass transfer, chemical reaction, and battery thermal runaway during the flue gas flow process; acquiring real-time operating parameters within the energy storage chamber, comparing the real-time operating parameters with the initial simulation results, and correcting the three-dimensional lithium battery energy storage chamber model based on the deviation data obtained from the comparison to obtain a corrected three-dimensional lithium battery energy storage chamber model; dividing the corrected three-dimensional lithium battery energy storage chamber model into blocks according to the pressure gradient, and performing flue gas spread simulation using calculation rules and mesh sizes with corresponding accuracy for different block regions to obtain flue gas spread simulation results. This invention constructs a three-dimensional CFD model that couples the multi-physics field effects of flue gas flow, heat transfer, mass transfer, chemical reaction, and battery thermal runaway. It also combines a deviation correction model between the real-time operating parameters of the energy storage compartment and the initial simulation results to achieve accurate simulation of flue gas propagation. This effectively solves the simulation deviation problems caused by inaccurate coupling of complex physical processes and idealized boundary conditions in existing technologies. It enables accurate simulation and real-time prediction of flue gas propagation in lithium battery energy storage compartments, providing a reliable basis for fire safety design and emergency decision-making in energy storage compartments. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating a method for modeling the spread of flue gas in a lithium battery energy storage chamber, as provided in an embodiment of the present invention;

[0044] Figure 2 A three-dimensional structural diagram of a lithium battery energy storage compartment provided in an embodiment of the present invention;

[0045] Figure 3 This is a schematic diagram of sensor arrangement provided in an embodiment of the present invention;

[0046] Figure 4 A block diagram of a lithium battery energy storage compartment flue gas spread modeling device provided in an embodiment of the present invention;

[0047] Figure 5 This is a block diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0049] Please see Figure 1 This embodiment provides a method for modeling the spread of flue gas in a lithium battery energy storage compartment, including the following steps:

[0050] S11: Establish a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics and conduct simulation to obtain initial simulation results; the three-dimensional lithium battery energy storage chamber model couples the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway in the flue gas flow process.

[0051] It should be noted that computational fluid dynamics (CFD) is a branch of science that combines fluid mechanics with numerical computing techniques. It simulates physical processes such as fluid flow, heat transfer, and mass transfer by numerically solving the governing equations related to fluid flow.

[0052] The three-dimensional lithium battery energy storage compartment model is a three-dimensional digital model constructed based on the actual spatial structure of the lithium battery energy storage compartment, used to replicate the physical environment of smoke spread inside the compartment.

[0053] In the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway, heat transfer refers to the heat transfer process between flue gas and the cabin structure and battery; mass transfer refers to the migration and diffusion process of various substances in flue gas; chemical reaction refers to the chemical interaction between flue gas components and between flue gas and cabin materials; battery thermal runaway refers to the chain exothermic reaction caused by battery overheating. The multi-physics field effects are the interaction and coupling of the above-mentioned physical and chemical processes.

[0054] The initial simulation results are the simulation output data (such as the initial heat source intensity, pressure and temperature distribution corresponding to the initial chemical reaction rate constant, etc.) based on the CFD-based three-dimensional lithium battery energy storage chamber model (which has taken into account multi-physics coupling and refined structural modeling) under the initial parameter settings.

[0055] This step is based on computational fluid dynamics theory to construct a three-dimensional model that can replicate the spatial structure of a lithium battery energy storage compartment. The model incorporates the multi-physics interactions of heat transfer, mass transfer, chemical reactions, and battery thermal runaway during the flue gas flow process to ensure that the model can reflect the real physicochemical mechanism of flue gas propagation. Then, the three-dimensional model is started for simulation, and the initial flue gas propagation simulation data, i.e., the initial simulation results, are output through model calculation.

[0056] S12: Obtain the real-time operating parameters inside the energy storage compartment, compare the real-time operating parameters with the initial simulation results, and correct the three-dimensional lithium battery energy storage compartment model based on the deviation data obtained from the comparison, so as to obtain the corrected three-dimensional lithium battery energy storage compartment model.

[0057] It should be noted that real-time operating parameters refer to the real-time status data inside the lithium battery energy storage compartment during actual operation. Deviation data refers to the difference between the real-time operating parameters and the initial simulation results. The corrected 3D lithium battery energy storage compartment model is obtained by adjusting the parameters of the initial 3D lithium battery energy storage compartment model based on the deviation data, and its ability to adapt to actual scenarios is superior to the initial model.

[0058] This step first obtains the real-time operating parameters inside the lithium battery energy storage chamber, then compares the real-time data with the initial simulation results dimension by dimension to calculate the deviation data between the two; based on the deviation data, the parameters related to the deviation in the initial three-dimensional lithium battery energy storage chamber model are adjusted to eliminate the deviation between the model and the actual scene, and finally the corrected three-dimensional lithium battery energy storage chamber model is obtained, improving the model's actual adaptability.

[0059] S13: The corrected three-dimensional lithium battery energy storage chamber model is divided into blocks according to the pressure gradient. For different block areas, the corresponding accuracy calculation rules and mesh size are used to simulate the flue gas spread and obtain the flue gas spread simulation results.

[0060] It should be noted that the pressure gradient refers to the rate of pressure change in different areas within the chamber, serving as the basis for delineating areas at risk of flue gas spread. Differences in pressure gradients reflect variations in the severity of flue gas spread. Segmentation refers to dividing the simulation area of ​​the corrected 3D lithium battery energy storage chamber model into multiple independent sub-regions based on the pressure gradient, achieving differentiated simulation.

[0061] This step involves dividing the modified 3D lithium battery energy storage chamber model into simulation regions based on the pressure gradient, clarifying the pressure characteristics of different sub-regions. For each sub-region's pressure characteristics, matching calculation rule accuracy and mesh size are configured, ensuring simulation accuracy in high-risk areas (large pressure gradients) through high-precision calculations, while balancing calculation efficiency in low-risk areas through appropriate accuracy. Subsequently, flue gas propagation simulations are performed in each sub-region, and the simulation data from each sub-region are integrated to obtain the complete flue gas propagation simulation results.

[0062] This embodiment provides a method for modeling the spread of flue gas in lithium battery energy storage chambers. This method constructs a three-dimensional lithium battery energy storage chamber model that couples heat transfer, mass transfer, chemical reactions, and multi-physics field effects related to battery thermal runaway, achieving an accurate characterization of the physicochemical mechanisms of flue gas spread. Simultaneously, it introduces a deviation-driven correction mechanism between real-time operating parameters and initial simulation results, effectively correcting the discrepancy between the model and the actual scenario. Combining a pressure gradient partitioning strategy and differentiated accuracy calculation rules, it optimizes the computational efficiency of low-risk areas while ensuring simulation accuracy in high-risk areas, ultimately achieving accurate simulation of flue gas spread in lithium battery energy storage chambers. This provides reliable data support for fire safety design and emergency decision-making in energy storage chambers, effectively solving the problems of insufficient model accuracy and inconsistencies between simulation results and actual operating conditions in existing technologies.

[0063] In one embodiment of the present invention, the step of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes:

[0064] S21: The Arrhenius equation is used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data are obtained.

[0065] The Arrhenius equation is a classic formula characterizing the quantitative relationship between chemical reaction rate and temperature; its expression is: Where k is the reaction rate constant and A is the pre-exponential factor. R is the activation energy of the reaction, T is the molar gas constant, and T is the thermodynamic temperature. In this step, considering the characteristics of the lithium battery's electrode materials and electrolyte composition, the values ​​of A and T in the formula are determined through experimental measurements or literature calibration. Parameters such as these are used to establish the correlation between the rate of chemical reactions such as oxidation decomposition and gas production during battery thermal runaway and temperature. The output thermal runaway reaction kinetic data includes indicators such as reaction rate, heat production power, and gas production rate at different temperatures.

[0066] S22: Correlate the thermal runaway reaction kinetics data with the energy and mass equations of the computational fluid dynamics model.

[0067] The CFD model's energy equation describes the heat transfer and distribution within the energy storage chamber, while the mass equation describes the transport and conservation of substances such as flue gas. In the correlation process, the heat generation power from the thermal runaway reaction kinetics data is used as the heat source term in the energy equation, and the gas generation rate is used as the material source term in the mass equation. This establishes a mapping relationship between the kinetic data and the equation parameters. Specifically, changes in the thermal runaway reaction rate directly affect the magnitudes of the heat source and material source terms, while the temperature field distribution calculated by the energy equation in turn affects the reaction rate constant. This constructs a correlation logic between reaction kinetics, energy transfer, and mass transfer, giving the originally independent equations a basis for coupling.

[0068] S23: Couple the correlated energy equation, mass equation, and thermal runaway reaction kinetics data to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

[0069] In the solution process, numerical methods commonly used in CFD, such as the finite volume method, are adopted to discretize the associated control equations and divide the computational domain of the three-dimensional lithium battery energy storage chamber into several grid cells. Taking the kinetic data from step S21 and the equation association relationship from step S22 as input, the discretized equation set is solved through iterative calculation. In each iteration, the reaction rate constant is calculated based on the current temperature field, and the temperature distribution is updated through the energy equation. At the same time, the material concentration distribution is updated through the mass equation based on the gas production rate. The iteration is repeated until the calculation results converge, and finally, the accurate simulation of the multi-physics coupling effect of heat transfer, mass transfer, and chemical reaction during battery thermal runaway is achieved. This ensures that the model can reflect the influence of thermal runaway heat generation on the temperature field, the feedback effect of the temperature field on the reaction rate, and the driving effect of the gas production process on the flow of flue gas in the chamber.

[0070] This embodiment constructs the core implementation path of a multi-physics coupled CFD model for lithium battery energy storage chambers through a process of dynamic description, equation association, and coupled solution. It deeply integrates the chemical reaction characteristics of battery thermal runaway with the heat transfer and mass transport laws within the energy storage chamber, solving the technical problem that traditional single-physics models cannot accurately reflect the interaction of various physical effects during thermal runaway. The output multi-physics coupled model can accurately capture the coordinated changes in heat generation, gas generation, flue gas flow, and structural heat exchange during thermal runaway.

[0071] In one embodiment of the present invention, the real-time operating parameters include at least temperature, smoke concentration, gas composition, and pressure.

[0072] In this embodiment, temperature is a direct indicator of the thermal runaway process, smoke concentration corresponds to the smoke spread state caused by thermal runaway gas production, gas composition is directly related to the chemical reaction products of the decomposition of materials such as electrodes and electrolytes during thermal runaway, and pressure change can intuitively reflect the combined effect of gas expansion and smoke flow inside the chamber. By collecting these four types of parameters in real time, multi-dimensional state information related to thermal runaway inside the energy storage chamber can be obtained more comprehensively.

[0073] The acquisition of the aforementioned real-time operating parameters is based on the three-dimensional structural features of the energy storage compartment and the sensor arrangement: such as Figure 2 As shown, the spatial layout of the battery modules, ventilation ducts, and depressurization channels inside the cabin is clearly defined, and then specifically combined with... Figure 3 The sensors are arranged as shown. Temperature sensors, smoke concentration sensors, gas composition sensors, and pressure sensors are installed in key areas within the lithium battery energy storage compartment (including the area around the battery modules, the vents, and near the pressure relief channels). The gas composition sensors can accurately detect thermal runaway characteristic gases such as CO2, H2, CO, and low-carbon hydrogen compounds. Each sensor transmits real-time collected parameter data to a dedicated data acquisition and processing system via wireless communication technology, ensuring the real-time performance and stability of the data transmission. The data acquisition and processing system compares and analyzes the received real-time parameters with the simulation results. Through a real-time correction algorithm based on data assimilation, it dynamically adjusts key parameters in the model, such as heat source intensity and chemical reaction rate constant, to ensure that the model simulation results accurately match the actual operating state of the energy storage compartment. For example, when the temperature sensor detects an abnormal temperature increase in a certain area, the heat source parameters for the corresponding area can be adjusted based on the real-time temperature data, and the model can be rerun to improve the accuracy of smoke spread prediction.

[0074] It is worth noting that this embodiment selects gas composition as a real-time operating parameter based on the principle of gas generation during thermal runaway in lithium-ion batteries. The gases released during thermal runaway in lithium-ion batteries are generated by the decomposition and mutual reactions of additives such as electrodes, electrolytes, SEI films, and binders. The main components of gas generated during battery thermal runaway include CO2, H2, CO, and hydrocarbons with fewer than 3 carbon atoms (CH4, C2H2, C2H4, C2H6, C3H6, and C3H8). Due to the complexity of the internal material system of lithium-ion batteries, including electrodes, electrolytes, binders, and special additives, and the differences in thermal runaway induction and detection methods, the composition of gas generated during battery thermal runaway varies in different studies. Some existing technologies have also detected O2, HF, HCl, NO, NO2, SO2, aldehydes, and electrolyte vapor in the gas generated during battery thermal runaway. The gas generation reaction in lithium-ion batteries is complex; different gases may be generated simultaneously, and the product of one reaction may be a reactant of another. The generation of several major gases can be explained by the following reactions.

[0075] (1) O2 mainly comes from the decomposition reaction of the cathode material at high temperature. The decomposition reaction of the ternary NCM electrode is as follows:

[0076]

[0077] (2) CO2 can be generated by the decomposition of the SEI membrane, the reaction of LiPF6 with the electrolyte, and the reaction of oxygen released from the positive electrode with the electrolyte.

[0078] SEI membrane decomposition reaction:

[0079]

[0080] The dealdehyde reaction of LiPF6 with the electrolyte, where R is an alkane group:

[0081]

[0082]

[0083]

[0084] In the presence of HF and water, alkyl lithium carbonate may be generated inside the battery through the decomposition of the SEI film or the reaction of EC with lithium in the negative electrode. Alkyl lithium carbonate can also react with HF or water to generate CO2, C2H6, and O2.

[0085]

[0086]

[0087]

[0088]

[0089] Lithium carbonate and HF may react at high temperatures to produce CO2. Here, the lithium carbonate is produced by the reduction reaction between EC and lithium in the negative electrode.

[0090]

[0091] In the presence of oxygen, the oxidation reaction (combustion) of the electrolyte with oxygen releases CO2.

[0092] EC:

[0093]

[0094] PC:

[0095]

[0096] DEC:

[0097]

[0098] DMC:

[0099]

[0100] (3) CO mainly comes from the incomplete combustion of hydrocarbons, and a small part comes from the reduction reaction of lithium ions to CO2.

[0101] The reduction reaction of lithium ions to CO2 gas:

[0102]

[0103] In the presence of oxygen, if the oxidation reaction between the electrolyte and oxygen is incomplete (incomplete combustion), CO will be released.

[0104] EC:

[0105]

[0106] PC:

[0107]

[0108] DEC:

[0109]

[0110] DMC:

[0111]

[0112] (4) H2 can be produced by the decomposition reaction of the binder polyvinylidene fluoride (PVdF) and carboxymethyl cellulose (CMC) at high temperature:

[0113] The decomposition reaction of polyvinylidene fluoride at temperatures above 260°C:

[0114]

[0115] The decomposition reaction of carboxymethyl cellulose at temperatures above 250°C:

[0116]

[0117] (5) C2H4 comes from the decomposition of the SEI film and the reduction reaction of lithium in EC and negative electrode.

[0118] Decomposition reaction of SEI membrane:

[0119]

[0120]

[0121] The reduction reaction of EC with lithium in the negative electrode:

[0122]

[0123]

[0124] (6) In the presence of H2, CH4 originates from the reaction of DMC and lithium ions. Furthermore, similar reactions can also produce C2H6 and C3H6:

[0125]

[0126] (7) C2H6 is partly derived from the reduction reaction of DMC, DEC and lithium in the negative electrode:

[0127]

[0128]

[0129] (8) C3H6 is partly derived from the reduction reaction of PC with lithium in the negative electrode:

[0130]

[0131] CH4 and C3H8 can also be produced by the reduction reaction of DEC with lithium in the negative electrode; C2H2 can be produced by the oxidation or dehydrogenation reaction of C2H4 on the negative electrode surface, or by the cracking reaction of CH4.

[0132] In one embodiment of the present invention, the step of correcting the three-dimensional lithium battery energy storage chamber model employs a data assimilation algorithm to achieve model correction, including:

[0133] S31: Extract the deviation data between the real-time operating parameters and the initial simulation results.

[0134] The real-time operating parameters are data collected by temperature sensors, smoke concentration sensors, gas composition sensors, and pressure sensors inside the lithium battery energy storage compartment.

[0135] The real-time operating parameters and the initial simulation results are compared and calculated point by point and time by time to extract the deviation data (such as the difference between the actual monitored temperature and the simulated temperature in a certain area, the difference between the actual pressure and the simulated pressure, etc.). This deviation data directly reflects the degree of fit between the initial model and the actual operating state of the energy storage cabin.

[0136] S32: Input the deviation data into the preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage cabin model; the data assimilation algorithm is an algorithm that obtains the adjustment values ​​of the parameters of the three-dimensional lithium battery energy storage cabin model based on the deviation data inversion.

[0137] The data assimilation algorithm performs parameter inversion based on deviation data, establishing a mapping relationship between deviation data and key model parameters (heat source intensity, chemical reaction rate constant). Due to the multi-physics coupling characteristics of the original model (coupling of thermal runaway reaction kinetics with energy and mass equations), heat source intensity directly affects the solution results of the energy equation (temperature field distribution), while the chemical reaction rate constant directly determines the thermal runaway gas production rate and reaction heat production efficiency (corresponding to the mass and energy equations). These two parameters are the core factors causing the deviation between simulation results and actual results. The preset data assimilation algorithm can reduce the adjustment range of deviation parameters by fusing real-time deviation data and inversely deriving: for example, if the deviation data of a certain area shows that the actual monitored temperature is much higher than the simulated temperature, the algorithm will inversely derive the adjustment value that needs to be increased for the heat source intensity in that area; if the CO2 generation in the gas composition monitoring is higher than the simulated value, the algorithm will output the corresponding adjustment value that increases the rate constant of related chemical reactions (such as SEI membrane decomposition and electrolyte oxidation), ultimately outputting accurate adjustment values ​​for heat source intensity and chemical reaction rate constant.

[0138] S33: Update the corresponding parameters of the three-dimensional lithium battery energy storage chamber model based on the adjustment values ​​to complete the model correction.

[0139] After obtaining the adjusted values, they are synchronously updated to the corresponding parameter modules of the 3D lithium battery energy storage module model: for the heat source intensity adjustment value, the heat source term in the model's energy equation is updated; for the chemical reaction rate constant adjustment value, the coupled thermal runaway reaction kinetic data (such as the reaction rate constant k in the Arrhenius formula) is updated. After the parameters are updated, the corrected model is rerun. At this time, the coupled solution results of the model's energy equation, mass equation, and thermal runaway reaction kinetic data will significantly reduce the deviation from the real-time monitoring data.

[0140] In one embodiment of the present invention, the modified three-dimensional lithium battery energy storage chamber model is divided into blocks according to the pressure gradient, and flue gas propagation simulation is performed using calculation rules and mesh sizes with corresponding accuracy for different block regions, including:

[0141] S41: Perform a full-domain pressure scan on the corrected 3D lithium battery energy storage chamber model, and divide it into high-risk and low-risk block areas based on the scanned pressure values; the high-risk block area is the area where the pressure value is greater than the set pressure threshold, and the low-risk block area is the area where the pressure value is less than or equal to the set pressure threshold.

[0142] For example, 10 kPa is set as the pressure threshold, and areas with pressure values ​​greater than 10 kPa are divided into high-risk areas, while areas with pressure values ​​less than or equal to 10 kPa are divided into low-risk areas.

[0143] S42: Configure double-precision calculation rules and a first-size calculation grid for high-risk block regions, and configure single-precision calculation rules and a second-size calculation grid for low-risk block regions; the first size is smaller than the second size.

[0144] For example, double-precision calculation rules and a 0.02m-sized calculation grid are configured for high-risk segmented areas to ensure the accuracy of capturing the flue gas flow and spread characteristics in these areas; single-precision calculation rules and a 0.08m-sized calculation grid are configured for low-risk segmented areas to reduce computational overhead while meeting basic simulation requirements. A balance between simulation accuracy and computational efficiency is achieved through differentiated matching of accuracy and grid size.

[0145] S43: Decompose the flue gas spread simulation task corresponding to the high-risk block area and the low-risk block area into several independent calculation sub-tasks.

[0146] S44: By running each independent computational subtask synchronously in parallel computing mode, and integrating the computational results of all subtasks, the simulation results of flue gas spread are obtained.

[0147] This step can employ GPU-accelerated parallel computing technology to distribute each independent computational subtask to multiple computing cores of the GPU for synchronous execution. By optimizing algorithms and data structures, data transmission and storage overhead can be reduced. After all subtasks have been executed, the computation results are integrated to obtain a complete smoke propagation simulation result, significantly improving overall computational efficiency.

[0148] Furthermore, the construction of the 3D lithium battery energy storage chamber model requires refined structural modeling. This includes precise 3D modeling of the internal structures such as battery modules, partitions, and ventilation ducts, as well as the internal structures within the battery modules, including individual battery cells, insulation materials, and pressure relief valves. After modeling using CAD software, the models are imported into CFD software, and a high-quality computational mesh is generated through mesh generation technology to ensure accurate capture of flue gas flow characteristics under complex structures. Model construction and simulation operation must adhere to preset parameter settings, including an ambient temperature of 25℃, an ambient pressure of 100kPa (1bar), and a wind speed of 0 (ignoring the influence of ambient wind). The Eular equation is used as boundary conditions, with an air composition of 20.95% O2 + 79.05% N2. The key output variables include pressure (kPa, bar) and flame propagation speed (m / s). Simultaneously, model reduction techniques can be introduced. By extracting the main characteristic modes of the model, the model is simplified, reducing degrees of freedom and further reducing computational load while ensuring accuracy, enabling rapid simulation and prediction of flue gas spread.

[0149] The refined physical model constructed in this invention fully considers the coupling of multiple physics fields and the complex structure inside the compartment, enabling it to more accurately simulate the real physical process of smoke spread inside the lithium battery energy storage compartment, thus improving the model's accuracy and reliability. Simultaneously, real-time monitoring data corrects the model, allowing it to dynamically adjust according to actual operating conditions and promptly reflect changes in smoke spread trends, providing real-time and accurate data for fire safety decisions. Furthermore, the use of efficient calculation methods to accelerate the simulation significantly shortens the simulation time, meeting real-time requirements and facilitating a rapid response and effective fire extinguishing and evacuation measures in the event of a fire.

[0150] Based on the same inventive concept, this application also provides a lithium battery storage compartment flue gas spread modeling device for implementing the above-mentioned lithium battery storage compartment flue gas spread modeling method. The solution provided by this device is similar to the solution described in the above-mentioned method. Therefore, the specific limitations in the embodiments of the lithium battery storage compartment flue gas spread modeling device provided below can be found in the limitations of the lithium battery storage compartment flue gas spread modeling method above, and will not be repeated here.

[0151] Please see Figure 4 This invention also provides a lithium battery energy storage compartment flue gas spread modeling device, comprising:

[0152] The model building module is used to build a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics and run the simulation to obtain initial simulation results. The three-dimensional lithium battery energy storage chamber model couples the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway in the flue gas flow process.

[0153] The model correction module is used to obtain real-time operating parameters inside the energy storage compartment, compare the real-time operating parameters with the initial simulation results, and correct the three-dimensional lithium battery energy storage compartment model based on the deviation data obtained from the comparison, so as to obtain the corrected three-dimensional lithium battery energy storage compartment model.

[0154] The flue gas propagation simulation module is used to divide the corrected three-dimensional lithium battery energy storage chamber model into blocks according to the pressure gradient. For different block areas, corresponding accuracy calculation rules and mesh sizes are used to simulate flue gas propagation and obtain the flue gas propagation simulation results.

[0155] Furthermore, in the model building module, the step of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes:

[0156] The Arrhenius equation was used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data were obtained.

[0157] The thermal runaway reaction kinetics data are correlated with the energy and mass equations of the computational fluid dynamics model;

[0158] The energy equation, mass equation, and thermal runaway reaction kinetics data after correlation are coupled and solved to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

[0159] Furthermore, in the model correction module, a data assimilation algorithm is used to implement model correction, including:

[0160] Extract the deviation data between real-time operating parameters and initial simulation results;

[0161] The deviation data is input into a preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage chamber model; the data assimilation algorithm is an algorithm that obtains the adjustment values ​​of the parameters of the three-dimensional lithium battery energy storage chamber model based on the deviation data inversion.

[0162] The corresponding parameters of the three-dimensional lithium battery energy storage chamber model are updated based on the adjustment values ​​to complete the model correction.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0164] Reference Figure 5 The present invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it implements the lithium battery energy storage compartment flue gas propagation modeling method as described in any of the above methods.

[0165] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 5 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0166] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0167] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0168] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the lithium battery energy storage compartment flue gas propagation modeling method as described in any of the above methods.

[0169] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0170] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the lithium battery energy storage compartment flue gas propagation modeling method as described in any of the above methods.

[0171] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0172] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0173] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0174] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for modeling the spread of flue gas in a lithium battery energy storage compartment, characterized in that, Includes the following steps: A three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics was established and simulated to obtain initial simulation results. The three-dimensional lithium battery energy storage chamber model coupled the multi-physics field effects of heat transfer, mass transfer, chemical reaction and battery thermal runaway in the flue gas flow process. The real-time operating parameters inside the energy storage compartment are obtained, and the real-time operating parameters are compared with the initial simulation results. Based on the deviation data obtained from the comparison, the three-dimensional lithium battery energy storage compartment model is corrected to obtain the corrected three-dimensional lithium battery energy storage compartment model. The modified three-dimensional lithium battery energy storage chamber model is divided into blocks according to the pressure gradient. For different block regions, the corresponding accuracy calculation rules and grid size are used to simulate the smoke spread, and the smoke spread simulation results are obtained.

2. The method for modeling the spread of flue gas in a lithium battery energy storage compartment according to claim 1, characterized in that, The process of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes: The Arrhenius equation was used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data were obtained. The thermal runaway reaction kinetic data are correlated with the energy equations and mass equations of the computational fluid dynamics model; The energy equation, mass equation, and thermal runaway reaction kinetics data after correlation are coupled and solved to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

3. The method for modeling the spread of flue gas in a lithium battery energy storage chamber according to claim 1, characterized in that, The real-time operating parameters include at least temperature, smoke concentration, gas composition, and pressure.

4. The method for modeling the spread of flue gas in a lithium battery energy storage compartment according to claim 1, characterized in that, In the step of correcting the three-dimensional lithium battery energy storage chamber model, a data assimilation algorithm is used to implement the model correction, including: Extract the deviation data between the real-time operating parameters and the initial simulation results; The deviation data is input into a preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage chamber model; the data assimilation algorithm is an algorithm that inverts the deviation data to obtain the parameter adjustment values ​​of the three-dimensional lithium battery energy storage chamber model. The corresponding parameters of the three-dimensional lithium battery energy storage chamber model are updated based on the adjustment values ​​to complete the model correction.

5. The method for modeling the spread of flue gas in a lithium battery energy storage compartment according to claim 1, characterized in that, The corrected three-dimensional lithium battery energy storage chamber model is divided into blocks according to the pressure gradient. For different block regions, corresponding accuracy calculation rules and mesh sizes are used to simulate flue gas propagation, including: A full-domain pressure scan is performed on the corrected three-dimensional lithium battery energy storage chamber model, and high-risk and low-risk block areas are divided according to the scanned pressure values. The high-risk block areas are areas where the pressure values ​​are greater than a set pressure threshold, and the low-risk block areas are areas where the pressure values ​​are less than or equal to the set pressure threshold. For the high-risk segmented regions, double-precision calculation rules and a calculation grid of a first size are configured; for the low-risk segmented regions, single-precision calculation rules and a calculation grid of a second size are configured; the first size is smaller than the second size. The flue gas spread simulation task corresponding to the high-risk block area and the low-risk block area is decomposed into several independent computational sub-tasks; The independent computational subtasks are run synchronously in parallel computing mode, and the computational results of all subtasks are integrated to obtain the simulation results of flue gas propagation.

6. A device for modeling the spread of flue gas in a lithium battery energy storage compartment, characterized in that, include: The model building module is used to build a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics and run the simulation to obtain initial simulation results; The three-dimensional lithium battery energy storage chamber model couples the heat transfer, mass transfer, chemical reaction and multi-physics field effects of battery thermal runaway during the flue gas flow process. The model correction module is used to obtain real-time operating parameters inside the energy storage compartment, compare the real-time operating parameters with the initial simulation results, and correct the three-dimensional lithium battery energy storage compartment model based on the deviation data obtained from the comparison, so as to obtain the corrected three-dimensional lithium battery energy storage compartment model. The flue gas propagation simulation module is used to divide the corrected three-dimensional lithium battery energy storage chamber model into blocks according to the pressure gradient. For different block areas, the corresponding accuracy calculation rules and grid size are used to simulate the flue gas propagation and obtain the flue gas propagation simulation results.

7. The lithium battery energy storage chamber flue gas propagation modeling device according to claim 6, characterized in that, In the model building module, the step of establishing a three-dimensional lithium battery energy storage chamber model based on computational fluid dynamics includes a multiphysics coupling process, which includes: The Arrhenius equation was used to describe the thermal runaway reaction kinetics of the battery, and thermal runaway reaction kinetic data were obtained. The thermal runaway reaction kinetic data are correlated with the energy equations and mass equations of the computational fluid dynamics model; The energy equation, mass equation, and thermal runaway reaction kinetics data after correlation are coupled and solved to achieve multi-physics coupling of the three-dimensional lithium battery energy storage chamber model.

8. The lithium battery energy storage compartment flue gas propagation modeling device according to claim 6, characterized in that, In the model correction module, a data assimilation algorithm is used to implement model correction, including: Extract the deviation data between the real-time operating parameters and the initial simulation results; The deviation data is input into a preset data assimilation algorithm to obtain the adjustment values ​​of the heat source intensity and chemical reaction rate constant in the three-dimensional lithium battery energy storage chamber model; the data assimilation algorithm is an algorithm that inverts the deviation data to obtain the parameter adjustment values ​​of the three-dimensional lithium battery energy storage chamber model. The corresponding parameters of the three-dimensional lithium battery energy storage chamber model are updated based on the adjustment values ​​to complete the model correction.

9. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes a method for modeling the spread of flue gas in a lithium battery energy storage compartment as described in any one of claims 1-5, according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for modeling the spread of flue gas in a lithium battery energy storage chamber as described in any one of claims 1-5.

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