A battery pack thermal runaway simulation method, device and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for simulating thermal runaway of a battery pack, relating to the field of power battery technology. The method includes: first, establishing a geometric model of the battery pack based on its spatial structure; then, configuring particulate matter ejection rules in the geometric model to simulate the gas flow field inside the battery pack, obtaining a first simulation model; further, configuring particulate matter motion rules in the first simulation model to simulate the motion process of particulate matter in the gas flow field, obtaining a second simulation model; finally, tracking the distribution of particulate matter after ejection in the second simulation model, and correcting the parameters of the gas flow field based on the distribution, obtaining a battery pack simulation model. The battery pack simulation model is used to simulate the ejection and diffusion behavior of particulate matter during thermal runaway of the battery pack. This application embodiment can be used for battery pack safety assessment, can reproduce the particulate matter ejection and diffusion behavior inside the battery pack, and improves the realism of the simulation.

Description

Technical Field

[0001] This application relates to the field of power battery technology, and in particular to a method, apparatus, and electronic device for simulating thermal runaway of a battery pack. Background Technology

[0002] With the rapid development of new energy vehicles, the safety issues of lithium-ion battery packs are becoming increasingly prominent. Thermal runaway is the most serious form of battery failure. When a cell in a battery pack experiences thermal runaway due to short circuits, high temperatures, or mechanical abuse, the cell will emit a large amount of corrosive particles. The release and accumulation of these particles within the battery pack can easily lead to short circuits or arcing in other cells or electronic components that have not yet runaway, and may even cause the entire pack to catch fire or explode.

[0003] Currently, most existing methods use simulation analysis on a single cell to assess the risk of thermal runaway, which makes it difficult to truly reflect the particulate matter ejection and diffusion process in the multi-cell coupled environment within a battery pack. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and electronic device for simulating thermal runaway of a battery pack, which aims to reproduce the emission and diffusion behavior of particulate matter within the battery pack and improve the realism of the simulation.

[0005] In a first aspect, this application provides a method for simulating thermal runaway of a battery pack. The method includes: establishing a geometric model of the battery pack based on its spatial structure; configuring particulate matter ejection rules in the geometric model to simulate the gas flow field inside the battery pack, thereby obtaining a first simulation model; configuring particulate matter motion rules in the first simulation model to obtain a second simulation model, wherein the particulate matter motion rules are used to simulate the motion process of particulate matter in the gas flow field; tracking the distribution of particulate matter after ejection in the second simulation model, and correcting the parameters of the gas flow field according to the distribution, thereby obtaining a battery pack simulation model, wherein the battery pack simulation model is used to simulate the ejection and diffusion behavior of particulate matter during thermal runaway of the battery pack.

[0006] The battery pack thermal runaway simulation method provided in this application establishes a geometric model based on the actual spatial structure of the battery pack and sequentially configures particulate matter ejection and motion rules. This allows for the simulation of the gas flow field induced by thermal runaway, and subsequently, the motion of particulate matter within it. Specifically, by tracking the distribution of particulate matter after ejection and adjusting the parameters of the gas flow field accordingly, a two-way coupling effect between the gas and particulate matter is achieved. This more realistically reflects the interaction between the gas and solid phases during battery pack thermal runaway, enabling the final battery pack simulation model to accurately simulate the ejection and diffusion behavior of particulate matter. This improves the accuracy and reliability of the simulation results and provides strong data support for battery pack safety performance evaluation and optimization design.

[0007] In conjunction with the first aspect mentioned above, in one possible implementation, the particle motion rules include collision behavior rules when particles collide with the wall in the first simulation model; the collision behavior rules include: when particles collide with the wall in the first simulation model, determining the reflection coefficient of the particles based on the collision velocity of the particles; determining the reflection velocity of the particles based on the reflection coefficient; and determining the motion of the particles during the collision behavior based on the reflection velocity and a preset critical velocity.

[0008] Based on the above technical means, this application determines the rebound coefficient by collision velocity and further determines the reflection velocity, and then compares the reflection velocity with the preset critical velocity, thereby accurately defining the motion state of the particulate matter after collision, providing a reliable physical model basis for subsequent accurate simulation of the diffusion trajectory and deposition location of particulate matter inside the battery pack.

[0009] In conjunction with the first aspect mentioned above, in one possible implementation, when the reflection velocity is less than a preset critical velocity, the motion is determined to be particle deposition on the wall surface; when the reflection velocity is greater than or equal to the preset critical velocity, the motion is determined to be particle rebound on the wall surface.

[0010] Based on the above technical means, this application can clearly distinguish between the deposition and rebound behavior of particles after collision by setting a preset critical velocity as a judgment threshold, thereby accurately reflecting the physical phenomenon of the interaction between particles and the wall surface and improving the accuracy of the simulation model in predicting the motion destination of particles.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, when the motion is that the particles bounce off the wall surface, the motion of the particles is updated according to the reflection velocity until the particles stop moving.

[0012] Based on the aforementioned technical means, this application can completely simulate the multiple collision processes of particles inside the battery pack by continuously tracking the rebounding particles until they stop moving, thus avoiding mass loss or trajectory deviation caused by interrupted simulation and ensuring the continuity and integrity of the simulation process.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the particulate matter motion rule further includes an escape behavior rule for particulate matter passing through the exhaust structure in the first simulation model. The escape behavior rule includes: obtaining the particle size of the particulate matter when it passes through the exhaust structure in the first simulation model; determining a filtration threshold corresponding to the filter based on the pore characteristic parameters of the filter in the exhaust structure, wherein the pore characteristic parameters are updated as the particulate matter is captured by the filter; determining that the particulate matter is captured by the exhaust structure when the particle size is greater than the filtration threshold; and determining that the particulate matter escapes at the exhaust structure when the particle size is less than or equal to the filtration threshold.

[0014] Based on the above technical means, this application can simulate the process of real-time changes in filter performance caused by particulate matter blockage by comparing the particle size with the dynamically updated filtration threshold. This allows for accurate prediction of the escape or capture of particles of different sizes at the exhaust structure, making the simulation model more consistent with the impact of filter performance degradation on particulate matter diffusion behavior under actual working conditions.

[0015] In conjunction with the first aspect mentioned above, in one possible implementation, thermal runaway simulation analysis of the battery pack is performed in the battery pack simulation model, including: based on the cell gas generation data and particulate matter emission parameters of the target battery pack, thermal runaway simulation is performed in the battery pack simulation model to obtain the positional distribution of the particulate matter, and the spatial structure of the target battery pack is consistent with that of the battery pack simulation model; based on the positional distribution, the state parameters related to the thermal runaway of the target battery pack are determined.

[0016] Based on the above-mentioned technical means, this application can obtain the spatial distribution characteristics of particulate matter during thermal runaway by inputting real cell gas generation data and eruption parameters into a simulation model with the same structure as the sample. In this way, key state parameters for quantifying the degree of thermal runaway hazard can be extracted, providing data support for evaluating the safety performance of the battery pack.

[0017] In conjunction with the first aspect mentioned above, in one possible implementation, the state parameters include the particulate matter accumulation mass and particulate matter concentration in a preset region of the battery pack simulation model. The preset region is a monitoring area pre-delineated based on the structural characteristics of the battery pack simulation model and / or historical deposition data of particulate matter during thermal runaway. Based on the location distribution, the state parameters related to thermal runaway of the target battery pack are determined, including: acquiring a first particulate matter based on its location distribution, wherein the first particulate matter is particulate matter falling into the preset region; accumulating the mass of the first particulate matter to obtain the particulate matter accumulation mass within the preset region; and determining the particulate matter concentration within the preset region based on the particulate matter accumulation mass and the volume of the preset region.

[0018] Based on the aforementioned technical means, this application can quantify the cumulative risk of particulate matter generated by thermal runaway in sensitive locations such as high-voltage connection points and weak insulation by delineating key monitoring areas and accumulating the mass and calculating the concentration of particulate matter falling into these areas. This provides an intuitive quantitative basis for battery pack safety design and the setting of early warning thresholds.

[0019] In conjunction with the first aspect mentioned above, in one possible implementation, the state parameters also include the gas flow velocity at the exhaust structure in the battery pack simulation model; determining the state parameters related to the thermal runaway of the target battery pack based on the position distribution also includes: obtaining a second particulate matter based on the position distribution, the second particulate matter being the particulate matter passing through the exhaust structure; obtaining the velocity of the second particulate matter as it passes through the exhaust structure; determining the gas flow velocity at the exhaust structure based on the velocity of the particulate matter, the particulate matter being carried away by the gas at the exhaust structure.

[0020] Based on the above-mentioned technical means, this application obtains the velocity of particulate matter passing through the exhaust structure and uses the physical relationship of particulate matter being carried by gas to infer the gas flow velocity, thereby realizing the indirect measurement of gas flow characteristics during the depressurization process of the battery pack. This provides a reference for evaluating depressurization efficiency and thermal runaway gas emission capability, while avoiding the difficulty of directly calculating complex flow fields in simulation.

[0021] In conjunction with the first aspect mentioned above, in one possible implementation, the parameters of the battery pack simulation model are calibrated through the following steps: A thermal runaway test is conducted on a battery pack sample to obtain experimental data related to thermal runaway; thermal runaway simulation analysis of the battery pack sample is performed in the battery pack simulation model to obtain state parameters related to thermal runaway; based on the experimental data and the difference between the state parameters and the experimental data, the parameters to be optimized in the simulation model are adjusted; the steps of battery pack thermal runaway simulation and adjusting the parameters to be optimized are repeated until the difference between the experimental data and the state parameters meets the preset requirements.

[0022] Based on the above technical means, this application establishes a closed-loop correction mechanism between the simulation model and the actual physical process by iteratively comparing and correcting the simulation results with the actual thermal runaway test data, thereby effectively calibrating the key physical parameters in the model and improving the reliability and prediction accuracy of the simulation model.

[0023] In conjunction with the first aspect mentioned above, in one possible implementation, the parameters to be optimized include at least one of the following: the reflection coefficient when particles collide with the wall, and the deposition parameters corresponding to the critical velocity at which particles are deposited on the wall; based on experimental data, and based on the difference between the state parameters and the experimental data, the parameters to be optimized in the simulation model are adjusted, including: based on the experimental data, using the difference between the state parameters and the experimental data as the loss value; based on the loss value, using Bayesian search, determining a set of parameters in the parameter value space that minimizes the loss value, as the adjusted parameters.

[0024] Based on the above technical means, this application adopts the Bayesian optimization algorithm to construct a loss function based on experimental data, and automatically searches for the optimal parameter combination that minimizes the loss value in the parameter space. This achieves efficient and automated verification of key physical parameters such as reflection coefficient and deposition parameters, avoids the subjectivity and inefficiency of manual trial and error, and ensures a high degree of consistency between the simulation model and the experimental results.

[0025] Secondly, this application provides a battery pack thermal runaway simulation device, comprising: a model building module and a rule configuration module. The model building module is used to build a geometric model of the battery pack based on its spatial structure. The rule configuration module is used to configure particulate matter ejection rules in the geometric model to simulate the gas flow field inside the battery pack, obtaining a first simulation model. The rule configuration module is also used to configure particulate matter motion rules in the first simulation model, obtaining a second simulation model, whereby the particulate matter motion rules simulate the motion process of particulate matter in the gas flow field. The rule configuration module is also used to track the distribution of particulate matter after ejection in the second simulation model and correct the parameters of the gas flow field based on the distribution, obtaining a battery pack simulation model. This battery pack simulation model is used to simulate the ejection and diffusion behavior of particulate matter during thermal runaway of the battery pack.

[0026] In conjunction with the second aspect above, in one possible implementation, the particle motion rules include collision behavior rules when particles collide with the wall in the first simulation model; the collision behavior rules include: when particles collide with the wall in the first simulation model, determining the reflection coefficient of the particles based on the collision velocity of the particles; determining the reflection velocity of the particles based on the reflection coefficient; and determining the motion of the particles during the collision behavior based on the reflection velocity and a preset critical velocity.

[0027] In conjunction with the second aspect above, in one possible implementation, when the reflection velocity is less than a preset critical velocity, the motion is determined to be particle deposition on the wall surface; when the reflection velocity is greater than or equal to the preset critical velocity, the motion is determined to be particle rebound on the wall surface.

[0028] In conjunction with the second aspect above, in one possible implementation, when the motion is that the particles bounce off the wall surface, the motion of the particles is updated according to the reflection velocity until the particles stop moving.

[0029] In conjunction with the second aspect above, in one possible implementation, the particulate matter motion rule further includes an escape behavior rule for particulate matter passing through the exhaust structure in the first simulation model. The escape behavior rule includes: obtaining the particle size of the particulate matter when it passes through the exhaust structure in the first simulation model; determining a filtration threshold corresponding to the filter based on the pore characteristic parameters of the filter in the exhaust structure, wherein the pore characteristic parameters are updated as the particulate matter is captured by the filter; determining that the particulate matter is captured by the exhaust structure when the particle size is greater than the filtration threshold; and determining that the particulate matter escapes at the exhaust structure when the particle size is less than or equal to the filtration threshold.

[0030] In conjunction with the second aspect above, in one possible implementation, the device further includes a simulation analysis module, used to: perform thermal runaway simulation in a battery pack simulation model based on the cell gas generation data and particulate matter emission parameters of the target battery pack, to obtain the positional distribution of the particulate matter, wherein the spatial structure of the target battery pack is consistent with that of the battery pack simulation model; and determine the state parameters related to the thermal runaway of the target battery pack based on the positional distribution.

[0031] In conjunction with the second aspect above, in one possible implementation, the state parameters include the particulate matter accumulation mass and particulate matter concentration in a preset region of the battery pack simulation model. The preset region is a monitoring area pre-delineated based on the structural characteristics of the battery pack simulation model and / or historical deposition data of particulate matter during thermal runaway. The simulation analysis module is specifically used for: acquiring a first particulate matter based on its location distribution, wherein the first particulate matter is particulate matter that has fallen into the preset region; accumulating the mass of the first particulate matter to obtain the particulate matter accumulation mass in the preset region; and determining the particulate matter concentration in the preset region based on the particulate matter accumulation mass and the volume of the preset region.

[0032] In conjunction with the second aspect mentioned above, in one possible implementation, the state parameters also include the gas flow velocity at the exhaust structure in the battery pack simulation model; the simulation analysis module is specifically used for: obtaining the second particulate matter based on its position distribution, the second particulate matter being the particulate matter passing through the exhaust structure; obtaining the velocity of the second particulate matter as it passes through the exhaust structure; and determining the gas flow velocity at the exhaust structure based on the velocity of the particulate matter, the particulate matter being carried along by the gas at the exhaust structure.

[0033] In conjunction with the second aspect above, in one possible implementation, the device further includes a model calibration module, used for: conducting thermal runaway tests on the battery pack sample to obtain test data related to thermal runaway of the battery pack sample; performing thermal runaway simulation analysis of the battery pack sample in a battery pack simulation model to obtain state parameters related to thermal runaway of the battery pack sample; adjusting the parameters to be optimized in the simulation model based on the test data and the difference between the state parameters and the test data; repeating the steps of battery pack thermal runaway simulation and adjusting the parameters to be optimized until the difference between the test data and the state parameters meets the preset requirements.

[0034] In conjunction with the second aspect above, in one possible implementation, the parameters to be optimized include at least one of the following: the reflection coefficient when particles collide with the wall, and the deposition parameters corresponding to the critical velocity at which particles are deposited on the wall; the model calibration module is specifically used to: take the experimental data as a benchmark, and use the difference between the state parameters and the experimental data as the loss value; based on the loss value, determine a set of parameters that minimize the loss value in the parameter value space through Bayesian search, and use them as the adjusted parameters.

[0035] Thirdly, this application provides an electronic device comprising: a processor and a memory; the memory storing processor-executable instructions; when the processor is configured to execute the instructions, the electronic device implements the method of the first aspect described above.

[0036] Fourthly, this application provides a computer-readable storage medium comprising: computer software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0037] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the steps of the relevant method described in the first aspect above, so as to implement the method of the first aspect above.

[0038] The beneficial effects of the second to fifth aspects mentioned above can be referred to the corresponding description of the first aspect, and will not be repeated here.

[0039] It should be noted that any of the possible implementations of any of the above aspects can be combined, provided that the solutions do not contradict each other. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating a battery pack thermal runaway simulation method provided in this application embodiment; Figure 2 A flowchart illustrating a particulate matter collision behavior processing method provided in an embodiment of this application; Figure 3 A flowchart illustrating a method for handling particulate matter escape behavior provided in an embodiment of this application; Figure 4A flowchart illustrating a battery pack simulation model application method provided in this application embodiment; Figure 5 A flowchart illustrating a method for determining the packing mass of particulate matter, provided as an embodiment of this application; Figure 6 This is a schematic diagram of the structural composition of a battery pack sample provided in an embodiment of this application; Figure 7 This is a schematic diagram of the composition of a battery pack thermal runaway simulation device provided in an embodiment of this application; Figure 8 This is a schematic diagram of a battery pack thermal runaway simulation device provided in an embodiment of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] It should be noted that in the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0044] In the embodiments of this application, the terms "first," "second," "third," "fourth," "fifth," and "sixth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," "fourth," "fifth," and "sixth" may explicitly or implicitly include one or more of that feature.

[0045] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0046] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0047] Current research on particulate matter ejected during battery thermal runaway largely focuses on the individual battery cell level, which is insufficient to address the research needs regarding the propagation and accumulation of particulate matter within the complex internal structure of battery packs. In the field of cell ejection simulation analysis, existing simulation models generally suffer from high computational complexity, low simulation efficiency, and difficulty in controlling simulation accuracy, thus failing to meet the practical needs of engineering applications.

[0048] Meanwhile, existing research on particulate matter emissions from battery packs is mostly limited to the optimization design of local structures, and does not carry out systematic safety protection design at the battery pack level, thus failing to fully address both the protection against particulate matter accumulation inside the battery pack and the overall smoke control requirements.

[0049] Based on this, this application provides a battery pack thermal runaway simulation method. By establishing a geometric model based on the actual spatial structure of the battery pack and sequentially configuring particulate matter ejection and motion rules, the method can simulate the gas flow field caused by thermal runaway, and thus simulate the motion process of particulate matter within it. In particular, by tracking the distribution of particulate matter after ejection and correcting the parameters of the gas flow field accordingly, a two-way coupling effect between gas and particulate matter is achieved. This more realistically reflects the interaction between the gas and solid phases during battery pack thermal runaway, enabling the final battery pack simulation model to accurately simulate the ejection and diffusion behavior of particulate matter, improving the accuracy and reliability of the simulation results, and providing strong data support for battery pack safety performance evaluation and optimization design.

[0050] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0051] The battery pack thermal runaway simulation method provided in this application can be directly applied to simulation analysis equipment. This equipment can be a locally deployed terminal computing device, a server cluster device deployed in the cloud, or a distributed computing device in which a local terminal and a cloud server collaborate. This application does not impose any specific limitations on this.

[0052] like Figure 1 As shown, the battery pack thermal runaway simulation method provided in this application includes the following steps S101-S104: S101. Based on the spatial structure of the battery pack, establish the geometric model of the battery pack.

[0053] In this embodiment, the spatial structure of the battery pack refers to the physical arrangement, dimensions, relative positions of the components within the battery pack, and the overall external shape of the battery pack. The geometric model, on the other hand, is a virtual three-dimensional model constructed using digital modeling techniques based on the spatial structure of the battery pack. It accurately maps the physical spatial characteristics of the battery pack and serves as the fundamental basis for conducting thermal runaway simulations of the battery pack.

[0054] In some embodiments, the spatial structure of the battery pack may include: the overall external dimensions of the battery pack, the arrangement and spacing of the internal cells, the position and size of various brackets and fixing structures, the layout and channel specifications of the exhaust structure, the distribution and connection of cavities inside the battery pack, and the spatial position and size parameters of various electronic components and pipelines inside the battery pack.

[0055] In some embodiments, the geometric model may include: a three-dimensional solid model that matches the actual spatial structure of the battery pack at a 1:1 scale, digital identifiers corresponding to each structure in the model, a mesh generation structure that can be used for simulation calculations, and model attribute settings that can be adapted to gas flow and particulate motion simulations.

[0056] In some embodiments, the simulation analysis equipment can obtain spatial structure data of the battery pack by retrieving the original manufacturer's design drawings and 3D modeling files. Alternatively, it can obtain spatial structure information of the battery pack after performing 3D scanning and dimensional measurement on an actual battery pack sample, providing accurate data support for the establishment of the geometric model.

[0057] In some embodiments, the simulation analysis device can also: verify and correct the acquired battery pack spatial structure data, remove redundant data and supplement missing structural parameters, and simplify the geometric model according to the accuracy requirements of the simulation analysis, retain the key structures that affect gas flow and particulate matter emission and diffusion, ignore the minor structures that have no substantial impact on the simulation results, and improve the efficiency of subsequent simulation calculations.

[0058] In one possible implementation, the simulation analysis equipment relies on specialized 3D modeling and simulation software to import verified and corrected data such as the spatial structural dimensions and component layout of the battery pack, and builds a basic 3D model according to the actual physical structure of the battery pack. Subsequently, based on the requirements of power battery particulate matter emission simulation, the model is meshed, dividing the internal space of the battery pack into multiple mesh units adapted for flow field and particulate matter motion calculations, thus completing the establishment of the battery pack geometric model, which is consistent with the actual spatial structure of the battery pack sample.

[0059] As can be seen from step S101, establishing a corresponding geometric model based on the actual spatial structure of the battery pack enables the geometric model to accurately reproduce the real physical spatial characteristics of the battery pack, laying a realistic model foundation for subsequent configuration of particulate matter emission rules and simulation of gas flow field, and avoiding distortion of simulation results due to deviation between the model and the actual structure.

[0060] S102. Configure particulate matter emission rules in the geometric model to simulate the gas flow field inside the battery pack in the geometric model, and obtain the first simulation model.

[0061] In this embodiment, the particulate matter ejection rules refer to the various logical rules and constraints set to simulate particulate matter ejection behavior during thermal runaway of the battery pack. They form the core rule system connecting the internal gas flow of the battery pack with the particulate matter ejection behavior. The gas flow field refers to the virtual flow field simulated in the geometric model, which reflects the internal gas flow state and distribution characteristics during thermal runaway of the battery pack. The first simulation model refers to the simulation model that accurately simulates the internal gas flow field of the battery pack after configuring the particulate matter ejection rules on the battery pack's geometric model; it is the foundational model for subsequent simulations of particulate matter motion.

[0062] In some embodiments, particulate matter emission rules may include: triggering rules that control the linkage between particulate matter emission and gas flow; association rules that regulate the matching relationship between particulate matter emission behavior and gas flow state; regional rules that limit the spatial location of particulate matter emission in the geometric model; and temporal rules that constrain the temporal sequence of particulate matter emission behavior in the model. These rules together constitute a particulate matter emission logic system adapted to battery pack thermal runaway scenarios.

[0063] In some embodiments, the gas flow field may include: the transient flow velocity of gas at different locations inside the battery pack, the gas flow direction, the distribution state of gas in each cavity and channel, and the flow field change characteristics caused by the obstruction of the internal structure of the battery pack.

[0064] In some embodiments, the first simulation model may include: a battery pack geometric model incorporating particulate ejection rules, relevant control equations for calculating gas flow, a flow field calculation grid adapted to the spatial structure of the battery pack, and a model calculation module capable of outputting gas flow parameters at various locations inside the battery pack.

[0065] In some embodiments, the simulation analysis equipment can configure basic linkage and correlation rules for particulate matter emission based on the actual physical mechanism of battery pack thermal runaway and combined with industry research results in the field of power batteries. Spatial region rules for particulate matter emission can be configured according to the cell layout and thermal runaway-prone areas in the battery pack geometric model. Furthermore, temporal sequence rules for particulate matter emission can be configured based on the development process of battery pack thermal runaway, thus completing the overall configuration system for particulate matter emission rules.

[0066] In some embodiments, the simulation analysis equipment can also: pre-verify the configured particulate matter emission rules, and verify through small-scale simulation tests whether the simulated gas flow field after rule configuration conforms to the physical laws of gas flow during battery pack thermal runaway. If deviations are found in the flow field simulation, the relevant parameters in the particulate matter emission rules can be adjusted in a timely manner. Furthermore, a parameter adjustment interface can be added to the configured particulate matter emission rules, facilitating flexible modification of rule parameters based on simulation requirements and experimental data, thereby improving the adaptability of the rules.

[0067] In one possible implementation, the simulation analysis equipment first selects the core region related to particulate matter emission within the established battery pack geometric model. Then, it sequentially establishes the linkage triggering rules for particulate matter emission and gas flow, and the correlation matching rules between emission behavior and flow field state. Simultaneously, it delineates the spatial triggering region for particulate matter emission within the model and sets temporal constraint rules, completing the configuration of all particulate matter emission rules. Based on the configured rules, it simulates the gas flow field inside the battery pack within the geometric model. Once the flow field simulation results stabilize and conform to the flow field characteristics of battery pack thermal runaway, the configured rules are combined with the simulated flow field to generate the first simulation model.

[0068] As can be seen from step S102, configuring particulate ejection rules in the geometric model and simulating the gas flow field accordingly to obtain the first simulation model can make the simulation of the gas flow field closely match the actual physical scenario of particulate ejection during thermal runaway of the battery pack, avoiding the disconnect between the flow field simulation and particulate ejection behavior. At the same time, it lays a realistic basic simulation model for subsequent configuration of particulate motion rules and simulation of the motion process of particulates in the flow field, ensuring the continuity and physical authenticity of subsequent simulation steps.

[0069] S103. Configure the particulate matter motion rules in the first simulation model to obtain the second simulation model.

[0070] Among them, the particulate motion rule is used to simulate the motion process of particulate matter in a gas flow field.

[0071] In this embodiment, the particulate motion rules refer to the logical constraints and behavioral judgment rules set to recreate the motion state of particulate matter in the gas flow field during thermal runaway of the battery pack. These rules are the core basis for simulating various motion behaviors of particulate matter in the flow field. The second simulation model refers to a simulation model formed after configuring the particulate motion rules based on the first simulation model. This model can accurately simulate the complete motion process of particulate matter in the gas flow field inside the battery pack, enabling the digital reconstruction of various motion behaviors of particulate matter in the flow field.

[0072] In some embodiments, the particulate motion rules may include: flow motion rules that regulate the overall migration of particulate matter with the gas flow field; contact motion rules that constrain the interaction between particulate matter and various structures within the model; state evolution rules that limit the change of the particulate matter's own motion state in the gas flow field; and behavior switching rules that control the change of particulate matter's motion behavior at specific structures in the model. These various rules work together to fully cover the entire process of particulate matter's motion behavior in the gas flow field.

[0073] In some embodiments, the simulation analysis equipment can configure the flow motion rules and state evolution rules of particles based on the basic principles of fluid mechanics and the physical characteristics of particle motion in a gas-phase flow field. It can also configure the contact motion rules of particles by combining the contact mechanisms between solid particles and different structures such as solid walls and exhaust structures. Furthermore, it can configure the behavior switching rules of particles based on the functional characteristics of different structures within the battery pack and the motion patterns of particles at corresponding positions, thus completing the overall configuration of the particle motion rules in a manner consistent with actual physical laws.

[0074] In some embodiments, the simulation analysis device can also: perform logical compatibility verification on the configured particulate motion rules to ensure smooth connection between the various motion rules and compatibility with the gas flow field characteristics in the first simulation model. Simultaneously, it sets up a hierarchical control interface for the particulate motion rules, allowing independent adjustment of different types of motion rules according to the simulation focus. It can also precisely associate the particulate motion rules with different structural regions in the first simulation model, ensuring that the rules are precisely triggered and take effect within the corresponding regions of the model.

[0075] In one possible implementation, the simulation analysis equipment first retrieves the overall characteristics and distribution information of the gas flow field within the pre-constructed first simulation model, and then establishes the core flow-following motion rules for particulate matter migrating with the gas flow field. Next, it configures basic contact motion rules between the particulate matter and the structures within the model, based on various structures. Simultaneously, it configures rules governing the evolution of the particulate matter's velocity, direction of motion, and other states within the flow field, as well as rules for behavior switching at specific structures. After systematically configuring all particulate matter motion rules, the rule system is deeply integrated with the gas flow field of the first simulation model, enabling the rules to drive the model to simulate the motion process of particulate matter within the gas flow field, ultimately forming the second simulation model.

[0076] As can be seen from step S103, configuring the particulate motion rules in the first simulation model and obtaining the second simulation model enables the simulation of particulate motion to be adapted to the actual gas flow field during the thermal runaway of the battery pack. This ensures the physical authenticity of the particulate motion process from the rule level. At the same time, it constructs a simulation model that can completely simulate various motion behaviors of particulates in the flow field, laying the core model foundation for subsequent tracking of the distribution of particulate matter after eruption and analysis of particulate matter diffusion behavior, and ensuring the orderly conduct of subsequent simulation steps and the rationality of simulation results.

[0077] S104. Track the distribution of particulate matter after eruption in the second simulation model, and correct the parameters of the gas flow field according to the distribution to obtain the battery pack simulation model.

[0078] Among them, the battery pack simulation model is used to simulate the eruption and diffusion behavior of particulate matter during the thermal runaway process of the battery pack.

[0079] In this embodiment, the distribution of particulate matter after eruption refers to the state and distribution characteristics of particulate matter in different spatial locations and structural regions within the model after completing various motion behaviors in the gas flow field of the second simulation model. The parameters of the gas flow field refer to various core parameters that characterize the gas flow state within the model and influence the gas flow law. The battery pack simulation model is the final simulation model formed on the basis of the second simulation model, after particulate matter distribution tracking and gas flow field parameter correction. It can completely simulate the particulate matter eruption and diffusion behavior during the entire process of battery pack thermal runaway and can accurately reflect the interaction relationship between the gas and solid phases.

[0080] In some embodiments, the distribution of particulate matter after ejection may include: the spatial distribution of particulate matter in the various cavities and structural gaps inside the battery pack; the accumulation distribution of particulate matter in different areas such as the inner wall of the model and the exhaust structure; and the dynamic motion distribution of particulate matter in different flow field regions in the gas flow field, which can comprehensively reflect the overall distribution state of particulate matter within the model.

[0081] In some embodiments, the parameters of the gas flow field may include: velocity parameters characterizing the gas flow speed, flow direction parameters reflecting the gas flow direction, field strength parameters reflecting the spatial distribution of the gas, and coupling parameters related to the interaction between the gas and particulate matter. These are the core elements for controlling the simulation effect of the gas flow field.

[0082] In some embodiments, the simulation analysis equipment can establish real-time tracking rules for particulate matter distribution based on the simulation function configuration of the second simulation model, thereby obtaining various distribution conditions of particulate matter after eruption. Based on the fundamental principles of fluid mechanics and gas-solid two-phase flow, combined with the physical characteristics of battery pack thermal runaway, basic value rules and correction adjustment rules for gas flow field parameters can be configured, clarifying the direction, range, and correlation logic of parameter correction. Furthermore, based on the current motion of the particulate matter and the requirements for flow field parameter correction, linkage and adaptation rules can be configured, laying the rule foundation for the construction of the battery pack simulation model.

[0083] In some embodiments, the simulation analysis equipment can also: configure multi-dimensional monitoring rules for particulate matter distribution tracking, enabling refined acquisition and recording of particulate matter distribution. Simultaneously, it can establish hierarchical correction rules for gas flow field parameters, allowing for parameter adjustments to varying degrees based on the matching degree between particulate matter distribution and the flow field. Furthermore, it can configure a storage module for rules and parameters for the battery pack simulation model, saving relevant configuration information for this model construction, facilitating subsequent model reuse and optimization. It can also establish simulation scenario verification rules for the model, ensuring that the model can adapt to different battery pack thermal runaway simulation scenarios.

[0084] In one possible implementation, the simulation analysis equipment first configures real-time tracking rules for particulate matter distribution in the pre-built second simulation model, and then enables the model's simulation operation function. These rules track and acquire various distribution patterns of particulate matter after its movement within the gas flow field. Next, it configures correlation analysis rules between particulate matter distribution and gas flow field parameters. Based on these rules, it analyzes the impact of particulate matter distribution on the gas flow field, determining the correction direction and logic for the flow field parameters. Then, according to the predetermined correction rules, it specifically corrects core parameters of the gas flow field, such as velocity and direction, to ensure the gas flow field is compatible with the particulate matter distribution. Finally, it deeply integrates the corrected gas flow field with the particulate matter motion rule system of the second simulation model, configuring simulation linkage rules for the entire thermal runaway process to further update the particulate matter motion, ultimately forming a battery pack simulation model capable of simulating particulate matter ejection and diffusion behavior during battery pack thermal runaway.

[0085] As can be seen from step S104, the distribution of particulate matter after eruption is tracked in the second simulation model and the parameters of the gas flow field are corrected accordingly to obtain the battery pack simulation model. This enables the simulation model to achieve bidirectional coupling simulation of gas flow field and particulate matter motion, accurately reflecting the mutual influence between gas and solid phases during battery pack thermal runaway, and avoiding simulation distortion caused by simulating only the flow field or particulate matter motion.

[0086] In this embodiment of the application, the particulate motion rules include collision behavior rules when particulates collide with the wall in the first simulation model.

[0087] For example, the collision behavior rules include: when a particle collides with the wall in the first simulation model, determining the particle's reflection coefficient based on the particle's collision velocity; then, determining the particle's reflection velocity based on the reflection coefficient; and finally, determining the particle's motion during the collision behavior based on the reflection velocity and a preset critical velocity.

[0088] In this embodiment, the collision velocity refers to the velocity of the particle at the instant it comes into contact with the wall in the first simulation model, and is a fundamental physical quantity for determining the particle's motion state after the collision. The reflection coefficient is a coefficient used to characterize the degree of velocity attenuation after the particle collides with the wall; it is related to the collision velocity and is a key parameter for calculating the particle's reflection velocity. The reflection velocity refers to the rebound velocity calculated based on the reflection coefficient after the particle collides with the wall. The preset critical velocity is a velocity threshold set to distinguish different motion outcomes of the particle after the collision, and is the core basis for determining the particle's collision behavior. The motion state of the collision behavior refers to the subsequent motion state of the particle after colliding with the wall, based on its own velocity and the preset threshold.

[0089] In some embodiments, the reflection coefficient may include: a normal reflection coefficient corresponding to the normal collision velocity of the particles, and a tangential reflection coefficient corresponding to the tangential collision velocity of the particles. The two types of reflection coefficients are respectively matched to the collision velocities of the particles in different directions, and are used to accurately calculate the reflection velocity in the corresponding directions.

[0090] For example, the formula for calculating the normal reflection coefficient is:

[0091] Collision speed When the value is greater than a preset threshold, the normal reflection coefficient is [value missing]. .

[0092] in, and All of these are constant coefficients, which can be calibrated based on thermal runaway simulation experiments on battery pack samples.

[0093] For example, the formula for calculating the tangential reflection coefficient is as follows:

[0094] Collision speed When the tangential reflection coefficient is greater than a preset threshold, the value is... .

[0095] in, and All of these are constant coefficients, which can be calibrated based on thermal runaway simulation experiments on battery pack samples.

[0096] In some embodiments, the reflection velocity may include the rebound velocity of the particle in the normal direction and the rebound velocity in the tangential direction after colliding with the wall. The two are calculated from the collision velocity and the reflection coefficient in the corresponding directions, reflecting the rebound motion characteristics of the particle after the collision.

[0097] In some embodiments, the preset critical velocity may include a critical determination velocity set for the normal reflection velocity of particulate matter, which is the core threshold for distinguishing between particulate matter deposition and rebound after collision.

[0098] In some embodiments, the motion of the collision behavior may include: the deposition state of the particles on the wall surface after the collision because the reflection velocity does not reach the threshold, and the rebound motion state from the wall surface after the reflection velocity reaches the threshold, which fully covers the main motion destination of the particles after the collision with the wall surface.

[0099] In some embodiments, the simulation analysis device can also: configure a continuous update rule for the motion state of particles after rebound; when the motion of the particle collision behavior is determined to be wall rebound, update the motion state parameters such as the particle's motion speed and direction in real time based on the calculated reflection velocity; at the same time, establish motion tracking rules for the particle after rebound; continuously monitor the updated motion state of the particle until the particle's velocity decays to the point of stopping due to multiple collisions, or other motion behaviors are generated due to other rules, so as to ensure the complete simulation of the motion process of the particle after collision.

[0100] In some embodiments, the simulation analysis device can also: configure multi-dimensional monitoring and extraction rules for collision velocity to ensure accurate acquisition of velocity data at the moment of collision between particles and the wall, and configure association rules for reflection coefficient that are compatible with different wall materials and different particle types, so that the determination of reflection coefficient is more in line with the actual collision scenario.

[0101] In one possible implementation, the processing logic for collision behavior rules is as follows: Figure 2As shown, the simulation analysis equipment first determines whether the current particle is an inertial particle (particulate matter with a certain collision velocity). If not, the particle trajectory is terminated directly. If so, the simulation analysis equipment first calculates the reflection coefficient, and then calculates the reflection velocity based on the reflection coefficient. It then determines whether the inertial particle meets the condition that the reflection velocity is greater than a critical value. If so, the particle velocity is updated, the particle trajectory is updated, and the particle motion is continued to be tracked. If not, the particle trajectory is terminated, completing the processing of this collision behavior.

[0102] In this embodiment, by configuring rules to determine the reflection coefficient based on the collision speed, and then determining the reflection speed based on the reflection coefficient, and subsequently determining the collision behavior based on the reflection speed and the preset critical speed, the collision behavior of particles and the wall can be simulated to closely match the actual physical collision mechanism. This ensures the logic and accuracy of the collision behavior determination from the rule level, accurately distinguishes the different motion situations of particles after collision, and lays a reliable rule foundation for the subsequent complete simulation of the motion trajectory and deposition position of particles inside the battery pack, thereby improving the realism and rationality of the particle motion simulation.

[0103] In this embodiment of the application, the particulate matter motion rules also include escape behavior rules for particulate matter when passing through the exhaust structure in the first simulation model.

[0104] For example, the escape behavior rule includes: First, obtaining the particle size as particulate matter passes through the exhaust structure in the first simulation model. Then, determining a filtration threshold corresponding to the filter based on the pore characteristic parameters of the filter in the exhaust structure. The pore characteristic parameters are updated as particulate matter is captured by the filter. Finally, comparing the particle size with the filtration threshold determines the motion of the particulate matter.

[0105] The motion parameters include: determining that particulate matter is captured by the exhaust structure when its particle size is greater than the filtration threshold; and determining that particulate matter escapes from the exhaust structure when its particle size is less than or equal to the filtration threshold.

[0106] In this embodiment, the exhaust structure refers to the structural region in the first simulation model corresponding to the actual pressure relief and exhaust functions of the battery pack. It is a key area where particulate matter may escape or be captured in the model. The pore characteristic parameters of the filter refer to various parameters used to characterize the pore state of the filter within the exhaust structure in the first simulation model, and are the core basis for determining the filtration threshold. The filtration threshold is a critical particle size value determined based on the filter pore characteristic parameters, used to determine whether particulate matter can pass through the filter. The movement of particulate matter here refers to the two motion states exhibited by particulate matter when passing through the exhaust structure: being captured by the exhaust structure or escaping from the exhaust structure due to differences in particle size.

[0107] In some embodiments, the exhaust structure may include: structural areas in the model corresponding to the battery pack pressure relief valve and explosion-proof valve, and channel structural areas connecting various pressure relief and exhaust components, which are the main channels for particulate matter to diffuse outward in the model.

[0108] In some embodiments, the pore characteristic parameters of the filter may include parameters that reflect the flow capacity of the filter pores, such as porosity, pore size, and resistance coefficient of the filter within the exhaust structure of the model. These parameters will change dynamically with the capture of particulate matter.

[0109] In some embodiments, the filtration threshold may include: a critical value for particulate matter size corresponding to different exhaust structure regions, determined based on the real-time pore characteristic parameters of the filter, which is adjusted synchronously with the update of the pore characteristic parameters.

[0110] In some embodiments, the movement of particulate matter may include: a state in which particulate matter is blocked and captured by a filter in the exhaust structure because its particle size exceeds the filtration threshold, and a state in which particulate matter passes smoothly through the exhaust structure and escapes outwards because its particle size meets the filtration threshold requirements.

[0111] In some embodiments, the simulation analysis equipment can also: configure zoning recognition rules for the exhaust structure to accurately distinguish exhaust structure regions with different functions and locations in the model and configure corresponding filter pore feature parameters for each; simultaneously, configure high-precision recognition rules for obtaining particulate matter size to ensure accurate extraction of particulate matter size data from the model. It can also establish dynamic update triggering rules for filter pore feature parameters, specifying the time nodes and triggering conditions for parameter updates, updating the corresponding parameters promptly after particulate matter is captured, and simultaneously adjusting the filter threshold. Furthermore, it can configure state processing rules after particulate matter is captured to perform state calibration and position recording of particulate matter captured by the exhaust structure within the model.

[0112] In one possible implementation, the processing logic for the escape behavior rule is as follows: Figure 3 As shown. When the simulation analysis equipment determines that the current particle is an active particle in a porous medium, it will check whether the particle diameter meets the condition of being greater than the filtration threshold. If so, it will update the cumulative flow, cumulative volume, and porosity, and update the filtration threshold according to the porosity to continue processing subsequent particles. If not, it will determine that the particle has escaped, and complete the processing of this escape behavior.

[0113] In this embodiment, configuration rules enable the acquisition of particle size as particulate matter passes through the exhaust structure, the determination of the filtration threshold based on dynamically updated filter pore characteristic parameters, and the determination of particulate matter movement by comparing the particle size with the filtration threshold. This allows the movement behavior of particulate matter at the exhaust structure to simulate the actual exhaust filtration conditions of the battery pack, accurately reproducing the dynamic performance changes of the filter caused by particulate matter capture, and ensuring the authenticity and rationality of the determination of particulate matter escape or capture from a rule-based perspective.

[0114] In this embodiment of the application, the battery pack simulation model can perform thermal runaway simulation analysis on similar battery packs with the same structure. The simulation analysis process is as follows: Figure 4 As shown, it includes S401-S402: S401. Based on the cell gas generation data and particulate matter emission parameters of the target battery pack, thermal runaway simulation is performed in the battery pack simulation model to obtain the positional distribution of particulate matter.

[0115] The target battery pack has the same spatial structure as the battery pack simulation model.

[0116] In this embodiment, the target battery pack refers to the power battery pack under study that requires thermal runaway simulation analysis, and is the object of this simulation analysis. Cell gas generation data refers to various data related to the gas generated when the cells in the battery pack experience thermal runaway; it is the core input data for simulating the gas flow field in the simulation. Particulate matter ejection parameters refer to various characteristic data related to the ejection of particulate matter during cell thermal runaway; it is the core input data for simulating particulate matter motion and diffusion in the simulation. Particulate matter location distribution refers to the distribution state and location characteristics of particulate matter in different spatial locations and structural regions within the battery pack simulation model after completing the thermal runaway simulation.

[0117] In some embodiments, cell gas generation data may include: gas generation rate data at different time stages during cell thermal runaway, gas percentage data corresponding to different types of gas, and time evolution data related to the cell gas generation process, which can fully reflect the gas generation pattern of cell thermal runaway.

[0118] In some embodiments, particulate matter ejection parameters may include particulate matter ejection rate data, ejection mass flow rate data, and particulate matter size distribution data, which can comprehensively reflect the ejection characteristics of particulate matter during thermal runaway of the battery cell.

[0119] In some embodiments, the positional distribution of particulate matter may include: the spatial coordinate distribution of particulate matter in the cavities and structural gaps inside the battery pack, the distribution of the accumulation position of particulate matter on the battery pack wall and the surface of various components, and the distribution of the aggregation and diffusion position of particulate matter near the exhaust structure, as well as the differentiated positional distribution of particulate matter of different sizes within the model.

[0120] In some embodiments, the simulation analysis equipment can directly retrieve the cell gas generation data and particulate matter emission parameters of a cell sample that matches the cell specifications used in the target battery pack. The cell gas generation data and particulate matter emission parameters of this cell sample can be obtained through an accelerating rate calorimeter (ARC) test.

[0121] For example, the process of obtaining cell gas generation data and particulate matter emission parameters of a battery cell sample through ARC testing is as follows: During the ARC test, the cell gas generation rate of the battery cell sample at each time period is statistically analyzed to determine the cell gas generation data. Then, particulate matter is emitted along with the cell gas generation, and the emission rate is consistent with the cell gas generation rate. Furthermore, after the ARC test, the total mass of the emitted particulate matter from the battery cell can be collected, and the particulate matter emission rate curve can be proportionally scaled so that the integral value of the curve equals the total mass of the emitted particulate matter. This curve can be used as the particulate matter emission mass flow rate. Simultaneously, the emitted particulate matter after the ARC test can be collected, and the particle size and its corresponding mass fraction can be measured to represent the particulate matter size distribution in the particulate matter emission parameters.

[0122] For example, the particle size distribution can be described using the Rosin-Rammler equation:

[0123] in, For a mass fraction exceeding this particle size, For particle size, The average particle size is This is the size distribution index.

[0124] In some embodiments, the simulation analysis equipment can also: configure particulate matter location monitoring rules at multiple time points during the thermal runaway simulation process, acquire the particulate matter location distribution and gas flow field distribution at different thermal runaway stages in real time, and realize dynamic tracking of the particulate matter diffusion process. Furthermore, the acquired particulate matter location distribution data can be classified and stored, and organized according to dimensions such as accumulation region and particulate matter size, facilitating subsequent data analysis and use.

[0125] In one possible implementation, the simulation analysis equipment first confirms the structural consistency between the battery pack simulation model and the target battery pack, completing the pre-simulation verification of the model. Then, it retrieves the gas generation data and particulate matter emission parameters of a cell sample with specifications consistent with the target battery pack, obtained through ARC testing. After standardizing and preprocessing the data, it imports the cell gas generation data and particulate matter emission parameters into the battery pack simulation model according to the model's input requirements. Next, it configures the thermal runaway simulation parameters in the model, enabling the model's thermal runaway simulation function. Based on the imported data, the model simulates the gas flow field and particulate matter emission and movement process during cell thermal runaway, while simultaneously tracking the movement trajectory of particulate matter within the model through its built-in monitoring function. Finally, after the thermal runaway simulation process is completed, the model's data extraction function obtains the distribution status and positional characteristics of particulate matter in various structural regions and spatial locations within the model, forming complete particulate matter positional distribution data.

[0126] As shown in step S401, thermal runaway simulation is performed in a structurally consistent battery pack simulation model based on the cell gas generation data and particulate matter emission parameters of the target battery pack, and the positional distribution of particulate matter is obtained. This allows the simulation process to closely match the actual cell characteristics of the target battery pack. Relying on real data obtained through ARC testing as simulation input, the realism and accuracy of the thermal runaway simulation are guaranteed from a data perspective. At the same time, the structural consistency between the battery pack simulation model and the target battery pack allows the positional distribution of particulate matter to accurately map the actual particulate matter distribution characteristics during thermal runaway of the target battery pack. This fully presents the accumulation and diffusion state of particulate matter within the battery pack, providing real and comprehensive basic data support for subsequent analysis of the thermal runaway risk of the target battery pack and extraction of relevant state parameters, thereby enhancing the practical reference value of the simulation analysis for the target battery pack.

[0127] S402. Based on the location distribution, determine the state parameters related to the thermal runaway of the target battery pack.

[0128] In the embodiments of this application, state parameters refer to various quantitative indicators that are determined based on the positional distribution of particulate matter and can quantitatively reflect the characteristics of particulate matter eruption and diffusion and the risk of thermal runaway during the thermal runaway of the target battery pack. They are the core basis for evaluating the thermal runaway safety performance of the target battery pack.

[0129] In some embodiments, state parameters may include: the mass of particulate matter accumulation and the concentration of particulate matter within a preset monitoring area inside the target battery pack, as well as the gas flow rate at the exhaust structure of the target battery pack. These parameters quantify the key characteristics of the thermal runaway process of the target battery pack from dimensions such as the degree of particulate matter accumulation, spatial concentration distribution, and gas emission characteristics, and intuitively reflect the degree of safety risk caused by thermal runaway.

[0130] In some embodiments, the simulation analysis equipment can also: pre-define sensitive areas requiring key monitoring based on the safety design requirements of the target battery pack and configure dedicated state parameter calculation rules; perform refined parameter statistics for key areas such as high-voltage connection points and weak insulation points; perform multi-dimensional analysis of the calculated state parameters; compare the changes in state parameters corresponding to different thermal runaway stages and different particle sizes; and compare the state parameters with preset safety thresholds to quickly determine the thermal runaway risk level of the target battery pack. Furthermore, the state parameters and analysis results can be visualized, presented intuitively in the form of charts, model annotations, etc., facilitating subsequent safety assessments and design optimizations.

[0131] In one possible implementation, the processing logic for determining the particulate matter accumulation mass of the monitoring area using simulation analysis equipment is as follows: Figure 5 As shown, the simulation analysis equipment can statistically analyze the mass of particulate matter accumulation within a region by defining a user variable. First, the user variable is initialized. Then, all grid cells within the sampling region are traversed. Finally, the particle mass is calculated and assigned to the user variable, thus completing the statistical analysis and recording of the mass of particulate matter accumulation within the monitoring region.

[0132] For example, simulation analysis equipment can pre-define preset monitoring areas in the battery pack simulation model, based on the structural characteristics and thermal runaway protection priorities of the target battery pack, to determine the required particulate matter accumulation mass and concentration. Then, based on the location distribution of the particulate matter, it extracts all relevant particulate matter data falling within each preset monitoring area from the model. Using configured mass accumulation rules, it sums and calculates the mass of all particulate matter within that area to obtain the particulate matter accumulation mass for each preset monitoring area. Next, it retrieves the corresponding spatial volume data for each preset monitoring area in the model and, according to the particulate matter concentration calculation logic, calculates the ratio between the particulate matter accumulation mass and the corresponding volume of each area to obtain the particulate matter concentration within each preset monitoring area. Simultaneously, it can separately statistically analyze the accumulation mass and concentration of particles with different sizes, thus determining the relevant state parameters.

[0133] In one possible implementation, the simulation analysis equipment determines the gas velocity at the battery pack exhaust structure as follows: First, it extracts all particulate matter-related data passing through the exhaust structure in the battery pack simulation model from the particulate matter's positional distribution, determining the trajectory and velocity data of these particulate matter as they pass through the exhaust structure. Combining the physical characteristics of particulate matter being entrained by gas in the gas flow field, the extracted particulate matter velocity as it passes through the exhaust structure is converted to obtain the gas velocity data at the exhaust structure. Simultaneously, it can statistically analyze the gas velocity at different locations and different thermal runaway stages of the exhaust structure by time period and region. Furthermore, it can combine the concentration of escaped particulate matter to calculate relevant parameters of the gas-solid two-phase flow at the exhaust structure, thus completing the determination of the gas velocity as a state parameter at the exhaust structure.

[0134] As shown in step S402, determining the state parameters related to thermal runaway of the target battery pack based on the positional distribution of particulate matter can transform the qualitative characteristics of the particulate matter distribution obtained from simulation into quantifiable specific parameters. This allows for a multi-dimensional and accurate characterization of the eruption, accumulation, diffusion, and gas flow characteristics of particulate matter during the thermal runaway of the target battery pack. It upgrades the assessment of the thermal runaway safety performance of the target battery pack from qualitative analysis to quantitative analysis, providing intuitive and accurate quantitative data support for subsequent thermal runaway safety protection design, pressure relief path optimization, and early warning threshold setting of the target battery pack. This enhances the scientific nature and relevance of battery pack safety design and assessment.

[0135] In this embodiment of the application, the parameters of the battery pack simulation model can be calibrated by conducting thermal runaway tests on the battery pack sample.

[0136] For example, firstly, a thermal runaway test can be conducted on a battery pack sample to obtain experimental data related to thermal runaway. Then, a simulation analysis device can be used to perform thermal runaway simulation analysis of the battery pack sample in a battery pack simulation model to obtain state parameters related to thermal runaway. Next, using the experimental data as a benchmark, the parameters to be optimized in the simulation model are adjusted based on the differences between the state parameters and the experimental data. Finally, the process of performing battery pack thermal runaway simulation and adjusting the parameters to be optimized is repeated on the simulation analysis device until the differences between the experimental data and the state parameters meet preset requirements.

[0137] In this embodiment, the battery pack sample refers to a physical prototype of a power battery pack used for conducting actual thermal runaway tests, providing real data references for simulation model calibration. Test data refers to various measured data collected after conducting thermal runaway tests on the battery pack sample, reflecting the actual thermal runaway process of the battery pack sample, and serves as the benchmark data for calibrating simulation model parameters. Parameters to be optimized refer to various model parameters in the battery pack simulation model that affect the accuracy of simulation results such as particulate matter motion and flow field simulation, and need to be adjusted and optimized in conjunction with test data.

[0138] In some embodiments, the test data may include: the measured mass of particulate matter accumulation and concentration in a specific area during the thermal runaway of the battery pack sample, the gas flow rate at the exhaust structure, and the measured data related to the particulate matter distribution at different stages of thermal runaway, which can comprehensively reflect the actual physical characteristics of the thermal runaway of the battery pack sample.

[0139] In some embodiments, the parameters to be optimized may include: calculation parameters of the tangential and normal reflection coefficients when particles collide with the wall, and deposition parameters such as the deposition rate corresponding to the critical velocity at which particles are deposited on the wall. These parameters directly affect the simulation accuracy of particle motion behavior.

[0140] In some embodiments, experimental data related to thermal runaway of the battery pack sample can be collected as follows: A flue gas duct is connected to the outside of the pressure relief valve of the battery pack sample. If there are multiple pressure relief valves, multiple flue gas ducts need to be connected, and all flue gas ducts are merged into a main duct. A flow monitor is placed near each pressure relief valve to monitor the gas flow rate. An opening is made on the main duct near the outlet, and the measuring head of a concentration meter is inserted. Runaway of the battery cells within the battery pack is triggered using a specific method, including various methods such as internal triggering and external triggering. One cell can be triggered, or multiple cells can be triggered simultaneously. During the thermal runaway of the battery cells, the particulate matter concentration is measured in real time using a concentration meter. After all the preset target cells have runaway, the pack is left to stand for a certain period of time until all particulate matter has settled. The pack is then opened for inspection, and particulate matter from specific areas is collected, numbered, and weighed.

[0141] For example, battery pack samples can be made using, for example Figure 6The battery pack sample structure shown is as follows. This battery pack sample has only one pressure relief valve; therefore, only one pipe needs to be connected to the outside of the explosion-proof valve. Holes are made on the side of the pipe for particulate matter concentration monitoring and gas flow rate monitoring, respectively, and gas flow rate and particulate matter concentration monitoring instruments are inserted into these holes. A target cell inside the battery pack sample can be triggered using built-in heating. If thermal runaway occurs in the target cell, heating is immediately stopped, and the sample is left to stand for 24 hours. Subsequently, the test battery pack sample is opened for inspection, and particulate matter from key areas is collected, weighed, and recorded. Simultaneously, the monitored gas flow rate and particulate matter concentration data are recorded.

[0142] In some embodiments, the simulation analysis device can: use the difference between experimental data and state parameters as the loss value, construct a loss function based on the experimental data, and clarify the calculation logic and quantification standard of the loss value. Simultaneously, it can include the calculation parameters of the reflection coefficient when particles collide with the wall and the deposition parameters corresponding to the critical velocity for particle deposition on the wall in the parameter range to be optimized, and define a reasonable value space for each parameter to be optimized. Furthermore, the simulation analysis device can use a Bayesian search algorithm based on the loss value to search within the parameter value space and determine the set of parameters that minimizes the loss value as the adjusted parameters. The simulation analysis device can also establish a calibration parameter transfer model, using a similarity evaluation matrix (based on topology, number of pressure relief valves, cooling method, etc.) for different battery pack structures to achieve cross-platform reuse of calibration parameters.

[0143] In one possible implementation, the simulation analysis equipment first receives various experimental data obtained after conducting thermal runaway tests on the battery pack sample. This data is then standardized and stored in a designated data module as a benchmark for model parameter calibration. Subsequently, the battery pack simulation model is invoked, and thermal runaway simulation analysis is performed according to the actual experimental conditions of the battery pack sample to obtain the thermal runaway-related state parameters of the battery pack sample corresponding to the experimental data. Next, the state parameters are compared one by one with the experimental data, and the difference between the two is calculated and used as the loss value to construct the corresponding loss function. Simultaneously, the parameters to be optimized are determined as the calculation parameters of the reflection coefficient of particle-wall collision and the deposition parameters corresponding to the critical velocity of particle deposition, and a reasonable value space is defined for each parameter to be optimized. Then, based on the loss value, a Bayesian search algorithm is initiated to search for the parameter combination that minimizes the loss value in the parameter value space. This combination is then used as the adjusted parameters and updated in the battery pack simulation model. Then, the thermal runaway simulation analysis is carried out again using the model with updated parameters to obtain new state parameters. The process of parameter adjustment, simulation analysis, and loss value calculation is repeated until the difference between the state parameters and the experimental data meets the preset error threshold requirements, thus completing the parameter calibration of the battery pack simulation model.

[0144] In this embodiment, by conducting thermal runaway tests on battery pack samples to obtain experimental data, and combining the state parameters obtained from simulation to adjust the parameters to be optimized in the model and iterating repeatedly until the differences meet the preset requirements, the parameters of the battery pack simulation model can be matched with the actual physical process of battery pack thermal runaway. A closed-loop correction mechanism for model parameters is formed based on real experimental data, which effectively improves the simulation accuracy of the simulation model. At the same time, the optimal parameter combination is determined by relying on the Bayesian search algorithm, replacing the manual trial and error parameter adjustment method, improving the efficiency and accuracy of parameter optimization. This allows the calibrated simulation model to more realistically reproduce the eruption and diffusion behavior of particulate matter during battery pack thermal runaway, ensuring the accuracy and reliability of subsequent simulation analysis results, and providing more realistic model support for safety research and design related to battery pack thermal runaway.

[0145] In this embodiment, by conducting thermal runaway tests on battery pack samples to obtain experimental data, and combining this with the state parameters obtained from simulation to adjust the parameters to be optimized in the model, iterating repeatedly until the differences meet preset requirements, the parameters of the battery pack simulation model can be matched with the actual physical process of battery pack thermal runaway. A closed-loop correction mechanism for model parameters based on real experimental data can effectively improve the simulation accuracy of the model. Relying on the Bayesian search algorithm to determine the optimal parameter combination improves the efficiency and accuracy of parameter optimization, allowing the calibrated simulation model to more realistically reproduce the eruption and diffusion behavior of particulate matter during battery pack thermal runaway, ensuring the accuracy and reliability of subsequent simulation analysis results, and providing more realistic model support for safety research and design related to battery pack thermal runaway.

[0146] In summary, the battery pack thermal runaway simulation method provided in this application has the following beneficial effects: High simulation efficiency and accuracy: A phased decoupled simulation strategy is adopted, reducing computation time while ensuring physical realism and solving the real-time challenge of multiphase flow simulation at the battery pack level. Prediction accuracy is further improved through experimental calibration. By introducing rules for particulate matter wall deposition, rebound, and escape, the simulation achieves good results and strong engineering applicability.

[0147] Comprehensive experimental verification: By conducting experiments on battery pack samples, three key parameters—particulate matter accumulation mass, gas flow rate, and concentration—are obtained simultaneously, enabling multi-dimensional verification of the simulation model and improving the credibility of the results.

[0148] Intelligent and transferable calibration: Bayesian optimization algorithm is introduced to realize automatic parameter calibration and establish a structural similarity evaluation matrix to support cross-platform parameter transfer, form a replicable technical standard, and shorten the development cycle of new projects.

[0149] Supports overall battery pack safety design: Breaking through the limitations of local optimization, it supports a holistic approach to battery pack design, identifies areas where particulate matter tends to accumulate, guides the protection design of key components, optimizes pressure relief paths and the layout of filtration systems, and comprehensively improves system-level safety performance.

[0150] In an exemplary embodiment, such as Figure 7 As shown, the battery pack thermal runaway simulation device includes a model building module 701 and a rule configuration module 702. The model building module 701 is used to build a geometric model of the battery pack based on its spatial structure. The rule configuration module 702 is used to configure particulate matter ejection rules in the geometric model to simulate the gas flow field inside the battery pack, obtaining a first simulation model. The rule configuration module 702 is also used to configure particulate matter motion rules in the first simulation model, obtaining a second simulation model. The particulate matter motion rules are used to simulate the motion process of particulate matter in the gas flow field. The rule configuration module 702 is also used to track the distribution of particulate matter after ejection in the second simulation model and correct the parameters of the gas flow field based on the distribution, obtaining a battery pack simulation model. The battery pack simulation model is used to simulate the ejection and diffusion behavior of particulate matter during thermal runaway of the battery pack.

[0151] In this embodiment of the application, the particle motion rules include collision behavior rules when particles collide with the wall in the first simulation model; the collision behavior rules include: when particles collide with the wall in the first simulation model, determining the reflection coefficient of the particles based on the collision velocity of the particles; determining the reflection velocity of the particles based on the reflection coefficient; and determining the motion of the particles during the collision behavior based on the reflection velocity and a preset critical velocity.

[0152] In this embodiment, when the reflection speed is less than a preset critical speed, the motion is determined to be particle deposition on the wall surface; when the reflection speed is greater than or equal to the preset critical speed, the motion is determined to be particle rebound on the wall surface.

[0153] In this embodiment of the application, when the motion is that the particles bounce off the wall surface, the motion of the particles is updated according to the reflection speed until the particles stop moving.

[0154] In this embodiment of the application, the particulate matter motion rule further includes an escape behavior rule for particulate matter passing through the exhaust structure in the first simulation model; the escape behavior rule includes: obtaining the particle size of the particulate matter when it passes through the exhaust structure in the first simulation model; determining a filtration threshold corresponding to the filter based on the pore characteristic parameters of the filter in the exhaust structure, wherein the pore characteristic parameters are updated as the particulate matter is captured by the filter; determining that the particulate matter is captured by the exhaust structure when the particle size is greater than the filtration threshold; and determining that the particulate matter escapes at the exhaust structure when the particle size is less than or equal to the filtration threshold.

[0155] In this embodiment of the application, the device further includes a simulation analysis module, used to: perform thermal runaway simulation in a battery pack simulation model based on the cell gas generation data and particulate matter emission parameters of the target battery pack, to obtain the position distribution of the particulate matter, wherein the battery pack simulation model is consistent with the structure of the target battery pack; and determine the state parameters related to the thermal runaway of the target battery pack based on the position distribution.

[0156] In this embodiment, the state parameters include the particle accumulation mass and particle concentration in a preset area of ​​the battery pack simulation model; the simulation analysis module is specifically used for: obtaining a first particle based on its location distribution, wherein the first particle is a particle that falls into the preset area, and the preset area is a monitoring area pre-delineated based on the structural features of the battery pack simulation model and / or historical deposition data of particles during thermal runaway; accumulating the mass of the first particle to obtain the particle accumulation mass in the preset area; and determining the particle concentration in the preset area based on the particle accumulation mass and the volume of the preset area.

[0157] In this embodiment of the application, the state parameters also include the gas flow velocity at the exhaust structure in the battery pack simulation model; the simulation analysis module is specifically used for: obtaining the second particulate matter according to the position distribution, the second particulate matter being the particulate matter passing through the exhaust structure; obtaining the movement speed of the second particulate matter when passing through the exhaust structure; determining the gas flow velocity at the exhaust structure based on the movement speed of the particulate matter, the particulate matter being carried by the gas at the exhaust structure.

[0158] In this embodiment, the device further includes a model calibration module, used for: conducting thermal runaway tests on battery pack samples to obtain test data related to thermal runaway of the battery pack samples; performing thermal runaway simulation analysis of the battery pack samples in a battery pack simulation model to obtain state parameters related to thermal runaway of the battery pack samples; adjusting the parameters to be optimized in the simulation model based on the test data and the difference between the state parameters and the test data; repeating the steps of battery pack thermal runaway simulation and adjusting the parameters to be optimized until the difference between the test data and the state parameters meets the preset requirements.

[0159] In this embodiment of the application, the parameters to be optimized include at least one of the following: the reflection coefficient when the particles collide with the wall, and the deposition parameters corresponding to the critical velocity at which the particles are deposited on the wall; the model calibration module is specifically used to: take the experimental data as a benchmark, and use the difference between the state parameters and the experimental data as the loss value; based on the loss value, determine a set of parameters that minimize the loss value in the parameter value space through Bayesian search, and use them as the adjusted parameters.

[0160] In an exemplary embodiment, this application also provides an electronic device, which can be the battery pack thermal runaway simulation device in the above method embodiments. For example... Figure 8 As shown, the battery pack thermal runaway simulation device may include a processor 801 and a memory 802. The memory 802 stores instructions executable by the processor 801. When the processor 801 is configured to execute instructions, it causes an electronic device, network device, or manager to perform the system functions described in the foregoing method embodiments.

[0161] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus 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 device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0163] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Furthermore, the functional units in the various embodiments of this application 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.

[0165] 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 readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for simulating thermal runaway of a battery pack, characterized in that, The method includes: Based on the spatial structure of the battery pack, a geometric model of the battery pack is established; The particulate matter emission rules are configured in the geometric model to simulate the gas flow field inside the battery pack, thus obtaining the first simulation model. By configuring particulate motion rules in the first simulation model, a second simulation model is obtained. The particulate motion rules are used to simulate the motion process of particulate matter in the gas flow field. In the second simulation model, the distribution of the particulate matter after eruption is tracked, and the parameters of the gas flow field are corrected according to the distribution to obtain a battery pack simulation model. The battery pack simulation model is used to simulate the eruption and diffusion behavior of particulate matter in the battery pack during thermal runaway.

2. The battery pack thermal runaway simulation method according to claim 1, characterized in that, The particle motion rules include collision behavior rules when the particles collide with the wall in the first simulation model; the collision behavior rules include: When a particulate matter collides with the wall in the first simulation model, the reflection coefficient of the particulate matter is determined based on the collision velocity of the particulate matter. The reflection velocity of the particulate matter is determined based on the reflection coefficient. Based on the reflection velocity and the preset critical velocity, the motion of the particulate matter during collision is determined.

3. The battery pack thermal runaway simulation method according to claim 2, characterized in that, When the reflection velocity is less than a preset critical velocity, the motion is determined to be the deposition of the particulate matter on the wall surface; When the reflection speed is greater than or equal to the preset critical speed, the motion is determined to be the particle bouncing off the wall surface.

4. The battery pack thermal runaway simulation method according to claim 3, characterized in that, When the motion condition is that the particle bounces off the wall surface, the motion condition of the particle is updated according to the reflection velocity until the particle stops moving.

5. The battery pack thermal runaway simulation method according to claim 1, characterized in that, The particulate matter motion rules also include escape behavior rules for particulate matter passing through the exhaust structure in the first simulation model; the escape behavior rules include: The particle size of the particulate matter is obtained when the particulate matter passes through the exhaust structure in the first simulation model; Based on the pore characteristic parameters of the filter in the exhaust structure, a filtration threshold corresponding to the filter is determined, and the pore characteristic parameters are updated as particulate matter is captured by the filter. When the particle size is greater than the filtration threshold, it is determined that the particle is captured by the exhaust structure; When the particle size is less than or equal to the filtration threshold, it is determined that the particle escapes at the exhaust structure.

6. The battery pack thermal runaway simulation method according to claim 1, characterized in that, The battery pack thermal runaway simulation analysis is performed in the battery pack simulation model, including: Based on the cell gas generation data and particulate matter emission parameters of the target battery pack, thermal runaway simulation is performed in the battery pack simulation model to obtain the positional distribution of particulate matter. The spatial structure of the target battery pack is consistent with that of the battery pack simulation model. Based on the location distribution, determine the state parameters related to the thermal runaway of the target battery pack.

7. The battery pack thermal runaway simulation method according to claim 6, characterized in that, The state parameters include the particulate matter accumulation mass and particulate matter concentration in a preset region of the battery pack simulation model; the preset region is a monitoring area pre-delineated based on the structural characteristics of the battery pack simulation model and / or historical deposition data of particulate matter during thermal runaway. The step of determining the state parameters related to the thermal runaway of the target battery pack based on the location distribution includes: Based on the location distribution, a first particulate matter is obtained, wherein the first particulate matter is the particulate matter that falls into the preset area; The mass of the first particulate matter is added together to obtain the mass of the accumulated particulate matter within the preset area; The particle concentration within the preset area is determined based on the particle accumulation mass and the volume of the preset area.

8. The battery pack thermal runaway simulation method according to claim 6, characterized in that, The state parameters also include the gas flow velocity at the exhaust structure in the battery pack simulation model; The step of determining the state parameters related to the thermal runaway of the target battery pack based on the location distribution further includes: Based on the location distribution, a second particulate matter is obtained, wherein the second particulate matter is particulate matter passing through the exhaust structure; The velocity of the second particulate matter as it passes through the exhaust structure is obtained; Based on the velocity of the particles, the gas flow rate at the exhaust structure is determined, and the particles are carried by the gas at the exhaust structure.

9. The battery pack thermal runaway simulation method according to any one of claims 1 to 8, characterized in that, The parameters of the battery pack simulation model are calibrated through the following steps: Thermal runaway tests were conducted on battery pack samples to obtain test data related to thermal runaway of the battery pack samples. In the battery pack simulation model, thermal runaway simulation analysis of the battery pack sample is performed to obtain the state parameters related to thermal runaway of the battery pack sample. Based on the experimental data, and considering the difference between the state parameters and the experimental data, the parameters to be optimized in the simulation model are adjusted. Repeat the steps of simulating thermal runaway of the battery pack and adjusting the parameters to be optimized until the difference between the test data and the state parameters meets the preset requirements.

10. The battery pack thermal runaway simulation method according to claim 9, characterized in that, The parameters to be optimized include at least one of the following: the reflection coefficient when the particles collide with the wall, and the deposition parameters corresponding to the critical velocity at which the particles are deposited on the wall. The step of adjusting the parameters to be optimized in the simulation model based on the experimental data and the differences between the state parameters and the experimental data includes: Based on the experimental data, the difference between the state parameters and the experimental data is taken as the loss value; Based on the loss value, a set of parameters that minimizes the loss value is determined in the parameter value space through Bayesian search, and these are used as the adjusted parameters.

11. A battery pack thermal runaway simulation device, characterized in that, The device includes: a model building module and a rule configuration module; The model building module is used to build a geometric model of the battery pack based on the spatial structure of the battery pack. The rule configuration module is used to configure particulate matter emission rules in the geometric model to simulate the gas flow field inside the battery pack in the geometric model and obtain a first simulation model. The rule configuration module is also used to configure particulate motion rules in the first simulation model to obtain a second simulation model. The particulate motion rules are used to simulate the motion process of particulate matter in the gas flow field. The rule configuration module is also used to track the distribution of the particulate matter after it is ejected in the second simulation model, and to correct the parameters of the gas flow field according to the distribution to obtain a battery pack simulation model. The battery pack simulation model is used to simulate the ejection and diffusion behavior of particulate matter in the battery pack during thermal runaway.

12. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the battery pack thermal runaway simulation method as described in any one of claims 1-10.

Images