Method for identifying aluminum bar welding failure in EOL expansion stage of battery pack

Abstract

The invention relates to a battery pack EOL expansion stage aluminum bar welding failure identification method, which comprises the following steps: acquiring aluminum bar welding tension actual measurement data through a horizontal drawing test, and taking the minimum tension as a failure judgment threshold value; a module three-dimensional digital model and a welding track file are obtained, a finite element model is established through geometric pretreatment and differential grid division, the aluminum bar is endowed with material attributes of a stress-strain curve, and Tie binding connection is established; explicit dynamic analysis is adopted, end plate constraint and EOL equivalent expansive force loads are applied, aluminum bar welding area section tension is extracted after simulation, and the aluminum bar welding area section tension is compared with a threshold value to judge the failure risk; and failure causes can be positioned based on tension discreteness, and an optimization direction can be given. Quantitative identification of the welding failure risk is achieved, simulation precision and efficiency are both considered, the risk can be pre-judged in advance, the research and development period is shortened, and technical support is provided for safety of the battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and specifically to a method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack. Background Technology

[0002] With the rapid development of the electrochemical energy storage industry, the safety and durability of large-capacity energy storage battery packs have become core research and development priorities. During long-term charge-discharge cycles, lithium-ion batteries experience continuous expansion forces due to SEI film growth, internal gas generation, and volume changes in active materials. These expansion forces gradually increase as the battery's health deteriorates, reaching a peak at the end of its lifespan (EOL). The expansion force of an energy storage cell during the EOL stage can reach as high as 20,000N-50,000N. As a key component for energy transfer and structural connection between cells, the aluminum busbar (ABB) is prone to failure at its welded joints to the cell terminals under the peak expansion force during the EOL stage, potentially leading to serious safety accidents such as thermal runaway, fire, and explosion. Therefore, a technical solution for accurately identifying the risk of ABB welding failure during the EOL expansion stage is urgently needed to support the structural design and welding process optimization of the ABB.

[0003] In existing technologies, research on the expansion force of battery modules mainly focuses on structural component strength testing and general expansion simulation, which has obvious limitations: most expansion force simulation methods only simulate the stress and strain state of module fixing components such as end plates, bolts, and straps by calculating the force on the end plates or applying expansion coefficients, without paying attention to the core failure risk point of aluminum bar welding. For example, the invention application with publication number CN117330407A discloses a "Battery Module Structural Component Expansion Force Testing Method, Failure Judgment Method and Device", which attempts to improve simulation accuracy by applying resistance force and expansion thrust step by step. However, the test object focuses on module fixing components and does not involve the welding area between aluminum bars and terminals between cells. The failure judgment is based on macroscopic parameters such as the stress and strain state and length change of the structural components, without establishing a specific evaluation standard for the welding parts. The scheme only applies resistance force and expansion force through theoretical calculation, without combining measured welding tensile force data to calibrate the simulation model, resulting in insufficient accuracy in identifying welding failure risks and failing to provide a clear direction for the optimization of aluminum bar welding process. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention aims to provide a method for identifying aluminum bar welding failures during the end-of-life (EOL) expansion stage of a battery pack. First, actual welding tensile force data of the aluminum bar is obtained through horizontal pull-out testing. The minimum welding tensile force is used as the failure criterion to ensure that the assessment criteria align with engineering realities. Then, finite element method (FEM) software is used to refine the mesh of the aluminum bar welding area, accurately establishing the tie-bonding relationship of the welded joint. Finally, dynamic explicit analysis is employed to simulate the actual expansion force during the EOL stage, extracting the welding area tensile force and comparing it with measured thresholds to achieve accurate identification of failure risks. This application fills the gap in existing technologies for identifying aluminum bar welding failures during the EOL expansion stage, providing efficient and accurate technical support for battery pack safety design.

[0005] The present invention provides the following solution to the above-mentioned technical problems: A method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack, comprising the following steps: Obtain measured data of the welding tensile strength of aluminum bar, and determine the failure judgment threshold of the welding tensile strength of aluminum bar based on the measured data; A three-dimensional data model of the battery pack is obtained, and geometric preprocessing and mesh generation are performed on the three-dimensional data model of the battery pack to establish a finite element model of the battery pack. Assign corresponding material properties to each component of the finite element model of the battery pack, and establish the binding connection between the aluminum bar welding area and the cell electrode based on the actual welding trajectory of the aluminum bar. The finite element model of the battery pack is imported into the simulation software and the analysis step is set. In the analysis step, load settings and boundary conditions simulating the expansion of the battery cell to the EOL state are applied, and the simulation calculation is submitted. After the simulation calculation is completed, the cross-sectional tensile force of each aluminum bar welding area in the finite element model is extracted and compared with the failure judgment threshold. If the cross-sectional tensile force of any aluminum bar welding area exceeds the failure judgment threshold, the aluminum bar is determined to have a welding failure risk.

[0006] This invention establishes a complete process encompassing measured threshold acquisition, refined finite element modeling, simulation analysis and load application, result extraction, and risk assessment. By directly correlating and comparing physical test data with refined simulation results, it forms a quantifiable virtual prediction and risk assessment closed-loop method for aluminum bar welding failure. This method focuses the assessment object from the generalized module structure to the weakest aluminum bar welding point in the battery pack, achieving precise risk identification. It can identify design or process defects before physical prototype manufacturing and testing, significantly shortening the R&D cycle and reducing the potential safety risk of thermal runaway caused by welding failure.

[0007] Preferably, the method further includes analyzing the causes of aluminum bar welding failure based on the measured data of the aluminum bar welding tensile strength of the sample, as follows: If the difference between the maximum and minimum welding tensile force in the measured data of the aluminum bar welding tensile force of the sample is greater than a preset first threshold, then the welding failure is determined to be caused by an unstable welding process, and the improvement direction is to optimize the welding process. If the difference between the maximum and minimum welding tensile force in the measured data of the aluminum bar welding tensile force of the sample is less than or equal to a preset first threshold, then the welding failure is determined to be caused by the aluminum bar structure design, and the improvement direction is to optimize the aluminum bar structure.

[0008] By discretizing failure causes based on measured data, we can accurately distinguish between process problems and design problems, avoid resource waste caused by blind optimization, and directly provide targeted improvement directions, offering clear R&D guidance for engineers.

[0009] Preferably, the measured data of the aluminum bar welding pull force is obtained in the following way: A battery cell and an aluminum bar sample are selected from the batch to be evaluated and welded together. A horizontal pull-out force test is performed on the welded sample. The maximum pull-out force value recorded when the weld point between the aluminum bar and the battery cell terminal is pulled to failure is the measured welding pull force data for a single sample. The minimum value selected from the multiple measured data is determined as the failure judgment threshold. The welding parameters are set as follows: weld penetration depth 0.5-2.0 mm, weld width 2.0-4.0 mm.

[0010] The threshold was determined to originate from destructive testing simulating actual stress conditions, and the minimum measured value was used as the criterion, ensuring the reliability of the risk assessment. A range of welding parameters was provided, enabling standardized testing conditions and guaranteeing the consistency and comparability of threshold data across different batches and under different personnel operations.

[0011] Preferably, the step of acquiring the three-dimensional data model of the battery pack, and performing geometric preprocessing and mesh generation on the three-dimensional data model of the battery pack to establish a finite element model of the battery pack includes: Import the 3D data model of the battery pack, including the 3D digital model information of the cells, aluminum foil, inter-cell foam, and end plates on both sides, into the finite element software. The battery pack 3D data model is based on the engineering design scheme of the battery pack module. It obtains the 3D design model of the module completed by the structural engineer. The model is exported after structural design verification and geometric cleaning (removing redundant features and repairing geometric defects). It contains the precise geometric information and assembly constraint relationship of the cells, aluminum bars, welding areas, foam between cells and end plates. The format is adapted to the import requirements of finite element preprocessing software (such as Hypermesh). The actual welding trajectory parameters of the aluminum bar are obtained, and the geometry of the aluminum bar is refined in the simulation software based on the actual welding trajectory parameters of the aluminum bar to create the geometric surface where the aluminum bar and the battery cell electrode overlap during welding. The actual welding trajectory parameters are obtained from the aluminum bar welding trajectory design drawings, which are jointly confirmed by structural engineers and welding process engineers, based on the structural design specifications of the battery pack module, the spatial layout dimensions of the cell terminals, and the preset welding process requirements. The drawings are output after the process feasibility is verified, and include precise geometric information of the welding start point, path trajectory, end point, and welding area boundary, which can be directly used for welding area modeling in the finite element model. The battery cell and aluminum bar are divided using different grid sizes, with the aluminum bar having a smaller grid size than the battery cell; the aluminum bar is divided into multiple solid grids along the thickness direction, and the welding area and the base material area of ​​the aluminum bar are divided into different components.

[0012] Refining the geometry based on the welding trajectory ensured that the simulation model accurately reproduced the actual welding area and location, laying the foundation for accurate subsequent analysis. Differentiated mesh sizes (fine for aluminum foil, coarse for the battery cell) effectively controlled the overall model size and improved computational efficiency while maintaining calculation accuracy for key components. Multi-layer solid meshing of the aluminum foil enabled accurate simulation of the stress gradient along the thickness direction during bending; separating the welding area from the base material area allowed for the separate and precise extraction of stress at the welding interface.

[0013] Preferably, the battery cell and aluminum bus are divided using different grid sizes, wherein the grid size of the aluminum bus is smaller than that of the battery cell; the aluminum bus is divided into multiple solid grids along the thickness direction, and the welding area and the base material area of ​​the aluminum bus are divided into different components, as detailed below: The overall basic grid size is 5mm, the grid of the battery cell is 10mm, and the grid of the aluminum foil is refined to 3mm; In terms of unit type, the battery cell outer shell is modeled using shell elements, the battery cell cover is modeled using hexahedral solid elements, and the meshes around the battery cell cover and the battery cell outer shell are treated with shared nodes; the aluminum bus is divided into hexahedral solid elements and divided into three layers of mesh along its thickness direction, and the welding area of ​​the aluminum bus and the base material area are divided into different components.

[0014] Preferably, the step of assigning corresponding material properties to each component of the battery pack finite element model and establishing the binding connection between the aluminum bar welding area and the cell terminal post in the simulation software based on the actual welding trajectory of the aluminum bar includes: Create an aluminum bar material card, input the density, elastic modulus, and Poisson's ratio of the aluminum bar, check the plasticity option and input the stress-strain curve to complete the assignment of cross-sectional properties to the aluminum bar material; Create master and slave surfaces according to the actual welding trajectory of the aluminum bar. Select the contact surface of the cell electrode post or cover plate and the aluminum bar as the master surface, and the geometric surface where the aluminum bar and the cell electrode post overlap during welding as the slave surface. The welding area of ​​the aluminum bar is bound to the cell terminal using the Tie binding command, simulating the welding relationship between the two.

[0015] By assigning aluminum bar a stress-strain curve that incorporates plasticity, it can simulate the yielding and permanent deformation that may occur under large expansion forces, far exceeding the simple linear elastic assumption, and significantly improving the physical realism and reliability of the simulation results.

[0016] Preferably, the step of importing the finite element model of the battery pack into the simulation software and setting the analysis step, applying load settings and boundary conditions to simulate the expansion of the battery cells to the EOL state in the analysis step, and submitting the simulation calculation includes: An explicit dynamic analysis step is created for the finite element model in the simulation software to simulate the large deformation and complex contact behavior during the cell expansion process; and in the field output request settings of the analysis step, the nodal force output option is activated to record the nodal force data for subsequent extraction of welding tensile force. In the analysis step, the six degrees of freedom of the fixed connection parts of the end plates on both sides of the module are constrained to simulate the actual fixed installation state of the battery module in the battery pack. In the analysis step, a temperature rise load simulating the expansion of the battery cell to the end of its life is applied to the core node set inside the battery cell. After the settings are completed, the finite element model is submitted for calculation until the simulation reaches the set end-of-life expansion state of the battery cell.

[0017] For the battery cell model to be tested, the equivalent expansion force parameter of its EOL stage is obtained from the battery cell engineer and input into the load application module. This parameter is determined by the battery cell engineer through full life cycle cyclic testing of the battery cell (such as charge and discharge cycles to the capacity decay threshold), in-situ expansion force monitoring experiments, and multi-physics field simulation coupling analysis. After consistency verification and engineering adaptation calibration of multiple batches of samples, it is finally output in the form of a quantitative parameter table (including peak force value, distribution coefficient, and action boundary) to ensure that it is completely consistent with the expansion behavior of the battery cell under the actual EOL state.

[0018] An explicit dynamic analysis step is adopted to adapt to the large deformation and complex contact behavior during the cell expansion process, solving the problem that static analysis cannot simulate transient expansion forces and improving the adaptability of the simulation scenario. Activating the nodal force output option ensures that welding tensile force data can be accurately extracted, avoiding the uselessness of simulation results due to missing output settings and ensuring the feasibility of risk assessment.

[0019] The six degrees of freedom of the fixed connection part of the constrained end plate are simulated to represent the actual installation state. Temperature rise loads are applied to simulate EOL expansion, ensuring that the loads and boundary conditions fully conform to engineering reality, avoiding a disconnect between theoretical assumptions and actual working conditions, and improving the reliability of the simulation results. Calculations are explicitly performed up to the EOL expansion state to ensure that the simulation focuses on the core scenario, avoids invalid calculations, and improves simulation efficiency.

[0020] The present invention also provides a battery pack EOL expansion stage aluminum bar welding failure identification system, comprising: The failure judgment threshold generation module is used to acquire the measured data of the aluminum bar welding tensile force and determine the failure judgment threshold of the aluminum bar welding tensile force based on the measured data. The finite element model construction module is used to acquire the three-dimensional data model of the battery pack, and to perform geometric preprocessing and mesh generation on the three-dimensional data model of the battery pack to establish the finite element model of the battery pack. The material and connection relationship assignment module is used to assign corresponding material properties to each component of the battery pack finite element model, and to establish the binding connection relationship between the aluminum bar welding area and the cell electrode based on the actual welding trajectory of the aluminum bar. The simulation analysis module is used to import the finite element model of the battery pack into the simulation software and set the analysis steps. In the analysis steps, load settings and boundary conditions are applied to simulate the expansion of the battery cell to the EOL state, and the simulation calculation is submitted. The risk identification module is used to extract the cross-sectional tensile force of each aluminum bar welding area in the finite element model after completing the simulation calculation, and compare it with the failure judgment threshold. If the cross-sectional tensile force of any aluminum bar welding area exceeds the failure judgment threshold, it is determined that the aluminum bar has a welding failure risk.

[0021] The present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described above.

[0022] The present invention also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described above are implemented.

[0023] The beneficial effects of this invention are as follows: This invention obtains actual welding tensile force data through horizontal pull-out testing, using the minimum welding tensile force as the failure threshold to avoid the distortion problems of purely theoretical simulations. Simultaneously, through detailed design such as weld area mesh refinement, assignment of plastic properties, and establishment of realistic binding relationships, it ensures that the simulation results highly match engineering reality, significantly improving the reliability of failure identification. Furthermore, it can differentiate between two types of failure causes—process instability and structural design—based on the discreteness of welding tensile force, directly providing targeted improvement directions.

[0024] In summary, this invention addresses the area of ​​aluminum bar welding failure during the EOL expansion stage of battery packs, which is not covered by existing technologies. It constructs a dedicated method for actual data calibration, simulation modeling, and quantitative judgment, solving the problem that traditional technologies only focus on module structural components and ignore aluminum bar welding risks, thus enabling the identification of failure risks.

[0025] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below. Specific embodiments of the present invention are given in detail in the following examples. Attached Figure Description

[0026] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart of a method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack, provided in Example 1; Figure 2 This is a schematic diagram of the horizontal pull-out force test performed on the battery cell and aluminum bar sample after welding in step 1 of Example 1. Figure 3 This is the aluminum bar welding trajectory design diagram obtained in step 2 of Example 1; Figure 4 This is a schematic diagram showing the geometric refinement of the aluminum bar welding area in step 2 of Example 1; Figure 5 This is a schematic diagram of the aluminum bar finite element mesh model in step 2 of Example 1; Figure 6 This is a complete view of the finite element mesh model of the battery pack in step 2 of Example 1; Figure 7 A screenshot of the interface for creating the aluminum bar material card using Hypermesh software in step 3 of Example 1; Figure 8 This is a schematic diagram showing the connection setup of the aluminum bar and the battery cell cover plate in step 3 of Example 1; Figure 9 This is a screenshot of the Abaqus analysis step settings module in step 4 of Example 1. Figure 10 This is a schematic diagram of the boundary constraint settings in the finite element software in step 4 of Example 1; Figure 11 This is a screenshot of the load module settings interface in the finite element software in step 4 of Example 1; Figure 12 This is a screenshot of the welding tensile force reading and setting interface during the post-processing of Abaqus software in step 5 of Example 1. Figure 13 This is a schematic diagram of the welding tensile force simulation reading results in step 5 of the embodiment. Detailed Implementation

[0027] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0028] Example 1 like Figure 1 As shown, this embodiment provides a method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack, including the following steps: S1. Obtain the measured data of the welding tensile strength of the aluminum bar, and determine the failure judgment threshold of the welding tensile strength of the aluminum bar based on the measured data, as follows: The measured data of the welding tensile strength of aluminum foil were obtained through the following methods: Cell and aluminum bar samples were selected from the batch to be evaluated and welded together. Horizontal pull-out force tests were then performed on the welded cell and aluminum bar samples. Figure 2 As shown, the maximum tensile force value recorded when the weld point between the aluminum bar and the battery cell terminal is pulled to failure is obtained; where the welding parameters are set as weld penetration depth 0.5-2.0mm and weld width 2.0-4.0mm.

[0029] In this embodiment, 10 samples were selected for pull-out force testing. The results are shown in Table 1. The minimum weld pull-out force was 1035.72 N, the maximum weld pull-out force was 3036.57 N, and the average weld pull-out force was 2131.57 N. The minimum value of 1035.72 N was selected from the measured data and determined as the failure judgment threshold.

[0030] Table 1. Measured data of aluminum bar welding tensile strength of samples from the tested batches. S2. Obtain the 3D data model of the battery pack, and perform geometric preprocessing and mesh generation on the 3D data model of the battery pack to establish the finite element model of the battery pack, as follows: Import the 3D data model of the battery pack, including the 3D digital model information of the cells, aluminum foil, inter-cell foam, and end plates on both sides, into the finite element software. The actual welding trajectory parameters of the aluminum bar are obtained, and the geometry of the aluminum bar is refined in the simulation software based on these parameters to create the geometric surface where the aluminum bar and the battery cell electrode overlap during welding. Figure 4 As shown; The actual welding trajectory parameters are obtained from the aluminum battery pack module's welding trajectory design drawings, which are jointly confirmed by structural engineers and welding process engineers, based on the structural design specifications of the battery pack module, the spatial layout dimensions of the cell terminals, and the preset welding process requirements. The drawings, after being verified for process feasibility, are output and contain precise geometric information on the welding start point, path trajectory, end point, and welding area boundaries. This information can be directly used for welding area modeling in the finite element model. Figure 3 As shown, the inner diameter of the aluminum bar weld is 7mm, the outer diameter is 11mm, and the weld width is 2mm.

[0031] The local details of the finite element mesh model constructed in this embodiment are as follows: Figures 5-6 As shown, the battery cells and aluminum foil are divided using different grid sizes. The overall basic grid size is 5mm, the grid size of the battery cells is 10mm, and the grid size of the aluminum foil is refined to 3mm. To accurately simulate the stress on the aluminum bar, it was divided into three layers of solid mesh along its thickness. The cell outer shell was modeled using shell elements, while the cell cover was modeled using hexahedral solid elements. The meshes around the cell cover and outer shell shared nodes. To facilitate post-processing for extracting the welding tensile force, the entire aluminum bar was meshed using hexahedral solid elements, and the welding area and the base material area were classified as different components, as detailed below: S3. Assign corresponding material properties to each component of the battery pack finite element model, and establish the binding connection between the aluminum bar welding area and the cell terminal based on the actual welding trajectory of the aluminum bar, as follows: like Figure 7 As shown, create an aluminum bar material card in Hypermesh, input the density, elastic modulus, and Poisson's ratio of the aluminum bar, check the Plastic option and input the stress-strain curve to complete the assignment of cross-sectional properties to the aluminum bar material. like Figure 8 As shown, a master-slave surface is created according to the actual welding trajectory of the aluminum bar. The contact surface between the battery cell electrode or cover plate and the aluminum bar is selected as the master surface, and the geometric surface where the aluminum bar and the battery cell electrode overlap during welding is selected as the slave surface. The welding area of ​​the aluminum bar is bound to the battery cell electrode using the Tie binding command to simulate the welding relationship between the two.

[0032] S4. Import the finite element model of the battery pack into the simulation software and set the analysis step. In the analysis step, apply the load settings and boundary conditions to simulate the expansion of the battery cells to the EOL state, and submit the simulation calculation, as follows: Import the finite element model of the battery pack into the Abaqus simulation software, such as Figure 9As shown, an explicit dynamic analysis step is created for the finite element model in the simulation software to simulate the large deformation and complex contact behavior during the cell expansion process; and in the field output request settings of the analysis step (Field OutputRequest in the Step module), the nodal force output option (NFORC option) is activated to record the nodal force data for subsequent extraction of welding tensile force; like Figures 10-11 As shown, in the analysis step, the six degrees of freedom of the fixed connection parts of the end plates on both sides of the module are constrained to simulate the actual fixed installation state of the battery module in the battery pack. In the analysis step, a temperature rise load simulating the expansion of the battery cell to the end of its life is applied to the core node set inside the battery cell. After the settings are completed, the finite element model is submitted for calculation until the simulation reaches the set end-of-life expansion state of the battery cell.

[0033] S5. After completing the simulation calculation, as follows: Figure 12 As shown, in the Abaqus post-processor, click "Create FreeBody Cut" to create a cross-section, then select the weld area corresponding to each aluminum bar, and click "OK" to confirm. Next, in the Options settings, check the "Show forces" command to extract the cross-sectional tensile force of each aluminum bar weld area in the finite element model and compare it with the failure judgment threshold. The results are as follows. Figure 13 As shown, the maximum welding tensile force is 1572.8N and the minimum is 1148.4N, both exceeding the failure judgment threshold of 1035.72N determined by S1 above. This indicates that the aluminum bar has a risk of welding failure at the end of the module's EOL expansion.

[0034] The difference between the maximum and minimum welding tensile strength of the aluminum bar welded samples tested in S6 and S1 is nearly 2000N, which is greater than the first preset threshold (500N). This indicates that there is a serious problem with the stability of the aluminum bar welding process, which is the main cause of welding failure. The improvement direction is to optimize the welding process.

[0035] If the difference between the maximum and minimum welding tensile force in the measured data of the aluminum bar welding tensile force of the sample is less than or equal to a preset first threshold, then the welding failure is determined to be caused by the aluminum bar structure design, and the improvement direction is to optimize the aluminum bar structure.

[0036] Example 2 This embodiment provides a battery pack EOL expansion stage aluminum bar welding failure identification system, including: The failure judgment threshold generation module is used to acquire the measured data of the aluminum bar welding tensile force and determine the failure judgment threshold of the aluminum bar welding tensile force based on the measured data. The finite element model construction module is used to acquire the three-dimensional data model of the battery pack, and to perform geometric preprocessing and mesh generation on the three-dimensional data model of the battery pack to establish the finite element model of the battery pack. The material and connection relationship assignment module is used to assign corresponding material properties to each component of the battery pack finite element model, and to establish the binding connection relationship between the aluminum bar welding area and the cell electrode based on the actual welding trajectory of the aluminum bar. The simulation analysis module is used to import the finite element model of the battery pack into the simulation software and set the analysis steps. In the analysis steps, load settings and boundary conditions are applied to simulate the expansion of the battery cell to the EOL state, and the simulation calculation is submitted. The risk identification module is used to extract the cross-sectional tensile force of each aluminum bar welding area in the finite element model after completing the simulation calculation, and compare it with the failure judgment threshold. If the cross-sectional tensile force of any aluminum bar welding area exceeds the failure judgment threshold, it is determined that the aluminum bar has a welding failure risk.

[0037] Example 3 This embodiment provides a computer storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described in Embodiment 1.

[0038] Example 4 This embodiment also provides an electronic device, including a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described in Embodiment 1.

[0039] The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0040] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device.

[0041] Memory can be volatile memory, such as random-access memory (RAM); memory can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. Memory can be a combination of the above-mentioned types of memory.

[0042] This invention can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0043] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention according to the description and above. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, based on the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack, characterized in that, include: Obtain measured data of the welding tensile strength of aluminum bar, and determine the failure judgment threshold of the welding tensile strength of aluminum bar based on the measured data; A three-dimensional data model of the battery pack is obtained, and geometric preprocessing and mesh generation are performed on the three-dimensional data model of the battery pack to establish a finite element model of the battery pack. Assign corresponding material properties to each component of the finite element model of the battery pack, and establish the binding connection between the aluminum bar welding area and the cell electrode based on the actual welding trajectory of the aluminum bar. The finite element model of the battery pack is imported into the simulation software and the analysis step is set. In the analysis step, load settings and boundary conditions simulating the expansion of the battery cell to the EOL state are applied, and the simulation calculation is submitted. After the simulation calculation is completed, the cross-sectional tensile force of each aluminum bar welding area in the finite element model is extracted and compared with the failure judgment threshold. If the cross-sectional tensile force of any aluminum bar welding area exceeds the failure judgment threshold, the aluminum bar is determined to have a welding failure risk.

2. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 1, characterized in that: The method also includes analyzing the causes of aluminum bar welding failure based on measured data of the aluminum bar welding tensile strength of the samples, as detailed below: If the difference between the maximum and minimum welding tensile force in the measured data of the aluminum bar welding tensile force of the sample is greater than a preset first threshold, then the welding failure is determined to be caused by an unstable welding process, and the improvement direction is to optimize the welding process. If the difference between the maximum and minimum welding tensile force in the measured data of the aluminum bar welding tensile force of the sample is less than or equal to a preset first threshold, then the welding failure is determined to be caused by the aluminum bar structure design, and the improvement direction is to optimize the aluminum bar structure.

3. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 1, characterized in that: The measured data of the aluminum bar welding pull force were obtained in the following way: a battery cell and an aluminum bar sample were selected from the batch to be evaluated and welded. A horizontal pull force test was performed on the welded sample. The maximum pull force value recorded when the weld point between the aluminum bar and the battery cell terminal was pulled to failure was the measured data of the welding pull force of a single sample. The minimum value was selected from the multiple measured data to determine the failure judgment threshold. The welding parameters were set as follows: weld penetration depth 0.5-2.0 mm, weld penetration width 2.0-4.0 mm.

4. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 1, characterized in that, The process of acquiring a 3D data model of the battery pack, performing geometric preprocessing and mesh generation on the 3D data model of the battery pack, and establishing a finite element model of the battery pack includes: Import the 3D data model of the battery pack, including the 3D digital model information of the cells, aluminum foil, inter-cell foam, and end plates on both sides, into the finite element software. The actual welding trajectory parameters of the aluminum bar are obtained, and the geometry of the aluminum bar is refined in the simulation software based on the actual welding trajectory parameters of the aluminum bar to create the geometric surface where the aluminum bar and the battery cell electrode overlap during welding. The battery cell and aluminum bar are divided using different grid sizes, with the aluminum bar having a smaller grid size than the battery cell; the aluminum bar is divided into multiple solid grids along the thickness direction, and the welding area and the base material area of ​​the aluminum bar are divided into different components.

5. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 4, characterized in that, The battery cell and aluminum bus are divided using different mesh sizes, with the aluminum bus's mesh size being smaller than that of the battery cell. The aluminum bus is further divided into multiple layers of solid mesh along its thickness direction, and the welding area and the base material area of ​​the aluminum bus are classified as different components, as detailed below: The overall basic grid size is 5mm, the grid of the battery cell is 10mm, and the grid of the aluminum foil is refined to 3mm; In terms of unit type, the battery cell outer shell is modeled using shell elements, the battery cell cover is modeled using hexahedral solid elements, and the meshes around the battery cell cover and the battery cell outer shell are treated with shared nodes; the aluminum bus is divided into hexahedral solid elements and divided into three layers of mesh along its thickness direction, and the welding area of ​​the aluminum bus and the base material area are divided into different components.

6. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 4, characterized in that, The process involves assigning corresponding material properties to each component of the battery pack finite element model and establishing a binding connection between the aluminum bar welding area and the cell terminal post based on the actual welding trajectory of the aluminum bar, including: Create an aluminum bar material card in the simulation software, input the density, elastic modulus, and Poisson's ratio of the aluminum bar, check the plasticity option and input the stress-strain curve to complete the assignment of cross-sectional properties of the aluminum bar material. Create master and slave surfaces according to the actual welding trajectory of the aluminum bar. Select the contact surface of the cell electrode post or cover plate and the aluminum bar as the master surface, and the geometric surface where the aluminum bar and the cell electrode post overlap during welding as the slave surface. The welding area of ​​the aluminum bar is bound to the cell terminal using the Tie binding command, simulating the welding relationship between the two.

7. The method for identifying aluminum bar welding failure during the EOL expansion stage of a battery pack as described in claim 1, characterized in that, The process of importing the finite element model of the battery pack into the simulation software and setting the analysis step, applying load settings and boundary conditions simulating cell expansion to the EOL state in the analysis step, and submitting the simulation calculation includes: An explicit dynamic analysis step is created for the finite element model in the simulation software to simulate the large deformation and complex contact behavior during the cell expansion process; and in the field output request settings of the analysis step, the nodal force output option is activated to record the nodal force data for subsequent extraction of welding tensile force. In the analysis step, the six degrees of freedom of the fixed connection parts of the end plates on both sides of the module are constrained to simulate the actual fixed installation state of the battery module in the battery pack. In the analysis step, a temperature rise load simulating the expansion of the battery cell to the end of its life is applied to the core node set inside the battery cell. After the settings are completed, the finite element model is submitted for calculation until the simulation reaches the set end-of-life expansion state of the battery cell.

8. A battery pack EOL expansion stage aluminum bar welding failure identification system, characterized in that, include: The failure judgment threshold generation module is used to acquire the measured data of the aluminum bar welding tensile force and determine the failure judgment threshold of the aluminum bar welding tensile force based on the measured data. The finite element model construction module is used to acquire the three-dimensional data model of the battery pack, and to perform geometric preprocessing and mesh generation on the three-dimensional data model of the battery pack to establish the finite element model of the battery pack. The material and connection relationship assignment module is used to assign corresponding material properties to each component of the battery pack finite element model, and to establish the binding connection relationship between the aluminum bar welding area and the cell electrode based on the actual welding trajectory of the aluminum bar. The simulation analysis module is used to import the finite element model of the battery pack into the simulation software and set the analysis steps. In the analysis steps, load settings and boundary conditions are applied to simulate the expansion of the battery cell to the EOL state, and the simulation calculation is submitted. The risk identification module is used to extract the cross-sectional tensile force of each aluminum bar welding area in the finite element model after completing the simulation calculation, and compare it with the failure judgment threshold. If the cross-sectional tensile force of any aluminum bar welding area exceeds the failure judgment threshold, it is determined that the aluminum bar has a welding failure risk.

9. A computer storage medium storing a computer program that, when executed by a processor, implements the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described in any one of claims 1-7.

10. An electronic device comprising a memory and a processor: the memory for storing computer-executable instructions, the processor for executing the computer-executable instructions, wherein the computer-executable instructions, when executed by the processor, implement the steps of the battery pack EOL expansion stage aluminum bar welding failure identification method as described in any one of claims 1-7.

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