A battery pack structure glue coating performance simulation method and device and electronic equipment
By obtaining the mechanical deformation results of the load-bearing components and constructing a realistic structural adhesive data model, combined with the VOF multiphase flow model, the idealization problem of the simulation model in the battery pack structural adhesive coating process was solved, achieving accurate prediction of dynamic filling behavior and optimization of process parameters, thus improving coating quality and thermal management efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DEEPAL AUTOMOBILE TECH CO LTD
- Filing Date
- 2026-04-23
- Publication Date
- 2026-07-14
AI Technical Summary
In the current process of applying structural adhesive to battery packs, the simulation models are mostly based on idealized geometry and static loads, which cannot accurately predict the dynamic filling behavior of the structural adhesive. This results in inconsistent coating quality, unstable thermal management, and low production line debugging efficiency.
By obtaining the mechanical deformation results of the load-bearing components, a realistic structural adhesive data model is constructed. The flow behavior of the structural adhesive is simulated by combining the VOF multiphase flow model, and a closed-loop iterative optimization mechanism is established to accurately predict the dynamic filling behavior and spatial distribution of the structural adhesive.
It significantly improves coating quality consistency and thermal management reliability, shortens the process parameter optimization cycle, reduces material loss and debugging costs, and improves production line debugging efficiency.
Smart Images
Figure CN122389464A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of battery testing technology, and specifically to a method, apparatus, and electronic device for simulating the performance of structural adhesive coating on a battery pack. Background Technology
[0002] Against the backdrop of the rapid development of the new energy vehicle industry, the structural reliability, thermal management efficiency, and manufacturing consistency of power batteries, as core components, have become a focus of industry attention. In current mainstream battery pack designs, structural adhesives are commonly used to achieve mechanical connections and heat conduction for thermal management system components such as cells and liquid cooling plates. This adhesive layer not only serves to buffer interface stress and reinforce the structure, but also directly affects the battery pack's thermal diffusion control capability, vibration durability, and overall life-cycle safety.
[0003] In industrial production, although industrial robots are widely used in adhesive application and pressing processes, traditional pressing processes still face severe challenges due to the increasing complexity of battery pack structures, the accumulation of manufacturing tolerances in components, and the fluctuations in the flatness of load-bearing parts. Insufficient actual coverage of structural adhesive can easily lead to localized heat buildup and structural failure risks. Process parameters rely on experience for adjustment, resulting in extended production line cycle times and fluctuating yield rates. The root cause lies in the fact that structural adhesive, as a typical non-Newtonian fluid, is affected by the coupling of multiple physical fields such as pressure gradient, substrate microstructure, contact angle, and time-varying viscosity characteristics, making it difficult to accurately predict the dynamic pressing process through experience or static simulation.
[0004] In summary, current technologies related to structural adhesives for power batteries have the following key limitations: (1) The simulation models are mostly based on idealized geometry and static loads, ignoring the influence of process variables such as actual deformation of the bearing and initial configuration of the adhesive layer on the flow behavior, and cannot reflect the real filling state during the pressing process; (2) The technical path is disconnected from the production process and lacks the closed-loop simulation capability that connects "mechanical deformation-colloid flow-coverage evaluation". It is difficult to realize the rapid verification and iterative optimization of the pressing parameters, which restricts the intelligent upgrading of the production line and the efficiency of quality control. Summary of the Invention
[0005] In view of the shortcomings of the prior art, the purpose of this application is to provide a method, device and electronic device for simulating the coating performance of structural adhesive for battery packs. It can accurately predict the dynamic filling behavior and spatial distribution of structural adhesive on complex curved substrates, provide a quantifiable and iterative digital verification means for the bonding process of power batteries, and significantly improve the consistency of coating quality, the reliability of thermal management and the efficiency of production line debugging.
[0006] In a first aspect, embodiments of this application provide a method for simulating the performance of structural adhesive coating on a battery pack, comprising: Obtain the mechanical deformation results of the load-bearing component under the preset pressure bonding process parameters; Based on the mechanical deformation results, a data model of the bearing component is constructed, and a data model of the structural adhesive and a data model of the battery cell are constructed. The lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the bearing component data model to form a conformal structural adhesive data model. Using the lowest point of the data model of the bearing component as a reference, a fluid calculation domain of a preset thickness is generated along the vertical direction; Set simulation parameters, apply a downward pressure load to the cell data model, simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour, and output the flow distribution pattern of the structural adhesive.
[0007] In the above technical solution, the mechanical deformation results of the carrier component under preset adhesive bonding process parameters are first obtained. Then, a data model is constructed based on the actual deformed carrier component, rather than using an ideal flat carrier component model. This allows the flow simulation of the structural adhesive to be based on the actual adhesive surface contour, improving the accuracy of the simulation results. The lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the carrier component to form a conformal structural adhesive data model, avoiding flow calculation errors caused by model misalignment or insufficient fit. This ensures that the initial state of the structural adhesive is consistent with the actual adhesive application conditions, laying an accurate model foundation for subsequent flow behavior simulation calculations. A fluid calculation domain of preset thickness is generated along the vertical direction using the lowest point of the carrier component data model as a reference. Compared with the random domain generation method without a reference, this ensures sufficient computational space for structural adhesive flow while avoiding the waste of computational power caused by an excessively large calculation domain, making the simulation calculation more efficient and more focused on the actual flow area of the adhesive surface. By setting simulation parameters based on the actual pressing process and applying a downward pressure load to the battery cell, the actual flow behavior of the structural adhesive along the bonding surface during the pressing process of the battery cell can be accurately simulated. It can accurately predict the dynamic filling behavior and spatial distribution of the structural adhesive on complex curved substrates, providing a quantifiable and iterative digital verification method for the pressing process of power batteries, and significantly improving the consistency of coating quality, the reliability of thermal management, and the efficiency of production line debugging.
[0008] In one embodiment, mechanical simulation of the carrier is performed based on preset adhesive bonding process parameters to obtain the mechanical deformation results of the carrier.
[0009] In the above technical solution, the mechanical deformation of the carrier directly determines the actual contour of the adhesive surface, which is the core basis for subsequent construction of conformal structural adhesive data models, generation of fluid computational domains, and simulation of structural adhesive flow behavior. Modeling based on real deformation results can avoid structural adhesive bonding deviations and flow calculation errors caused by using flat, ideal carrier models, allowing all subsequent simulation stages to be based on the actual adhesive surface morphology, significantly improving the accuracy of the overall simulation results. Furthermore, the mechanical simulation is based on preset pressing process parameters, rather than idealized general parameters, accurately matching key process conditions such as load, pressure application method, and pressing speed during actual pressing on the production line. This can realistically reproduce the stress and deformation of the carrier during the actual pressing process, and the obtained mechanical deformation results are highly consistent with the carrier morphology in actual production line operation, ensuring the authenticity of the data from the source of the simulation.
[0010] In one embodiment, the flatness parameters of the carrier are obtained, and a flatness data model of the carrier is constructed based on the flatness parameters; A finite element model of the battery pack is constructed, including a data model of the battery pack frame, a data model of the battery cells, a data model of the adhesive bonding fixture, and a data model of the battery pack support fixture. The flatness data model of the carrier component is assembled into the finite element model of the battery pack. The flatness data model of the carrier component and the battery pack frame data model are rigidly connected in the riveting area. The flatness data model of the carrier component is supported on the battery pack support fixture data model. The cell data model is initially set above the flatness data model of the carrier component and retains a preset gap. The adhesive bonding fixture data model is in contact with the upper surface of the cell data model. A force matching the preset adhesive bonding process parameters is applied to the adhesive bonding fixture data model, driving the cell data model to move downward, so that the cell data model contacts the flatness data model of the carrier and causes mechanical deformation of the flatness data model of the carrier. Output the deformation data of the load-bearing component obtained through mechanical simulation.
[0011] In the above technical solution, digital simulation is used to replace physical testing to obtain deformation data of load-bearing components, which significantly shortens the time of flow prediction, reduces trial and error losses of materials such as power batteries, cells, and structural adhesives, and lowers the cost of process research and development and debugging.
[0012] In one embodiment, the flatness parameters of the carrier are obtained by scanning with a coordinate measuring machine.
[0013] In the above technical solution, a coordinate measuring machine is used to scan and obtain the flatness parameters of the bearing component. It has the characteristics of high precision and high resolution, and can accurately capture the actual dimensional error and surface contour features of the bearing component, providing accurate basic data for constructing a flatness data model of the bearing component that fits the actual situation.
[0014] In one embodiment, the simulation parameters include the structural adhesive viscosity coefficient, the structural adhesive contact angle, and the cell pressure displacement.
[0015] In the above technical solution, the core physical properties of the structural adhesive, namely viscosity coefficient and contact angle, and the key process parameter of bonding, namely cell pressing displacement, are selected. This aligns with the non-Newtonian fluid characteristics of the structural adhesive and the force-flow law of the actual bonding process. From a parameter perspective, this ensures a high degree of consistency between the simulation process and physical reality, improving the accuracy of the structural adhesive flow simulation results. The targeted inclusion of the structural adhesive's own physical properties accurately reflects the differences in flow characteristics among different types of structural adhesives, solving the problem of flow prediction distortion caused by traditional simulations that do not consider adhesive properties, and adapting to the bonding process simulation needs of structural adhesives of different specifications. Simultaneously incorporating the core process parameter of cell pressing displacement establishes a direct correlation between process operation parameters and structural adhesive flow behavior. This allows for quantitative analysis of the impact of different pressing process parameters on the flow distribution and coverage of the structural adhesive, providing direct simulation data support for the optimization and selection of bonding process parameters.
[0016] In one embodiment, the simulation calculation of the flow behavior of the structural adhesive along the adhesive surface contour adopts the VOF multiphase flow model.
[0017] In the above technical solution, the VOF multiphase flow model can efficiently track the phase interface movement of structural adhesives with non-Newtonian fluid properties on the non-flat adhesive surface contour, solve the problem that traditional models are difficult to accurately simulate the flow boundary of the adhesive, effectively identify the unfilled area at the adhesive surface, and provide reliable data for adhesive coverage calculation.
[0018] In one embodiment, after outputting the flow distribution pattern of the structural adhesive, the method further includes: identifying unfilled areas between the carrier and the battery cell based on the flow distribution, and calculating the coverage rate of the structural adhesive; when the coverage rate is lower than a preset threshold, adjusting the pressing process parameters and iteratively simulating until the coverage rate meets the process requirements.
[0019] In the above technical solution, a preset coverage threshold is set, and a closed-loop optimization mechanism of simulation evaluation, parameter feedback, and iterative simulation is established. The simulation results are directly linked to process parameter adjustments, achieving automated and scientific optimization of the adhesive bonding process parameters. This replaces traditional experience-based trial-and-error adjustments, significantly improving the accuracy of process optimization. Furthermore, the iterative simulation process is based on the existing simulation model, eliminating the need for remodeling. It can quickly complete the flowability simulation verification after process parameter adjustments, shortening the process parameter optimization cycle and enabling rapid selection of optimal process parameters, meeting the needs of efficient production line debugging.
[0020] Secondly, embodiments of this application provide a battery pack structural adhesive coating performance simulation device, which includes: The mechanical deformation data acquisition module is used to acquire the mechanical deformation results of the bearing component under the action of preset pressure bonding process parameters; A three-dimensional model building module is used to build a load-bearing component data model based on the mechanical deformation results, and to build a structural adhesive data model and a battery cell data model, wherein the lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the load-bearing component data model to form a conformal structural adhesive data model. The fluid computational domain generation module is used to generate a fluid computational domain of a preset thickness along the vertical direction, based on the lowest point of the data model of the bearing component. The flow simulation calculation module is used to set simulation parameters, apply a downward pressure load to the cell data model, and simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour. The simulation results output module is used to output the flow distribution pattern of the structural adhesive.
[0021] In one embodiment, it further includes: The coverage analysis module is used to identify the unfilled area between the carrier and the battery cell based on the flow distribution pattern, and to calculate the coverage of the structural adhesive. The iterative optimization module, which is communicatively connected to the coverage analysis module and the mechanical deformation data acquisition module, is used to compare the structural adhesive coverage with a preset threshold. When the coverage is lower than the preset threshold, an adjustment command for the pressing process parameters is generated, and the mechanical deformation data acquisition module is triggered to re-acquire the mechanical deformation results based on the adjusted pressing process parameters. This drives the three-dimensional model construction module, the fluid computation domain generation module, and the flow simulation calculation module to perform a new round of simulation calculations until the structural adhesive coverage meets the preset process requirements.
[0022] Thirdly, embodiments of this application provide an electronic device, including: One or more processors; A memory communicatively connected to the one or more processors, the memory storing computer program instructions executable by the processors; When the processor executes the computer program instructions, it implements the steps of the above-mentioned battery pack structural adhesive coating performance simulation method. Attached Figure Description
[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application will be described below.
[0024] Figure 1 This is a flowchart illustrating the battery pack structural adhesive coating performance simulation method disclosed in the embodiments of this application; Figure 2This is a schematic diagram illustrating the interaction between the carrier data model, structural adhesive data model, and battery cell data model disclosed in the embodiments of this application. Figure 3 This is a schematic diagram illustrating the process of obtaining the mechanical deformation results of the load-bearing component disclosed in the embodiments of this application; Figure 4 This is a schematic diagram showing the state of the flatness data model of the carrier component disclosed in this application after it is assembled into the finite element model of the battery pack. Figure 5 This is a schematic diagram of one embodiment of the battery pack structural adhesive coating performance simulation device disclosed in this application. Figure 6 This is a schematic diagram of another embodiment of the battery pack structural adhesive coating performance simulation device disclosed in this application. Figure 7 This is a schematic diagram of the structure of the electronic device disclosed in the embodiments of this application; Figure 8 This is a simulation diagram of the flow distribution morphology of the structural adhesive disclosed in the embodiments of this application; Figure 9 This is a schematic diagram of the physical verification results of the flow distribution morphology of the structural adhesive disclosed in the embodiments of this application.
[0025] Explanation of reference numerals in the attached figures: 11-Bearing component data model, 12-Structural adhesive data model, 13-Cell data model, 14-Fluid computational domain. 21-Flatness data model of load-bearing component, 22-Data model of battery pack frame, 23-Data model of adhesive bonding fixture, 24-Data model of battery pack support fixture. 30-Simulation device; 31-Mechanical deformation data acquisition module; 32-3D model construction module; 33-Fluid computational domain generation module; 34-Flow simulation calculation module; 35-Simulation result output module; 36-Coverage analysis module; 37-Iterative optimization module. 41-Processor, 42-Memory, 43-Communication module. Detailed Implementation
[0026] The battery pack structural adhesive coating performance simulation method, apparatus, and electronic equipment described in this application will be further described in detail below with reference to the accompanying drawings and specific embodiments. The scope of protection of this application is not limited to the following embodiments; any equivalent modifications made by those skilled in the art based on the teachings of this application are within the scope of protection of this application.
[0027] It should be noted that the carrier component described in this application is a component in the power battery pack that is bonded to the battery cell with structural adhesive, preferably a liquid-cooled plate or a water-cooled plate. As the adhesive substrate for the structural adhesive, it directly affects the coating and filling effect of the structural adhesive. The preset pressing process parameters are the core process parameters of the pressing process in the mass production of power batteries, including but not limited to the pressing load, pressing speed, and pressing and holding time. The adhesive surface is the bonding surface where the carrier component and the structural adhesive come into contact.
[0028] The core of this application's embodiments lies in obtaining actual deformation data of the load-bearing component through mechanical simulation, constructing a simulation model that fits the actual working conditions based on the deformed load-bearing component, accurately predicting the flow behavior of the structural adhesive through fluid simulation, and optimizing the pressing process parameters through closed-loop iteration. This solves the technical problems of traditional simulation idealization modeling and disconnection from the production process, and realizes the digital and quantifiable simulation verification of the coating performance of the battery pack structural adhesive.
[0029] In one embodiment, this application provides a method for simulating the performance of structural adhesive coating on a battery pack. (See also...) Figure 1 As shown, it includes the following steps: Step 1: Obtain the mechanical deformation results of the bearing component under the preset pressure bonding process parameters.
[0030] Specifically, based on the preset adhesive bonding process parameters actually planned for the power battery production line, mechanical simulation is performed on the carrier component to simulate the actual stress and deformation state of the carrier component during the adhesive bonding process, and the mechanical deformation results of the carrier component are output. The mechanical deformation results include the deformation amount, deformation trend, and actual contour data of the adhesive surface at each position of the carrier component, providing core mechanical data support for subsequent modeling.
[0031] Step 2: Construct a data model of the load-bearing component based on the mechanical deformation results, and construct a data model of the structural adhesive and a data model of the battery cell. The lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the load-bearing component data model to form a conformal structural adhesive data model.
[0032] Specifically, see Figure 2 As shown, a data model 11 for the load-bearing component is constructed based on the aforementioned mechanical deformation results. Simultaneously, a data model 12 for the structural adhesive and a data model 13 for the battery cell are constructed based on the actual specifications of the structural adhesive and the three-dimensional dimensions of the battery cell. The lower surface of the structural adhesive data model 12 is precisely fitted and matched with the adhesive surface of the load-bearing component data model 11 to form a conformal structural adhesive data model, eliminating simulation errors caused by model misalignment and insufficient fit.
[0033] Step 3: Using the lowest point of the data model of the bearing component as a reference, generate a fluid calculation domain of a preset thickness along the vertical direction.
[0034] Specifically, see Figure 2 As shown, a fluid computation domain 14 of preset thickness is generated along the vertical direction, with the lowest point of the load-bearing component data model 11 as the reference. The preset thickness is determined based on the actual adhesive layer thickness and the cell pressing stroke during the power battery bonding process, and is preferably 15~25mm. This method ensures sufficient simulation computation space for the structural adhesive flow while avoiding the waste of computational power caused by an excessively large computation domain, allowing the simulation computation to focus on the actual flow area of the adhesive bonding surface.
[0035] Step 4: Set simulation parameters, apply a downward pressure load to the cell data model, simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour, and output the flow distribution pattern of the structural adhesive.
[0036] Specifically, simulation parameters are set based on the physical properties of the structural adhesive and the requirements of the bonding process. A downward pressure load matching the preset bonding process parameters is applied to the cell data model. The dynamic flow behavior of the structural adhesive along the adhesive surface contour is simulated and calculated. Finally, the flow distribution pattern of the structural adhesive on the adhesive surface is output. This pattern includes key information such as the spread range, thickness distribution, and flow trend of the structural adhesive.
[0037] The battery pack structural adhesive coating performance simulation method provided in this application can accurately predict the dynamic filling behavior and spatial distribution of structural adhesive on complex curved substrates, providing a quantifiable and iterative digital verification method for power battery bonding processes, significantly improving coating quality consistency, thermal management reliability, and production line debugging efficiency. Furthermore, the prediction time for the flowability of structural adhesive in the power battery bonding process is shortened from 15 days in traditional physical verification to 1 day; reducing material losses of power batteries, structural adhesive, and cells during physical verification, while avoiding disruption to existing production line plans during the physical verification process. As a preferred embodiment of this application, see [link to application]. Figure 3 As shown, in step one above, the mechanical deformation results of the load-bearing component are obtained through full-element finite element simulation, specifically including the following sub-steps: Step A: Obtain the flatness parameters of the bearing component, and construct the flatness data model 21 of the bearing component based on the flatness parameters.
[0038] Specifically, the flatness parameters of the carrier component are obtained through scanning with a coordinate measuring machine (CMM). That is, the CMM performs a full-surface scan of the carrier component to obtain its actual flatness parameters. These flatness parameters include the carrier component's surface contour, dimensional errors, and uneven deformation, among other real geometric data. Based on these flatness parameters, a flatness data model of the carrier component that matches its actual state is constructed, eliminating simulation deviations caused by idealized modeling from the outset.
[0039] Step B, see Figure 4As shown, a finite element model of the battery pack is constructed, including a battery pack frame data model 22, a cell data model 13, a bonding tooling data model 23, and a battery pack support tooling data model 24.
[0040] Step C, see Figure 4 As shown, the flatness data model 21 of the carrier component is assembled into the finite element model of the battery pack. The flatness data model 21 and the battery pack frame data model 22 are rigidly connected in the riveting area, matching the fixing method in actual assembly. The flatness data model 21 of the carrier component is supported on the battery pack support fixture data model 24, simulating the actual load-bearing state. The cell data model 13 is initially positioned above the flatness data model 21 of the carrier component with a preset gap, which is consistent with the initial adhesive layer thickness of the actual pressing process. The pressing fixture data model 23 contacts the upper surface of the cell data model 13, simulating the force applied by the pressing fixture.
[0041] Step D: Apply a force matching the preset adhesive bonding process parameters to the adhesive bonding tooling data model 23 to drive the cell data model 13 to move downward, so that the cell data model 13 contacts the flatness data model 21 of the carrier and causes the flatness data model 21 of the carrier to undergo mechanical deformation. Step E: After the simulation process stabilizes, output the deformation data of the load-bearing component obtained from the mechanical simulation.
[0042] This preferred embodiment uses digital simulation to replace physical testing to obtain deformation data of the load-bearing components, which significantly shortens the time of the flow prediction process, reduces trial and error losses of materials such as power batteries, cells, and structural adhesives, and lowers the cost of process development and debugging.
[0043] In step four above, the simulation parameters include the viscosity coefficient of the structural adhesive, the contact angle of the structural adhesive, and the cell pressing displacement. By selecting the core physical properties of the structural adhesive, namely the viscosity coefficient and contact angle, and the key process parameter of pressing, namely the cell pressing displacement, the simulation parameters are aligned with the non-Newtonian fluid characteristics of the structural adhesive and the force-flow law of the actual pressing process. This ensures a high degree of consistency between the simulation process and physical reality from the parameter perspective, improving the accuracy of the structural adhesive flow simulation results. Targeted inclusion of the structural adhesive's own physical properties accurately reflects the differences in flow characteristics among different types of structural adhesives, solving the problem of flow prediction distortion caused by traditional simulations that do not consider the adhesive properties, and adapting to the pressing process simulation needs of structural adhesives of different specifications. Simultaneously incorporating the core process parameter of cell pressing displacement establishes a direct correlation between process operation parameters and structural adhesive flow behavior. This allows for quantitative analysis of the impact of different pressing process parameters on the flow distribution and coverage of the structural adhesive, providing direct simulation data support for the optimization and selection of pressing process parameters.
[0044] Among them, the viscosity coefficient of the structural adhesive reflects its viscous characteristics and is matched with the type of structural adhesive, operating temperature, and shear rate. The contact angle of the structural adhesive reflects its wetting and spreading characteristics on the bonding surface of the load-bearing component and is determined by the material properties of the structural adhesive and the load-bearing component. The cell pressing displacement reflects the actual pressing stroke of the cell during the pressing process and is consistent with the pressing distance in the preset pressing process parameters.
[0045] In step four above, the simulation calculation of the structural adhesive's flow behavior along the adhesive bonding surface contour employs the VOF multiphase flow model. The VOF multiphase flow model can efficiently track the interface morphology and dynamic changes between the structural adhesive and air, accurately capturing the interface movement of the non-Newtonian fluid-like structural adhesive on a non-flat adhesive bonding surface contour, thus solving the problem of traditional models' difficulty in accurately simulating the adhesive flow boundary. Simultaneously, the VOF multiphase flow model can perform refined simulation of the transient process of structural adhesive flow, fully reproducing the dynamic changes of the structural adhesive from contact to spreading and filling during the cell pressing process, providing reliable data for adhesive coverage calculation.
[0046] In a preferred embodiment of this application, after outputting the flow distribution pattern of the structural adhesive, the method further includes: identifying unfilled areas between the carrier and the battery cell based on the flow distribution, and calculating the coverage rate of the structural adhesive; when the coverage rate is lower than a preset threshold, adjusting the pressing process parameters and iteratively simulating until the coverage rate meets the process requirements.
[0047] Specifically, based on the flow distribution pattern of the structural adhesive, the unfilled area between the carrier and the battery cell is accurately identified through the area recognition function of the simulation software, and the coverage rate of the structural adhesive is calculated based on the total area of the adhesive surface and the actual filling area of the structural adhesive.
[0048] The calculated structural adhesive coverage is compared with a preset threshold. If the coverage is lower than the threshold, the preset pressing process parameters are adjusted, such as the pressing load, pressing displacement, and pressing speed. Based on the adjusted parameters, steps one through four are re-executed to complete a new round of structural adhesive flow simulation. This comparison and iterative simulation process is repeated until the structural adhesive coverage meets the preset process requirements, at which point the optimal pressing process parameters are output. This closed-loop optimization mechanism is based on the existing simulation model, eliminating the need for remodeling. It allows for rapid flow simulation verification after parameter adjustments, achieving automated and scientific optimization of the pressing process parameters.
[0049] The preset threshold is set according to the structural reliability and thermal management efficiency requirements of the power battery pack, and is preferably ≥95%.
[0050] In one embodiment, this application provides a battery pack structural adhesive coating performance simulation device, see [link to relevant documentation]. Figure 5 As shown, the simulation device 30 includes: The mechanical deformation data acquisition module 31 is used to acquire the mechanical deformation results of the bearing component under the action of preset pressure bonding process parameters; The three-dimensional model construction module 32 is used to construct a load-bearing component data model based on the mechanical deformation results, and to construct a structural adhesive data model and a battery cell data model, wherein the lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the load-bearing component data model to form a conformal structural adhesive data model. The fluid computational domain generation module 33 is used to generate a fluid computational domain of a preset thickness along the vertical direction, based on the lowest point of the data model of the bearing component. The flow simulation calculation module 34 is used to set simulation parameters, apply a downward pressure load to the cell data model, and simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour. The simulation result output module 35 is used to output the flow distribution pattern of the structural adhesive.
[0051] As a preferred embodiment of this application, see [link to application]. Figure 6 As shown, the simulation device 30 also includes: Coverage analysis module 36 is used to identify unfilled areas between the carrier and the battery cell based on the flow distribution pattern, and to calculate the structural adhesive coverage. The iterative optimization module 37 is communicatively connected to the coverage analysis module and the mechanical deformation data acquisition module. It is used to compare the coverage of the structural adhesive with a preset threshold. When the coverage is lower than the preset threshold, it generates an adjustment command for the pressing process parameters and triggers the mechanical deformation data acquisition module to re-acquire the mechanical deformation results based on the adjusted pressing process parameters. This drives the three-dimensional model construction module, the fluid calculation domain generation module, and the flow simulation calculation module to perform a new round of simulation calculations until the coverage of the structural adhesive meets the preset process requirements.
[0052] In one embodiment, this application provides an electronic device that can run the above-described simulation method and be equipped with the above-described simulation device to realize the simulation calculation of the coating performance of the structural adhesive of the battery pack. See also Figure 7 As shown, the specific hardware structure includes: One or more processors 41 are used to execute computer program instructions and complete core operations such as data processing, model building, and simulation calculation during the simulation process; preferably, a multi-core processor is used to improve simulation calculation efficiency.
[0053] The memory 42 is communicatively connected to the one or more processors. The memory stores computer program instructions that can be executed by the processors. The computer program instructions are program code for implementing the above-mentioned battery pack structural adhesive coating performance simulation method, including finite element modeling code, fluid simulation code, coverage calculation code, parameter iterative optimization code, etc.
[0054] The input interface is used to receive basic data such as preset pressure bonding process parameters, structural adhesive physical property parameters, and flatness parameters of load-bearing components from external input. It can be connected to external equipment such as coordinate measuring machines and production line process parameter systems.
[0055] The output interface is used to output simulation results such as the flow distribution pattern of structural adhesive, adhesive coverage data, and optimal pressing process parameters. It can be connected to external devices such as monitors, printers, and production line control systems to realize the visualization of simulation results and the direct distribution of process parameters.
[0056] The communication module 43 is used to realize high-speed data communication between the processor 41 and the memory 42, the input interface and the output interface, so as to ensure the smoothness of the simulation process.
[0057] When the processor 41 executes the computer program instructions stored in the memory 42, it can implement the battery pack structural adhesive coating performance simulation method described in any of the above embodiments. The electronic device can be a desktop computer, workstation, industrial control computer or other device with high performance computing capabilities, and is suitable for the process research and development, debugging and quality control scenarios of power battery production lines.
[0058] To verify the effectiveness of the simulation method described in this application, the bonding process between the water-cooled plate (bearing component) and the battery cell of a certain power battery pack was used as the verification object. The structural adhesive coating performance was simulated using the method of this application, and compared with the traditional physical verification method. The verification results are shown in [link to relevant documentation]. Figure 8 and Figure 9 As shown, Figure 8 The simulation results shown are consistent with Figure 9 The results of the physical verification shown are highly consistent, which means that the simulation method can directly replace the traditional multiple physical bonding verifications, greatly reducing the loss of materials such as power batteries, structural adhesives, and cells during the physical verification process, and reducing the cost of process development and debugging.
[0059] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.
Claims
1. A method for simulating the performance of structural adhesive coating in battery packs, characterized in that, include: Obtain the mechanical deformation results of the load-bearing component under the preset pressure bonding process parameters; Based on the mechanical deformation results, a data model of the bearing component is constructed, and a data model of the structural adhesive and a data model of the battery cell are constructed. The lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the bearing component data model to form a conformal structural adhesive data model. Using the lowest point of the data model of the bearing component as a reference, a fluid calculation domain of a preset thickness is generated along the vertical direction; Set simulation parameters, apply a downward pressure load to the cell data model, simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour, and output the flow distribution pattern of the structural adhesive.
2. The method for simulating the performance of structural adhesive coating in battery packs according to claim 1, characterized in that: Mechanical simulation of the carrier component was performed based on preset adhesive bonding process parameters to obtain the mechanical deformation results of the carrier component.
3. The method for simulating the performance of structural adhesive coating in battery packs according to claim 2, characterized in that: Obtain the flatness parameters of the load-bearing component, and construct a flatness data model of the load-bearing component based on the flatness parameters; A finite element model of the battery pack is constructed, including a data model of the battery pack frame, a data model of the battery cells, a data model of the adhesive bonding fixture, and a data model of the battery pack support fixture. The flatness data model of the carrier component is assembled into the finite element model of the battery pack. The flatness data model of the carrier component and the battery pack frame data model are rigidly connected in the riveting area. The flatness data model of the carrier component is supported on the battery pack support fixture data model. The cell data model is initially set above the flatness data model of the carrier component and retains a preset gap. The adhesive bonding fixture data model is in contact with the upper surface of the cell data model. A force matching the preset adhesive bonding process parameters is applied to the adhesive bonding fixture data model, driving the cell data model to move downward, so that the cell data model contacts the flatness data model of the carrier and causes mechanical deformation of the flatness data model of the carrier. Output the deformation data of the load-bearing component obtained through mechanical simulation.
4. The method for simulating the performance of structural adhesive coating in battery packs according to claim 3, characterized in that: The flatness parameters of the bearing component are obtained by scanning with a coordinate measuring machine.
5. The method for simulating the performance of structural adhesive coating in battery packs according to claim 1, characterized in that: The simulation parameters include the viscosity coefficient of the structural adhesive, the contact angle of the structural adhesive, and the displacement of the battery cell under pressure.
6. The method for simulating the performance of structural adhesive coating in battery packs according to claim 1, characterized in that: The simulation calculation of the flow behavior of the structural adhesive along the adhesive surface contour uses the VOF multiphase flow model.
7. The method for simulating the performance of structural adhesive coating in battery packs according to claim 1, characterized in that: After outputting the flow distribution pattern of the structural adhesive, the method further includes: identifying unfilled areas between the carrier and the battery cell based on the flow distribution, and calculating the coverage rate of the structural adhesive; when the coverage rate is lower than a preset threshold, adjusting the pressing process parameters and iterating the simulation until the coverage rate meets the process requirements.
8. A simulation device for the performance of structural adhesive coating in battery packs, characterized in that, include: The mechanical deformation data acquisition module is used to acquire the mechanical deformation results of the bearing component under the action of preset pressure bonding process parameters; A three-dimensional model building module is used to build a load-bearing component data model based on the mechanical deformation results, and to build a structural adhesive data model and a battery cell data model, wherein the lower surface of the structural adhesive data model is fitted and matched with the adhesive surface of the load-bearing component data model to form a conformal structural adhesive data model. The fluid computational domain generation module is used to generate a fluid computational domain of a preset thickness along the vertical direction, based on the lowest point of the data model of the bearing component. The flow simulation calculation module is used to set simulation parameters, apply a downward pressure load to the cell data model, and simulate and calculate the flow behavior of the structural adhesive along the adhesive surface contour. The simulation results output module is used to output the flow distribution pattern of the structural adhesive.
9. The battery pack structural adhesive coating performance simulation device according to claim 8, characterized in that, Also includes: The coverage analysis module is used to identify the unfilled area between the carrier and the battery cell based on the flow distribution pattern, and to calculate the coverage of the structural adhesive. The iterative optimization module, which is communicatively connected to the coverage analysis module and the mechanical deformation data acquisition module, is used to compare the structural adhesive coverage with a preset threshold. When the coverage is lower than the preset threshold, an adjustment command for the pressing process parameters is generated, and the mechanical deformation data acquisition module is triggered to re-acquire the mechanical deformation results based on the adjusted pressing process parameters. This drives the three-dimensional model construction module, the fluid computation domain generation module, and the flow simulation calculation module to perform a new round of simulation calculations until the structural adhesive coverage meets the preset process requirements.
10. An electronic device, characterized in that, include: One or more processors; A memory communicatively connected to the one or more processors, the memory storing computer program instructions executable by the processors; When the processor executes the computer program instructions, it implements the steps of the battery pack structural adhesive coating performance simulation method as described in any one of claims 1 to 7.