Parameter determination method and device of engine hood, engine hood and vehicle

Abstract

The application provides a parameter determination method and device of an engine cover, the engine cover and a vehicle. The method comprises the following steps: obtaining a design parameter of the engine cover; calculating an energy absorption value of the engine cover corresponding to the design parameter, and calculating a collision loss value of the engine cover corresponding to the design parameter; performing optimization by using a multi-objective optimization algorithm, adjusting the design parameter, until the energy absorption value reaches a maximum energy value and the collision loss value reaches a minimum loss value, and obtaining an updated design parameter, wherein the updated design parameter is used to construct the engine cover. The scheme solves the problem of high design cost of the engine cover in the prior art.

Description

Technical Field

[0001] This application relates to the field of engine technology, and more specifically, to a method for determining the parameters of an engine hood, a device for determining the parameters of an engine hood, an engine hood, and a vehicle. Background Technology

[0002] In today's automotive industry, with increasingly stringent environmental regulations and rising consumer demands for fuel economy and safety, lightweighting has become an irreversible trend. Lightweighting not only reduces carbon emissions and increases mileage, but also improves vehicle handling and safety. Aluminum alloys, due to their high specific strength and excellent formability, have become a popular choice for body panel materials, especially in areas such as the hood, side panels, and doors.

[0003] The existing design process involves multiple independent design steps lacking a closed-loop connection. On the one hand, the establishment of material constitutive models is often based on simplifying assumptions, neglecting the complex dynamic mechanical behavior of materials under high strain rates, leading to significant deviations between simulation results and actual experiments. On the other hand, structural optimization typically focuses on static performance, failing to fully consider the dynamic energy absorption characteristics during actual collisions, which limits the improvement of the crash safety performance of the covering. Furthermore, current designs rely heavily on experience, resulting in high design costs for engine hoods. Summary of the Invention

[0004] The main objective of this application is to provide a method for determining the parameters of an engine hood, a device for determining the parameters of an engine hood, an engine hood, and a vehicle, so as to at least solve the problem of high design costs of engine hoods in the prior art.

[0005] To achieve the above objectives, according to one aspect of this application, a method for determining the parameters of an engine hood is provided, comprising: obtaining design parameters of the engine hood, wherein the design parameters include one or more of material grade, thickness distribution, shape of reinforcement members, number of reinforcement members, distribution of reinforcement members, shape of hollow areas, number of hollow areas, and distribution of hollow areas; calculating the energy absorption value of the engine hood corresponding to the design parameters, and calculating the collision loss value of the engine hood corresponding to the design parameters, wherein the energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the design parameters in the event of a collision, and the collision loss value is the stress value received by the target object in the event of a collision; and performing optimization using a multi-objective optimization algorithm to adjust the design parameters until the energy absorption value reaches the maximum energy value and the collision loss value reaches the minimum loss value, thereby obtaining updated design parameters, wherein the updated design parameters are used to construct the engine hood.

[0006] Optionally, a multi-objective optimization algorithm is used to optimize the design parameters until the absorbed energy value reaches its maximum value and the collision loss value reaches its minimum value, thus obtaining updated design parameters. This includes: using the multi-objective optimization algorithm to adjust the design parameters as variables multiple times, and obtaining the absorbed energy value and the collision loss value after each adjustment; extracting the maximum value among the multiple adjusted absorbed energy values, extracting the minimum value among the multiple adjusted collision loss values, and extracting the adjusted design parameters corresponding to the maximum absorbed energy value and the minimum collision loss value, thus obtaining the updated design parameters.

[0007] Optionally, the multi-objective optimization algorithm is used to adjust the design parameters as variables multiple times, and the absorbed energy value and the collision loss value are obtained after each adjustment of the design parameters. This includes: simulating the strain value of the material corresponding to the material grade using the Johnson-Cook model; constructing a finite element model of the engine hood using the design parameters to obtain the engine hood model; and using the multi-objective optimization algorithm, based on the strain value of the material obtained from the simulation, adjusting the design parameters in the engine hood model multiple times, and obtaining the absorbed energy value and the collision loss value after each adjustment of the design parameters.

[0008] Optionally, the strain value of the material corresponding to the material grade is simulated using the Johnson-Cook model, including: conducting a tensile test on the material corresponding to the material grade in the laboratory, constructing a strain curve of the material corresponding to the material grade, and obtaining a strain curve; fitting the strain curve of the material corresponding to the material grade using the Johnson-Cook model to obtain the simulated strain value of the material corresponding to the material grade.

[0009] Optionally, calculating the energy absorption value of the engine hood corresponding to the design parameters and the collision loss value of the engine hood corresponding to the design parameters includes: simulating a collision between the engine hood and the target object using simulation technology; and extracting the energy absorption value and the collision loss value of the engine hood and the target object corresponding to the design parameters during the simulation process using simulation software.

[0010] Optionally, after using a multi-objective optimization algorithm to optimize and adjust the design parameters until the absorbed energy value reaches the maximum energy value and the collision loss value reaches the minimum loss value, and obtaining updated design parameters, the method further includes: constructing a digital twin model of the engine hood using the updated design parameters to obtain a digital twin model of the engine hood; and displaying the digital twin model of the engine hood on a display device.

[0011] Optionally, after constructing a digital twin model of the engine hood using the updated design parameters to obtain the engine hood digital twin model, the method further includes: obtaining the actual absorbed energy value and the actual collision loss value, wherein the actual absorbed energy value is the actual kinetic energy absorbed by the engine hood corresponding to the design parameters under the condition of a collision, and the actual collision loss value is the actual stress value received by the target object under the condition of a collision; calculating the difference between the actual absorbed energy value and the absorbed energy value to obtain an energy difference value; calculating the difference between the actual collision loss value and the collision loss value to obtain a loss difference value; optimizing the engine hood digital twin model when the energy difference value is greater than or equal to a preset energy threshold, and / or when the loss difference value is greater than or equal to a preset loss threshold, to obtain an optimized engine hood digital twin model, wherein the optimization method includes at least finite element optimization; and displaying the optimized engine hood digital twin model on the display device.

[0012] According to another aspect of this application, a parameter determination device for an engine hood is provided, comprising: a first acquisition unit, configured to acquire design parameters of the engine hood, wherein the design parameters include one or more of material grade, thickness distribution, shape of reinforcement members, number of reinforcement members, distribution of reinforcement members, shape of hollow areas, number of hollow areas, and distribution of hollow areas; a second acquisition unit, configured to calculate the energy absorption value of the engine hood corresponding to the design parameters, and calculate the collision loss value of the engine hood corresponding to the design parameters, wherein the energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the design parameters in the event of a collision, and the collision loss value is the stress value received by a target object in the event of a collision; and an optimization unit, configured to perform optimization using a multi-objective optimization algorithm, adjust the design parameters until the energy absorption value reaches the maximum energy value and the collision loss value reaches the minimum loss value, and obtain updated design parameters, wherein the updated design parameters are used to construct the engine hood.

[0013] According to another aspect of this application, an engine hood is provided, which is manufactured according to any of the engine hood parameter determination methods described above.

[0014] According to another aspect of this application, a vehicle is provided, the vehicle including an engine hood, the engine hood being any of the aforementioned engine hoods.

[0015] By applying the technical solution of this application, a multi-objective optimization algorithm is used to iteratively adjust the design parameters. The goal is to maximize energy absorption while minimizing collision loss. This breaks through the single-objective optimization in traditional design, such as only pursuing lightweighting or energy absorption capacity. Instead, it seeks a balance among multiple mutually constraining objectives. It can rapidly iterate the design in a virtual environment, avoiding the high costs and time wasted on physical prototyping and testing, thereby reducing design costs. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining engine hood parameters is shown in an embodiment of this application.

[0018] Figure 2 A flowchart illustrating a method for determining the parameters of an engine hood according to an embodiment of this application is shown.

[0019] Figure 3 A schematic diagram of the structure of an engine cover plate is shown;

[0020] Figure 4 A schematic diagram of another engine cover plate is shown;

[0021] Figure 5 The first schematic diagram of the fitting is shown;

[0022] Figure 6 A second schematic diagram of the fitting is shown;

[0023] Figure 7 The third schematic diagram of the fitting is shown;

[0024] Figure 8 The fourth schematic diagram of the fitting is shown;

[0025] Figure 9 The first schematic diagram showing the selected collision point is shown;

[0026] Figure 10 A second schematic diagram showing the selected collision point is shown;

[0027] Figure 11 A schematic diagram of the curve at the first collision point is shown;

[0028] Figure 12 A schematic diagram of the curve at the first collision point is shown;

[0029] Figure 13 A schematic diagram of the curve at the first collision point is shown;

[0030] Figure 14 A structural block diagram of a parameter determination device for an engine hood provided according to an embodiment of this application is shown.

[0031] The above figures include the following reference numerals:

[0032] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0033] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0034] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0035] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0036] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0037] Currently, the automotive industry is facing the dual challenges of lightweighting and improving passive safety. While traditional 5xxx series aluminum alloys offer good formability for critical components such as engine hoods, their strength, bake-hardening effect, and recyclability are limited. 6xxx series aluminum alloys, on the other hand, possess excellent bake-hardening properties and higher ultimate strength, making them more suitable for lightweighting and energy absorption. However, their application to engine hood inner panels faces bottlenecks such as inaccurate material models, reliance on experience in structural design, and high testing costs.

[0038] In existing design processes, material selection, structural design, and performance verification are often disconnected. Material models used in CAE simulations are severely simplified and cannot accurately reflect the true mechanical behavior of materials under high strain rates, leading to significant discrepancies between simulation results and actual experiments. This necessitates multiple, time-consuming, and labor-intensive "design-prototype-testing" cycles. Therefore, the industry urgently needs a digital design closed-loop method that can bridge the gap between material properties and system performance, enabling precise, efficient design and reliability verification of body panels.

[0039] As described in the background section, the design cost of engine hoods in the prior art is relatively high. To solve the above problems, embodiments of this application provide a method for determining the parameters of an engine hood, a device for determining the parameters of an engine hood, an engine hood, and a vehicle.

[0040] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0041] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining engine hood parameters according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0042] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the engine hood parameter determination method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0043] This embodiment provides a method for determining the parameters of an engine hood that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0044] Figure 2 This is a flowchart illustrating a method for determining the parameters of an engine hood according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0045] Step S201: Obtain the design parameters of the engine hood, wherein the design parameters include one or more of the following: material grade, thickness distribution, shape of reinforcement, number of reinforcements, distribution of reinforcements, shape of hollow area, number of hollow areas, and distribution of hollow area.

[0046] Specifically, the material grade refers to the specific type of aluminum alloy material used to make up the engine hood, such as 6014. Different material grades have different mechanical properties, heat treatment characteristics, and cost-effectiveness ratios. Choosing the appropriate grade is crucial for the lightweight design and energy absorption efficiency of the engine hood.

[0047] Regarding the thickness distribution, the thickness of the various parts inside the engine hood is not uniform, but rather differentiated according to the role each area plays in a collision. Typically, areas subjected to greater stress are designed to be thicker to enhance local strength, while other areas are relatively thinner to reduce overall weight. This design optimizes material usage and improves structural efficiency.

[0048] Regarding the shape of reinforcement components, such as stiffeners, their geometry (e.g., corrugated, U-shaped, trapezoidal, etc.) directly affects their load-bearing capacity and distribution efficiency. Different shapes can reduce material usage while ensuring structural stability, thereby reducing the weight of the engine hood.

[0049] The number of reinforcements determines the density of the internal structure of the engine hood. Too many reinforcements will increase weight, while too few may affect structural rigidity. Properly planning the number of reinforcements is key to achieving lightweighting while ensuring structural strength and energy absorption performance.

[0050] The distribution of reinforcement components, and their arrangement within the engine hood, not only affects the overall structural stability but also the energy absorption distribution during a collision. Optimizing the distribution of reinforcement components ensures sufficient support for critical areas under impact, while avoiding over-design in unnecessary areas and saving materials.

[0051] The design of the hollowed-out or perforated areas aims to reduce material weight without compromising the overall structural strength. By precisely controlling the shape and size of the hollowed-out areas, unnecessary material can be reduced while ensuring necessary rigidity, thus achieving lightweighting.

[0052] The number of open areas, and the total number of open areas on the hood, are also factors that need careful consideration in lightweight design. Too many open areas may weaken the structure, while appropriate open areas can reduce weight while maintaining sufficient rigidity.

[0053] The distribution of open areas, specifically the location strategy of these open or perforated areas on the engine hood, directly affects the structure's safety and energy absorption performance. By distributing open areas in non-critical load-bearing regions, weight can be reduced and material utilization improved without compromising crash safety performance.

[0054] Step S202: Calculate the energy absorption value of the engine hood corresponding to the above design parameters, and calculate the collision loss value of the engine hood corresponding to the above design parameters, wherein the energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the above design parameters in the event of a collision, and the collision loss value is the stress value received by the target object in the event of a collision.

[0055] Specifically, the energy absorbed by the engine hood can be the kinetic energy absorbed by the engine hood under the design parameters in the event of a collision, obtained through simulation, and the collision loss value can be the stress value received by the target object in the event of a collision, obtained through simulation.

[0056] Step S203: A multi-objective optimization algorithm is used to optimize the design parameters until the energy absorption value reaches the maximum value and the collision loss value reaches the minimum value, thereby obtaining the updated design parameters. The updated design parameters are used to construct the engine hood.

[0057] Specifically, by utilizing multi-objective optimization algorithms, the energy absorption capacity of the engine hood can be maximized while protecting the target object. This is because multi-objective optimization algorithms can identify and explore the Pareto front in the parameter space, selecting a design scheme that satisfies both energy absorption requirements and reduces damage to the target object. The target object can be either a moving or inactive object.

[0058] Multi-objective optimization algorithms are applied to design parameter adjustments, aiming to simultaneously optimize energy absorption and collision loss values ​​until a balance is reached between maximizing energy absorption and minimizing loss. The specific implementation process is as follows: First, design parameters are incorporated as variables into the multi-objective optimization algorithm framework for iterative adjustments. Second, the energy absorption and collision loss values ​​obtained after each adjustment are recorded, and the maximum energy absorption value and minimum collision loss value, along with the corresponding design parameter combinations, are selected. Finally, these parameter combinations are determined as the updated design parameters to guide the structural optimization of the engine hood inner panel. This method, by comprehensively considering energy absorption efficiency and collision safety, achieves a comprehensive improvement in structural performance, ensuring that the product can effectively absorb collision energy while minimizing damage to the target object in practical applications.

[0059] Common multi-objective optimization algorithms include NSGA-II, SPEA2, MOEA / D, particle swarm optimization, ant colony optimization, and so on.

[0060] In this embodiment, a multi-objective optimization algorithm is used to iteratively adjust the design parameters. The goal is to maximize energy absorption while minimizing collision loss. This breaks through the single-objective optimization in traditional design, such as only pursuing lightweighting or energy absorption capacity. Instead, it seeks a balance among multiple mutually constraining objectives. It can rapidly iterate the design in a virtual environment, avoiding the high costs and time wasted on physical prototyping and testing, thereby reducing design costs.

[0061] In summary, the solution proposed in this application is an integrated design and verification method for automotive body panels (especially engine hoods) that combines materials science, structural optimization, and digital twin technology.

[0062] In the specific implementation process, a multi-objective optimization algorithm is used to optimize the above design parameters until the energy absorption value reaches the maximum value and the collision loss value reaches the minimum value, thus obtaining the updated design parameters. This can be achieved through the following steps: The above multi-objective optimization algorithm is used to adjust the above design parameters as variables multiple times, and the energy absorption value and collision loss value after each adjustment are obtained; the maximum value of the energy absorption value after multiple adjustments is extracted, the minimum value of the collision loss value after multiple adjustments is extracted, and the adjusted design parameters corresponding to the maximum value of the energy absorption value and the minimum value of the collision loss value are extracted to obtain the updated design parameters.

[0063] In this scheme, the iterative optimization process of the multi-objective optimization algorithm can not only accurately adjust the design parameters of the inner panel of the engine hood to meet the requirements of high-performance energy absorption and low-damage protection, but also greatly reduce the dependence on physical prototypes, thereby significantly reducing development costs.

[0064] Multi-objective optimization algorithms are optimization strategies that simultaneously consider multiple objective functions (energy absorption and collision loss in this case). Their goal is to find one or more compromise solutions that are optimal or suboptimal across all objectives, forming a non-dominated solution set (Pareto front). When designing the engine hood inner panel, design parameters (material grade, thickness distribution, stiffener layout, etc.) are treated as adjustable variables. Finite element method (FE) software is used for simulation to evaluate the energy absorption and protection performance after each parameter adjustment. Through iterative optimization, the algorithm can automatically find the set of design parameters that optimally absorb energy while meeting protection requirements. From all simulation results, the combination of design parameters that performs best in energy absorption (reaching the maximum value) and minimizes damage in collision loss (reaching the minimum value) is selected.

[0065] In some embodiments, the above-mentioned multi-objective optimization algorithm is used to adjust the design parameters as variables multiple times, and the energy absorption value and collision loss value after each adjustment of the design parameters are obtained. Specifically, this can be achieved through the following steps: simulating the strain value of the material corresponding to the above-mentioned material grade using the Johnson-Cook model; constructing a finite element model of the engine hood using the above-mentioned design parameters to obtain the engine hood model; and using the above-mentioned multi-objective optimization algorithm, based on the strain value of the material obtained from the simulation, adjusting the design parameters in the engine hood model multiple times, and obtaining the energy absorption value and collision loss value after each adjustment of the design parameters.

[0066] In this scheme, the Johnson-Cook model can accurately reflect the dynamic mechanical behavior of materials under high strain rates. Through parameter fitting, the material model can be closely linked with the actual physical properties, making the simulation prediction more accurate. The parameterized model allows the software to automatically adjust the design parameters and evaluate the performance of different schemes. Using multi-objective optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, the design parameters in the engine hood model are iteratively optimized. Through multi-objective optimization, the optimal solution can be found in the multi-dimensional design space such as material strength and structural layout, ensuring that the engine hood can not only effectively absorb collision energy, but also perform well in terms of protection. This can greatly reduce the dependence on physical prototypes, thereby significantly reducing development costs.

[0067] The above scheme focuses on the strain value of the material, using the Johnson-Cook model to accurately simulate the dynamic mechanical properties of 6xxx aluminum alloys, especially 6014 alloy. First, based on the Johnson-Cook model parameters corresponding to the material grade, a virtual model of the engine hood inner panel is constructed using finite element analysis software, with the inner panel thickness and stiffener layout considered as design variables. Then, a multi-objective optimization algorithm is used to iteratively adjust the structural parameters of the engine hood inner panel. After each parameter adjustment, the energy absorption and collision loss values ​​of the engine hood under a protective collision scenario are calculated, aiming to find the optimal design scheme that meets the high energy absorption requirements without excessively increasing collision loss. This process not only fully utilizes the high energy absorption potential of the 6xxx series aluminum alloys but also effectively avoids excessive collision loss, ensuring that the engine hood maintains low self-damage while protecting the target object. Through multiple rounds of optimization, the final designed inner panel maximizes energy absorption at key collision points while minimizing damage.

[0068] In one specific implementation, the scheme of this application includes establishing an object protection simulation model. The object model is a finite element model established according to the actual size of the object, and a three-dimensional solid mesh is used for meshing. The meshing accuracy is determined by using different element sizes for different parts. When setting material properties, different material properties are set according to the hardness of different parts of the object. In order to obtain the change of the object's acceleration over time during the calculation, an acceleration sensor is established inside the object.

[0069] The engine hood model consists of an outer hood panel, an inner hood panel, and internal reinforcing plates. The outer hood panel can be made of baked 6014 stainless steel with a thickness of 1.2mm, and the inner hood panel can be made of baked 5182 stainless steel with a thickness of 1.3mm. In the model building process, shell elements are used to create the finite element model, with quadrilateral elements being the primary element type. The components are connected using adhesive bonding or riveting. A model of the internal reinforcing plates of the hood assembly with adhesive riveting connections is created; see details below. Figure 3 , Figure 4 Finally, the entire assembly is assembled, and a finite element model of the hood assembly is established.

[0070] Specifically, the scheme of this application includes the construction of a material gene library and the establishment of a high-precision constitutive model: tensile tests are performed on the specimens, and specimens with different pre-stretch rates (0%, 4%, 7%) are prepared using 6014 aluminum alloy plates and 5182 aluminum alloy plates. All specimens undergo a simulated baking process: 180℃×20min (electrophoresis simulation) + 180℃×20min (painting simulation). Using a high-speed tensile testing machine, tensile tests are performed on the treated specimens at quasi-static (0.002 / s) and dynamic (e.g., 1 / s, 10 / s, 100 / s) strain rates to obtain complete engineering stress-strain curves. The engineering data are converted into real stress-plastic strain curves. The Johnson-Cook model is used to fit the data. The strain hardening parameters (Rp0.2, B, n) are fitted with the quasi-static data; the strain rate sensitivity coefficient m is fitted with the data at different strain rates, such as... Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown in Table 1, the high-confidence material parameters were finally obtained and used as input for subsequent simulations.

[0071] Table 1

[0072]

[0073] Specifically, the solution in this application includes parametric modeling and performance-driven optimization: In finite element software, a parametric model of the engine hood assembly (outer panel, inner panel, and reinforcing plate) is established. The initial thickness of the inner panel is set to 1.3 mm, and the layout, height, and angle of its reinforcing ribs are defined as design variables. The Johnson-Cook material parameters obtained in the preceding steps are assigned to the outer panel (6014) and the inner panel (6014). According to C-NCAP 2024 regulations, an object impactor model is set with an impact speed of 40 km / h and an angle of 50°, and an analysis area containing 6 standard impact points is delineated on the engine hood, such as... Figure 9 and Figure 10 As shown. Optimization objectives are set, for example, maximizing the energy absorption ratio of the inner panel at impact points 1 and 2; and minimizing the HIC15 value of the object throughout the impact process. Optimization algorithms (such as response surface methodology or genetic algorithms) are run, and the software automatically iteratively adjusts the structural parameters of the inner panel until the optimal design that satisfies all objectives and requirements is found. The optimized inner panel will exhibit a non-uniform stiffener layout optimized for energy absorption efficiency.

[0074] Specifically, in accordance with the 2021 version of the collision protection regulations, the front and rear sides of the hood were constrained, the impact speed was 40 km / h, and the impact angle was 50°. Six impact points were selected at different locations on the hood surface for simulation analysis. After calculation, displacement cloud maps, acceleration curves, and energy absorption distribution maps of the impact point locations were obtained.

[0075] Specifically, for example, collision point 1 has two peak values. At the first peak value (t=3ms), the force is transmitted to the inner plate, and both the inner and outer plates simultaneously exert resistance, causing the inner plate to undergo a large displacement. At the second peak value (t=16ms), the outer plate is at its maximum deformation and then begins to rebound. At collision point 2, the acceleration gradually increases during the collision. At t=4ms, the outer plate causes the inner plate to undergo a large displacement, and then both plates deform together. At collision point 3, the acceleration reaches its maximum value at t=12ms, at which point the outer plate is at its maximum deformation and then begins to rebound. At collision points 4 and 5, the acceleration reaches its maximum at t=9ms, at which point the outer plate is at its maximum deformation and then begins to rebound. At collision point 6, the acceleration reaches its maximum at t=5ms. Before this point, the force is mainly borne by the outer plate. At t=5ms, the force is transmitted to the inner plate, and both the inner and outer plates simultaneously exert resistance.

[0076] At impact points 1 and 2, there is direct interaction between the inner and outer panels, with the inner panel absorbing most of the energy during the collision. At impact points 4 and 5, there is no contact between the inner and outer panels; they are mainly connected by reinforcing plates, and energy is absorbed through the reinforcing plates and outer panels during the collision. At impact point 3, the outer panel mainly bears the impact; there is no contact between the outer and inner panels, and this point is not connected to the reinforcing members. During the collision, the outer panel primarily absorbs the energy.

[0077] Of the six selected impact points, the total system absorbed over 60% of the energy. At impact points 1, 2, 3, and 6, the energy was primarily absorbed by the inner and outer panels, accounting for over 88% of the total absorbed energy. Specifically, at impact points 1, 2, and 6, both the inner and outer panels participated in energy absorption, with the inner panel absorbing more energy than the outer panel. At impact point 3, the outer panel primarily absorbed energy, with the inner panel absorbing very little. At impact points 4 and 5, the inner and outer panels absorbed the maximum energy, accounting for 54.7% and 59.7% of the total absorbed energy, respectively, with over 40% of the absorbed energy being absorbed by the accessory reinforcement components.

[0078] To investigate the impact of aluminum alloy hairnets on the protective performance of objects, impact tests can be conducted at collision points 1, 2, 3, and 6. Considering practical considerations, to ensure that the same hairnet can be tested twice without mutual interference, impact points 2 and 3 can be selected for testing, and another impact test can be conducted separately at collision point 1.

[0079] In summary, collision points 1, 2, and 3 were ultimately selected as the test verification points.

[0080] In the specific implementation process, the strain value of the material corresponding to the above material grade is simulated using the Johnson-Cook model. This can be achieved through the following steps: a tensile test is performed on the material corresponding to the above material grade in the laboratory, and the strain curve of the material corresponding to the above material grade is constructed to obtain the strain curve; the strain curve of the material corresponding to the above material grade is fitted using the Johnson-Cook model to obtain the simulated strain value of the material corresponding to the above material grade.

[0081] This scheme can provide real mechanical property data of materials under high strain rates. High-speed tensile tests can simulate the strain rate environment in actual collision processes. The obtained curves reflect the real stress-strain response of materials under extreme conditions. The Johnson-Cook model can accurately describe the strain hardening effect and strain rate sensitivity of materials. Through parameter fitting, it can be ensured that the model has good predictive ability under different working conditions, thus obtaining more accurate simulated strain values ​​of materials.

[0082] To accurately simulate the dynamic mechanical behavior of 6xxx aluminum alloy under different pre-strain rates and baking processes, the Johnson-Cook model was employed. High-speed tensile tests were conducted on the 6xxx aluminum alloy to obtain engineering stress-strain curves at high and low strain rates, which were then converted into realistic stress-plastic strain curves. Using these curves, the Johnson-Cook model underwent parameter fitting, thereby constructing a high-confidence material model parameter library. This high-precision model accurately reflects the strain hardening and strain rate hardening characteristics of the material under actual collision conditions, providing a solid foundation for subsequent structural optimization and collision simulation. By applying the optimized model parameters to the parametric finite element model, the structural design of the engine hood inner panel can be guided, ensuring optimal energy absorption performance at critical collision points while meeting protective performance requirements.

[0083] Specifically, this plan includes the following:

[0084] Construction of the material gene library: High-speed tensile tests were conducted on candidate 6xxx aluminum alloys under different pre-strain rates (2%-8%) and baking processes to obtain their engineering stress-strain curves at high and low strain rates, and then converted into real stress-plastic strain curves to establish a material property database.

[0085] High-precision constitutive model establishment: Based on the above material property database, the Johnson-Cook model is used to fit the strain hardening and strain rate hardening behavior of the material to obtain high-confidence material model parameters;

[0086] The Johnson-Cook model expression is:

[0087] ;

[0088] In the formula, It is the equivalent plastic strain rate. , , and The Johnson-Cook model has four undetermined parameters. The yield strength of the material. The work hardening coefficient, The work hardening index, is the strain rate sensitivity coefficient.

[0089] Parametric finite element model construction: Establish a parametric finite element model including the outer plate, inner plate and reinforcing plate of the cover, where the thickness of the inner plate, the layout of the reinforcing ribs and the connection method with adjacent components are designable variables;

[0090] Performance-driven optimization: Using the specified impact conditions as boundary conditions, and with the optimization objectives of maximizing the energy absorption of the cover assembly at the specified collision point and minimizing the damage index (HIC value), the above-mentioned high-precision constitutive model and parametric finite element model are used for iterative optimization to determine the optimal material grade, thickness distribution and structural topology of the inner plate.

[0091] The aforementioned 6xxx aluminum alloy is 6014 alloy, and its optimized Johnson-Cook model parameters are: yield strength Rp0.2 of 213.6±10MPa, work hardening coefficient B of 377.8±15MPa, work hardening index n of 0.596±0.05, and strain rate sensitivity coefficient m of 0.00434±0.0005.

[0092] Specifically, to obtain the strain rate under high-speed tension, collision simulation analysis was first performed at three different collision points of the hair cover to obtain the element strain within the collision region of the hair cover. ε Over time t The variation curve of strain rate This can be obtained by calculating the strain time, that is:

[0093] .

[0094] Throughout the collision process, the strain rate range of the element near the first collision point is (0 / s, 18 / s), the strain rate range of the element near the second collision point is (0 / s, 70 / s), and the strain rate range of the element near the third collision point is (0 / s, 60 / s).

[0095] After preliminary analysis, the strain rate of the object being protected during the collision was determined to be (0 / s, 70 / s). Therefore, high-speed tensile tests were conducted on the material, selecting quasi-static tensile tests at strain rates of 0.002 / s and 0.02 / s, as well as dynamic tensile tests at strain rates of 2 / s, 20 / s, 60 / s, and 200 / s.

[0096] Different tensile specimens are required for different strain rates, and the specimen materials are selected as 5182 and 6014. Specifically, the outer panel is pre-stretched by 4% 6014 and the inner panel by 7% 5182. The oven baking process is simulated as follows: 180℃×20min simulates electrophoresis, and 180℃×20min simulates baking paint.

[0097] When performing collision simulations for object protection, the actual stress-strain curves of the material are required. Furthermore, the shield undergoes plastic deformation during the collision; therefore, the actual stress-strain curves during the plastic stage must be calculated. The actual stress-strain can be derived from engineering stress-strain calculations, as described below:

[0098] Real response In the formula, To adapt to engineering contingencies.

[0099] The actual stress is In the formula, , These are engineering stress and strain, respectively.

[0100] Plastic strain is ,

[0101] In the formula, For plastic strain, To respond realistically, For actual stress, It is the elastic modulus.

[0102] The data curve of the yield-tensile segment under a quasi-static strain rate of 0.002 / s was selected, and the true stress-plastic strain curve was obtained. Then, the strain hardening part of the JC model in Origin was used to perform nonlinear fitting on the curve, and the values ​​of three parameters Rp0.2, B, and n were obtained.

[0103] In some embodiments, the energy absorption value of the engine hood corresponding to the above design parameters and the collision loss value of the engine hood corresponding to the above design parameters are calculated. Specifically, this can be achieved through the following steps: using simulation technology to simulate the collision between the engine hood and the target object; using simulation software to extract the energy absorption value and the collision loss value of the engine hood and the target object corresponding to the above design parameters during the simulation process.

[0104] In this scheme, during the simulation process, the simulation software can record and analyze in detail the response of the engine hood structure under different collision conditions, including key mechanical parameters such as stress and strain distribution and displacement velocity. The simulation technology and performance parameter extraction methods can accurately evaluate the energy absorption efficiency and object protection level of the engine hood inner panel in a virtual environment.

[0105] In the above embodiments, simulation technology is used to perform high-precision simulations of the performance of automotive body panels based on 6xxx aluminum alloy, particularly the inner hood panel, after high-speed stretching and specific baking processes. Through parameter fitting of the Johnson-Cook model, an accurate description of the dynamic mechanical behavior of the material under different strain rates is achieved, thereby guiding the topology optimization of the body panel structure. The optimization process is based on impact conditions, aiming to maximize energy absorption and minimize collision loss, which can replace traditional physical crash testing and significantly reduce development costs.

[0106] In the specific implementation process, after using a multi-objective optimization algorithm to find the best design parameters and adjusting them until the energy absorption value reaches the maximum energy value and the collision loss value reaches the minimum loss value, the method further includes the following steps: using the updated design parameters to construct a digital twin model of the engine hood, and displaying the digital twin model of the engine hood on a display device.

[0107] In this scheme, once the optimization algorithm converges, a set of optimal design parameters will be obtained. At this point, the engine hood model needs to be reconstructed in the finite element software, and these design parameters will be applied to the model. During model construction, special attention must be paid to the selection of material properties and the precise input of structural dimensions to ensure that the digital twin model accurately reflects the performance characteristics of the physical entity. In this way, a virtual model that completely corresponds to the optimized engine hood can be created for subsequent performance prediction and verification. The constructed digital twin model is then rendered and displayed on a display device. This not only helps the design team intuitively understand the shape and characteristics of the final optimized structure, but also allows for the addition of real-time monitoring functions, such as sensor nodes for stress, temperature, and displacement. When the physical engine hood is in the manufacturing or testing phase, physical parameters can be fed back to the digital model in real time, achieving bidirectional data exchange between the physical entity and the virtual model, ensuring that both remain synchronized.

[0108] In the aforementioned scheme, a multi-objective optimization algorithm is used to optimize the design parameters of the inner panel of the hood, achieving the goal of maximizing energy absorption while minimizing collision loss, resulting in updated design parameters. Subsequently, a digital twin model of the hood is constructed based on these optimized parameters. This model not only includes the optimized inner panel structure but also integrates the outer panel, reinforcing plates, and their composite connection methods. The digital twin model can reflect the behavior of the physical hood under different operating conditions in real time, providing a precise predictive tool for subsequent design changes, material replacements, and quality control. Furthermore, the model can be visually displayed on a display device, facilitating the design team's visual evaluation of the optimization results and ensuring a high degree of consistency between the design scheme and actual performance. This effectively promotes the lightweight and safety design process of automotive body panels, reduces the need for physical testing, and saves development time and costs. In other embodiments not shown, further refining the model parameters, such as adjusting the layout of the connection areas or improving the material processing technology, can achieve more precise control over the hood's performance, improving design flexibility and efficiency.

[0109] In addition to the above, this application also includes the following: Based on the Johnson-Cook model, a machine learning algorithm is introduced to automatically adjust the parameters of the Johnson-Cook model according to different collision scenarios and working conditions to best match actual collision requirements. Engine hood test data under different collision scenarios are collected and organized, including but not limited to energy absorption values, collision loss values, strain rate, plastic strain, and temperature data; features related to material behavior are extracted from the dataset to construct an input feature vector; using the aforementioned feature vector and the corresponding Johnson-Cook model parameters, a regression or neural network model is trained, enabling the machine learning model to predict the optimal adjustment values ​​of material parameters based on the input features; before each collision simulation, the Johnson-Cook model parameters are adjusted using the aforementioned machine learning model according to the characteristics of the current collision scenario, ensuring that the model prediction is as consistent as possible with actual collision requirements; the adaptively optimized parameters are updated in the digital twin model for material performance input in collision simulation; the digital twin model calibration and verification process is repeated to ensure that the relative error of key performance indicators remains within 10% after adaptive optimization.

[0110] Specifically, first, a comprehensive collision dataset is constructed. This dataset needs to include performance data of actual engine hoods under various strain rates, plastic strains, temperatures, and different collision scenarios, including but not limited to energy absorption values, collision loss values ​​(such as HIC values), strain rate fluctuation ranges (from 0.001 / s to 100 / s), plastic strain levels (0.01 to 0.2), and collision temperatures (-20°C to 60°C). Data sources can include high-speed tensile tests in laboratories, actual road accident analyses, and existing literature and databases. Features closely related to material behavior, such as the aforementioned strain rate, plastic strain, and collision temperature, are selected from the dataset. These features will serve as input to a machine learning model to predict the optimal adjustment values ​​for the Johnson-Cook model parameters. Regression analysis or deep learning neural networks (such as convolutional neural networks (CNNs) or long short-term memory networks (LSTMs)) are used to train the machine learning model based on existing collision scenario data and corresponding Johnson-Cook model parameters. The goal is to teach the model how to predict the material parameters that best match the collision scenario based on its characteristics. Before each collision simulation, based on the characteristics of the current collision scenario (such as strain rate, plastic strain, and temperature), a trained machine learning model predicts the optimal adjustment values ​​for the Johnson-Cook model parameters. For example, in the case of a high-speed frontal collision, the model may predict a higher strain rate sensitivity coefficient *m* and a lower work hardening coefficient *B*. These predicted material parameters are then updated in the digital twin model and used as material property inputs for the next collision simulation. This ensures that the digital twin model provides high-precision simulation results even under non-standard conditions. Following the digital twin model calibration and verification process—namely, manufacturing physical prototypes, conducting physical collision tests, collecting test data, and calibrating the updated digital model by comparing the acceleration curves and HIC values ​​of the simulation and tests—the relative errors of key performance indicators are stabilized within 10%.

[0111] In some embodiments, after constructing a digital twin model of the engine hood using the updated design parameters, the method further includes the following steps: obtaining the actual absorbed energy value and the actual collision loss value, wherein the actual absorbed energy value is the actual kinetic energy absorbed by the engine hood corresponding to the design parameters under the condition of a collision, and the actual collision loss value is the actual stress value received by the target object under the condition of a collision; calculating the difference between the actual absorbed energy value and the absorbed energy value to obtain an energy difference value; calculating the difference between the actual collision loss value and the collision loss value to obtain a loss difference value; optimizing the engine hood digital twin model when the energy difference value is greater than or equal to a preset energy threshold, and / or when the loss difference value is greater than or equal to a preset loss threshold, to obtain an optimized engine hood digital twin model, wherein the optimization method includes at least finite element optimization; and displaying the optimized engine hood digital twin model on the display device.

[0112] This solution introduces the concept of a threshold by comparing actual test data with the prediction results of the digital twin model, thereby achieving precise calibration of the digital twin model, eliminating subtle differences between theory and practice, and ensuring that each model optimization is based on feedback from actual test data. This avoids blind trial and error, reduces unnecessary physical experiments, and significantly lowers development costs.

[0113] In the above embodiments, after obtaining the energy absorption and collision loss values ​​of the engine hood under actual collision conditions, they are compared with the corresponding values ​​predicted by the digital twin model to calculate the energy difference and loss difference. If the energy difference or loss difference exceeds a preset threshold, it indicates a significant difference between the model and the actual performance, requiring optimization of the engine hood digital twin model. This optimization process involves adjusting the parameters of the finite element model, such as changing material properties, structural details, or connection characteristics, to ensure that the model's prediction results are closer to the actual performance. The optimized model will be re-evaluated until the differences in key performance indicators are controlled within the preset threshold, thereby ensuring that the engine hood digital twin model can accurately reflect actual collision behavior and provide a reliable basis for subsequent design changes and material replacements. Finally, the optimized engine hood digital twin model will be displayed on a display device for engineers to intuitively analyze and verify.

[0114] For example, the energy threshold can be set to ±5J, and the loss threshold can be set to ±10%. Of course, other values ​​are also possible.

[0115] The engine hood assembly includes an outer panel, an inner panel, and a reinforcing plate. The inner panel is connected to the outer panel and the reinforcing plate using a composite connection method that combines adhesive bonding and riveting. The layout of the connection area is optimized and determined by the aforementioned digital twin drive design method.

[0116] Specifically, the inner panel of the car engine hood obtained by this method is made of 6014 aluminum alloy and undergoes 4%-7% pre-stretching treatment and baking hardening treatment at 180℃×20min+180℃×20min. Its single piece has a maximum energy absorption of not less than 83J in the C-NCAP impact test.

[0117] The structure of the inner panel is determined through the performance-driven optimization of the above scheme. At the key collision points corresponding to the center line of the engine hood and the hinge installation area, a non-uniformly distributed reinforcing rib network obtained by topology optimization is set, so that at these collision points, the energy absorption ratio of the inner panel exceeds 55% of the total energy absorption.

[0118] The calibrated digital twin model can predict and verify the performance of body panels after design changes or material replacements, replacing at least 30% of physical crash tests.

[0119] Based on the preliminary analysis, three locations—P1 (bottom right), P3 (center line), and P5 (top left)—were selected for physical collision testing. Longitudinal alignment was based on the laser-guided center line, while transverse alignment was based on the lower contour line, with lines spaced 100mm apart. During the specific experiment, the impact speed was 40km / h, the impactor angle was 50°, and the cover plate tilt angle was 13.36°.

[0120] For the object impact test, the main direct data collected is the acceleration-time curve. The performance of the object protection hood impact test is mainly evaluated through acceleration. At point P3, a significant dent appears on the outer panel of the hood after the impact. This is because, structurally, there is no obvious contact between the inner and outer panels at this point. The outer panel mainly bears the impact of the object, resulting in significant deformation. The acceleration at this point is also smaller than that at P1 and P2, with a maximum acceleration of 105.6g. At point P1, the deformation of the outer panel of the hood is not significant after the impact, but the acceleration is relatively large. This is because, structurally, the inner and outer panels are connected by structural adhesive at this point, and there is a reinforcing member on the underside of the inner panel. The stiffness at this point is relatively high. Therefore, during the object impact, the outer panel, the inner panel, and the reinforcing member at this location simultaneously bear the impact of the object, resulting in smaller deformation and a significantly higher acceleration. The maximum acceleration obtained in the test is 225.8g. At point P2, the outer panel of the hood showed a significant dent after the collision. This is because, structurally, there is no obvious contact between the inner and outer panels at this point. However, the inner and outer panels are connected by structural adhesive near this point. When the object is impacted, the outer panel mainly bears the impact at the point of impact, while the inner panel is also subjected to the impact force. Therefore, during the collision, the outer panel will show significant deformation at the point of impact. Furthermore, since the inner panel also participates in resisting the impact, the acceleration will be higher than that at point P3. The maximum acceleration obtained in the experiment was 237.5g.

[0121] A finite element simulation model was established, ensuring that the hair shield model, object model, and hair shield constraint positions were consistent with the actual experimental model. During modeling, the impact velocity of the object was guaranteed to be 40 km / h, the impactor angle to be 50°, and the tilt angle of the shield plate to be 13.36°. The outer plate of the hair shield was made of 6014 stainless steel, and the inner plate was made of 5182 stainless steel, consistent with the materials used in the experimental specimen.

[0122] Model calibration was performed using acceleration-time curves and HIC values ​​during the collision process.

[0123] ,

[0124] In the formula, , The start and end times of integration. For the object impactor in Acceleration at any moment.

[0125] Specifically, the solution in this application includes the calibration and verification of a digital twin model: Based on the optimized design results, a physical prototype of the engine hood is manufactured. In a physical crash test, key impact points (such as points 1, 2, and 3) identified in the precise impact optimization analysis are precisely identified. Acceleration-time curves from the test are collected, and the HIC15 value is calculated. The test data is compared with the simulation prediction results, as shown in the acceleration-time curves. Figure 11 , Figure 12 and Figure 13 As shown, the calculated relative error η of HIC15 is shown in Table 2.

[0126] Table 2

[0127]

[0128] If the error exceeds 10% (e.g., due to unconsidered factors such as connection stiffness in the initial simulation), the corresponding parameters in the finite element model (such as connector stiffness and boundary conditions) are adjusted in reverse to ensure a high degree of agreement between the simulation and experimental curves. After 1-2 rounds of calibration, a digital twin model with high consistency with the physical engine hood is obtained. This model can then be used for accurate prediction and rapid development of engine hoods for other vehicle models on this platform, achieving "one-time calibration, multiple applications." Through the above typical examples, the entire process of this solution from material basis to final verification is fully demonstrated, reflecting its systematic nature, innovation, and industrial applicability.

[0129] To investigate the effect of different wall thicknesses on the protective performance of the object, outer and inner plates of different thicknesses were analyzed, with the thickness of the inner and outer plates set to be the same. 6502 stainless steel was used, and true stress-true plastic strain curves were constructed at different strain rates. With increasing yield strength, the maximum acceleration and HIC15 showed an upward trend, and the rate of increase was relatively fast. This indicates that yield strength has a significant impact on HIC15; an increase of 0.1 mm in thickness resulted in an 80° increase in HIC15.

[0130] To investigate the impact of different materials on an object during collision, collision analyses were conducted on steel and aluminum shields. Based on material parameters provided by a car manufacturer, the steel shield's inner panel thickness was 0.6 mm with a yield strength of 150 MPa, while the outer panel thickness was 0.6 mm with a yield strength of 250 MPa. During collisions, the effect of strain rate on object protection was studied both with and without considering strain rate. Furthermore, to further investigate the impact of strain rate on object protection, different shield structures were used for different strain rates, resulting in corresponding object damage values ​​and acceleration-time curves.

[0131] Based on the steel and aluminum parameters provided by a car manufacturer, the steel hood inner panel has a wall thickness of 0.6mm and a yield strength of 150MPa; the outer panel has a wall thickness of 0.6mm and a yield strength of 250MPa. The aluminum alloy hood inner panel is made of 6502 stainless steel with a yield strength of 90MPa, and the outer panel is made of 6014 stainless steel with a yield strength of 214MPa. Both inner and outer panel wall thicknesses are 0.9mm, according to the car manufacturer's data. Collision simulation analysis was performed using both hood structures.

[0132] The curves for steel and aluminum materials were substituted into the finite element models of the two hood structures respectively to perform collision simulation calculations, obtaining the object damage values ​​and maximum accelerations. The maximum acceleration is material-dependent; the maximum acceleration of the steel hood is greater than that of the aluminum alloy hood. However, analyzing the object damage values, the hood structure has a more significant impact on the object damage values ​​than the hood material itself.

[0133] Collision simulation analysis was conducted on different engine hoods using steel and aluminum alloy materials to obtain the maximum acceleration and HIC15 value during the collision process. The maximum acceleration and HIC15 value of the steel engine hood were greater than those of the aluminum alloy engine hood.

[0134] The structure of the engine hood has a significant impact on object protection performance. The most significant way to improve object protection is to optimize the hood structure. Under the same hood conditions: without considering the influence of the m-value, steel, due to its thinner thickness, has better object protection performance than aluminum under certain conditions. Considering the influence of the m-value, steel, due to its strain rate sensitivity, shows a significant increase in material strength during high-speed collisions, while aluminum alloy is not sensitive to strain rate and maintains its static performance during high-speed collisions, exhibiting better object protection performance. Therefore, replacing steel with aluminum can improve object protection performance.

[0135] In addition to the above, this application also includes the following: based on the establishment of a high-precision constitutive model, dynamic prediction of the structural performance of the engine hood inner panel is achieved by real-time monitoring and intelligent analysis of the influence of external environmental factors on material properties. This study collects engine hood performance data under various environmental conditions, including temperature, humidity, and aging levels, to construct an environmental perception database. Using statistical analysis or machine learning algorithms, it investigates the impact of environmental factors on the Johnson-Cook model parameters, establishing a correlation model between environmental factors and material performance parameters. Temperature sensors, humidity sensors, and aging monitoring devices are installed on the engine hood to feed real-time environmental data back to the digital twin model. Based on the correlation model between environmental factors and material performance parameters, the material parameters in the digital twin model are automatically adjusted to reflect performance changes under current environmental conditions. Using the updated digital twin model, the performance of the engine hood under current environmental conditions is predicted, including key indicators such as energy absorption and collision loss values. Structural design parameters are adjusted based on the prediction results to improve its performance in specific environments. The accuracy of the environmental perception model is verified by comparing the crash test results of actual vehicles under different environmental conditions with the predictions of the digital twin model. Model parameters are then appropriately adjusted to ensure that the performance prediction error is controlled within 10%.

[0136] Specifically, performance data for the engine hood will be collected and compiled covering a wide temperature range (e.g., -40°C to 80°C), humidity variations (10% to 90% RH), and aging levels (from new parts to 5-year service life). This data should include, but is not limited to, energy absorption, impact loss coefficient (HIC value), material yield strength, elastic modulus, and fracture toughness. Data collection can be conducted through accelerated aging tests in laboratories, long-term outdoor exposure experiments, and analysis of real-vehicle driving data. Multiple regression analysis, principal component analysis, or machine learning algorithms (such as support vector machines and random forests) will be used to study the effects of environmental factors such as temperature, humidity, and aging levels on the Johnson-Cook model parameters. For example, it may be found that as temperature increases, the strain rate sensitivity coefficient *m* of the material decreases slightly; humidity variations may affect the material's yield strength *Rp0.2*; and the aging level is related to the plastic strain *n*. A mathematical model between environmental factors and material performance parameters will be established through statistical analysis. Multiple temperature sensors, humidity sensors, and aging monitoring devices (indirectly reflecting aging by monitoring coating thickness, hardness, etc.) will be installed on the inner and outer surfaces of the engine hood. Sensor data is transmitted in real time to the central processing unit via the vehicle network, and then uploaded to the cloud for comparison and analysis with data in the environmental perception database to provide real-time feedback on the environmental status of the vehicle with the hood. Based on the correlation model between environmental factors and material performance parameters, the parameters in the Johnson-Cook model can be automatically adjusted according to the real-time collected environmental data. For example, the strain rate sensitivity coefficient m can be appropriately reduced when the temperature rises, and the yield strength Rp0.2 can be adjusted when the humidity increases. In this way, the digital twin model can dynamically reflect the material properties under actual environmental conditions, improving the accuracy of predictions. Using the updated digital twin model, the performance changes of the hood under current environmental conditions are predicted. By analyzing the prediction results, designers can assess whether the hood still meets the protection regulations and adjust the structural design as needed, such as changing the inner panel thickness distribution or the layout of reinforcing ribs, to compensate for the performance degradation caused by environmental factors. By comparing the performance data predicted by the updated digital twin model with the crash test results of the actual vehicle under different environments, the effectiveness of the environmental perception prediction model is verified. If the prediction error exceeds 10%, it indicates a bias in the model's environmental adaptability analysis. Further optimization of the correlation model between environmental factors and material properties is needed, either by adjusting the regression model's parameters or using more complex learning algorithms. This process may require repeated iterations until the digital twin model maintains high prediction accuracy across various environments.

[0137] In summary, the solution proposed in this application pertains to vehicle body panels, particularly the inner hood panel. It innovatively utilizes 6xxx series aluminum alloys processed with specific pre-stretching and baking techniques, combined with a multi-layered energy-absorbing structure design. The core of this solution lies in constructing a high-precision digital twin model, from microscopic material constitutive model to macroscopic system collision response. The Johnson-Cook constitutive model accurately characterizes the dynamic mechanical behavior of the material under high strain rates, and this model guides the topology optimization of the inner panel, maximizing its energy absorption efficiency in critical collision areas. This solution achieves a single inner panel energy absorption of ≥83J, and through joint calibration of simulation and experiments, the error between the digital model and the physical entity in key performance indicators is less than 10%. This provides a closed-loop solution for precise material selection, rapid structural optimization, and product recyclability while ensuring excellent protective performance.

[0138] In summary, the proposed solution fully leverages the material potential of 6xxx aluminum alloys through an integrated design approach based on digital twins, producing automotive body panels that combine high energy absorption, lightweight, and easy recyclability, while significantly reducing development cycle and cost.

[0139] The core of this technical solution lies in constructing an integrated digital twin system encompassing materials, structure, and performance. This system begins with a detailed characterization of the dynamic mechanical properties of 6xxx aluminum alloys (such as 6014), establishing a high-precision Johnson-Cook constitutive model. This model is then embedded into a parametric finite element model, driving the automatic optimization of the inner panel structure with the goal of protecting the object's performance. Finally, the digital model is precisely calibrated through a very small number of physical experiments, resulting in a high-fidelity digital twin model that maps in real-time to the physical product. This model is not only used for initial design but also for subsequent design iterations, quality monitoring, and even recycling strategy evaluation.

[0140] The main advantages of this solution are: a disruptive design paradigm, shifting from "experiment-driven, test-and-verify" to "model-driven, predictive design," achieving digitalization and intelligentization of the design process; performance breakthroughs, through synergistic optimization of materials and structure, enabling the energy absorption efficiency of 6xxx aluminum alloy inner panels to surpass traditional materials, with single-piece energy absorption significantly exceeding regulatory requirements; improved development efficiency, as calibrated digital twin models can replace a large number of physical tests, shortening the development cycle by more than 30% and reducing development costs; and lifecycle management, providing a unified data foundation and value chain for lightweight product design, safety performance assessment, and even post-disposal material recycling.

[0141] This application also provides a parameter determination device for an engine hood. It should be noted that this parameter determination device can be used to execute the parameter determination method for an engine hood provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0142] The parameter determination device for the engine hood provided in the embodiments of this application will be described below.

[0143] Figure 14 This is a structural block diagram of an engine hood parameter determination device according to an embodiment of this application. Figure 14 As shown, the device includes:

[0144] The first acquisition unit 10 is used to acquire the design parameters of the engine hood, wherein the design parameters include one or more of the following: material grade, thickness distribution, shape of reinforcement, number of reinforcements, distribution of reinforcements, shape of hollow area, number of hollow areas, and distribution of hollow area.

[0145] The first calculation unit 20 is used to calculate the energy absorption value of the engine hood corresponding to the above design parameters and to calculate the collision loss value of the engine hood corresponding to the above design parameters. The energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the above design parameters in the event of a collision, and the collision loss value is the stress value received by the target object in the event of a collision.

[0146] The optimization unit 30 is used to perform optimization using a multi-objective optimization algorithm to adjust the above design parameters until the above absorbed energy value reaches the maximum energy value and the above collision loss value reaches the minimum loss value, thereby obtaining the updated design parameters, wherein the above updated design parameters are used to construct the above engine hood.

[0147] In this embodiment, a multi-objective optimization algorithm is used to iteratively adjust the design parameters. The goal is to maximize energy absorption while minimizing collision loss. This breaks through the single-objective optimization in traditional design, such as only pursuing lightweighting or energy absorption capacity. Instead, it seeks a balance among multiple mutually constraining objectives. It can rapidly iterate the design in a virtual environment, avoiding the high costs and time wasted on physical prototyping and testing, thereby reducing design costs.

[0148] In the specific implementation process, the optimization unit includes an adjustment module and an optimization module. The adjustment module is used to adjust the design parameters as variables multiple times using the multi-objective optimization algorithm, and obtain the absorbed energy value and the collision loss value after each adjustment of the design parameters. The optimization module is used to extract the maximum value of the absorbed energy value after multiple adjustments of the design parameters, extract the minimum value of the collision loss value after multiple adjustments of the design parameters, and extract the adjusted design parameters corresponding to the maximum value of the absorbed energy value and the minimum value of the collision loss value, so as to obtain the updated design parameters.

[0149] In this scheme, the iterative optimization process of the multi-objective optimization algorithm can not only accurately adjust the design parameters of the inner panel of the engine hood to meet the requirements of high-performance energy absorption and low-damage protection, but also greatly reduce the dependence on physical prototypes, thereby significantly reducing development costs.

[0150] In some embodiments, the adjustment module includes a simulation submodule, a construction submodule, and an adjustment submodule. The simulation submodule is used to simulate the strain values ​​of the material corresponding to the aforementioned material grade using the Johnson-Cook model. The construction submodule is used to construct the finite element model of the engine hood using the aforementioned design parameters to obtain the engine hood model. The adjustment submodule is used to use the aforementioned multi-objective optimization algorithm to adjust the aforementioned design parameters in the engine hood model multiple times based on the aforementioned strain values ​​of the aforementioned material obtained from the simulation, and to obtain the aforementioned absorbed energy value and the aforementioned collision loss value after each adjustment of the aforementioned design parameters.

[0151] In this scheme, the Johnson-Cook model can accurately reflect the dynamic mechanical behavior of materials under high strain rates. Through parameter fitting, the material model can be closely linked with the actual physical properties, making the simulation prediction more accurate. The parameterized model allows the software to automatically adjust the design parameters and evaluate the performance of different schemes. Using multi-objective optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, the design parameters in the engine hood model are iteratively optimized. Through multi-objective optimization, the optimal solution can be found in the multi-dimensional design space such as material strength and structural layout, ensuring that the engine hood can not only effectively absorb collision energy, but also perform well in terms of protection. This can greatly reduce the dependence on physical prototypes, thereby significantly reducing development costs.

[0152] In the specific implementation process, the simulation submodule is used to conduct tensile tests on the materials corresponding to the above material grades in the laboratory, construct the strain curve of the materials corresponding to the above material grades, and obtain the strain curve; the simulation submodule is used to fit the strain curve of the materials corresponding to the above material grades using the Johnson-Cook model to obtain the simulated strain value of the materials corresponding to the above material grades.

[0153] This scheme can provide real mechanical property data of materials under high strain rates. High-speed tensile tests can simulate the strain rate environment in actual collision processes. The obtained curves reflect the real stress-strain response of materials under extreme conditions. The Johnson-Cook model can accurately describe the strain hardening effect and strain rate sensitivity of materials. Through parameter fitting, it can be ensured that the model has good predictive ability under different working conditions, thus obtaining more accurate simulated strain values ​​of materials.

[0154] In some embodiments, the first calculation unit includes a simulation module and an extraction module. The simulation module is used to simulate the collision between the engine hood and the target object using simulation technology. The extraction module is used to extract the absorbed energy value and the collision loss value corresponding to the design parameters of the engine hood and the target object during the simulation process using simulation software.

[0155] In this scheme, during the simulation process, the simulation software can record and analyze in detail the response of the engine hood structure under different collision conditions, including key mechanical parameters such as stress and strain distribution and displacement velocity. The simulation technology and performance parameter extraction methods can accurately evaluate the energy absorption efficiency and object protection level of the engine hood inner panel in a virtual environment.

[0156] In the specific implementation process, the above-mentioned device also includes a construction unit and a first display unit. The construction unit is used to optimize the design parameters by using a multi-objective optimization algorithm until the energy absorption value reaches the maximum energy value and the collision loss value reaches the minimum loss value. After obtaining the updated design parameters, the digital twin model of the engine hood is constructed using the updated design parameters to obtain the digital twin model of the engine hood. The first display unit is used to display the digital twin model of the engine hood on a display device.

[0157] In this scheme, once the optimization algorithm converges, a set of optimal design parameters will be obtained. At this point, the engine hood model needs to be reconstructed in the finite element software, and these design parameters will be applied to the model. During model construction, special attention must be paid to the selection of material properties and the precise input of structural dimensions to ensure that the digital twin model accurately reflects the performance characteristics of the physical entity. In this way, a virtual model that completely corresponds to the optimized engine hood can be created for subsequent performance prediction and verification. The constructed digital twin model is then rendered and displayed on a display device. This not only helps the design team intuitively understand the shape and characteristics of the final optimized structure, but also allows for the addition of real-time monitoring functions, such as sensor nodes for stress, temperature, and displacement. When the physical engine hood is in the manufacturing or testing phase, physical parameters can be fed back to the digital model in real time, achieving bidirectional data exchange between the physical entity and the virtual model, ensuring that both remain synchronized.

[0158] In some embodiments, the above-described apparatus further includes a second acquisition unit, a second calculation unit, a third calculation unit, a model optimization unit, and a second display unit. The second acquisition unit is used to construct a digital twin model of the engine hood using the updated design parameters, and after obtaining the digital twin model of the engine hood, acquire the actual absorbed energy value and the actual collision loss value, wherein the actual absorbed energy value is the actual kinetic energy absorbed by the engine hood corresponding to the design parameters in the event of a collision, and the actual collision loss value is the actual stress value received by the target object in the event of a collision. The second calculation unit is used to calculate the difference between the actual absorbed energy value and the absorbed energy value to obtain an energy difference value. The third calculation unit is used to calculate the difference between the actual collision loss value and the collision loss value to obtain a loss difference value. The model optimization unit is used to optimize the digital twin model of the engine hood when the energy difference value is greater than or equal to a preset energy threshold, and / or when the loss difference value is greater than or equal to a preset loss threshold, to obtain an optimized digital twin model of the engine hood, wherein the optimization method includes at least finite element optimization. The second display unit is used to display the optimized digital twin model of the engine hood on the display device.

[0159] This solution introduces the concept of a threshold by comparing actual test data with the prediction results of the digital twin model, thereby achieving precise calibration of the digital twin model, eliminating subtle differences between theory and practice, and ensuring that each model optimization is based on feedback from actual test data. This avoids blind trial and error, reduces unnecessary physical experiments, and significantly lowers development costs.

[0160] The aforementioned engine hood parameter determination device includes a processor and a memory. The first acquisition unit, the second acquisition unit, and the optimization unit are all stored as program units in the memory, and the processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0161] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and the high design cost of engine hoods in existing technologies can be addressed by adjusting kernel parameters.

[0162] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0163] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the engine hood parameter determination method.

[0164] This invention provides a processor for running a program, wherein the program executes the method for determining the parameters of the engine hood.

[0165] This invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of a method for determining the parameters of an engine hood. The device in this article may be a server, PC, PAD, mobile phone, etc.

[0166] This application also provides a computer program product that, when executed on a data processing device, is adapted to perform a program that initializes a parameter determination method step having at least the following engine hood.

[0167] This application also provides an engine cover, which is manufactured according to any of the above-described engine cover parameter determination methods.

[0168] This application also provides a vehicle that includes an engine hood, the engine hood being the aforementioned engine hood.

[0169] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0170] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0175] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0176] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0177] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0179] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the parameters of an engine hood, characterized in that, include: Obtain the design parameters of the engine hood, wherein the design parameters include one or more of the following: material grade, thickness distribution, shape of reinforcement, number of reinforcements, distribution of reinforcements, shape of hollow area, number of hollow areas, and distribution of hollow area; Calculate the energy absorption value of the engine hood corresponding to the design parameters, and calculate the collision loss value of the engine hood corresponding to the design parameters, wherein the energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the design parameters in the event of a collision, and the collision loss value is the stress value received by the target object in the event of a collision. A multi-objective optimization algorithm is used to optimize the design parameters until the energy absorption value reaches the maximum energy value and the collision loss value reaches the minimum loss value, thereby obtaining updated design parameters. The updated design parameters are used to construct the engine hood. A multi-objective optimization algorithm is used to optimize the design parameters until the absorbed energy value reaches its maximum value and the collision loss value reaches its minimum value, thus obtaining updated design parameters. This includes: using the multi-objective optimization algorithm to adjust the design parameters multiple times as variables, and obtaining the absorbed energy value and the collision loss value after each adjustment; extracting the maximum value from the multiple adjusted absorbed energy values, extracting the minimum value from the multiple adjusted collision loss values, and extracting the adjusted design parameters corresponding to the maximum and minimum values ​​of the absorbed energy and collision loss values, thus obtaining the updated design parameters. The multi-objective optimization algorithm is used to adjust the design parameters as variables multiple times, and the absorbed energy value and collision loss value are obtained after each adjustment of the design parameters. This includes: simulating the strain value of the material corresponding to the material grade using a Johnson-Cook model; constructing a finite element model of the engine hood using the design parameters to obtain the engine hood model; and using the multi-objective optimization algorithm, based on the simulated strain value of the material, adjusting the design parameters in the engine hood model multiple times, and obtaining the absorbed energy value and collision loss value after each adjustment of the design parameters. The engine hood model consists of an outer hood panel, an inner hood panel, and an internal reinforcing plate. The outer hood panel is made of baked 6014 aluminum alloy with a thickness of 1.2mm, and the inner hood panel is made of baked 5182 material with a thickness of 1.3mm. Tensile tests were conducted on candidate 6014 aluminum alloys under different pre-strain rates and baking processes to obtain engineering stress-strain curves at various strain rates, which were then converted into true stress-plastic strain curves to establish a material property database. Based on this database, the strain hardening and strain rate hardening behaviors of the materials were fitted using the Johnson-Cook model to obtain the material model parameters with confidence levels. The expression for the Johnson-Cook model is as follows: In the formula, It is the equivalent plastic strain rate. , , and The Johnson-Cook model has four undetermined parameters. A parametric finite element model is established, including the outer and inner panels of the cover assembly, and the reinforcing plates. The thickness of the inner panel, the layout of the reinforcing ribs, and the connection method with adjacent components are designable variables. Using specified impact conditions as boundary conditions, and with the optimization objectives of maximizing energy absorption and minimizing damage indices at a specified collision point, iterative optimization is performed using a high-precision constitutive model and a parametric finite element model to determine the optimal material grade, thickness distribution, and structural topology of the inner panel. In the object protection collision simulation, the actual stress-strain curve of the material is used. The actual stress-strain can be derived from engineering stress-strain, and is described as follows: The actual strain is... In the formula, For engineering strain; the actual stress is In the formula, , These are engineering stress and strain, respectively; plastic strain is... In the formula, For plastic strain, To respond realistically, For actual stress, It is the elastic modulus.

2. The method according to claim 1, characterized in that, The strain values ​​of the material corresponding to the stated material grade were simulated using the Johnson-Cook model, including: Tensile tests were conducted on the material corresponding to the material grade in the laboratory, and strain curves of the material corresponding to the material grade were constructed to obtain strain curves; The strain curve of the material corresponding to the material grade is fitted using the Johnson-Cook model to obtain the simulated strain value of the material corresponding to the material grade.

3. The method according to claim 1, characterized in that, Calculate the energy absorption value of the engine hood corresponding to the design parameters, and calculate the collision loss value of the engine hood corresponding to the design parameters, including: Simulation technology was used to simulate a collision between the engine hood and the target object. Simulation software is used to extract the absorbed energy value and the collision loss value corresponding to the design parameters during the simulation process when the engine hood and the target object collide.

4. The method according to claim 1, characterized in that, After employing a multi-objective optimization algorithm to optimize the design parameters until the absorbed energy value reaches its maximum value and the collision loss value reaches its minimum value, and obtaining the updated design parameters, the method further includes: A digital twin model of the engine hood is constructed using the updated design parameters to obtain the digital twin model of the engine hood; The digital twin model of the engine hood is displayed on a display device.

5. The method according to claim 4, characterized in that, After constructing a digital twin model of the engine hood using the updated design parameters to obtain the digital twin model of the engine hood, the method further includes: Obtain the actual absorbed energy value and the actual collision loss value, wherein the actual absorbed energy value is the actual kinetic energy absorbed by the engine hood corresponding to the design parameters under the condition of a collision, and the actual collision loss value is the actual stress value received by the target object under the condition of a collision; Calculate the difference between the actual absorbed energy value and the absorbed energy value to obtain the energy difference; Calculate the difference between the actual collision loss value and the collision loss value to obtain the loss difference; When the energy difference is greater than or equal to a preset energy threshold, and / or when the loss difference is greater than or equal to a preset loss threshold, the digital twin model of the engine hood is optimized to obtain an optimized digital twin model of the engine hood, wherein the optimization method includes at least finite element optimization. The optimized digital twin model of the engine hood is displayed on the display device.

6. A parameter determining device for an engine hood, characterized in that, include: The first acquisition unit is used to acquire the design parameters of the engine hood, wherein the design parameters include one or more of the following: material grade, thickness distribution, shape of reinforcement, number of reinforcements, distribution of reinforcements, shape of hollow area, number of hollow areas, and distribution of hollow area; The first calculation unit is used to calculate the energy absorption value of the engine hood corresponding to the design parameters and to calculate the collision loss value of the engine hood corresponding to the design parameters, wherein the energy absorption value is the kinetic energy absorbed by the engine hood corresponding to the design parameters in the event of a collision, and the collision loss value is the stress value received by the target object in the event of a collision. An optimization unit is used to perform optimization using a multi-objective optimization algorithm to adjust the design parameters until the absorbed energy value reaches the maximum energy value and the collision loss value reaches the minimum loss value, thereby obtaining updated design parameters. The updated design parameters are used to construct the engine hood. The optimization unit includes an adjustment module and an optimization module. The adjustment module is used to adjust the design parameters multiple times using the multi-objective optimization algorithm, and obtain the absorbed energy value and the collision loss value after each adjustment of the design parameters. The optimization module is used to extract the maximum value among the absorbed energy values ​​after multiple adjustments of the design parameters, extract the minimum value among the collision loss values ​​after multiple adjustments of the design parameters, and extract the adjusted design parameters corresponding to the maximum value of the absorbed energy value and the minimum value of the collision loss value, to obtain the updated design parameters. The adjustment module includes a simulation submodule, a construction submodule, and an adjustment submodule. The simulation submodule is used to simulate the strain value of the material corresponding to the material grade using the Johnson-Cook model. The construction submodule is used to construct a finite element model of the engine hood using the design parameters to obtain the engine hood model. The adjustment submodule is used to use the multi-objective optimization algorithm to adjust the design parameters in the engine hood model multiple times based on the strain value of the material obtained from the simulation, and obtain the absorbed energy value and the collision loss value after each adjustment of the design parameters. The engine hood model consists of an outer hood panel, an inner hood panel, and an internal reinforcing plate. The outer hood panel is made of baked 6014 aluminum alloy with a thickness of 1.2mm, and the inner hood panel is made of baked 5182 material with a thickness of 1.3mm. Tensile tests were conducted on candidate 6014 aluminum alloys under different pre-strain rates and baking processes to obtain engineering stress-strain curves at various strain rates, which were then converted into true stress-plastic strain curves to establish a material property database. Based on this database, the strain hardening and strain rate hardening behaviors of the materials were fitted using the Johnson-Cook model to obtain the material model parameters with confidence levels. The expression for the Johnson-Cook model is as follows: In the formula, It is the equivalent plastic strain rate. , , and The Johnson-Cook model has four undetermined parameters. A parametric finite element model is established, including the outer and inner panels of the cover assembly, and the reinforcing plates. The thickness of the inner panel, the layout of the reinforcing ribs, and the connection method with adjacent components are designable variables. Using specified impact conditions as boundary conditions, and with the optimization objectives of maximizing energy absorption and minimizing damage indices at a specified collision point, iterative optimization is performed using a high-precision constitutive model and a parametric finite element model to determine the optimal material grade, thickness distribution, and structural topology of the inner panel. In the object protection collision simulation, the actual stress-strain curve of the material is used. The actual stress-strain can be derived from engineering stress-strain, and is described as follows: The actual strain is... In the formula, For engineering strain; the actual stress is In the formula, , These are engineering stress and strain, respectively; plastic strain is... In the formula, For plastic strain, To respond realistically, For actual stress, It is the elastic modulus.

7. An engine hood, characterized in that, The engine hood is manufactured using the engine hood parameter determination method according to any one of claims 1 to 5.

8. A vehicle, characterized in that, The vehicle includes an engine hood, which is the engine hood of claim 7.

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