Seat parameter optimization method based on driver comfort

Abstract

The invention provides a seat parameter optimization method based on driver comfort, and the method comprises the steps: building a driver restraint system finite element model based on a vehicle finite element model, a dummy model, and a pre-built safety belt model, a safety airbag model and a seat model; seat parameters including a seat backrest angle, a seat cushion angle, a seat leg support angle and seat filler rigidity are determined, an orthogonal experiment table with the seat parameters as experiment factors is established, and vehicle collision simulation is carried out based on the orthogonal experiment table and a driver restraint system finite element model to obtain a seat comfort simulation result. And constructing a response surface model of each individual pressure distribution index corresponding to the seat parameters based on the seat comfort simulation result, and performing iterative solution on the response surface models based on the seat comfort simulation result to obtain an optimization result of the seat parameters. By adopting the method and the device, the seat parameters can be optimized, so that the seat comfort is improved on the basis of ensuring the safety of a driver.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive seat design, and particularly to a method for optimizing seat parameters based on driver comfort. Background Art

[0002] Existing passive safety technologies still have limitations when dealing with drivers in abnormal sitting postures. For the driver restraint system, seat design is one of the important factors affecting comfort. Analyzing the seat comfort in the pre-collision restraint system is of great significance for improving driver comfort and providing more comprehensive safety protection.

[0003] Currently, autonomous vehicles still cannot completely avoid traffic accidents, and passive safety technologies still need to be continuously improved to provide more comfortable safety protection for drivers in the vehicle. In the future, advanced driver assistance technologies will perceive the driver's state, road environment, and degree of danger more accurately, enabling earlier collision warning times, which also leaves more design space for the driver restraint system.

[0004] However, the loads distributed by traditional restraint systems on drivers are uneven; in some collision conditions, the restraint system cannot effectively restrain the movement of the driver, thus causing additional injuries to the driver; unreasonable seat stiffness will cause discomfort to the driver or cause additional injuries to the driver. Therefore, it is actually difficult for existing passive safety technologies to provide relatively comfortable safety protection for drivers in the vehicle through the restraint system. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for optimizing seat parameters based on driver comfort to alleviate the above problems existing in the related technologies.

[0006] An embodiment of the present invention provides a method for optimizing seat parameters based on driver comfort, including: obtaining a vehicle finite element model, and configuring a dummy model, a pre-established seat belt model, an airbag model, and a seat model for the vehicle finite element model to establish a driver restraint system finite element model; wherein, the seat model is equipped with a leg support and an adjustable mechanism; determining seat parameters including seat backrest angle, seat cushion angle, seat leg support angle, and seat filler stiffness, and establishing an orthogonal experiment table with the seat parameters as experimental factors; performing vehicle collision simulation based on the orthogonal experiment table and the driver restraint system finite element model to obtain seat comfort simulation results; wherein, the seat comfort simulation results include output values corresponding to multiple body pressure distribution indicators; constructing a response surface model for each body pressure distribution indicator corresponding to the seat parameters based on the seat comfort simulation results, and performing iterative solution on the response surface model based on the seat comfort simulation results to obtain the optimization results of the seat parameters; wherein, the response surface model characterizes the relationship between the seat parameters and the body pressure distribution indicators.

[0007] For the method for optimizing seat parameters based on driver comfort provided by an embodiment of the present invention, first, a vehicle finite element model is obtained, and a dummy model, a pre-established seat belt model, an airbag model, and a seat model are configured for the vehicle finite element model to establish a driver restraint system finite element model. Then, seat parameters including seat backrest angle, seat cushion angle, seat leg support angle, and seat filler stiffness are determined, and an orthogonal experiment table with the seat parameters as experimental factors is established. After that, vehicle collision simulation is performed based on the orthogonal experiment table and the driver restraint system finite element model to obtain seat comfort simulation results. Then, a response surface model for each body pressure distribution indicator corresponding to the seat parameters is constructed based on the seat comfort simulation results, and iterative solution is performed on the response surface model based on the seat comfort simulation results to obtain the optimization results of the seat parameters. By adopting the above technology, a driver restraint system finite element model and an orthogonal experiment table can be established based on the vehicle finite element model and seat parameters, and a response surface model can be constructed through vehicle collision simulation, and then the seat parameters can be optimized by iterative solution of the response surface model, so as to improve seat comfort on the basis of ensuring driver safety.

[0008] Other features and advantages of the present invention will be described in the following specification, and, in part, will become apparent from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification, claims, and drawings.

[0009] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and detailed descriptions are made in conjunction with the accompanying drawings as follows. Description of the Drawings

[0010] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0011] Figure 1 It is a schematic flow chart of a seat parameter optimization method based on driver comfort in an embodiment of the present invention;

[0012] Figure 2 It is a flow chart of the finite element method in an embodiment of the present invention;

[0013] Figure 3 It is a detailed display diagram of the vehicle body structure and interior in an embodiment of the present invention;

[0014] Figure 4 It is a schematic diagram of dummy positioning in an embodiment of the present invention;

[0015] Figure 5 It is a schematic diagram of dummy adjustment in an embodiment of the present invention;

[0016] Figure 6 It is a pre-simulation schematic diagram of the THUMS dummy in an embodiment of the present invention;

[0017] Figure 7 It is a driver's motion response diagram in the real vehicle test and simulation test in an embodiment of the present invention;

[0018] Figure 8 It is a damage curve diagram of each part of the driver in an embodiment of the present invention;

[0019] Figure 9 It is a seat belt output parameter diagram in an embodiment of the present invention;

[0020] Figure 10 It is a seat contact force diagram in an embodiment of the present invention;

[0021] Figure 11 It is a compressive stress nephogram of the seat filler in an embodiment of the present invention;

[0022] Figure 12 It is a measurement example diagram of seat parameters in an embodiment of the present invention;

[0023] Figure 13 It is a process diagram of response surface model optimization in an embodiment of the present invention. Specific Embodiments

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0025] Currently, autonomous vehicles still cannot completely avoid traffic accidents, and passive safety technologies still need to be continuously improved to provide more comfortable safety protection for drivers inside the vehicle. The traditional restraint system distributes uneven loads on the driver; in certain collision conditions, the restraint system cannot effectively restrain the movement of the driver, thereby causing additional injuries to the driver; the unreasonable seat stiffness will cause discomfort to the driver or cause additional injuries to the driver. Therefore, it is actually difficult for existing passive safety technologies to provide relatively comfortable safety protection for drivers inside the vehicle through the restraint system. Based on this, an optimization method for seat parameters based on driver comfort provided by the embodiments of the present invention can alleviate the above problems existing in the related technologies.

[0026] See Figure 1 As shown, the optimization method for seat parameters based on driver comfort may include the following steps:

[0027] Step S102: Obtain a vehicle finite element model, and configure a dummy model, a pre-established seat belt model, an airbag model, and a seat model for the vehicle finite element model to establish a driver restraint system finite element model.

[0028] Among them, the seat model is equipped with a leg support and an adjustable mechanism.

[0029] Step S104: Determine the seat parameters including the seat backrest angle, the seat cushion angle, the seat leg support angle, and the seat filling stiffness, and establish an orthogonal experiment table with the seat parameters as experimental factors.

[0030] Step S106: Perform vehicle collision simulation based on the orthogonal experiment table and the driver restraint system finite element model to obtain seat comfort simulation results.

[0031] Among them, the seat comfort simulation results may include output values corresponding to multiple body pressure distribution indicators.

[0032] Step S108: Construct a response surface model for each body pressure distribution indicator corresponding to the seat parameters based on the seat comfort simulation results, and perform iterative solution on the response surface model based on the seat comfort simulation results to obtain the optimization results of the seat parameters.

[0033] Among them, the response surface model can characterize the relationship between the seat parameters and the body pressure distribution index.

[0034] A seat parameter optimization method based on driver comfort provided by an embodiment of the present invention can establish a finite element model of the driver restraint system and an orthogonal experiment table based on a vehicle finite element model and seat parameters, and construct a response surface model through vehicle collision simulation, and then optimize the seat parameters by iteratively solving the response surface model, so as to improve the seat comfort on the basis of ensuring the safety of the driver.

[0035] As a possible implementation, the dummy model can adopt the HybridⅢ 50th percentile dummy and the THUMS 50th percentile dummy; based on this, the above seat parameter optimization method based on driver comfort can further include: establishing a seat belt model, an airbag model and a seat model.

[0036] The damage parts that the HybridⅢ series dummies can simulate include the head, neck, chest, pelvis, thighs, and calves. The THUMS series dummies can simulate injuries such as fractures, ligament ruptures, brain injuries, and visceral injuries. Due to certain differences in the structure and appearance of the HybridⅢ dummy and the THUMS dummy, especially in the hip, leg, and back regions of the dummy, the body surface of the THUMS dummy is closer to that of a human. This difference makes the contact surface generated when the THUMS dummy comes into contact with the seat more continuous. Therefore, the HybridⅢ 50th percentile dummy can be mainly used to evaluate driver injuries, while the THUMS 50th percentile dummy is mainly used to evaluate driver comfort.

[0037] As a possible implementation, configuring the dummy model, the pre-established seat belt model, airbag model, and seat model for the vehicle finite element model in step S102 can include: dividing the vehicle finite element model into a cockpit, and embedding the dummy model, seat belt model, airbag model, and seat model into the cockpit.

[0038] The finite element model of a certain type of sedan that has been published can be obtained, and the integrity of its structure can be verified. The seat belt model and the airbag model can be established using PRIMER software, and at the same time, a seat model with a leg support and an adjustable mechanism can be established. The sitting posture of the dummy is adjusted through pre-simulation, and the dummy model, the seat belt model, and the seat model are placed into the driver-side restraint system sled model of a certain type of sedan. This model divides the cockpit and impacts a rigid wall at a speed of 56 km / h head-on. In the simulation, by applying displacements to the high-precision THUMS dummy (i.e., the 50th percentile THUMS dummy), the rotation of the joints of the dummy is achieved, so as to adjust the THUMS dummy to the predetermined sitting posture. The sitting posture of the HybridⅢ dummy (i.e., the 50th percentile HybridⅢ dummy) can be adjusted by adjusting the joint angles of each limb of the HybridⅢ dummy in the Ls-PrePost software. After adjusting the sitting posture of the dummy, the finite element model of the driver restraint system can be obtained. The finite element model of the driver restraint system can be used for vehicle collision simulation experiments. Then, according to the collision restraint evaluation criteria, the output values of the simulation experiments are compared with the injuries of each part of the occupant in the real vehicle test, and some parameters in the finite element model of the driver restraint system are output to judge whether the initial values of the finite element model of the driver restraint system are reasonable, so as to verify the effectiveness of the finite element model of the driver restraint system.

[0039] As an important part of the restraint system, the design and implementation of the seat belt model should ensure that it can effectively protect the driver in the event of a collision. The specific implementation steps for establishing and simulating the seat belt model are as follows:

[0040] Step 1.1, Modeling and Meshing: First, establish the finite element model of the seat belt in PRIMER software. The finite element model of the seat belt consists of 2D shell elements and 1D seatbelt elements, which are used to simulate the contact and interaction between the seat belt and the dummy's body and the slip ring. Specifically, the part of the seat belt in contact with the dummy uses 2D shell elements, while the part of the seat belt near the slip ring uses 1D seatbelt elements, so that the tightening and sliding process of the seat belt can be accurately simulated.

[0041] Step 1.2, Path and Positioning of the Seat Belt: During the modeling process, it is necessary to set the winding path of the seat belt and ensure the precise positioning of the contact points between the seat belt and the dummy. For example, the positions of the retractor and the slip ring, and the seat belt contact points on the dummy's shoulders, chest, abdomen, and hips need to be set according to the actual situation.

[0042] Step 1.3, Define the physical properties of the seatbelt: The material properties of the seatbelt are simulated using a material model (such as the *MAT_34 model, etc.), which can accurately reflect the mechanical behavior of the fabric material. At the same time, the friction coefficient of the seatbelt is set to 0.15 at the slip ring to simulate the friction force between the seatbelt and the slip ring.

[0043] Step 1.4, Seatbelt pre-tensioning and force limitation: The force limitation function of the seatbelt is achieved by setting the keyword *ELEMENT_SEATBELT_RETRACTOR to limit the tensile force of the seatbelt; the pre-tensioning function of the seatbelt is achieved by using the keyword *ELEMENT_SEATBELT_PRETENSIONER. By adjusting these settings, ensure that the seatbelt can effectively restrain the driver during a collision.

[0044] The airbag is another important occupant protection device. The airbag modeling and simulation process includes the following steps:

[0045] Step 2.1, Establish the original geometric model of the airbag: First, establish the initial geometric model of the airbag in the PRIMER software, set the airbag to be circular, generate two layers of meshes, and add two exhaust holes to the airbag.

[0046] Step 2.2, Airbag folding: Use the *Thin Fold folding method to fold the airbag in the PRIMER software. After each folding, it is necessary to re-mesh the meshes near the folding line to ensure that the geometric shape and structure of the airbag after folding adapt to the collision conditions.

[0047] Step 2.3, Inflation simulation and parameter setting: In HyperMesh, use the equal pressure method to simulate the airbag inflation process and define the mass flow curve of the airbag generator. At the same time, set the curve of the exhaust hole area changing with the absolute pressure. These parameters can effectively simulate the inflation and deployment process of the airbag during a collision.

[0048] Contact definition between the airbag and other components: To ensure good contact between the airbag and the steering wheel and the occupant, use the keywords *CONTACT_AIRBAG_SINGLE_SURFACE and *CONTACT_AUTOMATIC_SURFACE TO SURFACE to define the contact between the airbag and other components. The friction coefficient is set to 0.35 to ensure correct simulation of the friction behavior between the airbag and the driver or the steering wheel when the airbag deploys.

[0049] Exemplarily, the steps of embedding a Hybrid Ⅲ 50th percentile dummy into the cockpit may include: placing the Hybrid Ⅲ 50th percentile dummy in the cockpit and setting the initial sitting posture of the Hybrid Ⅲ 50th percentile dummy in the cockpit according to the preset collision test specifications; wherein, the initial sitting posture makes the torso of the Hybrid Ⅲ 50th percentile dummy fit with the seat back, and at the same time keeps the relative position of the head of the Hybrid Ⅲ 50th percentile dummy unchanged with respect to the reference point.

[0050] Exemplarily, the steps of embedding a THUMS 50th percentile dummy into the cockpit may include: placing the THUMS 50th percentile dummy in the cockpit and setting the sitting posture of the THUMS 50th percentile dummy in the cockpit by applying displacement to the THUMS 50th percentile dummy.

[0051] Exemplarily, the reference point may be the front door lock buckle mounting point of the vehicle body; the initial sitting posture is determined by multiple positioning parameters of the Hybrid Ⅲ 50th percentile dummy; as shown in Table 1, the multiple positioning parameters may include: the distances from the head of the dummy to the roof of the vehicle, the distance from the head of the dummy to the front windshield, the distance from the nose of the dummy to the edge of the steering wheel, the distance from the chest of the dummy to the instrument panel, the distance from the chest of the dummy to the center of the steering wheel, the distance from the knee joint of the dummy to the reference point, the distance from the head of the dummy to the reference point, and the distance from the H-point of the dummy to the reference point.

[0052] Table 1 Comparison Table of Dummy Positioning Parameters for Simulation and Test

[0053]

[0054] The dummy model plays an important role in simulating the responses and injuries of the occupants during a collision simulation. The relevant implementation steps of the dummy model in the collision simulation are as follows:

[0055] Step 3.1, Dummy positioning and sitting posture adjustment: According to the collision test specifications, it is first necessary to accurately position the dummy. Taking the front door lock buckle mounting point of the vehicle body as the reference point, adjust the relative position between the H-point of the dummy and the door lock so that the torso of the dummy fits with the seat back, and at the same time ensure that the position relationship between the head and the door lock is reasonable. Each joint of the dummy (such as the shoulder, hip, knee, etc.) needs to be adjusted according to the predetermined sitting posture parameters.

[0056] Step 3.2, Definition of the contact between the dummy and vehicle interior components: Use finite element units to define the contact between the dummy and components such as the seat, steering wheel, airbag, etc. The contact between the dummy and vehicle interior components should simulate their motion responses and possible collision injuries. In the simulation, the dummy evaluates the injury situation through contact with components such as the airbag and steering wheel.

[0057] Step 3.3, Use of THUMS dummy: Since the body surface of the THUMS dummy is more conformable to the actual human body structure, the THUMS dummy can be used for more detailed injury simulation, such as simulating internal injuries of the human body (such as fractures, internal organ injuries, etc.) to improve the simulation accuracy. The THUMS dummy needs to adjust the joints by applying additional displacements to simulate the correct sitting posture.

[0058] In the collision simulation, the entire restraint system (including seat belt model, airbag model, dummy, etc.) needs to be placed in the sled model simulating the occupant compartment for analysis. The specific steps are as follows:

[0059] Step 4.1, Model division and combination: As needed, the front occupant compartment of the vehicle is divided into a quarter, retaining the interior parts such as the steering wheel, brake pedal, driver's side instrument panel, and seat. At the same time, the seat belt, airbag, and dummy models are embedded in the cockpit to simulate the collision process.

[0060] Step 4.2, Collision simulation: In the simulation, by setting the simulation conditions, the collision process will automatically trigger the restraint of the seat belt, the deployment of the airbag, and the motion response of the dummy. This can effectively evaluate the safety and comfort of the restraint system in protecting the driver.

[0061] Through the above Steps 4.1 and 4.2, the seat belt model, airbag model, and dummy model can be integrated into a complete collision simulation system to evaluate the effect of the restraint system in the case of a collision.

[0062] Such as Figure 2As shown, the finite element method can be used to analyze collision problems. The implementation process of the finite element method is divided into three stages: preprocessing, solution, and postprocessing. In the preprocessing stage, finite element models are established using software such as HyperMesh, PRIMER, and LS-PrePost to complete mesh generation, element type definition, material property setting, and boundary condition loading, ensuring model accuracy and solution effectiveness. In the solution stage, LS-DYNA is used for collision analysis to handle large displacement, large rotation, and nonlinear material problems, which is suitable for explicit dynamic simulation. In the postprocessing stage, simulation reports are generated using HyperView and HyperGraph for optimization analysis. Specifically, the collision process is visualized using HyperView and the result curves are plotted using HyperGraph to support design optimization. Each stage is closely connected to ensure the accuracy of the simulation model and the reliability of the results. Automobile collision analysis is a typical structural simulation problem handled by the finite element method, usually carried out in three stages: preprocessing, solution, and postprocessing. First, in the preprocessing stage, engineers use software such as HyperMesh to generate meshes, dividing the entire model into small finite element cells, each with a specific shape and size to ensure calculation accuracy. At the same time, material properties (such as elastic modulus, density, etc.) are defined, corresponding materials are assigned to each cell, and loads (such as collision forces, pressures, etc.) and boundary conditions (such as fixed ends, symmetry conditions, etc.) are set. For example, in automobile collision simulation, different materials may need to be set for the vehicle body and the dummy, and the collision position and initial velocity are defined. Then, in the solution stage, a suitable solver (such as LS-DYNA) is selected to perform dynamic solution on the collision model. The solver will perform iterative calculations according to the type of problem (such as nonlinear, large deformation, dynamic analysis, etc.) to obtain the stress, strain, and displacement distributions at each moment. Finally, in the postprocessing stage, software such as HyperView is used to extract the solution results (such as maximum deformation, stress distribution, energy loss, etc.) to generate result reports to help engineers analyze the structural performance. If some components are damaged or deformed too much during the collision process, engineers can also optimize the model according to the simulation results, such as improving the structural safety and performance by changing materials, strengthening certain areas, or adjusting the load distribution.

[0063] As Figure 3 As shown, based on the finite element model of a certain type of sedan developed by the Center for Collision Safety and Analysis (CCSA) at George Mason University in the United States, the three-dimensional data model of the whole vehicle was reconstructed using reverse engineering technology, and the material information of each component was obtained to ensure the consistency of the mechanical properties of the three-dimensional data model of the whole vehicle with the actual sedan. The three-dimensional data model of the whole vehicle includes 1,086 components such as the vehicle body structure, engine, chassis, and seats, with a total of 2,255,361 nodes and 2,257,280 elements, which can support complex collision simulation analysis.

[0064] Dummies are key substitutes for simulating occupant injuries in crash tests, and their initial sitting postures have important impacts on their motion responses and injury degrees during crashes. Therefore, the accuracy of dummy positioning is the basis for ensuring the precision of experimental results. As Figure 4 shown, referring to the NCAP test procedures, the installation point of the front door latch of the vehicle body can be used as a reference point, and the H-point of the dummy (the connection point between the torso and the thigh in a two-dimensional or three-dimensional human model, representing the position of the midpoint of the hip joint in the vehicle after the driver is seated) can be used as the reference for driver positioning. The steps for dummy positioning mainly include: First, confirm the relative position between the H-point of the dummy and the door latch, and adjust the pelvic angle of the dummy so that the dummy's torso fits the seat backrest, while ensuring that the relative position between the dummy's head and the door latch remains consistent. Through these steps, the accurate positioning of the dummy in the finite element model is ensured, thereby improving the simulation accuracy of the crash test. Figure 4 It also shows the symbols and definition methods of the dummy positioning parameters in Table 1.

[0065] As Figure 5 shown, the sitting posture of the HybridⅢ dummy can be adjusted with reference to the dummy positioning parameters in the NCAP test procedures. Figure 5 It shows the relative distances between the body surface positioning points of the driver in the simulation model and the vehicle body and interior positioning points. In crash tests, the initial sitting posture of the dummy has an important impact on the motion response and final injury of the dummy during the crash. Therefore, the positioning accuracy of the dummy is the basis for ensuring the accuracy of experimental test results. The positioning process of the driver can be simulated in the finite element model with reference to the dummy positioning parameters in the NCAP test procedures. First, take the installation point of the front door latch of the vehicle body as the vehicle body reference point and the H-point of the dummy as the driver reference point, and confirm the relative position between the H-point and the door latch. By adjusting the pelvic angle of the dummy, make the upper body of the dummy's torso fit the seat backrest, while ensuring that the relative position between the dummy's head and the door latch remains consistent. In the Ls-Prepost software, the joint angles of each limb of the dummy can be adjusted to reach the predetermined position. The joints involved include the shoulders, arms, wrists, thighs, knees, calves, and ankles. Finally, the relative distances between the dummy positioning points in the simulation model and the vehicle body and interior positioning points are shown in Table 1. The results show that the deviation between the dummy positioning parameters in the simulation and those in the test does not exceed 5%, verifying the consistency of the sitting posture of the driver in the simulation and that in the test.

[0066] As Figure 6As shown, the process of THUMS dummy pre-simulation mainly involves using CT scan data to generate a digital full-body model and constructing a finite element mesh based on this full-body model. The specific steps of THUMS dummy pre-simulation can include: (1) CT scan data acquisition: First, use a high-resolution CT scan device to scan the human body to obtain accurate scan data. These scan data include three-dimensional images of various parts of the human body, which can accurately display the shapes and positions of bones, muscles, internal organs, etc. (2) Digital model construction: Use medical image processing software to process and reconstruct the scan data obtained from CT scans to form a complete digital human model. The digital human model includes the geometric shape of the human body, tissue distribution, and organ structure. (3) Finite element mesh generation: Use finite element analysis software (such as HyperMesh, Abaqus, etc.) to convert the digital human model into a finite element mesh. The process of generating the finite element mesh includes dividing the entire three-dimensional model into many small units with specific physical properties, and these units can simulate the physical properties of various parts of the human body, such as the stiffness of bones and the flexibility of tissues. Through the finite element mesh, the THUMS dummy can perform detailed mechanical analyses on different parts of the human body (such as fractures, brain injuries, internal organ injuries, etc.) in collision simulations. These analyses can not only accurately simulate the movement of the human body in collisions but also penetrate to the tissue level to accurately simulate various types of human injuries. Therefore, this process of THUMS dummy pre-simulation combines high-resolution CT scans and digital modeling to ensure that the THUMS dummy can truly reproduce the human body structure and response in simulations, thus providing more accurate collision simulations and injury predictions than traditional physical dummies.

[0067] However, the THUMS dummy lacks a mechanical structure inside and cannot simulate the rotation of joints through a hinged mechanical structure like the HybridⅢ dummy. To solve this problem, the THUMS dummy can be adjusted to a predetermined sitting position by applying additional displacements. Specifically, as Figure 6 shown, the parts of the dummy that do not need to be adjusted can be fixed on a rigid body bracket, while the joints of the dummy that need to rotate are connected by 1D elements and displacements are applied to achieve the rotation of the joints by utilizing the contact behavior of the dummy itself.

[0068] The rigid body support is composed of rigid elements (such as beam elements or rigid body elements) and is used to fix and support the dummy model. Finite element software (such as HyperMesh) can be used to define the rigid body support and connect it to the dummy model to restrict the relative movement between the rigid body support and the dummy. 1D elements are usually rod elements or spring elements and are used to connect joints that need to rotate. The 1D elements can be placed at joint locations (such as the knee joint, hip joint, etc.) by defining appropriate connection points and directions to effectively simulate the rotation or relative displacement of the joints. When constructing the 1D elements, it is necessary to ensure that the direction of the 1D elements is consistent with the direction of joint rotation, and at the same time, appropriate element types (such as springs, rods, etc.) should be set to simulate the elasticity and movement of the joints. When applying displacement to the THUMS dummy model, the magnitude of the applied displacement needs to be determined according to the predetermined sitting posture, and usually, the displacement magnitude is selected based on the simulation purpose or experimental data. For example, the displacement magnitude of the knee joint may be 5°, 10° or larger, and it is adjusted specifically according to the sitting posture angle. The application of displacement is usually completed by applying boundary conditions or loads, and it can be achieved through "displacement control" or "kinematic control" in the finite element software. The process of applying displacement is to gradually control the displacement magnitude to make the joint gradually reach the target angle. During the calculation process, a solver (such as LS-DYNA) is used for nonlinear static or dynamic analysis, and the application of displacement at the dummy joints is usually carried out in time steps. In each calculation step, the applied displacement guides the rotation of the dummy model joints. The specific way of applying displacement is as follows: for the knee joint, assume that the applied displacement is 10 mm or 5°, and then, according to the stiffness, mass distribution and external loads of the model, calculate the force required to reach this displacement and ensure that the dummy model can correctly simulate the rotation of the joint. The simulation of the self-contact behavior of the dummy can be achieved by defining the contact surface (such as the soft contact surface at the joint), and specifically, the contact force and the interaction between the contact points can be captured by defining contact elements in the calculation. Appropriate contact algorithms (such as the penalty function method or the Lagrange multiplier method) can be used to simulate the contact behavior at the joints to ensure that the joints can rotate naturally when displacement is applied.

[0069] To reduce the computational load of the finite element model, the complete vehicle model can be segmented to retain the cockpit, and interior components such as the steering wheel, brake pedal, driver-side instrument panel, and seat are retained in the vehicle model. Then, the seat belt model, airbag model, and dummy model are placed in the cockpit to conduct vehicle collision simulations to evaluate the safety of the restraint system. After completing the configuration of the seat belt model, airbag model, and dummy model, vehicle collision simulations are carried out. During the simulation process, collision events are simulated to observe the force on the occupant, the degree of injury, and the protection effect of the seat belt and airbag.

[0070] such as Figure 7As shown, to further evaluate the collision performance of the model, the NCAP frontal collision test procedure was used for simulation and compared with the real vehicle test data. Figure 7 It shows the motion responses of the driver at different times in the real vehicle test and the simulation test. In the simulation test, the vehicle was set to hit a rigid wall at a certain speed (56 km / h) head-on, and the installation position of the accelerometer was the same as that in the NCAP test. The simulation results show that the deformation of the vehicle front end in the simulation test is highly consistent with that in the real vehicle test, verifying the high accuracy of the established finite element model of the driver restraint system in predicting collision behavior.

[0071] When using the NCAP frontal collision test procedure for simulation, the simulation input data includes initial conditions, collision parameters, airbag parameters, time step, etc. The initial conditions include the initial moment when the collision occurs (t = 0 ms). At the initial moment, the vehicle contacts the rigid wall, and the dummy and the airbag are in a static state before the collision. The collision parameters can include the collision speed (such as the collision speed of the vehicle, the collision angle, etc.) and the relevant characteristics of the rigid wall (such as the elasticity, shape, size, etc. of the rigid wall). The airbag parameters can include data such as the deployment time, inflation pressure, and inflation speed of the airbag. The time step of the simulation process needs to be set small enough to ensure that the simulation can accurately track the motion response of the dummy at each moment during the simulation. During the simulation, the motion response of the dummy is affected by various mechanical constraints during the collision process, which are specifically reflected in the following time nodes:

[0072] t = 0 ms: At the initial moment of the collision, the dummy and the vehicle in the simulation model contact the wall and start to accelerate.

[0073] t = 22 ms: The airbag starts to pop out of the airbag housing. At this time, there is no obvious displacement of the dummy's head. The inflation process of the airbag will be simulated in the simulation, and the force exerted on the dummy's head due to the change in air pressure will be recorded.

[0074] t = 64 ms: The driver's head contacts the airbag. At this moment, the collision force between the head and the airbag will be calculated in the simulation, and the kinematic data such as the acceleration and angular velocity of the dummy's head will be updated.

[0075] t = 110 ms: The airbag deflates, and the steering column of the steering wheel collapses. At this time, the simulation system will start to calculate the reaction force of the steering wheel deformation on the dummy, and at the same time consider the dynamic effect of the airbag deflation process on the dummy.

[0076] The simulation output data includes motion response data, damage values, time history diagrams, etc. The motion response data mainly includes the changes in physical quantities such as acceleration, displacement, and velocity of the dummy's head, chest, lower limbs, etc. By analyzing these data, it can be judged whether the dynamic responses in the simulation test and the actual collision test are consistent. The damage values are mainly calculated based on the stress and strain conditions of each part of the dummy. The damage values can include chest compression, head impact force, etc. The damage values help to judge the degree of injury of the dummy in the collision and compare with the damage data in the actual test. Through the time history diagrams in the simulation, the motion changes of each part can be analyzed, such as the acceleration of the head, the displacement of the chest, etc.

[0077] As Figure 8 shown, the damage curves of each part of the occupant are output according to the NCAP regulations. Figure 8 (a) in it shows the head acceleration curve. Figure 8 (b) in it shows the chest acceleration curve. Figure 8 (c) in it shows the neck shear force curve. Figure 8 (d) in it shows the neck tension curve. Figure 8 (e) in it shows the neck bending moment curve. Figure 8 (f) in it shows the thigh axial force curve. Based on Figure 8 the following conclusions can be drawn: The peak phase of the head acceleration curve in the simulation test is consistent with the result of the real vehicle test, but the peak value of the head acceleration curve in the simulation test is slightly higher than that of the head acceleration curve in the real vehicle test; The growth trends of the chest acceleration curves in the simulation test and the real vehicle test are similar, but the peak value of the chest acceleration curve in the simulation test is slightly lower than that of the chest acceleration curve in the real vehicle test; The peaks and trends of the neck injury curves (including the neck shear force curve, neck tension curve, and neck bending moment curve) in the simulation test and the real vehicle test are similar, but the phase at which the peak value of the corresponding neck injury curve appears in the simulation test is later than the phase at which the peak value of the corresponding neck injury curve appears in the real vehicle test; The peaks and trends of the thigh axial force curves in the simulation test and the real vehicle test are similar, but the phase difference of the peak values of the thigh axial force curves in the simulation test and the real vehicle test is relatively large, which may be related to the initial position of the thigh placement.

[0078] In addition, the finite element model of the driver restraint system also needs to output some parameters in the restraint system to check whether the initial values of the restraint system parameters in the finite element model of the driver restraint system are reasonable. As Figure 9 shown, the finite element model of the driver restraint system outputs the webbing in-and-out quantity curve of the seat belt retractor, the seat belt force curve at the retractor outlet, and the seat belt waist force curve. Based on Figure 9It can be seen that the changing trends of the webbing input / output curve of the seat belt retractor and the seat belt force curve at the retractor outlet in the simulation test are consistent with the actual situation respectively. The peak values and trends of the seat belt force curves in the simulation test and the real vehicle test are similar, indicating that the initial values of the seat belt parameters in the finite element model of the driver restraint system are relatively reasonable.

[0079] As Figure 10 shown, the effectiveness of the finite element model of the driver restraint system can be judged by outputting the contact forces generated between the driver and the seat at the contact surface by the finite element model of the driver restraint system. These contact forces include: the contact force between the driver's buttocks and the seat cushion, the contact force between the driver's back and the seat backrest, and the contact force between the driver's legs and the seat leg rest. Figure 10 The curves showing the variation of the magnitudes of these three contact forces with time in the simulation test are presented.

[0080] As Figure 11 shown, the effectiveness of the finite element model of the driver restraint system can be judged by outputting the stress nephogram of the seat cushion filler by the finite element model of the driver restraint system. Figure 11 (a) in it is the stress nephogram of the seat backrest. The backrest is the part of the seat that supports the back; Figure 11 (b) in it is the stress nephogram of the seat cushion. The cushion supports the buttocks and thighs; Figure 11 (c) in it is the stress nephogram of the seat leg rest. The leg rest supports the calves or feet. According to Figure 11 it can be known that from the perspective of stress distribution, the compressive stress of the seat cushion filler is symmetrically distributed left and right, and the compressive stress distribution of the seat cushion filler shows a distribution law of spreading outwards from the center. This stress distribution is reasonable.

[0081] Generally speaking, in the simulation collision test and the real vehicle collision test, the overall changing trends and peak values of the head injury curve, chest injury curve, neck injury curve and thigh injury curve of the driver are basically the same; there are no abnormalities in the parameters output by the restraint system, and the contact forces generated by the driver on the seat and the compressive stress generated by the seat cushion filler are relatively reasonable, indicating that the established finite element model of the driver restraint system is effective.

[0082] As Figure 12As shown, the seat parameters may include the backrest angle (θ1), the seat cushion angle (θ2), the leg rest angle (θ3) of the seat, and the stiffness (K) of the seat cushion filler. During the driver's sitting on the seat, different body pressures will be generated on the backrest, seat cushion, and leg rest of the seat respectively. The body pressure distribution indexes mainly may include the maximum pressure (P), the maximum pressure gradient (PG), the average pressure (PA), the pressure distribution uniformity (SPD), and the contact area (A). These indexes help to evaluate the comfort and support of the seat. For the convenience of data analysis and result reading, these seat parameters and these body pressure distribution indexes can be used for the quantitative evaluation of the seat performance. Specifically, the seat parameters can be used as factors to design an orthogonal experiment to obtain reliable data, and the response surface model of each body pressure index is constructed by using the polynomial regression method. Subsequently, the seat parameters are optimized according to the response surface model.

[0083] As shown in Table 2, a unified form can be used to represent the seat parameters and the body pressure distribution indexes of each part of the seat. Using this representation method facilitates the reading of the subsequent orthogonal experiment result data.

[0084] Table 2 Seat Parameter and Body Pressure Variable Table

[0085]

[0086] Table 3 shows that the experimental factors selected for the orthogonal experiment are the backrest angle (θ1), the seat cushion angle (θ2), the leg rest angle (θ3) of the seat, and the stiffness (K) of the seat cushion filler, and five levels are set for these four experimental factors.

[0087] Table 3 Orthogonal Experiment Factor and Level Table

[0088]

[0089] Table 4 represents the L 25 (6 5 ) orthogonal table. Since the input variables (i.e., the backrest angle, the seat cushion angle, the leg rest angle of the seat, and the stiffness of the seat cushion filler) are 4, according to the relational expression between the number N of basis functions and the number n of input variables it can be known that the basis functions for constructing the response surface are at least 15. To improve the representativeness of the data, the number of experiments to be carried out can be increased to 1.5 times the number of basis functions, which means that at least 23 sample points need to be obtained. Through SPSS analysis, it can be known that the L 25 (6 5 ) orthogonal table just meets the experimental requirements. According to the orthogonal table selection principle, the orthogonal table can be designed by referring to the factors and levels arranged in Table 3.

[0090] Table 5 shows the specific experimental arrangement table of the seat parameters (i.e., the orthogonal experimental table). As can be seen from Table 3, 25 sample points are obtained through the orthogonal experiment. These sample points include 4 input variables and 5 response values. Taking the 4 factors and 5 levels of Table 3, the variable parameters shown in Table 2 are input. The 4 input variables represent that the seat has 4 different parameters that can be adjusted.

[0091] Orthogonal table of six factors and five levels

[0092]

[0093] Experimental arrangement table of Table 5

[0094]

[0095] As a possible implementation manner, the orthogonal experimental table may include multiple input values corresponding to the seat backrest angle, seat cushion angle, seat leg rest angle, and seat filler stiffness of the seat respectively; based on this, the above step S106 (i.e., performing vehicle collision simulation based on the orthogonal experimental table and the finite element model of the driver restraint system to obtain the seat comfort simulation result) may include:

[0096] Step A1, establishing multiple input parameter groups of the seat model in the finite element model of the driver restraint system based on the input values, and setting the input data of the finite element model of the driver restraint system; wherein, the input data includes the initial moment when the vehicle collides, vehicle collision parameters, airbag parameters, and simulation time step.

[0097] Step A2, performing vehicle collision simulation through the finite element model of the driver restraint system using its input data according to each input parameter group respectively to obtain the simulation result corresponding to each input parameter group; wherein, the simulation result includes each body pressure distribution index and the output value corresponding to the corresponding input parameter group.

[0098] Continuing with the previous example, the 25 sample points generated by the experimental arrangement table shown in Table 5 can be used as 25 input parameter groups. After setting the input data of the finite element model of the driver restraint system, these 25 input parameter groups can be respectively input into the finite element model of the driver restraint system for vehicle collision simulation, so as to perform 25 vehicle collision simulations through the finite element model of the driver restraint system to obtain the results of these 25 vehicle collision simulations.

[0099] Table 6 shows the output variables representing the body pressure distribution of the seat backrest, seat cushion, and leg rest obtained by the finite element model of the driver restraint system when different variable parameters (i.e., 25 different input parameter groups) are input into the finite element model of the driver restraint system.

[0100] Comfort simulation result table of Table 6

[0101]

[0102] As a possible implementation manner, before the above step S106 (i.e., performing vehicle collision simulation based on the orthogonal experiment table and the finite element model of the driver restraint system to obtain the seat comfort simulation result), the above seat parameter optimization method based on driver comfort may further include: normalizing the input values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle, and the seat filler stiffness respectively.

[0103] Exemplarily, each input parameter group may include the normalized values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle, and the seat filler stiffness respectively (the values obtained after normalizing the values in the 25 sample points shown in Table 5), or each input parameter group may include the input values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle, and the seat filler stiffness respectively (the values in the 25 sample points shown in Table 5).

[0104] Adopting the above normalization dimensionless processing method (i.e., normalizing the data of each seat parameter in Table 5 by the normalization method) will not change the distribution law of the data, but only scale the values, which can not only compress all the data within the range of 0 to 1, but also ensure the consistency of the data characteristics. The expression of normalization can be: C is the normalized value, x is the value of the original data before normalization, x min is the minimum value of the original data, x max is the maximum value of the original data.

[0105] As a possible implementation manner, constructing the response surface model of each body pressure distribution index corresponding to the seat parameters in the above step S108 may include: for each body pressure distribution index, based on the input parameter group and the output value corresponding to the body pressure distribution index, using the polynomial regression method to fit the response surface function expression corresponding to the body pressure distribution index, and taking the response surface function expression as the response surface model corresponding to the body pressure distribution index; wherein, the response surface function expression takes the seat parameters as independent variables and the body pressure distribution index as the dependent variable.

[0106] The response surface function expressions corresponding to each body pressure distribution index can be fitted by using the polynomial regression method with the corresponding data in Table 5 and Table 6.

[0107] Exemplarily, the response surface function expression of the contact area can be:

[0108]

[0109] The response surface function expression of the maximum pressure can be:

[0110]

[0111] The response surface function expression of the average pressure can be:

[0112]

[0113] The response surface function expression of the maximum pressure gradient index can be:

[0114]

[0115] The response surface function expression of the pressure distribution uniformity can be:

[0116]

[0117] According to the above five response surface function expressions, it can be seen that the various adjustment parameters of the seat have different effects on the evaluation index (i.e., the body pressure distribution index), and the interaction between different seat adjustment parameters will also affect the final evaluation index. The response surface function expression actually represents the relationship between the body pressure distribution index and the seat parameters, and this relationship provides a basis for the subsequent algorithm optimization.

[0118] In order to judge the effectiveness of the response surface model, after normalizing the data in Table 5 and Table 6 and then fitting the response surface function expressions corresponding to each body pressure distribution index (i.e., the above five response surface function expressions) through the polynomial regression method, the determination coefficient and adjusted determination coefficient of the response surface model corresponding to each body pressure distribution index can be calculated respectively. Table 7 shows the determination coefficient R 2 and the adjusted determination coefficient R Adj 2 , the closer the values of the determination coefficient R 2 and the adjusted determination coefficient R Adj 2 are to 1, the better the degree of model fitting. According to Table 7, it can be known that among all the response surface models, the response surface models of the maximum pressure and the pressure distribution uniformity perform poorly, which may be caused by the instability of the response value of a certain output; generally, the determination coefficients of all the response surface models exceed 0.8, indicating that all the response surface models meet the requirements of the response surface analysis, proving the effectiveness of the established response surface model.

[0119] Table 7 Example table of determination coefficient and adjusted determination coefficient

[0120]

[0121] Generally speaking, it is very necessary to make the data dimensionless (i.e., normalize it), which solves the problem that it is difficult to comprehensively analyze the numerical values of each index. The method of making the data dimensionless by using the normalization method not only meets the requirement of simple data processing process but also meets the requirement of reliable data processing results. The effectiveness of the established response surface model is verified according to the normalized results.

[0122] As a possible implementation manner, in the above step S108, the response surface model is iteratively solved based on the seat comfort simulation results, and the optimization results of the seat parameters may include: performing a correlation analysis on the seat comfort simulation results, and determining the target body pressure distribution index based on the correlation analysis results; determining the target response surface model corresponding to the target body pressure distribution index from the response surface model, and using a preset genetic algorithm to iteratively solve the target response surface model to obtain the optimization results.

[0123] Exemplarily, the target body pressure distribution index may include the contact area and the maximum pressure; based on this, the step of using a preset genetic algorithm to iteratively solve the target response surface model to obtain the optimization results may include: aiming at maximizing the contact area and setting the constraint conditions of the seat parameters, using a preset genetic algorithm to iteratively solve the first target response surface model corresponding to the contact area to obtain the first optimization results of the seat parameters; aiming at minimizing the maximum pressure and setting the constraint conditions of the seat parameters, using a preset genetic algorithm to iteratively solve the second target response surface model corresponding to the maximum pressure to obtain the second optimization results of the seat parameters; determining the optimization results based on the first optimization results and the second optimization results.

[0124] In the actual application process, the step of determining the optimization results based on the first optimization results and the second optimization results may include: if each input parameter group includes the normalized values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle, and the seat filling stiffness respectively, then perform anti-normalization on the first optimization results and the second optimization results, and use the anti-normalization results of the first optimization results and the second optimization results respectively as the optimization results; if each input parameter group includes the input values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle, and the seat filling stiffness respectively, then use the first optimization results and the second optimization results as the optimization results.

[0125] Continuing with the previous example, as shown in Table 8, the Pearson correlation coefficient table can be used to represent the strength of the correlation relationship. By studying the correlation between these five response values, the relationship between one response value and other response values can be found. Thus, the effect of optimizing the overall response values can be achieved only by optimizing one response value.

[0126] Table 8 Pearson correlation coefficient table

[0127]

[0128] As can be seen from Table 8, there is a significant negative correlation between the contact area and the average pressure; there is a weak correlation between the contact area and the maximum pressure, the maximum pressure gradient, and the pressure distribution uniformity; there is a significant positive correlation between the maximum pressure and the maximum pressure gradient and the pressure distribution uniformity. According to the screening principle of influencing factors (that is, screening out the significantly relevant influencing factors and the less relevant influencing factors from the five influencing factors of contact area, average pressure, maximum pressure, maximum pressure gradient, and pressure distribution uniformity), the average pressure (significantly correlated with the contact area), the maximum pressure gradient (significantly correlated with the maximum pressure but weakly correlated with the contact area), and the pressure distribution uniformity (significantly correlated with the maximum pressure but weakly correlated with the contact area) can be removed as the three evaluation indicators, and the contact area and the maximum pressure can be selected to establish an optimization objective function; for the evaluation indicator of the contact area, an increase in its value means that the driver is in more sufficient contact with the seat support surface. From the correlation between the contact area and the average pressure, it can be known that the average pressure generated by the increase in the contact area is lower. Therefore, the maximum value of the contact area (represented by A max can be used) as the optimization objective; while an increase in the value of the evaluation indicator of the maximum pressure means that the pressure distribution on the seat support surface is uneven, which also explains the significant positive correlation between the maximum pressure and the maximum pressure gradient and the pressure distribution uniformity. Therefore, the minimum value of the maximum pressure (represented by P min can be used) as the optimization objective. To ensure the convergence and reliability of the calculation results, after determining the optimization objectives, the four independent variables of the seat backrest angle (θ1), the seat cushion angle (θ2), the seat leg support angle (θ3), and the seat filling stiffness (K) can be set with constraints to obtain the following two response surface models:

[0129] A max = f(θ1, θ2, θ3, K)

[0130] P min = f(θ1, θ2, θ3, K)

[0131] A max represents that the optimization objective is to maximize the contact area, and P min represents that the optimization objective is to minimize the maximum pressure; the constraint conditions for these two response surface models are: 20° ≤ θ1 ≤ 40°; 10° ≤ θ2 ≤ 20°; 50° ≤ θ3 ≤ 70°; 0.7 ≤ K ≤ 1.3.

[0132] Such as Figure 13As shown, after establishing the response surface models corresponding to the contact area and the maximum pressure respectively, the population size can be set to 20, the number of genetic generations can be set to 50, and the crossover probability can be set to 0.8. The NSGA-II genetic algorithm is used to solve the response surface models corresponding to the contact area and the maximum pressure respectively. After about 200 iterations, a feasible optimal solution appears. According to the characteristics of the genetic algorithm, this trend persists until the end of the iteration. Finally, the two response surface models obtain the optimal solutions after 1001 iterations respectively. Since the data was normalized previously, Figure 13 the numerical values on the vertical axis in

[0133] do not represent the true values of the corresponding indicators. Generally speaking, the response surface models corresponding to the contact area and the maximum pressure can be established according to the Pearson correlation coefficient table, and the NSGA-II genetic algorithm is used to iteratively solve the response surface models corresponding to the contact area and the maximum pressure respectively. Finally, the optimal solutions are obtained after multiple iterations.

[0134] As shown in Table 9, the optimal solution obtained from the response surface model corresponding to the contact area is denoted as the optimal parameter 1, and the optimal solution obtained from the response surface model corresponding to the maximum pressure is denoted as the optimal parameter 2. The optimized parameter results are compared with the initial parameters of the constraint system. According to Table 9, after optimization, the seat adjustment angle becomes larger and the stiffness of the seat cushion becomes smaller.

[0135] Table 9 Comparison Table of Optimized Parameters and Initial Parameters

[0136]

[0137] Continuing with the previous example, in order to verify the effectiveness of the above seat parameter optimization method based on driver comfort, as shown in Table 10, the optimal parameter 1 and the optimal parameter 2 shown in Table 9 can be substituted into the finite element model of the driver restraint system for simulation respectively, and the results obtained from the simulation of the finite element model of the driver restraint system are compared with the optimization results of the response surface model (that is, the optimized objective function values corresponding to the optimal solutions of the response surface model. Specifically, for the optimal parameter 1, it is the contact area value, and for the optimal parameter 2, it is the maximum pressure value). As can be seen from Table 10, the deviations between the simulation results and the optimization results of the two response surface models are 5.7% and 10.3% respectively, and the deviations are both less than 15%.

[0138] Table 10 Comparison Table of Simulation Results and Response Surface Model Results

[0139]

[0140] Generally speaking, it can be seen from the comparison results of the two optimal parameters and the initial parameters of the constraint system that the optimized seat adjustment angle becomes larger, and the stiffness of the seat filling after optimization is also smaller, which means that the driver will obtain a more comfortable experience in a seat with a reclined posture and softer filling; the two optimal parameters are also brought into the finite element model for simulation, so as to prove that the obtained response surface model is relatively accurate and certain optimization effects have been achieved through iterative solution of the response surface model.

[0141] In summary, applying the above seat parameter optimization method based on driver comfort to improve seat comfort is a feasible solution, and at the same time, the above seat parameter optimization method based on driver comfort has important guiding significance for exploring the sitting posture of the driver before a collision.

[0142] Unless otherwise specifically stated, the relative steps, numerical expressions and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0143] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0144] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0145] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present invention, used to illustrate the technical solutions of the present invention, rather than limiting it. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field of the present invention can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A seat parameter optimization method based on driver comfort, characterized in that Including: Obtain a vehicle finite element model, and configure a dummy model, a pre-established seat belt model, an airbag model, and a seat model for the vehicle finite element model to establish a driver restraint system finite element model; wherein, the seat model is equipped with a leg support and an adjustable mechanism; Determine seat parameters including seat backrest angle, seat cushion angle, seat leg support angle, and seat filler stiffness, and establish an orthogonal experiment table with the seat parameters as experimental factors; Based on the orthogonal experiment table and the driver restraint system finite element model, conduct vehicle collision simulation to obtain seat comfort simulation results; wherein, the seat comfort simulation results include output values corresponding to multiple body pressure distribution indicators; Based on the seat comfort simulation results, construct a response surface model for each body pressure distribution indicator corresponding to the seat parameters, and perform iterative solution on the response surface model based on the seat comfort simulation results to obtain the optimized results of the seat parameters; wherein, the response surface model characterizes the relationship between the seat parameters and the body pressure distribution indicators.

2. The seat parameter optimization method based on driver comfort according to claim 1, wherein Performing iterative solution on the response surface model based on the seat comfort simulation results to obtain the optimized results of the seat parameters, including: Conduct a correlation analysis on the seat comfort simulation results, and determine the target body pressure distribution indicators based on the correlation analysis results; Determine the target response surface model corresponding to the target body pressure distribution indicators from the response surface model, and use a preset genetic algorithm to perform iterative solution on the target response surface model to obtain the optimized results.

3. The seat parameter optimization method based on driver comfort according to claim 2, characterized in that, The target body pressure distribution indicators include contact area and maximum pressure; Using a preset genetic algorithm to perform iterative solution on the target response surface model to obtain the optimized results, including: Taking maximizing the contact area as the goal and setting the constraint conditions of the seat parameters, using a preset genetic algorithm to perform iterative solution on the first target response surface model corresponding to the contact area to obtain the first optimized results of the seat parameters; Taking minimizing the maximum pressure as the goal and setting the constraint conditions of the seat parameters, using a preset genetic algorithm to perform iterative solution on the second target response surface model corresponding to the maximum pressure to obtain the second optimized results of the seat parameters; Determine the optimized results based on the first optimized results and the second optimized results.

4. The method for optimizing seat parameters based on driver comfort according to claim 3, characterized in that, The orthogonal experiment table includes multiple input values corresponding to the seat backrest angle, seat cushion angle, seat leg support angle, and seat filler stiffness respectively; Based on the orthogonal experiment table and the driver restraint system finite element model, conduct vehicle collision simulation to obtain seat comfort simulation results, including: Based on the input values, establish multiple input parameter groups of the seat model in the driver restraint system finite element model, and set the input data of the driver restraint system finite element model; wherein, the input data includes the initial moment of vehicle collision, vehicle collision parameters, airbag parameters, and simulation time step; Using the finite element model of the driver restraint system and the input data, vehicle collision simulation is carried out according to each input parameter group respectively to obtain the simulation results corresponding to each input parameter group; wherein, the simulation results include each body pressure distribution index and the output value corresponding to the corresponding input parameter group.

5. The seat parameter optimization method based on driver comfort according to claim 4, wherein, Based on the seat comfort simulation results, a response surface model of each body pressure distribution index corresponding to the seat parameters is constructed, including: For each body pressure distribution index, based on the input parameter group and the output value corresponding to the body pressure distribution index, a response surface function expression corresponding to the body pressure distribution index is fitted by the polynomial regression method, and the response surface function expression is used as the response surface model corresponding to the body pressure distribution index; wherein, the response surface function expression takes the seat parameters as independent variables and the body pressure distribution index as the dependent variable.

6. The seat parameter optimization method based on driver comfort according to claim 1, wherein Configure a dummy model, a pre-established seat belt model, an airbag model and a seat model for the vehicle finite element model, including: Divide the vehicle finite element model into a cockpit, and embed the dummy model, the seat belt model, the airbag model and the seat model into the cockpit.

7. The method for optimizing seat parameters based on driver comfort according to claim 1, wherein The dummy model adopts the HybridⅢ 50th percentile dummy and the THUMS 50th percentile dummy; the method further includes: establishing the seat belt model, the airbag model and the seat model; Embedding the HybridⅢ 50th percentile dummy into the cockpit includes: placing the HybridⅢ 50th percentile dummy in the cockpit and setting the initial sitting posture of the HybridⅢ 50th percentile dummy in the cockpit according to the preset collision test specification; wherein, the initial sitting posture makes the torso of the HybridⅢ 50th percentile dummy fit with the seat backrest, and at the same time keeps the relative position of the head of the HybridⅢ 50th percentile dummy and the reference point unchanged; Embedding the THUMS 50th percentile dummy into the cockpit includes: placing the THUMS 50th percentile dummy in the cockpit and setting the sitting posture of the THUMS 50th percentile dummy in the cockpit by applying displacement to the THUMS 50th percentile dummy.

8. The method for optimizing seat parameters based on driver comfort according to claim 7, wherein The reference point is the installation point of the front door lock of the vehicle body; the initial sitting posture is determined by multiple positioning parameters of the HybridⅢ 50th percentile dummy; the multiple positioning parameters include: the distances from the dummy's head to the roof of the vehicle, the distance from the dummy's head to the front windshield, the distance from the dummy's nose to the edge of the steering wheel, the distance from the dummy's chest to the instrument panel, the distance from the dummy's chest to the center of the steering wheel, the distance from the dummy's knee joint to the reference point, the distance from the dummy's head to the reference point, and the distance from the dummy's H-point to the reference point.

9. The method for optimizing seat parameters based on driver comfort according to claim 4, wherein Before carrying out vehicle collision simulation based on the orthogonal experiment table and the finite element model of the driver restraint system to obtain the seat comfort simulation results, the method further includes: normalizing the input values corresponding to the seat backrest angle, the seat cushion angle, the seat leg rest angle and the seat filler stiffness respectively. Each of the input parameter groups includes the normalized values corresponding to the seat back angle, seat cushion angle, seat leg rest angle, and seat cushion stiffness respectively, or each of the input parameter groups includes the input values corresponding to the seat back angle, seat cushion angle, seat leg rest angle, and seat cushion stiffness respectively.

10. The method for optimizing seat parameters based on driver comfort according to claim 9, wherein, Determining the optimization result based on the first optimization result and the second optimization result includes: If each of the input parameter groups includes the normalized values corresponding to the seat back angle, seat cushion angle, seat leg rest angle, and seat cushion stiffness respectively, then denormalize the first optimization result and the second optimization result, and use the denormalized results of the first optimization result and the second optimization result respectively as the optimization result; If each of the input parameter groups includes the input values corresponding to the seat back angle, seat cushion angle, seat leg rest angle, and seat cushion stiffness respectively, then use the first optimization result and the second optimization result as the optimization result.

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