Analysis method of the impact of automobile front-end structure design on collision signal

Abstract

The present invention relates to an analysis method for the influence of automobile front-end structural design on collision signals. During automobile product development, especially the development of modified products, the method provides an effective and reliable judgment basis for determining in advance whether an ACU calibration algorithm needs to be adjusted, offers a solution for situations in which algorithm requirements are not met, and completes reliability verification of the analysis method during a later stage of actual vehicle collision. High-speed dynamic nonlinear implicit finite element calculations are used to obtain 10K high-frequency collision signal data, and the signals under different collision conditions are analyzed and compared to determine whether they meet the margin requirements of the main ignition algorithm. This determines whether the ACU calibration algorithm should be retained or adjusted, and completes reliability verification of the analysis method during a later stage of actual vehicle collision development, thereby avoiding additional development costs and shortening the development cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile collision, in particular to a method for analyzing the influence of automobile front-end structural design on collision signals. Background Art

[0002] The existing technology is to collect collision signals through real-vehicle destructive collision tests in the later stage of vehicle development and compare them with the collision signals of the basic vehicle. If the signal difference is small, there is no need to adjust the calibration algorithm. If the signal difference is large, the calibration algorithm needs to be adjusted and additional sample vehicles and experiments need to be added for re-verification.

[0003] The drawback of existing technologies is that the risks of the impact of changes in the front-end structural design of the car on the collision signal are all accumulated in the later stages of product development, and there is a high risk of additional development costs and extended development cycles. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for analyzing the impact of automobile front-end structural design on collision signals, which shortens the development cycle and reduces development costs.

[0005] The technical solution adopted by the present invention is:

[0006] A method for analyzing the impact of automobile front-end structural design on collision signals.

[0007] Step S10, receiving the front-end structural design data of the modified vehicle model;

[0008] Step S11, performing high-speed dynamic nonlinear implicit finite element calculation on the frontal collision working condition of the modified model, i.e., CAE model analysis and calculation of the collision calibration working condition;

[0009] It is to make all the parts of the vehicle into a digital model, set the boundary conditions for the model, and use the LS-DYNA collision solver to calculate the theoretical values of the vehicle's motion, deformation, speed change curve, acceleration curve and other indicators through numerical calculation;

[0010] Step S12, obtaining 10K high-frequency signal data of collision at key positions of the vehicle body;

[0011] When creating a digital model, define the signals to be output. For example, define a small square below the vehicle's B-pillar to simulate the acceleration sensor in a real experiment. After the high-speed dynamic nonlinear implicit finite element calculation is completed, the 10K acceleration high-frequency signal at this location can be read through the calculation post-processing software.

[0012] Step S13, 10K high frequency signal is converted to 1K or 2K data; because of the current industry technology, the change between each two points of the high frequency signal is too large, such asFigure 4 As shown, the ACU algorithm cannot directly recognize 10K high-frequency signals. The conversion method requires defining the API interface protocol and inputting it into the ACU calibration algorithm for simulation calculation.

[0013] The purpose of inputting the algorithm pool is to select the algorithm that is most likely to process these 1K or 2K signals. First of all, the algorithm will not be programmed from scratch. All algorithms have been gradually iterated through generations of ACU controller products and have been verified on vehicles. Therefore, long-term accumulation will form an algorithm pool. For different collision signals, we need to select the best and safest algorithm from the algorithm pool to ensure the safety of the ACU ignition strategy, such as Figure 5 ;

[0014] Step S14, determining whether the signal under different collision conditions meets the signal misfire algorithm margin (domain) requirement. If the signal misfire requirement is not met, jump to step S16; otherwise, jump to step S15;

[0015] After obtaining relevant frontal collision signals and processing them in the algorithm pool, a matrix similar to Table 1 is generated. As development requirements increase and the number of frontal collision scenarios increases, the matrix becomes even larger. If all primary ignition algorithms have a margin greater than 50%, the requirements are considered met. A margin greater than 50% means that scaling any collision signal curve by 50% will not affect the ignition decision shown in Table 1. As shown in Table 1, if all primary ignition algorithms have a margin greater than 50%, the requirements are considered met.

[0016] Step S15, change the design and jump to step S10;

[0017] Step S16, judging whether the signals under different collision conditions meet the signal ignition algorithm margin (domain) requirement, if the signal ignition requirement is met, jump to step S20, otherwise jump to step S17;

[0018] Step S17, determining whether the cause is the suspension system rupture moment, if so, jump to step S18, otherwise jump to step S19;

[0019] Step S18, change the suspension system design and jump to step S10;

[0020] Step S19, adjust the ACU calibration algorithm and jump to step S21;

[0021] Step S20: Using the ACU hardware and algorithm of the previous generation vehicle model;

[0022] Step S21, verifying the reliability of the calibration algorithm through a real vehicle collision;

[0023] Unreliable means: For example, in a 13km head-on collision, the ACU strategy requires no airbag deployment. This means that in a 13km collision, the airbag is not allowed to deploy, otherwise it will cause harm to the occupants. During real-car collision verification, if the airbag deploys, the algorithm is considered unreliable.

[0024] Step S22, determining whether the signal under the collision condition meets the signal ignition algorithm margin requirement. If it meets the signal ignition requirement, the process ends; otherwise, the process jumps to step S23;

[0025] Step S23: calibrate the new algorithm based on the actual vehicle physical signal, and then jump to step S21.

[0026] Preferably, a finite element model of a car collision is established in a computer, and the boundaries and loads of the model are set according to the actual collision test conditions. The large deformation of the car collision is solved through a numerical calculation solver, and computer graphics processing and data processing technology are used to read the animation and other results of the car collision.

[0027] Preferably, as long as the parameters that need to be calculated and output are set when building the automobile collision finite element model, these data can be directly read in the automobile collision post-processing calculation software. These data mainly refer to the vehicle collision acceleration signal (g), the speed change of the vehicle collision (mm / s), and the deformation of the whole vehicle after the collision (mm).

[0028] These data (10K high-frequency signal data of collision at key positions of the vehicle body) are process data that must be guaranteed when developing automobile collision performance and calibrating the ACU. Without these data, there is no way to analyze whether our development and calibration technology is correct. Taking the acceleration signal as an example, the smaller the acceleration signal, the smaller the damage to the human body.

[0029] The input parameters required for the ACU calibration algorithm are input into the ACU calibration algorithm calculation pool. The purpose is to select the algorithm conditions in the algorithm pool that best match the input parameters and write them into the ACU hardware; to determine whether the signals under different collision conditions meet the main ignition algorithm margin (domain) requirements.

[0030] Only by determining the margin requirements of the main ignition algorithm can we determine whether the currently used ACU calibration algorithm meets the airbag and seatbelt deployment strategy and is suitable for this vehicle.

[0031] The ACU calibration algorithm is retained or adjusted (assuming that these signals meet the margin (domain) requirements of the main ignition algorithm); ultimately, the reliability of the calibration algorithm is verified through actual vehicle crashes.

[0032] Preferably, the front-end structure design scheme data in step S10 includes: front bumper, headlights, fog lights, air intake grille, center grid, LOGO, and center support data.

[0033] The beneficial effects of the present invention compared to the prior art are as follows:

[0034] This method for analyzing the impact of vehicle front-end structural design on crash signals provides an effective and reliable basis for determining in advance whether the ACU calibration algorithm needs adjustment during vehicle product development, especially during the development of modified models. It also offers a solution for situations where the algorithm doesn't meet requirements. The reliability of the analysis method is then verified during the subsequent actual vehicle crash phase.

[0035] The present invention analyzes the impact of automobile front-end structural design on collision signals using high-speed dynamic nonlinear implicit finite element calculations to obtain 10K high-frequency collision signal data. The signals under different collision conditions are analyzed and compared to determine whether they meet the requirements of the main ignition or non-ignition algorithm. This determines whether to retain or adjust the ACU calibration algorithm. The reliability of the analysis method is verified during the actual vehicle collision phase in the later stages of development, minimizing the probability and possibility of additional development costs and extending the development cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Flowchart of the analysis method for the impact of vehicle front-end structural design on collision signals;

[0037] Figure 2 Comparison of the front-end structural design of the base model and the modified model;

[0038] Figure 3 After the front-end structure of the car is modified, the ACU algorithm calibration is prone to errors in the frontal collision condition;

[0039] Figure 4 Collision 10K high frequency signal data;

[0040] Figure 5 Data conversion and computational physics flow chart;

[0041] Figure 6 Calculate the collision deformation area map. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and embodiments:

[0043] like Figure 3 The 15 km RCAR and 40 km ODB are shown, two frontal collision conditions, Figure 3 15 km RCAR (left), 40 km ODB (right).

[0044] ACU algorithm calibration is prone to errors because, after amplifying 50% of the 15km RCAR collision signal, it is easy for it to intersect with the 40km ODB collision signal. Once the signals intersect, the firing algorithm cannot determine whether to deploy the airbags. In fact, industry standards and the market require that the RCAR signal not be deployed at 15km, but that the ODB signal must be deployed to protect the occupants.

[0045] Figure 3 A head-on collision is an example of a frontal collision scenario, where ACU algorithm calibration is most prone to errors. During a collision, the ACU algorithm is unaware of external factors such as the collision speed, direction, and angle; it can only interpret acceleration signals. However, the industry has established guidelines for ACU algorithm processing. For example, at a 15 km / h (RCAR) acceleration, airbag deployment is not performed, while at a 40 km (ODB) acceleration, airbag deployment is required. The acceleration signals for these two collision scenarios are poorly differentiated. Without processing, the ACU will confuse the two signals, leading to control errors and false airbag deployment.

[0046] Figure 6 This explains step S17. During a collision, whether the engine mount breaks significantly influences the vehicle's collision acceleration signal. L2 represents the distance from the vehicle's front end to the engine mount. During the calculation in step S11, it is simultaneously determined whether the collision deformation zone has reached L2. If so, it indicates that the engine mount breakage affected the vehicle's collision acceleration signal during the collision. If the deformation does not reach L2, the influence of S17 can be eliminated. L1 refers to the X-axis distance of the vehicle's front end structure and is used only to indicate its position within the vehicle.

[0047] A physical device is used to convert high frequency signals. The device is a hardware device with computing and processing functions, and defines an API interface protocol, which can read and identify 10K high frequency signals, and convert 10K signals ( Figure 4 ) is converted and output as 1K or 2K signal.

[0048] Attachment Figure 1-6 It can be seen that a method for analyzing the impact of automobile front-end structural design on collision signals is

[0049] Step S10, receiving the front-end structural design data of the modified vehicle model;

[0050] Step S11, performing high-speed dynamic nonlinear implicit finite element calculation on the frontal collision working condition of the modified model, i.e., CAE model analysis and calculation of the collision calibration working condition;

[0051] It is to make all the parts of the vehicle into a digital model, set the boundary conditions for the model, and use the LS-DYNA collision solver to calculate the theoretical values of the vehicle's motion, deformation, speed change curve, acceleration curve and other indicators through numerical calculation;

[0052] Step S12, obtaining 10K high-frequency signal data of collision at key positions of the vehicle body;

[0053] When creating a digital model, define the signals to be output. For example, define a small square below the vehicle's B-pillar to simulate the acceleration sensor in a real experiment. After the high-speed dynamic nonlinear implicit finite element calculation is completed, the 10K acceleration high-frequency signal at this location can be read through the calculation post-processing software.

[0054] Step S13, 10K high frequency signal is converted to 1K or 2K data. Because of the current industry technology, the change between each two points of the high frequency signal is too large, such as Figure 4 As shown, the ACU algorithm cannot directly recognize 10K high-frequency signals. The conversion method requires defining the API interface protocol and inputting it into the ACU calibration algorithm for simulation calculation.

[0055] The purpose of inputting the algorithm pool is to select the algorithm that is most likely to process these 1K or 2K signals. First of all, the algorithm is not written from scratch. All algorithms are gradually iterated through generations of ACU controller products and have been verified on vehicles. Therefore, long-term accumulation will form an algorithm pool. For different collision signals, we need to select the best and safest algorithm from the algorithm pool to ensure the safety of the ACU ignition strategy; Figure 5 ;

[0056] Step S14, determining whether the signal under different collision conditions meets the signal misfire algorithm margin (domain) requirement. If the signal misfire requirement is not met, jump to step S16; otherwise, jump to step S15;

[0057] After obtaining relevant frontal collision signals and processing them in the algorithm pool, a matrix similar to Table 1 is generated. As development requirements increase and the number of frontal collision scenarios increases, the matrix becomes even larger. If all primary ignition algorithms have a margin greater than 50%, the requirements are considered met. A margin greater than 50% means that scaling any collision signal curve by 50% will not affect the ignition decision shown in Table 1. As shown in Table 1, if all primary ignition algorithms have a margin greater than 50%, the requirements are considered met.

[0058] Step S15, change the design and jump to step S10;

[0059] Step S16, judging whether the signals under different collision conditions meet the signal ignition algorithm margin (domain) requirement, if the signal ignition requirement is met, jump to step S20, otherwise jump to step S17;

[0060] Step S17, determining whether the cause is the suspension system rupture moment, if so, jump to step S18, otherwise jump to step S19;

[0061] Step S18, change the suspension system design and jump to step S10;

[0062] Step S19, adjust the ACU calibration algorithm and jump to step S21;

[0063] Step S20: Using the ACU hardware and algorithm of the previous generation vehicle model;

[0064] Step S21, verifying the reliability of the calibration algorithm through a real vehicle collision;

[0065] Unreliable means: For example, in a 13km head-on collision, the ACU strategy requires no airbag deployment. This means that in a 13km collision, the airbag is not allowed to deploy, otherwise it will cause harm to the occupants. During real-car collision verification, if the airbag deploys, the algorithm is considered unreliable.

[0066] Step S22, determining whether the signal under the collision condition meets the signal ignition algorithm margin requirement. If it meets the signal ignition requirement, the process ends; otherwise, the process jumps to step S23;

[0067] Step S23: calibrate the new algorithm based on the actual vehicle physical signal, and then jump to step S21.

[0068] Preferably, a finite element model of a car collision is established in a computer, and the boundaries and loads of the model are set according to the actual collision test conditions. The large deformation of the car collision is solved through a numerical calculation solver, and computer graphics processing and data processing technology are used to read the animation and other results of the car collision.

[0069] Preferably, as long as the parameters that need to be calculated and output are set when building the automobile collision finite element model, these data can be directly read in the automobile collision post-processing calculation software. These data mainly refer to the vehicle collision acceleration signal (g), the speed change of the vehicle collision (mm / s), and the deformation of the whole vehicle after the collision (mm).

[0070] These data (10K high-frequency signal data of collision at key positions of the vehicle body) are process data that must be guaranteed when developing automobile collision performance and calibrating the ACU. Without these data, there is no way to analyze whether our development and calibration technology is correct. Taking the acceleration signal as an example, the smaller the acceleration signal, the smaller the damage to the human body.

[0071] The input parameters required for the ACU calibration algorithm are input into the ACU calibration algorithm calculation pool. The purpose is to select the algorithm conditions in the algorithm pool that best match the input parameters and write them into the ACU hardware; to determine whether the signals under different collision conditions meet the main ignition algorithm margin (domain) requirements.

[0072] Only by determining the margin requirements of the main ignition algorithm can we determine whether the currently used ACU calibration algorithm meets the airbag and seatbelt deployment strategy and is suitable for this vehicle.

[0073] The ACU calibration algorithm is retained or adjusted (assuming that these signals meet the margin (domain) requirements of the main ignition algorithm); ultimately, the reliability of the calibration algorithm is verified through actual vehicle crashes.

[0074] Step S10: The front-end structure design scheme data includes: front bumper, headlights, fog lights, air intake grille, center grille, LOGO, and center support data.

[0075] Table 1: Various collision conditions and ACU algorithm margin requirements for the calibration algorithm:

[0076] ;

[0077] This method for analyzing the impact of vehicle front-end structural design on crash signals provides an effective and reliable basis for determining in advance whether the ACU calibration algorithm needs adjustment during vehicle product development, especially during the development of modified models. It also offers a solution for situations where the algorithm doesn't meet requirements. The reliability of the analysis method is then verified during the subsequent actual vehicle crash phase.

[0078] The present invention analyzes the impact of automobile front-end structural design on collision signals using high-speed dynamic nonlinear implicit finite element calculations to obtain 10K high-frequency collision signal data. The signals under different collision conditions are analyzed and compared to determine whether they meet the requirements of the main ignition or non-ignition algorithm. This determines whether to retain or adjust the ACU calibration algorithm. The reliability of the analysis method is verified during the actual vehicle collision phase in the later stages of development, minimizing the probability and possibility of additional development costs and extending the development cycle.

[0079] The existing ACU calibration technology relies entirely on physical testing and requires two complete rounds of physical collisions. In the first round of collisions, all signals are collected, and the ACU calibration algorithm is processed based on the collected signals. The software program is rewritten in the ACU controller hardware. The updated ACU hardware is installed in the actual vehicle in the second round of collision tests, and the collision test is carried out again. The reliability of the ACU calibration algorithm is determined based on the detonation of the airbags and seat belts.

[0080] The present invention simulates the collision environment in a computer and obtains the collision signal through high-speed dynamic nonlinear implicit finite element numerical calculation. The high-speed dynamic nonlinear implicit finite element numerical calculation replaces the first round of collision test, directly reads the collision acceleration signal in the numerical calculation, and inputs it into the ACU calibration algorithm for processing.

[0081] The above description is only a preferred embodiment of the present invention and does not limit the structure of the present invention in any form. Any modification, equivalent change and modification of the above embodiment based on the technical essence of the present invention shall fall within the scope of the technical solution of the present invention.

Claims

1. A method for analyzing the impact of automobile front-end structural design on collision signals, characterized in that: Step S10, receiving the front-end structural design data of the modified vehicle model; Step S11, performing high-speed dynamic nonlinear implicit finite element calculation on the frontal collision working condition of the modified model, i.e., CAE model analysis and calculation of the collision calibration working condition; Step S12, obtaining 10K high-frequency signal data of collision at key positions of the vehicle body; Step S13, converting the 10K high-frequency signal into 1K or 2K data and inputting it into the ACU calibration algorithm for simulation calculation; Step S14, determining whether the signal under different collision conditions meets the signal misfire algorithm margin requirement, if not, jump to step S16, otherwise jump to step S15; Step S15, change the design and jump to step S10; Step S16, judging whether the signals under different collision conditions meet the signal ignition algorithm margin requirements, if the signal ignition requirements are met, jump to step S20, otherwise jump to step S17; Step S17, determining whether the cause is the suspension system rupture moment, if so, jump to step S18, otherwise jump to step S19; Step S18, change the suspension system design and jump to step S10; Step S19: Adjust the ACU calibration algorithm and jump to step S21; Step S20: Use the ACU hardware and algorithm of the previous generation vehicle model; Step S21, verifying the reliability of the calibration algorithm through a real vehicle collision; Step S22, determining whether the signal under the collision condition meets the signal ignition algorithm margin requirement. If it meets the signal ignition requirement, the process ends; otherwise, the process jumps to step S23; Step S23: calibrate the new algorithm based on the actual vehicle physical signal, and then jump to step S21.

2. The method for analyzing the impact of automobile front-end structural design on collision signals according to claim 1, characterized in that: A finite element model of a car collision is established in the computer. By comparing it with the actual collision test conditions, the model's boundaries and loads are set. The large deformation of the car collision is calculated through a numerical calculation solver, and computer graphics processing and data processing technology are used to read the animated results of the car collision.

3. The method for analyzing the impact of automobile front-end structural design on collision signals according to claim 1, characterized in that: When building the finite element model of a car collision, the parameters that need to be calculated and output are set. The data is directly read in the car collision post-processing calculation software. The data includes: vehicle collision acceleration signal, vehicle collision speed change, and vehicle deformation after the collision.

4. The method for analyzing the impact of automobile front-end structural design on collision signals according to claim 1, characterized in that: Step S10: The front-end structure design scheme data includes: front bumper, headlights, fog lights, air intake grille, center grille, LOGO, and center support data.

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