Vehicle load determination method, device, equipment, storage medium and program product
By combining road spectrum data and iterative calculations of dynamic models, and optimizing stiffness test data and drive data, the problem of determining the accuracy of vehicle load under misuse conditions is solved, achieving higher precision in acquiring suspension system loads and supporting vehicle design and durability verification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHONGQING JINKANG NEW ENERGY VEHICLE CO LTD
- Filing Date
- 2026-04-02
- Publication Date
- 2026-07-24
AI Technical Summary
Existing methods for determining vehicle loads under misuse conditions have low accuracy, making it difficult to accurately obtain the load on the suspension system, which affects the forward design and fatigue durability analysis of the suspension system, brackets, and connectors.
By acquiring road spectrum data, inertial parameters, and drive data of the experimental vehicle, a dynamic model is constructed. Combined with stiffness test data of the suspension sample, iterative calculations are performed to determine the vehicle load. By combining physical road spectrum testing with virtual simulation iteration, the stiffness test data and drive data are optimized to improve accuracy.
It improves the accuracy of vehicle load under misuse conditions, reduces testing costs, avoids dependence on expensive sensors, and can be directly used for product forward design and durability verification. The results are highly consistent with the actual test results, thus improving the reliability of the design scheme.
Smart Images
Figure CN122452018A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, device, storage medium, and program product for determining vehicle load. Background Technology
[0002] During vehicle research and development and design, it is necessary to obtain the loads borne by the powertrain mounting system of the test vehicle under various misuse conditions (such as high-speed bump crossings and impacts from severe road conditions). Obtaining accurate loads is a crucial foundation for the forward design, fatigue durability analysis, and optimization of the vehicle's mounting system, brackets, and connectors.
[0003] However, the methods for determining the load of vehicles under misuse conditions in related technologies suffer from low accuracy. Summary of the Invention
[0004] Based on this, this application addresses the aforementioned technical problems by providing a method, apparatus, device, storage medium, and program product for determining vehicle load, which can improve the accuracy of vehicle load under misuse conditions.
[0005] In a first aspect, this application provides a method for determining vehicle load, comprising:
[0006] The road spectrum data, powertrain inertial parameters and drive data of the experimental vehicle were obtained, as well as the stiffness test data of the suspension sample in the experimental vehicle were obtained.
[0007] A dynamic model of the experimental vehicle is constructed based on the inertial parameters and the stiffness test data;
[0008] The vehicle load is determined by iterative calculations based on the dynamic model, the driving data, and the road spectrum data; the vehicle load is used for vehicle design.
[0009] The aforementioned method for determining vehicle loads involves acquiring road spectrum data, powertrain inertial parameters, and drive data of the experimental vehicle, as well as stiffness test data of the suspension components within the experimental vehicle. A dynamic model of the experimental vehicle is then constructed based on the inertial parameters and stiffness test data. This allows for iterative calculations using the dynamic model, drive data, and road spectrum data to determine the vehicle loads used for vehicle design. Compared to related technologies that rely entirely on dynamic models for simulation to obtain vehicle loads, this application combines real road spectrum data obtained from physical road spectrum testing with virtual simulation iterations for load extraction. Therefore, it can more accurately determine the vehicle loads of the powertrain suspension system under misuse conditions.
[0010] In an optional embodiment of the first aspect, obtaining the stiffness test data of the suspension sample in the experimental vehicle includes:
[0011] The suspension sample was subjected to stiffness testing to obtain the initial stiffness test data of the suspension sample.
[0012] The initial stiffness test data is corrected based on the road spectrum data to obtain the stiffness test data.
[0013] In this embodiment, the stiffness of the suspension sample can be tested first to obtain the initial stiffness test data of the suspension sample. Then, the initial stiffness test data can be corrected according to the road spectrum data. Therefore, the stiffness test data can be accurately obtained through the correction method of iterative simulation.
[0014] In an optional embodiment of the first aspect, the step of correcting the initial stiffness test data based on the road spectrum data to obtain the stiffness test data includes:
[0015] Based on the size information of the suspended sample, a stiffness analysis is performed on the suspended sample to obtain multiple virtual stiffness points;
[0016] The optimization objective is determined based on the road spectrum data;
[0017] The stiffness values of the plurality of virtual stiffness points are iteratively simulated and calculated according to the optimization objective to obtain the optimized values of the plurality of virtual stiffness points;
[0018] The initial stiffness test data is corrected based on the optimized values of the multiple virtual stiffness points to obtain the stiffness test data.
[0019] In this embodiment, the stiffness values of multiple virtual stiffness points can be iteratively simulated and calculated based on road spectrum data. This allows for the comparison and fitting of the tested stiffness values with theoretical values, yielding optimized values for multiple virtual stiffness points. Therefore, the initial stiffness test data is corrected based on the optimized values of these virtual stiffness points. Consequently, the theoretical values of multiple virtual stiffness points (or curve slopes) can be fitted onto the measured curve, ultimately resulting in accurate stiffness test data.
[0020] In an optional embodiment of the first aspect, determining the vehicle load by iterative calculation based on the dynamic model, the driving data, and the road spectrum data includes:
[0021] The driving data is used to drive the dynamic model to obtain response data;
[0022] Determine the difference between the response data and the road spectrum data. If the difference exceeds a preset difference threshold, update the driving data and use the updated driving data as the new driving data. Then return to the step of using the driving data to drive the dynamic model.
[0023] If the difference value does not exceed the preset difference threshold, the suspension component force of the suspension sample is determined based on the driving data, and the vehicle load is determined based on the suspension component force.
[0024] In this embodiment, using road spectrum data as the target, the displacement driving signal can be continuously adjusted through an iterative algorithm, ultimately making the response signal output by the dynamic model (such as key point acceleration) infinitely close to the measured road spectrum data. In this way, the six-component force load of the suspension sample can be accurately derived by iteratively optimizing the driving data.
[0025] In an optional embodiment of the first aspect, the method further includes:
[0026] When the range extender engine of the experimental vehicle is started, the cylinder pressure of the experimental vehicle is obtained.
[0027] The process of using the driving data to drive the dynamic model and obtain response data includes:
[0028] The dynamic model is driven using the driving data and the cylinder pressure to obtain the response data.
[0029] In this embodiment, when the range extender engine of the experimental vehicle starts, the cylinder pressure of the experimental vehicle can be accurately obtained, and the driving dynamics model can be effectively driven in the range extender engine start-up scenario using the driving data and cylinder pressure.
[0030] In an optional embodiment of the first aspect, the method further includes:
[0031] When the experimental vehicle is in four-wheel drive range-extending mode, the half-shaft torque of the experimental vehicle is obtained;
[0032] The process of using the driving data to drive the dynamic model and obtain response data includes:
[0033] The dynamic model is driven using the driving data and the half-shaft torque to obtain the response data.
[0034] In this embodiment, when the test vehicle is in four-wheel drive range-extending mode, the half-shaft torque of the test vehicle can be accurately obtained, and the driving dynamics model can be effectively driven in four-wheel drive range-extending mode using the driving data and half-shaft torque.
[0035] Secondly, this application also provides a vehicle load determining device, comprising:
[0036] The acquisition module is used to acquire road spectrum data, powertrain inertial parameters and drive data of the test vehicle, as well as stiffness test data of the suspension sample in the test vehicle.
[0037] A construction module is used to construct a dynamic model of the experimental vehicle based on the inertial parameters and the stiffness test data;
[0038] The determination module is used to perform iterative calculations based on the dynamic model, the driving data, and the road spectrum data to determine the vehicle load; the vehicle load is used for vehicle design.
[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.
[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.
[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above aspects.
[0042] Regarding the beneficial effects of any of the technical solutions in the second to fifth aspects mentioned above, refer to the beneficial effects of the corresponding technical solutions in the first aspect; repeated examples will not be listed here. Attached Figure Description
[0043] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 This is a schematic flowchart of an optional method for determining vehicle load in one embodiment;
[0045] Figure 2 This is a schematic diagram of an optional structure of the experimental vehicle in one embodiment;
[0046] Figure 3 This is a schematic diagram illustrating one possible location for strain gauge placement in one embodiment.
[0047] Figure 4 This is a schematic diagram of an optional process for obtaining stiffness test data in one embodiment;
[0048] Figure 5 This is an optional comparative diagram of the theoretical and measured values of a nonlinear stiffness curve in one embodiment;
[0049] Figure 6 This is a schematic diagram illustrating one possible location of multiple virtual stiffness points in one embodiment;
[0050] Figure 7 This is a schematic diagram of an optional position of the rubber limiting contact in one embodiment;
[0051] Figure 8 This is a schematic diagram of an optional process for determining vehicle load in one embodiment;
[0052] Figure 9 This is a schematic diagram of an optional structure for driving data in one embodiment;
[0053] Figure 10 This is a schematic flowchart of an optional method for determining vehicle load in another embodiment;
[0054] Figure 11 This is a schematic diagram of an optional structure of a vehicle load determination device in one embodiment;
[0055] Figure 12 This is a schematic diagram of an optional internal structure of a computer device in one embodiment. Detailed Implementation
[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.
[0057] The terms "first," "second," etc., used in this application may be used to describe various elements, but these elements are not limited by these terms. These terms are used only to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.
[0058] During vehicle research and development and design, it is necessary to obtain the loads borne by the powertrain mounting system of the test vehicle under various misuse conditions (such as high-speed bump crossings and impacts from severe road conditions). Obtaining accurate loads is a crucial foundation for the forward design, fatigue durability analysis, and optimization of the vehicle's mounting system, brackets, and connectors.
[0059] Among related technologies, methods for determining vehicle loads under misuse conditions include the following: First, installing six-component / three-component force sensors on the suspension system to directly measure the load. This method is extremely costly, the bulky sensors alter the system's dynamic characteristics, and it is difficult to implement in the space-constrained engine compartment of a range-extended powertrain. Second, indirectly calculating the suspension load by measuring the six-component force at the wheel center and combining it with a whole vehicle model. This method is greatly affected by the accuracy of the whole vehicle model and cannot accurately reflect the contribution of internal powertrain excitations (such as engine combustion impact and motor torque fluctuations) to the suspension load. Third, relying entirely on multibody dynamics models for simulation, resulting in strong model dependence. Because under misuse conditions, suspension components often enter the nonlinear stiffness region (such as the contact hard-limit stage), it is difficult to accurately obtain the dynamic mechanical characteristic parameters of the suspension components, leading to a large deviation between the pure simulation results and the actual load, especially under high-frequency impact conditions. In summary, the above-mentioned methods for determining vehicle loads under misuse conditions suffer from low accuracy.
[0060] In view of this, embodiments of this application propose a method, apparatus, device, storage medium, and program product for determining vehicle load, which can improve the accuracy of vehicle load under misuse conditions.
[0061] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.
[0062] In one embodiment, such as Figure 1 As shown, a method for determining vehicle load is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can be applied to any computer device, and therefore can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0063] S101, acquire road spectrum data, powertrain inertial parameters and drive data of the experimental vehicle, and acquire stiffness test data of the suspension sample in the experimental vehicle.
[0064] Among them, the experimental vehicle is a prototype vehicle used for testing and vehicle design, for example, such as Figure 2 As shown, Figure 2The diagram shows the structure of the experimental vehicle, including the body, subframe, left and right suspension prototypes, tie rod suspension prototypes, and powertrain (or range extender). The powertrain is the core power source of the vehicle and can include the engine and transmission. The suspension prototypes are physical prototype parts created during vehicle development to verify the performance of the engine mounting system; typically, the suspension prototypes connect the powertrain to the body / frame.
[0065] The inertial parameters may include mass, center of mass, and moment of inertia. Road spectrum data includes driving data collected by the prototype vehicle during actual road driving; for example, road spectrum data may include, but is not limited to, acceleration, displacement, and strain. Drive data refers to preset displacement drive signals applied to the powertrain. Stiffness test data may be a nonlinear stiffness curve representing the relationship between suspension force and displacement.
[0066] In this embodiment, the terminal can collect the inertial parameters of the powertrain of the experimental vehicle before testing it, and pre-set the drive data of the powertrain. Furthermore, the measured suspension samples can be pre-installed on the experimental vehicle to ensure that the torque of all connecting bolts is consistent with the design state. Additionally, the following sensors can be arranged on the experimental vehicle: first, three-dimensional acceleration sensors can be arranged on the active and passive sides (including auxiliary tie rods, etc.) of each suspension sample, and at the highest and lowest points of the powertrain; second, at least two displacement sensors can be arranged at each of the highest and lowest points of the powertrain to monitor the overall displacement and attitude of the powertrain; third, such as... Figure 3 As shown, strain gauges can be attached to the stress concentration areas of the vehicle body side and power side brackets of each suspension sample, wherein, Figure 3 The arrow pointing to the strain gauge on the tie rod bracket indicates the strain gauge placement position.
[0067] Therefore, the terminal can test the experimental vehicle and collect road spectrum data according to the test specifications for each misuse condition. During road spectrum collection, each condition is measured at least three times to ensure the validity of the road spectrum data. The methods for obtaining the road spectrum data of the experimental vehicle can include, but are not limited to, the following two: In one optional implementation, the terminal can directly use the road spectrum data as the road spectrum data of the experimental vehicle. In another optional implementation, the terminal can also perform preprocessing such as channel selection and filtering on the road spectrum data, and verify the consistency of the data before and after processing through pseudo-damage calculation. If the pseudo-damage result indicates that the consistency of the data before and after processing is not higher than 99%, the road spectrum data needs to be reprocessed; if the pseudo-damage result indicates that the consistency of the data before and after processing is higher than 99%, the processed road spectrum data can be used as the road spectrum data of the experimental vehicle.
[0068] Furthermore, the terminal can acquire stiffness test data of the suspension samples in the experimental vehicle. The acquisition of stiffness test data for the suspension samples can be achieved in two ways, but are not limited to: In one optional implementation, the terminal can perform a static stiffness test on the suspension samples of the experimental vehicle to obtain a nonlinear stiffness curve, which can then be used as the stiffness test data of the suspension samples. In another optional implementation, the terminal can first perform a static stiffness test on the suspension samples of the experimental vehicle, and then correct the results of the static stiffness test to obtain the stiffness test data of the suspension samples.
[0069] S102, construct the dynamic model of the experimental vehicle based on inertial parameters and stiffness test data.
[0070] In this embodiment, to accurately simulate the impact of structural deformation, the terminal can use finite element software to perform rigid-flexible coupling modeling of structures such as suspension brackets (e.g., active and passive ends), subframes, and related body parts (e.g., suspension towers) based on inertial parameters and stiffness test data, obtaining a modal neutral file (.mnf). This modal neutral file contains core data such as the mass, stiffness, modal shape, and modal coordinates of the flexible body. Therefore, the modal neutral file can be imported into a multibody dynamics simulation platform, and the original rigid body components can be replaced with flexible bodies to construct the dynamic model of the experimental vehicle. The dynamic model can include a multibody dynamics model containing flexible bodies and a powertrain dynamics model.
[0071] S103, based on the dynamic model, driving data and road spectrum data, iterative calculations are performed to determine the vehicle load; the vehicle load is used for vehicle design.
[0072] Vehicle load refers to the load borne by the powertrain mounting system of the test vehicle under various extreme misuse conditions, and is used for vehicle design. For example, vehicle load may include the load of the mounting.
[0073] In this embodiment, the terminal can use driving data to drive the dynamic model, and update and perform virtual iterative calculations on the driving data based on the driving results and road spectrum data, thereby ultimately determining the vehicle load based on the continuously updated driving data.
[0074] The aforementioned method for determining vehicle loads involves acquiring road spectrum data, powertrain inertial parameters, and drive data of the experimental vehicle, as well as stiffness test data of the suspension components within the experimental vehicle. A dynamic model of the experimental vehicle is then constructed based on the inertial parameters and stiffness test data. This allows for iterative calculations using the dynamic model, drive data, and road spectrum data to determine the vehicle loads used for vehicle design. Compared to related technologies that rely entirely on dynamic models for simulation to obtain vehicle loads, this application combines real road spectrum data obtained from physical road spectrum testing with virtual simulation iterations for load extraction. Therefore, it can more accurately determine the vehicle loads of the powertrain suspension system under misuse conditions.
[0075] In one embodiment, such as Figure 4 As shown, a method for obtaining stiffness test data is provided, namely, "obtaining stiffness test data of the suspension sample in the experimental vehicle" in S101 above, including:
[0076] S201, Perform a stiffness test on the suspended sample to obtain the initial stiffness test data of the suspended sample.
[0077] In this embodiment, the terminal can perform static stiffness testing on the suspension sample of the experimental vehicle used in the test process to obtain a nonlinear stiffness curve, which can then be determined as the initial stiffness test data of the suspension sample. The nonlinear stiffness curve is measured as close as possible to the hard limit point. For example, as shown... Figure 5 As shown, Figure 5 This is a schematic diagram comparing the theoretical and measured values of the nonlinear stiffness curve. Furthermore, the damping characteristics of the hard-limit point can also be measured.
[0078] S202, the initial stiffness test data is corrected based on the road spectrum data to obtain the stiffness test data.
[0079] Because the suspended sample has entered a highly nonlinear region (both sides of the curve peak and trough) under misuse conditions, and the initial stiffness test data is usually missing or inaccurate in this region, the results of subsequent iterative calculations based on the initial stiffness test data often fail to meet the iterative accuracy requirements. Therefore, in this embodiment, the terminal can extract at least some stiffness points from the initial stiffness test data of the suspended sample, and perform iterative simulation calculations on the stiffness values of these stiffness points based on road spectrum data to obtain optimized stiffness values for these stiffness points. Thus, the terminal can use these optimized stiffness values to correct the initial stiffness test data, obtaining stiffness test data. The stiffness test data can be a corrected nonlinear stiffness curve.
[0080] In this embodiment, the stiffness of the suspension sample can be tested first to obtain the initial stiffness test data of the suspension sample. Then, the initial stiffness test data can be corrected according to the road spectrum data. Therefore, the stiffness test data can be accurately obtained through the correction method of iterative simulation.
[0081] In one embodiment, a method for obtaining stiffness test data through correction is provided, namely, "correcting the initial stiffness test data based on road spectrum data to obtain stiffness test data" in S202 above, including:
[0082] Based on the size information of the suspended sample, a stiffness analysis was performed on the suspended sample to obtain multiple virtual stiffness points.
[0083] The optimization objective is determined based on the road spectrum data.
[0084] Based on the optimization objective, iterative simulation calculations are performed on the stiffness values of multiple virtual stiffness points to obtain optimized values for these points.
[0085] The initial stiffness test data is corrected based on the optimized values of multiple virtual stiffness points to obtain the stiffness test data.
[0086] In this embodiment, the terminal can perform stiffness analysis on the suspended sample using finite element analysis software based on the sample's size information to obtain multiple virtual stiffness points. For example, as shown... Figure 6 As shown, multiple virtual stiffness points can be Figure 6 The three limiting stiffness points. Among them, combined with Figure 7 As shown, Figure 6 The virtual stiffness point 1 in the figure represents the point where the rubber is fully compressed after the rubber is in contact with the limiting contact. Figure 7 Region a in the diagram is in contact with region d. Figure 6 The virtual stiffness point 2 in the figure represents the point where the rubber is compressed by 1 / 2 after the rubber is in contact with the limiting contact. Figure 7 In the rubber region c, region a is in contact with region b, and the rubber in region c is compressed by 1 / 2. Figure 6 The virtual stiffness point 3 in the figure represents the point where the rubber is compressed by 1 / 3 after the rubber is in contact with the limiting contact. Figure 7 In the diagram, region a is in contact with region b, and the rubber in region c is compressed by 1 / 3.
[0087] Therefore, the terminal can use the stiffness values of multiple virtual stiffness points as optimization variables and the displacement obtained by integrating the acceleration of the active end as the input excitation signal. In addition, the terminal can determine the optimization target based on road spectrum data. For example, it can use measured road spectrum data such as the acceleration of the passive end of the suspension and the strain of the support as the optimization target.
[0088] Furthermore, the terminal can employ multi-objective optimization software from the ISIGHT platform (such as a multi-island genetic algorithm) to iteratively simulate and calculate the stiffness values of multiple virtual stiffness points according to the optimization objective, thereby obtaining optimized values for the multiple virtual stiffness points. In one exemplary implementation, the terminal can use the stiffness values of multiple virtual stiffness points as initial data and input the initial data into an iterative model (such as the Simcode component) for the first round of iterative calculation, obtaining the first round of optimized values for the multiple virtual stiffness points. Thus, the terminal can determine whether the first round of optimized values meets the optimization objective. If the first round of optimized values meets the optimization objective, they can be used as the optimized values for the multiple virtual stiffness points. If the first round of optimized values does not meet the optimization objective, the stiffness values of the multiple virtual stiffness points can be corrected or updated, and the corrected stiffness values can be used as new initial data. The process then returns to the step of inputting the initial data into the iterative model for the first round of iterative calculation, until optimized values for the multiple virtual stiffness points that satisfy the objective function are obtained through iteration.
[0089] Subsequently, the terminal can replace the stiffness values of multiple virtual stiffness points in the initial stiffness test data with optimized values of multiple virtual stiffness points, thereby correcting the initial stiffness test data and obtaining the stiffness test data.
[0090] In this embodiment, the stiffness values of multiple virtual stiffness points can be iteratively simulated and calculated based on road spectrum data. This allows for the comparison and fitting of the tested stiffness values with theoretical values, yielding optimized values for multiple virtual stiffness points. Therefore, the initial stiffness test data is corrected based on the optimized values of these virtual stiffness points. Consequently, the theoretical values of multiple virtual stiffness points (or curve slopes) can be fitted onto the measured curve, ultimately resulting in accurate stiffness test data.
[0091] In one embodiment, such as Figure 8 As shown, a method for determining vehicle load is provided, namely, "determining vehicle load by iterative calculation based on dynamic model, driving data and road spectrum data" in S103 above, including:
[0092] S301 uses a driving data-driven dynamics model to obtain response data.
[0093] For example, in a pendulum-type three-point suspension system, as shown in Table 1 below, three Z-axis, two Y-axis, and three X-axis displacement drive channels can be set in the dynamic model to fully control the rigid body motion of the dynamic model. Wherein, as... Figure 9 As shown, Figure 9The diagram shows schematics in each direction. Simultaneously, if the number of suspension samples increases, the number of drive data (i.e., displacement drive signals) can be increased accordingly. The core principle is that the number of X and Z-axis displacement drives and suspension samples should be consistent, and the number of Y-axis drive data should be consistent with the number of connected objects of the suspension samples, such as the suspension samples being connected to the main beam and subframe.
[0094] Table 1
[0095] Model-driven Expected Channel Monitoring Channel 3 Z-axis displacement drive signals passive end acceleration of suspension and air filter bracket Assembly acceleration at highest and lowest points 3 X-axis displacement drive signals Acceleration of the active end of the suspension and air filter bracket Displacement of the highest and lowest points of the assembly One Y-axis displacement drive signal Suspension bracket with three-way force (optional) Stent strain 3-6 Two torque direct drive signals
[0096] In this embodiment, the terminal can first generate a white noise excitation signal and use it as the initial driving signal for the dynamic model. The simulation outputs the white noise response signal corresponding to the white noise excitation signal, and calculates the transfer function and inverse transfer function of the dynamic system. Thus, the dynamic model and road spectrum data can be imported into commercial virtual iterative software, and displacement driving signals in each direction can be set according to the inverse transfer function of the dynamic system. Furthermore, the dynamic model is driven by the displacement driving signals in each direction, and response data is obtained through simulation. The response data can be the response signal corresponding to the displacement driving signal.
[0097] S302, determine whether the difference between the response data and the road spectrum data exceeds a preset difference threshold. If the difference exceeds the preset difference threshold, proceed to S303; if the difference does not exceed the preset difference threshold, proceed to S304.
[0098] S303, update the driver data, use the updated driver data as the new driver data, and return to execute S301.
[0099] S304, determine the six components of the suspension force of the suspension sample based on the driving data, and determine the vehicle load based on the six components of the suspension force.
[0100] In this embodiment, the terminal can compare the simulated response data with the measured road spectrum data to determine whether the difference between the response data and the road spectrum data exceeds a preset difference threshold. If the difference between the response data and the road spectrum data exceeds the preset difference threshold, the driving data can be updated, and the updated driving data can be used as the new driving data. The updated driving data is then used to drive the dynamics model to obtain new response data, thereby determining whether the difference between the new response data and the road spectrum data exceeds the preset difference threshold. This process is iterated until the response data is infinitely close to the measured road spectrum data.
[0101] If the difference between the response data and the road spectrum data does not exceed a preset difference threshold, iterative convergence can be performed based on the time domain, frequency domain, and pseudo-damage of the response data and the road spectrum data. If the response data at this point does not meet the preset accuracy target in at least one dimension (time domain, frequency domain, and pseudo-damage), the driving data can be updated, and iteration can continue using the updated driving data until the response data meets the preset accuracy target in multiple dimensions (time domain, frequency domain, and pseudo-damage). At this point, the suspension six-component force of the suspension sample can be determined based on the driving data, and this suspension six-component force can be defined as the vehicle load.
[0102] In this embodiment, using road spectrum data as the target, the displacement driving signal can be continuously adjusted through an iterative algorithm, ultimately making the response signal output by the dynamic model (such as key point acceleration) infinitely close to the measured road spectrum data. In this way, the six-component force load of the suspension sample can be accurately derived by iteratively optimizing the driving data.
[0103] In one embodiment, an implementation of the driving dynamics model is provided, namely, the above method further includes:
[0104] The cylinder pressure of the experimental vehicle was obtained when the range extender engine of the experimental vehicle was started.
[0105] Based on this, S301 includes:
[0106] Response data are obtained by using drive data and cylinder pressure, and a drive dynamics model.
[0107] In this embodiment, if the range extender engine of the experimental vehicle starts during the test, the crankshaft angle signal and cylinder pressure of the experimental vehicle under the corresponding operating conditions need to be measured in advance on the test bench. Therefore, if engine fuel replenishment is in operation, a crankshaft connecting rod mechanism can be established in the dynamic model, and the driving data and the measured cylinder pressure can be used to jointly drive the dynamic model to obtain response data. The cylinder pressure is a constant input signal and does not participate in the subsequent iterative loop process.
[0108] In this embodiment, when the range extender engine of the experimental vehicle starts, the cylinder pressure of the experimental vehicle can be accurately obtained, and the driving dynamics model can be effectively driven in the range extender engine start-up scenario using the driving data and cylinder pressure.
[0109] In another embodiment, a method for implementing a driving dynamics model is provided, wherein the above method further includes:
[0110] The half-shaft torque of the test vehicle was obtained when the test vehicle was in four-wheel drive range-extending mode.
[0111] Based on this, S301 includes:
[0112] Response data are obtained by using drive data and half-shaft torque to drive dynamics model.
[0113] In this embodiment, if the experimental vehicle is in four-wheel drive range-extended mode (i.e., the range extender assembly is integrated with the drive motor), torque sensors need to be placed on both sides of the drive shaft of the experimental vehicle to accurately extract the half-shaft torque. Therefore, if the experimental vehicle is in four-wheel drive mode, a half-shaft torque drive signal can be established in the dynamic model. This means that the drive data and the measured half-shaft torque can be used together to drive the dynamic model and obtain response data. The half-shaft torque is a constant input signal and does not participate in subsequent iterative loops.
[0114] In this embodiment, when the test vehicle is in four-wheel drive range-extending mode, the half-shaft torque of the test vehicle can be accurately obtained, and the driving dynamics model can be effectively driven in four-wheel drive range-extending mode using the driving data and half-shaft torque.
[0115] In summary, based on all the above embodiments, this application also provides a method for determining vehicle load, such as... Figure 10 As shown, the method includes:
[0116] S401, acquire road spectrum data, powertrain inertial parameters and drive data of the test vehicle, as well as drive data of the suspension sample in the test vehicle.
[0117] S402, Perform stiffness testing on the suspended sample to obtain the initial stiffness test data of the suspended sample;
[0118] S403, based on the size information of the suspended sample, perform stiffness analysis on the suspended sample to obtain multiple virtual stiffness points;
[0119] S404, the optimization objective is determined based on road spectrum data;
[0120] S405, according to the optimization objective, iterative simulation calculations are performed on the stiffness values of multiple virtual stiffness points to obtain the optimized values of multiple virtual stiffness points;
[0121] S406, The initial stiffness test data is corrected based on the optimized values of multiple virtual stiffness points to obtain stiffness test data;
[0122] S407, a dynamic model of the experimental vehicle was constructed based on inertial parameters and stiffness test data;
[0123] S408 acquires the cylinder pressure of the test vehicle when the range extender engine of the test vehicle starts.
[0124] S409, when the test vehicle is in four-wheel drive range-extending mode, obtains the half-shaft torque of the test vehicle;
[0125] S410 uses drive data, cylinder pressure, and half-shaft torque to create a drive dynamics model and obtain response data.
[0126] S411, determine whether the difference between the response data and the road spectrum data exceeds a preset difference threshold. If the difference exceeds the preset difference threshold, proceed to S412; if the difference does not exceed the preset difference threshold, proceed to S413.
[0127] S412, update the driver data, use the updated driver data as the new driver data, and return to execute S410.
[0128] S413, determine the six components of the suspension force of the suspension sample based on the driving data, and determine the vehicle load based on the six components of the suspension force.
[0129] Based on the above embodiments, the embodiments of this application can achieve at least the following beneficial effects: First, it can combine the real road spectrum data obtained from physical road spectrum testing with the virtual simulation iteration method for load extraction, thus more accurately obtaining the vehicle load of the powertrain suspension system of the experimental vehicle under misuse conditions. Second, it can perform parameter inversion optimization for the hard limit region with the strongest nonlinearity, greatly improving the extraction accuracy of suspension load under misuse conditions. Third, it avoids the use of expensive and bulky six-component force sensors, and adopts common acceleration, displacement, and torque sensors, reducing testing costs and implementation difficulty. Fourth, it does not rely on the difficult-to-obtain wheel center six-component force, but directly targets the suspension system, and the results can be directly used for product forward design and durability verification, making it highly practical. Fifth, it solves the simulation bottleneck caused by the difficulty in accurately measuring the stiffness of the suspension hard limit region, and effectively handles strong nonlinear problems through the approach of "co-simulation + parameter inversion".
[0130] Furthermore, the method of this application embodiment has been verified through specific vehicle model projects. The results show that after the suspension load extracted by the above method is used for durability simulation, the predicted fatigue damage results are highly consistent with the actual test results, which significantly improves the reliability of the design scheme and the first-time success rate.
[0131] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.
[0132] Based on the same inventive concept, this application also provides a vehicle load determining apparatus for implementing the vehicle load determining method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more vehicle load determining apparatus embodiments provided below can be found in the limitations of the vehicle load determining method described above, and will not be repeated here.
[0133] In one exemplary embodiment, such as Figure 11 As shown, a vehicle load determination device is provided, comprising: an acquisition module 501, a construction module 502, and a determination module 503, wherein:
[0134] The acquisition module 501 is used to acquire road spectrum data, powertrain inertial parameters and drive data of the test vehicle, as well as stiffness test data of the suspension sample in the test vehicle.
[0135] Module 502 is used to construct a dynamic model of the experimental vehicle based on inertial parameters and stiffness test data.
[0136] The determination module 503 is used to perform iterative calculations based on the dynamic model, driving data, and road spectrum data to determine the vehicle load; the vehicle load is used for vehicle design.
[0137] In one embodiment, the acquisition module 501 includes:
[0138] The stiffness testing unit is used to perform stiffness tests on the suspended sample and obtain the initial stiffness test data of the suspended sample.
[0139] The correction unit is used to correct the initial stiffness test data based on the road spectrum data to obtain the stiffness test data.
[0140] In one embodiment, the correction unit is specifically used for:
[0141] Based on the size information of the suspended sample, a stiffness analysis was performed on the suspended sample to obtain multiple virtual stiffness points;
[0142] Determine the optimization objective based on road spectrum data;
[0143] Based on the optimization objective, the stiffness values of multiple virtual stiffness points are iteratively simulated and calculated to obtain the optimized values of multiple virtual stiffness points;
[0144] The initial stiffness test data is corrected based on the optimized values of multiple virtual stiffness points to obtain the stiffness test data.
[0145] In one embodiment, the determining module 503 includes:
[0146] The driving unit is used to drive the dynamic model with driving data to obtain response data;
[0147] The update unit is used to determine the difference between the response data and the road spectrum data. If the difference exceeds the preset difference threshold, the driving data is updated and the updated driving data is used as the new driving data. The unit then returns to the step of using the driving data to drive the dynamic model.
[0148] The determination unit is used to determine the suspension six-component force of the suspension sample based on the driving data if the difference value does not exceed the preset difference threshold, and to determine the vehicle load based on the suspension six-component force.
[0149] In one embodiment, the vehicle load determining device further includes:
[0150] The first acquisition module is used to acquire the cylinder pressure of the test vehicle when the range extender engine of the test vehicle is started.
[0151] The drive unit includes:
[0152] The first drive subunit is used to obtain response data by employing drive data and cylinder pressure, and driving dynamics model.
[0153] In one embodiment, the vehicle load determining device further includes:
[0154] The second acquisition module is used to acquire the half-shaft torque of the test vehicle when the test vehicle is in four-wheel drive range-extending mode.
[0155] The drive unit includes:
[0156] The second drive subunit is used to obtain response data by employing drive data and half-shaft torque to drive the dynamics model.
[0157] Each module in the aforementioned vehicle load determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.
[0158] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for determining vehicle load. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0159] Those skilled in the art will understand that Figure 12 The structure shown is a block diagram of a partial structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0160] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0161] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.
[0162] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program mentioned can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0165] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method for determining vehicle load, characterized in that, The method includes: The road spectrum data, powertrain inertial parameters and drive data of the experimental vehicle were obtained, as well as the stiffness test data of the suspension sample in the experimental vehicle were obtained. A dynamic model of the experimental vehicle is constructed based on the inertial parameters and the stiffness test data; The vehicle load is determined by iterative calculations based on the dynamic model, the driving data, and the road spectrum data; the vehicle load is used for vehicle design.
2. The method according to claim 1, characterized in that, The acquisition of stiffness test data for the suspension samples in the experimental vehicle includes: The suspension sample was subjected to stiffness testing to obtain the initial stiffness test data of the suspension sample. The initial stiffness test data is corrected based on the road spectrum data to obtain the stiffness test data.
3. The method according to claim 2, characterized in that, The step of correcting the initial stiffness test data based on the road spectrum data to obtain the stiffness test data includes: Based on the size information of the suspended sample, a stiffness analysis is performed on the suspended sample to obtain multiple virtual stiffness points; The optimization objective is determined based on the road spectrum data; The stiffness values of the plurality of virtual stiffness points are iteratively simulated and calculated according to the optimization objective to obtain the optimized values of the plurality of virtual stiffness points; The initial stiffness test data is corrected based on the optimized values of the multiple virtual stiffness points to obtain the stiffness test data.
4. The method according to any one of claims 1-3, characterized in that, The step of determining the vehicle load through iterative calculations based on the dynamic model, the driving data, and the road spectrum data includes: The driving data is used to drive the dynamic model to obtain response data; Determine the difference between the response data and the road spectrum data. If the difference exceeds a preset difference threshold, update the driving data and use the updated driving data as the new driving data. Then return to the step of using the driving data to drive the dynamic model. If the difference value does not exceed the preset difference threshold, the suspension component force of the suspension sample is determined based on the driving data, and the vehicle load is determined based on the suspension component force.
5. The method according to claim 4, characterized in that, The method further includes: When the range extender engine of the experimental vehicle is started, the cylinder pressure of the experimental vehicle is obtained. The process of using the driving data to drive the dynamic model and obtain response data includes: The dynamic model is driven using the driving data and the cylinder pressure to obtain the response data.
6. The method according to claim 4, characterized in that, The method further includes: When the experimental vehicle is in four-wheel drive range-extending mode, the half-shaft torque of the experimental vehicle is obtained; The process of using the driving data to drive the dynamic model and obtain response data includes: The dynamic model is driven using the driving data and the half-shaft torque to obtain the response data.
7. A device for determining vehicle load, characterized in that, The device includes: The acquisition module is used to acquire road spectrum data, powertrain inertial parameters and drive data of the test vehicle, as well as stiffness test data of the suspension sample in the test vehicle. A construction module is used to construct a dynamic model of the experimental vehicle based on the inertial parameters and the stiffness test data; The determination module is used to perform iterative calculations based on the dynamic model, the driving data, and the road spectrum data to determine the vehicle load; the vehicle load is used for vehicle design.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.