Suspension optimization method, device and equipment

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses a suspension optimization method, device and equipment, and the method comprises the steps: obtaining preset parameters of all parts in a target suspension, and carrying out the modeling of the target suspension based on the preset parameters, and obtaining an initial suspension model; respectively calculating the sensitive degree of each structure parameter in the target suspension relative to the test fitting curve, and determining a to-be-calibrated parameter of which the sensitive degree reaches a preset condition from each structure parameter; on the basis of the difference between the test fitting curve and the simulation fitting curve, the to-be-calibrated parameters are calibrated, and a calibrated suspension model is obtained; and optimizing the calibrated suspension model by taking the target optimization value of the target optimization object as an optimization target to obtain an optimized suspension model. Thus, optimization is performed on the basis of the calibrated suspension model to obtain the optimized suspension model of the target suspension, so that optimization of the target suspension better conforms to the actual situation of the target suspension, and the optimization effect of the target suspension is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of vehicle technology, and specifically to a suspension optimization method, device and equipment. Background Art

[0002] The suspension system not only affects the driving comfort and stability of the car, but also directly affects the vehicle's handling performance. At present, the parameters of the suspension system are usually optimized and adjusted to achieve the vehicle's handling stability; however, the parameters of the suspension system are numerous and complex, and often affect each other, resulting in complex optimization of the suspension system, making it difficult to obtain the optimization value that meets actual needs, and the optimization effect is poor. Summary of the invention

[0003] In view of the above problems, embodiments of the present invention provide a suspension optimization method, device and apparatus, which are used to solve the problem of poor optimization effect on the suspension system in the prior art.

[0004] According to one aspect of an embodiment of the present invention, a suspension optimization method is provided, the method comprising:

[0005] Obtaining preset parameters of various components in the target suspension, and modeling the target suspension based on the preset parameters to obtain an initial suspension model;

[0006] The sensitivity of each structural parameter in the target suspension relative to the test fitting curve is calculated respectively, and based on the sensitivity, the parameters to be calibrated whose sensitivity reaches the preset conditions are determined from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameters on the test fitting curve;

[0007] Based on the difference between the test fitting curve and the simulation fitting curve, the parameters to be calibrated are calibrated to calibrate the initial suspension model to obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model;

[0008] The target optimization object and the target optimization value of the target optimization object are obtained, and the target optimization value of the target optimization object is used as the optimization target to optimize the calibrated suspension model to obtain the optimized suspension model.

[0009] According to another aspect of an embodiment of the present invention, there is provided a suspension optimization device, the device comprising:

[0010] A suspension model building module is used to obtain preset parameters of various components in the target suspension, and to model the target suspension based on the preset parameters to obtain an initial suspension model;

[0011] The module for constructing parameters to be calibrated is used to calculate the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determine the parameters to be calibrated whose sensitivity reaches the preset conditions from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameters on the test fitting curve;

[0012] The suspension model calibration module is used to calibrate the parameters to be calibrated based on the difference between the test fitting curve and the simulation fitting curve, so as to calibrate the initial suspension model and obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model;

[0013] The suspension model optimization module is used to obtain the target optimization object and the target optimization value of the target optimization object, and optimize the calibrated suspension model with the target optimization value of the target optimization object as the optimization target to obtain the optimized suspension model.

[0014] According to another aspect of an embodiment of the present invention, there is provided a computer device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0015] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute any of the suspension optimization methods or operations of the suspension optimization method as described above.

[0016] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, in which at least one executable instruction is stored. When the executable instruction is executed on a suspension optimization device / equipment, the suspension optimization device / equipment performs any operation of the suspension optimization method described above.

[0017] The embodiment of the present invention models the target suspension based on preset parameters to obtain an initial suspension model, and calibrates the initial suspension model to obtain a calibrated suspension model, so that the calibrated suspension model is more consistent with the actual data of the target suspension. At the same time, optimization is performed on the basis of the calibrated suspension model to obtain an optimized suspension model of the target suspension, so that the optimization of the target suspension is more consistent with the actual situation of the target suspension, thereby improving the optimization effect of the target suspension.

[0018] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0020] Figure 1 is a flow chart of a suspension optimization method provided by an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a suspension structure in a suspension optimization method provided by an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of K&C characteristic characterization information in a suspension optimization method provided by an embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of a flow chart of calculating sensitivity in a suspension optimization method provided by an embodiment of the present invention;

[0024] FIG5( a ) is a schematic diagram showing the sensitivity of various structural parameters to roll and camber;

[0025] FIG5( b ) is a schematic diagram showing the sensitivity of various structural parameters to the roll center height;

[0026] FIG5( c ) is a schematic diagram showing the sensitivity of various structural parameters to the roll toe;

[0027] FIG5( d ) is a schematic diagram showing the sensitivity of various structural parameters to the lateral force camber;

[0028] FIG5( e ) is a schematic diagram showing the sensitivity of various structural parameters to the lateral force toe-in;

[0029] FIG5( f ) is a schematic diagram showing the sensitivity of various structural parameters to the lateral track;

[0030] FIG5( g ) is a schematic diagram showing the sensitivity of various structural parameters to the longitudinal force camber;

[0031] Figure 5(h) is a schematic diagram showing the sensitivity of various structural parameters to the longitudinal force toe-in;

[0032] FIG5(i) is a schematic diagram showing the sensitivity of various structural parameters to the long wheelbase;

[0033] Figure 6 It is a schematic diagram of a flow chart of calibrating an initial suspension model to obtain a calibrated suspension model in a suspension optimization method provided by an embodiment of the present invention;

[0034] FIG. 7( a ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the camber angle of the front left suspension in an embodiment of the present invention;

[0035] FIG7( b ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the toe angle of the front left suspension in an embodiment of the present invention;

[0036] FIG7( c ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the longitudinal displacement of the front left suspension in an embodiment of the present invention;

[0037] FIG7( d ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left toe-in according to an embodiment of the present invention;

[0038] FIG7( e ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the vertical stiffness of the front left suspension in an embodiment of the present invention;

[0039] FIG7( f) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension toe angle in an embodiment of the present invention;

[0040] FIG7( g) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension toe angle in an embodiment of the present invention;

[0041] FIG7(h) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension in an embodiment of the present invention;

[0042] FIG8( a ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension camber angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0043] FIG8( b ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0044] FIG8( c ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the longitudinal displacement of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0045] FIG8( d ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left toe-in after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0046] FIG8( e ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the vertical stiffness of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0047] FIG8( f ) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0048] FIG8(g) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0049] FIG8(h) is a schematic diagram of a test fitting curve and a simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention;

[0050] Fig. 9 is a schematic diagram of a flow chart of optimizing a calibrated suspension model in a suspension optimization method provided by an embodiment of the present invention;

[0051] Fig.10 is an input and output schematic diagram of optimizing an optimization function in an embodiment of the present invention;

[0052] Fig.11 is a comparison diagram of the front left toe-in before and after optimization in an embodiment of the present invention;

[0053] Fig.12 is a comparison diagram of the Ackerman angle error before and after optimization in an embodiment of the present invention;

[0054] Fig.13 is a schematic structural diagram of an embodiment of a suspension optimization device provided by an embodiment of the present invention;

[0055] Fig.14 It is a schematic diagram of the structure of an embodiment of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0057] The suspension system not only affects the driving comfort and stability of the car, but also directly affects the vehicle's handling performance. At present, the parameters of the suspension system are usually optimized and adjusted to achieve the vehicle's handling stability; however, the parameters of the suspension system are numerous and complex, and often affect each other, making the optimization of the suspension system complex and difficult to achieve, and it is difficult to obtain the optimization value that meets actual needs, resulting in poor optimization effect.

[0058] Based on this, an embodiment of the present invention provides a suspension optimization method, which includes: obtaining preset parameters of each component in a target suspension, and modeling the target suspension based on the preset parameters to obtain an initial suspension model; respectively calculating the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determining the parameters to be calibrated whose sensitivity reaches the preset conditions from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameters on the test fitting curve; based on the difference between the test fitting curve and the simulation fitting curve, calibrating the parameters to be calibrated to calibrate the initial suspension model to obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model; obtaining the target optimization object and the target optimization value of the target optimization object, and optimizing the calibrated suspension model with the target optimization value of the target optimization object as the optimization target to obtain an optimized suspension model. In this way, the target suspension is modeled based on preset parameters to obtain an initial suspension model, and the initial suspension model is calibrated to obtain a calibrated suspension model, so that the calibrated suspension model is more consistent with the actual data of the target suspension. At the same time, the optimized suspension model of the target suspension is obtained by optimizing the calibrated suspension model, so that the optimization of the target suspension is more consistent with the actual situation of the target suspension, thereby improving the optimization effect of the target suspension.

[0059] A specific embodiment of a suspension optimization method of the present invention is described below. Figure 1 It is a flowchart of a suspension optimization method provided by an embodiment of the present invention. This specification provides method operation steps such as the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in the order shown in the embodiment or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 1 As shown, the method may include:

[0060] Step 110: Obtain preset parameters of various components in the target suspension, and model the target suspension based on the preset parameters to obtain an initial suspension model.

[0061] In the embodiments of the present invention, the suspension refers to a system in a vehicle or mechanical equipment for supporting the weight of the vehicle body and absorbing impact and vibration from the road surface. During the design and production process of the vehicle, it is necessary to adjust the suspension parameters to optimize the characteristic curve of the suspension so that the suspension can meet the requirements of vehicle operation stability. The target suspension is a suspension that needs to be adjusted and optimized in parameters. The various components in the target suspension include bushings, stabilizer bars, springs and other components of the target suspension. Figure 2 is a schematic diagram of a suspension structure in a suspension optimization method provided by an embodiment of the present invention, Figure 2 The standard model of the McPherson suspension structure is shown in , and on the basis of this model, the target suspension is modeled by setting the preset parameters of each component to obtain the initial suspension model. The preset parameters may include relevant design parameters of the whole vehicle, such as the whole vehicle digital model, the whole vehicle mass, etc., and the main design parameters of the target suspension, such as the hard point coordinates of the suspension, the stiffness characteristics of the spring in the suspension, the FV characteristics of the shock absorber, the stiffness characteristics of the lateral stabilizer bar, and the stiffness characteristics and installation direction of the rubber bushing, etc. Figure 2 The suspension structure shown in is only an example. In the actual optimization process, the corresponding type of suspension structure model can be used for modeling according to the specific structure of the target suspension, and no specific limitation is made here.

[0062] In an optional implementation, the target suspension may be modeled using ADAMS software.

[0063] Step 120: Calculate the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determine the parameters to be calibrated whose sensitivity meets the preset conditions from among the structural parameters.

[0064] In the embodiment of the present invention, the structural parameters are design parameters of each component or structure in the target suspension, that is, specific parameter values ​​of each structure in the target suspension, such as wheel center position, shock absorber installation position, spring installation position, etc. Since the characteristic curve involves many structural parameters, and these structural parameters also affect each other, it is necessary to analyze and calculate the sensitivity of each structural parameter to the characteristic curve, that is, each curve in the test fitting curve, so as to determine the parameters to be calibrated from the structural parameters according to the sensitivity.

[0065] In the embodiment of the present invention, the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension, and the test fitting curve can reflect the actual characteristics of the target suspension. The sensitivity is used to characterize the influence of the structural parameters on the test fitting curve; the sensitivity can be divided into positive sensitivity and negative sensitivity, the positive sensitivity indicates that the structural parameter has a positive influence on the curve, and the negative sensitivity indicates that the structural parameter has a negative influence on the curve; the larger the value of the sensitivity, the stronger the ability of the structural parameter to influence the curve. In other words, the sensitivity is a positive value, and the larger the positive value, the stronger the ability of the structural parameter to positively influence the curve; the sensitivity is a negative value, and the larger the negative value, the stronger the ability of the structural parameter to negatively influence the curve.

[0066] In an embodiment of the present invention, the preset condition may be that the sensitivity is negative sensitivity, and the value of the sensitivity among the sensitivities of various structural parameters is the largest or greater than a preset value, that is, the structural parameter with the strongest negative impact capability is determined as the parameter to be calibrated.

[0067] Step 130: Based on the difference between the test fitting curve and the simulation fitting curve, the parameters to be calibrated are calibrated to calibrate the initial suspension model to obtain a calibrated suspension model.

[0068] In the embodiment of the present invention, the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model, and the simulation fitting curve is a reflection of the characteristics of the initial suspension model. Since there may be errors or insufficient measurement accuracy in the measurement of the data of the initial suspension model, such as errors in the measurement of the inner point position of the steering rod, insufficient measurement accuracy of the bushing installation direction, etc., and there may be inconsistencies between the state of the initial suspension model and the actual state of the target suspension, there may also be individual differences in the measurement of the stiffness value of the bushing, and the rod flexibility is not considered in the modeling process, etc., the initial suspension model cannot fully fit the actual situation of the target suspension, so it is necessary to calibrate the initial suspension model based on the simulation fitting curve and the test fitting curve, calibrate and adjust the parameters to be calibrated, and obtain a calibrated suspension model so that the suspension model is more in line with the actual situation of the target suspension.

[0069] In the embodiment of the present invention, the characteristic curve of the target suspension is a K&C (Kinematics and Compliance) characteristic curve. Figure 3 is a schematic diagram of K&C characteristic characterization information in a suspension optimization method provided by an embodiment of the present invention, such as Figure 3As shown, the K&C characteristics include suspension K (Kinematics) characteristics, suspension C (Compliance) characteristics, and steering characteristics. Among them, the K characteristic characterizes the kinematic characteristics of the suspension system, which is used to describe the geometric trajectory and change law of the wheel during the movement of the suspension system, including the parallel wheel jump and the roll vertical force of the wheel. The parallel wheel jump specifically includes vertical stiffness, longitudinal displacement of the horizontal wheel center, lateral displacement of the horizontal wheel center, horizontal jump toe, horizontal jump camber and horizontal jump caster, etc. The roll vertical force specifically includes roll toe, roll camber, roll stiffness and roll center height, etc.; the C characteristic characterizes the compliance characteristics of the suspension system, which is used to describe the suspension system's ability to absorb road unevenness and vehicle handling stability, including the lateral force loading and longitudinal force loading of the wheel. The lateral force loading specifically includes lateral force toe, lateral force camber and lateral force flexibility, etc. The longitudinal force loading specifically includes longitudinal force toe, longitudinal force camber and longitudinal force flexibility, etc.; the steering characteristics include Ackerman angle and steering transmission ratio. In other words, the characteristic curve includes multiple curves that represent different information respectively.

[0070] In the embodiment of the present invention, based on the difference between the test fitting curve and the simulation fitting curve, the calibration parameters are adjusted to make the simulation fitting curve fit the test fitting curve as closely as possible, so that the calibrated suspension model obtained by calibration is more consistent with the actual situation of the target suspension.

[0071] Step 140: Obtain a target optimization object and a target optimization value of the target optimization object, and optimize the calibrated suspension model with the target optimization value of the target optimization object as the optimization target to obtain an optimized suspension model.

[0072] In the embodiment of the present invention, the target optimization object and the target optimization value of the target optimization object can be determined according to the actual requirements of the target suspension and the relevant data distribution of the K&C characteristics of the calibrated suspension model, and no specific restrictions are made here; for example, since the positive value of the roll toe in the K characteristic will increase the oversteering tendency of the vehicle and have an adverse effect on the operational stability of the whole vehicle, the roll toe is set as the target optimization object, and the roll stiffness in the K characteristic is decomposed according to the roll gradient in the whole vehicle operational stability test to obtain its target value, so the roll stiffness is set as the target optimization object, and its target value is set to its corresponding target optimization value. Based on the set target optimization object and target optimization value, the calibrated suspension model is optimized so that the target optimization object can reach the target optimization value, thereby obtaining an optimized suspension model.

[0073] In an optional embodiment, Figure 4 FIG. 1 is a flow chart of calculating sensitivity in a suspension optimization method provided by an embodiment of the present invention. Figure 4As shown, the above step 120: respectively calculating the sensitivity of each structural parameter in the target suspension relative to the test fitting curve may include the following steps:

[0074] Step 410: constructing a sensitivity model corresponding to the structural parameters based on the experimental fitting curve and the structural parameters.

[0075] In the embodiment of the present invention, a sensitivity model corresponding to the structural parameters is constructed based on the differential of the experimental fitting curve and the structural parameters. The mathematical function corresponding to the sensitivity model can be shown as the following formula (1):

[0076]

[0077] Among them, S is the sensitivity, f(X) is the value of the characteristic curve, and X i is the parameter value of the structural parameter, Represents differential.

[0078] Step 420: Perform mathematical analysis on the sensitivity model to obtain the sensitivity corresponding to each structural parameter.

[0079] In an embodiment of the present invention, after constructing the sensitivity model, the parameter values ​​of each structural parameter are adjusted, and the characteristic curve is simulated based on the adjusted parameter values ​​to obtain multiple simulation fitting curves, which are input into the sensitivity model and mathematically analyzed in combination with a two-factor orthogonal test algorithm to obtain the sensitivity corresponding to each structural parameter.

[0080] The simulation curve data corresponding to each structural parameter is obtained, and based on the simulation curve data and the preset test method, the sensitivity model is analyzed to obtain the sensitivity corresponding to the structural parameter. The simulation curve data is used to characterize the simulation fitting curve corresponding to different parameter values ​​of the structural parameter, that is, the parameter value used by the structural parameter in the suspension model is adjusted to obtain the simulation fitting curve corresponding to different parameter values, and the sensitivity model is analyzed based on the changes in the simulation fitting curve corresponding to different parameter values ​​to obtain the sensitivity.

[0081] In an optional implementation, the mathematical analysis of the sensitivity model can be as follows:

[0082] Step a1: adjusting the parameter values ​​of various structural parameters, and simulating the characteristic curve of the target suspension based on the adjusted parameter values ​​to obtain simulation fitting curves corresponding to multiple parameter values.

[0083] Step a2: Discretize the simulation fitting curves corresponding to multiple parameter values ​​to obtain simulation data corresponding to each structural parameter.

[0084] In step a2, in order to facilitate the software to analyze the sensitivity model with two-factor orthogonal experimental design, the simulation fitting curve is discretized to obtain simulation data. For example, for the hard point coordinates, the data at ±5mm of the original coordinates are selected; for the bushing stiffness, the data at 0.5 times and 2 times the original stiffness are selected; for the bushing installation direction, the Euler angle is used to represent the data at ±20deg of the original bushing installation direction.

[0085] Step a3: Input the simulation data into the sensitivity model, and analyze the sensitivity model based on a two-factor orthogonal test algorithm to obtain the sensitivity corresponding to the structural parameters.

[0086] In step a3, since the characteristic curve involves multiple parameters, for example, for the K characteristic curve, all the curves are characteristic curves of a certain position and posture parameter of the wheel with the change of wheel jump height, rather than characteristic curves associated with a certain structural parameter. The change of structural parameters will affect the trend of the characteristic curve. Therefore, when analyzing the sensitivity model of each curve, it not only involves structural parameters, but also involves parameters originally associated with it. For the K characteristic curve, the parameter is wheel jump height, and for the C characteristic curve, the parameter is longitudinal force or lateral force. Based on this, when analyzing the sensitivity model, a two-factor orthogonal experimental design is adopted, and data analysis is performed based on ISIGHT software to obtain the sensitivity of each structural parameter corresponding to different curves.

[0087] In an optional implementation, the structural parameters of the target suspension may include: front wheel center position (hpl_wheel_center), shock absorber lower mounting point position (hpl_strut_lwr_mount), shock absorber upper mounting point position (hpl_top_mount), spring upper mounting point position (hpl_spring_upper), spring lower mounting point position (hpl_spring_lower), control arm outer point position (hpl_lca_outer), control arm rear point position (hpl_lca_rear), control arm front point position (hpl_lca_front), steering rod outer point position (hpl_tierod_outer), steering rod inner point position (hpl_tierod_inner ), bushing stiffness coefficient at the connection between the anti-roll bar and the subframe (factor_anti), bushing stiffness coefficient at the connection between the upper mounting point of the shock absorber and the vehicle body (factor_top_mount), bushing stiffness coefficient at the connection between the front point of the control arm and the subframe (factor_lca_front), bushing stiffness coefficient at the connection between the rear point of the control arm and the subframe (factor_lca_rear), bushing installation direction at the connection between the front point of the control arm and the subframe (euler_lca_front), bushing installation direction at the connection between the rear point of the control arm and the subframe (euler_lca_rear), installation direction at the connection between the upper mounting point of the shock absorber and the vehicle body (euler_top_mount), and anti-roll bar radius (anti_roll_radius), etc. Among them, the above structural parameters can be decomposed in various directions to further determine the influence of the structural parameters on the characteristic curve of the target suspension in various directions. For example, the front point position of the control arm (hpl_lca_front) can be decomposed in the x, y, and z directions to obtain hpl_lca_front_x, hpl_lca_front_y, and hpl_lca_front_z.

[0088] In an optional implementation, a mathematical analysis is performed on the sensitivity model to obtain the sensitivity of each structural parameter relative to different characteristic curves. Figure 5(a)-Figure 5(i)The sensitivity of each structural parameter relative to some characteristic curves is shown. Among them, FIG5(a) is a schematic diagram of the sensitivity of each structural parameter relative to roll camber. As shown in FIG5(a), black represents positive sensitivity and white represents negative sensitivity. FIG5(a) shows that the position of the mounting point on the shock absorber in the x direction (hpl_top_mount_fx) and the position of the mounting point on the shock absorber in the y direction (hpl_strut_lwr_mount_y) have the strongest positive influence on roll camber, and the position of the mounting point on the shock absorber in the y direction (hpl_top_mount_y) has the strongest negative influence on roll camber.

[0089] Figure 5(b) is a schematic diagram of the sensitivity of various structural parameters to the roll center height. Figure 5(b) shows that the radius of the lateral stabilizer bar (anti_roll_radius) has the strongest positive influence on the roll center height (roll_M), and the installation position of the bushing on the stabilizer bar in the z direction (hpl_arb_bushing_z) has the strongest negative influence on the roll center height (roll_M).

[0090] Figure 5(c) is a schematic diagram of the sensitivity of various structural parameters to roll toe. Figure 5(c) shows that the position of the outer point of the steering rod in the z direction (hpl_tierod_outer_z) has the strongest positive influence on the roll toe (roll_toe), and the position of the inner point of the steering rod in the z direction (hpl_tierod_inner_z) has the strongest negative influence on the roll toe (roll_toe).

[0091] Figure 5(d) is a schematic diagram of the sensitivity of various structural parameters to the lateral force camber. Figure 5(d) shows that the strong influence on the lateral force camber (lat_camber) is a positive influence. Among them, the position of the mounting point in the x direction on the shock absorber (hpl_top_mount_fx) has the strongest positive influence on the lateral force camber (lat_camber).

[0092] Figure 5(e) is a schematic diagram of the sensitivity of various structural parameters to the lateral force toe-in. Figure 5(e) shows that the coefficient of the steering rack in the z direction (factor_rack_fz) has the strongest positive influence on the lateral force toe-in (lat_toe), and the position of the front point of the control arm in the z direction (hpl_lca_front_fz) has the strongest negative influence on the lateral force toe-in (lat_toe).

[0093] Figure 5(f) is a schematic diagram of the sensitivity of various structural parameters to the lateral track. Figure 5(f) shows that the front point position of the control arm (hpl_lca_front) has the strongest positive influence on the lateral track (lat_wheel_track), and the stiffness coefficient of the bushing in the z direction at the connection between the anti-roll bar and the subframe (factor_anti_fz) has the strongest negative influence on the lateral track (lat_wheel_track).

[0094] Figure 5(g) is a schematic diagram of the sensitivity of various structural parameters to the longitudinal force camber. Figure 5(g) shows that the strong influence on the longitudinal force camber (long_camber) is a negative influence. Among them, the stiffness coefficient of the bushing in the x direction at the connection between the rear point of the control arm and the subframe (factor_lca_rear_fx) has the strongest negative influence on the longitudinal force camber (long_camber).

[0095] Figure 5(h) is a schematic diagram of the sensitivity of various structural parameters to the longitudinal toe-in. Figure 5(h) shows that the installation direction of the shock absorber upper mounting point and the vehicle body connection (euler_top_mount) has the strongest positive influence on the longitudinal toe-in (long_toe), and the stiffness coefficient of the bushing in the x direction at the shock absorber upper mounting point and the vehicle body connection (factor_top_mount_fx) has the strongest negative influence on the longitudinal toe-in (long_toe).

[0096] Figure 5(i) is a schematic diagram of the sensitivity of various structural parameters to the long wheelbase. Figure 5(i) shows that the strong influence on the long wheelbase (long_wheel_base) is a negative influence. Among them, the stiffness coefficient of the bushing in the x direction at the connection between the shock absorber mounting point and the vehicle body (factor_top_mount_fx) has the strongest negative influence on the long wheelbase (long_wheel_base).

[0097] The above are merely examples of the sensitivity of various structural parameters to the characteristic curve, and actually include more or less sensitivity to the characteristic curve.

[0098] In an optional implementation, according to the sensitivity, the parameters to be calibrated are selected from the above structural parameters: the position of the front point of the control arm in the z direction (hpl_lca_front_z), the position of the outer point of the steering rod in the z direction (hpl_tierod_outer_z), the position of the outer point of the steering rod in the x direction (hpl_tierod_outer_x), the position of the lower mounting point of the shock absorber in the y direction (hpl_strut_lwr_mount_y), the stiffness coefficient of the bushing at the connection between the front point of the control arm and the subframe in the x direction (factor_lca_fr ont_fx), the stiffness coefficient of the bushing at the connection between the anti-roll bar and the subframe in the x direction (factor_anti_fx), the stiffness coefficient of the bushing in the radial direction of the connection between the rear point of the control arm and the subframe (factor_lca_rear_rx), the stiffness coefficient of the bushing in the radial direction of the connection between the rear point of the control arm and the subframe (factor_lca_front_rz), the stiffness coefficient of the bushing in the x direction of the connection between the rear point of the control arm and the subframe (factor_lca_rear_fx) and the coefficient of the steering rack in the z direction (factor_rack_fz).

[0099] In an optional embodiment, Figure 6 FIG. 1 is a flow chart of calibrating an initial suspension model to obtain a calibrated suspension model in a suspension optimization method provided by an embodiment of the present invention. Figure 6 As shown, the above step 130: based on the difference between the test fitting curve and the simulation fitting curve, calibrating the parameters to be calibrated to calibrate the initial suspension model to obtain a calibrated suspension model, may include the following steps:

[0100] Step 610: taking the minimum difference between the test fitting curve and the simulation fitting curve as the optimization goal, calibrate the parameters to be calibrated to obtain the calibration values ​​of the parameters to be calibrated.

[0101] In an embodiment of the present invention, in order to make the calibrated suspension model conform to the actual situation of the target suspension, that is, the simulation fitting curve corresponding to the calibrated suspension model can be consistent with the experimental fitting curve, the difference between the experimental fitting curve and the simulation fitting curve is minimized as the optimization goal, and the parameters to be calibrated are calibrated to obtain the calibration values ​​of the parameters to be calibrated.

[0102] For example, Figure 7(a)-Figure 7(h)It is a schematic diagram of the simulation fitting curve and the test fitting curve in the embodiment of the present invention. Figure 7(a) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the camber angle of the front left suspension in the embodiment of the present invention; wherein, the test original curve is the curve corresponding to the test data of the target suspension, and the curve has not been fitted, and directly reflects the distribution of the test data; the test fitting curve is the curve obtained by fitting based on the test data, and the simulation original curve, corresponding to the simulation fitting curve, is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model. As shown in Figure 7(a), there are large differences between the simulation fitting curve corresponding to the camber angle of the front left suspension, the test original curve, and the test fitting curve in the front and rear sections of the curve.

[0103] Figure 7(b) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle in an embodiment of the present invention. As shown in Figure 7(b), there are large differences between the simulation fitting curve corresponding to the front left suspension toe angle and the test original curve and the test fitting curve in the front and rear sections of the curve.

[0104] Figure 7(c) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the longitudinal displacement of the front left suspension in an embodiment of the present invention. As shown in Figure 7(c), there are large differences between the simulation fitting curve corresponding to the longitudinal displacement of the front left suspension and the original test curve and the test fitting curve in the front and rear sections of the curve; compared with the test fitting curve, although the curve trend of the simulation fitting curve is consistent with the original experimental curve, there are still large differences.

[0105] FIG7( d ) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left toe-in embodiment of the present invention. As shown in FIG7( d ), there is a large difference between the simulation fitting curve corresponding to the front left toe-in and the test original curve and the test fitting curve.

[0106] Figure 7(e) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the vertical stiffness of the front left suspension in an embodiment of the present invention. As shown in Figure 7(e), the simulation fitting curve corresponding to the vertical stiffness of the front left suspension is relatively consistent with the test original curve and the test fitting curve.

[0107] Figure 7(f) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle in an embodiment of the present invention. The schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle shown in Figure 7(f) is a schematic diagram of the characteristic curve related to the longitudinal force. As shown in Figure 7(f), the simulation fitting curve corresponding to the front left suspension toe angle is relatively consistent with the test original curve and the test fitting curve, but there are large differences in the front and end sections of the curve.

[0108] Figure 7(g) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle in an embodiment of the present invention. The schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle shown in Figure 7(g) is a schematic diagram of the characteristic curve related to the lateral force. As shown in Figure 7(g), the curve trends of the simulation fitting curve corresponding to the front left suspension toe angle are relatively consistent with the test original curve and the test fitting curve, but there are still large differences.

[0109] Figure 7(h) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension in an embodiment of the present invention. As shown in Figure 7(h), the curve trends of the simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension are relatively consistent with the test original curve and the test fitting curve, but there are still large differences.

[0110] As can be seen from the above example, there are large differences between the simulation fitting curve and the test fitting curve and the original test data. The simulation fitting curve cannot represent the actual situation of the target suspension. Therefore, the difference between the test fitting curve and the simulation fitting curve is minimized as the optimization goal, and the parameters to be calibrated are calibrated to obtain the calibration values ​​of the parameters to be calibrated. Specifically, the test fitting curve and the simulation fitting curve are fitted with functions to obtain the fitting functions corresponding to the test fitting curve and the simulation fitting curve; the calibration function of the parameters to be calibrated is determined based on the second norm of the difference between the fitting functions corresponding to the test fitting curve and the simulation fitting curve; the calibration function is optimized with the function value of the calibration function being zero as the optimization goal to obtain the calibration values ​​of the parameters to be calibrated.

[0111] In an optional implementation, in order to quantify the difference between the simulation fitting curve and the test fitting curve, function fitting is performed on the simulation fitting curve and the test fitting curve respectively, and fitting functions corresponding to the simulation fitting curve and the test fitting curve are obtained. In the K characteristic curve, function fitting is performed in different ways according to different characterization information. For example, the vertical stiffness is fitted as a linear function within the wheel hop range due to the existence of upper and lower limit blocks, and the camber angle, lateral displacement, longitudinal displacement, toe angle, and caster angle are fitted as quadratic or cubic functions; in the C characteristic curve, the characteristic curves corresponding to each characterization information are all fitted as linear functions. For example, the lateral force toe-in, longitudinal force toe-in, etc. are all fitted as linear functions.

[0112] In an optional implementation, when constructing the calibration function, for the K characteristic curve, the kinematic characteristics of the target suspension are made to match the test data as closely as possible by reasonably arranging and selecting the hard point coordinates. Accordingly, the calibration function is actually a function of the difference between the test fitting curve and the simulation fitting curve corresponding to each hard point coordinate, which can be specifically shown in the following formula (2):

[0113]

[0114] Among them, F i is the calibration function, o i is the function value of the i-th hard point coordinate in the fitting function corresponding to the test fitting curve, b i is the function value of the i-th hard point coordinate in the fitting function corresponding to the simulation fitting curve; r represents the total number of hard point coordinates.

[0115] In an optional implementation, since there are multiple parameters to be calibrated and optimized, and multiple curves to be calibrated and optimized are involved, a multi-objective optimization strategy is adopted when optimizing the calibration function with the function value of the calibration function being zero as the optimization target. The multi-objective optimization strategy can select the NSGA-Ⅱ genetic algorithm (Non-dominated Sorting Genetic Algorithm II, non-dominated sorting genetic algorithm with elite strategy), where the number of populations can be set to 50 and the evolution is set to 50 generations.

[0116] In an optional implementation, the calibration function is optimized to obtain calibration values ​​corresponding to the parameters to be calibrated as shown in Table 1 below:

[0117] Table 1

[0118]

[0119]

[0120] Step 620: Based on the calibration values ​​of the parameters to be calibrated, the initial suspension model is adjusted to obtain a calibrated suspension model.

[0121] In the embodiment of the present invention, the parameter values ​​of the parameters to be calibrated in the initial suspension model are adjusted to calibration values, thereby obtaining a calibrated suspension model.

[0122] Exemplarily, corresponding to the above Figure 7(a)-Figure 7(h) , Figure 8(a)-Figure 8(h) A schematic diagram of a simulation fitting curve and an experimental fitting curve after the calibration of the parameters to be calibrated is completed is shown. FIG8(a) is a schematic diagram of an experimental fitting curve and a simulation fitting curve corresponding to the camber angle of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention; wherein, the simulation curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the calibrated suspension model after the calibration of the parameters to be calibrated is completed. Comparing FIG7(a) and FIG8(a), the simulation fitting curve corresponding to the camber angle of the front left suspension is more in line with the original test curve after calibration, and the difference between the front and rear sections of the curve is reduced a lot.

[0123] FIG8(b) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing FIG7(b) with FIG8(b), the simulation fitting curve corresponding to the front left suspension toe angle after calibration, although there is still a large difference between the original test curve and the test fitting curve, is closer to the original test curve and the test fitting curve than the original simulation fitting curve.

[0124] FIG8(c) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the longitudinal displacement of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. By comparing FIG7(c) and FIG8(c), the simulation fitting curve corresponding to the longitudinal displacement of the front left suspension is closer to the original test curve after calibration, and the difference between the front and rear sections of the curve is much reduced.

[0125] Fig. 8(d) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left toe-in after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing Fig. 7(d) and Fig. 8(d), the simulation fitting curve corresponding to the front left toe-in is more consistent with the test original curve and the test fitting curve after calibration.

[0126] Figure 8(e) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the vertical stiffness of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing Figure 7(e) and Figure 8(e), before calibration, the simulation fitting curve corresponding to the vertical stiffness of the front left suspension is relatively consistent with the original test curve and the test fitting curve. After calibration, the simulation fitting curve corresponding to the vertical stiffness of the front left suspension still maintains a high consistency with the original test curve and the test fitting curve.

[0127] Fig. 8(f) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing Fig. 7(f) and Fig. 8(f), the simulation fitting curve corresponding to the front left suspension toe angle and the simulation fitting curve corresponding to the longitudinal displacement of the front left suspension are more consistent with the original test curve and the test fitting curve after calibration, and the difference between the front and rear sections of the curve is much reduced.

[0128] FIG8(g) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the front left suspension toe angle after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing FIG7(g) and FIG8(g), the simulation fitting curve corresponding to the front left suspension toe angle is more closely aligned with the test original curve and the test fitting curve after calibration.

[0129] Fig. 8(h) is a schematic diagram of the test fitting curve and the simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension after the calibration of the parameters to be calibrated is completed in an embodiment of the present invention. Comparing Fig. 7(h) and Fig. 8(h), the simulation fitting curve corresponding to the longitudinal flexibility of the front left suspension is more consistent with the original test curve and the test fitting curve after calibration.

[0130] In an optional embodiment, Fig. 9 FIG. 1 is a flow chart of optimizing a calibrated suspension model in a suspension optimization method provided by an embodiment of the present invention. Fig. 9 As shown, in the above step 140, the target optimization value of the target optimization object is used as the optimization target, and the calibrated suspension model is optimized to obtain the optimized suspension model, which may include the following steps:

[0131] Step 910: Based on the target optimization object, construct an optimization function and determine the parameters to be optimized corresponding to the optimization function.

[0132] In the embodiment of the present invention, an optimization function is constructed based on the target optimization object, and the parameters to be optimized are selected from the structural parameters according to the constructed optimization function. When selecting the parameters to be optimized, in order to ensure the optimization effect of the target optimization object while reducing the impact on other characteristics in the K&C characteristics, the parameters to be optimized can be selected according to the sensitivity, and the parameters with the largest positive value of sensitivity to the target optimization object and the smallest sensitivity to other characteristics are selected as the parameters to be optimized. For example, when the target optimization object is the roll toe, roll stiffness and C characteristics in the K characteristic, the parameters to be optimized can be selected as the position of the front point of the control arm in the x direction (factor_lca_front_fx), the position of the front point of the control arm in the z direction (factor_lca_front_fz), the stiffness coefficient of the bushing in the x direction at the connection between the rear point of the control arm and the subframe (factor_lca_rear_fx), and the position of the mounting point on the shock absorber in the x direction (factor_top_mount_fx).

[0133] Step 920: Construct constraint conditions based on a preset error range.

[0134] In the embodiment of the present invention, since the change of the hard point position will affect the Ackerman angle error, in order to minimize the change of the hard point position, a preset error range is set to include the Ackerman angle error being less than the preset angle error, and a constraint condition is constructed based on this.

[0135] In an optional implementation, the current Ackerman angle error is set as a preset angle error, thereby limiting the size of the optimized Ackerman angle error to prevent the change in the hard point position from exceeding the change in the hard point position in the suspension model before optimization. Specifically, based on the steering angle of the outer steering wheel and the steering angle weighted function of the inner steering wheel in the target suspension, the current Ackerman angle error is calculated, and the current Ackerman angle error is used as the preset angle error; the optimized Ackerman angle error is less than or equal to the preset angle error as a constraint condition. The constraint condition can be shown in the following formula (3):

[0136]

[0137] Among them, φ x is the optimized Ackerman angle error, φ is the current Ackerman angle error; β ^ is the actual steering angle of the outer steering wheel, β is the theoretical steering angle of the outer steering wheel, and f(α) is the weighted function of the steering angle of the inner steering wheel.

[0138] Step 930: Based on the preset optimization algorithm and constraint conditions, the optimization function is optimized to obtain the optimization value corresponding to the parameter to be optimized.

[0139] In the embodiment of the present invention, since multiple target optimization objects and parameters to be optimized are involved, the preset optimization algorithm can be a multi-objective optimization algorithm. Based on the multi-objective optimization algorithm and constraints, the optimization function is optimized to obtain the optimization values ​​corresponding to the parameters to be optimized.

[0140] In an optional embodiment, Fig.10 is a schematic diagram of input and output of optimizing the optimization function in an embodiment of the present invention, such as Fig.10 As shown, for the C characteristic curve, there will be multiple groups of optimization values ​​during optimization. At this time, a group of optimization values ​​can be selected according to user needs as the optimization values ​​of the parameters to be optimized that are finally used.

[0141] Corresponding to the example shown in step 910, the optimization function is optimized, and the optimization values ​​corresponding to the parameters to be optimized are obtained as follows: factor_lca_front_fx=0.57, factor_lca_front_fz=0.53, factor_lca_rear_fx=0.47, factor_top_mount_fx=1.87. At the same time, the values ​​of the target optimization objects before and after the optimization are shown in Table 2 below:

[0142] Table 2

[0143] Target optimization object Before optimization Target value After optimization Toe-in (deg / deg) 0.0372 -0.0234 -0.0229 Roll stiffness (N*m / deg) 700.1 797 793.8 Ackerman angle error 176.99 ≤176.99 175.23 Lateral force compliance (mm / N) 1.52e-4 2.00e-4 2.48e-4 Lateral force camber (deg / N) -2.68e-4 -4.69e-5 -4.65e-5 Lateral force toe-in (deg / N) 1.43e-4 1.00e-4 1.28e-4 Longitudinal force compliance (mm / N) 2.84e-3 2.84e-3 3.80e-3 Longitudinal force camber (deg / N) 3.06e-5 4.00e-5 5.31e-5 Longitudinal force toe-in (deg / N) -4.50e-5 -1.00e-4 -5.44e-5

[0144] Step 940: Based on the optimization values ​​corresponding to the parameters to be optimized, the calibrated suspension model is adjusted to obtain an optimized suspension model.

[0145] In an embodiment of the present invention, the parameter value corresponding to the parameter to be optimized in the calibrated suspension model is adjusted to the optimized value, thereby obtaining an optimized suspension model. Fig.11 is a comparison diagram of the front left toe-in before and after optimization in an embodiment of the present invention, Fig.12 is a comparison diagram of the Ackerman angle error before and after optimization in the embodiment of the present invention, such as Fig.11 and Fig.12 As shown in the figure, after optimization, the change trend of the front left toe-in slows down, and when the roll angle is 0, the front left toe-in reaches -0.0229, and the optimization target is achieved from the positive value to the negative value, and the Ackerman angle error is basically consistent with that before optimization, which can meet the constraints.

[0146] In an optional embodiment, after obtaining the optimized suspension model, in order to verify the optimization ability of the optimized suspension model on the operational stability of the vehicle, a whole vehicle model corresponding to the target suspension is established based on the optimized suspension model, and the operational stability of the whole vehicle model is verified based on the preset verification test. Wherein, when establishing the whole vehicle model, the optimized suspension model and the preset tire model are used to establish the whole vehicle model on the ADAMS platform, and then the operational stability is verified based on the whole vehicle model; the preset verification test may include steady-state rotation test, serpentine test, angular step test and other tests. The whole vehicle model established according to the optimized suspension model obtained in the above steps is verified for operational stability, and the results are shown in Table 3 below:

[0147] Table 3

[0148]

[0149] In Table 3, the values ​​in brackets before and after optimization are scoring values, and the higher the scoring value, the better the performance. It can be seen that the optimization of the roll toe according to the above steps increases the understeer of the vehicle, and the optimization of the stabilizer bar stiffness suppresses the body roll angle, thereby improving the operational stability of the vehicle.

[0150] The suspension optimization method provided by the embodiment of the present invention obtains preset parameters of each component in the target suspension, and models the target suspension based on the preset parameters to obtain an initial suspension model; respectively calculates the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determines the parameters to be calibrated whose sensitivity reaches the preset conditions from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameters on the test fitting curve; based on the difference between the test fitting curve and the simulation fitting curve, the parameters to be calibrated are calibrated to calibrate the initial suspension model to obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model; the target optimization object and the target optimization value of the target optimization object are obtained, and the calibrated suspension model is optimized with the target optimization value of the target optimization object as the optimization target to obtain an optimized suspension model. In this way, the target suspension is modeled based on preset parameters to obtain an initial suspension model, and the initial suspension model is calibrated to obtain a calibrated suspension model, so that the calibrated suspension model is more consistent with the actual data of the target suspension. At the same time, the optimized suspension model of the target suspension is obtained by optimizing the calibrated suspension model, so that the optimization of the target suspension is more consistent with the actual situation of the target suspension, thereby improving the optimization effect of the target suspension.

[0151] The embodiment of the present invention further provides a suspension optimization device, Fig.13 is a schematic diagram of the structure of an embodiment of a suspension optimization device provided by an embodiment of the present invention, such as Fig.13 As shown, the device 1300 includes:

[0152] The suspension model building module 1310 is used to obtain preset parameters of various components in the target suspension, and to model the target suspension based on the preset parameters to obtain an initial suspension model;

[0153] The module 1320 for constructing parameters to be calibrated is used to calculate the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determine the parameters to be calibrated whose sensitivity reaches the preset conditions from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameters on the test fitting curve;

[0154] The suspension model calibration module 1330 is used to calibrate the parameters to be calibrated based on the difference between the test fitting curve and the simulation fitting curve, so as to calibrate the initial suspension model and obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model;

[0155] The suspension model optimization module 1340 is used to obtain a target optimization object and a target optimization value of the target optimization object, and optimize the calibrated suspension model with the target optimization value of the target optimization object as the optimization target to obtain an optimized suspension model.

[0156] The device and method embodiments in the embodiments of this application are based on the same application concept.

[0157] Fig.14 The schematic diagram of the structure of the embodiment of the computer device provided by the embodiment of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the computer device.

[0158] like Fig.14 As shown, the computer device may include: a processor (processor) 1402 , a communication interface (Communications Interface) 1404 , a memory (memory) 1406 , and a communication bus 1408 .

[0159] The processor 1402, the communication interface 1404, and the memory 1406 communicate with each other via a communication bus 1408. The communication interface 1404 is used to communicate with other devices such as a client or other server network elements. The processor 1402 is used to execute a program 1410, which can specifically execute the relevant steps in the above-mentioned embodiment of the suspension optimization method.

[0160] Specifically, the program 1410 may include program code including computer executable instructions.

[0161] The processor 1402 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computer device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0162] The memory 1406 is used to store the program 1410. The memory 1406 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0163] The program 1410 may be specifically called by the processor 1402 to enable the computer device to execute the relevant steps in the above-mentioned embodiment of the suspension optimization method.

[0164] It can be understood by those skilled in the art that Fig.14 The structure shown is only for illustration and does not limit the structure of the above-mentioned device. Fig.14 More or fewer components as shown, or with Fig.14 Different configurations are shown.

[0165] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one executable instruction. When the executable instruction runs on a suspension optimization device / equipment, the suspension optimization device / equipment performs the operation of any suspension optimization method described above.

[0166] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0167] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0168] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0169] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the present invention. The word "comprising" does not exclude the existence of elements or steps not listed in the present invention. The word "one" or "an" preceding an element does not exclude the existence of multiple such elements. The present invention can be implemented by means of hardware including several different elements and by means of appropriately programmed computers. In a unit in which several devices are listed, several of these devices can be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names. The steps in the above embodiments should not be understood as limiting the order of execution unless otherwise specified.

Claims

1. A suspension optimization method, characterized in that: The method comprises: Acquiring preset parameters of various components in a target suspension, and modeling the target suspension based on the preset parameters to obtain an initial suspension model; The sensitivity of each structural parameter in the target suspension relative to the test fitting curve is calculated respectively, and based on the sensitivity, the parameters to be calibrated whose sensitivity reaches the preset conditions are determined from each structural parameter; the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameter on the test fitting curve; Based on the difference between the test fitting curve and the simulation fitting curve, the parameters to be calibrated are calibrated to calibrate the initial suspension model to obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model; A target optimization object and a target optimization value of the target optimization object are obtained, and the target optimization value of the target optimization object is used as an optimization target to optimize the calibrated suspension model to obtain an optimized suspension model.

2. The method according to claim 1, characterized in that The respectively calculating the sensitivity of each structural parameter in the target suspension relative to the test fitting curve comprises: Based on the test fitting curve and the structural parameters, construct a sensitivity model corresponding to the structural parameters; The sensitivity model is mathematically analyzed to obtain the sensitivity corresponding to each of the structural parameters.

3. The method according to claim 2, characterized in that The performing mathematical analysis on the sensitivity model to obtain the sensitivity corresponding to the structural parameter includes: Adjusting the parameter values ​​of each of the structural parameters, and simulating the characteristic curve of the target suspension based on the adjusted parameter values ​​to obtain simulation fitting curves corresponding to a plurality of parameter values; Discretizing the simulation fitting curves corresponding to the plurality of parameter values ​​respectively to obtain simulation data corresponding to each of the structural parameters; The simulation data is input into the sensitivity model, and the sensitivity model is analyzed based on a two-factor orthogonal test algorithm to obtain the sensitivity corresponding to the structural parameters.

4. The method according to claim 1, characterized in that: The step of calibrating the parameters to be calibrated based on the difference between the test fitting curve and the simulation fitting curve to calibrate the initial suspension model to obtain a calibrated suspension model includes: Taking the minimum difference between the test fitting curve and the simulation fitting curve as the optimization goal, calibrating the parameter to be calibrated to obtain a calibration value of the parameter to be calibrated; Based on the calibration values ​​of the parameters to be calibrated, the initial suspension model is adjusted to obtain the calibrated suspension model.

5. The method according to claim 4, characterized in that The step of calibrating the parameter to be calibrated by taking the difference between the test fitting curve and the simulation fitting curve as the minimum optimization target to obtain the calibration value of the parameter to be calibrated includes: Performing function fitting on the test fitting curve and the simulation fitting curve respectively to obtain fitting functions corresponding to the test fitting curve and the simulation fitting curve; Determining a calibration function for the parameter to be calibrated based on the second norm of the difference between the fitting functions corresponding to the test fitting curve and the simulation fitting curve; Taking the function value of the calibration function as zero as the optimization target, the calibration function is optimized to obtain the calibration value of the parameter to be calibrated.

6. The method according to claim 1, characterized in that The step of optimizing the calibrated suspension model by taking the target optimization value of the target optimization object as the optimization target to obtain the optimized suspension model comprises: Based on the target optimization object, construct an optimization function, and determine the parameters to be optimized corresponding to the optimization function; Construct constraints based on the preset error range; Based on the preset optimization algorithm and the constraint conditions, the optimization function is optimized to obtain the optimization value corresponding to the parameter to be optimized; Based on the optimization value corresponding to the parameter to be optimized, the calibrated suspension model is adjusted to obtain the optimized suspension model.

7. The method according to claim 6, characterized in that The preset error range includes that the Ackerman angle error is less than the preset angle error; and the constraint condition is constructed based on the preset error range, including: Calculating a current Ackerman steering angle error based on a steering angle of an outer steering wheel and a steering angle weighting function of an inner steering wheel in the target suspension, and using the current Ackerman steering angle error as the preset steering angle error; The optimized Ackerman angle error is less than or equal to the preset angle error, which is used as the constraint condition.

8. The method according to claim 1, characterized in that The method further comprises: Based on the optimized suspension model, establishing a whole vehicle model corresponding to the target suspension; Based on the preset verification test, the operational stability of the whole vehicle model is verified.

9. A suspension optimization device, characterized in that: The device comprises: A suspension model building module, used for acquiring preset parameters of various components in a target suspension, and modeling the target suspension based on the preset parameters to obtain an initial suspension model; a module for constructing parameters to be calibrated, for respectively calculating the sensitivity of each structural parameter in the target suspension relative to the test fitting curve, and based on the sensitivity, determining the parameters to be calibrated whose sensitivity reaches a preset condition from each of the structural parameters; the test fitting curve is obtained by fitting the characteristic curve of the target suspension based on the test data of the target suspension; the sensitivity is used to characterize the influence of the structural parameter on the test fitting curve; A suspension model calibration module, used for calibrating the parameters to be calibrated based on the difference between the test fitting curve and the simulation fitting curve, so as to calibrate the initial suspension model and obtain a calibrated suspension model; the simulation fitting curve is obtained by simulating and fitting the characteristic curve of the target suspension based on the initial suspension model; The suspension model optimization module is used to obtain a target optimization object and a target optimization value of the target optimization object, and optimize the calibrated suspension model with the target optimization value of the target optimization object as the optimization target to obtain an optimized suspension model.

10. A computer device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the suspension optimization method as described in any one of claims 1-8.

Images