CN109117557B - Suspension rubber bushing optimization method - Google Patents
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- CN109117557B CN109117557B CN201810924406.7A CN201810924406A CN109117557B CN 109117557 B CN109117557 B CN 109117557B CN 201810924406 A CN201810924406 A CN 201810924406A CN 109117557 B CN109117557 B CN 109117557B
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Abstract
The invention discloses a suspension rubber bushing optimization method, which comprises the following steps: acquiring material data of a suspension rubber bushing to be optimized; acquiring rigidity data of a suspension rubber bushing to be optimized; acquiring installation angle information of a suspension rubber bushing to be optimized and whole vehicle parameters; multi-station simulation is carried out by utilizing multi-body dynamics software or CAE analysis software to obtain the performance information of the whole vehicle; determining performance parameters to be optimized based on the whole vehicle performance information; the magnitude of influence of the stiffness data and the installation angle information on the performance parameters to be optimized; and optimizing the optimization variables. When the method optimizes the suspension rubber bushing, not only the rigidity of the suspension rubber bushing is considered, but also the influence of the installation angle of the suspension rubber bushing on the whole vehicle is considered, and compared with the prior art, the method has the advantages that the considered factors are more comprehensive, the obtained optimization scheme is more diversified, and the optimization effect is better.
Description
Technical Field
The invention relates to the technical field of automobile accessories, in particular to a suspension rubber bushing optimization method.
Background
The suspension rubber bushing has good vibration and noise reduction capability and is widely used on modern automobiles. In the later stage of the whole vehicle design, key parameters are determined, the changeable space is smaller, and if the control stability and smoothness of the whole vehicle are required to be improved on the basis, the design can be realized only by means of reasonable design of the suspension rubber bushing.
In the prior art, the influence of the rigidity of the suspension rubber bushing on the performance of the whole vehicle is only focused on the optimization of the suspension rubber bushing, and the optimization scheme is limited only by considering the rigidity parameter, so that the optimization scheme is too single and an ideal optimization effect is difficult to achieve.
Therefore, how to provide a more perfect optimization method for the suspension rubber bushing becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention discloses a suspension rubber bushing optimization method, when the suspension rubber bushing is optimized, not only the rigidity of the suspension rubber bushing is considered, but also the influence of the installation angle of the suspension rubber bushing on the whole vehicle is considered, and compared with the prior art, the consideration factors are more comprehensive, the obtained optimization scheme is more diversified, and the optimization effect is better.
In order to solve the technical problems, the invention adopts the following technical scheme:
the suspension rubber bushing optimization method comprises the following steps:
acquiring material data of the suspension rubber bushing to be optimized;
acquiring rigidity data of a suspension rubber bushing to be optimized, wherein the rigidity data comprises tensile rigidity and torsional rigidity of the suspension rubber bushing to be optimized along the directions X, Y and Z, and X, Y and Z are the directions perpendicular to each other;
acquiring installation angle information of a suspension rubber bushing to be optimized and whole vehicle parameters;
inputting the whole vehicle parameters, the installation angle information of the suspension rubber bushing to be optimized and the rigidity data of the suspension rubber bushing to be optimized into multi-body dynamics software or CAE analysis software to perform multi-station simulation, and obtaining whole vehicle performance information;
determining performance parameters to be optimized based on the whole vehicle performance information;
based on the rigidity data and the installation angle information, selecting the rigidity data and/or the installation angle information as optimization variables;
and optimizing the optimization variable by adopting an optimization algorithm.
Preferably, the acquiring the material data of the suspension rubber bushing to be optimized includes:
and carrying out a uniaxial tensile test, a biaxial tensile test and a plane shearing test on the test rod to obtain stress-strain curves of the three tests, wherein the material of the test rod is the same as the material of the suspension rubber bushing to be optimized.
Preferably, the acquiring rigidity data of the suspension rubber bushing to be optimized includes:
obtaining a to-be-selected constitutive model;
inputting the material data into finite element simulation software, and performing constitutive model coefficient fitting on the constitutive models to be selected based on the material data to obtain stress-strain curves of each constitutive model to be selected;
selecting a to-be-selected constitutive model with a stress-strain curve closest to material data as an optimal constitutive model;
and carrying out finite element simulation on the suspension rubber bushing to be optimized based on the optimal constitutive model, and obtaining the rigidity data.
Preferably, the whole vehicle parameters comprise hard point information of front and rear suspensions, spring stiffness, damping coefficient, steering system hard point information, gear-rack transmission ratio, tire stiffness, engine suspension position and engine stiffness data.
Preferably, the multi-operating simulation includes any one or more of a steering wheel angle step input test, a steering wheel angle pulse input test, a serpentine travel test, a double lane change simulation test, a random road surface input test, and a pulse input test.
Preferably, the vehicle performance information comprises operation stability and/or smoothness, wherein the operation stability selects an operation stability objective evaluation system based on a total variance evaluation method, and the smoothness selects a vertical acceleration root mean square value of the chassis when the vehicle runs at a speed of 60km/h on a random input cement road surface as an evaluation index.
Preferably, the performance parameter to be optimized is an objective function, and the determining the performance parameter to be optimized based on the whole vehicle performance information includes:
when the whole vehicle performance information comprises the stability, the stability objective evaluation system can obtain an stability objective evaluation index J T The objective function comprises a first sub-function minf (x 1 ) Wherein, minf (x 1 )＝min(J T )，min(J T ) Objective evaluation index J for stability of operation T Is the minimum of (2);
when the whole vehicle performance information comprises smoothness, the smoothness evaluation index is a chassis vertical acceleration root mean square value alpha RMS The objective function includes a second sub-function minf (x 1 ) Wherein, minf (x 2 )＝min(α RMS )，min(α RMS ) Is the root mean square value alpha of the vertical acceleration of the chassis RMS Is a minimum of (2).
Preferably, when the vehicle performance information includes stability and smoothness, the objective function is minf (x), where minf (x) =ω 1 minf(x 1 )+ω 2 minf(x 2 )，ω 1 Weights omega as the first subfunction 2 Weights are the second subfunction.
Preferably, sensitivity analysis is carried out on the rigidity and the installation angle information of the suspension rubber bushing, and the influence of the rigidity data and the installation angle information on the performance parameters to be optimized is judged.
Preferably, the optimization algorithm is an NSGA-II optimization algorithm.
In summary, the invention discloses a suspension rubber bushing optimization method, which comprises the following steps: acquiring material data of the suspension rubber bushing to be optimized; acquiring rigidity data of a suspension rubber bushing to be optimized, wherein the rigidity data comprises tensile rigidity and torsional rigidity of the suspension rubber bushing to be optimized along the directions X, Y and Z, and X, Y and Z are the directions perpendicular to each other; acquiring installation angle information of a suspension rubber bushing to be optimized and whole vehicle parameters; inputting the whole vehicle parameters, the installation angle information of the suspension rubber bushing to be optimized and the rigidity data of the suspension rubber bushing to be optimized into multi-body dynamics software or CAE analysis software to perform multi-station simulation, and obtaining whole vehicle performance information; determining performance parameters to be optimized based on the whole vehicle performance information; based on the rigidity data and the installation angle information, selecting the rigidity data and/or the installation angle information as optimization variables; and optimizing the optimization variable by adopting an optimization algorithm. When the method optimizes the suspension rubber bushing, not only the rigidity of the suspension rubber bushing is considered, but also the influence of the installation angle of the suspension rubber bushing on the whole vehicle is considered, and compared with the prior art, the method has the advantages that the considered factors are more comprehensive, the obtained optimization scheme is more diversified, and the optimization effect is better.
Drawings
FIG. 1 is a flow chart of a method of optimizing a suspension rubber bushing of the present disclosure;
FIG. 2 is a schematic diagram of a uniaxial stretching fitted curve of the coefficient fitting of the constitutive model in the invention;
FIG. 3 is a schematic diagram of a biaxially oriented fitted curve of the present invention fitted with constitutive model coefficients;
FIG. 4 is a schematic view of a planar shear fit curve fitted by constitutive model coefficients in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the invention discloses a suspension rubber bushing optimization method, which comprises the following steps:
s101, acquiring material data of the suspension rubber bushing to be optimized;
s102, acquiring rigidity data of a suspension rubber bushing to be optimized, wherein the rigidity data comprise tensile rigidity and torsional rigidity of the suspension rubber bushing to be optimized along the directions X, Y and Z, and X, Y and Z are directions perpendicular to each other;
taking a Macpherson suspension as an example, a rubber bushing is respectively arranged at the front and rear of the control arm, and the determination of the installation angle information is to determine the relative position of the suspension rubber bushing and the whole vehicle coordinate system. The whole vehicle coordinate system takes the midpoint of the connecting line of the centers of the two front wheels as the origin of coordinates, the X direction points to the rear of the whole vehicle, the Y direction points to the right hand direction of the driver, and the Z direction is vertically upward. The suspension rubber bushing also has X, Y, Z three directions, and the key point is to determine the Z direction for the front bushing of the McPherson suspension control arm; for the rear bushing of the Macpherson suspension control arm, the Z direction is generally vertical upwards, the key is to determine the direction of the hollow radial direction, and the other bushing directions are all the position relations of the corresponding coordinate directions and the whole vehicle coordinate directions.
S103, acquiring installation angle information of a suspension rubber bushing to be optimized and whole vehicle parameters;
s104, inputting the whole vehicle parameters, the installation angle information of the suspension rubber bushing to be optimized and the rigidity data of the suspension rubber bushing to be optimized into multi-body dynamics software or CAE analysis software for multi-station simulation to obtain whole vehicle performance information;
s105, determining performance parameters to be optimized based on the whole vehicle performance information;
s106, based on the rigidity data and the installation angle information, selecting the rigidity data and/or the installation angle information as optimization variables;
and S107, optimizing the optimization variable by adopting an optimization algorithm.
When the method optimizes the suspension rubber bushing, not only the rigidity of the suspension rubber bushing is considered, but also the influence of the installation angle of the suspension rubber bushing on the whole vehicle is considered, and compared with the prior art, the method has the advantages that the considered factors are more comprehensive, the obtained optimization scheme is more diversified, and the optimization effect is better.
In specific implementation, the acquiring the material data of the suspension rubber bushing to be optimized includes:
and carrying out a uniaxial tensile test, a biaxial tensile test and a plane shearing test on the test rod to obtain stress-strain curves of the three tests, wherein the material of the test rod is the same as the material of the suspension rubber bushing to be optimized.
And the test rod is manufactured by adopting the material for manufacturing the suspension rubber bushing to be optimized, and then the test rod is tested, so that the stress-strain curve of the material for manufacturing the suspension rubber bushing to be optimized can be obtained, and the subsequent simulation is facilitated.
In a specific implementation, the obtaining the rigidity data of the suspension rubber bushing to be optimized includes:
obtaining a to-be-selected constitutive model;
inputting the material data into finite element simulation software, and performing constitutive model coefficient fitting on the constitutive models to be selected based on the material data to obtain stress-strain curves of each constitutive model to be selected;
selecting a to-be-selected constitutive model with a stress-strain curve closest to material data as an optimal constitutive model;
and carrying out finite element simulation on the suspension rubber bushing to be optimized based on the optimal constitutive model, and obtaining the rigidity data.
Before finite element simulation of a rubber bushing is carried out, an optimal constitutive model is required to be selected, then coefficients are fitted, the constitutive model commonly used at present comprises a Yeoh constitutive model, an Arruda-Boyce constitutive model and a 3-order Ogden constitutive model, material data are input into finite element simulation software, a stress strain curve obtained after which coefficient is fitted to the constitutive model to be selected is observed, the data obtained after a test rod is tested are closest to each other, and the constitutive model to be selected is selected as the optimal constitutive model. As shown in fig. 2 to 4, the constitutive model to be selected after the fitting coefficient is compared with the data obtained after the test is performed on the test rod, and the Yeoh constitutive model is closest to the test curve, so that the Yeoh constitutive model is selected as the optimal constitutive model for the finite element simulation of the suspension rubber bushing. The simulation result can be more accurate by selecting the optimal constitutive model, so that the optimization result is more accurate, and the optimization effect is improved.
In specific implementation, the whole vehicle parameters comprise hard point information of front and rear suspensions, spring stiffness, damping coefficient, steering system hard point information, gear-rack transmission ratio, tire stiffness, engine suspension position and engine stiffness data.
When optimizing, need consider optimizing the influence to whole car parameter, this application acquires multiple whole car parameter, and the factor of considering is more comprehensive, can avoid appearing improving the performance to a certain parameter, reduces the emergence of the condition of performance to another parameter.
When the method is specifically implemented, the multi-working condition simulation comprises any one or more of a steering wheel angle step input test, a steering wheel angle pulse input test, a snake-shaped running test, a double-lane-shifting simulation test, a random road surface input test and a pulse input test.
In specific implementation, the whole vehicle performance information comprises operation stability and/or smoothness, wherein the operation stability selects an operation stability objective evaluation system based on a total variance evaluation method, and the smoothness selects a vertical acceleration root mean square value of a chassis when the vehicle runs at a speed of 60km/h on a random input cement road surface as an evaluation index.
The parameters and the evaluation indexes in the whole vehicle performance information are in a mature calculation mode, wherein the operation stability selects an operation stability objective evaluation system based on a total variance evaluation method, and the reference documents are as follows:
wu Lian bushing stiffness characteristics and structural parameter optimization based on the stability of the whole vehicle [ D ]: university of gilin, 2011.
The smoothness selects the root mean square value of the vertical acceleration of the chassis when the vehicle runs at the speed of 60km/h on the random input cement pavement as an evaluation index. The references are:
shi Multi-body dynamics study of influence of suspension rubber bushings on ride comfort of automobiles [ D ]. Beijing: university of Beijing, university of Italian 2016.
The optimization parameters are to select proper rigidity and installation angle of the suspension rubber bushing as the optimization parameters. In order to improve the optimization efficiency, sensitivity analysis is carried out on the rigidity and the installation angle of the suspension rubber bushing, factors with larger influence on the objective function are selected as final optimization variables, the variation range of the optimization parameters is required to be within +/-50% during optimization, and the condition that the later processing is difficult due to overlarge variation range is prevented.
Through the technical scheme, the optimization efficiency can be improved, the reasonable change interval of the optimization parameters is set, and the later processing and manufacturing difficulty level is reduced.
In specific implementation, the performance parameter to be optimized is an objective function, and the determining the performance parameter to be optimized based on the whole vehicle performance information includes:
when the whole vehicle performance information comprises the stability, the stability objective evaluation system can obtain an stability objective evaluation index J T The objective function comprises a first sub-function minf (x 1 ) Wherein, minf (x 1 )＝min(J T )，min(J T ) Objective evaluation index J for stability of operation T Is the minimum of (2);
when the whole vehicle performance information comprises smoothness, the smoothness evaluation index is a chassis vertical acceleration root mean square value alpha RMS The objective function includes a second sub-function minf (x 1 ) Wherein, minf (x 2 )＝min(α RMS )，min(α RMS ) Is the root mean square value alpha of the vertical acceleration of the chassis RMS Is a minimum of (2).
In a specific implementation, when the vehicle performance information includes stability and smoothness, the objective function is minf (x), where minf (x) =ω 1 minf(x 1 )+ω 2 minf(x 2 )，ω 1 Weights omega as the first subfunction 2 Weights are the second subfunction.
In specific implementation, sensitivity analysis is carried out on the rigidity and the installation angle information of the suspension rubber bushing, and the influence of the rigidity data and the installation angle information on the performance parameters to be optimized is judged.
After the simulation is completed, the performance parameters, namely the objective function, which need to be optimized are determined through the simulation result. In order to improve the optimization efficiency, sensitivity analysis is carried out on the rigidity and the installation angle of the suspension rubber bushing, and factors with larger influence on the objective function are selected as final optimization variables. When optimizing, each objective function is not an average weight, and the objective function with the emphasis is selected to increase the weight, so that the optimization process is more prone to improving the worse performance parameters.
In specific implementation, the optimization algorithm is an NSGA-II optimization algorithm.
The improved NSGA-II optimization algorithm can avoid mass reproduction of repeated individuals, improves population diversity, and comprises the following specific algorithm content citations:
liu Wei, shi Wenku, gui Longming, etc. suspension system multi-objective optimization based on ride and steering stability [ J Jilin university journal (ergonomic edition), 2011,41 (5): 1199-1204.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be understood that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. The suspension rubber bushing optimization method is characterized by comprising the following steps of:
acquiring material data of a suspension rubber bushing to be optimized;
acquiring rigidity data of a suspension rubber bushing to be optimized, wherein the rigidity data comprises tensile rigidity and torsional rigidity of the suspension rubber bushing to be optimized along the directions X, Y and Z, and X, Y and Z are the directions perpendicular to each other;
acquiring installation angle information of a suspension rubber bushing to be optimized and whole vehicle parameters;
inputting the whole vehicle parameters, the installation angle information of the suspension rubber bushing to be optimized and the rigidity data of the suspension rubber bushing to be optimized into multi-body dynamics software or CAE analysis software to perform multi-station simulation, and obtaining whole vehicle performance information;
determining performance parameters to be optimized based on the whole vehicle performance information; the whole vehicle performance information comprises operation stability and/or smoothness, wherein the operation stability selects an operation stability objective evaluation system based on a total variance evaluation method, and the smoothness selects a vertical acceleration root mean square value of a chassis when the vehicle runs at a speed of 60km/h on a random input cement road surface as an evaluation index; the performance parameters to be optimized are target functions, and determining the performance parameters to be optimized based on the whole vehicle performance information comprises the following steps:
when the whole vehicle performance information comprises the stability, the stability objective evaluation system can obtain an stability objective evaluation index J T The objective function comprises a first sub-function minf (x 1 ) Wherein, minf (x 1 )＝min(J T )，min(J T ) Is thatObjective evaluation index J of stability T Is the minimum of (2);
when the whole vehicle performance information comprises smoothness, the smoothness evaluation index is a chassis vertical acceleration root mean square value alpha RMS The objective function includes a second sub-function minf (x 1 ) Wherein, minf (x 2 )＝min(α RMS )，min(α RMS ) Is the root mean square value alpha of the vertical acceleration of the chassis RMS Is the minimum of (2);
based on the rigidity data and the installation angle information, selecting the rigidity data and/or the installation angle information as optimization variables;
and optimizing the optimization variable by adopting an optimization algorithm.
2. The suspension rubber bushing optimization method according to claim 1, wherein the acquiring material data of the suspension rubber bushing to be optimized includes:
and carrying out a uniaxial tensile test, a biaxial tensile test and a plane shearing test on the test rod to obtain stress-strain curves of the three tests, wherein the material of the test rod is the same as the material of the suspension rubber bushing to be optimized.
3. The suspension rubber bushing optimization method according to claim 1, wherein the acquiring rigidity data of the suspension rubber bushing to be optimized includes:
obtaining a to-be-selected constitutive model;
inputting the material data into finite element simulation software, and performing constitutive model coefficient fitting on the constitutive models to be selected based on the material data to obtain stress-strain curves of each constitutive model to be selected;
selecting a to-be-selected constitutive model with a stress-strain curve closest to material data as an optimal constitutive model;
and carrying out finite element simulation on the suspension rubber bushing to be optimized based on the optimal constitutive model, and obtaining the rigidity data.
4. The method of optimizing the suspension rubber bushing of claim 1, wherein the vehicle parameters include hard point information, spring rate, damping coefficient, steering system hard point information, rack and pinion gear ratio, tire stiffness, engine suspension position, and engine stiffness data for front and rear suspensions.
5. The suspension rubber bushing optimization method of claim 1, wherein the multiple-operating simulation includes any one or more of a steering wheel angle step input test, a steering wheel angle pulse input test, a serpentine travel test, a double lane change simulation test, a random road surface input test, and a pulse input test.
6. The method for optimizing a suspension rubber bushing according to claim 1, wherein when the vehicle performance information includes stability and smoothness, an objective function is minf (x), wherein minf (x) =ω 1 minf(x 1 )+ω 2 minf(x 2 )，ω 1 Weights omega as the first subfunction 2 Weights are the second subfunction.
7. The suspension rubber bushing optimization method according to claim 1, wherein sensitivity analysis is performed on the rigidity and the installation angle information of the suspension rubber bushing, and the magnitude of influence of the rigidity data and the installation angle information on the performance parameters to be optimized is determined.
8. The suspension rubber bushing optimization method of claim 1, wherein the optimization algorithm is an NSGA-ii optimization algorithm.
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| CN111310380B (en) * | 2020-03-19 | 2023-08-04 | 江苏吉麦新能源车业有限公司 | Design and development method for suspension rubber bushing structure of electric vehicle power assembly |
| CN112498523B (en) * | 2020-11-16 | 2022-05-03 | 东风柳州汽车有限公司 | Vehicle assembling method and device |
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| CN113312703B (en) * | 2021-05-27 | 2022-12-30 | 奇瑞汽车股份有限公司 | Simulation method and device for automobile bushing and computer storage medium |
| CN113536468B (en) * | 2021-07-30 | 2024-07-16 | 宜宾凯翼汽车有限公司 | Optimization method for K & C characteristics of multi-working-condition and multi-target suspension |
| CN117367829A (en) * | 2023-10-23 | 2024-01-09 | 哈尔滨东安汽车发动机制造有限公司 | Method for testing influence of assembly carrying angle on drivability of whole vehicle |
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