CN117725765A - Vehicle suspension multi-objective optimization method, device and medium based on response analysis - Google Patents
Vehicle suspension multi-objective optimization method, device and medium based on response analysis Download PDFInfo
- Publication number
- CN117725765A CN117725765A CN202410171988.1A CN202410171988A CN117725765A CN 117725765 A CN117725765 A CN 117725765A CN 202410171988 A CN202410171988 A CN 202410171988A CN 117725765 A CN117725765 A CN 117725765A
- Authority
- CN
- China
- Prior art keywords
- variable
- value
- determining
- variables
- comfort
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000004044 response Effects 0.000 title claims abstract description 74
- 238000004458 analytical method Methods 0.000 title claims abstract description 65
- 239000000725 suspension Substances 0.000 title claims abstract description 49
- 238000005457 optimization Methods 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000004088 simulation Methods 0.000 claims abstract description 87
- 238000013461 design Methods 0.000 claims abstract description 62
- 230000000694 effects Effects 0.000 claims abstract description 47
- 238000005316 response function Methods 0.000 claims description 46
- 230000006870 function Effects 0.000 claims description 31
- 230000003993 interaction Effects 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 7
- 238000000540 analysis of variance Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 5
- 238000005211 surface analysis Methods 0.000 description 11
- 230000008569 process Effects 0.000 description 7
- 238000004590 computer program Methods 0.000 description 6
- 239000006096 absorbing agent Substances 0.000 description 5
- 239000002131 composite material Substances 0.000 description 5
- 230000002452 interceptive effect Effects 0.000 description 5
- 230000035939 shock Effects 0.000 description 5
- 239000007787 solid Substances 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000000551 statistical hypothesis test Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000012938 design process Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000013329 compounding Methods 0.000 description 1
- 238000013401 experimental design Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Vehicle Body Suspensions (AREA)
Abstract
The invention relates to the technical field of data processing, and discloses a vehicle suspension multi-objective optimization method, device and medium based on response analysis. According to the method, through determining all design variables and all comfort parameters of a vehicle suspension, constructing an objective function, obtaining all first variable combinations and all second variable combinations based on the determined first variables and second variables, substituting experimental data of the first variables into a simulation model for each first variable combination to obtain simulation data, determining a first response analysis curved surface so as to obtain optimized values of all the first variables, substituting experimental data of the second variables and optimized values of the first variables into the simulation model for each second variable combination to obtain simulation data, determining optimized values of all the second variables so as to realize optimized solution of the remaining design variables, solving the problem of poor optimization effect in the prior art, and greatly improving the optimization efficiency.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a vehicle suspension multi-objective optimization method, device and medium based on response analysis.
Background
In the design process of the automobile suspension, the design requirement is combined, and the automobile design parameters are adjusted, so that an optimized automobile design scheme is obtained, and the method has important significance in reducing the design cost and improving the automobile performance. According to the performance requirements in the vehicle design process, design parameters such as a running spring, a shock absorber assembly, a differential, wheels and the like are required to be determined.
Existing solutions typically have each department designed the individual components for which it is responsible. However, because of the complexity of the design of the chassis, the influence of parameters such as the running spring, the shock absorber assembly, the differential, the wheels and the like on the performance adjustment of the vehicle is not independent and irrelevant, so that the conventional scheme generally has the problem of poor optimization effect.
In view of this, the present invention has been made.
Disclosure of Invention
In order to solve the technical problems, the invention provides a vehicle suspension multi-objective optimization method, device and medium based on response analysis, and solves the problem of poor optimization effect in the prior art.
The embodiment of the invention provides a vehicle suspension multi-objective optimization method based on response analysis, which comprises the following steps:
determining each design variable and each comfort parameter of a vehicle suspension, constructing an objective function according to each comfort parameter, and determining a first variable and a second variable in all the design variables;
determining each first variable combination and each second variable combination, wherein the first variable combination comprises three first variables and the second variable combination comprises one first variable and two second variables;
substituting experimental data of the first variable combinations into a simulation model for each first variable combination to obtain simulation data of each comfort parameter, determining a first response analysis curved surface based on the experimental data of the first variable combinations and the simulation data of each comfort parameter, and determining an optimized value of each first variable based on the first response analysis curved surface and the objective function;
substituting experimental data of the second variables in the second variable combination and optimized values of the first variables into a simulation model for each second variable combination to obtain simulation data of each comfort parameter, determining a second response analysis curved surface based on the experimental data of the second variables in the second variable combination, the optimized values of the first variables and the simulation data of each comfort parameter, and determining optimized values of each second variable based on the second response analysis curved surface and the objective function.
The embodiment of the invention provides electronic equipment, which comprises:
a processor and a memory;
the processor is configured to execute the steps of the vehicle suspension multi-objective optimization method based on response analysis according to any of the embodiments by calling the program or the instructions stored in the memory.
Embodiments of the present invention provide a computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of the response analysis-based vehicle suspension multi-objective optimization method of any of the embodiments.
The embodiment of the invention has the following technical effects:
determining each design variable and each comfort parameter of a vehicle suspension, constructing an objective function according to each comfort parameter, determining a first variable and a second variable in all the design variables to obtain each first variable combination and each second variable combination, substituting experimental data of the first variable into a simulation model for each first variable combination to obtain simulation data of each comfort parameter, determining a first response analysis curved surface based on the experimental data and the simulation data to obtain an optimized value of each first variable in the first variable combination, obtaining an optimized value of each first variable in the first wheel optimization based on response surface analysis, substituting the experimental data of the second variable and the optimized value of the first variable into the simulation model for each second variable combination to obtain simulation data of each comfort parameter, determining a second response analysis curved surface based on the experimental data, the optimized value and the simulation data to obtain an optimized value of each second variable in the second variable combination, obtaining an optimized value of each second variable in the second wheel optimization in a point-surface composite mode, realizing optimal solution to the remaining design variable, the method can be used for the vehicle suspension through the response surface analysis of the response surface, the response surface can be improved, the optimal effect can be obtained by the solution of the optimal design parameters of the vehicle suspension, the solution can be achieved through the solution of the response surface analysis of the optimal design parameters, the solution of the optimal suspension can be achieved through the solution of the optimal solution of the critical factors, and the optimal solution can be achieved through the solution of the optimal solution of the critical design parameters, and the optimization efficiency of design parameters is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for multi-objective optimization of a vehicle suspension based on response analysis provided by an embodiment of the invention;
FIG. 2 is a schematic illustration of an experimental cube provided by an embodiment of the present invention;
FIG. 3 is a schematic view of an experimental rectangular pyramid provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the invention, are within the scope of the invention.
The vehicle suspension multi-objective optimization method based on response analysis provided by the embodiment of the invention is mainly suitable for determining the final value of each design parameter in the vehicle suspension according to the comfort performance requirement of the vehicle suspension design, for example, determining the final value of the design parameters such as a running spring, a shock absorber assembly, a suspension buffer block and the like. The vehicle suspension multi-objective optimization method based on response analysis provided by the embodiment of the invention can be executed by electronic equipment such as a computer.
Fig. 1 is a flowchart of a vehicle suspension multi-objective optimization method based on response analysis according to an embodiment of the present invention. Referring to fig. 1, the response analysis-based vehicle suspension multi-objective optimization method specifically includes:
s110, determining each design variable and each comfort parameter of the vehicle suspension, constructing an objective function according to each comfort parameter, and determining a first variable and a second variable in all the design variables.
The design variables may be various related component variables that need to be determined to be valued during the design of the vehicle suspension, such as a running spring, a shock absorber assembly, a suspension damper, a vehicle hard point, a bushing, and the like.
The comfort parameter may be a parameter used to measure vehicle comfort during vehicle suspension design, such as tire vertical stiffness, ride stiffness, tire runout, tire longitudinal displacement, tire axial displacement, brake anti-pitch angle, drive anti-pitch angle, roll center height, suspension runout travel, vehicle suspension spring rate, shock absorber transfer ratio, and the like.
After each comfort parameter is determined, in order to obtain a vehicle suspension design scheme with optimal comfort, a parameter target value of each comfort parameter can be set according to the vehicle suspension performance requirement, and then an objective function is constructed according to the parameter target value. The parameter target value may be a target value that the comfort parameter is expected to reach.
In a specific embodiment, constructing the objective function from each comfort parameter comprises:
acquiring a parameter target value of each comfort parameter; an objective function is constructed based on the differences between the parameter target values and the actual output values of all comfort parameters.
Specifically, a root mean square function of all the differences can be calculated as an objective function according to the differences between the parameter target values and the actual output values of all the comfort parameters. The actual output value may be each value of the comfort parameter in the response analysis curved surface obtained by the response surface analysis.
By way of example, the objective function may satisfy the following formula:
；
in the method, in the process of the invention,representing the result of the calculation of the objective function +.>For the actual output value of the ith comfort parameter,/->For the parameter target value of the i-th comfort parameter, n is the number of comfort parameters.
In the above embodiment, the objective function is constructed by the difference between the parameter target value and the actual output value of the comfort parameter, so that the following scheme for making the actual output value approach to the parameter target value can be conveniently obtained by optimization, the vehicle suspension design scheme with optimal comfort is obtained, and the performance requirement of the vehicle suspension design is met.
Furthermore, after determining each design variable of the vehicle suspension, a first variable and a second variable may be determined among all the design variables, wherein the first variable may be a design variable requiring a first round of optimization, and the second variable may be a design variable requiring a second round of optimization.
Specifically, all design variables may be divided into first variables and second variables according to the importance (or criticality) of all design variables. If the design variable with larger influence on the vehicle comfort is determined to be the first variable, the optimization value of each first variable is obtained through the first round of optimization, so that each first variable is optimized preferentially, the design variable with smaller influence on the vehicle comfort is determined to be the second variable, the optimization value of each second variable is obtained through the combination of the second round of optimization on the basis of the first round of optimization result, and the finally obtained suspension design scheme is further ensured to meet the comfort requirement.
For example, the design variables of the running spring, the damper assembly, the suspension damper block, etc. may be taken as the first variable, and the design variables of the vehicle hard spot, the bushing, etc. may be taken as the second variable.
S120, determining each first variable combination and each second variable combination, wherein the first variable combination comprises three first variables, and the second variable combination comprises one first variable and two second variables.
Specifically, after all the design variables are divided into each first variable and each second variable, three first variables can be taken as a group, and each first variable is combined to obtain a plurality of first variable combinations; and combining the second variables by taking one first variable and two second variables as a group to obtain a plurality of second variable combinations.
For example, permutation and combination of the first variables without replacement can be performed, and three first variables are selected each time, so as to obtain a plurality of first variable combinations. And, can carry out the permutation and combination that does not put back to all second variables, select two second variables and first variable at a time, obtain a plurality of second variable combinations.
S130, substituting experimental data of the first variable combinations into a simulation model for each first variable combination to obtain simulation data of each comfort parameter, determining a first response analysis curved surface based on the experimental data of the first variable combinations and the simulation data of each comfort parameter, and determining an optimized value of each first variable based on the first response analysis curved surface and an objective function.
In an embodiment of the present invention, after the first variable combination and the second variable combination are determined, a first round of optimization may be performed. In the first round of optimization process, response surface analysis is carried out for each first variable combination to obtain the optimized value of each first variable.
Specifically, for each first variable combination, the value range (i.e., the upper and lower bounds) of each first variable can be determined first, and then a three-dimensional area of a feasible domain is formed according to the value ranges of three first variables, and experimental data is provided for simulation through the three-dimensional area, and response surface analysis is performed according to the experimental data of each first variable and the simulation value of the comfort parameter.
Taking a first variable combination as an example, in a specific embodiment, experimental data of the first variable combination is substituted into a simulation model to obtain simulation data of each comfort parameter, and the method comprises the following steps:
step 11, determining a center point, shaft points and cube points of an experimental cube based on the value range of each first variable in the first variable combination;
step 12, determining experimental data of a first variable combination based on a center point, each axis point and each cube point of an experimental cube, wherein the experimental data comprises a plurality of experimental values of each variable;
and 13, inputting experimental data of the first variable combinations into a simulation model so that the simulation model determines simulation values of all the comfort parameters according to the experimental values of all the first variables and initial values of other design variables except the first variable combinations to obtain simulation data of all the comfort parameters.
In step 11, a three-dimensional feasible region may be determined according to the value range of each first variable in the first variable combination, and a center point, each axis point and each cube point of the experimental cube are set.
Wherein the x, y, z axes of the experimental cube may represent three first variables in the first variable combination, respectively. Fig. 2 is a schematic diagram of an experimental cube according to an embodiment of the present invention, where, as shown in fig. 2, the number of cube points of the experimental cube is 8, and the number of cube points is solid dots in the graph, which represents a corresponding upper bound or a corresponding lower bound of the first variable; the number of the axis points of the experimental cube is 6, and the axis points are solid star points in the graph, represent corresponding first variables and take the median; the number of center points of the experimental cube is 1, which is the solid hexagonal point in the graph, representing the median of all the first variables.
Further, in step 12, after the experimental cube is constructed and the center point, the axis point and the cube point thereof are determined, the experimental cube can be sampled by the full factor center composite design to obtain experimental data of the first variable combination. The experimental data is composed of a plurality of experimental values of each first variable in the first variable combination.
Further, in step 13, experimental data generated based on center compounding may be substituted into a simulation model, where the simulation model may be a model simulating a real vehicle suspension system, and is used to calculate simulation values of corresponding comfort parameters according to the values of the input design variables; the simulation model outputs simulation values of all the comfort parameters based on Adams, and simulation data of all the comfort parameters are obtained. Wherein for other design variables than the first combination of variables, the simulation model may be considered as such design variables to take corresponding initial values.
Through the steps 11-13, the acquisition of experimental data based on the central composite design is realized, the comprehensiveness of the experimental data can be ensured, the simulation precision is ensured, the calculation efficiency is improved, and the subsequent response surface analysis according to the experimental data and the simulation data is facilitated.
After experimental data of the first variable combination and simulation data of each comfort parameter are obtained, response surface analysis can be performed based on the experimental data and the simulation data, and a first response analysis curved surface is obtained.
The first response analysis curved surface is composed of a plurality of points, each point is used for describing the value of each first variable and the actual output value of the corresponding comfort parameter, and the actual output value can be obtained through calculation of a response function obtained in the response analysis process.
In a specific embodiment, the method for determining the first response analysis surface based on the experimental data of the first variable combination and the simulation data of each comfort parameter includes the following steps:
step 21, determining a main effect value between each comfort parameter and a first-order item of each first variable in the first variable combination and an interactive effect value between each comfort parameter and a second-order cross item of each first variable in the first variable combination based on experimental data of the first variable combination and simulation data of each comfort parameter;
step 22, determining a response function between the first variable combination and each comfort parameter based on the main effect value and the interaction effect value;
and step 23, determining a first response analysis curved surface according to the response function corresponding to each comfort parameter.
In step 21, first order terms of the first variables in the first variable combination and second order cross terms of the first variables may be constructed. The second-order cross term may refer to multiplication of any two first variables, or square of any one first variable.
Further, the main effect value between each first-order item and each comfort parameter and the interaction effect value between each second-order cross item and each comfort parameter can be analyzed according to the experimental data of the first variable combination and the simulation data of each comfort parameter. The larger the main effect value or the interaction effect value is, the stronger the relevance between the corresponding item and the comfort parameter is, namely the larger the influence on the value of the comfort parameter is.
Further, in step 22, for each comfort parameter, the items with low relevance to the comfort parameter may be removed according to the main effect value between the first-order items and the comfort parameter and the interactive effect value between the second-order cross items and the comfort parameter.
For the above step 22, optionally, determining a response function between the first variable combination and each comfort parameter based on the main effect value and the interaction effect value, includes:
judging whether a main effect value and an interactive effect value between the comfort parameter and each first variable are smaller than a preset significance threshold value or not according to each comfort parameter; if the effect value smaller than the preset significance threshold exists, eliminating a first-order item or a second-order cross item corresponding to the effect value in a response function corresponding to the comfort parameter.
Specifically, taking a comfort parameter as an example, whether each main effect value and each interaction effect value are smaller than a preset significance threshold value or not can be judged, if the effect value smaller than the preset significance threshold value exists, the effect value indicates that a first-order item or a second-order cross item corresponding to the effect value has smaller influence on the comfort parameter, and the corresponding first-order item or second-order cross item can be removed.
The aim of simplifying the response function can be achieved by eliminating the items with low relevance between the comfort parameters, and the accuracy of the response function constructed later can be further ensured, so that the accuracy of suspension optimization is further ensured.
Further, after the first-order term or the second-order cross term with the effect value smaller than the preset significance threshold is removed, the response function between the rest term and the comfort parameter can be analyzed again according to the experimental data and the simulation data, namely, the relation between the rest term and the comfort parameter is fitted, and the response function is obtained. Wherein the response function is used to describe the relationship between the comfort parameter and the design variables.
After the response function corresponding to each comfort parameter is obtained, in consideration of the situation that the fitting effect of the response function is poor possibly, in order to further ensure the accuracy of the response function, variance analysis can be performed on the response function.
Optionally, for step 22 above, after determining the response function between the first variable combination and each comfort parameter, further includes:
for each comfort parameter, performing variance analysis on the response function based on the actual output value of the response function corresponding to the comfort parameter;
and carrying out significance test on the response function according to the analysis of variance results, and if the significance test fails, re-determining experimental data of the first variable combination and simulation data of the comfort parameter so as to re-determine a main effect value and an interaction effect value between the comfort parameter and each first variable.
Specifically, for each comfort parameter, the variance analysis may be performed on the response function according to the actual output value of the response function corresponding to the comfort parameter (i.e., the response function takes the value of the calculated comfort parameter through the input design variable), for example, the variance analysis may be performed through the actual output value of the response function and the simulation data of the comfort parameter, so as to obtain the T value and the P value.
Further, the statistical significance test can be performed on the response function through the T value and the P value, if the T value is larger than the set threshold and the P value is smaller than the significance level, the response function is determined to pass the statistical significance test and have statistical significance, otherwise, the response function is determined to not pass the statistical significance test, at the moment, experimental data of the first variable combination and simulation data of the comfort parameter can be redetermined to re-perform response surface analysis, a new main effect value and an interaction effect value are obtained, and a new response function is further constructed.
In the embodiment, after the response function corresponding to the comfort parameter is obtained, the significance test is carried out on the response function, so that the reliability of the response function passing the test can be determined to be higher, the fitting effect is better, the accuracy of subsequent optimization can be ensured, and for the response function which does not pass the test, the steps can be returned to acquire experimental data and analyze the response surface again, so that a new response function is obtained, and the accuracy of analyzing the response surface is further improved.
Further, for each first variable combination, a first response analysis curved surface can be determined based on the response surface analyzer according to the response function corresponding to each comfort parameter obtained by analysis. If the value of the first variable in the first variable combination is input into the response function, an actual output value of the comfort parameter output by the response function is obtained, the operation is repeated, a plurality of points are obtained, and then a first response analysis curved surface is constructed according to all the points.
Through the steps 21-23, analysis of the effect values of the first-order items and the second-order interaction items of the first variables in the first variable combination can be realized, so that influence of the first-order items and the second-order interaction items on comfort parameters is determined, a response function is conveniently constructed according to the effect values, a first response analysis curved surface is obtained, response surface analysis is realized, and accuracy of analysis results is ensured.
Further, after the first response analysis curved surface is obtained, the output result of the objective function may be minimized, that is, the difference between the actual output value of the response function and the target value of the parameter is minimized, and the point satisfying the target is searched for, thereby obtaining the optimized result of the first variable combination.
In a specific embodiment, determining the optimized value of each first variable based on the first response analysis surface and the objective function includes:
searching the final point meeting the target in the first response analysis curved surface by taking the output result of the objective function as the target, and obtaining the optimized value of each first variable based on the final point; the first response analysis curved surface is composed of a plurality of points, and each point is used for describing the value of each first variable and the actual output value of the corresponding comfort parameter.
The objective function is constructed based on the difference between the actual output value and the parameter target value, so that the output result of the objective function is minimized as a target, the difference between the actual output value and the parameter target value can be minimized, and the value of each first variable which enables the actual output value to be close to the parameter target value can be found.
Specifically, a point where the actual output value meets the target may be found in the first response analysis curved surface as the final point, and then the value of each first variable corresponding to the final point is used as the optimized value of each first variable.
By the embodiment, the optimal value which minimizes the difference between the actual output value and the parameter target value can be obtained, and the comfort performance requirement of the vehicle suspension design can be met.
It should be noted that, for each first variable combination, the above operation may be repeated until the optimized values of the first variables in all the first variable combinations are obtained.
S140, substituting experimental data of the second variables in the second variable combination and optimized values of the first variables into a simulation model to obtain simulation data of each comfort parameter, determining a second response analysis curved surface based on the experimental data of the second variables in the second variable combination, the optimized values of the first variables and the simulation data of each comfort parameter, and determining optimized values of each second variable based on the second response analysis curved surface and an objective function.
After optimizing all the first variable combinations, a second round of optimization, i.e. optimizing each second variable in all the second variable combinations, may be performed in combination with the results of the first round of optimization.
Specifically, in the second round of optimization, the central composite experimental method can be improved to a point-plane experimental method, namely, the value of the first variable which is already optimized in the second variable combination is fixed to be an optimized value, namely, fixed to be a point, so that experimental data of each second variable are determined in each plane of the experimental rectangular pyramid, and simulation data of comfort parameters can be conveniently obtained through a simulation model in the following steps.
Taking a second variable combination as an example, in a specific embodiment, substituting the experimental data of the second variable in the second variable combination and the optimized value of the first variable into the simulation model includes the following steps:
step 31, determining a vertex of the experimental rectangular pyramid based on the optimized value of the first variable in the second variable combination;
step 32, determining the center point, each axis point and the rest vertexes of the experimental rectangular pyramid based on the value range of each second variable in the second variable combination;
step 33, determining experimental data of a second variable in the second variable combination based on the center point, each axis point and the rest of each vertex of the experimental rectangular pyramid;
and step 34, inputting the experimental data of the second variable and the optimized value of the first variable in the second variable combination into the simulation model, so that the simulation model determines simulation values of all the comfort parameters according to the experimental values of the second variable, the optimized values of the first variable and initial values of other design variables except the second variable combination, and obtains the simulation data of all the comfort parameters.
In steps 31-32, the optimized value of the first variable in the second variable combination may be first used as one vertex of the experimental rectangular pyramid. And, according to the value range of each second variable in the second variable combination, each remaining vertex is set as the upper and lower bounds of the corresponding second variable, each axis point is set as the median of the corresponding second variable, and the center point is set as the median of all the second variables.
Fig. 3 is a schematic view of an experimental rectangular pyramid according to an embodiment of the present invention, where x, y, and z axes represent two second variables and first variables, respectively. As shown in fig. 3, where the solid star points may represent the optimized values of the first variables, the remaining vertices, axes points on the surface, and center points are experimental values of the second variables in the second round of optimization.
Further, in step 33, according to the constructed experimental rectangular pyramid, sampling may be performed on the bottom surface of the experimental rectangular pyramid, so as to obtain experimental data of each second variable in the second variable combination.
Further, in step 34, the experimental data of the second variable in the second variable combination and the optimized value of the first variable may be input into a simulation model, and the simulation value of each comfort parameter is calculated by the simulation model, so as to obtain the simulation data of each comfort parameter, where the simulation model may be regarded as such design variables to take initial values for other design variables except the second variable combination.
Through the steps 32-34, the experimental data based on the improved point-plane experimental design is obtained, and the experimental data of the second variable in the second variable combination can be obtained under the condition that the value of the first variable in the second variable combination is ensured to be an optimized value, so that response plane analysis is conducted on the remaining design variables according to the first round of optimization results, and the optimization efficiency is improved.
Specifically, after the simulation data of each comfort parameter is obtained, the main effect value of the first order term and the interactive effect value of the second order term in the second variable combination can be determined according to the experimental data of the second variable in the second variable combination, the optimized value of the first variable and the simulation data of each comfort parameter, and further, the response function between the second variable combination and each comfort parameter is determined according to the effect value and the interactive effect value, and the detailed process can refer to the description of the first variable combination.
Further, a second response analysis curved surface can be determined according to the response function corresponding to each comfort parameter, the output result of the objective function is minimized as a target, and the final point meeting the target is searched in the second response analysis curved surface, so that the optimized value of each second variable is obtained. The second response analysis curved surface is composed of a plurality of points, and each point is used for describing the value of each second variable and the actual output value of the corresponding comfort parameter.
It should be noted that, for each second variable combination, the above operation may be repeated until the optimized values of the second variables in all the second variable combinations are obtained.
After the optimized values of all the first variables and the optimized values of all the second variables are determined, the optimized values of all the design variables can be used as a final suspension design scheme to realize simulation experiments and optimization adjustment of the vehicle suspension.
The invention has the following technical effects: the method comprises the steps of determining each design variable and each comfort parameter of a vehicle suspension, constructing an objective function according to each comfort parameter, determining a first variable and a second variable in all the design variables, obtaining each first variable combination and each second variable combination, substituting experimental data of the first variable into a simulation model for each first variable combination, obtaining simulation data of each comfort parameter, determining a first response analysis curved surface based on the experimental data and the simulation data, obtaining an optimized value of each first variable in the first variable combination, obtaining an optimized value of each first variable in first wheel optimization based on response surface analysis, substituting the experimental data of the second variable and the optimized value of the first variable into the simulation model for each second variable combination, obtaining the optimized value of each second variable in the second variable combination in a point-surface composite mode, obtaining the optimized value of each second variable in the second wheel optimization, and realizing the optimized solution of the remaining design variable.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, electronic device 400 includes one or more processors 401 and memory 402.
The processor 401 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities and may control other components in the electronic device 400 to perform desired functions.
Memory 402 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM) and/or cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions may be stored on the computer readable storage medium that can be executed by the processor 401 to implement the response analysis based vehicle suspension multi-objective optimization method and/or other desired functions of any of the embodiments of the present invention described above. Various content such as initial arguments, thresholds, etc. may also be stored in the computer readable storage medium.
In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected by a bus system and/or other forms of connection mechanisms (not shown). The input device 403 may include, for example, a keyboard, a mouse, and the like. The output device 404 may output various information to the outside, including early warning prompt information, braking force, etc. The output device 404 may include, for example, a display, speakers, a printer, and a communication network and remote output devices connected thereto, etc.
Of course, only some of the components of the electronic device 400 that are relevant to the present invention are shown in fig. 4 for simplicity, components such as buses, input/output interfaces, etc. are omitted. In addition, electronic device 400 may include any other suitable components depending on the particular application.
In addition to the methods and apparatus described above, embodiments of the invention may also be a computer program product comprising computer program instructions which, when executed by a processor, cause the processor to perform the steps of the response analysis based vehicle suspension multi-objective optimization method provided by any of the embodiments of the invention.
The computer program product may write program code for performing operations of embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present invention may also be a computer-readable storage medium, having stored thereon computer program instructions, which when executed by a processor, cause the processor to perform the steps of the response analysis based vehicle suspension multi-objective optimization method provided by any of the embodiments of the present invention.
The computer readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present application. As used in this specification, the terms "a," "an," "the," and/or "the" are not intended to be limiting, but rather are to be construed as covering the singular and the plural, unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method or apparatus comprising such elements.
It should also be noted that the positional or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the positional or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or element in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Unless specifically stated or limited otherwise, the terms "mounted," "connected," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.
Claims (10)
1. A response analysis-based multi-objective optimization method for a vehicle suspension, comprising:
determining each design variable and each comfort parameter of a vehicle suspension, constructing an objective function according to each comfort parameter, and determining a first variable and a second variable in all the design variables;
determining each first variable combination and each second variable combination, wherein the first variable combination comprises three first variables and the second variable combination comprises one first variable and two second variables;
substituting experimental data of the first variable combinations into a simulation model for each first variable combination to obtain simulation data of each comfort parameter, determining a first response analysis curved surface based on the experimental data of the first variable combinations and the simulation data of each comfort parameter, and determining an optimized value of each first variable based on the first response analysis curved surface and the objective function;
substituting experimental data of the second variables in the second variable combination and optimized values of the first variables into a simulation model for each second variable combination to obtain simulation data of each comfort parameter, determining a second response analysis curved surface based on the experimental data of the second variables in the second variable combination, the optimized values of the first variables and the simulation data of each comfort parameter, and determining optimized values of each second variable based on the second response analysis curved surface and the objective function.
2. The method of claim 1, wherein said constructing an objective function from each comfort parameter comprises:
acquiring a parameter target value of each comfort parameter;
an objective function is constructed based on the differences between the parameter target values and the actual output values of all comfort parameters.
3. The method of claim 1, wherein substituting the experimental data of the first variable combination into a simulation model to obtain simulation data for each comfort parameter comprises:
determining a center point, each axis point and each cube point of the experimental cube based on the value range of each first variable in the first variable combination;
determining experimental data of the first variable combination based on a center point, each axis point and each cube point of the experimental cube, wherein the experimental data comprises a plurality of experimental values of each variable;
and inputting the experimental data of the first variable combination into a simulation model, so that the simulation model determines simulation values of all the comfort parameters according to the experimental values of all the first variables and initial values of other design variables except the first variable combination, and obtains the simulation data of all the comfort parameters.
4. The method of claim 1, wherein determining a first response analysis surface based on the experimental data for the first variable combination and the simulation data for each comfort parameter comprises:
determining a main effect value between each comfort parameter and a first-order item of each first variable in the first variable combination and an interaction effect value between each comfort parameter and a second-order cross item of each first variable in the first variable combination based on experimental data of the first variable combination and simulation data of each comfort parameter;
determining a response function between the first variable combination and each comfort parameter based on the main effect value and the interaction effect value;
and determining a first response analysis curved surface according to the response function corresponding to each comfort parameter.
5. The method of claim 4, wherein determining a response function between the first variable combination and each comfort parameter based on the main effect value and the interaction effect value comprises:
judging whether a main effect value and an interaction effect value between each comfort parameter and each first variable are smaller than a preset significance threshold value or not according to each comfort parameter;
if the effect value smaller than the preset significance threshold exists, eliminating a first-order item or a second-order cross item corresponding to the effect value in a response function corresponding to the comfort parameter.
6. The method of claim 4, further comprising, after determining the response function between the first variable combination and each comfort parameter:
for each comfort parameter, performing analysis of variance on a response function corresponding to the comfort parameter based on an actual output value of the response function;
and carrying out significance test on the response function according to the analysis of variance results, and if the significance test fails, re-determining experimental data of the first variable combination and simulation data of the comfort parameter so as to re-determine a main effect value and an interaction effect value between the comfort parameter and each first variable.
7. The method of claim 2, wherein determining the optimized value for each first variable based on the first response analysis surface and the objective function comprises:
searching the final point meeting the target in the first response analysis curved surface by taking the output result of the objective function as the target, and obtaining the optimized value of each first variable based on the final point;
the first response analysis curved surface is composed of a plurality of points, and each point is used for describing the value of each first variable and the actual output value of the corresponding comfort parameter.
8. The method of claim 1, wherein substituting the experimental data for the second variable in the second variable combination with the optimized value for the first variable into the simulation model comprises:
determining a vertex of the experimental rectangular pyramid based on the optimized value of the first variable in the second variable combination;
determining a center point, each axis point and the rest vertexes of the experimental rectangular pyramid based on the value range of each second variable in the second variable combination;
determining experimental data of a second variable in the second variable combination based on the center point, each axis point and the remaining vertexes of the experimental rectangular pyramid;
inputting experimental data of the second variable and the optimized value of the first variable in the second variable combination into a simulation model, so that the simulation model determines simulation values of all comfort parameters according to the experimental value of the second variable, the optimized value of the first variable and initial values of other design variables except the second variable combination, and simulation data of all comfort parameters are obtained.
9. An electronic device, the electronic device comprising:
a processor and a memory;
the processor is configured to execute the steps of the response analysis-based vehicle suspension multi-objective optimization method according to any one of claims 1 to 8 by calling a program or instructions stored in the memory.
10. A computer-readable storage medium storing a program or instructions that cause a computer to perform the steps of the response analysis-based vehicle suspension multi-objective optimization method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410171988.1A CN117725765B (en) | 2024-02-07 | 2024-02-07 | Vehicle suspension multi-objective optimization method, device and medium based on response analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410171988.1A CN117725765B (en) | 2024-02-07 | 2024-02-07 | Vehicle suspension multi-objective optimization method, device and medium based on response analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117725765A true CN117725765A (en) | 2024-03-19 |
| CN117725765B CN117725765B (en) | 2024-05-31 |
Family
ID=90207268
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410171988.1A Active CN117725765B (en) | 2024-02-07 | 2024-02-07 | Vehicle suspension multi-objective optimization method, device and medium based on response analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117725765B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866676A (en) * | 2015-05-27 | 2015-08-26 | 东南大学 | Bondbeam cable-stayed bridge sensor layout method based on two-phase multi-scale model correction |
| CN108446528A (en) * | 2018-06-01 | 2018-08-24 | 上汽通用五菱汽车股份有限公司 | Front suspension optimum design method, device and computer readable storage medium |
| CN112084708A (en) * | 2020-09-04 | 2020-12-15 | 西南交通大学 | AGV system optimization configuration method based on response surface and genetic algorithm |
| CN113742953A (en) * | 2020-05-29 | 2021-12-03 | 广州汽车集团股份有限公司 | Method and device for analyzing and optimizing abnormal sound of vehicle, controller and storage medium |
| US20230140696A1 (en) * | 2021-11-03 | 2023-05-04 | Electronics And Telecommunications Research Institute | Method and system for optimizing parameter intervals of manufacturing processes based on prediction intervals |
| CN116522475A (en) * | 2023-03-30 | 2023-08-01 | 重庆长安汽车股份有限公司 | Multidisciplinary optimization design method for suspension system and storage medium |
| CN117235871A (en) * | 2023-11-13 | 2023-12-15 | 湘潭大学 | Two-stage response surface model correction method for steel-concrete composite beam bridge |
| CN117290965A (en) * | 2023-11-22 | 2023-12-26 | 中汽研汽车检验中心(广州)有限公司 | Vehicle model simulation test simulation method, equipment and medium of vehicle simulation software |
-
2024
- 2024-02-07 CN CN202410171988.1A patent/CN117725765B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866676A (en) * | 2015-05-27 | 2015-08-26 | 东南大学 | Bondbeam cable-stayed bridge sensor layout method based on two-phase multi-scale model correction |
| CN108446528A (en) * | 2018-06-01 | 2018-08-24 | 上汽通用五菱汽车股份有限公司 | Front suspension optimum design method, device and computer readable storage medium |
| CN113742953A (en) * | 2020-05-29 | 2021-12-03 | 广州汽车集团股份有限公司 | Method and device for analyzing and optimizing abnormal sound of vehicle, controller and storage medium |
| CN112084708A (en) * | 2020-09-04 | 2020-12-15 | 西南交通大学 | AGV system optimization configuration method based on response surface and genetic algorithm |
| US20230140696A1 (en) * | 2021-11-03 | 2023-05-04 | Electronics And Telecommunications Research Institute | Method and system for optimizing parameter intervals of manufacturing processes based on prediction intervals |
| CN116522475A (en) * | 2023-03-30 | 2023-08-01 | 重庆长安汽车股份有限公司 | Multidisciplinary optimization design method for suspension system and storage medium |
| CN117235871A (en) * | 2023-11-13 | 2023-12-15 | 湘潭大学 | Two-stage response surface model correction method for steel-concrete composite beam bridge |
| CN117290965A (en) * | 2023-11-22 | 2023-12-26 | 中汽研汽车检验中心(广州)有限公司 | Vehicle model simulation test simulation method, equipment and medium of vehicle simulation software |
Non-Patent Citations (1)
| Title |
|---|
| 李佳豪: "机器人谐波减速器帽型柔轮疲劳分析与优化", 《中国优秀硕士学位论文全文数据库信息科技辑》, 15 January 2023 (2023-01-15) * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117725765B (en) | 2024-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8775338B2 (en) | Computer-implemented systems and methods for constructing a reduced input space utilizing the rejected variable space | |
| CN116227042B (en) | Vehicle windage coefficient determination method, apparatus and storage medium | |
| CN112308454B (en) | Method, device, equipment and storage medium for evaluating automobile steering stability | |
| CN116560895B (en) | Fault diagnosis method for mechanical equipment | |
| Shojaeefard et al. | An efficient sensitivity analysis method for modified geometry of Macpherson suspension based on Pearson correlation coefficient | |
| CN111931335A (en) | Suspension system equivalent parameter correction method and system, medium and electronic equipment | |
| Hidalgo et al. | Real-time dynamic simulations of large road vehicles using dense, sparse, and parallelization techniques | |
| CN117725765B (en) | Vehicle suspension multi-objective optimization method, device and medium based on response analysis | |
| CN117313249B (en) | Whole vehicle wind noise voice definition prediction method, equipment and storage medium | |
| CN113408070B (en) | Engine parameter determining method, device, equipment and storage medium | |
| Chen et al. | Robust design for improved vehicle handling under a range of maneuver conditions | |
| Shiiba et al. | Flexible multibody simulation of automotive systems with non-modal model reduction techniques | |
| CN112100747A (en) | Vehicle body framework topology optimization method, device, equipment and medium | |
| CN112861261A (en) | Vehicle body attachment point dynamic stiffness post-processing method and device and electronic equipment | |
| CN117744416B (en) | Vehicle stability optimization method, device and medium based on sensitivity analysis | |
| CN115526320A (en) | Neural network model inference acceleration method, apparatus, electronic device and medium | |
| CN117708997B (en) | Vehicle suspension design parameter determination method, device and medium based on genetic algorithm | |
| CN117725764B (en) | Regression model-based vehicle chassis multi-objective optimization method, equipment and medium | |
| CN115388906A (en) | Pose determination method and device, electronic equipment and storage medium | |
| CN117744410B (en) | Interval multi-objective optimization method, equipment and medium based on vehicle stability requirement | |
| CN115270584B (en) | Lightweight method suitable for new energy electric vehicle battery bracket | |
| CN115993965B (en) | Flexibly configurable low-code linkage system | |
| Duchanoy et al. | Optimal redesign of a shock absorber using physical variables and the full dynamics of the suspension system | |
| CN117725685B (en) | Multi-objective optimization method and equipment for vehicle operability | |
| CN111723926A (en) | Training method and training device for neural network model for determining image parallax |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |