CN113343352B - Chassis part target decomposition method based on robust optimization - Google Patents
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Abstract
The invention discloses a chassis part target decomposition method based on robust optimization, which comprises the following steps: step 1: establishing a suspension K & C characteristic parametric analysis model, and step 2: performing DOE analysis on the K characteristic of the suspension; and step 3: establishing a hard point coordinate and suspension K characteristic index approximate model; and 4, step 4: performing suspension hard point coordinate robust optimization based on suspension K characteristic indexes; and 5: performing DOE analysis on the suspension C characteristics; step 6: establishing an approximate model of the rigidity of the elastic part and the characteristic index of the suspension C; and 7: the method comprises the following steps of (1) optimizing the rigidity of a suspension elastic part in a multi-objective mode based on suspension C characteristic indexes; and 8: and outputting the rigidity of the hard point and the elastic part of the suspension. According to the invention, the initial state and the design range of the design index of the part are determined through numerical optimization, various performance indexes of the whole vehicle are comprehensively balanced, and the problem that the system level target of the driving performance is decomposed to the design index of the part of the chassis in the forward development of the chassis can be solved.
Description
Technical Field
The invention relates to the field of automobile running performance analysis, in particular to a method for decomposing a running performance system level target into a chassis part design index in a forward development part primary design stage of an entire automobile chassis.
Background
The key parts of the chassis mainly refer to a rod system structure forming a front suspension and a rear suspension, and elastic parts such as a lining, a spring and the like. The design indexes of the chassis parts mainly comprise: the chassis hard point is the mounting point or the motion reference point of the part and the rigidity of the elastic piece. The chassis hard point provides a geometric boundary for the design of parts such as a chassis rod system and the like, and the rigidity of the elastic part provides a basis for the design of the outer characteristic curve of the shock absorber, the spiral spring and the rubber bushing, so as to guide the detailed design of the structure of the shock absorber, the spiral spring and the rubber bushing.
The hard points of the chassis directly determine the motion track of the chassis rod system, thereby influencing the posture change of tires when wheels jump along with the ground in the running process of the vehicle and directly influencing the operation stability of the vehicle. The chassis bushing is mainly used for reducing the abrasion among metals, isolating road noise and obviously influencing the smoothness; meanwhile, the bushing is flexible and can elastically deform in a certain range, and certain influence is exerted on the movement of the chassis bar system, namely the operation stability of the vehicle is influenced. The coil spring rate mainly affects the ride comfort of the vehicle.
The design of chassis driving performance parts mainly relates to chassis hard point design, liner rigidity design in all directions and spring rigidity design. At present, the chassis parts of the host factory are mainly designed in a reverse development mode. The design index range and the initial state of the parts are determined by the benchmarks, so that the multi-performance balance of the whole vehicle is difficult to ensure to reach the standard, and the influence of the performance degradation of the parts on the performance of the whole vehicle cannot be considered.
Therefore, a decomposition method from a system target to a part design index is a key technology for forward development of the chassis, and is a bottom-layer implementation way for creating a chassis personalized style based on user requirements.
Disclosure of Invention
The invention provides a chassis part target decomposition method based on robust optimization, which aims to solve the problem that a driving performance system level target is decomposed to a chassis part design index in forward development of a chassis, determine the initial state and the design range of the part design index through numerical optimization, comprehensively balance various performance indexes of a whole vehicle and achieve the purpose of achieving the driving performance system level target of the whole vehicle and personalized manufacturing of chassis performance.
The technical scheme of the invention is as follows:
the invention discloses a chassis part target decomposition method based on robust optimization, which comprises the steps of building a suspension K & C characteristic analysis model through a multi-body dynamics modeling technology, and carrying out parameterization on chassis hard points, bushing rigidity and spiral spring rigidity to form a parameterized suspension analysis model; depending on a suspension K characteristic analysis model, developing DOE analysis on suspension K characteristics and establishing a hard point and suspension K characteristic index approximate model; performing hard point robust optimization by taking a suspension K characteristic index as a constraint based on a K characteristic approximation model to obtain a hard point design scheme; updating a hard point of a suspension C model, developing DOE analysis on the suspension C characteristic by depending on a suspension C characteristic analysis model, and establishing an approximate model of the rigidity of the elastic component and the C characteristic index; performing multi-objective optimization by taking the C characteristic index of the suspension as a constraint and optimization target based on the C characteristic approximate model to obtain a rigidity design scheme of the elastic component; and outputting a suspension hard point and rigidity design scheme, and completing the decomposition from the driving performance system level target to the part design index.
The method comprises the following concrete implementation steps:
step 1: establishing suspension K & C characteristic parametric analysis model
Step 1.1: establishing a C characteristic analysis model of the suspension in multi-body analysis software, determining the installation positions of a suspension rod system and an elastic component by inputting the coordinate position of a hard point, establishing the suspension rod system by inputting the mass and the rotational inertia of each rod piece, establishing the elastic component by inputting the rigidity of each elastic component in each direction, and establishing a constraint relation between each rod piece and each elastic component through a hinge to complete the establishment of the C characteristic analysis model of the suspension.
Step 1.2: and establishing a K characteristic analysis model on the basis of the C characteristic analysis model, establishing a constraint relation at the position of the bushing in the C model by using a hinge, and inhibiting the attribute of the original bushing to complete the establishment of the suspension K characteristic analysis model.
Step 1.3: compiling a script file by using programming languages such as Python/Matlab and the like, extracting suspension hard point coordinates, bushing stiffness and spring stiffness in a suspension model file, defining variables to describe the hard point coordinates, the bushing stiffness and the spring stiffness, and finishing model file updating through the variables; and calling a multi-body analysis software solving module through a script to perform automatic simulation solving and automatic result extraction. And realizing parameterization of the suspension K characteristic analysis model and the C characteristic analysis model.
Step 2: suspension K characteristic DOE analysis
And (3) taking the coordinate variable of the hard point of the suspension defined in the step 1.3 as a design variable, establishing a DOE (design of element) design matrix by using a Latin hypercube method, calling the parameterized suspension K model established in the step 1.3 to perform K characteristic analysis, extracting K characteristic indexes of the suspension, and completing the DOE analysis of the K characteristic of the suspension.
And 3, step 3: establishing a hard point coordinate and suspension K characteristic index approximate model
And (3) calculating a correlation coefficient of the hard point coordinate and the suspension K characteristic index according to the DOE analysis result obtained in the step (2), screening a key hard point coordinate variable influencing the suspension K characteristic index according to the correlation coefficient, and establishing an approximate model by taking the key hard point coordinate variable as a design variable and the suspension K characteristic index as a response.
And 4, step 4: suspension hard point coordinate robust optimization based on suspension K characteristic index
Giving a hard point position deviation distribution function at the installation position of the bushing, based on the K characteristic approximate model established in the step 3, taking the suspension K characteristic index as constraint, estimating a response standard deviation by using a first-order moment method (FOSM method), and performing hard point position robust optimization by taking the minimum response standard deviation as an optimization target; and (3) calling a suspension K characteristic analysis model to verify the optimization result, returning to the step 2 to increase DOE sample points and update a DOE design matrix if the requirement of the K characteristic index is not met, and obtaining a hard point design scheme if the requirement of the K characteristic index is met.
And 5: suspension C characteristic DOE analysis
And (3) updating the hard point position of the suspension C model established in the step (1.3) by using the hard point coordinates obtained in the step (4), establishing a DOE (design of element) design matrix by using the stiffness variables of the suspension bushing and the spring defined in the step (1.3) as design variables and by using a Latin hypercube method, calling the parameterized suspension C model established in the step (1.3) to perform C characteristic analysis and extracting suspension C characteristic indexes, and completing the DOE analysis of the suspension C characteristics.
Step 6: establishing an approximate model of the rigidity of the elastic component and the C characteristic index of the suspension
And 5, calculating correlation coefficients of the rigidity of the bush, the rigidity of the spring and the characteristic index of the suspension C according to the DOE analysis result obtained in the step 5, screening the rigidity of the key bush and the rigidity of the spring which influence the characteristic index of the suspension C according to the correlation coefficients, and establishing an approximate model by using the rigidity of the key bush and the rigidity of the spring as design variables and the characteristic index of the suspension C as response.
And 7: suspension elastic part rigidity multi-objective optimization based on suspension C characteristic index
Based on the C characteristic approximate model established in the step 6, multi-objective optimization is carried out by taking the C characteristic index of the suspension as a constraint or optimization target and taking the rigidity of the key bushing and the rigidity of the spring as design variables; and (3) after an optimization result is obtained, calling a suspension C characteristic analysis model to verify the optimization result, returning to the step 5 to increase DOE sample points and update a DOE design matrix if the C characteristic index requirement is not met, and obtaining an elastic component rigidity design scheme if the C characteristic index requirement is met.
And 8: output suspension hard point and elastic component rigidity
And (4) outputting the hard point design scheme obtained in the step (4) and the elastic component rigidity design scheme obtained in the step (7) in a text form to finish the whole method flow.
The invention has the following advantages and beneficial effects:
1. the target decomposition method of the chassis parts has the characteristics of reducing the influence of the lining on the K characteristic to the maximum extent through hard point robust optimization, realizing K & C characteristic design decoupling, and reducing the multi-target optimization dimension and optimization difficulty of the chassis parts.
2. According to the chassis part target decomposition method, automation of model solving is achieved through a parametric modeling technology in a response solving process, simulation solving time is reduced through introducing an approximate model technology in an optimization process, and model calling solving times are reduced through introducing an approximate estimation method (FOSM method) in a response standard deviation solving process. The invention can effectively improve the design efficiency of chassis parts.
Drawings
FIG. 1 is a flow chart of a chassis component target decomposition method based on robust optimization;
FIG. 2 is a diagram of a multi-body dynamics model of a four-bar linkage structure of the rear suspension.
Detailed Description
In order to better illustrate the objects and advantages of the present invention, the following example of the decomposition of the rear suspension component object of the Changan vehicle model is further described with reference to the table and the accompanying drawings, and the specific implementation steps are as follows:
step 1: establishing suspension K & C characteristic parametric analysis model
Step 1.1: a C characteristic analysis model of a rear suspension is established in multi-body analysis software Adams/Car, and the rear suspension is of a four-bar linkage structure, as shown in figure 2. The method comprises the steps of determining the installation positions of a suspension rod system and an elastic component by inputting the coordinate position of a hard point, establishing the suspension rod system by inputting the mass and the rotational inertia of each rod piece, establishing the elastic component by inputting the rigidity of each elastic component in each direction, establishing a constraint relation between each rod piece and the elastic component through a hinge, and completing the establishment of a C characteristic analysis model of the rear suspension.
Step 1.2: and establishing a K characteristic analysis model on the basis of the C characteristic analysis model, establishing a constraint relation at the position of the bushing in the C model by using a hinge, and inhibiting the attribute of the original bushing to complete the establishment of the K characteristic analysis model of the rear suspension.
Step 1.3: writing a script file by using Matlab, extracting suspension hard point coordinates, bushing stiffness and spring stiffness in a rear suspension model file, and defining variables to describe the hard point coordinates, the bushing stiffness and the spring stiffness, wherein the hard point coordinates are defined in a sub file, the bushing stiffness is defined in a bus, and the spring stiffness is defined in a spr, and updating of a corresponding model file is completed through the variables; and calling an Adams/Car solving module through a script to perform simulation automatic solving and result automatic extraction. And parameterization of a K characteristic analysis model and a C characteristic analysis model of the rear suspension is realized.
Step 2: suspension K-characteristic DOE analysis
And (3) taking the coordinate variable of the hard point of the rear suspension defined in the step 1.3 as a design variable, wherein the specific variable and the value range are shown in the table 1.
TABLE 1 rear suspension design variables
A DOE design matrix was built using the latin hypercube method, giving 801 sample points. And (3) calling the parameterized rear suspension K model established in the step 1.3 to perform K characteristic analysis and extract a rear suspension K characteristic index, wherein the K characteristic index is shown in table 2, and the DOE analysis of the rear suspension K characteristic is completed through automatic simulation solution.
TABLE 2 rear suspension K characteristic index
And step 3: establishing a hard point coordinate and suspension K characteristic index approximate model
And (3) calculating a correlation coefficient between the hard point coordinate and the K characteristic index of the rear suspension according to the DOE analysis result obtained in the step (2), and selecting a design variable with the absolute value of the correlation coefficient larger than 0.25 as a key hard point coordinate variable, wherein the specific variable is shown in a table 3. And establishing an approximate model by using the selected key hard point coordinate variable as a design variable and the K characteristic index of the rear suspension as a response and using a least square method.
TABLE 3 Key hard Point coordinate variables
And 4, step 4: suspension hard point coordinate robust optimization based on suspension K characteristic index
And setting the position deviation distribution function of the key hard points as a normal distribution function, wherein the standard deviation of the normal distribution function is 0.1 mm. Based on the K characteristic approximation model established in the step 3, the K characteristic index of the rear suspension is used as constraint, a first-order moment method (FOSM method) is used for estimating the response standard deviation, the quadratic sum of the response standard deviations is used as an optimization target for carrying out hard point position robust optimization, and the optimization result is shown in a table 4.
TABLE 4 rear suspension hard point coordinate robust optimization results
And (5) calling a rear suspension K characteristic analysis model to verify the optimization result, wherein the verification result is shown in a table 5. From the results of table 5, it can be seen that the sum of squares of the standard deviations of the responses is reduced from 0.9589 to 0.9396, and the response results satisfy the K characteristic index requirements, and the obtained solution is the hard spot design solution.
Table 5 verification table for robust optimization result of hard point coordinates of rear suspension
And 5: suspension C characteristic DOE analysis
And (4) updating the hard point position of the rear suspension C model established in the step (1.3) by using the hard point coordinates obtained in the step (4), and taking the rigidity variables of the rear suspension bushing and the spring rigidity defined in the step (1.3) as design variables, wherein the specific variables and the value range are shown in a table 6.
TABLE 6 rear suspension spring design variables
The DOE design matrix was built using the latin hypercube method, giving 500 sample points. And (3) calling the parameterized rear suspension C model established in the step 1.3 to perform C characteristic analysis and extract a suspension C characteristic index, and completing suspension C characteristic DOE analysis, wherein the C characteristic index is shown in a table 7.
TABLE 7 rear suspension C characteristic index
Step 6: establishing an approximate model of the rigidity of the elastic component and the C characteristic index of the suspension
And (5) calculating correlation coefficients of the rigidity of the bushing, the rigidity of the spring and the characteristic index of the suspension C according to the DOE analysis result obtained in the step (5), and selecting design variables with the absolute values of the correlation coefficients larger than 0.25 as the rigidity of the key bushing and the rigidity of the spring, wherein the key design variables are shown in a table 8. And establishing an approximate model by using a least square method by taking the rigidity of the key bushing and the rigidity of the spring as design variables and the characteristic index of the suspension C as response.
TABLE 8 Key design variables for rear suspension spring
And 7: suspension elastic part rigidity multi-objective optimization based on suspension C characteristic index
And (4) performing multi-objective optimization by taking the C characteristic index of the rear suspension as a constraint or optimization target and the rigidity of the key bushing and the rigidity of the spring as design variables on the basis of the C characteristic approximate model established in the step (6), wherein the optimization result is shown in a table 9.
TABLE 9 optimization results of the characteristics of the rear suspension spring C
And calling a rear suspension C characteristic analysis model to verify the optimization result, wherein the verification result is shown in a table 10. It can be seen from table 10 that the verification result satisfies the requirement of the C characteristic index, and the obtained optimized design scheme is the design scheme of the rigidity of the elastic component.
Table 10 verification table for C characteristic optimization results of rear suspension elastic member
And 8: output suspension hard point and elastic component rigidity
And (4) outputting the hard point design scheme obtained in the step (4) and the elastic component rigidity design scheme obtained in the step (7) in a text form to finish the whole method flow.
Claims (6)
1. A chassis part target decomposition method based on robust optimization is characterized by comprising the following steps:
step 1: establishing a suspension K & C characteristic parametric analysis model, which comprises the following steps: establishing a suspension C characteristic analysis model, establishing a suspension K characteristic analysis model, and carrying out parameterization on the suspension K characteristic analysis model and the suspension C characteristic analysis model;
step 2: performing suspension K characteristic DOE analysis;
and step 3: establishing a hard point coordinate and suspension K characteristic index approximate model;
and 4, step 4: suspension hard point coordinate robust optimization based on suspension K characteristic index
Giving a hard point position deviation distribution function at the installation position of the bushing, based on the K characteristic approximate model established in the step 3, taking the suspension K characteristic index as constraint, estimating a response standard deviation by using a first-order moment method (FOSM method), and performing hard point position robust optimization by taking the minimum response standard deviation as an optimization target; calling a suspension K characteristic analysis model to verify an optimization result, returning to the step 2 to increase DOE sample points and update a DOE design matrix if the requirement of the K characteristic index is not met, and obtaining a hard point design scheme if the requirement of the K characteristic index is met;
and 5: performing DOE analysis on the suspension C characteristics;
step 6: establishing an approximate model of the rigidity of the elastic component and the C characteristic index of the suspension
And 7: suspension elastic part rigidity multi-objective optimization based on suspension C characteristic index
Based on the C characteristic approximate model established in the step 6, multi-objective optimization is carried out by taking the C characteristic index of the suspension as a constraint or optimization target and taking the rigidity of the key bushing and the rigidity of the spring as design variables; after the optimization result is obtained, calling a suspension C characteristic analysis model to verify the optimization result, returning to the step 5 to increase DOE sample points and update a DOE design matrix if the C characteristic index requirement is not met, and obtaining an elastic component rigidity design scheme if the C characteristic index requirement is met;
and 8: and outputting the rigidity of the hard point and the elastic component of the suspension.
2. The chassis component object decomposition method as recited in claim 1, wherein said step 1 comprises:
step 1.1: establishing a C characteristic analysis model of the suspension in multi-body analysis software, determining the installation positions of a suspension rod system and an elastic component by inputting the coordinate position of a hard point, establishing the suspension rod system by inputting the mass and the rotational inertia of each rod piece, establishing the elastic component by inputting the rigidity of each elastic component in each direction, establishing a constraint relation between each rod piece and each elastic component through a hinge, and completing the establishment of the C characteristic analysis model of the suspension;
step 1.2: establishing a K characteristic analysis model on the basis of the C characteristic analysis model, establishing a constraint relation at the position of the bushing in the C model by using a hinge, and inhibiting the attribute of the original bushing to complete the establishment of the suspension K characteristic analysis model;
step 1.3: compiling a script file, extracting suspension hard point coordinates, bushing stiffness and spring stiffness in a suspension model file, defining variables to describe the hard point coordinates, the bushing stiffness and the spring stiffness, and finishing model file updating through the variables; and calling a multi-body analysis software solving module through a script to perform automatic simulation solving and automatic result extraction, so as to realize parameterization of the suspension K characteristic analysis model and the C characteristic analysis model.
3. The chassis part target decomposition method according to claim 2, wherein the step 2 is to use the suspension hard point coordinate variable defined in the step 1.3 as a design variable, establish a DOE design matrix by using a Latin hypercube method, call the parameterized suspension K model established in the step 1.3 to perform K characteristic analysis, extract a suspension K characteristic index, and complete suspension K characteristic DOE analysis.
4. The chassis component target decomposition method according to claim 1, 2 or 3, wherein in the step 3, a correlation coefficient between a hard point coordinate and a suspension K characteristic index is calculated according to the DOE analysis result obtained in the step 2, a key hard point coordinate variable affecting the suspension K characteristic index is screened according to the correlation coefficient, and an approximate model is established by taking the key hard point coordinate variable as a design variable and the suspension K characteristic index as a response.
5. The chassis part target decomposition method according to claim 1, 2 or 3, wherein step 5 is to update the hard point position of the suspension C model established in step 1.3 by using the hard point coordinates obtained in step 4, establish a DOE design matrix by using the suspension bushing stiffness and spring stiffness variables defined in step 1.3 as design variables and using a latin hypercube method, and perform C characteristic analysis by calling the parameterized suspension C model established in step 1.3 and extracting suspension C characteristic indexes to complete suspension C characteristic DOE analysis.
6. The chassis part target decomposition method according to claim 1, 2 or 3, wherein step 6 is to calculate correlation coefficients of the bushing stiffness and the spring stiffness with the suspension C characteristic index according to the DOE analysis result obtained in step 5, screen key bushing stiffness and spring stiffness which affect the suspension C characteristic index according to the correlation coefficients, and establish an approximate model by using the key bushing stiffness and spring stiffness as design variables and the suspension C characteristic index as response.
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