CN109918836B - Bogie suspension parameter optimization matching method - Google Patents
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Abstract
The invention provides a bogie suspension parameter optimization matching method, and relates to the technical field of suspension parameter optimization design of rolling stock. Establishing a vehicle dynamics simulation model; performing suspension parameter matching optimization analysis, determining input variables and value ranges thereof, output evaluation indexes and threshold settings thereof required by the model, establishing a joint simulation module for vehicle dynamics model simulation and MATLAB, performing characteristic value simulation calculation on the vehicle dynamics model according to an optimization target, and extracting an optimized target value obtained by simulation through a joint simulation interface; the method comprises the steps of obtaining a needed suspension parameter group from a large number of random parameters in a layer-by-layer screening mode, finally processing optimization result data by a discrete statistical method, mining matching relations among suspension parameters by combining a three-dimensional histogram, and selecting specific parameter values of suspension parameter matching combinations meeting requirements and corresponding optimization target values of the specific parameter values according to actual requirements.
Description
Technical Field
The invention relates to the technical field of suspension parameter optimization design of rolling stock.
Background
The transverse snaking motion stability is one of the most concerned problems in the design of a wheel-rail train bogie, and the primary suspension and the secondary suspension of the bogie, particularly the suspension parameters in the horizontal direction, play a key role in improving the transverse stability of the train bogie and considering other performances of the train. Reasonable suspension parameter matching is beneficial to improving the lateral stability of the train and resisting the influence of suspension parameters and perturbation of wheel-rail contact parameters on the stability of the system (called system robustness). Therefore, research for optimizing the matching of bogie suspension parameters is crucial to the design of trains.
For a complex train suspension system, besides the fact that the secondary horizontal stiffness parameters mainly affect the curve passing performance of a train, other horizontal direction suspension parameters such as primary longitudinal positioning stiffness, primary transverse positioning stiffness, anti-snaking shock absorber damping, secondary transverse shock absorber damping, anti-snaking shock absorber rubber joint stiffness and secondary transverse shock absorber rubber joint stiffness mainly affect the transverse running stability of the train. With the rapid development of high-speed railways, the requirements on the dynamic performance of vehicle systems are higher and higher. The stability of the transverse movement of the train is an important condition for the safe operation of the train, once a vehicle system has the hunting instability of a bogie, the operation quality of the train is rapidly deteriorated, the acting force of a wheel rail is enhanced, a line is damaged, and even a derailment accident can be caused seriously. Therefore, it is necessary to optimize the suspension parameters for the lateral stability of the train.
The traditional dynamic evaluation indexes of the high-speed train mainly comprise indexes such as a transverse stability index, a vertical stability index, a derailment coefficient, a wheel load shedding rate, a wheel axle transverse force and an overturning coefficient. The indexes are obtained by neglecting the influence of the perturbation of the bogie suspension parameters and the wheel-rail contact parameters on the dynamic performance of the train under the condition that the suspension parameters and the wheel-rail contact parameters of the train are assumed to be constant values. The two parameters have large disturbance in the manufacturing and actual operation of the vehicle, and the two parameters have large influence on the operation safety of the train. In addition, the indexes are obtained by simulation under the normal equivalent taper operating condition, the stability change of the train under the low taper operating condition is not considered, and the low-speed train shaking phenomenon of the train under the low taper operating condition often occurs, so that the riding comfort of the train is seriously influenced.
Key contents of this study include: the optimization target and the threshold value thereof can be flexibly and detailedly defined according to specific requirements, and the required suspension parameter group is obtained from a large amount of random parameters in a layer-by-layer screening mode; the low taper stability, the normal taper stability, the suspension parameter robustness and the equivalent taper robustness are proposed and considered as targets for optimization; and (5) researching a suspension parameter matching rule by adopting a discrete statistical method.
Disclosure of Invention
The invention aims to provide a bogie suspension parameter optimization matching method, which can effectively solve the technical problem of defining the matching target value range of bogie suspension parameters in detail according to actual requirements.
The purpose of the invention is realized by the following technical scheme: a bogie suspension parameter optimization matching method comprises the following steps:
firstly, performing suspension parameter optimization analysis according to a vehicle dynamics model, determining input variables, value ranges, output evaluation indexes and threshold settings required by the model, establishing a combined simulation module by using a SIMPACK scripting language to simulate the vehicle dynamics model and MATLAB, and uniformly acquiring N groups of random parameters aiming at the input variables by using the MATLAB, wherein N is 50000; the input variables comprise six key suspension parameters of the bogie which influence the transverse stability of the train, namely primary suspension longitudinal rigidity, primary suspension transverse rigidity, anti-snake shock absorber damping, secondary transverse shock absorber damping, anti-snake shock absorber joint rigidity and secondary transverse shock absorber joint rigidity;
step two, endowing the called random parameters to a vehicle dynamics model, carrying out low taper stability simulation on the model at the speed of 200km/h, and extracting a stability index zeta through a SIMPACK and MATLAB combined simulation interfacelow；
Step three, judging the low taper stability index zeta of the vehiclelowIf the requirements are not met, calling a next group of random parameters and turning to the second step, and giving the vehicle dynamics model to continue the low taper stability simulation until zeta satisfaction appearslowWhen the index requires random parameters, the next step is carried out;
step four, satisfying zeta obtained in step threelowThe vehicle dynamics model under the random parameter required by the index carries out the normal taper stability simulation under the speed of 350km/h, and the stability index zeta is extractednorm(ii) a And judging whether the requirements are met or not, if not, calling the next groupAnd (5) random parameters are changed to the second step until zeta is satisfied simultaneouslylowAnd ζnormWhen the index requires random parameters, the next step is carried out;
step five, satisfying zeta obtained in step fourlowAnd ζnormCalculating the robustness of the suspension parameter of the vehicle dynamics model under the random parameter required by the index, and extracting the stability index std (zeta)par) (ii) a Judging whether the parameters meet the requirements, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylow、ζnormAnd std (ζ)par) When the index requires random parameters, the next step is carried out;
step six, satisfying zeta obtained in step fivelow、ζnormAnd std (ζ)par) Calculating the robustness of equivalent taper by dynamics under random parameters required by the indexes, and extracting a stability index std (zeta lambda); judging whether the parameters meet the requirements, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylow、ζnorm、std(ζpar) And std (ζ)λ) When the index requires random parameters, the group of data is stored, and the next group of random parameters is continuously called, and the step two is carried out to carry out the next cycle;
after the next group of random parameters is called, judging whether the called random parameters are empty sets, if not, continuing to circulate in the second step, and if so, calling all the generated groups of random parameters, and finishing the matching optimization;
and seventhly, performing data processing on the obtained optimized result by adopting a normalization and discrete statistics method, selecting a parameter specific value of the suspension parameter matching combination meeting the requirement and a corresponding optimized target value from the optimized result data according to actual requirements, and further mining the matching relation between the suspension parameters by combining a three-dimensional histogram.
The method relates to four sub-targets of a low taper stability index, a normal taper stability index, a suspension parameter robustness index and an equivalent taper robustness index in the performance indexes of the rolling stock, wherein thresholds are respectively set for the four sub-targets in the optimization process, and when a vehicle system endowed with random parameters meets the threshold range set by the four sub-targets, the random parameters are stored, namely, the set threshold range is met and the random parameters are regarded as the optimization result.
The vehicle dynamics model is established and suspension parameter optimization analysis is carried out, and the specific operation is as follows:
firstly, a vehicle dynamic model is established according to the CRH3 structural parameters. Determining important input variables and value ranges thereof required to be designed by the model based on the existing rule of stability of the train transverse stability; and according to the actual running condition of the model, the low taper stability index zetalowZeta, normal taper stability indexnorm, suspensionHanging parameter robustness index std (ζ)par) And the equivalent taper robustness index std (ζ)λ) The optimization target range is included, a total of four optimization targets are taken as outputs, and the thresholds of the four optimization targets are set. Since the present study is primarily directed to train lateral stability, the initial parameters (i.e., input variables) include six key suspension parameters of the bogie that affect train lateral stability, such as primary suspension longitudinal stiffness kpx, primary suspension lateral stiffness kpy, anti-hunting shock absorber damping csx, secondary lateral shock absorber damping csy, anti-hunting shock absorber joint stiffness kncsx, and secondary lateral shock absorber joint stiffness kncsy. And then, establishing a joint simulation module by using a SIMPACK scripting language and MATLAB for vehicle dynamics model simulation, and generating 50000 groups of uniformly distributed random parameters aiming at input variables by using the MATLAB.
And giving the generated random parameters to a vehicle dynamics model one by one, and sequentially carrying out low taper stability simulation, normal taper stability simulation and suspension parameter robustness and equivalent taper robustness calculation on the model. Judging the simulation result after each simulation, and if the simulation result does not meet the requirement, calling the next group of random parameters and transferring to the second step; and carrying out next simulation calculation under the condition of meeting the requirements. When the vehicle system endowed with the group of random parameters meets the requirements of all four optimization targets, the group of random parameters is stored, the next group of random parameters is called to continue simulation, and finally all the suspension parameter groups meeting the requirements of all four optimization targets are obtained from a large number of random parameters. After the next group of random parameters is called each time, whether the called random parameters are empty sets needs to be judged, if not, the step two can be carried out to continue circulation, if yes, all the generated random parameters are called, and the matching optimization is finished.
The obtained suspension parameter optimization matching data needs to be mined, and the concrete operations are as follows:
because the optimized data are numerous, the matching relation between the suspension parameters is not easy to find, and the optimized matching result is subjected to data processing; the optimized matching result data obtained after normalization processing is adopted, six suspension parameter distributions with different sizes can be compared on one graph, and specific parameter values and corresponding optimized target values of 6 typical suspension parameter matching combinations selected from the optimized result data according to actual requirements are selected; in addition, discrete statistical processing is carried out on the optimized matching result data, and the matching relation between the suspension parameters is further mined by combining the three-dimensional histogram.
The invention effectively combines the optimization matching of bogie suspension parameters with the discrete statistics, solves the problem that the study of the matching relationship of the suspension parameters becomes abnormal and difficult due to a plurality of suspension parameter groups and a plurality of suspension parameter types in the study process of the optimization matching relationship of the bogie suspension parameters, finally realizes the optimization design process of the bogie suspension parameters with high efficiency at low calculation cost, and can excavate the matching relationship of the suspension parameters, thereby ensuring that the operation of a high-speed train can take into account a plurality of indexes such as low taper stability, normal taper stability, suspension parameter robustness and equivalent taper robustness, and the like, processes the optimization matching data of the suspension parameters by the method of the discrete statistics, and selects the combination which is most in line with the design requirement from the optimization matching data. The method has important significance and obvious engineering practical application value for improving the design and analysis capability of the bogie suspension parameters.
Compared with the prior art, the invention has the advantages and effects that:
firstly, putting the low taper stability into the optimization target range
The existing research mainly aims at optimizing and designing suspension parameters by taking traditional dynamic indexes of a vehicle model as targets, the dynamic performance of a vehicle under normal taper is considered, the change of the system stability of the vehicle system when the vehicle system operates under the working condition of lower equivalent taper is ignored, and the vehicle is easy to shake at low speed under the working condition of low taper, so that the riding comfort of a train is seriously influenced. The invention not only puts forward that the low taper stability index is brought into the optimization range of the suspension parameters, but also optimizes the vehicle suspension parameters from a plurality of angles by taking the normal taper stability index, the suspension parameter robustness index and the equivalent taper robustness index into consideration. And the method is optimized in a layer-by-layer screening mode, so that a more reasonable suspension parameter matching mode is obtained, and conditions are provided for obtaining a more reliable bogie suspension parameter optimization result.
Secondly, optimizing and matching data processing on suspension parameters by adopting a discrete statistical method to obtain a combination meeting requirements
Because the optimization result data volume obtained by optimizing in a layer-by-layer screening mode is large, the data analysis is very difficult, and the suspension parameter matching relationship is not easy to study. The research adopts a discrete statistical method to process a large amount of optimized matching data of the suspension parameters, further excavates the matching relation between the suspension parameters of the bogie by combining a three-dimensional histogram, and has important significance and obvious practical engineering application value for improving the design and analysis capability of the suspension parameters of the bogie.
Drawings
FIG. 1 is a flow chart of the present invention for rapid analysis of optimal matching of bogie suspension parameters
FIG. 2 illustrates suspension parameter optimized match data after normalization and discrete statistics in accordance with the present invention
FIG. 3 is a graph showing the matching relationship between kpx, csx and kncsx according to the present invention
FIG. 4 is a graph showing the matching relationship between kpx, kpy and csy according to the present invention
Detailed Description
The invention is further described below with reference to the accompanying drawings:
firstly, performing suspension parameter optimization analysis according to a vehicle dynamics model, determining input variables and value ranges thereof, output evaluation indexes and threshold settings thereof, wherein the input variables and the value ranges thereof are required by the model, the threshold settings are shown in table 1, establishing a joint simulation module by using vehicle dynamics model simulation and MATLAB through SIMPACK scripting language, and uniformly acquiring N groups of random parameters aiming at the input variables through the MATLAB, wherein N is 50000;
TABLE 1 threshold settings for four optimization objectives
Step two, endowing the called random parameters to a vehicle dynamics model, carrying out low taper stability simulation on the model at the speed of 200km/h, and extracting the stability index zeta through a SIMPACK and MATLAB combined simulation interfacelow；
Step three, judging the low taper stability index zeta of the vehiclelowIf the requirements are not met, calling a next group of random parameters and turning to the second step, and giving the vehicle dynamics model to continue the low taper stability simulation until zeta satisfaction appearslowWhen the index requires random parameters, the next step is carried out;
step four, satisfying zeta obtained in step threelowThe vehicle dynamics model under the random parameter required by the index carries out the normal taper stability simulation under the speed of 350km/h, and the stability index zeta is extractednorm(ii) a And judging whether the requirements are met, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylowAnd ζnormWhen the index requires random parameters, the next step is carried out;
step five, satisfying zeta obtained in step fourlowAnd ζnormCalculating the robustness of the suspension parameter of the vehicle dynamics model under the random parameter required by the index, and extracting the stability index std (zeta)par) (ii) a Judging whether the requirement is met, if the requirement is not met,invoking the next set of random parameters and turning to step two until zeta is satisfied simultaneouslylow、ζnormAnd std (ζ)par) When the index requires random parameters, the next step is carried out;
step six, meeting zeta-demand obtained in step fivelow、ζnormAnd std (ζ)par) Calculating the robustness of equivalent taper by dynamics under random parameters required by the index, and extracting the stability index std (zeta)λ) (ii) a Judging whether the parameters meet the requirements, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylow、ζnorm、std(ζpar) And std (ζ)λ) When the index requires random parameters, the group of data is stored, and the next group of random parameters is continuously called, and the step two is carried out to carry out the next cycle;
after the next group of random parameters is called, judging whether the called random parameters are empty sets, if not, continuing to circulate in the second step, and if so, calling all the generated groups of random parameters, and finishing the matching optimization;
and seventhly, performing data processing on the obtained optimization result by adopting a normalization and discrete statistics method, selecting specific parameter values of suspension parameter matching combinations meeting requirements and corresponding optimization target values from the optimization result data according to actual requirements, and further mining the matching relation between the suspension parameters by combining a three-dimensional histogram.
1. Constructing a kinetic model and generating random parameters
And establishing a vehicle dynamic model according to the CRH3 structural parameters. Determining important input variables and value ranges thereof required to be designed by the model based on the existing rule of stability of the train transverse stability; and according to the actual running condition of the model, the low taper stability index zetalowZeta, normal taper stability indexnormAnd suspension parameter robustness index std (ζ)par) And the equivalent taper robustness index std (ζ)λ) The optimization target range is included, a total of four optimization targets are used as output, and four optimization targets are setA threshold value of the optimization objective. Since the present study is primarily directed to train lateral stability, the initial parameters (i.e., input variables) include six key suspension parameters of the bogie that affect train lateral stability, such as primary suspension longitudinal stiffness kpx, primary suspension lateral stiffness kpy, anti-hunting shock absorber damping csx, secondary lateral shock absorber damping csy, anti-hunting shock absorber joint stiffness kncsx, and secondary lateral shock absorber joint stiffness kncsy. And then, establishing a joint simulation module by using a SIMPACK scripting language and MATLAB for vehicle dynamics model simulation, and generating 50000 groups of uniformly distributed random parameters aiming at input variables by using the MATLAB.
2. Simulation calculation and selection of suspension parameter set meeting four optimization targets
And giving the generated random parameters to a vehicle dynamics model one by one, and sequentially carrying out low taper stability simulation, normal taper stability simulation and suspension parameter robustness and equivalent taper robustness calculation on the model. Judging the simulation result after each simulation, and if the simulation result does not meet the requirement, calling the next group of random parameters and transferring to the second step; and carrying out next simulation calculation under the condition of meeting the requirements. When the vehicle system endowed with the group of random parameters meets the requirements of all four optimization targets, the group of random parameters is stored, the next group of random parameters is called to continue simulation, and finally all suspension parameter groups meeting the requirements of all four optimization targets are obtained from a large number of random parameters. After the next group of random parameters is called each time, whether the called random parameters are empty sets needs to be judged, if not, the step two can be carried out to continue circulation, if yes, all the generated random parameters are called, and the matching optimization is finished.
3. Processing optimized data by adopting discrete statistical method
First, normalization processing is performed on the obtained optimization result data, and the processing result is shown in fig. 2 (a). In the figure, horizontal axes 1-6 respectively represent six suspension parameters needing to be optimized, wherein the six suspension parameters comprise primary suspension longitudinal stiffness kpx, primary suspension transverse stiffness kpy, anti-snaking shock absorber damping csx, secondary transverse shock absorber damping csy, anti-snaking shock absorber joint stiffness kncsx and secondary transverse shock absorber joint stiffness kncsy, the vertical axis is probability distribution of all suspension parameter values between 0 and 1 after normalization, each line in the figure represents each group of suspension parameter groups meeting all four optimization targets, and obvious rules can be seen among the six suspension parameters. The 6 typical suspension parameter matching combinations selected from the optimization result data according to the actual requirements are respectively represented by s 1-s 6, and the corresponding parameter specific values and the optimization target values are respectively shown in table 2 and table 3. In addition, two matching types can be obtained from table 1, wherein s 1-s 3 belong to the first matching type, and the vehicle system is combined by adopting a larger value csx to match with a smaller value kpx; s 4-s 6 are of the second type of matching, with the vehicle system taking on a smaller value of csx.
Then, the initial range of six suspension parameters to be optimized is evenly divided into five parts, the five parts are numbered in the order from small to large and respectively correspond to 1-5, and the results are shown in table 4. And performing discrete statistical processing on the optimization result data according to the distribution range, wherein the result is shown in fig. 2 (b). In the figure, the horizontal axes 1-6 represent six suspension parameters to be optimized respectively, and the vertical axes are numbers 1-5 after the initial range of the six suspension parameters is divided. Each line in the graph also represents each set of suspension parameters that meets the optimization objectives.
TABLE 2 four typical suspension parameter matching optimization results
TABLE 3 target values for four exemplary suspension match optimizations
TABLE 4 initial Range distribution of six suspension parameters
Finally, in order to further analyze the matching rules among the suspension parameters, a three-dimensional histogram of the suspension parameter matching combination is obtained according to the data in fig. 2(b), as shown in fig. 3 and 4, in the graph, 1-5 of the X axis and the Y axis are numbers 1-5 after the initial range of the suspension parameters needing to be optimized is divided. Fig. 3 is a matching law diagram of kpx, csx and kncsx, wherein an X axis is a series of longitudinal stiffness kpx, a Y axis is a series of transverse stiffness kpx, a Z axis is the number of times of occurrence of effective suspension parameters, and 5 sub-diagrams are obtained on the premise that kncsx is obtained in corresponding five number range values. It can be seen from the figure that when a smaller parameter value is selected by kncsx, the number of effective parameter matching groups is counted the most, and no matter the values of kpx and csx, an effective parameter set appears. Namely, the suspension parameter matching has stronger adaptability when the kncsx takes a smaller value. Fig. 4 is a graph of the matching laws of kpx, kpy, and csy, where the X-axis is a series of longitudinal stiffness kpx, the Y-axis is anti-hunting damper damping csx, the Z-axis is the number of times the suspension parameter occurs within the number range, and the 5 sub-graphs are obtained under the premise of corresponding five number range values of csy. It can be seen from the figure that the larger csy, the more statistics it occurs, and that kpx and kpy exhibit a negative correlation rule when csy has the smallest value, i.e. the larger kpx, the smaller kpy is needed for matching.
Claims (4)
1. A bogie suspension parameter optimization matching method comprises the following steps:
firstly, performing suspension parameter optimization analysis according to a vehicle dynamics model, determining input variables, value ranges, output evaluation indexes and threshold settings required by the model, establishing a combined simulation module by using a SIMPACK scripting language to simulate the vehicle dynamics model and MATLAB, and uniformly acquiring N groups of random parameters aiming at the input variables by using the MATLAB, wherein N is 50000; the input variables comprise six key suspension parameters of the bogie which influence the transverse stability of the train, namely primary suspension longitudinal rigidity, primary suspension transverse rigidity, anti-snake shock absorber damping, secondary transverse shock absorber damping, anti-snake shock absorber joint rigidity and secondary transverse shock absorber joint rigidity;
step two, endowing the acquired random parameters to a vehicle dynamics model, carrying out low taper stability simulation on the model at the speed of 200km/h, and extracting a stability index zeta through a SIMPACK and MATLAB combined simulation interfacelow；
Step three, judging the low taper stability index zeta of the vehiclelowIf the requirements are not met, calling a next group of random parameters and turning to the second step, and giving the vehicle dynamics model to continue the low taper stability simulation until zeta satisfaction appearslowWhen the index requires random parameters, the next step is carried out;
step four, satisfying zeta obtained in step threelowThe vehicle dynamics model under the random parameter required by the index carries out the normal taper stability simulation under the speed of 350km/h, and the stability index zeta is extractednorm(ii) a And judging whether the requirements are met, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylowAnd ζnormWhen the index requires random parameters, the next step is carried out;
step five, satisfying zeta obtained in step fourlowAnd ζnormCalculating the robustness of suspension parameters by using a vehicle dynamics model under random parameters required by indexes, and extracting a stability index std (zeta)par) (ii) a Judging whether the parameters meet the requirements, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylow、ζnormAnd std (ζ)par) When the index requires random parameters, the next step is carried out;
step six, meeting zeta-demand obtained in step fivelow、ζnormAnd std (ζ)par) Calculating the equivalent taper robustness of dynamics under random parameters required by the indexes, and extracting a stability index std (zeta)λ) (ii) a Judging whether the parameters meet the requirements, if not, calling the next group of random parameters and turning to the second step until zeta is met simultaneouslylow、ζnorm、std(ζpar) And std (ζ)λ) When the random parameter required by the index is required, the group of data is stored, and the next group of random parameters is continuously called and the step two is carried outAnd carrying out the next cycle;
after the next group of random parameters is called, judging whether the called random parameters are empty sets, if not, continuing to circulate in the second step, and if so, calling all the generated groups of random parameters, and finishing the matching optimization;
and seventhly, performing data processing on the obtained optimization result by adopting a normalization and discrete statistics method, selecting specific parameter values of suspension parameter matching combinations meeting requirements and corresponding optimization target values from the optimization result data according to actual requirements, and further mining the matching relation between the suspension parameters by combining a three-dimensional histogram.
2. The bogie suspension parameter optimization matching method according to claim 1, wherein the method comprises the following steps: the method relates to four sub-targets of a low taper stability index, a normal taper stability index, a suspension parameter robustness index and an equivalent taper robustness index in the performance indexes of the rolling stock, wherein thresholds are respectively set for the four sub-targets in the optimization process, and when a vehicle system endowed with random parameters meets the threshold range set by the four sub-targets, the random parameters are stored, namely the threshold range is met and the random parameters are regarded as an optimization matching result.
3. The bogie suspension parameter optimization matching method according to claim 1, wherein the method comprises the following steps: the optimization analysis of the suspension parameters according to the vehicle dynamic model comprises the following specific operations:
firstly, performing dynamic modeling according to CRH3 type vehicle structural parameters, and determining input variables and value ranges thereof required to be designed by the model based on existing rules of train transverse stability and suspension parameter values; and according to the actual running condition of the model, the low taper stability index zetalowZeta, normal taper stability indexnormAnd suspension parameter robustness index std (ζ)par) And the equivalent taper robustness index std (ζ)λ) Including the optimization target range, four advantages in totalTaking the target as output, and setting thresholds of four optimization targets; then, establishing a joint simulation module for vehicle dynamics model simulation and MATLAB through SIMPACK scripting language, and generating 50000 groups of uniformly distributed random parameters aiming at input variables through the MATLAB according to the values of suspension parameters of the existing vehicle; giving the generated random parameters to a vehicle dynamic model one by one, and sequentially carrying out low taper stability simulation, normal taper stability simulation and suspension parameter robustness and equivalent taper robustness calculation on the model; judging the simulation result after each simulation, carrying out next simulation calculation on the condition of meeting the requirement, and otherwise, directly calling the next group of random parameters and transferring to the second step; when the vehicle system endowed with the group of random parameters meets the requirements of all four optimization targets, the group of random parameters is stored, the next group of random parameters is called to continue simulation, and finally all suspension parameter groups meeting the requirements of all four optimization targets are obtained from a large number of random parameters; after the next group of random parameters is called each time, whether the called random parameters are empty sets needs to be judged, if not, the step two can be carried out to continue circulation, if yes, all the generated random parameters are called, and the matching optimization is finished.
4. The bogie suspension parameter optimization matching method according to claim 2, characterized in that: the data of the optimized matching result needs to be mined, and the specific operation is as follows:
because the optimized data are numerous, the matching relation between the suspension parameters is not easy to find, and the optimized matching result is subjected to data processing; the optimized matching result data obtained after normalization processing is adopted, six suspension parameter distributions with different sizes can be compared on one graph, and specific parameter values and corresponding optimized target values of 6 typical suspension parameter matching combinations selected from the optimized result data according to actual requirements are selected; in addition, discrete statistical processing is carried out on the optimized matching result data, and the matching relation between the suspension parameters is further mined by combining the three-dimensional histogram.
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