CN112765735B - Optimization method for suspension parameters of virtual rail train - Google Patents
Optimization method for suspension parameters of virtual rail train Download PDFInfo
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Abstract
The invention discloses an optimization method of virtual rail train suspension parameters, which comprises the steps of obtaining structural parameters of a target virtual rail train and the irregularity grade of a road, and carrying out dynamic modeling on the target virtual rail train to obtain a dynamic model of the target virtual rail train; according to the rough degree grade of the road, establishing a rough degree condition of the road corresponding to the target virtual rail train; carrying out parameterized modeling on the suspension of the target virtual rail train according to the dynamic model and the irregularity condition to obtain a parameterized model of the suspension; carrying out optimization calculation on a parameterized model of the suspension by taking three objective functions of a train stability index, a comfort index and a tire wear index as objects to obtain optimized suspension parameters; the method can solve the problem that a method for evaluating and optimizing the suspension parameters is lacked in the prior art, and has the advantages of comprehensive coverage influence factors, reliable calculation and wide application range.
Description
Technical Field
The invention relates to the technical field of rail trains, in particular to a method for optimizing suspension parameters of a virtual rail train.
Background
In recent years, with the rapid development of urban traffic, many different types of urban traffic systems have been developed to solve the problems of severe traffic congestion, energy shortage, and air pollution. Compared with the traditional railway, the articulated train has the advantages of energy conservation, environmental protection, small turning radius, low running noise, large passenger capacity and the like. The method can be used as a supplement of a large-scale urban rail transit system and can also be used as a main traffic mode of medium and small cities. In recent years, china has proposed a new type of transportation system, named virtual rail train system, which has the ability to track automatically along expected lanes. The device consists of three vehicle groups, and rubber tires and hub motors are adopted, so that the device can run on the existing roads in cities. The virtual rail train has the characteristics of complex structure, strong maneuverability and small curve passing performance.
In order to optimize the structural design of the virtual rail train public transportation system and ensure the safety and reliability of the operation of the virtual rail train public transportation system, the evaluation of the dynamic performance of the virtual rail train public transportation system is necessary. At present, the research on the dynamic performance evaluation of the virtual rail train is very rare, and the research on the virtual rail train is on the design of a controller based on a simplified dynamic model, neglects certain vibration characteristics of a vehicle system and dynamic interaction among parts of a mechanical system, and does not consider the effect of road surface irregularity.
And the dynamic performance of the virtual rail train is greatly influenced by a suspension system, particularly the key parameters of the suspension, and the selection of the key parameters of the suspension is reasonable or not, so that the dynamic performance of the virtual rail train directly has great influence on the motion stability, safety and comfort of the train.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the optimization method of the suspension parameters of the virtual rail train, which can solve the problem that the method for evaluating and optimizing the suspension parameters is lacked in the prior art.
In order to solve the technical problem, the invention adopts the following technical scheme:
the method for optimizing the suspension parameters of the virtual rail train comprises the following steps:
s1, acquiring structural parameters of the target virtual rail train and the irregularity grade of the road, and performing dynamic modeling on the target virtual rail train to obtain a dynamic model of the target virtual rail train;
s2, according to the rough level of the road, establishing rough conditions of the road corresponding to the target virtual rail train;
s3, carrying out parameterized modeling on the suspension of the target virtual rail train according to the dynamic model and the irregularity condition to obtain a parameterized model of the suspension;
and S4, carrying out optimization calculation on the parameterized model of the suspension by taking three objective functions of a train stability index, a comfort index and a tire wear index as objects to obtain optimized suspension parameters.
The method for optimizing the suspension parameters of the virtual rail train provided by the invention has the main beneficial effects that:
the invention realizes the parameterization establishment of the suspension of the virtual rail train to a space dynamics model by combining the interaction among the suspension system, the steering system and the road, fully ensures the full consideration of the interaction and the connection among all the structures, and ensures the accurate simulation of the suspension performance, thereby ensuring the reliability of the optimization result.
Drawings
Fig. 1 is a flow chart of a method for optimizing suspension parameters of a virtual rail train according to the present invention.
Fig. 2 is a model diagram of a spatial structure of a virtual rail train.
FIG. 3 is a diagram showing a comparison between road unevenness and a simulation structure.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which:
fig. 1 is a flowchart of a method for optimizing suspension parameters of a virtual rail train according to the present invention.
The method for optimizing the suspension parameters of the virtual rail train comprises the following steps of:
s1, acquiring structural parameters of the target virtual rail train and the irregularity grade of the road, and performing dynamic modeling on the target virtual rail train to obtain a dynamic model of the target virtual rail train;
further, the virtual rail train steering systems in the scheme all adopt a disconnected steering trapezoidal mechanism driven by a gear rack, and the subsequent description is also carried out based on the structure.
Wherein, as shown in fig. 2, the structural parameter includes the length L of the knuckle arm in the steering mechanism3Length L of pull rod2And the distance L between adjacent end points of the two side pull rods1And also includes the relative angles between the components.
The grade of the unevenness of the road can be divided into five grades from good to bad.
Further, the method for obtaining the target virtual rail train dynamics model comprises the following steps:
s1-1, changing the structural parameters of the steering mechanism of the target virtual rail train into coordinated parameters;
specifically, the coordinate parameter is a planar two-dimensional coordinate converted based on the aforementioned steering mechanism size and relative angle, and includes:
knuckle arm end and wheel pin joint C1And C2Coordinates of (2)
And
hinge point A of the other end of the knuckle arm and the pull rod1And A2Coordinates of (2)
And
hinge point B of gear rack mechanism and pull rod in steering mechanism1And B2Coordinates of (2)
And
and S1-2, calculating the steering angle of the target virtual rail train according to the coordinate parameters.
Further, it comprises the following steps:
s1-2-1, when the rack in the gear rack mechanism is transversely displaced by y in steering, B1The new coordinates of (a) are:
wherein the content of the first and second substances,
and
respectively is changed B1Point abscissa and ordinate;
s1-2-2, according to B1Calculating A according to the new coordinate and mechanical transmission relation1New coordinates of (2):
wherein the content of the first and second substances,
and
respectively is A after change1、C1Point abscissa and ordinate;
s1-2-3, the steering angle alpha of the left wheel is as follows:
s1-2-4, repeating the steps S1-2-1 to S1-2-3 to obtain the steering angle beta of the right wheel:
the steering angles of the left and right wheels are constrained and determined by the formulas of S1-2-3 and S1-2-4 by the lateral displacement of the rack. I.e., given a lateral rack displacement, the steering angle of the left and right wheels is dictated by the steering system.
And S1-3, calculating the steering constraint condition of the target virtual rail train according to the steering angle and the structure parameters.
Specifically, the steering constraint conditions are as follows:
wherein the content of the first and second substances,
in the formula, kt、ctVertical stiffness and damping, respectively, of the wheel, ztIs the vertical displacement of the tyre, zr0The unevenness of the road surface is the unevenness of the road surface,
the radial displacement change rate of the tire is shown, and delta r is the radial compression of the tire; kappaxwAnd alphaywRespectively the amount and angle of slip of the wheel, BxAnd ByLongitudinal and transverse stiffness coefficients, CxAnd CyForm factors in the longitudinal and transverse directions, respectively, DxAnd DyPeak values in the longitudinal and transverse directions, respectively, ExAnd EyLongitudinal and transverse curvature factors, respectively.
In addition, the left suspension system, the right suspension system and the wheels are independent in movement and stress, so that the left suspension system, the right suspension system and the wheels are combined together in a model to effectively evaluate the dynamic characteristics of the train.
S2, establishing an irregularity condition of the road corresponding to the target virtual rail train according to the irregularity grade of the road;
further, the irregularity condition is:
s2-1, calculating the irregularity z of the unilateral road according to the irregularity1：
Where G (n) is the power spectral density function of spatial frequency n, which is:
in the formula, phiiTo represent the random number of the phase angle, let-nminAnd-n abovemaxThe frequency limit is 0.01m-1And 10m-1；
Since the virtual rail trains actually travel on two roads parallel to each other, the irregularity of the roads on both sides needs to be calculated separately.
S2-2, calculating the irregularity z of the road on the other side2：
Wherein beta isiIs a random phase angle, G, between 0 and 2 pixAnd (n) is a cross-power spectrum, A, B, C, D, E is a fitting coefficient of a polynomial, and all parameters are empirical parameters.
The scheme adopts different parameters of different levels of road irregularity, as shown in the following table 1:
TABLE 1 parameters of different road irregularities
Based on the formulas of S2-1 and S2-2, road irregularity on the left and right sides can be simulated. FIG. 3 is a class A road irregularity. It can be seen that the road irregularity on the left and right sides and the irregularity obtained by modeling keep good consistency, thereby verifying the effectiveness of the model.
And S3, carrying out parameterized modeling on the suspension of the target virtual rail train according to the dynamic model and the irregularity condition to obtain a parameterized model of the suspension.
Preferably, the suspension model is obtained by calling a subvar file in Simpack software through matlab software and substituting the adjusted structural parameters into the subvar file.
The subvar file is provided with a suspension model, and the requirements can be met only by adjusting corresponding parameters of the suspension model.
And S4, carrying out optimization calculation on the parameterized model of the suspension by taking three objective functions of the train stability index, the comfort index and the tire wear index as objects to obtain optimized suspension parameters.
The optimization calculation is obtained by NSGA-II multi-target genetic algorithm.
Further, the stationarity index W is:
wherein A represents a vibration acceleration, f represents a vibration frequency, and F (f) represents a frequency correction coefficient;
comfort index NMVComprises the following steps:
in the formula, ax、ay、azRespectively in the longitudinal and transverse directionsAnd effective value of vertical acceleration, WdRespectively representing the weight coefficients of the acceleration, XP representing the coverage area of the bottom surface of the vehicle, and 95 representing the distribution probability;
the tire wear index WI is:
where μ is the tire-ground friction coefficient, TxAnd TyRespectively representing longitudinal and transverse slip forces, vxAnd vyRespectively, the longitudinal and lateral slip rates, and S the average area of contact of each tire with the ground.
Optimizing suspension parameter variables of the virtual rail train by a multi-objective genetic algorithm NSGA-II, obtaining a series of pareto optimal solutions by multi-objective optimization, optimizing design variables and optimization targets tend to converge through iterative calculation of the optimization algorithm, optimizing suspension parameters, improving performance indexes of the virtual rail train, and finally selecting one optimized suspension parameter by a minimum distance method.
The above description is for the purpose of describing particular embodiments of the present invention in order to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the particular embodiments, and it will be apparent to those of ordinary skill in the art that various changes may be made therein without departing from the spirit and scope of the present invention as defined and defined in the appended claims, and all such changes are intended to be protected by the present invention.
Claims (8)
1. A method for optimizing suspension parameters of a virtual rail train is characterized by comprising the following steps:
s1, obtaining the structural parameters of the target virtual rail train and the rough grade of the road, wherein the structural parameters comprise the length L of a steering knuckle arm in a steering mechanism3Length L of pull rod2And the distance L between adjacent end points of the two side pull rods1Performing dynamic modeling on the target virtual rail train to obtain a target virtual railA dynamic model of a railroad train, comprising:
s1-1, changing the structural parameters of the steering mechanism of the target virtual rail train into coordinate parameters;
s1-2, calculating the steering angle of the target virtual rail train according to the coordinated parameters;
s1-3, calculating a steering constraint condition of the target virtual rail train according to the steering angle and the structural parameters;
s2, establishing an irregularity condition of the road corresponding to the target virtual rail train according to the irregularity grade of the road;
s3, carrying out parameterized modeling on the suspension of the target virtual rail train according to the dynamic model and the irregularity condition to obtain a parameterized model of the suspension;
and S4, carrying out optimization calculation on the parameterized model of the suspension by taking three objective functions of a train stability index, a comfort index and a tire wear index as objects to obtain optimized suspension parameters.
2. The method of optimizing virtual rail train suspension parameters of claim 1, wherein the coordinated parameters comprise:
knuckle arm end and wheel pin joint C1And C2Coordinates of (2)
And
hinge point A of the other end of the knuckle arm and the pull rod1And A2Coordinates of (2)
And
hinge joint of gear rack mechanism and pull rod in steering mechanismPoint B1And B2Of (2)
And
3. the method for optimizing suspension parameters of a virtual rail train as claimed in claim 2, wherein the calculation method of the steering angle is:
s1-2-1, when the rack in the gear rack mechanism is transversely displaced by y in steering, B1The new coordinates of (c) are:
wherein the content of the first and second substances,
and
respectively is changed B1Point abscissa and ordinate;
s1-2-2, according to B1New coordinate and mechanical transmission relation of (2), calculating A1New coordinates of (2):
wherein, the first and the second end of the pipe are connected with each other,
and
respectively is A after change1、C1Point abscissa and ordinate;
s1-2-3, the steering angle alpha of the left wheel is as follows:
s1-2-4, repeating the steps S1-2-1 to S1-2-3 to obtain the steering angle beta of the right wheel:
4. the method of optimizing virtual rail train suspension parameters of claim 3, wherein the steering constraints are:
wherein the content of the first and second substances,
in the formula, kt、ctVertical stiffness and damping, respectively, of the wheel, ztIs the vertical displacement of the tyre, zr0In order to obtain the unevenness of the road surface,
the tire radial displacement change rate is shown, and delta r is tire radial compression; kappaxwAnd alphaywRespectively the amount and angle of slip of the wheel, BxAnd ByLongitudinal and transverse stiffness coefficients, CxAnd CyForm factor, D, in the longitudinal and transverse directions, respectivelyxAnd DyPeak values in the longitudinal and transverse directions, respectively, ExAnd EyLongitudinal and transverse curvature factors, respectively.
5. The method of optimizing virtual rail train suspension parameters of claim 4, wherein the out-of-flatness condition is:
s2-1, calculating the irregularity z of the unilateral road according to the irregularity1：
Where G (n) is a power spectral density function of spatial frequency n, which is:
in the formula, phiiTo represent the random number of the phase angle, let-nminAnd-n abovemaxThe frequency limit is 0.01m-1And 10m-1；
S2-2, calculating the irregularity z of the road on the other side2：
Wherein beta isiIs a random phase angle, G, between 0 and 2 pix(n) is the cross-power spectrum and A, B, C, D, E is the fitting coefficient of the polynomial.
6. The method for optimizing the suspension parameters of the virtual rail train according to claim 5, wherein the suspension model is obtained by calling a subvar file in Simpack software through matlab software and substituting the adjusted structural parameters into the subvar file.
7. The method according to claim 6, wherein the optimization calculation is obtained by NSGA-I multi-objective genetic algorithm.
8. The method of optimizing virtual rail train suspension parameters of claim 7, wherein the stationarity index W is:
wherein A represents a vibration acceleration, f represents a vibration frequency, and F (f) represents a frequency correction coefficient;
comfort index NMVComprises the following steps:
in the formula, ax、ay、azRespectively representing effective values of longitudinal, transverse and vertical acceleration, WdRespectively representing the weight coefficients of the acceleration, XP representing the coverage area of the bottom surface of the vehicle, and 95 representing the distribution probability;
the tire wear index WI is:
where μ is the tire-ground friction coefficient, TxAnd TyRespectively representing longitudinal and transverse slip forces, vxAnd vyRespectively, the longitudinal and lateral slip rates, and S the contact area of each tire with the ground.
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