CN116933384A - Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution - Google Patents
Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution Download PDFInfo
- Publication number
- CN116933384A CN116933384A CN202310647692.8A CN202310647692A CN116933384A CN 116933384 A CN116933384 A CN 116933384A CN 202310647692 A CN202310647692 A CN 202310647692A CN 116933384 A CN116933384 A CN 116933384A
- Authority
- CN
- China
- Prior art keywords
- braking
- brake
- vehicle
- model
- axis
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 91
- 238000005457 optimization Methods 0.000 title claims abstract description 70
- 239000000725 suspension Substances 0.000 claims abstract description 51
- 230000000694 effects Effects 0.000 claims abstract description 18
- 238000013433 optimization analysis Methods 0.000 claims abstract description 12
- 238000012360 testing method Methods 0.000 claims description 40
- 238000004088 simulation Methods 0.000 claims description 33
- 238000013461 design Methods 0.000 claims description 31
- 230000001133 acceleration Effects 0.000 claims description 16
- 238000006073 displacement reaction Methods 0.000 claims description 14
- 230000008569 process Effects 0.000 claims description 14
- 230000009471 action Effects 0.000 claims description 13
- 238000013016 damping Methods 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000035945 sensitivity Effects 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000000611 regression analysis Methods 0.000 claims description 4
- 239000000853 adhesive Substances 0.000 claims description 3
- 230000001070 adhesive effect Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 description 13
- 238000010586 diagram Methods 0.000 description 11
- 238000011160 research Methods 0.000 description 6
- 230000035939 shock Effects 0.000 description 6
- 239000006096 absorbing agent Substances 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000010835 comparative analysis Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000010206 sensitivity analysis Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 101100136092 Drosophila melanogaster peng gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 229960000555 fenyramidol Drugs 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012933 kinetic analysis Methods 0.000 description 1
- 239000000463 material Substances 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
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- 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
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Automation & Control Theory (AREA)
- Regulating Braking Force (AREA)
Abstract
The invention discloses a vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load, which comprises the steps of step (Step 1) constructing a multi-axle special vehicle dynamics model; step2, verifying a multi-axis special vehicle dynamics model; step3, performing brake performance optimization analysis on the multi-axis special vehicle dynamics model, and improving the brake performance of the whole vehicle; the method synthesizes the existing vehicle modeling theory, utilizes Adams/Car to construct a multi-axis special vehicle dynamics model comprising a whole vehicle inertia characteristic, a suspension assembly, a brake assembly and a steering assembly, and analyzes the whole vehicle dynamics characteristic by taking the brake performance as an emphasis; meanwhile, the Adams/weight module is utilized to optimize the pipeline pressure distribution coefficient, and a five-axis and equivalent three-axis braking force coordination relation control method is established based on the axle load dynamic distribution relation, so that the thought is expanded for the problem of optimizing the braking performance of the special vehicle, and the method has the characteristics of high model refinement degree, capability of fully restoring the real vehicle mechanical system and good effect of optimizing the braking performance of the multi-axis special vehicle.
Description
Technical Field
The invention relates to the technical field of vehicle power optimization, in particular to a vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load.
Background
The multi-axle special vehicle has high maneuverability and safety, so that the multi-axle special vehicle is widely applied to the national defense and military industry, and a transportation medium and a bearing platform are provided for large equipment. The dynamics of the vehicle determines the operability of the whole vehicle and the safety of personnel and equipment, wherein the braking performance is the basic guarantee of the safety of the whole vehicle. At present, aiming at the whole vehicle dynamics modeling, dynamics models with different degrees of freedom are mainly built by means of platforms such as MATLAB, trucksim, adams and the like according to dynamics characteristics such as stability and braking performance of emphasis analysis, and the dynamics models are used as bases for optimizing the dynamics characteristics of the vehicle. Document "Li Shaohua, yang Shaopu, chen Liqun. Three-way coupled nonlinear heavy-duty car modeling and kinetic analysis [ J ]. Vibration and shock,2014, 33 (22): LI Shao-hua, YANG Shao-pu, CHEN Li-qun. Three-way coupled nonlinear heavy vehicle modeling and dynamics analysis [ J ]. Journal ofVibration and Shock,2014, 33 (22): 131-138' builds a three-way coupling triaxial Car mathematical model with 23 degrees of freedom, and uses a numerical integration method to compare with an Adams/Car model to respectively analyze the effectiveness of the operation stability, the braking performance and the smoothness, and has few related parameters and high calculation efficiency. Document "Lin Zhichao, multiaxial distributed electrically driven vehicle dynamics modeling and state estimation study [ D ]. Wuhan: university of martial arts, 2018.Lin Zhi-char. Research on Dynamic Modeling and State Estimation ofMulti-axis Distributed Electric Drive Vehicle [ D ]. Wuhan: wuhan University ofTechnology,2018, "four-axis vehicle model with 23 degrees of freedom is constructed by using simulink", and the estimation accuracy of the dynamics of the whole vehicle is improved by combining vehicle parameter estimation. For the brake performance optimization analysis of the heavy multi-axle vehicle, the control precision can be improved by perfecting a brake system model. Literature "SUH M W, PARKYK, KWIN S J.branching performance simulation for a tractor-semitrailer vehicle with an air brake systems.proceedings of the Institution of Mechanical Engineers [ J ]. Journal ofAutomobile Engineering,2002, 216 (1): 43-54"," Santos, diego, cabral, et al, anodel method for controlling anABS (Anti-lock Braking System) for heavyv velcle, sae paper 360039, 2008 and "He L, u JL, peng ML, et al, modeling and Co-Simulation forAir Brake System ofHeavy Truck [ J ]. Advanced Materials Research,2012, 466-467 (2): 1109-1114' are added with a pneumatic braking system loop and a valve structure model, fully consider the influence of pneumatic delay on braking performance, and provide an analysis basis for optimizing braking performance. Document "He Qian. AMESim-based pneumatic brake System modeling and optimization design [ D ]. University of Huazhong science and technology, 2017.HE Qi-guang. Modeling and optimization design of pneumatic brake system based on AMESim [ D ]. Wuhan: huazhong University ofScience and Technology, 2017' use AMESim to construct a refined braking module to promote the response speed of braking control, and combine Adams/Car to analyze the optimization effect of the braking performance of the whole vehicle in straight line and turning, and because the modeling of the emphasis dynamics of Adams/Car does not generally consider the influence of a pneumatic system, the better optimization effect can be achieved through the combined simulation. The learner controls the slip rate to be near the optimal slip rate by adding an ABS control module or a dynamics model based on Adams in a whole vehicle mathematical model, combining a classical logic threshold algorithm, a sliding mode extremum searching algorithm, a phase plane method, a fuzzy algorithm and the like, optimizes the steering braking performance or the longitudinal braking performance, reduces the braking distance and improves the lateral stability of the vehicle body; literature "early brightness Qi Fuwei, wang Yanbo, etc. step control of automotive anti-lock system solenoid valves [ J ]. University of gilin (engineering edition), 2014, 44 (04): 907-911.CHU Liang,QI Fu-wei, WANGyan-bo, et al, ladder control of solenoid valve of automobile anti-lock brake system [ J ]. Journal of Jilin University (Engineering Edition), 2014, 44 (04): 907-911' adopts a step boost control mode to optimize the switching response characteristic of the electromagnetic valve in the braking system, thereby improving the braking stability of the vehicle on a separated road surface. Document "[15] Zheng Hongyu, wang Linlin, ma Shenao, etc.. Passenger car brake force distribution control algorithm based on load and slip ratio [ J ]. Chinese highway journal, 2015, 28 (08): zheng Hong-yu, WANG Lin-Lin, MA Shen-ao, et al passenger carbraking force distribution control algorithmbased on load and slip rate [ J ]. Journal ofChina Highway,2015, 28 (08): 120-126' reduces the impact of load variation on braking distance and reduces the impact of excessive front and rear wheel slip difference on braking stability. However, the method does not consider the influence of pipeline pressure distribution and axle load dynamic distribution on the braking performance of the multi-axle special vehicle, so that the built power model is low in refinement degree, and the mechanical system and key mechanical characteristics of the real vehicle cannot be effectively restored;
in view of the above problems, there is a need to design a method for optimizing the braking performance of a multi-axle feature vehicle based on pipeline pressure distribution and axle load dynamic distribution, so as to solve the problems in the prior art.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load, which synthesizes the existing vehicle modeling theory, utilizes Adams/Car to construct a multi-axle special vehicle dynamics model comprising the whole vehicle inertia characteristics, suspension components, braking components, steering components and the like, and analyzes the whole vehicle dynamics characteristics by taking the braking performance as an emphasis; meanwhile, from the perspective of perfecting a whole vehicle model, the pipeline pressure distribution coefficient is optimized by utilizing an Adams/weight module with the aim of improving the braking performance, a method for constructing a five-axis and equivalent three-axis braking force coordination relation is provided on the basis of an original model, the thought is expanded for the problem of optimizing the braking performance of the special vehicle, and the method has the characteristics of high model refinement degree, capability of fully recovering a real vehicle mechanical system and good effect of optimizing the braking performance of the multi-axis special vehicle.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load comprises the following steps of
Step1, constructing a dynamics model of the multi-axis special vehicle
Step101, constructing a multi-axis special vehicle braking system model;
step102, constructing a suspension system model;
step103, constructing a tire system model;
step104, performing multi-axis vehicle model matching;
step2, verifying the multi-axis special vehicle dynamics model constructed in the Step 1;
and step3, performing brake performance optimization analysis on a multi-axis special vehicle dynamics model, and optimizing the whole vehicle brake performance by utilizing an Adams/weight optimized brake pipeline pressure distribution coefficient method and a brake force coordination control relation optimization method.
Preferably, the process of constructing the multi-axis special vehicle brake system model in Step101 includes
(1) Irrespective of the pipeline arrangement and valve parts in the actual pneumatic braking system, a disc clamp type brake is adopted, the rolling resistance of wheels during braking is ignored, the brake lining is uniformly contacted with the brake disc, and the steady-state braking moment is derived from the braking pipeline pressure in proportional relation with the braking pedal force, namely
Wherein: t is a braking torque; f (F) 0 Positive pressure of the brake pad on the brake disc for a single side; mu is the friction coefficient of the brake disc; r is R e The effective radius of action of the brake disc; a is the effective acting area of the pressure of the brake chamber; p is the brake chamber pressure; beta is the pressure distribution coefficient of the front and rear brake pipelines; η is a conversion coefficient of the brake pedal force into the brake chamber pressure; f is a brake pedal force set in a simulation manner;
(2) Setting the effective acting radius R of the brake disc e To refer to the distance from the center of the sector area to the center of the brake disc, the unit area of the lining is rdθdr, and the corresponding friction moment is μpr 2 dθdr, p is the liner unit area pressure, and the unilateral braking torque acting on the brake disc is:
wherein: θ is the radian of the fan-shaped lining; r is R 1 Is the inner radius of the brake lining; r is R 2 Is the outer radius of the brake lining;
(3) Positive pressure F of single side lining of brake disc 0 The method comprises the following steps:
the effective radius of action of the brake disc can be obtained by the formulas (1) - (3):
(4) During braking, a single wheel is subjected to braking moment provided by a braking system and moment of ground adhesion force to the wheel center, and a kinetic equation is as follows:
wherein: i is the rotational inertia of the wheel; omega is the angular velocity of the wheel; t is the braking moment generated by the brake, F x The adhesive force provided for the ground, R is the rolling radius of the wheel;
(5) The brake system simulation model is constructed by adding a revolute pair to connect each brake with a corresponding axle, constructing a data communication device, transmitting position information of a brake disc, a clamp and wheels, determining positioning parameters of a suspension system in the direction of a rotation central shaft of the brake, transmitting braking moment values to each wheel through a sensor, and constructing the brake system simulation model.
Preferably, the suspension system model construction process of Step102 includes
(1) The vehicle is provided with a double-cross arm independent suspension, the longitudinal axis of the vehicle body is bilaterally symmetrical, the three axles are non-steering axles, the other four axles are steering axles, the rigidity and the damping of the vehicle suspension are considered, and the model is based on a single-degree-of-freedom 1/4 dynamic equation to obtain a suspension dynamic model:
wherein: i represents the i-th axis i=1, 2, …,5,; f (F) zi The dynamic axle load applied to the ith axle; k (k) i Equivalent stiffness for each suspension; c i Equivalent damping for each suspension; Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively;
(2) And determining the space structure of the suspension by setting the position information of each key point, and selecting the kinematic pair constraint to realize the kinematic connection relation among all parts in the suspension to obtain the suspension system simulation model.
Preferably, the tire system model building process of Step103 includes
(1) Using PAC2002 tire templates, adopting a magic formula mathematical solution model, namely:
Y(x)＝Dsin{Carctan[Bx-E(Bx-arctan(Bx))]} (7)
wherein: y (x) represents a longitudinal force, a lateral force; the corresponding x inputs are the longitudinal slip ratio and the tire slip angle; B. c, D, E the tire parameters are respectively stiffness factor, shape factor, peak factor and curvature factor;
(2) Building property attribute files in conjunction with test data
Wherein the test data are tested by a tire testing machine and obtained through interpolation calculation, and the attribute file consists of independent data units:
(3) When the tire model is matched and integrated, the tire model is automatically positioned to the corresponding wheel hub through the communication device of each wheel, the space position of the wheel is defined by the toe-in angle and the camber angle, and the contact relation between the rotation pair constraint tire and the ground is constructed by rotating the rotation pair constraint tire around the axle center of the wheel hub and vertical pair constraint tire.
Preferably, the process of performing brake performance optimization analysis on the multi-axis special vehicle dynamics model in Step3 includes
Step301, adopting a brake pipeline pressure distribution coefficient optimization method based on Adams/weight, analyzing the sensitivity of a distribution coefficient beta to three optimization targets by utilizing a matrix test design principle, and optimally analyzing pipeline pressure distribution coefficients of a front brake circuit and a rear brake circuit by a multi-axis special vehicle dynamics model;
step302, a braking force coordination control relation optimization method based on dynamic allocation of axle load is adopted, a five-axis and equivalent three-axis braking force coordination relation is provided, a multi-axis special vehicle dynamics model is utilized to analyze and demonstrate the braking force coordination relation optimization effect, and the braking performance of the whole vehicle is improved.
Preferably, the optimizing method of brake pipeline pressure distribution coefficient based on Adams/weight in Step301 uses matrix test design principle to analyze the sensitivity of distribution coefficient beta to three optimizing targets, and the process of optimizing the pipeline pressure distribution coefficients of front and rear brake circuits by using multi-axis special vehicle dynamics model includes
(1) Based on the original structure of the braking system, namely that braking actions are respectively applied to front and rear braking pipelines, initially setting a front braking pipeline pressure distribution coefficient beta in the Car module;
(2) By utilizing an Adams/weight matrix test design principle, analyzing different influences of a design variable beta on three optimization targets of a braking distance, a vehicle body transverse displacement and a vehicle body vertical acceleration, and determining an optimal objective function value, namely a minimum braking distance, a minimum vehicle body transverse displacement and a minimum vehicle body vertical acceleration by combining constraint conditions of pipeline pressure distribution coefficients;
(3) Adopting a scanning design method suitable for a single factor, dividing the rule of the design variable in a constraint range, and finally carrying out data fitting by using a quadratic regression analysis model to obtain an equation predicted value with the minimum error with an actual observed value;
(4) And (3) evaluating the fitting goodness of the optimization targets, and after performing repeated iterative simulation tests by utilizing a multi-axis special vehicle dynamics model based on the heavy braking working condition at the initial speed of 100km/h, analyzing the sensitivity of the design variables to the three optimization targets, and optimizing and adjusting the distribution coefficient beta.
Preferably, the method for optimizing the coordination control relationship of the braking force based on dynamic allocation of axle load in Step302 proposes a coordination relationship of the braking force of five axles and equivalent three axles, and the process for analyzing and proving the optimization effect of the coordination relationship of the braking force by using the multi-axle special vehicle dynamics model comprises the following steps of
(1) The control method of the braking force coordination relation based on axle load distribution converts the coordination relation of front and rear braking pipelines into five-axis axle load coordination relation, and constructs a model of each axle distribution coefficient state variable beta based on a suspension dynamics model i ：
Wherein: Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively;l ir is the longitudinal distance from the center of the ith axle to the pitch center;
(2) Simulating a severe braking condition under high-speed running of 100km/h by utilizing a multi-axis special vehicle dynamics model, analyzing a braking performance optimization effect of a five-axis coordination relation compared with a front-rear pipeline braking coordination relation, and realizing braking performance optimization of an emergency braking condition under high-speed running;
(3) And an equivalent triaxial braking force coordination control relation is provided by combining a 6S/6M braking control mode of the multi-axis special vehicle, a heavy braking condition under 100km/h high-speed running is simulated by utilizing a dynamics model of the multi-axis special vehicle, and a braking performance optimization effect of the equivalent triaxial coordination relation compared with a five-axis coordination relation is analyzed, so that braking performance optimization of an emergency braking condition under high-speed running is realized.
The beneficial effects of the invention are as follows: the invention discloses a vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load, which is improved compared with the prior art in that:
the invention designs a multiaxial characteristic vehicle braking performance optimization method based on pipeline pressure distribution and axle load dynamic distribution, which takes multiaxial special vehicles as research objects, and utilizes an Adams/Car platform to construct a refined whole vehicle dynamics model comprising a braking system, a suspension system, a steering system and the like by analyzing a strong coupling mechanical system of the multiaxial special vehicles; the reliability of the model is verified under the working condition of 60km/h initial speed emergency braking in a road test experiment, and a method for optimizing the pressure distribution coefficient of a brake pipeline by utilizing Adams/weight and a method for optimizing the coordination control relation of the braking force are provided to improve the braking performance of 100km/h emergency braking of the whole vehicle; the results show that: the optimization method based on Adams/weight reduces the braking distance and vertical acceleration, and improves the longitudinal braking safety and vertical stability of the vehicle; the braking force coordination control method is better in effectiveness, compared with the original model, the braking distance is effectively reduced, and the transverse stability of an emergency braking working condition is improved; compared with the equivalent three-axis coordination relation which is adaptive to the 6S/6M brake control mode, the five-axis coordination relation has the advantages of improving the effect, expanding the thought of exploring the problem of optimizing the brake performance of the multi-axis special vehicle, having high model refinement degree, being capable of fully restoring the mechanical system of the real vehicle and having good effect of optimizing the brake performance of the multi-axis special vehicle.
Drawings
FIG. 1 is a flow chart of the vehicle braking performance optimization method based on dynamic distribution of line pressure and axle load of the present invention.
FIG. 2 is a differential view of the friction surface of a brake pad of the present invention.
FIG. 3 is a diagram of a simulation model of a multi-axle vehicle braking system of the present invention.
Fig. 4 is a diagram of the suspension topology of the present invention.
Fig. 5 is a simulation model diagram of the multi-axle vehicle suspension system of the present invention.
FIG. 6 is a graph of the characteristics of a shock absorber according to the present invention.
Fig. 7 is a graph of the longitudinal stress of the front axle tire of the present invention.
Fig. 8 is a graph of the matching of the whole vehicle dynamics model of the multi-axle vehicle of the invention.
FIG. 9 is a diagram of a pilot plant of the present invention.
FIG. 10 is a graph of a comparative analysis of the model verification of the present invention.
FIG. 11 is a flow chart of the inventive weight optimization design method.
FIG. 12 is a graph of the optimization and comparison of the distribution coefficients of the pipeline pressure according to the present invention.
Fig. 13 is a diagram of a dynamic distribution model of the axle load of the multi-axle vehicle of the present invention.
Fig. 14 is a graph showing the result of improvement of the braking force co-ordination relationship in accordance with the present invention.
FIG. 15 is a chart of equivalent triaxial and five-axis cooperative relationship braking performance according to the present invention.
Wherein in fig. 6, fig. (a) is a spring rate characteristic diagram, fig. (b) is a damping characteristic diagram, and fig. (c) is a stopper stiffness characteristic diagram;
in fig. 9, a diagram (a) is a vehicle body posture sensor, a diagram (b) is a vibration monitoring unit, and a diagram (c) is an acceleration sensor;
in fig. 10, a graph (a) is a braking distance versus graph, a graph (b) is a braking deceleration versus graph, and a graph (c) is a braking speed versus graph;
in fig. 12, fig. 12 (a) is a front-rear braking distance comparison graph, fig. b is a front-rear vehicle body lateral displacement comparison graph, and fig. c is a front-rear vertical acceleration comparison graph;
in fig. 14, a graph (a) is a braking distance graph, a graph (b) is a braking deceleration graph, and a graph (c) is a vehicle body lateral displacement graph;
in fig. 15, a braking distance graph is shown in fig. (a), a braking deceleration graph is shown in fig. (b), and a vehicle body lateral displacement graph is shown in fig. (c).
Detailed Description
In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1: referring to the vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load shown in the attached drawings 1-15, the method synthesizes the existing vehicle modeling theory, utilizes Adams/Car to construct a multi-axis special vehicle dynamics model comprising whole vehicle inertia characteristics, suspension components, braking components, steering components and the like, and analyzes the whole vehicle dynamics characteristic by taking the braking performance as an emphasis; meanwhile, from the perspective of perfecting a whole vehicle model, an Adams/weight module is utilized to optimize a pipeline pressure distribution coefficient with the aim of improving the braking performance, a method for constructing a five-axis and equivalent three-axis braking force coordination relation is provided on the basis of an original model, and the thought is developed for the problem of optimizing the braking performance of a special vehicle; the specific process comprises the following steps
Step1, constructing a dynamics model of the multi-axis special vehicle
By utilizing a multi-body system dynamics modeling theory supported by a virtual prototype technology, constructing a kinematic equation of each part by means of a vehicle structure movement characteristic analysis simulation platform Adams/Car and adopting a Lagrangian multiplier method, applying driving constraint and kinematic pair constraint, accurately solving the system dynamics characteristics among all mechanisms of a vehicle, and carrying out whole vehicle dynamics analysis by combining a virtual test field;
step101 construction of a model of a brake system of a multi-axle special vehicle
(1) The brake system model constructed in the embodiment adopts a disc caliper brake without considering the pipeline arrangement and valve components in an actual pneumatic brake system; neglecting wheel rolling resistance during braking, assuming uniform contact between the brake lining and the brake disc, the steady-state braking torque is mainly derived from brake line pressure in a proportional relationship with brake pedal force, i.e
Wherein: t is a braking torque; f (F) 0 Positive pressure of the brake pad on the brake disc for a single side; mu is the friction coefficient of the brake disc; r is R e The effective radius of action of the brake disc; a is the effective acting area of the pressure of the brake chamber; p is the brake chamber pressure; beta is the pressure distribution coefficient of the front and rear brake pipelines; eta is the conversion coefficient of converting the brake pedal force into the brake chamber pressure, and the default value is 0.1/mm 2 The method comprises the steps of carrying out a first treatment on the surface of the F is a brake pedal force set in a simulation manner;
(2) Radius of action R of brake disc e Refers to the distance from the center of the sector area to the center of the brake disc, and as shown in FIG. 2, the unit area of the lining is rdθdr, and the corresponding friction torque is μpr 2 dθdr, p is the liner unit area pressure, the single-side braking torque acting on the brake disc is:
wherein: θ is the radian of the fan-shaped lining; r is R 1 Is the inner radius of the brake lining; r is R 2 Is the outer radius of the brake lining;
(3) Positive pressure F of single side lining of brake disc 0 Can be expressed as:
the effective radius of action of the brake disc can be obtained by the formulas (1) - (3):
(4) During braking, a single wheel is subjected to braking moment provided by a braking system and moment of ground adhesion force to the wheel center, and a kinetic equation is as follows:
wherein: i is the rotational inertia of the wheel; omega is the angular velocity of the wheel; t is the braking moment generated by the brake, F x The adhesive force provided for the ground, R is the rolling radius of the wheel;
(5) The method comprises the steps of connecting each brake with a corresponding axle through adding a rotating pair, constructing a data communication device, transmitting position information of a brake disc, a clamp and wheels, transmitting a suspension system positioning parameter capable of determining the direction of a rotation central shaft of the brake, transmitting a braking moment value to each wheel through a sensor, and constructing a braking system simulation model, wherein the simulation model is shown in fig. 3;
step102 construction of suspension System model
(1) The 5-axle vehicle studied in the embodiment adopts a double-cross arm independent suspension, the longitudinal axis of the vehicle body is bilaterally symmetrical, the four axles except the three axles are non-steering axles are steering axles, and the topological relation of the suspension is shown in figure 4; considering suspension stiffness and damping, the model is based on a 1/4 dynamics equation with single degree of freedom:
wherein: i represents the i-th axis i=1, 2, …,5,; f (F) zi The dynamic axle load applied to the ith axle; k (k) i Equivalent stiffness for each suspension; c i Equivalent damping for each suspension; Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively;
(2) During modeling, the space structure of the suspension is determined by setting the position information of each key point, and reasonable kinematic pair constraint is selected to realize the kinematic connection relation among all parts in the suspension, so that an obtained suspension system simulation model is shown in figure 5;
(3) The suspension model is mainly related to steering of wheels, swinging of an upper cross arm and a lower cross arm relative to a frame, multidimensional movement of a shock absorber and the like, and the nonlinear characteristic of the suspension model directly influences the safety of working conditions such as turning and braking of a vehicle; in order to accurately describe the characteristics, the action of a shock absorber is simulated by setting characteristic parameters of a spring, a damping and a limiting block, a characteristic curve of the shock absorber is obtained by adopting a cubic spline interpolation function according to test data, and a suspension system model is obtained as shown in fig. 6;
step103 building a tire System model
(1) The whole vehicle realizes the driving, braking and steering actions through a tire system, and the analysis of the braking performance is more focused on the adhesion characteristics of the tire and the ground and the cornering stiffness characteristics of the tire; selecting PAC2002 tire templates, and adopting a magic formula mathematical solution model, namely:
Y(x)＝Dsin{Carctan[Bx-E(Bx-arctan(Bx))]} (7)
wherein: y (x) represents a longitudinal force, a lateral force; the corresponding x inputs are the longitudinal slip ratio and the tire slip angle; B. c, D, E the tire parameters are respectively stiffness factor, shape factor, peak factor and curvature factor;
(2) And (3) constructing a characteristic attribute file by combining test data, wherein the test data are obtained by means of a tire tester test and interpolation calculation, the attribute file consists of 20 independent data units and is used for simulating longitudinal and lateral stress, aligning, overturning moment and the like of a tire, and partial parameters are shown in table 1:
table 1: tire solution model parameters
(3) When the tire model is matched and integrated, the tire model is automatically positioned to the corresponding wheel hub through the communication device of each wheel, and the space positions of the wheels are defined by the toe-in angle and the camber angle, so that the contact relation between the rotation pair constraint tire and the ground and the contact relation between the vertical pair constraint tire and the ground are constructed; taking a severe braking condition as an example, namely an emergency braking condition that a driver is fully pedaled to rapidly realize the maximum braking strength, a longitudinal stress curve of the front axle tire under a mathematical solution model according to a magic formula is shown in fig. 7;
step104, performing multi-axis vehicle model matching
The whole vehicle model comprises a suspension system, a braking system, a tire system, a frame system, a steering system, a power assembly, a driving model and the like; on the premise of not influencing the model precision, simplifying the vehicle body and the bearing equipment, and adding the equivalent sprung mass to the mass center of the whole vehicle by measuring the mass characteristics of the vehicle body and the bearing equipment; the modeling principle is followed from a basic template (template) to each Subsystem (Subsystem), and finally each Subsystem model is integrated into a multi-axle vehicle whole vehicle model (Assembly), so that the space structure, the quality characteristics and the connection relation of each part of the real vehicle are fully restored; as shown in fig. 8, model packaging is performed by using defined parameter variables, quality characteristic parameters obtained according to experiments and deformation mechanical parameters, and a mathematical solution model which is fit with the full-load 50t working condition of a real vehicle is constructed; simulating a test pavement to construct a three-dimensional smooth straight pavement with an attachment coefficient of 1 and constant road width as a simulation environment;
step2, verifying the multi-axis special vehicle dynamics model constructed in the Step1
In order to ensure that the dynamics simulation model of the multi-axis special vehicle can better simulate the dynamics characteristics in the running process of a real system, the reliability and the accuracy of the dynamics simulation model need to be verified through a road test;
step201 road test system
The test equipment adopted in the road test is mainly a dynamic parameter acquisition unit and a matched vehicle body attitude sensor; a vibration monitoring unit and an axial acceleration sensor; the pedal force, the wheel speed and the pipeline pressure sensor are all three parts, and part of test equipment is shown in figure 9;
an attitude sensor is arranged at the top of a vehicle body, an axial acceleration sensor is arranged at a key point of a vehicle frame, a pedal force sensor is arranged at a brake pedal, a wheel speed sensor is arranged at a wheel hub, a whole vehicle test system is formed by a synchronous cable and a data acquisition and monitoring unit arranged in a cab, and finally the synchronous cable and the data acquisition and monitoring unit are transmitted to a PC end data analysis platform so as to acquire dynamic parameters such as a course angle, a roll angle, various accelerations, a wheel speed and the like of the vehicle;
step202 road test verification
According to the built whole vehicle test system, real vehicle tests such as different braking strength, splay, round and the like are developed according to national standard requirements; in consideration of the test running safety, an emergency braking working condition with a test braking initial speed of 60km/h is selected, the accuracy of a simulation model is compared and verified, and a verification result is shown in fig. 10;
the test results show that:
(1) The whole vehicle simulation model has good braking effect;
(2) The braking speed, the braking distance and the braking deceleration simulation curve can be better fit to the test result, and the change trend of the actual braking working condition is accurately reflected;
(3) The simulation and test fitting have certain errors under the influence of uncertain factors such as test sites, weather, drivers and the like; the arrangement position of the sensor cannot completely restore the mass center position of the model, and the vehicle body shake of the real vehicle has a larger influence on the data acquisition of the acceleration sensor, so the accuracy of the graph (b) of fig. 10 is relatively lower, but is within an acceptable range;
(4) The model can be used for researching the performance of the real vehicle and is used as a simulation analysis platform for exploring the problem of optimizing the braking performance;
step3, performing brake performance optimization analysis on the multi-axis special vehicle dynamics model
The highest running speed requirement of the multi-axis special vehicle is 100km/h, and the multi-axis special vehicle is used for bearing special equipment, so that in order to explore the braking safety under high-speed running by utilizing a simulation model, on the basis of the original braking system structure, a braking pipeline pressure distribution coefficient optimization analysis method based on Adams/weight, a braking force coordination relation control method based on axle load distribution and a braking force coordination control relation optimization method are provided to further optimize the braking performance of the model;
step301, adopting an optimization method of the pressure distribution coefficient of the brake pipeline based on Adams/weight, analyzing the sensitivity of the distribution coefficient beta to three optimization targets by using a matrix test design principle, and performing optimization analysis on the pressure distribution coefficients of the front and rear brake circuits by using a multi-axis special vehicle dynamics model (Adams/weight distribution coefficient optimization analysis)
Based on the original structure of the braking system, the 1 and 2 shafts are arranged on the front braking pipeline, the 3 to 5 shafts are arranged on the rear braking pipeline, the braking action is respectively applied to the front braking pipeline and the rear braking pipeline, the pressure distribution coefficient beta of the front braking pipeline in the Car module is preliminarily set to be 0.49, and the beta is optimally analyzed by Adams/weight:
(1) Based on the original structure of the braking system, namely that braking actions are respectively applied to front and rear braking pipelines, initially setting a front braking pipeline pressure distribution coefficient beta in the Car module;
(2) By combining with the Adams/weight matrix test design principle, analyzing different influences of a design variable beta on three optimization targets of a braking distance, a vehicle body transverse displacement and a vehicle body vertical acceleration, namely analyzing multiple optimization objective function values by a single design variable, and obtaining a design method as shown in figure 11; beta is in the range of 0.45-0.69, and takes the beta as a constraint condition; determining an optimal objective function value, namely a minimum braking distance value, a minimum vehicle body transverse displacement value and a minimum vehicle body vertical acceleration value;
(3) The design variable rule can be divided in a constraint range by adopting a scanning design method (Study-Sweep) suitable for a single factor; the test design type is a complete factorial design so as to be suitable for the situation that the design variables and the horizontal number of the embodiment are fewer; finally, performing data fitting by using a quadratic regression analysis model to obtain an equation predicted value with the minimum error from the actual observed value;
(4) To evaluate the fitting goodness of the optimization targets, based on the heavy braking working condition at the initial speed of 100km/h, after 25 iterative tests are carried out, the sensitivity of the design variable beta to the three optimization targets is analyzed, and the analysis results are shown in tables 2 and 3;
table 2: sensitivity analysis of design variables to optimization objectives
Table 3: optimization objective fitness evaluation
R2 and R2adj represent the fitting quality, and the closer to 1, the better; p represents the availability of each item in the fitting process, and the smaller the number is, the more useful items are; R/V represents the relation between the calculated value of the model and the original data, and the model prediction result is better as the value is higher, so that the optimization target fitting goodness is high and the model prediction result is good as known from the weight fitting regression analysis result. According to the combination of sensitivity analysis results, the different influence trends of the brake pipeline pressure distribution coefficient on the brake distance, the vehicle body lateral displacement and the vehicle body pitching acceleration are comprehensively considered for the problem of brake performance optimization, the lateral stability and the vertical stability of the vehicle body are considered while the longitudinal brake distance is reduced, and the primary and secondary optimization targets are selected according to actual requirements; the distribution coefficient after optimization is adjusted to 0.615 from 0.49, the comparative analysis of the braking performance is shown in fig. 12, and the specific simulation results are shown in table 4;
table 4: optimizing front-to-rear brake performance contrast
As can be seen by combining the analysis of the chart data, the brake distance of the whole vehicle is reduced by 0.79m and the maximum vertical acceleration is reduced by 0.01m/s by optimizing the pipeline pressure distribution coefficient through weight 2 But the maximum vehicle body lateral displacement is increased by 10.3mm. Although certain transverse stability is sacrificed, the vehicle body which is not beyond the national standard is not allowed to exceed the requirement of a 3m wide lane, so that the longitudinal braking safety and the vertical stability of the whole vehicle under the working condition of high-speed driving emergency braking are improved; meanwhile, the pipeline pressure distribution coefficient optimization method based on the weight can be used for exploring the problem of optimizing the braking performance of the whole vehicle, and has certain feasibility;
step302, adopting a braking force coordination control relation optimization method based on dynamic allocation of axle load to propose a five-axis and equivalent three-axis braking force coordination relation, and utilizing a multi-axis special vehicle dynamics model to analyze and demonstrate the braking force coordination relation optimization effect (braking force coordination relation optimization analysis)
(1) The braking force distribution coefficient of the whole vehicle model based on the four-wheel drive two-axle vehicle template only coordinates front and rear braking pipelines, but the axle load distribution relation of each axle of the multi-axle vehicle is more complex compared with that of the two-axle vehicle, and the axle load transfer problem generated during braking of a larger load cannot be ignored; the braking force coordination relation control method based on axle load distribution converts the coordination relation of front and rear brake pipelines into five-axis axle load coordination relation, and constructs a state variable beta of each axle distribution coefficient of a model based on a suspension dynamic model, namely formula (6), according to a multi-axle load dynamic distribution model shown in fig. 13 i ：
Wherein: Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively; l (L) ir Is the longitudinal distance from the center of the ith axle to the pitch center;
(2) The severe braking condition under the high-speed running of 100km/h is simulated, the influence of the braking force coordination relation before and after the improvement on the braking performance is shown in fig. 14, and the specific simulation result is shown in table 5;
table 5: coordination relation for improving front and rear braking performance
As can be seen by combining the analysis of the chart data, after the braking force coordination relation is improved, the braking distance of the emergency braking working condition under the high-speed running is shortened by 2.8m, and the maximum braking deceleration (absolute value) which can be achieved by the vehicle body is increased by 0.49m/s 2 The maximum transverse displacement of the vehicle body is reduced by 88.6mm, the longitudinal braking safety is improved, and the braking strength and the transverse stability during high-speed running are improved. Therefore, the method can effectively improve the braking performance of the whole vehicle, and has better improving effect than a method for optimizing the pressure distribution coefficient of the front and rear brake pipelines;
(3) The feasibility and effectiveness of the control method are determined through the improved analysis of the braking force coordination relationship, the 6S/6M braking control mode adopted by the research object of the embodiment is combined, the adaptive braking force coordination relationship control method is further explored, and an equivalent triaxial axle load distribution coordination control system is built based on an original whole vehicle model; constructing distribution coefficient variables according to a triaxial axle load dynamic distribution model, wherein the front two axles are equivalent to one axle, the rear two axles are equivalent to one axle, the braking performance under five-axle coordination control is compared with that of the brake is shown in figure 15, and the specific simulation results are shown in table 6;
table 6: equivalent triaxial and five-axis coordination relation brake performance comparison
As can be seen by combining the analysis of the chart data, compared with the five-axis coordination control, the influence of the equivalent triaxial axle load distribution coordination control on the braking performance is reduced by 0.49m, and the maximum braking deceleration (absolute value) is increased by 0.125m/s 2 However, the maximum transverse position of the vehicle body is increased by 0.5mm, the overall braking performance is improved slightly, the effectiveness of the equivalent triaxial axle load distribution coordination control method is shown, and a simulation platform is provided for exploring the matching with the 6S/6M control mode of the whole vehicle braking.
Step303 conclusion
In the embodiment, a detailed dynamics model of a certain heavy multi-shaft special vehicle is built based on Adams/Car, the mechanical system and key mechanical characteristics of the vehicle are fully restored, and the accuracy and reliability of the model are verified through a road test experiment; the braking performance of a complex multi-axis vehicle system is explored by effectively utilizing a virtual prototype technology, and the braking performance optimization problem of a whole vehicle model is analyzed by two methods, so that the conclusion is as follows:
(1) Based on the optimization method of the pressure distribution coefficient of the Adams/weight pipeline, different influence degrees on the braking distance, the lateral displacement of the vehicle body and the pitching acceleration of the vehicle body are comprehensively considered, although the braking stability of the whole vehicle is slightly reduced, the longitudinal braking safety and the vertical stability of the whole vehicle under the high-speed driving working condition are improved, and the method has certain feasibility as a method for optimizing the braking performance of the whole vehicle model;
(2) For improved analysis of braking force coordination relation, from the most original front and rear braking pipeline distribution to a coordination control mode combining the five-axis axle load distribution of the multi-axis vehicle, the braking distance and the transverse displacement of the vehicle body can be effectively reduced, and the braking safety and the braking stability of the vehicle under the high-speed running working condition are better improved than those of a pipeline pressure distribution coefficient optimization method;
(3) Compared with a five-axis control mode, the equivalent three-axis coordination control mode has a smaller lifting effect on braking performance, and can be used as a research basis of a 6S/6M control mode of braking of the whole vehicle;
therefore, the method can provide a research thought and an exploration method for the brake performance optimization analysis of the whole vehicle.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The vehicle braking performance optimization method based on the dynamic distribution of pipeline pressure and axle load is characterized by comprising the following steps of: comprising the steps of
Step1, constructing a dynamics model of the multi-axis special vehicle
Step101, constructing a multi-axis special vehicle braking system model;
step102, constructing a suspension system model;
step103, constructing a tire system model;
step104, performing multi-axis vehicle model matching;
step2, verifying the multi-axis special vehicle dynamics model constructed in the Step 1;
and step3, performing brake performance optimization analysis on a multi-axis special vehicle dynamics model, and optimizing the whole vehicle brake performance by utilizing an Adams/weight optimized brake pipeline pressure distribution coefficient method and a brake force coordination control relation optimization method.
2. The vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load according to claim 1, wherein: the process for constructing the multi-axis special vehicle brake system model in Step101 comprises
(1) Irrespective of the pipeline arrangement and valve parts in the actual pneumatic braking system, a disc clamp type brake is adopted, the rolling resistance of wheels during braking is ignored, the brake lining is uniformly contacted with the brake disc, and the steady-state braking moment is derived from the braking pipeline pressure in proportional relation with the braking pedal force, namely
Wherein: t is a braking torque; f (F) 0 Positive pressure of the brake pad on the brake disc for a single side; mu is the friction coefficient of the brake disc; r is R e The effective radius of action of the brake disc; a is the effective acting area of the pressure of the brake chamber; p is the brake chamber pressure; beta is the pressure distribution coefficient of the front and rear brake pipelines; η is a conversion coefficient of the brake pedal force into the brake chamber pressureThe method comprises the steps of carrying out a first treatment on the surface of the F is a brake pedal force set in a simulation manner;
(2) Setting the effective acting radius R of the brake disc e To refer to the distance from the center of the sector area to the center of the brake disc, the unit area of the lining is rdθdr, and the corresponding friction moment is μpr 2 dθdr, p is the liner unit area pressure, and the unilateral braking torque acting on the brake disc is:
wherein: θ is the radian of the fan-shaped lining; r is R 1 Is the inner radius of the brake lining; r is R 2 Is the outer radius of the brake lining;
(3) Positive pressure F of single side lining of brake disc 0 The method comprises the following steps:
the effective radius of action of the brake disc can be obtained by the formulas (1) - (3):
(4) During braking, a single wheel is subjected to braking moment provided by a braking system and moment of ground adhesion force to the wheel center, and a kinetic equation is as follows:
wherein: i is the rotational inertia of the wheel; omega is the angular velocity of the wheel; t is the braking moment generated by the brake, F x The adhesive force provided for the ground, R is the rolling radius of the wheel;
(5) The brake system simulation model is constructed by adding a revolute pair to connect each brake with a corresponding axle, constructing a data communication device, transmitting position information of a brake disc, a clamp and wheels, determining positioning parameters of a suspension system in the direction of a rotation central shaft of the brake, transmitting braking moment values to each wheel through a sensor, and constructing the brake system simulation model.
3. The vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load according to claim 1, wherein: the suspension system model construction process of Step102 includes
(1) The vehicle is provided with a double-cross arm independent suspension, the longitudinal axis of the vehicle body is bilaterally symmetrical, the three axles are non-steering axles, the other four axles are steering axles, the rigidity and the damping of the vehicle suspension are considered, and the model is based on a single-degree-of-freedom 1/4 dynamic equation to obtain a suspension dynamic model:
wherein: i represents the i-th axis i=1, 2, …,5,; f (F) zi The dynamic axle load applied to the ith axle; k (k) i Equivalent stiffness for each suspension; c i Equivalent damping for each suspension; Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively;
(2) And determining the space structure of the suspension by setting the position information of each key point, and selecting the kinematic pair constraint to realize the kinematic connection relation among all parts in the suspension to obtain the suspension system simulation model.
4. The vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load according to claim 1, wherein: the tire system model construction process of Step103 includes
(1) Using PAC2002 tire templates, adopting a magic formula mathematical solution model, namely:
Y(x)＝Dsin{Carctan[Bx-E(Bx-arctan(Bx))]} (7)
wherein: y (x) represents a longitudinal force, a lateral force; the corresponding x inputs are the longitudinal slip ratio and the tire slip angle; B. c, D, E the tire parameters are respectively stiffness factor, shape factor, peak factor and curvature factor;
(2) Building property attribute files in conjunction with test data
Wherein the test data are tested by a tire testing machine and obtained through interpolation calculation, and the attribute file consists of independent data units:
(3) When the tire model is matched and integrated, the tire model is automatically positioned to the corresponding wheel hub through the communication device of each wheel, the space position of the wheel is defined by the toe-in angle and the camber angle, and the contact relation between the rotation pair constraint tire and the ground is constructed by rotating the rotation pair constraint tire around the axle center of the wheel hub and vertical pair constraint tire.
5. The vehicle braking performance optimization method based on dynamic distribution of pipeline pressure and axle load according to claim 1, wherein: the process of performing brake performance optimization analysis on the multi-axis special vehicle dynamics model in Step3 comprises
Step301, adopting a brake pipeline pressure distribution coefficient optimization method based on Adams/weight, analyzing the sensitivity of a distribution coefficient beta to three optimization targets by utilizing a matrix test design principle, and optimally analyzing pipeline pressure distribution coefficients of a front brake circuit and a rear brake circuit by a multi-axis special vehicle dynamics model;
step302, a braking force coordination control relation optimization method based on dynamic allocation of axle load is adopted, a five-axis and equivalent three-axis braking force coordination relation is provided, a multi-axis special vehicle dynamics model is utilized to analyze and demonstrate the braking force coordination relation optimization effect, and the braking performance of the whole vehicle is improved.
6. The method for optimizing vehicle braking performance based on dynamic distribution of line pressure and axle load according to claim 5, characterized in that: the method for optimizing the brake pipeline pressure distribution coefficient based on Adams/weight in Step301, which uses the matrix test design principle to analyze the sensitivity of the distribution coefficient beta to three optimization targets, and optimizes the pipeline pressure distribution coefficients of the front and rear brake circuits through a multi-axis special vehicle dynamics model, comprises the following steps of
(1) Based on the original structure of the braking system, namely that braking actions are respectively applied to front and rear braking pipelines, initially setting a front braking pipeline pressure distribution coefficient beta in the Car module;
(2) By utilizing an Adams/weight matrix test design principle, analyzing different influences of a design variable beta on three optimization targets of a braking distance, a vehicle body transverse displacement and a vehicle body vertical acceleration, and determining an optimal objective function value, namely a minimum braking distance, a minimum vehicle body transverse displacement and a minimum vehicle body vertical acceleration by combining constraint conditions of pipeline pressure distribution coefficients;
(3) Adopting a scanning design method suitable for a single factor, dividing the rule of the design variable in a constraint range, and finally carrying out data fitting by using a quadratic regression analysis model to obtain an equation predicted value with the minimum error with an actual observed value;
(4) And (3) evaluating the fitting goodness of the optimization targets, and after performing repeated iterative simulation tests by utilizing a multi-axis special vehicle dynamics model based on the heavy braking working condition at the initial speed of 100km/h, analyzing the sensitivity of the design variables to the three optimization targets, and optimizing and adjusting the distribution coefficient beta.
7. The method for optimizing vehicle braking performance based on dynamic distribution of line pressure and axle load according to claim 5, characterized in that: the process of providing a five-axis and equivalent three-axis braking force coordination relationship by adopting the braking force coordination control relationship optimization method based on dynamic axle load distribution and utilizing a multi-axis special vehicle dynamics model to analyze and demonstrate the braking force coordination relationship optimization effect comprises the following steps of
(1) The control method of the braking force coordination relation based on axle load distribution converts the coordination relation of front and rear braking pipelines into five-axis axle load coordination relation, and constructs a model of each axle distribution coefficient state variable beta based on a suspension dynamics model i ：
Wherein: Δz i Andthe vertical deformation and the corresponding change rate of the suspension are respectively; l (L) ir Is the longitudinal distance from the center of the ith axle to the pitch center;
(2) Simulating a severe braking condition under high-speed running of 100km/h by utilizing a multi-axis special vehicle dynamics model, analyzing a braking performance optimization effect of a five-axis coordination relation compared with a front-rear pipeline braking coordination relation, and realizing braking performance optimization of an emergency braking condition under high-speed running;
(3) And an equivalent triaxial braking force coordination control relation is provided by combining a 6S/6M braking control mode of the multi-axis special vehicle, a heavy braking condition under 100km/h high-speed running is simulated by utilizing a dynamics model of the multi-axis special vehicle, and a braking performance optimization effect of the equivalent triaxial coordination relation compared with a five-axis coordination relation is analyzed, so that braking performance optimization of an emergency braking condition under high-speed running is realized.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310647692.8A CN116933384A (en) | 2023-06-02 | 2023-06-02 | Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310647692.8A CN116933384A (en) | 2023-06-02 | 2023-06-02 | Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116933384A true CN116933384A (en) | 2023-10-24 |
Family
ID=88381709
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310647692.8A Pending CN116933384A (en) | 2023-06-02 | 2023-06-02 | Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116933384A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117601818A (en) * | 2024-01-23 | 2024-02-27 | 中国第一汽车股份有限公司 | Method and system for analyzing response time of brake-by-wire controller and vehicle |
-
2023
- 2023-06-02 CN CN202310647692.8A patent/CN116933384A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117601818A (en) * | 2024-01-23 | 2024-02-27 | 中国第一汽车股份有限公司 | Method and system for analyzing response time of brake-by-wire controller and vehicle |
| CN117601818B (en) * | 2024-01-23 | 2024-04-16 | 中国第一汽车股份有限公司 | Method and system for analyzing response time of brake-by-wire controller and vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111391822B (en) | Automobile transverse and longitudinal stability cooperative control method under limit working condition | |
| CN111845775A (en) | Joint estimation method for driving state and inertia parameters of distributed driving electric automobile | |
| CN113359457B (en) | High-dimensional dynamic model resolving device and method for intelligent vehicle chassis area controller | |
| CN112009577B (en) | Control method for semi-active suspension of heavy truck cab | |
| CN113239450B (en) | Interconnected air suspension multi-objective optimization method based on interval analysis | |
| CN116933384A (en) | Vehicle braking performance optimization method based on pipeline pressure and axle load dynamic distribution | |
| Heydinger et al. | Vehicle dynamics modelling for the national advanced driving simulator | |
| CN112937588B (en) | Vehicle stability analysis method for ice and snow track road condition | |
| Li et al. | Optimization method of vehicle handling stability based on response surface model with D-optimal test design | |
| Ozdalyan | Development of a slip control anti-lock braking system model | |
| CN111994085A (en) | Estimation method for vehicle driving stability area under complex road condition | |
| Lowndes | Development of an intermediate DOF vehicle dynamics model for optimal design studies | |
| CN113935107B (en) | Vehicle model modeling method suitable for ice and snow road surface | |
| CN115406669A (en) | Multi-axle special vehicle rollover index optimization method | |
| Azman et al. | Influence of anti-dive and anti-squat geometry in combined vehicle bounce and pitch dynamics | |
| Mastinu et al. | The influence of the body inertia tensor on the active safety and ride comfort of road vehicles | |
| Wade-Allen et al. | Validation of a non-linear vehicle dynamics simulation for limit handling | |
| CN112784355A (en) | Fourteen-degree-of-freedom vehicle dynamics model modeling method based on multi-body dynamics | |
| CN113449378A (en) | Transverse load transfer rate calculation method based on vehicle suspension deformation | |
| CN112987574A (en) | Cloud control intelligent chassis system based on multiple intelligent agents and control method | |
| Liu et al. | Multi-Objective Control of Dynamic Chassis Considering Road Roughness Class Recognition | |
| Zeng et al. | Analysis and Design of Suspension State Observer for Wheel Load Estimation | |
| Wang | Application of pid algorithm in drive fault-tolerant control of four-wheel drive electric vehicle | |
| Bakar et al. | Active suspension system in improving ride and handling performance of electric vehicle conversion | |
| Hamersma | ABS braking on rough terrain |
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 |