Intelligent passenger car rollover stabilization pneumatic structure and track control integrated design method

Abstract

The invention discloses a pneumatic structure and track control integrated design method for rollover stability of an intelligent passenger car. The method comprises the following steps: step 1, establishing an approximate model for a target function by adopting Gaussian process regression; step 2, establishing a passenger car rollover prevention mathematical model; 3, the passenger car rollover prevention mathematical model is decomposed into a system layer mathematical model and a plurality of subsystem layer mathematical models of different classes of variables and different subjects through pneumatic structure class variables and track control class variables; 4, calculating the mathematical model of the system layer by using a genetic algorithm, and calculating the mathematical models of the plurality of subsystem layers of different subjects by using a sequential quadratic algorithm; step 5, checking a calculation result of the step 4, and if the result meets a convergence condition, determining that the scheme is reasonable and the optimization work is completed; and if not, returning to the step 2. According to the method, a passenger car rollover prevention pneumatic structure-track control parameter global optimal scheme can be obtained, and rollover stability and obstacle avoidance safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to an integrated design method for a pneumatic structure and trajectory control for rollover stabilization of an intelligent passenger vehicle. Background Art

[0002] Since traditional structural optimization and control system design belong to different stages and fields, they are currently mainly carried out in a serial manner, that is, the sequential optimization design of optimizing the structure first and then optimizing the control system. In the structural design stage, the influence of obstacle avoidance trajectory and body posture is not considered, and during control design, the optimal planning can only be performed on the existing structure. During the high-speed obstacle avoidance and lane changing process of intelligent buses, there is a strong coupling relationship between the body posture and aerodynamic load and obstacle avoidance trajectory. The serial optimization design will separate the coupling between structural parameters and control parameters, and between parameters and structural performance and service performance, greatly reducing the feasible domain of optimization, and can only achieve local optimization improvement on the existing structure, and cannot achieve the global optimal matching of the physical structure and control system.

[0003] In order to ensure that the high performance, high reliability and rapid response design requirements of smart buses are met while obtaining the optimal design scheme for aerodynamic layout and obstacle avoidance safety, the applicant proposed an integrated design method for aerodynamic structure and trajectory control for rollover stability of smart buses.

[0004] The invention patent with announcement number CN118428240B discloses a method for optimizing the rollover collision safety assessment of a large bus based on a genetic algorithm. This technical solution focuses on the collision rollover situation rather than the impact of side wind and the bus obstacle avoidance trajectory on the rollover during high-speed driving.

[0005] Due to the different object scenes, the proposed design variables are also different. The aerodynamic structure design variables proposed by the applicant are the front top arc radius, front side arc radius, side arc radius, and tail upturn angle, and the trajectory control design variables are the steering wheel control amplitude, steering wheel control time period, etc., while the structural design variables proposed by the invention patent with announcement number CN118428240B are the length, width, height, and suspension system stiffness of the whole vehicle, and the control variable is the dynamic control system, and the control variables are unclear.

[0006] Moreover, the two are different in function construction, optimization framework design and algorithm design. Summary of the invention

[0007] The present invention provides an integrated design method for aerodynamic structure and trajectory control for rollover stability of an intelligent bus. It uses a multidisciplinary comprehensive consideration method to simultaneously optimize the anti-rollover design of the bus's exterior structure and obstacle avoidance trajectory on the basis of studying the traditional structural concept design, so as to ensure that the optimal obstacle avoidance path for the bus is obtained while reducing the sensitivity to side winds. The integrated design scheme of aerodynamic structure and trajectory control for rollover stability of an intelligent bus based on multidisciplinary design optimization fully considers the coupling relationship between traditional structural system design and control system design, and coordinates multiple disciplines to provide a more reasonable design scheme for actual engineering needs. To achieve the above-mentioned purpose, the present invention adopts the following technical scheme:

[0008] A method for integrating aerodynamic structure and trajectory control for rollover stability of intelligent buses comprises the following steps:

[0009] Step 1: Use Gaussian process regression to establish an approximate model for the objective function;

[0010] Step 2, establishing a mathematical model for preventing bus rollover;

[0011] Step 3: Decompose the bus anti-rollover mathematical model into a system-level mathematical model and multiple subsystem-level mathematical models of different categories of variables and different disciplines based on aerodynamic structure variables and trajectory control variables;

[0012] Step 4: Calculate the system-level mathematical model using a genetic algorithm, and calculate multiple subsystem-level mathematical models of different disciplines using a sequential quadratic algorithm;

[0013] Step 5: Check the calculation results of step 4. If the results meet the convergence conditions, the solution is reasonable and the optimization work is completed; if it does not converge, return to step 2.

[0014] Furthermore, in step 2, the bus rollover prevention mathematical model is:

[0015] min=M(z i )

[0016] stg(z i )=0

[0017] h(z i )≤0

[0018] z L ≤z i ≤z U

[0019] i=1,2,3,…,N

[0020] Where M is the objective function; z i is the i-th design variable; g is the equality constraint; h is the inequality constraint; zU ,z L are the maximum and minimum values ​​of z respectively.

[0021] Furthermore, in step 3, a system-level mathematical model is:

[0022] min=M(z i )

[0023]

[0024] z L ≤z i ≤z U

[0025] i=1,2,3,…,N

[0026] j=1,2,3,…,n

[0027] Where M is the system-level objective function; z j is the jth design variable at the system level; t ij Feedback design variable values ​​to the subsystem level; J i is the consistency constraint of subject i; s is the total number of design variables; ε is the relaxation factor; z U ,z L are the maximum and minimum values ​​of z respectively;

[0028] The mathematical models of multiple subsystem layers of different disciplines are:

[0029]

[0030] stg i (t i )=0

[0031] h(z i )≤0

[0032] t L ≤t ij ≤t U

[0033] j=1,2,3,…,n

[0034] where t i is the design variable of subject i; t ij is the j-th design variable of subject i; z j Assign expected values ​​to the system layer; g i is the inequality constraint of subject i; h i is the equality constraint of subject i; t U , t L are the maximum and minimum values ​​of t respectively.

[0035] Furthermore, the aerodynamic structure variables include a front top arc radius, a front side arc radius, a side arc radius and a tail tilt angle.

[0036] Furthermore, the trajectory control variables include steering wheel control amplitude and steering wheel control time period.

[0037] The beneficial effects of the present invention are:

[0038] 1. The present invention adopts an integrated design method of pneumatic structure and trajectory control, combining the pneumatic structure system and the trajectory control system together, thus avoiding the irrationality of the traditional sequential design method.

[0039] 2. The present invention adopts a multidisciplinary design method, which improves the design efficiency of the traditional single-disciplinary design method, saves calculation time, and reduces the cost of experimental design.

[0040] 3. The present invention has low cost and high efficiency, and can be designed in the conceptual design stage of buses, thereby reducing the risk of increased R&D cycle and R&D costs caused by later design changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the present invention.

[0042] The drawings are only used for illustrative purposes and should not be construed as limitations on this patent. In order to better illustrate this embodiment, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0044] This embodiment uses the integrated design of aerodynamic structure and trajectory control of a certain type of passenger car to provide a detailed description.

[0045] For the integrated design of aerodynamic structure and trajectory control of a certain type of bus, the main consideration is the impact of the bus's external aerodynamic structure and trajectory control parameters on the anti-rollover effect when avoiding obstacles at high speed.

[0046] After analysis, the front top arc, rear top arc, side arc radius and tail upturn angle were selected as the design variables of the aerodynamic structure; the steering wheel control amplitude and three time periods were selected as the trajectory control design variables, and the optimization work was carried out with the goal of minimizing the degree of bus rollover during high-speed obstacle avoidance.

[0047] The value ranges of the aerodynamic structure and trajectory control design variables are shown in Table 1.

[0048] Table 1 Aerodynamic structure and trajectory control design variables

[0049] Design variables Initial Value Upper limit Lower limit Front top arc radius 200mm 350mm 30mm Front arc radius 200mm 350mm 30mm Side arc radius 200mm 350mm 30mm Tail upturn angle 6° 9° 3° Steering wheel control amplitude 100° 150° 50° Steering wheel control time period 1 0.3s 0.5s 0.1s Steering wheel control time period 2 0.3s 0.5s 0.1s Steering wheel control time period 3 0.3s 0.5s 0.1s

[0050] The peak value of the lateral load transfer rate during high-speed obstacle avoidance is taken as the main performance index parameter; the lateral safety distance is taken as the performance index during obstacle avoidance; the aerodynamic lateral force and aerodynamic roll moment coefficient are taken as the performance indexes of the aerodynamic structure system, which can describe the crosswind sensitivity of the bus during high-speed obstacle avoidance. The initial values ​​of each performance index before optimization are shown in Table 2.

[0051] Table 2 Initial values ​​of performance indicators before optimization

[0052] LTR Y <![CDATA[C s ]]> <![CDATA[C rm ]]> 0.772 4.65m 1.022 -1.334

[0053] According to an integrated design method of aerodynamic structure and trajectory control for rollover stability of intelligent buses, the optimization process is divided into the following steps:

[0054] Step 1: Use Gaussian process regression to establish an approximate model for the objective function;

[0055] Step 2: Establish a mathematical model for preventing bus rollover, as shown in the following formula:

[0056] min=M(z i )

[0057] stLTR(z i )≤0.772

[0058] i=1,2,3,4,5,6,7,8

[0059] Where M is the objective function; z i is the i-th design variable; LTR is the peak lateral load transfer rate constraint; Y is the lateral safety distance constraint; C s , C rm is the aerodynamic coefficient constraint; z U ,z L are the maximum and minimum values ​​of z respectively.

[0060] Step 3: Decompose the bus anti-rollover mathematical model into a system-level mathematical model and multiple subsystem-level mathematical models of different categories of variables and different disciplines based on aerodynamic structure variables and trajectory control variables;

[0061] The decomposed system-level mathematical model is:

[0062] min=M(z i )

[0063]

[0064] zL ≤z i ≤z U

[0065] i=1,2,3,…,N

[0066] j=1,2,3,…,n

[0067] Where M is the system-level objective function; z j is the jth design variable at the system level; t ij Feedback design variable values ​​to the subsystem level; J i is the consistency constraint of subject i; s is the total number of design variables; ε is the relaxation factor; z U ,z L are the maximum and minimum values ​​of z respectively;

[0068] Decompose the subsystem level mathematical models of multiple disciplines to introduce the trajectory control subsystem level mathematical models and the aerodynamic structure subsystem level mathematical models in detail;

[0069] Mathematical model of trajectory control subsystem layer:

[0070] Lateral load transfer rate subsystem level:

[0071]

[0072] t L ≤t 1j ≤t U

[0073] j=1,2,3,4

[0074] Lateral safety distance subsystem layer:

[0075]

[0076] t L ≤t 2j ≤t U

[0077] j=1,2,3,4

[0078] Mathematical model of aerodynamic structure subsystem layer:

[0079] Aerodynamic lateral force coefficient subsystem layer:

[0080]

[0081] t L ≤t 3j ≤t U

[0082] j=1,2,3,4

[0083] Aerodynamic roll moment coefficient subsystem layer:

[0084]

[0085] t L ≤t 4j ≤t U

[0086] j=1,2,3,4

[0087] where t i is the design variable of subject i; t ij is the j-th design variable of subject i; z j Assign expected values ​​to system layers; U , t L are the maximum and minimum values ​​of t respectively.

[0088] Step 4: Calculate the system-level mathematical model using a genetic algorithm, and calculate multiple subsystem-level mathematical models of different disciplines using a sequential quadratic algorithm;

[0089] Step 5: The results are shown in Table 3, which meet the convergence conditions.

[0090] Table 3 Calculation results

[0091]

[0092] The present invention adopts a multidisciplinary aerodynamic structure and trajectory control integrated design method to obtain the global optimal solution of the passenger car anti-rollover aerodynamic structure-trajectory control parameters, thereby improving the rollover stability and obstacle avoidance safety.

[0093] The above embodiments are only used to illustrate rather than limit the technical solutions of the present invention. Although the present invention is described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

[0094] If the words "first", "second", etc. are used in this document to limit components, those skilled in the art should know that the use of "first" and "second" is only to facilitate the description of the present invention and simplify the description. Unless otherwise stated, the above words have no special meaning.

[0095] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0096] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

Claims

1. An integrated design method for aerodynamic structure and trajectory control for rollover stability of intelligent buses, characterized in that The steps include: Step 1: Use Gaussian process regression to establish an approximate model for the objective function; Step 2, establishing a mathematical model for preventing bus rollover; Step 3: Decompose the bus anti-rollover mathematical model into a system-level mathematical model and multiple subsystem-level mathematical models of different categories of variables and different disciplines based on aerodynamic structure variables and trajectory control variables; Step 4: Calculate the system-level mathematical model using a genetic algorithm, and calculate multiple subsystem-level mathematical models of different disciplines using a sequential quadratic algorithm; Step 5: Check the calculation results of step 4. If the results meet the convergence conditions, the solution is reasonable and the optimization work is completed; if it does not converge, return to step 2.

2. The integrated design method of aerodynamic structure and trajectory control for rollover stability of intelligent passenger vehicles according to claim 1 is characterized in that: In step 2, the bus rollover prevention mathematical model is: min=M(z i ) s.t.g(z i )=0 h(z i )≤0 With L ≤of i ≤of U i=1,2,3,…,N Where M is the objective function; z i is the i-th design variable; g is the equality constraint; h is the inequality constraint; z U ,z L are the maximum and minimum values ​​of z respectively.

3. The integrated design method of aerodynamic structure and trajectory control for rollover stability of intelligent passenger vehicles according to claim 1 is characterized in that: In step 3, a system-level mathematical model is: min=M(z i ) With L ≤of i ≤of U i=1,2,3,…,N j=1,2,3,…,n Where M is the system-level objective function; z j is the jth design variable at the system level; t ij Feedback design variable values ​​to the subsystem level; J i is the consistency constraint of subject i; s is the total number of design variables; ε is the relaxation factor; z U ,z L are the maximum and minimum values ​​of z respectively; The mathematical models of multiple subsystem layers of different disciplines are: s.t.g i (t i )=0 h(z i )≤0 t L ≤t ij ≤t U j=1,2,3,…,n where t i is the design variable of subject i; t ij is the j-th design variable of subject i; z j Assign expected values ​​to the system layer; g i is the inequality constraint of subject i; h i is the equality constraint of subject i; t U , t L are the maximum and minimum values ​​of t respectively.

4. The integrated design method of aerodynamic structure and trajectory control for rollover stability of intelligent passenger vehicles according to claim 1 is characterized in that: The aerodynamic structure variables include the front top arc radius, the front side arc radius, the side arc radius and the tail upturn angle.

5. The integrated design method of aerodynamic structure and trajectory control for rollover stability of intelligent passenger vehicles according to claim 1 is characterized in that: The trajectory control variables include a steering wheel control amplitude and a steering wheel control time period.

Images