Optimal design and optimal control collaborative articulated vehicle design optimization method and device

Abstract

The invention discloses an articulated vehicle design optimization method and device cooperating with optimal design and optimal control, and relates to the technical field of vehicle design optimizing.The method comprises the steps that firstly, a multi-degree-of-freedom dynamic model of an articulated vehicle is constructed based on the Lagrange dynamics theory, then residual constraints are determined according to the multi-degree-of-freedom dynamic model, and the optimal design and optimal control of the articulated vehicle are determined; boundary constraints are established according to the driving working conditions and working boundaries of all subsystems of the articulated vehicle, then design parameters and control parameters serve as optimization variables at the same time, and a multi-target collaborative optimization problem is constructed; finally, the multi-objective collaborative optimization problem is converted into a nonlinear optimization problem, a solver is used for solving the problem, and an optimal control parameter set and an optimal design parameter set are obtained and used for development of an articulated vehicle and development of a control algorithm of the articulated vehicle. According to the method, an optimal design and optimal control cooperation problem oriented to the articulated vehicle is constructed, and the design parameters and the control parameters of the articulated vehicle can be optimized at the same time based on the multi-degree-of-freedom dynamic model of the articulated vehicle.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle design optimization, and in particular to a method and device for optimizing the design of an articulated vehicle with coordinated optimal design and optimal control. Background Art

[0002] Articulated vehicles, with their unique design and functions, have shown significant advantages in the field of transportation. First, articulated vehicles have a strong carrying capacity and can carry a large amount of cargo, which significantly improves transportation efficiency. Secondly, the flexible replacement characteristics of trailers make them suitable for a variety of transportation scenarios, whether it is long-distance logistics or urban distribution, they can respond flexibly, improving the adaptability and flexibility of transportation. In addition, the high transportation efficiency of articulated vehicles can not only reduce transportation time, but also reduce overall costs, allowing companies to maintain their competitive advantage in a highly competitive market. Moreover, its energy-saving and emission-reduction effects are significant, which meets the needs of modern society for environmental protection and sustainable development. Therefore, articulated vehicles have significant advantages in improving the comprehensive economic benefits of road transportation, and are widely used in trunk logistics, port hubs, large logistics centers, etc.

[0003] Due to the rapid changes in technology and market demand in the automotive industry, manufacturers continue to invest in research and development and introduce new technologies to meet consumers' ever-increasing demands for performance, safety and environmental protection. At the same time, fierce market competition has forced manufacturers to speed up product updates to maintain market competitiveness. These factors have jointly promoted the rapid replacement of articulated vehicles. Therefore, new R&D processes are needed to shorten the production cycle in the field of platform interconnection and software.

[0004] Traditional new vehicle development generally adopts the method of empirical design-experimental verification. However, the structure of articulated vehicles is very complex. During the design process, it is often impossible to know whether the local and overall strength can meet the design requirements. Improvements can only be made through rounds of tests, which requires a long design cycle and high design and trial production costs, making it difficult to adapt to market demand. In addition, the traditional vehicle development method calculates the design parameters based on the requirements, and then optimizes the parameters based on the static model. After the optimization is completed, control optimization is performed for specific working conditions, resulting in small optimization space and poor dynamic operation effects. In addition, each subsystem is designed separately, which limits the overall performance of the vehicle. Therefore, in order to improve the development efficiency and quality of articulated vehicles, it is necessary to develop more advanced simulation-based vehicle optimization methods to shorten the development cycle and reduce costs. Summary of the invention

[0005] The purpose of the present application is to provide an articulated vehicle design optimization method and device that coordinates optimal design and optimal control, which can simultaneously optimize the design parameters and control parameters of the articulated vehicle based on the multi-degree-of-freedom dynamics model of the articulated vehicle.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control, comprising the following steps:

[0008] A multi-degree-of-freedom dynamic model of an articulated vehicle is constructed based on Lagrangian dynamics theory.

[0009] Residual constraints are determined based on the multi-degree-of-freedom dynamics model, and boundary constraints are established according to the driving conditions and the working boundaries of each subsystem of the articulated vehicle.

[0010] The design parameters and control parameters are taken as optimization variables at the same time, and a multi-objective collaborative optimization problem is constructed based on residual constraints, boundary constraints and optimization variables.

[0011] The multi-objective collaborative optimization problem is transformed into a nonlinear optimization problem, and the problem is solved using a nonlinear optimization problem solver to obtain the optimal control parameter set and the optimal design parameter set; the optimal control parameter set is used for the development of the articulated vehicle control algorithm, and the optimal design parameter set is used for the development of the articulated vehicle.

[0012] In a second aspect, the present application provides an articulated vehicle design optimization device for coordinated optimal design and optimal control, comprising the following modules:

[0013] Dynamic model building module, used to build a multi-degree-of-freedom dynamic model of an articulated vehicle based on Lagrangian dynamics theory;

[0014] The relevant constraint establishment module is used to determine the residual constraints based on the multi-degree-of-freedom dynamics model and establish boundary constraints according to the driving conditions and the working boundaries of each subsystem of the articulated vehicle;

[0015] Multi-objective optimization problem construction module, which is used to take design parameters and control parameters as optimization variables at the same time, and construct multi-objective collaborative optimization problems based on residual constraints, boundary constraints and optimization variables;

[0016] The problem conversion and NLP solving module is used to convert the multi-objective collaborative optimization problem into a nonlinear optimization problem, and use the nonlinear optimization problem solver to solve the problem to obtain the optimal control parameter set and the optimal design parameter set; the optimal control parameter set is used for the development of the articulated vehicle control algorithm, and the optimal design parameter set is used for the development of the articulated vehicle.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] The present application provides a method and device for optimizing the design of articulated vehicles with coordinated optimal design and optimal control. In the method, a multi-degree-of-freedom dynamic model of an articulated vehicle is first constructed based on the Lagrangian dynamics theory, and then residual constraints are determined based on the multi-degree-of-freedom dynamic model, and boundary constraints are established according to the driving conditions and the working boundaries of each subsystem of the articulated vehicle. Then, the design parameters and control parameters are used as optimization variables at the same time, and a multi-objective collaborative optimization problem is constructed based on the residual constraints, boundary constraints and optimization variables; finally, the multi-objective collaborative optimization problem is converted into a nonlinear optimization problem, and the problem is solved using a nonlinear optimization problem solver to obtain an optimal control parameter set and an optimal design parameter set, which can be used for the development of articulated vehicles and the development of their control algorithms. The present application constructs an optimal design and optimal control collaborative problem for articulated vehicles, which can realize the simultaneous optimization of its design parameters and control parameters based on the multi-degree-of-freedom dynamic model of the articulated vehicle. The optimal design parameters can be used for vehicle design, and the optimal control parameters can be used as a reference for the subsequent online control strategy formulation. The scheme of the present application can apply gradient information to expand the optimization space and has a good optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A flowchart of an articulated vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0021] Figure 2 A detailed flow chart of step S4 in a method for optimizing an articulated vehicle design by coordinating optimal design and optimal control provided in one embodiment of the present application.

[0022] Figure 3 A schematic diagram of the functional modules of an articulated vehicle design optimization device for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0023] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0026] In an exemplary embodiment, Figure 1 As shown, a method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control is provided, comprising the following steps:

[0027] S1. A multi-degree-of-freedom dynamics model of an articulated vehicle is constructed based on Lagrangian dynamics theory. In this embodiment, the multi-degree-of-freedom dynamics model of an articulated vehicle can be expressed by the following formula:

[0028]

[0029] Among them, T t represents the kinetic energy of the tractor, Q t,b represents the generalized force on the sprung mass of the tractor, Q t,u represents the generalized force on the unsprung mass of the tractor, q t,b represents the generalized coordinates of the sprung mass of the tractor, q t,u The generalized coordinates of the unsprung mass of the tractor, T s represents the kinetic energy of the trailer, Q s,b represents the generalized force on the sprung mass of the trailer, Q s,u represents the generalized force on the unsprung mass of the trailer, q s,b The generalized coordinates of the sprung mass of the trailer, q s,u It represents the generalized coordinates of the unsprung mass of the trailer. The point signs on the parameters are the derivative symbols.

[0030]

[0031] Among them, M t,u and J t,u are the mass matrix and moment of inertia matrix of the unsprung mass of the tractor, M t,b and J t,b are the mass matrix and moment of inertia matrix of the tractor sprung mass, M s,u and J s,u are the mass matrix and moment of inertia matrix of the unsprung mass of the trailer, M s,b and J s,bare the mass matrix and moment of inertia matrix of the sprung mass of the trailer respectively.

[0032] Kinetic energy of the tractor T t The kinetic energy of the tractor's sprung mass and the kinetic energy of the tractor's unsprung mass; the kinetic energy of the trailer is T s Includes the kinetic energy of the sprung mass of the trailer and the kinetic energy of the unsprung mass of the trailer.

[0033] The kinetic energy of the sprung mass of the tractor can be expressed as follows:

[0034]

[0035] Among them, T t,b is the kinetic energy of the sprung mass of the tractor, V t,b is the generalized speed of the tractor body, V t,u,i is the component of the unsprung mass velocity of the tractor in the vehicle body coordinate system, M t,u,i is the mass matrix of the unsprung mass of the tractor, h A,b With h b,u,i is the transformation matrix corresponding to the sprung mass and the unsprung mass, M gb is the generalized mass matrix obtained after sorting.

[0036] The kinetic energy of the unsprung mass of the tractor can be expressed as follows:

[0037]

[0038] Among them, T t,u is the kinetic energy of the unsprung mass of the tractor, V t,uz is the vertical velocity of the unsprung mass of the tractor, ω t,u is the angular velocity of the unsprung mass of the tractor, J t,u is the moment of inertia matrix of the unsprung mass of the tractor.

[0039] The calculation method of the kinetic energy of the sprung mass of the trailer and the kinetic energy of the unsprung mass of the trailer is similar to the calculation method of the kinetic energy of the sprung mass of the tractor and the kinetic energy of the unsprung mass of the tractor, and will not be repeated here.

[0040] Specifically in this embodiment, the generalized force on the sprung mass of the tractor, the generalized force on the unsprung mass of the tractor, the generalized force on the sprung mass of the trailer, and the generalized force on the unsprung mass of the trailer can be obtained by the principle of virtual work:

[0041]

[0042] Where F is the total generalized force matrix, r t,b is the virtual displacement of the sprung mass of the tractor, F t,b is the generalized force matrix of the sprung mass of the tractor, rt,u is the virtual displacement of the unsprung mass of the tractor, F t,u is the generalized force matrix of the unsprung mass of the tractor, r s,b is the virtual displacement of the sprung mass of the trailer, F s,b is the generalized force matrix acting on the sprung mass of the trailer, r s,u is the virtual displacement of the unsprung mass of the trailer, F s,u is the generalized force matrix acting on the unsprung mass of the trailer.

[0043] Specifically in this embodiment, the generalized force matrix of the sprung mass of the tractor is as follows:

[0044]

[0045] Among them, F t,x,i is the longitudinal force at the contact point between the ith tire of the tractor and the ground, δ t,i is the rotation angle input of the ith tire of the tractor, F t,y,i is the lateral force between the tractor's i-th tire and the ground, ψ t is the yaw angle of the tractor, F t,wx is the longitudinal component of aerodynamic drag, F hitch,x is the longitudinal hinge force, F t,wy is the lateral component of aerodynamic drag, F hitch,y is the lateral hinge force, F t,bs,i is the suspension force acting on the ith tire of the tractor, F t,wz is the downforce of aerodynamic drag, m t,b is the sprung mass of the tractor, g is the acceleration due to gravity, F hitch,z is the vertical hinge force, y t,u,i is the lateral coordinate of the suspension mounting point in the vehicle body coordinate system, Z t is the height of the center of sprung mass of the tractor, T t,d,i is the torque input of the ith tire of the tractor, M t,wx is the component of the aerodynamic drag moment around the x-axis, M hitch,x is the hinge moment about the x-axis, x t,u,i is the longitudinal coordinate of the suspension mounting point in the vehicle body coordinate system, M t,wy is the component of the aerodynamic drag moment around the y-axis, M hitch,y is the hinge moment about the y-axis, M t,z,i is the self-aligning torque of the contact point between the ith tire of the tractor and the ground, x t,w,i is the longitudinal coordinate of the tire center in the vehicle body coordinate system, y t,w,i is the lateral coordinate of the tire center in the vehicle body coordinate system, M t,wz is the component of the aerodynamic drag moment around the z-axis, M hitch,z is the hinge moment about the z-axis.

[0046] The generalized force matrix of the unsprung mass of the tractor is as follows:

[0047]

[0048] Among them, T t,d,fr is the torque input of the right front tire of the tractor, M t,y,fr is the rolling resistance moment of the right front tire of the tractor, F t,x,fr is the longitudinal force at the contact point between the right front tire of the tractor and the ground, z t,u,fr is the center height of the right front tire of the tractor, T t,d,rr is the torque input of the right rear tire of the tractor, M t,y,rr is the rolling resistance moment of the right rear tire of the tractor, F t,x,rr is the longitudinal force at the contact point between the right rear tire of the tractor and the ground, z t,u,rr F is the center height of the right rear tire of the tractor. t,z,fr is the vertical force on the right front tire of the tractor, F t,bs,fr is the suspension force acting on the right front tire of the tractor, m t,u,fr is the mass of the right front tire of the tractor, F t,z,rr is the vertical force on the right rear tire of the tractor, F t,bs,rr is the suspension force acting on the right rear tire of the tractor, m t,u,rr is the mass of the right rear tire of the tractor, and g is the acceleration due to gravity.

[0049] S2. Determine the residual constraints based on the multi-degree-of-freedom dynamics model, and establish boundary constraints according to the driving conditions and the working boundaries of each subsystem of the articulated vehicle.

[0050] S3. Use the design parameters and control parameters as optimization variables at the same time, and construct a multi-objective collaborative optimization problem based on residual constraints, boundary constraints and optimization variables. In this embodiment, the constructed multi-objective collaborative optimization problem is shown in the following formula:

[0051]

[0052] Where J is the objective function value; t f is the end time, t0 is the start time, J p is a penalty function to constrain acceleration and braking from being performed simultaneously, w is the weight factor of the penalty function, f[] is a multi-degree-of-freedom dynamics model, x is a state parameter set, and x min and x max are the upper and lower bounds of the state parameters; u is the control parameter set, u min and u max are the upper and lower bounds of the state parameters; t is the time step, p is the design parameter set, and p min and p maxare the upper and lower bounds of the state parameters; g[] is the path constraint function, g min and g max are the upper and lower bounds of the path constraint function; b[] is the boundary constraint function, b min and b max are the upper and lower bounds of the boundary constraint function.

[0053] S4. Convert the multi-objective collaborative optimization problem into a nonlinear optimization problem, and use a nonlinear optimization problem solver to solve the problem to obtain an optimal control parameter set and an optimal design parameter set. The optimal control parameter set is used for the development of the articulated vehicle control algorithm, and the optimal design parameter set is used for the development of the articulated vehicle. In an exemplary embodiment, any one of a local collocation method, a difference method, or an automatic scaling method is used to convert the multi-objective collaborative optimization problem into a nonlinear optimization problem.

[0054] In this embodiment, if Figure 2 In the flowchart shown, step S4 specifically includes the following steps:

[0055] S41. Linearly interpolate the state parameters and control parameters respectively to obtain the state parameters and control parameters at each interpolation moment, so as to reconstruct the optimization variables and obtain the reconstructed optimization variables; the interpolation moments include the start moment, the end moment and several intermediate moments between the start moment and the end moment; the reconstructed optimization variables include the design parameters and the state parameters and control parameters at each interpolation moment.

[0056] S42. Calculate the gradient of the objective function with respect to the reconstructed optimization variable.

[0057] S43. Based on the reconstructed optimization variables, the residual constraints, the boundary constraints and the path constraints are reconstructed respectively to obtain the reconstructed residual constraints, the reconstructed path constraints and the reconstructed boundary constraints.

[0058] S44, respectively calculating a first Jacobian matrix of the reconstructed residual constraint, a second Jacobian matrix of the reconstructed path constraint, and a third Jacobian matrix of the reconstructed boundary constraint.

[0059] S45. Based on the above results, a nonlinear programming solver is called to solve and obtain an optimal control parameter set and an optimal design parameter set. The reconstructed optimization variables, the objective function, the gradient of the objective function to the reconstructed optimization variables, the reconstructed residual constraint, the reconstructed path constraint, the reconstructed boundary constraint, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix are used as inputs, and a nonlinear programming solver is used to solve and obtain an optimal control parameter set and an optimal design parameter set.

[0060] In order to better understand the collaborative optimal design and optimal control of an articulated vehicle design optimization method proposed in the above embodiment of the present application, in the following example, an articulated commercial vehicle with a configuration of a three-axle rear-wheel drive tractor + a three-axle trailer is taken as an example to illustrate the application of the collaborative optimal design and optimal control of an articulated vehicle design optimization method, including the following steps:

[0061] Step 1: Construction of multi-degree-of-freedom vehicle dynamics model.

[0062] The input of the articulated commercial vehicle model is the steering wheel angle and the driving or braking torque of each wheel. The tire rotation is driven by the wheel torque and the longitudinal ground force. The input of the tire model module is the wheel rotation angular velocity and wheel center speed, and the output is the tire force and torque. The vertical movement of the unsprung mass is generated by the vertical force of the tire and the suspension force. The hinge point is equivalent to a spring-damper system, and the articulation force is calculated based on the movement of the tractor and trailer. The movement of the vehicle body is determined by the longitudinal and lateral tire forces, aerodynamics, suspension forces, and articulation forces. In order to improve the calculation efficiency, the vectorized programming method is applied to establish a vehicle dynamics model that supports vectorized calculation.

[0063] The dynamic behavior of a simplified vehicle composed of multiple rigid components is represented by a multi-degree-of-freedom vehicle model. The bodies of the tractor and trailer can move in the longitudinal, lateral and vertical directions, and rotate around the xyz axes. The 12 wheels have 12 rotational degrees of freedom, and due to the existence of the suspension system, the 12 wheels have 12 vertical degrees of freedom. A total of 36 degrees of freedom.

[0064] In the modeling process, O-xyz is used to represent the earth coordinate system, O1-x1y1z1 is used to represent the tractor body coordinate system, and O2-x2y2z2 is used to represent the trailer body coordinate system. The generalized coordinates of multiple degrees of freedom are expressed by vectors as follows:

[0065]

[0066] Where q represents the generalized coordinates of the vehicle model; subscript t represents the tractor, subscript s represents the trailer, and q bv represents the position vector of the center of mass of the vehicle body, X, Y, Z are the absolute displacements of the center of mass along the global coordinate system; q bω represents the rotation angle of the vehicle body’s center of mass, φ, ψ are the rotation angles of the vehicle body around the three axes of the global coordinate system; q uθ represents the angular velocity sequence generated by the rotation of the six tires on both sides, θ i represents the angular velocity of tire rotation; q uz represents the vertical displacement sequence of the six tires on both sides, z iIndicates the vertical displacement of the tire; the 6 tires are distinguished by subscripts, the subscript u, fr represents the right tire of the first axle, that is, the right front tire, u, fl represents the left tire of the first axle, that is, the left front tire, u, mr represents the right tire of the second axle, that is, the right middle tire, u, ml represents the left tire of the second axle, that is, the left middle tire, u, rr represents the right tire of the third axle, that is, the right rear tire, and u, rl represents the left tire of the third axle, that is, the left rear tire.

[0067] The coordinates of the centers of the six tires of the tractor in the tractor body coordinate system in the x1-y1 plane are as follows:

[0068]

[0069] Among them, l t represents the longitudinal distance between the three axles of the tractor and the center of mass, w t Indicates the wheelbase of three axles. The subscript f indicates the first axle, m indicates the second axle, and r indicates the third axle.

[0070] Correspondingly, the coordinates of the centers of the six tires of the trailer in the trailer body coordinate system in the x2-y2 plane are as follows:

[0071]

[0072] The unsprung mass, that is, the relative vertical position of the tire in the vehicle coordinate system can be expressed by its absolute coordinates, the vehicle roll angle, the pitch angle and the xy coordinates in the vehicle coordinate system, as shown in the following formula:

[0073]

[0074] The vehicle's 36-degree-of-freedom dynamics equation is derived based on Lagrangian dynamics, as shown below:

[0075]

[0076] Among them, T t is the kinetic energy of the tractor, T s represents the kinetic energy of the trailer, Q t,b represents the generalized force on the sprung mass of the tractor, Q t,u represents the generalized force on the unsprung mass of the tractor, q t,b represents the generalized coordinates of the sprung mass of the tractor, q t,u represents the generalized coordinates of the tractor's six tires, i.e., the unsprung mass, including the tire rotation angle and vertical position; Q s,b represents the generalized force on the sprung mass of the trailer, Q s,u represents the generalized force on the unsprung mass of the trailer, q s,b The generalized coordinates of the sprung mass of the trailer, q s,uThe generalized coordinates of the trailer's six tires, i.e. the unsprung mass, including the tire angles and vertical positions.

[0077] The calculation process of kinetic energy of a tractor in an articulated vehicle is introduced by taking the tractor as an example. The calculation process of kinetic energy of a trailer is exactly the same as that of a tractor, so it will not be described here.

[0078] When calculating the kinetic energy of the sprung mass, the kinetic energy of the tire's lateral and longitudinal motion is taken into account, as shown in the following formula:

[0079]

[0080] Among them, V t,b 、V t,ui are the components of the generalized velocity of the tractor body and the unsprung mass velocity in the body coordinate system, M t,b 、M t,ui are the mass matrices of the tractor body and the unsprung mass, respectively. Through coordinate transformation and matrix calculation, the kinetic energy of the sprung mass can be expressed as follows:

[0081]

[0082] The kinetic energy of the unsprung mass is composed of the rotational and vertical motion of the tire, as shown below:

[0083]

[0084] Among them, V t,uz ,ω t,u are the vertical velocity and angular velocity of the six tires of the unsprung mass, M t,u , J t,u are the mass matrix and moment of inertia matrix of the unsprung mass, respectively. According to matrix calculation, the above formula can be converted into the form of generalized coordinates and generalized mass matrix:

[0085]

[0086] In addition, the vertical forces acting on the sprung mass (vehicle body) include the weight of the vehicle body, suspension force, vertical air resistance, and vertical articulation force. The longitudinal forces include the lateral force generated between the tire and the ground, the longitudinal force and the component of air resistance in the global coordinate system, and the longitudinal articulation force. The lateral force includes the component of the lateral and longitudinal forces of the tire on the ground, and the lateral articulation force. The three-axis torques include the torques of the above forces on the center of mass, the tire self-aligning torque, the air resistance torque, the reaction torque on the drive tire, and the torque at the articulation point.

[0087] The vertical force on the tire is composed of the tire's own weight and suspension force, as shown in the following formula:

[0088] F t,z,i =F t,bs,i +mt,u,i g.

[0089] The longitudinal slip rate of the tire contact point with the ground is calculated as follows:

[0090]

[0091] Apply the tire magic formula to calculate tire longitudinal force, lateral force, self-aligning moment, rolling resistance moment, and yaw resistance moment.

[0092] The suspension force is composed of spring force and damping force. When the stiffness and damping ratio are constant values, the suspension force can be expressed as follows:

[0093]

[0094] The calculation of the air resistance part first calculates the air resistance slip angle. Assuming that the air is still and the wind speed is 0, the air resistance slip angle is the vehicle center of mass slip angle. Then, the air resistance coefficient is calculated by linear interpolation and cubic spline interpolation based on the air resistance slip angle. Then, the air resistance factor is calculated according to the following formula:

[0095]

[0096] The air resistance can be calculated based on the air resistance factor.

[0097] Based on the calculation of the tire force and suspension model above, the generalized force matrix of the sprung mass of the tractor is as follows:

[0098]

[0099] The forces acting on the unsprung mass of the tractor include suspension force, ground vertical force, and tire weight, and the torques acting on it include driving torque and tire rolling resistance torque. Its generalized force matrix is ​​expressed as follows:

[0100]

[0101] Similarly, the generalized force matrix of the sprung mass and unsprung mass of the trailer can be derived. Based on the force analysis and the principle of virtual work, the generalized forces can be derived as shown in the following equations:

[0102]

[0103] According to Lagrangian mechanics and D'Alembert's principle, the generalized equation of motion of the sprung mass of the tractor is as follows:

[0104]

[0105] The generalized equation of motion for the unsprung mass of the tractor is given by:

[0106]

[0107] The generalized equation of motion for the sprung mass of the trailer is given by:

[0108]

[0109] The generalized equation of motion for the unsprung mass of the trailer is given by:

[0110]

[0111] The second-order differentials of all state variables can be obtained through the above generalized equations of motion, which can be converted into residual constraints as the basis for constructing subsequent optimal control problems.

[0112] Step 2: Construct a multi-objective collaborative optimization problem of collaborative optimal design and optimal control.

[0113] In this part, you need to complete the objective function, upper and lower bounds of constraints, and guess the initial value. You also need to set some optimization-related parameters, such as the conversion method m. trans 、Differential method diff , allocation points N n , scaling setting scal, the parameters plot that need to be drawn in the iteration, and the weight w of the control parameter to be smoothed u wait.

[0114] According to the vehicle dynamics model established in step 1, provide the input of the problem, including the initial guess value x0 of the state quantity, the initial guess value u0 of the control quantity, and the terminal time t f0 and design variable p0.

[0115]

[0116] Where N x,usr is the number of nodes of the state variable provided by the user, and t is the number of nodes that convert 0 to t f Discretized into N x,usr Time series of points N u,usr is the number of nodes of the control variable provided by the user, n x is the number of state variables of the dynamic equation, n u is the number of control variables. If the terminal time is also a parameter to be optimized, then n tf is 1, otherwise it is 0, n p is the number of design parameters, represents a two-dimensional matrix with i rows and j columns. For the global transformation method, the initial values ​​of the start time and end time of each stage will be generated by the optimal control framework.

[0117] The above state variables, control variables, terminal time, upper and lower bounds of design variables and inequality constraints g are given by the following formula:

[0118]

[0119] Among them, n g is the number of path constraints. In addition to the above initial values ​​and upper and lower bounds, the problem input also includes the Lagrangian term of the cost function Meyer Item First-order dynamic constraint function f, path constraint function g, boundary constraint function b.

[0120] Step 3: Perform nonlinear transformation and solution of multi-objective collaborative optimization problems.

[0121] Before solving the problem, it is necessary to convert the problem into a large-scale NLP problem, and then call the underlying NLP solver to solve the problem.

[0122] In the problem conversion part, the format of variables and functions in the problem input is converted into the format required by the NLP solver. In this part, the local collocation method, the difference method, the scaling method, etc. are developed in this embodiment.

[0123] Step 3.1: Local matching method.

[0124] Step 3.1.1: NLP solver variables.

[0125] In the local collocation method, the continuous state and control quantities in the entire time interval can be discretized into N n nodes, the new variable is given by the following formula:

[0126]

[0127] Among them, x0 represents the initial guess value of the state quantity, u0 represents the initial guess value of the control quantity, t f0 represents the initial guess value of the terminal time, p0 represents the initial guess value of the design variable, N x,usr is the number of nodes of the state variable, and t is the number of nodes from 0 to t f Discretized into N x,usr A time series of points, N u,usr is the number of nodes of the control variable provided by the user, n x is the number of state variables of the dynamic equation, n u is the number of control variables. If the terminal time is also a parameter to be optimized, then n tf is 1, otherwise it is 0, n p is the number of design parameters, Represents a two-dimensional matrix with i rows and j columns.

[0128] Then, the discrete state variables, control variables and static variables are reconstructed into NLP column vectors:

[0129]

[0130] Among them, x i represents the state quantity at the i-th node, u i represents the control quantity at the i-th node, t f represents the terminal time, and p represents the design variable.

[0131] The upper and lower bounds of the variables in step 2 should also be reconstructed based on the reconstructed NLP variables.

[0132] Step 3.1.2: Constraints.

[0133] 1) Residual constraints of the Trapezoidal method:

[0134] Residual Constraint ζ for Trapezoidal Method k It can be expressed as:

[0135]

[0136] Among them, k represents the residual constraint at the kth node, x k represents the state quantity at the kth node, x k+1 Represents the state quantity at the k+1th node.

[0137] The application matrix is ​​calculated as:

[0138]

[0139] The transformation matrix T s1 and T s2 As shown below:

[0140]

[0141] 2) Path constraints and boundary constraints:

[0142] The path constraint g(x,u,p,t) is a function of the state quantity, control quantity, design variable and time, and the boundary constraint is a function of the initial state and terminal state, as shown in the following formulas:

[0143]

[0144] Boundary constraints b(x0,t0,x f ,t f ,p) is a function of initial state quantity, initial time, terminal state quantity, terminal time, and design variables.

[0145] Finally, after all constraints are calculated, the NLP constraints are expressed as:

[0146]

[0147] All residual constraints are bounded by 0.

[0148] Step 3.1.3: Jacobians matrix.

[0149] 1) Jacobians matrix of residual constraints:

[0150] Without considering the state and control quantities at the midpoint of each discrete time interval, the residual constraint is x a ,u a ,x b ,u b ,t f ,p function,x a ,u a ,x b ,u b ,t f ,p specific values ​​are as follows:

[0151]

[0152] To avoid repeated calculations, matrix calculations are performed. First, the derivatives of ζ with respect to x, u, and p are calculated. and

[0153] Therefore, the Jacobians matrix can be simplified to:

[0154]

[0155] In the formula is a matrix whose i-th column is 1 and the rest of the elements are all 0, T a ,T b Given by:

[0156] 2) Jacobians matrix of path constraints and boundary constraints:

[0157] First, calculate the derivative of the path constraint g with respect to x, u, p, t

[0158]

[0159] Then calculate the boundary constraints b for x0,x f ,p,t derivative:

[0160]

[0161] Step 3.1.4: Cost function.

[0162] The input cost function consists of Mayer terms and Lagrange terms, which can be expressed as follows:

[0163]

[0164] Step 3.1.5: Gradient of the cost function.

[0165] The cost function is [x,u,t f ,p], so its gradient to state variables and control variables is as follows:

[0166]

[0167] Then calculate the gradient of the cost function with respect to the terminal time and the design variables, and finally obtain the NLP gradient:

[0168]

[0169] Step 3.2: Difference method.

[0170] Most NLP solvers require the first-order derivatives of the constraint c(y), and accurate calculation of these derivatives helps to achieve faster convergence. The difference method implemented in this patent is the central difference. The central finite difference approximation method is:

[0171]

[0172] Step 3.3: Scaling.

[0173] Step 3.3.1: Variable scaling.

[0174] Apply linear scaling methods to scale state variables, control variables, terminal times, and design variables:

[0175]

[0176]

[0177] Step 3.3.2: Constrained scaling.

[0178] Apply the gradient-based average norm method to scale the constraint function and the cost function for faster convergence:

[0179] The residual constraint is scaled as follows:

[0180]

[0181] Scaling of path constraints is similar and is not listed here.

[0182] Step 3.3.3: Scaling of the Jacobians matrix.

[0183] The relationship between scaling variables and scaling constraints is considered in the scaling of the Jacobians matrix. The scaling of the Jacobians matrix of the residual constraint is defined as follows:

[0184]

[0185] Similarly, the scaling of the Jacobians matrix for path constraints is:

[0186]

[0187] After the above problem transformation, the transformed problem is input into the underlying solver for solution, which can solve the optimal control parameters and optimal design parameters at the same time. The design parameters are defined according to the requirements; the control parameters are the tire angle and torque input. It can be used for the development of articulated vehicles and the development of their control algorithms.

[0188] The above-mentioned embodiment of the present application constructs an optimal design and optimal control collaborative problem for articulated vehicles, which can realize the simultaneous optimization of its design parameters and control parameters based on the dynamic model of the articulated vehicle. The optimal design parameters can be used for vehicle design, and the optimal control parameters can be used as a reference for the subsequent online control strategy formulation; the framework can apply gradient information, expand the optimization space, and has a good optimization effect, which can improve the development efficiency of articulated commercial vehicles. This is mainly related to the construction of the optimal design and optimal control problems, the rationality of vehicle dynamics modeling, and the efficient solution of the problem. In addition, in this embodiment, for large-scale multi-degree-of-freedom optimal design and optimal control problems, the solution stability and efficiency of the problem are greatly improved. This is mainly due to the vectorized dynamics modeling method of the vehicle, and in the solution part of the optimal problem, the direct point matching method is improved, and the automatic normalization algorithm is applied to achieve a stable and fast solution to large-scale collaborative optimal problems.

[0189] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned coordinated optimal design and optimal control articulated vehicle design optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more device embodiments provided below can refer to the limitations of the coordinated optimal design and optimal control articulated vehicle design optimization method above, and will not be repeated here.

[0190] In an exemplary embodiment, Figure 3As shown, a design optimization device for an articulated vehicle with coordinated optimal design and optimal control is provided, comprising the following modules:

[0191] Dynamic model building module, used to build a multi-degree-of-freedom dynamic model of an articulated vehicle based on Lagrangian dynamics theory;

[0192] The relevant constraint establishment module is used to determine the residual constraints based on the multi-degree-of-freedom dynamics model and establish boundary constraints according to the driving conditions and the working boundaries of each subsystem of the articulated vehicle;

[0193] Multi-objective optimization problem construction module, which is used to take design parameters and control parameters as optimization variables at the same time, and construct multi-objective collaborative optimization problems based on residual constraints, boundary constraints and optimization variables;

[0194] The problem conversion and NLP solving module is used to convert the multi-objective collaborative optimization problem into a nonlinear optimization problem, and use the nonlinear optimization problem solver to solve the problem to obtain the optimal control parameter set and the optimal design parameter set; the optimal control parameter set is used for the development of the articulated vehicle control algorithm, and the optimal design parameter set is used for the development of the articulated vehicle.

[0195] certainly, Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Figure 3 One or at least two components of the system shown.

[0196] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the design of an articulated vehicle with coordinated optimal design and optimal control is implemented.

[0197] Those skilled in the art will understand that Figure 4The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0198] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0199] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0200] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0201] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control, characterized in that: include: The multi-degree-of-freedom dynamics model of the articulated vehicle is constructed based on the Lagrangian dynamics theory; Determining residual constraints based on the multi-degree-of-freedom dynamics model, and establishing boundary constraints based on driving conditions and working boundaries of each subsystem of the articulated vehicle; Taking the design parameters and the control parameters as optimization variables at the same time, and constructing a multi-objective collaborative optimization problem based on the residual constraint, the boundary constraint and the optimization variable; The multi-objective collaborative optimization problem is converted into a nonlinear optimization problem, and a nonlinear optimization problem solver is used to solve the problem to obtain an optimal control parameter set and an optimal design parameter set; the optimal control parameter set is used for the development of an articulated vehicle control algorithm, and the optimal design parameter set is used for the development of an articulated vehicle.

2. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 1, characterized in that: The multi-degree-of-freedom dynamic model of an articulated vehicle can be expressed as follows: Among them, T t represents the kinetic energy of the tractor, Q t,b represents the generalized force on the sprung mass of the tractor, Q t,u represents the generalized force on the unsprung mass of the tractor, q t,b represents the generalized coordinates of the sprung mass of the tractor, q t,u The generalized coordinates of the unsprung mass of the tractor, T s represents the kinetic energy of the trailer, Q s,b represents the generalized force on the sprung mass of the trailer, Q s,u represents the generalized force on the unsprung mass of the trailer, q s,b The generalized coordinates of the sprung mass of the trailer, q s,u It represents the generalized coordinates of the unsprung mass of the trailer, and the point sign on the parameter is the derivative symbol; Among them, M t,u and J t,u are the mass matrix and moment of inertia matrix of the unsprung mass of the tractor, M t,b and J t,b are the mass matrix and moment of inertia matrix of the tractor sprung mass, M s,u and J s,u are the mass matrix and moment of inertia matrix of the unsprung mass of the trailer, M s,b and J s,b are the mass matrix and moment of inertia matrix of the sprung mass of the trailer respectively.

3. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 2, characterized in that: The kinetic energy of the tractor includes the kinetic energy of the sprung mass of the tractor and the kinetic energy of the unsprung mass of the tractor, and the kinetic energy of the trailer includes the kinetic energy of the sprung mass of the trailer and the kinetic energy of the unsprung mass of the trailer; The kinetic energy of the sprung mass of the tractor can be expressed as follows: Among them, T t,b is the kinetic energy of the sprung mass of the tractor, V t,b is the generalized speed of the tractor body, V t,u,i is the component of the unsprung mass velocity of the tractor in the vehicle body coordinate system, M t,u,i is the mass matrix of the unsprung mass of the tractor, h A,b With h b,u,i is the transformation matrix corresponding to the sprung mass and the unsprung mass, M gb is the generalized mass matrix obtained by sorting; The kinetic energy of the unsprung mass of the tractor can be expressed as follows: Among them, T t,u is the kinetic energy of the unsprung mass of the tractor, V t,uz is the vertical velocity of the unsprung mass of the tractor, ω t,u is the angular velocity of the unsprung mass of the tractor, J t,u is the moment of inertia matrix of the unsprung mass of the tractor; The kinetic energy of the sprung mass of the trailer and the kinetic energy of the unsprung mass of the trailer are calculated in a similar manner to the kinetic energy of the sprung mass of the tractor and the kinetic energy of the unsprung mass of the tractor.

4. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 2, characterized in that: The generalized forces on the sprung mass of the tractor, the unsprung mass of the tractor, the sprung mass of the trailer, and the unsprung mass of the trailer can be obtained by the principle of virtual work: Where F is the total generalized force matrix, r t,b is the virtual displacement of the sprung mass of the tractor, F t,b is the generalized force matrix of the sprung mass of the tractor, r t,u is the virtual displacement of the unsprung mass of the tractor, F t,u is the generalized force matrix of the unsprung mass of the tractor, r s,b is the virtual displacement of the sprung mass of the trailer, F s,b is the generalized force matrix acting on the sprung mass of the trailer, r s,u is the virtual displacement of the unsprung mass of the trailer, F s,u is the generalized force matrix acting on the unsprung mass of the trailer.

5. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 4, characterized in that: The generalized force matrix of the sprung mass of the tractor is as follows: Among them, F t,x,i is the longitudinal force at the contact point between the ith tire of the tractor and the ground, δ t,i is the rotation angle input of the ith tire of the tractor, F t,y,i is the lateral force between the tractor's i-th tire and the ground, ψ t is the yaw angle of the tractor, F t,wx is the longitudinal component of aerodynamic drag, F hitch,x is the longitudinal hinge force, F t,wy is the lateral component of aerodynamic drag, F hitch,y is the lateral hinge force, F t,bs,i is the suspension force acting on the ith tire of the tractor, F t,wz is the downforce of aerodynamic drag, m t,b is the sprung mass of the tractor, g is the acceleration due to gravity, F hitch,z is the vertical hinge force, y t,u,i is the lateral coordinate of the suspension mounting point in the vehicle body coordinate system, Z t is the height of the center of sprung mass of the tractor, T t,d,i is the torque input of the ith tire of the tractor, M t,wx is the component of the aerodynamic drag moment around the x-axis, M hitch,x is the hinge moment about the x-axis, x t,u,i is the longitudinal coordinate of the suspension mounting point in the vehicle body coordinate system, M t,wy is the component of the aerodynamic drag moment around the y-axis, M hitch,y is the hinge moment about the y-axis, M t,z,i is the self-aligning torque of the contact point between the ith tire of the tractor and the ground, x t,w,i is the longitudinal coordinate of the tire center in the vehicle body coordinate system, y t,w,i is the lateral coordinate of the tire center in the vehicle body coordinate system, M t,wz is the component of the aerodynamic drag moment around the z-axis, M hitch,z is the hinge moment about the z-axis.

6. The method for design optimization of articulated vehicles by coordinated optimal design and optimal control according to claim 4, characterized in that: The generalized force matrix of the unsprung mass of the tractor is as follows: Among them, T t,d,fr is the torque input of the right front tire of the tractor, M t,y,fr is the rolling resistance moment of the right front tire of the tractor, F t,x,fr is the longitudinal force at the contact point between the right front tire of the tractor and the ground, z t,u,fr is the center height of the right front tire of the tractor, T t,d,rr is the torque input of the right rear tire of the tractor, M t,y,rr is the rolling resistance moment of the right rear tire of the tractor, F t,x,rr is the longitudinal force at the contact point between the right rear tire of the tractor and the ground, z t,u,rr F is the center height of the right rear tire of the tractor. t,z,fr is the vertical force on the right front tire of the tractor, F t,bs,fr is the suspension force acting on the right front tire of the tractor, m t,u,fr is the mass of the right front tire of the tractor, F t,z,rr is the vertical force on the right rear tire of the tractor, F t,bs,rr is the suspension force acting on the right rear tire of the tractor, m t,u,rr is the mass of the right rear tire of the tractor, and g is the acceleration due to gravity.

7. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 1, characterized in that: The multi-objective collaborative optimization problem is as follows: Where J is the objective function value; t f is the end time, t0 is the start time, J p is a penalty function to constrain acceleration and braking from being performed simultaneously, w is the weight factor of the penalty function, f[] is a multi-degree-of-freedom dynamics model, x is a state parameter set, and x min and x max are the upper and lower bounds of the state parameters; u is the control parameter set, u min and u max are the upper and lower bounds of the state parameters; t is the time step, p is the design parameter set, and p min and p max are the upper and lower bounds of the state parameters; g[] is the path constraint function, g min and g max are the upper and lower bounds of the path constraint function; b[] is the boundary constraint function, b min and b max are the upper and lower bounds of the boundary constraint function.

8. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 7, characterized in that: The multi-objective collaborative optimization problem is transformed into a nonlinear optimization problem, and a nonlinear optimization problem solver is used to solve the problem to obtain an optimal control parameter set and an optimal design parameter set, which specifically include: Linearly interpolating the state parameters and the control parameters respectively to obtain the state parameters and the control parameters at each interpolation time, so as to reconstruct the optimization variables to obtain the reconstructed optimization variables; the interpolation time includes the starting time, the ending time and a plurality of intermediate times between the starting time and the ending time; the reconstructed optimization variables include the design parameters and the state parameters and the control parameters at each interpolation time; Calculating the gradient of the objective function with respect to the reconstructed optimization variable; Based on the reconstructed optimization variables, the residual constraint, the boundary constraint and the path constraint are reconstructed to obtain a reconstructed residual constraint, a reconstructed path constraint and a reconstructed boundary constraint; Respectively calculating a first Jacobian matrix of the reconstructed residual constraint, a second Jacobian matrix of the reconstructed path constraint, and a third Jacobian matrix of the reconstructed boundary constraint; Taking the reconstructed optimization variables, the objective function, the gradient of the objective function with respect to the reconstructed optimization variables, the reconstructed residual constraints, the reconstructed path constraints, the reconstructed boundary constraints, the first Jacobian matrix, the second Jacobian matrix and the third Jacobian matrix as inputs, a nonlinear programming solver is used to solve the problem to obtain an optimal control parameter set and an optimal design parameter set.

9. The method for optimizing the design of an articulated vehicle by coordinating optimal design and optimal control according to claim 1, characterized in that: The multi-objective collaborative optimization problem is transformed into a nonlinear optimization problem by using any one of a local collocation method, a difference method or an automatic scaling method.

10. An articulated vehicle design optimization device for coordinated optimal design and optimal control, characterized in that: include: Dynamic model building module, used to build a multi-degree-of-freedom dynamic model of an articulated vehicle based on Lagrangian dynamics theory; A related constraint establishment module, used to determine residual constraints based on the multi-degree-of-freedom dynamics model, and to establish boundary constraints according to driving conditions and working boundaries of various subsystems of the articulated vehicle; A multi-objective optimization problem construction module, used to use the design parameters and the control parameters as optimization variables at the same time, and to construct a multi-objective collaborative optimization problem based on the residual constraint, the boundary constraint and the optimization variable; The problem conversion and NLP solving module is used to convert the multi-objective collaborative optimization problem into a nonlinear optimization problem, and use a nonlinear optimization problem solver to solve the problem to obtain an optimal control parameter set and an optimal design parameter set; the optimal control parameter set is used for the development of an articulated vehicle control algorithm, and the optimal design parameter set is used for the development of an articulated vehicle.

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