Vehicle design optimization method and device cooperating with optimal design and optimal control

Abstract

The invention discloses a 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 vehicle dynamics model is constructed based on Lagrange dynamics, and a power assembly quality model is constructed; the method comprises the following steps: before constructing a collaborative optimization problem, performing simulation control under a preset working condition based on a multi-degree-of-freedom vehicle dynamics model and a power assembly quality model to obtain an initial solution, and constructing a multi-target collaborative optimization problem according to the initial solution, a design parameter set, a control parameter set and related constraints; and finally, converting a multi-objective collaborative optimization problem into a nonlinear optimization problem, and solving the problem by using a solver to obtain an optimal control parameter set and an optimal design parameter set. According to the method, a vehicle-oriented collaborative optimization problem is constructed, the nonlinear optimization problem is solved after being converted, and the design parameters and the control parameters of the vehicle can be optimized at the same time.

Description

Technical Field

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

[0002] In recent years, the pace of new car launches has continued to accelerate, the global automotive market has changed rapidly, the development of intelligent vehicles has advanced by leaps and bounds, product iterations have been rapid, the global vehicle R&D delivery cycle has been shortened exponentially, and performance requirements have been continuously improved. After the driverless car cancels the cockpit, there are a large number of potential forms, and rapid development and iteration are urgently needed. New R&D processes are needed to shorten the production cycle in the field of platform interconnection and software. At present, the average R&D cycle of a new car has been shortened from 5 to 7 years in the past to 2 to 3 years. The design and development of vehicles in the new era have the following requirements: more forms, shorter cycles, and higher requirements.

[0003] In the vehicle design of traditional new car R&D, the existing vehicle design method mainly adopts an experience-based method, combining existing research experience to improve the design of the vehicle body and select power system components that meet the needs. It has the advantages of short design cycle and strong pertinence. However, this method mainly conducts qualitative analysis based on experience and cannot theoretically guarantee that the solution is optimal in terms of structure, performance, etc. In terms of vehicle lightweight design, the traditional method can only select specific optimization solutions based on qualitative evaluation. The optimization efficiency is low and the optimal effect cannot be guaranteed. It is impossible to optimize the parameters of various parts of the parts in a targeted manner based on specific analysis results.

[0004] In terms of vehicle parameter optimization methods, they are currently divided into several categories: 1) Parameter optimization methods based on static models, although they have high computational efficiency, have poor practical application effects due to the dynamic working process of the vehicle. 2) Optimization methods based on evolutionary algorithms can consider the dynamic model of the vehicle, but because gradient information is not applied, the optimization efficiency is low. 3) Optimization algorithms based on dynamic models using dynamic programming or indirect methods are extremely sensitive to the scale of problem solving and it is difficult to achieve rapid optimization of more parameters.

[0005] In addition, there is a coupling relationship between the vehicle's design parameters and its control parameters, and the above optimization method only optimizes the design parameters or control parameters, which greatly reduces the global optimization space and limits the space for improving vehicle performance. It is difficult to achieve optimal performance by optimizing the design parameters and control parameters separately. The optimization space can be expanded, but the problem established belongs to the multi-degree-of-freedom optimal design and optimal control problem, with a large parameter scale, extremely challenging to solve, and difficult to coordinate the optimization of design and control parameters. Summary of the invention

[0006] The purpose of this application is to provide a 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 vehicle based on a vehicle dynamics model.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a vehicle design optimization method for coordinated optimal design and optimal control, comprising the following steps:

[0009] According to the vehicle type, a multi-degree-of-freedom vehicle dynamics model is constructed based on Lagrangian dynamics.

[0010] According to the dependence of power system mass and output torque on design parameters, a powertrain mass model is constructed.

[0011] Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, simulation control is carried out under preset working conditions to obtain the initial solution.

[0012] Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, a multi-objective collaborative optimization problem is constructed according to the initial solution, design parameter set, control parameter set and related constraints.

[0013] 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.

[0014] In a second aspect, the present application provides a vehicle design optimization device for coordinated optimal design and optimal control, comprising the following modules:

[0015] The dynamics model building module is used to build a multi-degree-of-freedom vehicle dynamics model based on Lagrangian dynamics according to the vehicle type.

[0016] The quality model building module is used to build a powertrain quality model based on the dependence of the powertrain quality and output torque on the design parameters.

[0017] The initial solution generation module is used to perform simulation control under preset working conditions based on a multi-degree-of-freedom vehicle dynamics model and a powertrain mass model to obtain an initial solution.

[0018] The multi-objective optimization problem construction module is used to construct a multi-objective collaborative optimization problem based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model according to the initial solution, design parameter set, control parameter set and related constraints.

[0019] 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.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a vehicle design optimization method and device for coordinated optimal design and optimal control. In the method, a multi-degree-of-freedom vehicle dynamics model is first constructed based on Lagrangian dynamics, and a powertrain mass model is constructed based on the dependence of the power system mass and output torque on the design parameters; before constructing the collaborative optimization problem, based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, simulation control is performed under preset working conditions to obtain an initial solution, and then a multi-objective collaborative optimization problem is constructed based on the initial solution, the design parameter set, the control parameter set and related constraints; then 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. The present application constructs a vehicle-oriented optimal design and optimal control collaborative problem, which can realize the simultaneous optimization of its design parameters and control parameters based on the vehicle dynamics model. 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

[0022] 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.

[0023] Figure 1 A flowchart of a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0024] Figure 2 A schematic diagram of a vehicle dynamics model architecture in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0025] Figure 3 A schematic diagram of the 14 degrees of freedom of a vehicle in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0026] Figure 4A schematic diagram of a vehicle xy plane in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0027] Figure 5 A schematic diagram of tire-road contact in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0028] Figure 6 A schematic diagram of vehicle forces in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0029] Figure 7 A schematic diagram of the forces and moments acting on the unsprung mass in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0030] Figure 8 A schematic diagram of the XY coordinates of an example track in a vehicle design optimization method for coordinated optimal design and optimal control provided in an embodiment of the present application.

[0031] Fig. 9 A schematic diagram of the trajectory curvature of an example track in a vehicle design optimization method for coordinated optimal design and optimal control provided in an embodiment of the present application.

[0032] Fig.10 A schematic diagram of the posture of a vehicle in a curved coordinate system in a vehicle design optimization method for coordinated optimal design and optimal control provided in one embodiment of the present application.

[0033] Fig.11 A schematic diagram of a collaborative optimal design and optimal control algorithm framework used in a collaborative optimal design and optimal control vehicle design optimization method provided in an embodiment of the present application.

[0034] Fig.12 A schematic diagram of the functional modules of a vehicle design optimization device for coordinated optimal design and optimal control provided in one embodiment of the present application. DETAILED DESCRIPTION

[0035] 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.

[0036] 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.

[0037] In an exemplary embodiment, Figure 1 As shown, a vehicle design optimization method for coordinated optimal design and optimal control is provided, comprising the following steps:

[0038] A1. According to the vehicle type, a multi-degree-of-freedom vehicle dynamics model is constructed based on Lagrangian dynamics. The multi-degree-of-freedom vehicle dynamics model can be expressed by the following formula:

[0039]

[0040] Where T is the kinetic energy of the vehicle power system, Q b is the generalized force on the sprung mass, Q u is the generalized force on the unsprung mass, q b represents the degree of freedom of the vehicle body, q u represents the rotation and vertical displacement of the four tires, X A , Y A and Z A are the absolute displacement of the center of mass along the geodetic coordinate system, φ, ψ are the rotation angles of the vehicle body around the three axes of the geodetic coordinate system; θ ufr ,θ ufl ,θ urr ,θ url They represent the angular velocity sequences generated by the rotation of the four tires: right front, left front, right rear, and left rear. ufr ,z ufl ,z urr ,z url Respectively represent the vertical displacement sequence of the four tires, namely the right front, left front, right rear and left rear.

[0041] The kinetic energy of the vehicle power system includes the kinetic energy of the sprung mass and the kinetic energy of the unsprung mass; the kinetic energy of the sprung mass can be expressed by the following formula:

[0042]

[0043] Among them, T b is the kinetic energy of the sprung mass, V b is the component of the generalized velocity of the vehicle body in the vehicle body coordinate system, M b is the mass matrix of the vehicle body, V u,i is the speed of the ith tire in the vehicle coordinate system, M u,i is the mass matrix of the ith tire, h A,b is the transformation matrix between the vehicle coordinate system and the earth coordinate system, h b,u,i is the transformation matrix between the vehicle body coordinate system and the i-th tire coordinate system.

[0044] The kinetic energy of the unsprung mass can be expressed by the following equation:

[0045]

[0046] Among them, T u is the kinetic energy of the unsprung mass, V uz is the vertical speed of the four tires, M u is the mass matrix of the unsprung mass, ω u is the rotational angular velocity of the four tires, J u is the moment of inertia matrix of the unsprung mass.

[0047] The generalized forces on the sprung mass and the generalized forces on the unsprung mass are expressed by the following equations:

[0048]

[0049] Where F is the generalized force on the vehicle, r b For F b is the generalized force matrix of the sprung mass, r u For F u is the generalized force matrix acting on the unsprung mass.

[0050] The generalized force matrix acting on the sprung mass can be expressed as follows:

[0051]

[0052] Among them, F x,i is the longitudinal force at the contact point between the ith tire and the ground, δ i is the rotation angle input of the i-th tire, F y,i is the lateral force at the contact point between the ith tire and the ground, F wx is the longitudinal air resistance, F wy is the lateral air resistance, F bs,i is the suspension force acting on the i-th tire, g is the acceleration due to gravity, T d,i is the torque input of the ith tire, M wx is the air resistance moment around the x-axis, M wy is the air resistance moment around the y-axis, Z A is the height of the vehicle center of mass, M z,i is the self-aligning torque of the ith tire, x w,i is the longitudinal position of the ith tire in the vehicle coordinate system, y w,i is the lateral position of the i-th tire in the vehicle body coordinate system, M wz is the air resistance moment about the z-axis.

[0053] The generalized force matrix acting on the unsprung mass can be expressed as follows:

[0054]

[0055] Among them, T d,fr , T d,fl , T d,rr and T d,rl is the torque input of the right front, left front, right rear and left rear tires, M y,fr 、M y,fl 、M y,rr and M y,rl are the rolling resistance moments of the right front, left front, right rear and left rear tires, respectively, F x,fr 、F x,fl 、F x,rr and F x,rl are the longitudinal forces at the contact points between the right front, left front, right rear and left rear tires and the ground, z w,fr 、z w,fl 、z w,rr and z w,rl are the vertical positions of the center of mass of the right front, left front, right rear and left rear tires, respectively, and F z,fr 、F z,fl 、F z,rr and F z,rl are the vertical forces on the right front, left front, right rear and left rear tires respectively, F bs,fr 、F bs,fl 、F bs,rr and F bs,rl are the suspension forces acting on the right front, left front, right rear and left rear tires, m w,fr 、m w,fl 、m w,rr and m w,rl are the tire masses of the right front, left front, right rear and left rear tires respectively.

[0056] A2. According to the dependence of the power system mass and output torque on the design parameters, a powertrain mass model is constructed. The powertrain mass model includes a motor mass model and a transmission mass model; the motor mass model can be expressed by the following formula:

[0057]

[0058] Among them, m d is the motor mass, ρ m is the motor quality factor, P max is the maximum mechanical power of the motor, n b is the base speed of the motor.

[0059] The transmission mass model can be expressed by the following formula:

[0060]

[0061] Among them, mgt is the total mass of all gear sets in the gearbox, m g,j is the mass of the jth pair of gear sets, N g is the number of gear sets.

[0062] A3. Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, simulate the control under the preset working condition to obtain an initial solution. In this embodiment, step A3 is specifically: based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, a driver model is constructed to control the vehicle to drive under the working condition of the path tracking model, and a time series of control quantity input and corresponding state quantity output are obtained as the initial solution.

[0063] The path tracking model can be expressed by the following formula:

[0064]

[0065] Among them, s is the travel distance of the road, n is the vertical distance of the road, χ is the sideslip angle of the center of mass, is the road curvature, X is the longitudinal position of the vehicle's center of mass, θ is the heading angle, θ=ψ-χ, and Y is the lateral position of the vehicle's center of mass; the road curvature can be expressed by the following formula:

[0066]

[0067] Where dx and ddx are the first-order and second-order gradients of the X coordinate, and dy and ddy are the first-order and second-order gradients of the Y coordinate, respectively.

[0068] The driver model includes a longitudinal controller and a lateral controller; the longitudinal controller is based on PID control logic, and the lateral controller is based on PD control logic.

[0069] The longitudinal controller of the driver model is as follows:

[0070]

[0071] Among them, T d (n) is the desired driving torque input at the nth time step, n is the time step, e(n) is the tracking error of the longitudinal velocity at the nth time step, K p is the proportional gain coefficient of the longitudinal controller, T i is the integration time, T d is the derivative time.

[0072] The lateral controller of the driver model is as follows:

[0073] δ(n)=K p1 e1(n)+K d1(e1(n)-e1(n-1))+K p2 e2(n)+K d2 (e2(n)-e2(n-1)).

[0074] Where δ(n) is the expected driving angle input at the nth time step, K p1 and K p2 is the proportional gain coefficient of the lateral controller, K d1 and K d2 is the derivative gain coefficient of the lateral controller, e1(n) and e2(n) are the vertical distance error and yaw angle tracking error respectively.

[0075] A4. Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, a multi-objective collaborative optimization problem is constructed according to the initial solution, design parameter set, control parameter set and related constraints.

[0076] A5. Convert the multi-objective collaborative optimization problem into a nonlinear optimization problem, and use a nonlinear optimization problem solver to solve the problem, and obtain an optimal control parameter set and an optimal design parameter set. Specifically in this embodiment, the nonlinear optimization problem is shown as follows:

[0077]

[0078] p=[n b ,β,i g ],n p =3.

[0079] u=[δ,T m1 ,T m2 ,T m3 ,T m4 ],n u =4.

[0080] T w =[T m1 i g ,T m2 i g ,T m3 i g ,T m4 i g ].

[0081] Among them, J is the objective function value, t f is the terminal time, J p is a penalty function to constrain acceleration and braking from operating simultaneously, w is the weight factor of the penalty function, f[] is the dynamic model equation, x is the state parameter set, u is the control parameter set, t is the time step, p is the design parameter set, X A,b and Y A,bis the absolute displacement of the center of mass along the geodetic coordinate system, φ, ψ are the rotation angles of the vehicle body around the three axes of the geodetic coordinate system, z is the vertical height of the center of mass of the vehicle body, and z ufr 、z ufl 、z urr and z url are the vertical heights of the centroids of the four tires, θ ufr ,θ ufl ,θ urr and θ url are the rotation angles of the four tires, s is the travel distance of the road, n is the vertical distance of the road, χ is the sideslip angle of the center of mass, and n b is the reference speed of the motor, β is the high constant power speed ratio of the motor, i g is the transmission ratio, n p is the number of design parameters, δ is the front wheel steering angle of the vehicle, T m1 , T m2 , T m3 and T m4 are the output torques of the four motors, n u To control the number of parameters, T w is the torque acting on the four tires.

[0082] In another exemplary embodiment of the present application, in step A5, the local collocation method, the difference method and the automatic scaling method can be used to transform the multi-objective collaborative optimization problem into a nonlinear optimization problem.

[0083] In order to better understand the vehicle design optimization method for coordinated optimal design and optimal control proposed in the above embodiment of the present application, in the following example, taking a 4-wheel independent electric drive vehicle as an example, the method includes the following steps:

[0084] Step 1: Construction of multi-degree-of-freedom vehicle dynamics model.

[0085] In this step, according to the vehicle type, a vectorization method is used to build a high-precision vehicle dynamics model with multiple degrees of freedom. In order to more accurately describe the overall dynamic behavior of a four-wheel drive vehicle with independent suspension in terms of turning, braking, acceleration, etc., and to facilitate real-time simulation calculations, a multi-degree-of-freedom vehicle dynamics model is built based on Lagrangian dynamics. In order to accurately describe the dynamic behavior of the vehicle, it is necessary to estimate the external forces acting on the vehicle as accurately as possible, so the tire force is calculated based on the magic formula tire model. At the same time, in order to improve the calculation efficiency, vectorized programming is used to establish a multi-degree-of-freedom vehicle dynamics model that supports vectorized calculations.

[0086] like Figure 2As shown in Figure 1, the vehicle dynamics model framework is given, which includes the interaction between the vehicle body, suspension, unsprung mass and tire model. The driver model provides tire angle and torque input. d,i is the torque input of each tire, δ i is the rotation angle input of each tire, ω i is the rotation speed of each tire, F x,i 、F y,i is the lateral force and longitudinal force of each tire, Vb i are the speeds of the connection points between the vehicle body and the suspension, Zb i is the vertical coordinate of the connection point between the vehicle body and the suspension, F sb,i The vertical force exerted by the suspension on the vehicle body is equal in magnitude and opposite in direction to the vertical force exerted by the suspension on the tire, so -F is used in the calculation. sb,i Indicates that D i is the vertical displacement of the tire, F z,i V is the vertical force exerted on the tire by the ground. u,i is the vector of the lateral and longitudinal velocities of the i-th tire, ∑F i ,∑M i It is the resultant force and moment in three directions acting on the vehicle body.

[0087] The input of the multi-degree-of-freedom vehicle dynamics model is the steering wheel angle and wheel torque. The tire rotation is driven by the wheel torque and the longitudinal ground force. The tire model input is the wheel rotation angular velocity and wheel center speed, and the output is the tire force and torque. The vertical motion of the unsprung mass is generated by the tire vertical force and suspension force, and the movement of the vehicle body is generated by the longitudinal and lateral tire forces, aerodynamics, and suspension forces.

[0088] In this example, with respect to step A1 in the above method embodiment, the dynamic behavior of a simplified vehicle composed of multiple rigid components is represented by a multi-degree-of-freedom vehicle dynamics model, such as Figure 3 As shown, the vehicle body can move in the longitudinal, lateral and vertical directions, and rotate around the xyz axes. Each of the four tires has two degrees of freedom, which are rotation and vertical movement, that is, a vehicle dynamics model with 14 degrees of freedom.

[0089] In the modeling process, O-XYZ is used to represent the geodetic coordinate system. b -x b y b z b Represents the vehicle coordinate system, and the generalized coordinates of multiple degrees of freedom are expressed by vectors:

[0090]

[0091] Among them, the first row q bIt represents the degree of freedom of the vehicle body, that is, the absolute displacement of the center of mass along the earth coordinate system and the rotation around the three axes, X A ,Y A ,Z A is the absolute displacement of the center of mass along the global coordinate system; φ, ψ are the rotation angles of the vehicle body around the three axes of the global coordinate system; the second row q u represents the rotation and vertical displacement of the four tires, θ ufr ,θ ufl ,θ urr ,θ url They represent the angular velocity sequences generated by the rotation of the four tires: right front, left front, right rear, and left rear. ufr ,z ufl ,z urr ,z url They represent the vertical displacement sequence of the four tires, namely the right front, left front, right rear and left rear. The vector representation of the velocity is as follows:

[0092]

[0093] The dot above the variable indicates the derivative of the variable. The xy coordinates of the four wheel centers in the vehicle body coordinate system are:

[0094]

[0095] Where l is the longitudinal distance between the vehicle axle and the center of mass, w is the wheelbase, l and w are the distances in the vehicle coordinate system, and the relative position of the unsprung mass in the vehicle coordinate system is z w It can be represented by its absolute coordinates, roll angle, pitch angle and xy coordinates in the vehicle coordinate system. The vehicle xy plane is as follows: Figure 4 As shown, the specific expression is as follows:

[0096]

[0097] The vertical velocity of the unsprung mass in the vehicle coordinate system can be expressed as:

[0098]

[0099] The multi-degree-of-freedom dynamics model of the vehicle can be expressed using Lagrangian dynamics as follows:

[0100]

[0101] Where T is the kinetic energy of the system, Q b ,Q u It is the generalized force acting on the sprung mass and the unsprung mass.

[0102] The kinetic energy of the system includes the kinetic energy of the sprung mass and the kinetic energy of the unsprung mass. The following solves the two separately:

[0103] Kinetic energy of sprung mass T b As shown below:

[0104]

[0105] Among them, V b 、V ui are the components of the generalized velocity of the vehicle body and the velocity of the unsprung mass in the vehicle body coordinate system, M b 、M ui are the mass matrices of the vehicle body and the unsprung mass, respectively. There is a coordinate transformation between the vehicle body coordinate system and the earth coordinate system, and the transformation matrix is:

[0106]

[0107] From the above formula, the vehicle speed in the vehicle body coordinate system is:

[0108]

[0109] The lateral and longitudinal velocities of the four wheels in the vehicle coordinate system are as follows:

[0110]

[0111] The mass matrix of the vehicle body and the mass matrix of the four tires are as follows:

[0112]

[0113] Through the above calculation, the kinetic energy of the sprung mass can be expressed as:

[0114]

[0115] The kinetic energy of the unsprung mass is composed of the rotational and vertical motion of the tire, as shown below:

[0116]

[0117] Among them, V uz ,ω u are the vertical speed and angular velocity of the four unsprung tires, M u , J u They are the mass matrix and moment of inertia matrix of the unsprung mass. The calculation method is similar to that of the sprung mass.

[0118] The generalized forces acting on the vehicle in the dynamic model can be composed of the generalized forces acting on the sprung mass and the unsprung mass; among them, the vertical forces acting on the sprung mass include gravity, suspension force, and front and rear air downforce, the longitudinal forces include tire longitudinal force and air resistance, and the lateral forces only include the lateral force of the tires acting on the ground.

[0119] The motion of a vehicle depends on the forces and moments applied to the tires, so accurate modeling of tire-road interaction forces is crucial for vehicle dynamics modeling and simulation. Figure 5 The tire-road contact diagram is shown below, and the magic formula is combined to accurately model the tire-road interaction force.

[0120] The effective radius of the wheel is calculated as follows:

[0121]

[0122] Among them, R i,0 is the initial radius of the tire without external force, F z0,i is the nominal tire load, K t,i is the tire elastic stiffness, F z,i is the vertical force currently exerted on the tire by the ground, B reff , D reff 、F reff They are the effective rolling radius under low load stiffness, peak value, and high load stiffness respectively. The vertical force is composed of gravity and suspension force:

[0123] F z,i =F bs,i +m u,i g.

[0124] Among them, F z,i is the vertical force on the tire, F bs,i is the suspension force, m u,i g is the weight of the tire, and the longitudinal slip rate of the tire contact point with the ground is calculated as follows:

[0125]

[0126] The wheel center speed is as follows:

[0127]

[0128] Tire slip angle calculation:

[0129]

[0130] The longitudinal force at the tire's contact point with the ground is described by the magic formula as follows:

[0131] F x,i =(D x,i sin(C x,i arctan(B x,i κ i -E x,i (B x,i κ i -arctan(Bx,i κ i ))))+SV x,i )G xα,i .

[0132] The lateral force at the point where the tire contacts the ground is described by the magic formula as follows:

[0133] F y,i =(D y,i sin(C y,i arctan(B y,i α i -E y,i (B y,i α i -arctan(B y,i α i ))))+SV y,i )G yκ,i +SV yκ ,.

[0134] The tire overturning moment is described by the magic formula as shown below:

[0135]

[0136] The magic formula for the rolling resistance moment of a tire is as follows:

[0137]

[0138] The tire self-aligning torque is described by the magic formula as shown below:

[0139] M z =-t(F y -S Vyk )+M zr +sF x .

[0140] Next, we analyze the suspension force, which 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:

[0141]

[0142] The spring force on the spring is expressed as the spring stiffness k bs,i and deformation Δl s.i The damping force is a function of the damping c d,i and Function of speed.

[0143] The spring stiffness can be a constant value or a nonlinear function, while the damping ratio can be a constant or a nonlinear function of the damping rate, as shown in the following equation. In this case, the spring force and damping force are nonlinear functions of the deformation and damping rate, respectively.

[0144]

[0145] In this example, the nonlinear function is described by a table and one-dimensional interpolation. The deformation of the spring travel Δl s.i is the wheel runout ΔD i function, which can be obtained by the transmission ratio λ s,i calculate:

[0146] Δl s,i =λ s,i ΔD i .

[0147] Among them, the wheel runout ΔD i is the vertical displacement of the wheel, or the relative vertical movement of the axle relative to the vehicle reference frame, which can be defined as:

[0148] ΔD i =z w,i -z w0,i .

[0149] The deformation velocity of the suspension damping can be expressed as:

[0150]

[0151] Among them, z w,i is the vertical position of the unsprung mass in the vehicle coordinate system, z w0,i As its initial value. Finally, the suspension force acting on each wheel can be expressed as:

[0152] F bs,i =λ s,i F bss,i .

[0153] Next, the air resistance model is derived. First, the air resistance slip angle is calculated. Assuming that the air is still and the wind speed is 0, the air resistance slip angle is the vehicle's center of mass slip angle, which can be written as:

[0154]

[0155] Then, the air drag coefficient is calculated based on the air resistance sideslip angle through linear interpolation and cubic spline interpolation methods.

[0156]

[0157] Calculate the air resistance factor:

[0158]

[0159] Substituting the above results into the air resistance, the air resistance can be calculated as:

[0160]

[0161] Vehicle force diagram Figure 6 As shown. Therefore, the generalized force matrix of the sprung mass is as follows:

[0162]

[0163] The forces and moments on the unsprung mass are Figure 7 As shown, its generalized force matrix is ​​expressed as:

[0164]

[0165] The forces and moments in the formula have been solved in the previous article, δ i is the tire angle input, T d,i For each tire torque input, based on force analysis and the principle of virtual work, the generalized force can be derived as shown in the following equation:

[0166]

[0167] According to Lagrangian mechanics and D'Alembert's principle, the generalized equations of motion for the rigid sprung mass and the unsprung mass are derived in the form of the following equations. Directly using the differential generalized mass matrix can improve the computational efficiency.

[0168]

[0169] Step 2: Construction of powertrain mass model.

[0170] In this example, for step A2 in the above method embodiment, considering that the transmission system quality modeling mainly focuses on the dependency between the transmission system quality and the design parameters, a quality model of the motor and the gearbox is constructed to facilitate the lightweight design of the vehicle.

[0171] First, derive the motor mass model: The drive motor selected in this example is an alternating current (AC) motor because of its high output power and light weight. Based on the basic theory of AC motor design, the mass and maximum power P are derived. max , the basic speed n. The following formula is the basic reference equation when designing AC motors:

[0172]

[0173] Where P' is the apparent power, N pis the number of phases, E is the induced electromotive force, I is the current, f is the frequency of the current, N is the number of series turns of the stator, K w is the winding coefficient, Φ m is the maximum magnetic flux per pole, B is the magnetic flux density through iron, A Fe is the cross-sectional area of ​​the iron, J is the current density, A Cu is the total cross-sectional area of ​​the winding coil, p is the number of pole pairs, N b is the basic speed of the motor. m And the apparent power P', the maximum mechanical power P of the motor can be obtained max :

[0174]

[0175] In the formula, K E is the ratio of electromotive force to terminal voltage, cosφ is the power factor, and is usually 0.85 for induction motors.

[0176] According to the above equation, the relationship between the power and mass of the motor can be derived. The cross-sectional area A of the iron Fe and the total cross-sectional area of ​​the winding coil A Cu They are proportional to the square of the base unit "length":

[0177]

[0178] The volume is proportional to the cube of the base unit length:

[0179] V d ∝l 3 .

[0180] Based on the above equation, the motor mass can be expressed as:

[0181]

[0182] Among them, ρ m is the motor quality factor.

[0183] Next, derive the gearbox mass model: The gearbox is one of the most massive components in the powertrain, so reducing the weight of the gearbox is the primary consideration in vehicle lightweight design. Apply the basic equations of the relationship between the gear size, transmission power, input shaft gear speed, and gear ratio of the helical gear / spur gear set:

[0184]

[0185] Among them, d sis the center distance between the two shafts (mm), w is the gear width (mm), assuming that all gears have the same width, P is the transmitted power (kW), n is the speed of the input gear (rpm), K is the surface durability coefficient (N / mm2), and i is the gear ratio.

[0186] When the physical units of power and speed are kW and Rpm respectively, the torque on the input shaft Tin can be expressed as:

[0187]

[0188] From this we can get:

[0189]

[0190] The gear ratio and center distance can be expressed as:

[0191]

[0192] From the above two equations, we can deduce:

[0193]

[0194] d in is the diameter of the input gear, d o is the output gear diameter, and we can get:

[0195]

[0196] The mass of a gear set can be expressed as:

[0197]

[0198] ψ is the gear volume filling factor, ρ is the mass density of the gear, and combining the above equations we can get:

[0199]

[0200] So the mass of all gear sets in the entire gearbox can be estimated as:

[0201]

[0202] Among them, m g is the mass of a pair of gear sets, m gt is the total mass of all gear sets.

[0203] Step 3: Model and simulate the preset working conditions to obtain the initial solution.

[0204] In this example, for step A3 in the above method embodiment, the preset working condition is the Nürburgring track as an example, and the XY coordinates of the track are as follows: Figure 8 This data can be obtained through GPS or extracted from commercial or open source maps.

[0205] The curvature of a road can be calculated given XY coordinates:

[0206]

[0207] In a curvilinear coordinate system, a road can be described by its curvature and arc length, such as Fig. 9 The following figure shows the curvature of the track of an example track, with the origin at [0,0]. dx, ddx, dy, and ddy are the first-order and second-order gradients of the XY coordinates, respectively.

[0208] In the curvilinear coordinate system, the position of the vehicle on the road can be described by the travel distance s, the normal distance n from the reference trajectory, and the heading angle θ at the current travel distance, as Fig.10 As shown, the heading angle is recorded as:

[0209] θ=ψ-χ.

[0210] The derivative of the travel distance s can be expressed in terms of the longitudinal and lateral velocities of the vehicle in the global coordinate system:

[0211]

[0212] The derivative of the vertical distance n can be expressed as:

[0213]

[0214] The derivative of the center of mass sideslip angle χ can be expressed as:

[0215]

[0216] In addition, a driver model is developed to control the vehicle to follow the reference trajectory on a given road. The driver model is divided into longitudinal control and lateral control.

[0217] The purpose of longitudinal control is to make the vehicle track a given reference longitudinal velocity, which is based on the PID control logic:

[0218]

[0219] Where, T d (n) is the expected driving torque, n is the time step, e is the tracking error of the longitudinal velocity, K p is the proportional gain, T i is the integration time, T d is the derivative time.

[0220] The purpose of lateral control is to generate an appropriate steering angle to control the vehicle to track a reference trajectory. Considering the delayed response of the vehicle, a lateral controller based on PD control is proposed to accurately track a given reference trajectory.

[0221] δ(n)=K p1 e1(n)+K d1 (e1(n)-e1(n-1))+K p2 e2(n)+K d2 (e2(n)-e2(n-1)).

[0222] In the formula, K p1 , K p2 is the proportional gain, K d1 , K d2 is the derivative gain, e1 and e2 are the vertical distance and yaw angle tracking errors:

[0223] e1=n,e2=ψ ref -ψ.

[0224] Step 4: Construction of multi-objective collaborative optimization problem.

[0225] In this example, for step A4 in the above method embodiment, the following is adopted: Fig.11 The collaborative optimal design and optimal control algorithm framework shown in the figure provides the input of the problem based on the vehicle dynamics model established in the previous article. The input of the problem includes at least 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.

[0226]

[0227] Among them, 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 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 n of the dynamic equation 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.

[0228] 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, ng is the number of path constraints:

[0229]

[0230] 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 The first-order dynamic constraint function f, the path constraint function g, and the boundary constraint function b, the input and output dimensions of the above functions are shown in the following formula:

[0231]

[0232] In addition to the above parameters, the question input also includes the parameters in the following table:

[0233] Table 1 Other input parameters of the optimization problem

[0234]

[0235]

[0236] Step 5: Transform and solve the problem.

[0237] In this example, for step A5 in the above method embodiment, in the problem conversion part, the format of the variables and functions in the above problem input is converted into the format required by the NLP solver. In this part, you can choose the local collocation method, the difference method and the automatic scaling method. When performing problem conversion, the following steps are included:

[0238] Step 5.1: In the local collocation method, the continuous state and control variables in the entire time interval can be discretized into N using the linear interpolation method. n nodes, the new variable is given by the following formula:

[0239]

[0240] Then, the discrete state variables, control variables and static variables are reconstructed into NLP column vectors:

[0241]

[0242] For the Hermite-Simpson method, when the control variable in each sampling interval is also selected as the parameter to be optimized, the reconstructed NLP variable is:

[0243]

[0244] When the state quantity in each sampling interval is also selected as the parameter to be optimized, the reconstructed NLP variable is as follows:

[0245]

[0246] The upper and lower bounds of the variables in the previous article must also be reconstructed based on the reconstructed NLP variables.

[0247] Step 5.2: Constraints.

[0248] 1) Residual constraints of the Hermite-Simpson method.

[0249] Based on the third-order Hermite interpolation, at x k With x k+1 The state variables between and its derivatives It can be deduced as follows:

[0250]

[0251] Based on the Hermite-Simpson method, The residual constraint at It can be expressed as:

[0252]

[0253] Apply linear interpolation to calculate the value at x k With x k+1 The control variables between

[0254]

[0255] In order to reduce repeated calculations in numerical calculations, matrix calculation method is applied, so the state variable and control variables It can be expressed as:

[0256]

[0257] is the dynamic equation of the system:

[0258]

[0259] Transformation matrix T s1 and T s2 for:

[0260]

[0261] The residual constraint ζ can be expressed by applying matrix calculation as:

[0262]

[0263] 2) Residual constraints of the Trapezoidal method.

[0264] Residual Constraint ζ for Trapezoidal Method k It can be expressed as:

[0265]

[0266] The application matrix is ​​calculated as:

[0267]

[0268] 3) Path constraints and boundary constraints.

[0269] Path constraints are functions of state variables, control variables, design variables, and time, while boundary constraints are functions of initial and terminal states:

[0270]

[0271] Finally, after all constraints are calculated, the NLP constraints are expressed as:

[0272]

[0273] All the lower and upper bounds of residual constraints are 0, and the upper and lower bounds of path constraints and boundary constraints have been given in the previous article.

[0274] Step 5.3: Jacobians matrix.

[0275] 1) Jacobians matrix of residual constraints

[0276] 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:

[0277]

[0278] To avoid repeated calculations, perform matrix calculations and first calculate the derivatives of ζ with respect to x, u, and p:

[0279]

[0280] Therefore, the Jacobians matrix can be simplified to:

[0281]

[0282] 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:

[0283] 2) Jacobians matrix of path constraints and boundary constraints

[0284] First, calculate the derivative of the path constraint g with respect to x, u, p, t:

[0285]

[0286] Then calculate the boundary constraints b for x0,x f ,p,t derivative:

[0287]

[0288] Step 5.4: Solve the cost function.

[0289] The input cost function consists of Mayer terms and Lagrange terms, which can be expressed as:

[0290]

[0291] Step 5.5: Find the gradient of the cost function.

[0292] The cost function is [x,u,t f ,p], so its gradient to state variables and control variables is as follows:

[0293] Then calculate the gradient of the cost function with respect to the terminal time and the design variables, and finally obtain the NLP gradient:

[0294]

[0295] After the above problem transformation, the transformed problem is input into the underlying solver for solving, which can solve the optimal control parameters and the optimal design parameters at the same time.

[0296] The form of the nonlinear optimization problem is as follows:

[0297]

[0298] State variables: Design variables: p = [n b ,β,i g ],n p =3, control variable: u = [δ, Tm1 ,T m2 ,T m3 ,T m4 ],n u =4, tire torque: T w =[T m1 i g ,T m2 i g ,T m3 i g ,T m4 i g ].

[0299] After the above input, the problem transformation is completed, and the NLP problem solver is used to solve the transformed problem to obtain the vehicle state quantity and control quantity of the time series, as well as the optimized shortest time and the corresponding optimal design variables. In specific applications, it is divided into two parts. The optimal design variables solved by optimization can be used for vehicle development, such as motor parameter selection. The state quantity and control quantity obtained by the solution can be used to develop the control algorithm.

[0300] In the above method embodiment, the present application constructs a collaborative optimal design and optimal control framework for vehicles, which can optimize the design parameters and control parameters based on the vehicle dynamic model at the same time. 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 have a good optimization effect. 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 this embodiment, for large-scale multi-degree-of-freedom optimal design and optimal control problems, the solution stability and solution 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.

[0301] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned vehicle design optimization method for coordinated optimal design and optimal control. 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 vehicle design optimization method for coordinated optimal design and optimal control above, and will not be repeated here.

[0302] In an exemplary embodiment, Fig.12 As shown, a vehicle design optimization device for coordinated optimal design and optimal control is provided, comprising the following modules:

[0303] The dynamics model building module is used to build a multi-degree-of-freedom vehicle dynamics model based on Lagrangian dynamics according to the vehicle type.

[0304] The quality model building module is used to build a powertrain quality model based on the dependence of the powertrain quality and output torque on the design parameters.

[0305] The initial solution generation module is used to perform simulation control under preset working conditions based on a multi-degree-of-freedom vehicle dynamics model and a powertrain mass model to obtain an initial solution.

[0306] The multi-objective optimization problem construction module is used to construct a multi-objective collaborative optimization problem based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model according to the initial solution, design parameter set, control parameter set and related constraints.

[0307] 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.

[0308] certainly, Fig.12 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Fig.12 One or at least two components of the system shown.

[0309] The technical features of the above embodiments may be combined arbitrarily. 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.

[0310] 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 vehicle design optimization method for coordinated optimal design and optimal control, characterized in that: include: According to the vehicle type, a multi-degree-of-freedom vehicle dynamics model is constructed based on Lagrangian dynamics; According to the dependence of power system mass and output torque on design parameters, a powertrain mass model is constructed; Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, performing simulation control under a preset working condition to obtain an initial solution; Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, construct a multi-objective collaborative optimization problem according to the initial solution, the design parameter set, the control parameter set and related constraints; 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.

2. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 1, characterized in that: The multi-degree-of-freedom vehicle dynamics model can be expressed by the following formula: Where T is the kinetic energy of the vehicle power system, Q b is the generalized force on the sprung mass, Q u is the generalized force on the unsprung mass, q b represents the degree of freedom of the vehicle body, q u represents the rotation and vertical displacement of the four tires, X A , Y A and Z A are the absolute displacement of the center of mass along the geodetic coordinate system, are the rotation angles of the vehicle body around the three axes of the geodetic coordinate system; θ ufr ,θ ufl ,θ urr ,θ url They represent the angular velocity sequences generated by the rotation of the four tires: right front, left front, right rear, and left rear. ufr ,z ufl ,z urr ,z url Respectively represent the vertical displacement sequence of the four tires, namely the right front, left front, right rear and left rear.

3. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 2, characterized in that: The kinetic energy of the vehicle power system includes the kinetic energy of the sprung mass and the kinetic energy of the unsprung mass; the kinetic energy of the sprung mass can be expressed by the following formula: Among them, T b is the kinetic energy of the sprung mass, V b is the component of the generalized velocity of the vehicle body in the vehicle body coordinate system, M b is the mass matrix of the vehicle body, V u,i is the speed of the ith tire in the vehicle coordinate system, M u,i is the mass matrix of the ith tire, h A,b is the transformation matrix between the vehicle coordinate system and the earth coordinate system, h b,u,i is the transformation matrix between the vehicle body coordinate system and the i-th tire coordinate system; The kinetic energy of the unsprung mass can be expressed by the following equation: Among them, T u is the kinetic energy of the unsprung mass, V uz is the vertical speed of the four tires, M u is the mass matrix of the unsprung mass, ω u is the rotational angular velocity of the four tires, J u is the moment of inertia matrix of the unsprung mass.

4. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 2, characterized in that: The generalized forces on the sprung mass and the generalized forces on the unsprung mass are expressed by the following equations: Where F is the generalized force on the vehicle, r b For F b is the generalized force matrix of the sprung mass, r u For F u is the generalized force matrix acting on the unsprung mass; The generalized force matrix acting on the sprung mass can be expressed as follows: Among them, F x,i is the longitudinal force at the contact point between the ith tire and the ground, δ i is the rotation angle input of the i-th tire, F y,i is the lateral force at the contact point between the ith tire and the ground, F wx is the longitudinal air resistance, F wy is the lateral air resistance, F bs,i is the suspension force acting on the i-th tire, g is the acceleration due to gravity, T d,i is the torque input of the ith tire, M wx is the air resistance moment around the x-axis, M wy is the air resistance moment around the y-axis, Z A is the height of the vehicle center of mass, M z,i is the self-aligning torque of the ith tire, x w,i is the longitudinal position of the ith tire in the vehicle coordinate system, y w,i is the lateral position of the i-th tire in the vehicle body coordinate system, M zy is the air resistance moment around the z-axis; The generalized force matrix acting on the unsprung mass can be expressed as follows: Among them, T d,fr , T d,fl , T d,rr and T d,rl is the torque input of the right front, left front, right rear and left rear tires, M y,fr 、M y,fl 、M y,rr and M y,rl are the rolling resistance moments of the right front, left front, right rear and left rear tires, respectively, F x,fr 、F x,fl 、F x,rr and F x,rl is the longitudinal force at the contact points between the right front, left front, right rear and left rear tires and the ground, z w,fr 、z w,fl 、z w,rr and z w,rl are the vertical positions of the center of mass of the right front, left front, right rear and left rear tires, respectively, and F z,fr 、F z,fl 、F z,rr and F z,rl is the vertical force on the right front, left front, right rear and left rear tires, F bs,fr 、F bs,fl 、F bs,rr and F bs,rl is the suspension force acting on the right front, left front, right rear and left rear tires, m w,fr 、m w,fl 、m w,rr and m w,rl are the tire masses of the right front, left front, right rear and left rear tires respectively.

5. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 1, characterized in that: The powertrain mass model includes a motor mass model and a transmission mass model; the motor mass model can be expressed by the following formula: Among them, m d is the motor mass, ρ m is the motor quality factor, P max is the maximum mechanical power of the motor, n b is the reference speed of the motor; The transmission mass model can be expressed by the following formula: Among them, m gt is the total mass of all gear sets in the gearbox, m g,j is the mass of the jth pair of gear sets, N g is the number of gear sets.

6. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 2, characterized in that: Based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, simulation control is performed under preset working conditions to obtain an initial solution. Specifically, based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, a driver model is constructed to control the vehicle to drive under the working conditions of a path tracking model, and a time series of control quantity input and corresponding state quantity output are obtained as the initial solution.

7. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 6, characterized in that: The path tracking model can be expressed by the following formula: Wherein, s is the travel distance of the road, n is the vertical distance of the road, χ is the sideslip angle of the center of mass, C is the road curvature, X is the longitudinal position of the center of mass of the vehicle, θ is the heading angle, θ=ψ-χ, and Y is the lateral position of the center of mass of the vehicle; the road curvature can be expressed by the following formula: Among them, dx and ddx are the first-order and second-order gradients of the X coordinate, and dy and ddy are the first-order and second-order gradients of the Y coordinate, respectively; The driver model includes a longitudinal controller and a lateral controller; the longitudinal controller is based on PID control logic, and the lateral controller is based on PD control logic; The longitudinal controller of the driver model is as follows: Among them, T d (n) is the desired driving torque input at the nth time step, n is the time step, e(n) is the tracking error of the longitudinal velocity at the nth time step, K p is the proportional gain coefficient of the longitudinal controller, T i is the integration time, T d is the derivative time; The lateral controller of the driver model is as follows: δ(n)=K p1 e1(n)+K d1 (e1(n)-e1(n-1))+K p2 e2(n)+K d2 (e2(n)-e2(n-1)): Where δ(n) is the expected driving angle input at the nth time step, K p1 and K p2 is the proportional gain coefficient of the lateral controller, K d1 and K d2 is the derivative gain coefficient of the lateral controller, e1(n) and e2(n) are the vertical distance error and yaw angle tracking error respectively.

8. The vehicle design optimization method of coordinated optimal design and optimal control according to claim 1, characterized in that: The nonlinear optimization problem is expressed as follows: p=[n b ,β,i g ],n p =3; u=[δ,T m1 ,T m2 ,T m3 ,T m4 ],n u =4; T w =[T m1 i g ,T m2 i g ,T m3 i g ,T m4 i g ]; Among them, J is the objective function value, t f is the terminal time, J p is a penalty function to constrain acceleration and braking from operating simultaneously, w is the weight factor of the penalty function, f[] is the dynamic model equation, x is the state parameter set, u is the control parameter set, t is the time step, p is the design parameter set, X A,b and Y A,b is the absolute displacement of the center of mass along the geodetic coordinate system, are the rotation angles of the vehicle body around the three axes of the geodetic coordinate system, z is the vertical height of the center of mass of the vehicle body, and z ufr 、z ufl 、z urr and z url is the vertical height of the center of mass of the four tires, θ ufr ,θ ufl ,θ urr and θ url is the rotation angle of the four tires, s is the travel distance of the road, n is the vertical distance of the road, χ is the sideslip angle of the center of mass, n b is the reference speed of the motor, β is the high constant power speed ratio of the motor, i g is the transmission ratio, n p is the number of design parameters, δ is the front wheel steering angle of the vehicle, T m1 , T m2 , T m3 and T m4 is the output torque of the four motors, n u To control the number of parameters, T w is the torque acting on the four tires.

9. The vehicle design optimization method of coordinated 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 the local collocation method, the difference method and the automatic scaling method.

10. A vehicle design optimization device for coordinated optimal design and optimal control, characterized in that: include: A dynamics model building module is used to build a multi-degree-of-freedom vehicle dynamics model based on Lagrangian dynamics according to the vehicle type; A quality model building module is used to build a powertrain quality model based on the dependence of the powertrain quality and output torque on the design parameters; An initial solution generating module, used to perform simulation control under preset working conditions based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model to obtain an initial solution; A multi-objective optimization problem construction module, used to construct a multi-objective collaborative optimization problem based on the multi-degree-of-freedom vehicle dynamics model and the powertrain mass model, according to the initial solution, the design parameter set, the control parameter set and related constraints; 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.

Images