Dynamic real-time modeling method for heavy vehicle

Abstract

The invention relates to a dynamic real-time modeling method for a heavy vehicle. Comprising the following steps: establishing a three-degree-of-freedom vehicle dynamics model and a tire model, and obtaining a vehicle state and a chassis dynamics state; establishing a pavement state equation to obtain a pavement state; the cargo state is judged, and a vehicle and road load integrated nonlinear state equation containing state input and scheduling parameters is established in combination with the vehicle state, the chassis dynamics state and the road surface state; obtaining a mapping relation between the vehicle configuration, the road surface state and the cargo state and the observable state space; constructing a kernel function based on linear feature extraction according to the vehicle and road load integrated nonlinear state equation; and based on the kernel function, discarding the initial state of the window in real time under the data integration recursion moving window, and incorporating the data at the k + 1 moment to obtain an updated vehicle-road integrated linear state equation. The method is used for heavy vehicle dynamics modeling, and real-time state updating is achieved.

Description

Technical Field

[0001] The invention belongs to the field of vehicle engineering and dynamic system modeling, and in particular relates to a real-time dynamics modeling method for a heavy vehicle. Background Art

[0002] With the rapid development of the logistics industry and autonomous driving technology, heavy-duty vehicles have strong loading capacity and are widely used. However, the vehicle states involved are numerous and complex, and road conditions and cargo states also need to be considered. In order to accurately control heavy-duty vehicles for a series of engineering applications, it is urgent to propose a real-time modeling method for heavy-duty vehicle dynamics that is widely applicable, versatile, and can achieve real-time updates of vehicle states. First of all, although the traditional method based on physical modeling has strong interpretability, it relies on complex experimental parameters and assumptions, and it is difficult to accurately reflect the dynamic behavior under extreme conditions and cannot achieve real-time updates of vehicle data; due to the performance optimization and control algorithm design of heavy-duty vehicles, higher requirements are put forward for vehicle dynamics modeling, and real-time modeling methods based on physical-data updates have emerged, but they still cannot break through the fixed scene limitations of "special vehicles for special purposes", resulting in inaccurate vehicle data updates, so there are disadvantages in dealing with variable road environments and versatility. In summary, the current heavy-duty vehicle dynamics modeling methods still have the following shortcomings:

[0003] Dedicated vehicle for dedicated use: Model design is often targeted at specific vehicles and is only applicable to specific application scenarios, resulting in a lengthy vehicle development cycle and the inability to flexibly respond to a variety of mission requirements.

[0004] Fixed scenario limitations: Most existing models assume specific road conditions (such as flatness or a specific friction coefficient) and cargo configurations, and cannot adapt to complex road and cargo changes.

[0005] Lack of real-time performance: Traditional models are usually built in an offline manner, which makes it difficult to update them in real time according to dynamic changes in vehicle status and environment, thus affecting control accuracy. Summary of the invention

[0006] The purpose of the present invention is to provide a real-time dynamic modeling method for heavy-duty vehicles, which breaks through the limitations of traditional dynamic models of "special vehicles for special purposes and fixed scenarios" and realizes the construction of a unified dynamic model and real-time status update of heavy-duty vehicles.

[0007] The technical solution to achieve the purpose of the present invention is: a real-time dynamics modeling method for heavy vehicles, comprising the following steps:

[0008] S1: Establish a three-degree-of-freedom vehicle dynamics model and tire model, and obtain the vehicle state and chassis dynamics state;

[0009] S2: Establish the road surface state equation and obtain the road surface state;

[0010] S3: Determine the cargo state, combine the vehicle state, chassis dynamics state and road state, establish the vehicle-road-load integrated nonlinear state equation containing state input and scheduling parameters; obtain the mapping relationship between vehicle configuration, road state and cargo state and the observable state space;

[0011] S4: Based on the vehicle-road integrated nonlinear state equation, a Koopman kernel function based on linearized feature extraction is constructed;

[0012] S5: Based on the Koopman kernel function, the initial state of the window is discarded in real time under the data integration recursive moving window, and the data at time k+1 is incorporated to obtain the updated vehicle-road integrated linear state equation.

[0013] Furthermore, the three-degree-of-freedom vehicle dynamics model established in S1 is:

[0014] (1)

[0015] Where m is the vehicle mass, F f is the air resistance, F w is the rolling resistance, is the longitudinal acceleration, is the lateral acceleration, is the wheel angle, is the wheelbase, j=l,r represents the front and rear wheels of the vehicle respectively, i=f,m,l represents the front axle, middle axle and rear axle of the vehicle respectively, L i is the distance from the front and rear axles to the center of mass of the vehicle, and are the vehicle yaw rate and yaw acceleration, respectively. is the moment of inertia of the vehicle body around the Z1 axis, I x is the moment of inertia of the vehicle body around the X1 axis, and Respectively The longitudinal and lateral tire forces of the wheels; When the wheel is in front of the vehicle's center of gravity " is positive, and negative when it is behind the center of gravity of the vehicle; When the wheel is on the left side of the vehicle ” Take positive, and take negative when it is on the right side of the vehicle;

[0016] The tire model is:

[0017] (2)

[0018] Where Y is the lateral force, X is the slip angle, D is the peak factor, B is the stiffness factor, C is the curve shape factor, E is the curve curvature factor, S is his the horizontal drift of the curve, S v The curve drifts vertically.

[0019] Furthermore, the road state equation established in S2 is:

[0020] (3)

[0021] Where m is the vehicle mass, is the sideslip angle, is the vehicle speed, is the vehicle acceleration, g is the acceleration due to gravity, is the air resistance coefficient, is the contact area between the wheel and the ground, is the air density, is the engine torque, is the engine transmission ratio, is the gearbox ratio, r is the wheel radius, is the transmission efficiency, and f is the ground friction.

[0022] Furthermore, the “determine the state of the goods” in S3 is to establish a goods state equation to determine the rigidity and flexibility of the goods; specifically:

[0023] S31: When the vehicle is stationary, the vertical force balance equation of the vehicle is obtained from the geometric relationship between moment balance, force balance and suspension spring as follows:

[0024] (4)

[0025] In the formula, is the unloaded mass of the vehicle, , , are the spring stiffness of the front, middle and rear suspensions respectively. = = ; , , are the suspension forces of the front, middle and rear axles respectively, l f , l m , l r They are the distance from the front axle to the center of mass, the distance from the middle axle to the center of mass, and the distance from the rear axle to the center of mass;

[0026] S32: The calculation and analysis of the static vertical load on the wheel is as follows:

[0027] (5)

[0028] In the formula, , are the static vertical loads on the right and left wheels of the front axle, , are the static vertical loads on the right and left wheels of the intermediate shaft, , are the static vertical loads on the right and left wheels of the rear axle respectively;

[0029] S33: The dynamic vertical load of the wheel is calculated and analyzed as follows:

[0030] (6)

[0031] In the formula is the vertical displacement of the center of gravity of each wheel, is the displacement of each suspension, is the vertical displacement of the vehicle body, is the height difference between the inside and outside of the road surface; is the vehicle slip angle, is the vehicle yaw angle; is the spring stiffness of each wheel, is the spring stiffness of each suspension, is the tire damping of each wheel, is the dynamic vertical load of each wheel; When the wheel is in front of the vehicle's center of gravity "When it is positive, the cargo is flexible; when it is behind the center of gravity of the vehicle, it is negative, and the cargo is rigid; when the When the wheel is on the left side of the vehicle "When it is positive, the cargo is rigid; when it is on the right side of the vehicle, it is negative, and the cargo is flexible.

[0032] Furthermore, in S3, “combining the vehicle state, chassis dynamics state and road state, establishing a vehicle-road integrated nonlinear state equation including state input and scheduling parameters” is specifically

[0033] S34: Transform equation (1) into , , The displayed equations are used to set the vehicle dynamics equations, road state equations and cargo state equations to form a general heavy vehicle model in a nonlinear state space form:

[0034] (7)

[0035] Where h s is the height of the vehicle's center of mass from the ground, m s is the fully loaded mass, F x 、F y are lateral force and longitudinal force respectively, M z is the torque of the vehicle around the z-axis;

[0036] S35: After linearization, the vehicle-road integrated vehicle state space equation is obtained:

[0037] (8)

[0038] (9)

[0039] (10)

[0040] Where F c is the control force, L c is the control gain, A is the system matrix, X b is the state vector, B is the input matrix, U is the input vector, C is the bias vector, A F is the additional system matrix, is the input vector increment; F is the final control input, is the gain coefficient; is the time derivative of the state vector, B F Enter the matrix for the force;

[0041] The general nonlinear state equation expression is as follows:

[0042] (11)

[0043] In the formula is the time derivative of the state vector, is a nonlinear function describing the system dynamics, To predict control power;

[0044] Define the state vector of the system ,get:

[0045] ,

[0046]

[0047] Furthermore, S4 “Constructing Koopman kernel function based on linearized feature extraction” is specifically as follows:

[0048] (12)

[0049] In the formula , is the scheduling parameter; , , ;

[0050] S42: Discretize equation (12) and express it as:

[0051] (13)

[0052] In the formula

[0053] S43: Introducing the Klock inner product, equation (13) can be written as:

[0054] (14)

[0055] Where A jk (j=0, 1, 2, 3) is the lumped state correlation matrix, B jk (j=0, 1, 2, 3) is the lumped input correlation matrix, is the Klock inner product operator, , , is the data trajectory;

[0056] S44: The equation of equation (13) at the previous moment is expressed as:

[0057] (15)

[0058] Through the inverse operation of the matrix, the system matrix is ​​solved :

[0059] (16)

[0060] S45: A Koopman kernel function based on the Koopman operator and with a moving window is obtained to recursively update the vehicle state data, and its formula is expressed as:

[0061] (17)

[0062] middle is the Koopman operator and also the system transformation matrix, , , are the data trajectories of input variables, state variables, and scheduling parameter variables, respectively. , They are transfer variable matrix and update variable matrix respectively.

[0063] Furthermore, S5 includes:

[0064] S51: , , They are the data tracks of input variables, state variables, and scheduling parameter variables. The moving window method is used to update the data in real time. The window length is n d data points, define a moving window and express it as:

[0065] (18)

[0066] middle is a data snapshot matrix, which contains a combination of input, state, and scheduling parameters. It is a moving window data set in real-time modeling. is the input matrix, which contains arrive System input u, X k is the state matrix, which contains arrive The system state at time x, X Fk is the future state matrix, which includes arrive Future system states in time; and are the Klock inner product matrices of the input state and the scheduling parameter ρ, is the scheduling parameter vector, n d The number of data points in the moving window; is the Klock inner product operator, used to generate the combination between input and state and scheduling parameters;

[0067] S52: Based on equation (17) and equation (18), the moving window is rewritten as:

[0068] (19)

[0069] S53: Using the vehicle status data collected by formula (18), the system transformation matrix is ​​expressed as:

[0070] (20)

[0071] S54: The recursive update formula is expressed as:

[0072] (twenty one)

[0073] S55: Assume that the relationship between the system state x at time steps k and k+1 is:

[0074] (twenty two)

[0075] S56: Using the data points collected at time k+1 , , Update the system transformation matrix , to achieve real-time update of heavy-duty vehicle-road integrated modeling data, and the updated integrated state space is expressed as:

[0076] (twenty three)

[0077] In the formula is the state matrix at time k after the update, is the longitudinal velocity at time k after the update, is the lateral velocity at time k after the update, and are the vehicle yaw velocity and yaw acceleration at time k after update respectively.

[0078] Compared with the prior art, the present invention has the following significant advantages:

[0079] The present invention establishes an integrated universal model to update vehicle data in real time. The present invention is based on the vehicle physical model and uses a moving window to iteratively update the vehicle status. Compared with the traditional physical modeling method, the present invention breaks through the limitation of one vehicle for one use, and the integrated modeling can capture dynamic changes in real time in a complex road environment; compared with the physical-data modeling method, the present invention can not only comprehensively consider the impact of the three elements of vehicle, road and cargo on vehicle control, but the established moving window can also be updated in real time according to the dynamic changes of vehicle status and environment, and the data of the previous moment is discarded in real time to reduce the model calculation amount and improve the model accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 The figure is a flow chart of the real-time modeling method of the present invention.

[0081] Figure 2 This is a calculation flow chart of the real-time state update of the recursive moving window of the present invention. DETAILED DESCRIPTION

[0082] The present invention discloses a comprehensive physical big data modeling method based on invariant physical laws and time-varying information data, which is specifically used for heavy-duty vehicle dynamics modeling and real-time status update. Aiming at the problem that the traditional vehicle dynamics model has poor parameter update capability and that big data modeling methods such as neural networks are out of touch with the actual physical state, the mapping relationship between the system dynamics including vehicle configuration, road state and cargo specificity and the observable state space is deeply analyzed, a Kronecker product vehicle nonlinear state matrix including state input and scheduling parameters is constructed, and a Koopman kernel function based on linearized feature extraction is constructed to describe the physical state of the vehicle; a moving window vehicle state real-time update method based on data integration recursion is designed, the initial state data of the window is discarded in real time under the selected window length and the latest data at the k+1 moment is included, the real-time state of the "vehicle-road load" is iterated and solved based on the time series prediction theory, and finally a physical-information comprehensive vehicle dynamics modeling method with both physical explanation and adaptive update capability is established.

[0083] like Figure 1-2 As shown, a real-time dynamic modeling method for a heavy vehicle comprises the following steps:

[0084] S1: Establish a three-degree-of-freedom vehicle dynamics model and tire model, and obtain the vehicle state and chassis dynamics state;

[0085] S2: Establish the road surface state equation and obtain the road surface state;

[0086] S3: Determine the cargo state, combine the vehicle state, chassis dynamics state and road state, establish the vehicle-road-load integrated nonlinear state equation containing state input and scheduling parameters; obtain the mapping relationship between vehicle configuration, road state and cargo state and the observable state space;

[0087] S4: Based on the vehicle-road integrated nonlinear state equation, a Koopman kernel function based on linearized feature extraction is constructed;

[0088] S5: Based on the Koopman kernel function, the initial state of the window is discarded in real time under the data integration recursive moving window, and the data at time k+1 is incorporated to obtain the updated vehicle-road integrated linear state equation.

[0089] Furthermore, the three-degree-of-freedom vehicle dynamics model established in S1 is:

[0090] (1)

[0091] Where m is the vehicle mass, F f is the air resistance, F w is the rolling resistance, is the longitudinal acceleration, is the lateral acceleration, is the wheel angle, is the wheelbase, j=l,r represents the front and rear wheels of the vehicle respectively, i=f,m,l represents the front axle, middle axle and rear axle of the vehicle respectively, L i is the distance from the front and rear axles to the center of mass of the vehicle, and are the vehicle yaw rate and yaw acceleration, respectively. is the moment of inertia of the vehicle body around the Z1 axis, I x is the moment of inertia of the vehicle body around the X1 axis, and Respectively The longitudinal and lateral tire forces of the wheels; When the wheel is in front of the vehicle's center of gravity " is positive, and negative when it is behind the center of gravity of the vehicle; When the wheel is on the left side of the vehicle ” Take positive, and take negative when it is on the right side of the vehicle;

[0092] The tire model is:

[0093] (2)

[0094] Where Y is the lateral force, X is the slip angle, D is the peak factor, B is the stiffness factor, C is the curve shape factor, E is the curve curvature factor, S is h is the horizontal drift of the curve, S v The vertical drift of the curve.

[0095] Furthermore, the road state equation established in S2 is:

[0096] (3)

[0097] Where m is the vehicle mass, is the sideslip angle, is the vehicle speed, is the vehicle acceleration, g is the acceleration due to gravity, is the air resistance coefficient, is the contact area between the wheel and the ground, is the air density, is the engine torque, is the engine transmission ratio, is the gearbox ratio, r is the wheel radius, is the transmission efficiency, and f is the ground friction.

[0098] Furthermore, the “determine the state of the goods” in S3 is to establish a goods state equation to determine the rigidity and flexibility of the goods; specifically:

[0099] S31: When the vehicle is stationary, the vertical force balance equation of the vehicle is obtained from the geometric relationship between moment balance, force balance and suspension spring as follows:

[0100] (4)

[0101] In the formula, is the unloaded mass of the vehicle, , , are the spring stiffness of the front, middle and rear suspensions respectively. = = ; , , are the suspension forces of the front, middle and rear axles respectively, l f , l m , l r They are the distance from the front axle to the center of mass, the distance from the middle axle to the center of mass, and the distance from the rear axle to the center of mass;

[0102] S32: The calculation and analysis of the static vertical load on the wheel is as follows:

[0103] (5)

[0104] In the formula, , are the static vertical loads on the right and left wheels of the front axle, , are the static vertical loads on the right and left wheels of the intermediate shaft, , are the static vertical loads on the right and left wheels of the rear axle respectively;

[0105] S33: The dynamic vertical load of the wheel is calculated and analyzed as follows:

[0106] (6)

[0107] In the formula is the vertical displacement of the center of gravity of each wheel, is the displacement of each suspension, is the vertical displacement of the vehicle body, is the height difference between the inside and outside of the road surface; is the vehicle slip angle, is the vehicle yaw angle; is the spring stiffness of each wheel, is the spring stiffness of each suspension, is the tire damping of each wheel, is the dynamic vertical load of each wheel; When the wheel is in front of the vehicle's center of gravity "When it is positive, the cargo is flexible; when it is behind the center of gravity of the vehicle, it is negative, and the cargo is rigid; when the When the wheel is on the left side of the vehicle "When it is positive, the cargo is rigid; when it is on the right side of the vehicle, it is negative, and the cargo is flexible.

[0108] Furthermore, in S3, “combining the vehicle state, chassis dynamics state and road state, establishing a vehicle-road integrated nonlinear state equation including state input and scheduling parameters” is specifically

[0109] S34: Transform equation (1) into , , The displayed equations are used to set the vehicle dynamics equations, road state equations and cargo state equations to form a general heavy vehicle model in a nonlinear state space form:

[0110] (7)

[0111] Where h s is the height of the vehicle's center of mass from the ground, ms is the fully loaded mass, F x 、F y are lateral force and longitudinal force respectively, M z is the torque of the vehicle around the z-axis;

[0112] S35: After linearization, the vehicle-road integrated vehicle state space equation is obtained:

[0113] (8)

[0114] (9)

[0115] (10)

[0116] Where F c is the control force, L c is the control gain, A is the system matrix, X b is the state vector, B is the input matrix, U is the input vector, C is the bias vector, A F is the additional system matrix, is the input vector increment; F is the final control input, is the gain coefficient; is the time derivative of the state vector, B F Enter the matrix for the force;

[0117] The general nonlinear state equation expression is as follows:

[0118] (11)

[0119] In the formula is the time derivative of the state vector, is a nonlinear function describing the system dynamics, To predict control power;

[0120] Define the state vector of the system ,get:

[0121] ,

[0122]

[0123] Furthermore, S4 “Constructing Koopman kernel function based on linearized feature extraction” is specifically as follows:

[0124] S41: Formula (1) is expressed as a continuous-time linear parameter-varying system, and the expression is as follows:

[0125] (12)

[0126] In the formula , is the scheduling parameter; , , ;

[0127] S42: Discretize equation (12) and express it as:

[0128] (13)

[0129] In the formula

[0130] S43: Introducing the Klock inner product, equation (13) can be written as:

[0131] (14)

[0132] Where A jk (j=0, 1, 2, 3) is the lumped state correlation matrix, B jk (j=0, 1, 2, 3) is the lumped input correlation matrix, is the Klock inner product operator, , , is the data trajectory;

[0133] S44: The equation of equation (13) at the previous moment is expressed as:

[0134] (15)

[0135] Through the inverse operation of the matrix, the system matrix is ​​solved :

[0136] (16)

[0137] S45: A Koopman kernel function based on the Koopman operator and with a moving window is obtained to recursively update the vehicle state data, and its formula is expressed as:

[0138] (17)

[0139] middle is the Koopman operator and also the system transformation matrix, , , are the data trajectories of input variables, state variables, and scheduling parameter variables, respectively. , They are transfer variable matrix and update variable matrix respectively.

[0140] Furthermore, S5 includes:

[0141] S51: , , They are the data tracks of input variables, state variables, and scheduling parameter variables. The moving window method is used to update the data in real time. The window length is n d data points, define a moving window and express it as:

[0142] (18)

[0143] middle is a data snapshot matrix, which contains a combination of input, state, and scheduling parameters. It is a moving window data set in real-time modeling. is the input matrix, which contains arrive System input u, X k is the state matrix, which contains arrive The system state at time x, X Fk is the future state matrix, which includes arrive Future system states in time; and are the Klock inner product matrices of the input state and the scheduling parameter ρ, is the scheduling parameter vector, n d The number of data points in the moving window; is the Klock inner product operator, used to generate the combination between input and state and scheduling parameters;

[0144] S52: Based on equation (17) and equation (18), the moving window is rewritten as:

[0145] (19)

[0146] S53: Using the vehicle status data collected by formula (18), the system transformation matrix is ​​expressed as:

[0147] (20)

[0148] S54: The recursive update formula is expressed as:

[0149] (twenty one)

[0150] S55: Assume that the relationship between the system state x at time steps k and k+1 is:

[0151] (twenty two)

[0152] S56: Using the data points collected at time k+1 , , Update the system transformation matrix , to achieve real-time update of heavy-duty vehicle-road integrated modeling data, and the updated integrated state space is expressed as:

[0153] (twenty three)

[0154] In the formula is the state matrix at time k after the update, is the longitudinal velocity at time k after the update, is the lateral velocity at time k after the update, and are the vehicle yaw velocity and yaw acceleration at time k after update respectively.

[0155] The present invention breaks through the limitations of the traditional dynamic model of "special vehicle for special purpose, fixed scene". It can not only comprehensively consider the three elements of vehicle, road and cargo, but also capture dynamic changes in real time to meet the modeling and control needs of heavy vehicles in complex environments.

Claims

1. A real-time dynamic modeling method for heavy vehicles, characterized in that: The steps include: S1: Establish a three-degree-of-freedom vehicle dynamics model and tire model, and obtain the vehicle state and chassis dynamics state; S2: Establish the road surface state equation and obtain the road surface state; S3: Determine the cargo state, combine the vehicle state, chassis dynamics state and road state, establish the vehicle-road-load integrated nonlinear state equation containing state input and scheduling parameters; obtain the mapping relationship between vehicle configuration, road state and cargo state and observable state space; S4: Based on the vehicle-road integrated nonlinear state equation, a Koopman kernel function based on linearized feature extraction is constructed; S5: Based on the Koopman kernel function, the initial state of the window is discarded in real time under the data integration recursive moving window, and the data at time k+1 is incorporated to obtain the updated vehicle-road integrated linear state equation.

2. The method according to claim 1, characterized in that The three-degree-of-freedom vehicle dynamics model established in S1 is: (1) In the formula is the vehicle mass, is the air resistance, is the rolling resistance, is the longitudinal acceleration, is the lateral acceleration, is the wheel angle, is the wheelbase, Represents the front and rear wheels of the vehicle respectively, They represent the front axle, middle axle and rear axle of the vehicle respectively. is the distance from the front and rear axles to the center of mass of the vehicle, and are the vehicle yaw rate and yaw acceleration, respectively. is the moment of inertia of the vehicle body around the Z1 axis, I x is the moment of inertia of the vehicle body around the X1 axis, and are the longitudinal tire force and lateral tire force of the first wheel respectively; when the first When the wheel is in front of the vehicle's center of gravity " is positive, and negative when it is behind the center of gravity of the vehicle; When the wheel is on the left side of the vehicle ” Take positive, and take negative when it is on the right side of the vehicle; The tire model is: (2) Where Y is the lateral force, X is the slip angle, D is the peak factor, B is the stiffness factor, C is the curve shape factor, E is the curve curvature factor, S is h is the horizontal drift of the curve, S v The vertical drift of the curve.

3. The method according to claim 2, characterized in that The road state equation established in S2 is: (3) Where m is the vehicle mass, is the sideslip angle, is the vehicle speed, is the vehicle acceleration, g is the acceleration due to gravity, is the air resistance coefficient, is the contact area between the wheel and the ground, is the air density, is the engine torque, is the engine transmission ratio, is the gearbox ratio, r is the wheel radius, is the transmission efficiency, and f is the ground friction.

4. The method according to claim 3, characterized in that In S3, "determine the cargo state" is to establish the cargo state equation and determine the rigidity and flexibility of the cargo; specifically: S31: When the vehicle is stationary, the vertical force balance equation of the vehicle is obtained from the geometric relationship between moment balance, force balance and suspension spring as follows: (4) In the formula, is the unloaded mass of the vehicle, , , are the spring stiffness of the front, middle and rear suspensions respectively. = = ; , , are the suspension forces of the front, middle and rear axles respectively, l f , l m , l r They are the distance from the front axle to the center of mass, the distance from the middle axle to the center of mass, and the distance from the rear axle to the center of mass; S32: The calculation and analysis of the static vertical load on the wheel is as follows: (5) In the formula, , are the static vertical loads on the right and left wheels of the front axle, , are the static vertical loads on the right and left wheels of the intermediate shaft, , are the static vertical loads on the right and left wheels of the rear axle respectively; S33: The dynamic vertical load of the wheel is calculated and analyzed as follows: (6) In the formula is the vertical displacement of the center of gravity of each wheel, is the displacement of each suspension, is the vertical displacement of the vehicle body, is the height difference between the inside and outside of the road surface; is the vehicle slip angle, is the vehicle yaw angle; is the spring stiffness of each wheel, is the spring stiffness of each suspension, is the tire damping of each wheel, is the dynamic vertical load of each wheel; When the wheel is in front of the vehicle's center of gravity "When it is positive, the cargo is flexible; when it is behind the center of gravity of the vehicle, it is negative, and the cargo is rigid; when the When the wheel is on the left side of the vehicle "When it is positive, the cargo is rigid; when it is on the right side of the vehicle, it is negative, and the cargo is flexible.

5. The method according to claim 4, characterized in that In S3, "combining the vehicle state, chassis dynamics state and road state, establishing a vehicle-road integrated nonlinear state equation containing state input and scheduling parameters" is specifically S34: Transform equation (1) into , , The displayed equations are used to set the vehicle dynamics equations, road state equations and cargo state equations to form a general heavy vehicle model in a nonlinear state space form: (7) Where h s is the height of the vehicle's center of mass from the ground, m s is the fully loaded mass, F x 、F y are lateral force and longitudinal force respectively, M z is the torque of the vehicle around the z-axis; S35: After linearization, the vehicle-road integrated vehicle state space equation is obtained: (8) (9) (10) Where F c is the control force, L c is the control gain, A is the system matrix, X b is the state vector, B is the input matrix, U is the input vector, C is the bias vector, A F is the additional system matrix, is the input vector increment; F is the final control input, is the gain coefficient; is the time derivative of the state vector, B F Enter the matrix for the force; The general nonlinear state equation expression is as follows: (11) In the formula is the time derivative of the state vector, is a nonlinear function describing the system dynamics, To predict control power; Define the state vector of the system ,get: , 。 6. The method according to claim 5, characterized in that S4 "Constructing Koopman kernel function based on linear feature extraction" is specifically: S41: Formula (1) is expressed as a continuous-time linear parameter-varying system, and the expression is as follows: (12) In the formula , ， is the scheduling parameter; ， ， ； S42: Discretize equation (12) and express it as: (13) In the formula , ， S43: Introducing the Klock inner product, equation (13) can be written as: (14) Where A jk (j=0, 1, 2, 3) is the lumped state correlation matrix, B jk (j=0, 1, 2, 3) is the lumped input correlation matrix, is the Klock inner product operator, , , is the data trajectory; S44: The equation of equation (13) at the previous moment is expressed as: (15) Through the inverse operation of the matrix, the system matrix is ​​solved : (16) S45: A Koopman kernel function based on the Koopman operator and with a moving window is obtained to recursively update the vehicle state data, and its formula is expressed as: (17) middle is the Koopman operator and also the system transformation matrix, , , are the data trajectories of input variables, state variables, and scheduling parameter variables, respectively. , They are transfer variable matrix and update variable matrix respectively.

7. The method according to claim 6, characterized in that S5 include: S51: , , They are the data tracks of input variables, state variables, and scheduling parameter variables. The moving window method is used to update the data in real time. The window length is n d data points, define a moving window and express it as: (18) In the formula is a data snapshot matrix, which contains a combination of input, state, and scheduling parameters. It is a moving window data set in real-time modeling. is the input matrix, which contains arrive System input u, X k is the state matrix, which contains arrive The system state at time x, X Fk is the future state matrix, which includes arrive Future system states in time; and are the Klock inner product matrices of the input state and the scheduling parameter ρ, is the scheduling parameter vector, n d The number of data points in the moving window; is the Klock inner product operator, used to generate the combination between input and state and scheduling parameters; S52: Based on equation (17) and equation (18), the moving window is rewritten as: (19) S53: Using the vehicle status data collected by formula (18), the system transformation matrix is ​​expressed as: (20) S54: The recursive update formula is expressed as: (21) S55: Assume that the relationship between the system state x at time steps k and k+1 is: (22) S56: Using the data points collected at time k+1 , , Update the system transformation matrix , to achieve real-time update of heavy-duty vehicle-road integrated modeling data, and the updated integrated state space is expressed as: (23) In the formula is the state matrix at time k after the update, is the longitudinal velocity at time k after the update, is the lateral velocity at time k after the update, and are the vehicle yaw velocity and yaw acceleration at time k after update respectively.

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