Six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation method based on improved extended Kalman filtering

Abstract

The invention provides a six-degree-of-freedom low-speed heavy-load vehicle key parameter estimation method based on improved extended Kalman filtering, and the low-speed heavy-load vehicle is a multi-axis distributed electric drive articulated vehicle. The six-degree-of-freedom low-speed heavy-load vehicle key parameter estimation method comprises the following steps: establishing a six-degree-of-freedom vehicle dynamics model considering lateral motion, yaw motion, roll motion and acting force of a hinge point, and obtaining a state equation and an observation equation of a system; and on the basis of the collected related data of the to-be-estimated vehicle, solving the established six-degree-of-freedom vehicle dynamics model by using an improved extended Kalman filtering algorithm to obtain an estimation result of the key parameters of the vehicle. The six-degree-of-freedom low-speed heavy-load vehicle key parameter estimation method provided by the invention is easy to implement, high in anti-interference capability and high in estimation precision, and is suitable for state estimation of low-speed heavy-load vehicles under complex road working conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicles, and particularly relates to a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering. Background Technique

[0002] In recent years, low-speed heavy-duty vehicles have been widely used in specific industrial scenarios such as tunnels, ports, and mines due to their advantages of strong load-bearing capacity, high transportation efficiency, and low energy consumption. The sideslip angle and yaw rate are key parameters for measuring vehicle driving stability and realizing intelligent control. Traditional vehicle parameter estimation methods mainly focus on two-axle high-speed vehicles, and there is less research on low-speed heavy-duty vehicles, especially distributed electric drive vehicles with articulations.

[0003] Accurate vehicle parameter estimation highly depends on a high-precision dynamic model that can accurately reflect the actual dynamic characteristics of the vehicle. However, for low-speed heavy-duty vehicles, especially distributed electric drive vehicles with articulation structures, the complexity of the model increases sharply. It is necessary to consider the interaction between various parts of the vehicle, the non-linear contact force between the tires and the ground, accurately describe the kinematic constraints at the articulation, and the dynamic response of the electric drive system. These factors together result in numerous model parameters and strong coupling, posing higher requirements for achieving accurate parameter estimation.

[0004] How to achieve accurate estimation of low-speed heavy-duty vehicle parameters and ensure the driving safety of low-speed heavy-duty vehicles in mines, ports and other occasions is still a technical challenge to be solved urgently.

[0005] In order to solve the above problems, people have been seeking an ideal technical solution. Summary of the Invention

[0006] Based on this, in view of the above technical problems, it is necessary to provide a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering. By establishing a six-degree-of-freedom vehicle dynamic model considering lateral motion, yaw motion, and roll motion, the complex motion state of the low-speed heavy-duty vehicle can be described more comprehensively and accurately, and accurate estimation of the key parameters of the low-speed heavy-duty vehicle can be realized.

[0007] To achieve the above object, the first aspect of the present invention provides a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering. The low-speed heavy-duty vehicle is a multi-axle distributed electric drive articulated vehicle, including the following steps:

[0008] Establish a six-degree-of-freedom vehicle dynamic model considering lateral motion, yaw motion, roll motion, and the acting force at the articulation point to obtain the state equation and observation equation of the system;

[0009] The six-degree-of-freedom dynamic model is as follows:

[0010] The leading vehicle:

[0011]

[0012] The following vehicle:

[0013]

[0014] The constraint equations of the front and rear vehicle bodies:

[0015]

[0016] In the formula, m1 and m2 are the vehicle masses of the leading vehicle and the following vehicle respectively; u1 and u2 are the longitudinal speeds of the leading vehicle and the following vehicle respectively; β1 and β2 are the centroid sideslip angles of the front and rear vehicles respectively; ω1 and ω2 are the yaw angular velocities of the front and rear vehicles respectively; m s1 and m s2 are the sprung masses of the front and rear vehicles respectively; h1 and h2 are the distances from the centroids of the sprung masses of the front and rear vehicles to the roll centers respectively; are the roll angles of the front and rear vehicles respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the axle angles of the vehicle respectively, i = 1, 2, 3, 4, 5, 6; F T is the force at the hinge point; I Z1 and I z2 are the moments of inertia of the front and rear vehicles about the Z axis respectively; I 1xz and I 2xz are the yaw-roll inertia products of the front and rear vehicles about the centroid respectively; a1 is the distance from the first axle of the leading vehicle to the centroid of the leading vehicle; b1 is the distance from the second axle of the leading vehicle to the centroid of the leading vehicle; c1 is the distance from the third axle of the leading vehicle to the centroid of the leading vehicle; d1 is the distance from the centroid of the leading vehicle to the hinge point between the front and rear vehicles; d2 is the distance from the centroid of the following vehicle to the hinge point between the front and rear vehicles; c2 is the distance from the first axle of the following vehicle to the centroid of the following vehicle; b2 is the distance from the second axle of the following vehicle to the centroid of the following vehicle; a2 is the distance from the third axle of the following vehicle to the centroid of the following vehicle; I 1xx and I 2xx are the moments of inertia of the front and rear vehicles about the X axis respectively; K1 and K2 are the roll stiffnesses of the front and rear vehicles respectively; C1 and C2 are the roll dampings of the front and rear vehicles respectively; h 1c and h 2c are the distances from the roll centers of the front and rear vehicles to the hinge devices respectively; φ is the hinge angle;

[0017] The state equation and observation equation of the system are:

[0018]

[0019] Z = CX + DU

[0020] where X is the state variable of the system; [[M -1 N] is the system matrix; U is the system input variable; [[M -1 P] is the input matrix; Z is the observable variable of the system, which is the lateral acceleration a of the leading vehicle y1 , and can be measured by on-vehicle sensors;

[0021] Based on the relevant data of the vehicle to be estimated collected, the six-degree-of-freedom vehicle dynamics model established is solved using the improved extended Kalman filter algorithm to obtain the estimation results of the key parameters of the vehicle.

[0022] In a possible embodiment of the first aspect, based on the relevant data of the vehicle to be estimated collected, the six-degree-of-freedom vehicle dynamics model established is solved using the improved extended Kalman filter algorithm to obtain the estimation results of the key parameters of the vehicle, including:

[0023] According to the state equation and observation equation of the six-degree-of-freedom vehicle dynamics model established above, the standardized state equation and observation equation are obtained:

[0024]

[0025] z(t) = h(x(t), u(t), v(t))

[0026] where w(t) and v(t) are the process noise and observation noise respectively, and it is assumed that both are white noises and uncorrelated with each other; x(t) and u(t) are the state vector and input vector respectively;

[0027] Based on the standard state equation and observation equation and the input vector of the vehicle to be estimated collected, the improved extended Kalman filter algorithm is used to estimate the key parameters of the vehicle.

[0028] In a possible embodiment of the first aspect, the estimating the key parameters of the vehicle by using the improved extended Kalman filter algorithm based on the standard state equation and observation equation and the input vector of the vehicle to be estimated collected includes:

[0029] The first-order Taylor formula is used to linearize the standard state equation and observation equation to obtain their respective Jacobian matrices;

[0030] Set the initial values of the state vector and state error covariance to be and p0, start the time update process, and input the state estimate value at time k The error covariance P of the state estimation system k and the input u of the system k into the time update equation of the state estimation system for state prediction and state error covariance prediction to obtain the one-step state prediction value at time k + 1 and the one-step predicted value of the error covariance of the state estimation system

[0031] The time update equation of the state estimation system is as follows:

[0032]

[0033] In the formula, is the one-step predicted value of the state at time k + 1; is the state estimated value at time k; u k is the input vector at time k; w k is the process noise at time k; is the one-step predicted value of the error covariance of the state estimation system at time k + 1; A k is the Jacobian matrix of the state equation at time k; P k is the error covariance estimated value of the state estimation system at time k; Q k is the covariance matrix of the process noise at time k;

[0034] Based on the forgetting factor adjustment mechanism, the one-step predicted value of the state error covariance is dynamically corrected according to the current observation residual to obtain the corrected state error covariance matrix

[0035] Start the measurement update process, and use the one-step predicted value of the state at time k + 1 The corrected state error covariance matrix of the state estimation system at time k + 1 Input it into the measurement update equation of the state estimation system to obtain the state estimated value at time k + 1 and the error covariance P of the state estimation system k+1 ;

[0036] The measurement update equation of the state estimation system is as follows:

[0037]

[0038] In the formula, is the state estimated value at time k + 1; is the one-step state prediction; K k+1 is the Kalman gain matrix; z k+1 is the observed value at time k + 1; is the observed value obtained by substituting the one-step predicted value of the state at time k + 1; P k+1 is the error covariance of the state estimation system; I is the identity matrix; H k+1 is the Jacobian matrix of the observation equation at time k + 1; is the corrected state error covariance matrix;

[0039] Among them, K k+1 The expression of is:

[0040] In the formula, R k is the covariance matrix of the observation noise at time k.

[0041] Based on the observation residual information at the current moment, the Sage-Husa filter is introduced to dynamically adjust the process noise and the observation noise;

[0042] The above steps complete one cycle of the time update and measurement update process. Then, the posterior estimation result will be fed back to the prediction module to restart the time update process of the next cycle.

[0043] To achieve the above object, the second aspect of the present invention provides a key parameter estimation device for a six-degree-of-freedom low-speed heavy-duty vehicle based on an improved extended Kalman filter, which is applied to a multi-axle distributed electric drive articulated vehicle, including:

[0044] A dynamic model establishment module, which is used to establish a six-degree-of-freedom vehicle dynamic model considering lateral motion, yaw motion, roll motion, and the acting force at the articulation point:

[0045] The front vehicle:

[0046]

[0047] The rear vehicle:

[0048]

[0049]

[0050] The constraint equation of the front and rear vehicle bodies:

[0051]

[0052] In the formula, m1 and m2 are the total vehicle masses of the front vehicle and the rear vehicle respectively; u1 and u2 are the longitudinal speeds of the front vehicle and the rear vehicle respectively; β1 and β2 are the center-of-mass sideslip angles of the front and rear vehicles respectively; ω1 and ω2 are the yaw angular velocities of the front and rear vehicles respectively; m s1 、m s2 are the sprung masses of the front and rear vehicles respectively; h1 and h2 are the distances from the centers of mass of the sprung masses of the front and rear vehicles to the roll centers respectively; are the roll angles of the front and rear vehicles respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the steering angles of each axle of the vehicle respectively, i = 1, 2, 3, 4, 5, 6; F T is the acting force at the articulation point; I Z1 、I Z2are the moments of inertia of the front and rear vehicles about the Z-axis respectively; I 1xz and I 2xz are the yaw-roll products of inertia of the front and rear vehicles about the center of mass respectively; a1 is the distance from the first axle of the front vehicle to the center of mass of the front vehicle; b1 is the distance from the second axle of the front vehicle to the center of mass of the front vehicle; c1 is the distance from the third axle of the front vehicle to the center of mass of the front vehicle; d1 is the distance from the center of mass of the front vehicle to the articulation point between the front and rear vehicles; d2 is the distance from the center of mass of the rear vehicle to the articulation point between the front and rear vehicles; c2 is the distance from the first axle of the rear vehicle to the center of mass of the rear vehicle; b2 is the distance from the second axle of the rear vehicle to the center of mass of the rear vehicle; a2 is the distance from the third axle of the rear vehicle to the center of mass of the rear vehicle; I 1xx and I 2xx are the moments of inertia of the front and rear vehicles about the X-axis respectively; K1 and K2 are the roll stiffnesses of the front and rear vehicles respectively; C1 and C2 are the roll damping of the front and rear vehicles respectively; h 1c and h 2c are the distances from the roll centers of the front and rear vehicles to the articulation device respectively; φ is the articulation angle;

[0053] A state equation and observation equation acquisition module, configured to obtain a state equation and an observation equation of the system based on the six-degree-of-freedom dynamics model:

[0054]

[0055] Z = CX + DU

[0056] where X is the state quantity of the system; [M -1 N] is the system matrix; U is the system input quantity; [M -1 P] is the input matrix; Z is the observable quantity of the system, which is the lateral acceleration a y1 of the front vehicle and can be measured by an on-vehicle sensor;

[0057] A parameter estimation module, configured to solve the established six-degree-of-freedom vehicle dynamics model by using an improved extended Kalman filter algorithm based on the relevant data of the vehicle to be estimated collected, and obtain the estimation results of the key parameters of the vehicle.

[0058] To achieve the above object, a third aspect of the present invention provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus;

[0059] The memory is used to store a computer program;

[0060] The processor, when executing the program stored in the memory, implements the six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation method as described in the first aspect.

[0061] To achieve the above object, a fourth aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the key parameter estimation method for a six-degree-of-freedom low-speed heavy vehicle as described in the first aspect.

[0062] To achieve the above object, a fifth aspect of the present invention provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the key parameter estimation method for a six-degree-of-freedom low-speed heavy vehicle as described in the first aspect.

[0063] The beneficial effects of the present invention are as follows:

[0064] 1. High estimation accuracy: The present invention establishes a six-degree-of-freedom vehicle dynamics model considering lateral motion, yaw motion, roll motion, and the constraint equations between the front and rear vehicle bodies. Among them, the six degrees of freedom are the three degrees of freedom of lateral, yaw, and roll motion of the front vehicle and the three degrees of freedom of lateral, yaw, and roll motion of the rear vehicle. And due to the existence of the hinge, the existence of the hinge force between the front and rear vehicles needs to be considered during the model establishment process; the constraint equations between the vehicle bodies are to avoid redundant degrees of freedom of the model, and can more comprehensively and accurately describe the complex motion state of the low-speed heavy vehicle, thereby improving the estimation accuracy of the key vehicle parameters.

[0065] 2. Easy to implement: The parameter estimation method proposed by the present invention is based on common measurable data of the vehicle, such as longitudinal speed, lateral acceleration, etc., which can be easily obtained through on-vehicle sensors.

[0066] 3. Accurately obtaining the key vehicle parameters is of crucial significance for optimizing vehicle performance and ensuring driving safety. The method of the present invention can output high-precision vehicle parameters in real time, providing safety guarantees for low-speed heavy vehicles in complex scenarios such as mines and ports. Description of the Drawings

[0067] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0068] Figure 1 It is a flowchart of the extended Kalman filter algorithm for estimating a six-degree-of-freedom low-speed heavy vehicle proposed by the present invention.

[0069] Figure 2 It is a model diagram of the lateral and yaw motion of a six-degree-of-freedom vehicle.

[0070] Figure 3 It is a model diagram of the roll motion of the vehicle.

[0071] Figure 4 It is a comparison chart between the reference sideslip angle and the estimated sideslip angle of the leading vehicle, the reference yaw rate and the estimated yaw rate of the leading vehicle, the reference sideslip angle and the estimated sideslip angle of the trailing vehicle, and the reference yaw rate and the estimated yaw rate of the trailing vehicle under sinusoidal operating conditions.

[0072] Figure 5 It is a comparison chart between the reference sideslip angle and the estimated sideslip angle of the leading vehicle, the reference yaw rate and the estimated yaw rate of the leading vehicle, the reference sideslip angle and the estimated sideslip angle of the trailing vehicle, and the reference yaw rate and the estimated yaw rate of the trailing vehicle under fishhook operating conditions.

[0073] Figure 6 It is a schematic structural diagram of the computer device of the present invention. Specific embodiments

[0074] Low-speed articulated heavy vehicles (such as large semi-trailer trucks, construction machinery vehicles, etc.) have a complex structure (articulated connection), a large load, a high center of gravity and a special distribution. The six degrees of freedom of the vehicle (lateral, yaw, roll, longitudinal, pitch, vertical) have a significant impact on the running stability, maneuverability and safety.

[0075] (1) Influence of lateral movement:

[0076] Poor lateral stability: Articulated vehicles have a long body and a large mass. When turning at low speed, the lateral movements of the front and rear parts (tractor and trailer) are out of sync, and it is easy to occur "jackknifing" or fishtailing.

[0077] Low tire load limit: The heavy load causes the lateral force of the tire to approach the limit, and it is easy to skid when turning sharply at low speed or on a wet and slippery road surface, especially in the trailer part.

[0078] Path tracking error: The articulated structure causes a delay in the steering response and an increase in the lateral offset, which may scrape against surrounding obstacles (such as narrow construction sites or warehouses).

[0079] (2) Influence of yaw movement:

[0080] Moment coupling at the articulation point: The yaw movement directions of the tractor and the trailer conflict, which is easy to cause "snake oscillation", especially when driving in reverse at low speed or on soft ground.

[0081] Understeer / oversteer sensitivity: The heavy load causes uneven distribution of the axle loads, and the yaw directions of the tractor and the trailer conflict, which may suddenly get out of control.

[0082] Risk of trailer fishtailing: Due to its large inertia, the yaw rate of the trailer lags behind that of the tractor, and it is easy to swing out when turning (such as a right-angle bend).

[0083] (3) Influence of roll movement:

[0084] High center of gravity exacerbates roll: The center of gravity of heavy-duty vehicles is high, and the roll moment is large, which is likely to cause the trailer to roll over (such as the rollover accident of a muck truck when turning).

[0085] Risk of cargo offset: Loose cargo (such as sand and gravel, containers) may shift during roll, further damaging stability.

[0086] Overload of suspension system: When passing through uneven roads at low speed, roll may cause excessive compression of the unilateral suspension, exacerbating tire wear.

[0087] Specifically, when a low-speed articulated heavy-duty vehicle is running, the priority of each degree of freedom needs to be comprehensively judged based on safety risks and handling requirements. Among them, the applicant has found through research that the core priorities are lateral, yaw, and roll motions.

[0088] Roll motion is directly related to the risk of rollover and is the most fatal safety threat to low-speed heavy-duty vehicles. The yaw out-of-control of articulated vehicles (such as snake-like swinging, fishtailing) is likely to cause chain accidents. Lateral sliding and folding effects are typical problems during low-speed turning.

[0089] Moreover, the articulated structure of low-speed articulated heavy-duty vehicles will also cause serious coupling between each degree of freedom. For example, the combination of roll and lateral makes the body tilt, resulting in uneven tire ground contact pressure and a decrease in lateral grip, triggering side slip; the combination of yaw and roll makes the centrifugal force increase when the trailer yaws, further raising the roll angle.

[0090] To solve the above problems, this embodiment provides a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering. Particularly considering the importance of sideslip angle, yaw rate, roll angle, and articulation point in the intelligent control of low-speed articulated heavy-duty vehicles, a dynamic model suitable for low-speed articulated heavy-duty vehicles is established. Usually, low-speed articulated heavy-duty vehicles are multi-axle distributed electric drive articulated vehicles.

[0091] The following further describes the technical solution of the present invention in detail through specific embodiments.

[0092] Embodiment 1

[0093] As Figure 1 shown, taking a six-axle distributed electric drive articulated vehicle with three axles for both the front vehicle and the rear vehicle as an example, the present invention provides a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering, including the following steps:

[0094] 1) According to the vehicle parameters to be estimated, establish a six-degree-of-freedom vehicle dynamic model considering lateral motion, yaw motion, roll motion, and the acting force of the articulation point, and obtain the state equation and observation equation of the system.

[0095] 1.1) AsFigure 1 As shown in Figure 2 a coordinate system is respectively set at the center of mass of the leading vehicle and the trailing vehicle, and a leading vehicle dynamics model and a trailing vehicle dynamics model are respectively established according to the two coordinate systems and the vehicle to be estimated.

[0096] Furthermore, in an articulated vehicle, the articulation point is the key connection point between the leading vehicle and the trailing vehicle. The articulation point allows the front and rear vehicle bodies to rotate relative to each other around a specific axis to achieve flexible steering. Under low-speed heavy-load conditions, if the articulation point is insufficiently constrained, the steering may be inaccurate and even cause the vehicle to lose control; if it is over-constrained, the steering flexibility will decrease, affecting the operation efficiency. Therefore, the kinematic constraints at the articulation point must be considered, that is, the constraint equations of the front and rear vehicle bodies are established.

[0097] Specifically, under heavy-load conditions, the articulation point needs to evenly transfer the forces and torques between the front and rear vehicles. Ignoring the interaction between the front and rear vehicle bodies will cause the model to be unable to accurately reflect the load distribution, structural deformation or vibration characteristics. Therefore, during the design process of the leading vehicle dynamics model and the trailing vehicle dynamics model, the acting forces at the articulation point need to be considered.

[0098] Specifically, the leading vehicle dynamics model has three degrees of freedom, including the leading vehicle yaw motion equation, the leading vehicle lateral motion equation, and the leading vehicle roll motion equation. The specific expressions are as follows:

[0099] Leading vehicle lateral motion equation:

[0100] Leading vehicle yaw motion equation:

[0101] Leading vehicle roll motion equation:

[0102] The trailing vehicle dynamics model has three degrees of freedom, including the trailing vehicle yaw motion equation, the trailing vehicle lateral motion equation, and the trailing vehicle roll motion equation. The specific expressions are as follows:

[0103] Trailing vehicle yaw motion equation:

[0104] Trailing vehicle lateral motion equation:

[0105] Trailing vehicle roll motion equation:

[0106] Specifically, in this embodiment, the constraint equation of the front and rear vehicle bodies is:

[0107]

[0108] In the formula, m1 and m2 are the vehicle masses of the leading vehicle and the trailing vehicle respectively; u1 and u2 are the longitudinal speeds of the leading vehicle and the trailing vehicle respectively; β1 and β2 are the sideslip angles of the centers of mass of the leading vehicle and the trailing vehicle respectively; ω1 and ω2 are the yaw angular velocities of the leading vehicle and the trailing vehicle respectively; m s1 、m s2 are the sprung masses of the leading vehicle and the trailing vehicle respectively; h1 and h2 are the distances from the centers of mass of the sprung masses of the leading vehicle and the trailing vehicle to the roll centers respectively; are the roll angles of the leading vehicle and the trailing vehicle respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the steering angles of each axle of the vehicle respectively, i = 1, 2, 3, 4, 5, 6; F T is the force at the articulation point; I Z1 、I Z2 are the moments of inertia of the leading vehicle and the trailing vehicle about the Z-axis respectively; I 1xz 、I 2xz are the yaw-roll products of inertia of the leading vehicle and the trailing vehicle about the centers of mass respectively; a1 is the distance from the first axle of the leading vehicle to the center of mass of the leading vehicle; b1 is the distance from the second axle of the leading vehicle to the center of mass of the leading vehicle; c1 is the distance from the third axle of the leading vehicle to the center of mass of the leading vehicle; d1 is the distance from the center of mass of the leading vehicle to the articulation point between the leading vehicle and the trailing vehicle; d2 is the distance from the center of mass of the trailing vehicle to the articulation point between the leading vehicle and the trailing vehicle; c2 is the distance from the first axle of the trailing vehicle to the center of mass of the trailing vehicle; b2 is the distance from the second axle of the trailing vehicle to the center of mass of the trailing vehicle; a2 is the distance from the third axle of the trailing vehicle to the center of mass of the trailing vehicle; I 1xx 、I 2xx are the moments of inertia of the leading vehicle and the trailing vehicle about the X-axis respectively; K1 and K2 are the roll stiffnesses of the leading vehicle and the trailing vehicle respectively; C1 and C2 are the roll dampings of the leading vehicle and the trailing vehicle respectively; h 1c 、h 2c are the distances from the roll centers of the leading vehicle and the trailing vehicle to the articulation device respectively; φ is the articulation angle.

[0109] Since the speed of the low-speed heavy-duty vehicle is relatively low in the actual application scenario, a linear tire model is adopted in this embodiment. The linear tire model is a simplified model that only considers the linear relationship between the forces and slip amounts of the tire in the longitudinal, lateral, and vertical directions, and ignores some non-linear effects, such as the elastic deformation and friction characteristics of the tire. Therefore, in actual applications, it is necessary to make adjustments and corrections in combination with specific situations.

[0110] In this embodiment, the lateral tire reaction force F yi = k i α i , where k i is the tire cornering stiffness and α i is the side slip angle of the i-th tire. The calculation formula is as follows:

[0111]

[0112] The front wheel steering angle can be obtained by the ratio of the steering wheel angle to the angular transmission ratio of the steering system, i.e.:

[0113]

[0114] According to the Ackermann steering principle and geometric relationships, with the small angle assumption, the wheel steering angles of each axis are shown as follows:

[0115]

[0116] where φ is the angle between the front and rear vehicles, and R1 and R2 are the distances from the mass centers of the front and rear vehicles to the instantaneous steering center, respectively.

[0117] It should be noted that in this embodiment, the lateral reaction force F at the axle level is adopted, i = 1, 2, 3, 4, 5, 6, rather than the lateral reaction force at the tire level. The advantage of this is that the dynamic model can be simplified to quickly evaluate the overall performance such as vehicle stability and lateral acceleration response. yi

[0118] 1.2) According to the established six-degree-of-freedom vehicle dynamic model, the state equation and the observation equation are obtained:

[0119] Rewrite the above six-degree-of-freedom vehicle dynamic model into matrix form:

[0120]

[0121]

[0122] In the formula, M11 = dm1u1; M12 = I z1 ; M14 = -dm s1 h1;

[0123] M21 = m1u1; M24 = -m s1 h1; M25 = m2u2; M28 = -m s2 h2;

[0124] M35 = dm2m2; M36 = -I z2 ; M38 = I 2xz -dm s2 h2;

[0125] M51 = h 1c m1u1 + m s1 h1u1; M52 = I 1xz ; M54 = -h 1c m s1 h1 - I 1xx -2m s1 h1 2 ; ​

[0126] M65 = h 2c m2u2 + m s2 h2u2; M66 = I 2xz ; M68 = -h 2c m s2 h2 - I 2xx -2m s2 h2 2 ;

[0127] N11 = (a + d)k1 + (d - b)k2 + (d - c)a3;

[0128]

[0129]

[0130] N53 = K1 - m s1 gh1; N54 = C1N65 = (k4 + k5 + k6)h 2c ;

[0131]

[0132] N68 = C2;

[0133] According to the above formulas, the system state equation is obtained:

[0134]

[0135] The observable quantity of the system is the lateral acceleration a of the vehicle ahead y1 , and the calculation formula is as follows:

[0136]

[0137] Combining the system state equation and the formula for the lateral acceleration of the vehicle ahead, the system observation equation is obtained:

[0138] Z = CX + DU

[0139] The system state equation and the observation equation are obtained:

[0140]

[0141] Z = CX + DU

[0142] where X is the state quantity of the system; [M -1 N] is the system matrix; U is the system input quantity;

[0143] [M -1 P] is the input matrix; Z is the observable quantity of the system, which is the lateral acceleration a of the vehicle ahead y1 , and can be measured by on-vehicle sensors.

[0144] That is:

[0145]

[0146]

[0147] Among them, [M -1 N] is the system matrix, and [M -1 P] is the input matrix; the expressions of M, N, P, C, and D are respectively:

[0148]

[0149]

[0150] Among them, a 11 , a 12 , a 13 , a 14 , a 15 , a 16 , a 17 , a 18 are the first row elements of the system matrix [M -1 N]; b 11 , b 12 , b 13 , b 14 , b 15 , b 16 are the first row elements of the input matrix [M -1 P].

[0151] 2) Based on the collected relevant data of the vehicle to be estimated, use the improved extended Kalman filter algorithm to solve the established six-degree-of-freedom vehicle dynamics model to obtain the estimation results of the vehicle key parameters.

[0152] Specifically, it includes the following steps:

[0153] 2.1) According to the state equation and observation equation of the above-established six-degree-of-freedom vehicle dynamics model, rewrite the standardized state equation and observation equation:

[0154]

[0155] z(t) = h(x(t), u(t), v(t))

[0156] Among them, w(t) and v(t) are the process noise and observation noise respectively. It is assumed that both are white noises and are uncorrelated with each other; x(t) and u(t) are the state vector and input vector respectively.

[0157] 2.2) Based on the standard state equation and observation equation, and collecting the input vector of the vehicle to be estimated, the improved extended Kalman filter algorithm is used to estimate the key parameters of the vehicle.

[0158] Specifically, it includes the following steps:

[0159] 2.2.1) Use the first-order Taylor formula to linearize the standard state equation and observation equation to obtain their respective Jacobian matrices:

[0160]

[0161] In the formula, A is the Jacobian matrix of the partial derivative of the nonlinear function f with respect to the state quantity x, and H is the Jacobian matrix of the partial derivative of the nonlinear function h with respect to the state quantity x.

[0162] 2.2.2) Set the initial values of the state vector and state error covariance to and p0, start the time update process, and input the state estimate value the error covariance P of the state estimation system k and the input u of the system k into the time update equation of the state estimation system for state prediction and state error covariance prediction to obtain the one-step state prediction value and the one-step prediction value of the error covariance of the state estimation system

[0163] Specifically, the time update equation is:

[0164]

[0165] In the formula, is the one-step state prediction value at the (k + 1)th moment; is the state estimate value at the kth moment; u k is the input vector at the kth moment; w k is the process noise at the kth moment; is the one-step prediction value of the error covariance of the state estimation system at the (k + 1)th moment; A k is the Jacobian matrix of the state equation at the kth moment; P k is the error covariance estimate value of the state estimation system at the kth moment; Q k is the covariance matrix of the process noise at the kth moment.

[0166] 2.2.3) Based on the forgetting factor adjustment mechanism, dynamically correct the one-step prediction value of the state error covariance according to the current observation residual to obtain the corrected state error covariance matrix

[0167] The covariance correction equation is:

[0168]

[0169] where y k is the observation residual at time k + 1, λ min is the lower limit of the forgetting factor, taking values between 0.95 and 0.99; a is a parameter for adjusting the sensitivity, taking values between 1 and 5.

[0170] It can be understood that by introducing the forgetting factor, when the system parameters change over time, new data can better reflect the parameter change situation than old data.

[0171] 2.2.4) Start the measurement update process, and input the one-step predicted value of the state at time k + 1 The corrected state error covariance matrix of the state estimation system at time k + 1 into the measurement update equation of the state estimation system to obtain the state estimation value at time k + 1 and the error covariance P of the state estimation system k+1 .

[0172] Specifically, the measurement update equation is:

[0173]

[0174] where is the state estimation value at time k + 1; is the one-step state prediction; K k+1 is the Kalman gain matrix; z k+1 is the observation value at time k + 1; is the observation value obtained by substituting the one-step predicted value of the state at time k + 1; P k+1 is the error covariance of the state estimation system; I is the identity matrix; H k+1 is the Jacobian matrix of the observation equation at time k + 1; is the state error covariance matrix corrected based on the forgetting factor.

[0175] Among them, the expression of K k+1 is:

[0176]

[0177] where R k is the covariance matrix of the observation noise at time k.

[0178] 2.2.5) Based on the observation residual information at the current moment, the Sage-Husa filter is introduced to dynamically adjust the process noise and observation noise, so as to estimate and correct the statistical characteristics of the process noise and observation noise in real time, reduce the system model error and improve the filtering accuracy.

[0179] The noise matrix update equation is as follows:

[0180]

[0181] In the formula, d k+1 is the adaptive adjustment factor, which takes values between 0.95 and 0.99.

[0182] The above steps complete the time update and measurement update processes for one cycle. Then, the posterior estimation result will be fed back to the prediction module to restart the time update process for the next cycle. Repeat steps 2.2.2 and 2.2.3 in this way to obtain the parameter estimation values at each moment of the vehicle.

[0183] Next, the correctness of this state estimation method will be verified by combining specific embodiments, and a Trucksim&Simulink co-simulation platform will be built. The sine working condition and the fishhook working condition are selected. Due to the special body structure of the multi-axle distributed electric drive articulated vehicle, the driving speed is relatively low in the actual application scenario. Therefore, 20 km / h and 40 km / h are selected as the test vehicle speeds. The sampling time is 0.001 s. The simulation results are as Figure 4 and Figure 5 shown.

[0184] As can be seen from the figure, the estimation effect of the sideslip angle of the center of mass is good, and the average absolute error of the estimated sideslip angle of the center of mass of the front and rear vehicles at different vehicle speeds under the sine working condition and the fishhook working condition is within 0.18. The estimation effect of the yaw rate is good, and the average absolute error of the estimated yaw rate of the front and rear vehicles at different vehicle speeds under the sine working condition and the fishhook working condition is within 0.47. The estimated curve basically coincides with the reference curve.

[0185] Matters not covered by this invention are well-known technologies.

[0186] It should be understood that although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0187] Embodiment 2

[0188] Based on the same inventive concept, an embodiment of the present application also provides a six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation device for implementing the six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation method involved above. The implementation solution provided by this device to solve the problem is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation device based on the improved extended Kalman filter provided below can refer to the limitations on the six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation method in the above text, and will not be repeated here.

[0189] The six-degree-of-freedom low-speed heavy-duty vehicle key parameter estimation device based on the improved extended Kalman filter includes:

[0190] A dynamic model establishment module for establishing a six-degree-of-freedom vehicle dynamic model considering lateral motion, yaw motion, roll motion, and the acting force of the hinge point:

[0191] The front vehicle:

[0192]

[0193] The rear vehicle:

[0194]

[0195] The front and rear vehicle body constraint equations:

[0196]

[0197] In the formula, m1 and m2 are the vehicle masses of the front vehicle and the rear vehicle respectively; u1 and u2 are the longitudinal speeds of the front vehicle and the rear vehicle respectively; β1 and β2 are the center-of-mass sideslip angles of the front and rear vehicles respectively; ω1 and ω2 are the yaw angular velocities of the front and rear vehicles respectively; m s1 、m s2are the masses above the front and rear vehicle springs respectively; h1 and h2 are the distances from the mass centers of the masses above the front and rear vehicle springs to the roll center respectively; are the roll angles of the front and rear vehicles respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the angles of rotation of each vehicle axle respectively, i = 1, 2, 3, 4, 5, 6; F T is the force at the hinge point; I Z1 、I Z2 are the moments of inertia of the front and rear vehicles about the Z-axis respectively; I 1xz 、I 2xz are the yaw-roll products of inertia of the front and rear vehicles about the mass center respectively; a1 is the distance from the first axle of the front vehicle to the mass center of the front vehicle; b1 is the distance from the second axle of the front vehicle to the mass center of the front vehicle; c1 is the distance from the third axle of the front vehicle to the mass center of the front vehicle; d1 is the distance from the mass center of the front vehicle to the hinge point between the front and rear vehicles; d2 is the distance from the mass center of the rear vehicle to the hinge point between the front and rear vehicles; c2 is the distance from the first axle of the rear vehicle to the mass center of the rear vehicle; b2 is the distance from the second axle of the rear vehicle to the mass center of the rear vehicle; a2 is the distance from the third axle of the rear vehicle to the mass center of the rear vehicle; I 1xx 、I 2xx are the moments of inertia of the front and rear vehicles about the X-axis respectively; K1 and K2 are the roll stiffnesses of the front and rear vehicles respectively; C1 and C2 are the roll dampings of the front and rear vehicles respectively; h 1c 、h 2c are the distances from the roll centers of the front and rear vehicles to the hinge device respectively; φ is the hinge angle;

[0198] The state equation and observation equation acquisition module is used to obtain the state equation and observation equation of the system based on the six-degree-of-freedom dynamic model:

[0199]

[0200] Z = CX + DU

[0201] where X is the state quantity of the system; [M -1 N] is the system matrix; U is the system input quantity; [M -1 P] is the input matrix; Z is the observable quantity of the system, which is the lateral acceleration a y1 of the front vehicle and can be measured by in-vehicle sensors;

[0202] The parameter estimation module is used to solve the established six-degree-of-freedom vehicle dynamic model by using the improved extended Kalman filter algorithm based on the relevant data of the vehicle to be estimated collected, and obtain the estimation results of the key parameters of the vehicle.

[0203] Example 3

[0204] This embodiment provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 6As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0205] Those skilled in the art can understand that Figure 6 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0206] Embodiment 4

[0207] Based on the above embodiments, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle as described in Embodiment 1.

[0208] Embodiment 5

[0209] Based on the above embodiments, this embodiment provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle as described in Embodiment 1.

[0210] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0211] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0212] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that it is still possible to modify the specific implementation manners of the present invention or perform equivalent replacements on some technical features; without departing from the spirit of the technical solutions of the present invention, they should all be covered within the scope of the technical solutions claimed by the present invention.

Claims

1. A method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filter. The low-speed heavy-duty vehicle is a multi-axle distributed electric drive articulated vehicle, and is characterized in that It includes the following steps: Establish a six-degree-of-freedom vehicle dynamics model considering lateral motion, yaw motion, roll motion, and the forces at the articulation points, and obtain the state equation and observation equation of the system; The six-degree-of-freedom dynamics model is as follows: Leading vehicle: Following vehicle: Constraint equation for the front and rear vehicle bodies: Where, m1 and m2 are the vehicle masses of the leading vehicle and the trailing vehicle respectively; u1 and u2 are the longitudinal speeds of the leading vehicle and the trailing vehicle respectively; β1 and β2 are the centroid sideslip angles of the leading vehicle and the trailing vehicle respectively; ω1 and ω2 are the yaw angular velocities of the leading vehicle and the trailing vehicle respectively; m s1 and m s2 are the unsprung masses of the leading vehicle and the trailing vehicle respectively; h1 and h2 are the distances from the centroids of the unsprung masses of the leading vehicle and the trailing vehicle to the roll centers respectively; are the roll angles of the leading vehicle and the trailing vehicle respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the angles of each vehicle axle respectively, i = 1, 2, 3, 4, 5, 6; F T is the force at the hinge point; I Z1 and I Z2 are the moments of inertia of the leading vehicle and the trailing vehicle about the Z axis respectively; I 1xz and I 2xz are the yaw-roll products of inertia of the leading vehicle and the trailing vehicle about their centroids respectively; a1 is the distance from the first axle of the leading vehicle to the centroid of the leading vehicle; b1 is the distance from the second axle of the leading vehicle to the centroid of the leading vehicle; c1 is the distance from the third axle of the leading vehicle to the centroid of the leading vehicle; d1 is the distance from the centroid of the leading vehicle to the hinge point between the leading vehicle and the trailing vehicle; d2 is the distance from the centroid of the trailing vehicle to the hinge point between the leading vehicle and the trailing vehicle; c2 is the distance from the first axle of the trailing vehicle to the centroid of the trailing vehicle; b2 is the distance from the second axle of the trailing vehicle to the centroid of the trailing vehicle; a2 is the distance from the third axle of the trailing vehicle to the centroid of the trailing vehicle; I 1xx and I 2xx are the moments of inertia of the leading vehicle and the trailing vehicle about the X axis respectively; K1 and K2 are the roll stiffnesses of the leading vehicle and the trailing vehicle respectively; C1 and C2 are the roll dampings of the leading vehicle and the trailing vehicle respectively; h 1c and h 2c are the distances from the roll centers of the leading vehicle and the trailing vehicle to the hinge device respectively; φ is the hinge angle; The state equation and observation equation of the system are: Z = CX + DU where X is the state variable of the system; [[M -1 N] is the system matrix; U is the system input variable; [[M -1 P] is the input matrix; Z is the observable variable of the system, which is the lateral acceleration a y1 of the vehicle ahead and can be measured by in-vehicle sensors; Based on the collected relevant data of the vehicle to be estimated, use the improved extended Kalman filter algorithm to solve the established six-degree-of-freedom vehicle dynamics model, and obtain the estimation results of the key parameters of the vehicle.

2. A method for estimating key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filtering according to claim 1, characterized in that Based on the collected relevant data of the vehicle to be estimated, use the improved extended Kalman filter algorithm to solve the established six-degree-of-freedom vehicle dynamics model, and obtain the estimation results of the key parameters of the vehicle, including: According to the state equation and observation equation of the established six-degree-of-freedom vehicle dynamics model above, obtain the standardized state equation and observation equation: z(t) = h(x(t), u(t), v(t)) where w(t) and v(t) are the process noise and observation noise respectively, and it is assumed that both are white noises and uncorrelated; x(t) and u(t) are the state vector and input vector respectively; Based on the standard state equation and observation equation and the input vector of the vehicle to be estimated collected, use the improved extended Kalman filter algorithm to estimate the key parameters of the vehicle.

3. The key parameter estimation method for a six-degree-of-freedom low-speed heavy-duty vehicle based on an improved extended Kalman filter according to claim 2, characterized in that The method of using the improved extended Kalman filter algorithm to estimate the key parameters of the vehicle based on the standard state equation and observation equation and the input vector of the vehicle to be estimated collected includes: Use the first-order Taylor formula to linearize the standard state equation and observation equation to obtain their respective Jacobian matrices; Set the initial values of the state vector and the state error covariance to and p0, start the time update process, and use the state estimate value at time k The error covariance P of the state estimation system k and the system input u k Input them into the time update equation of the state estimation system to perform state prediction and state error covariance prediction, and obtain the one-step state prediction value at time k+1 and the one-step prediction value of the error covariance of the state estimation system The time update equation of the state estimation system is: In the formula, is the one-step predicted value of the state at time k + 1; is the state estimated value at time k; u k is the input vector at time k; w k is the process noise at time k; is the one-step predicted value of the error covariance of the state estimation system at time k + 1; A k is the Jacobian matrix of the state equation at time k; P k is the error covariance estimated value of the state estimation system at time k; Q k is the covariance matrix of the process noise at time k; Based on the forgetting factor adjustment mechanism, the one-step predicted value of the state error covariance is dynamically corrected according to the observation residual at the current moment to obtain the corrected state error covariance matrix Start the measurement update process, and use the one-step predicted value of the state at time k+1 The corrected state error covariance matrix of the state estimation system at time k+1 Input it into the measurement update equation of the state estimation system to obtain the state estimation value at time k+1 and the error covariance P of the state estimation system k+1 ; The measurement update equation of the state estimation system is: Wherein, is the state estimation value at the (k + 1)-th moment; is the one-step state prediction; K k+1 is the Kalman gain matrix; z k+1 is the observation value at the (k + 1)-th moment; is the observation value obtained by substituting the one-step state prediction value at the (k + 1)-th moment; P k+1 is the error covariance of the state estimation system; I is the identity matrix; H k+1 is the Jacobian matrix of the observation equation at the (k + 1)-th moment; is the corrected state error covariance matrix; Among which K k+1 has the following expression: where R k is the covariance matrix of the observation noise at time k. Based on the observation residual information at the current moment, introduce Sage-Husa filtering to dynamically adjust the process noise and observation noise for the time update of the next cycle.

4. An estimation device for key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle based on improved extended Kalman filter, which is applied to a multi-axle distributed electric drive articulated vehicle, and is characterized in that It includes: A dynamics model establishment module for establishing a six-degree-of-freedom vehicle dynamics model considering lateral motion, yaw motion, roll motion, and the forces at the articulation points; Leading vehicle: Following vehicle: Constraint equation for the front and rear vehicle bodies: Where, m1 and m2 are the vehicle masses of the leading vehicle and the trailing vehicle respectively; u1 and u2 are the longitudinal speeds of the leading vehicle and the trailing vehicle respectively; β1 and β2 are the sideslip angles of the centers of mass of the leading vehicle and the trailing vehicle respectively; ω1 and ω2 are the yaw angular velocities of the leading vehicle and the trailing vehicle respectively; m s1 and m s2 are the unsprung masses of the leading vehicle and the trailing vehicle respectively; h1 and h2 are the distances from the centers of mass of the unsprung masses of the leading vehicle and the trailing vehicle to the roll centers respectively; are the roll angles of the leading vehicle and the trailing vehicle respectively; F yi is the lateral tire reaction force, i = 1, 2, 3, 4, 5, 6; δ i are the steering angles of each axle of the vehicle respectively, i = 1, 2, 3, 4, 5, 6; F T is the force at the hinge point; I Z1 and I Z2 are the moments of inertia of the leading vehicle and the trailing vehicle about the Z axis respectively; I 1xz and I 2xz are the yaw-roll products of inertia of the leading vehicle and the trailing vehicle about their centers of mass respectively; a1 is the distance from the first axle of the leading vehicle to the center of mass of the leading vehicle; b1 is the distance from the second axle of the leading vehicle to the center of mass of the leading vehicle; c1 is the distance from the third axle of the leading vehicle to the center of mass of the leading vehicle; d1 is the distance from the center of mass of the leading vehicle to the hinge point between the leading vehicle and the trailing vehicle; d2 is the distance from the center of mass of the trailing vehicle to the hinge point between the leading vehicle and the trailing vehicle; c2 is the distance from the first axle of the trailing vehicle to the center of mass of the trailing vehicle; b2 is the distance from the second axle of the trailing vehicle to the center of mass of the trailing vehicle; a2 is the distance from the third axle of the trailing vehicle to the center of mass of the trailing vehicle; I 1xx and I 2xx are the moments of inertia of the leading vehicle and the trailing vehicle about the X axis respectively; K1 and K2 are the roll stiffnesses of the leading vehicle and the trailing vehicle respectively; C1 and C2 are the roll dampings of the leading vehicle and the trailing vehicle respectively; h 1c and h 2c are the distances from the roll centers of the leading vehicle and the trailing vehicle to the hinge device respectively; φ is the hinge angle; A state equation and observation equation acquisition module for obtaining the state equation and observation equation of the system based on the six-degree-of-freedom dynamics model: Z = CX + DU where X is the state variable of the system; [[M -1 N] is the system matrix; U is the system input variable; [[M -1 P] is the input matrix; Z is the observable variable of the system, which is the lateral acceleration a of the vehicle ahead y1 , and can be measured by on-vehicle sensors; A parameter estimation module for using the improved extended Kalman filter algorithm to solve the established six-degree-of-freedom vehicle dynamics model based on the collected relevant data of the vehicle to be estimated, and obtaining the estimation results of the key parameters of the vehicle.

5. A computer device, characterized in that: It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used to store computer programs; The processor, when executing the programs stored on the memory, realizes the method for estimating the key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by the processor, realizes the method for estimating the key parameters of a six-degree-of-freedom low-speed heavy-duty vehicle as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the key parameter estimation method for a six-degree-of-freedom low-speed heavy vehicle according to any one of claims 1 to 3.

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