Electric four-wheel drive passenger car state parameter estimation method and device and electronic equipment

Abstract

The invention provides an electric four-wheel drive passenger car state parameter estimation method and device and electronic equipment. The electric four-wheel drive passenger car state parameter estimation method comprises the steps that high-degree-of-freedom dynamic models of rotating motion, transverse operation, yawing motion and longitudinal motion of four drive wheels of a car are built; establishing an unscented filtering framework based on the high-degree-of-freedom dynamic model; a time driving interface is reserved in the unscented filtering frame; and based on an interactive multi-model algorithm and an unscented Kalman filtering algorithm, according to the time driving interface, evaluating the state parameters of the vehicle to obtain the state parameters of the vehicle. According to the method, the high-degree-of-freedom vehicle dynamics model is constructed, and unscented Kalman filtering and an interactive multi-model algorithm are combined, so that the vehicle state parameters are accurately evaluated, and the accuracy and robustness of vehicle motion control are improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle design and manufacturing, and in particular to a method, device and electronic equipment for estimating state parameters of an electric four-wheel drive bus. Background Art

[0002] Smart electric vehicles are complex systems with multiple degrees of freedom (DOFs), exhibiting strong coupling and nonlinear characteristics. Their motions encompass not only traditional lateral, longitudinal, and yaw movements, but also involve the electric drive system, energy management system, and complex sensor networks. To effectively analyze and control these motions, high-DOF dynamic models are necessary. These models more accurately reflect the vehicle's actual motion, including the rotational motion of the four drive wheels as well as lateral and longitudinal motion. These models not only improve computational accuracy but also provide a more reliable foundation for subsequent state estimation and control.

[0003] Smart electric vehicles exhibit strong coupling between their multiple degrees of freedom and significant nonlinearity. These characteristics complicate state parameter estimation. Traditional state parameter estimation methods, such as Kalman filtering and extended Kalman filtering, are effective for linear systems. However, their accuracy and robustness are poor for highly nonlinear systems like smart electric vehicles. Furthermore, these traditional methods often require significant computational resources, making them unsuitable for applications requiring high real-time performance. Therefore, there is an urgent need for advanced estimation methods that can address the complex dynamic behavior of smart electric vehicles.

[0004] In summary, as an important part of future transportation, the research on key state parameter estimation methods of smart electric vehicles has important theoretical and practical significance. However, the accuracy and robustness of current state parameter estimation are poor. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, device and electronic equipment for estimating the state parameters of an electric four-wheel drive passenger bus to alleviate the current phenomenon of poor accuracy and robustness of state parameter estimation. By combining unscented Kalman filtering and interacting multiple model algorithm, high-precision state parameter estimation can be achieved under complex driving conditions, providing strong guarantees for the safety and stability of smart electric vehicles.

[0006] In the first aspect, an embodiment of the present invention provides a method for estimating the state parameters of an electric four-wheel drive bus, comprising: constructing a high-degree-of-freedom dynamic model of the vehicle's four driving wheels' rotational motion, lateral operation, yaw motion, and longitudinal motion; establishing an unscented filtering framework based on the above-mentioned high-degree-of-freedom dynamic model; the above-mentioned unscented filtering framework reserves a time-driven interface; based on the interactive multi-model algorithm and the unscented Kalman filtering algorithm, according to the above-mentioned time-driven interface, the state parameters of the above-mentioned vehicle are evaluated to obtain the state parameters of the above-mentioned vehicle.

[0007] In a preferred embodiment of the present invention, the vehicle is in a state where the vehicle is in a state where the vehicle is not subjected to wheel motion caused by road dynamic load, air resistance, and grade resistance. The step of constructing a high-degree-of-freedom dynamic model of the rotational motion, lateral movement, yaw motion, and longitudinal motion of the four drive wheels of the vehicle comprises: constructing a high-degree-of-freedom dynamic model of the longitudinal motion of the four drive wheels based on the following formula: A high degree of freedom dynamic model of the lateral motion of the four driving wheels is constructed based on the following formula: A high-degree-of-freedom dynamic model of the yaw motion of the four driving wheels is constructed based on the following formula: A high-degree-of-freedom dynamic model of the rotational motion of the four driving wheels is constructed based on the following formula:

[0008] J w ·dω ij / dt=T tij -T bij -F xij r w ; Among them, among them, T tij Indicates the driving torque, T bij is the braking torque, r w is the wheel radius, J w is the moment of inertia of the wheel relative to the axis of rotation; d f is the front axle track, d r is the rear axle track, F xij 、F yij 、ω ij They represent the longitudinal force, lateral force and tire speed of the four wheels of the vehicle respectively. The subscripts i=f, r represent the front axle and rear axle of the vehicle respectively. The subscripts j=r, l represent the left wheel and right wheel of the vehicle respectively. fr, fl, rr, rl represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively. m represents the total vehicle mass. s represents the unsprung mass; β and δ represent the vehicle's side slip angle and front wheel turning angle, respectively; a and b are the distances from the center of mass to the front and rear axles of the intelligent electric four-wheel drive bus when it is stationary; v x is the longitudinal acceleration of the vehicle, v y The lateral acceleration of the vehicle, represents the first derivative of the above longitudinal acceleration, represents the first derivative of the yaw rate.

[0009] In a preferred embodiment of the present invention, the step of constructing a high degree of freedom dynamic model of the rotational motion, lateral motion, yaw motion, and longitudinal motion of the four driving wheels of the vehicle further includes: constructing an expression for the vertical load of each tire of the vehicle; including:

[0010]

[0011] Among them, F z represents the vertical force of the vehicle; the subscripts i = f, r represent the front axle and rear axle of the vehicle respectively, the subscripts j = q, p represent the left wheel and right wheel of the vehicle respectively, and thus the subscripts fq, fp, rq, and rp represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle respectively; m, m s 、m uf 、m ur Represents the total vehicle mass, vehicle sprung mass, and vehicle front and rear unsprung mass respectively; C φf 、C φr Respectively represent the front and rear roll stiffness of the vehicle; K φf , K φr Respectively represent the front and rear roll damping coefficients of the vehicle; V x 、V y 、ω z 、ω x They represent the vehicle's longitudinal velocity, lateral velocity, yaw rate, and sideslip rate, respectively; g represents the acceleration due to gravity; h represents the acceleration due to gravity; cg 、h uf 、h ur Respectively represent the height of the vehicle's center of mass and the front and rear wheel offset centers from the ground; respectively represent the height of the vehicle's front and rear roll centers; B f 、B r Respectively represent the front and rear track widths of the vehicle; L f 、L r Respectively represent the front and rear wheelbase of the vehicle; φ represents the roll angle, represents the differential of the above vehicle longitudinal velocity, Express the derivative of the vehicle's lateral velocity above; construct expressions for the linear tire longitudinal and lateral forces; including: Among them, F x 、F y They represent the longitudinal force and lateral force of the four wheels of the vehicle respectively; α and λ represent the tire side slip angle and slip rate respectively; C y 、k μ Indicates tire cornering stiffness and linear region μ-λ ij Image slope.

[0012] In a preferred embodiment of the present invention, the step of constructing a high degree of freedom dynamic model of the rotational motion, lateral motion, yaw motion and longitudinal motion of the four driving wheels of the vehicle further includes: performing nonlinear tire modeling on the tires of the vehicle; the expression of the nonlinear tire modeling is: Y = Dsin{Carctan[B(X+S h )-E(B(X+S h )-arctanB(X+S h ))]}Wherein, Y is the output value, which represents the lateral force or longitudinal force of the vehicle tire; D is the peak parameter, which represents the maximum amplitude of the output value; C is the curvature parameter, which represents the shape of the control output curve; arctan(·) is the inverse tangent function; B is the stiffness factor, which represents the slope of the influence curve; X is the input variable, which is the slip angle or slip rate; S h is the lateral offset, which represents the horizontal displacement of the input variable X; E is the shape factor, which is used to adjust the asymptotic behavior of the curve; when Y is the lateral force of the tire, the lateral force expression of the above tire is: F y =Dsin{Carctan[B(α+S h )-E(B(α+S h )-arctanB(α+S h ))]}+S v Where C = a0, B=a3sin[2arctan(F z / a4)] / CD,E=a6F z +a7, where a0 to a7 are pre-measured tire fitting parameters; when Y is the tire longitudinal force, the longitudinal force expression of the above tire is: F x =Dsin{Carctan[B(s+S h )-E(B(s+S h )-arctanB(s+S h ))]}+S v in,

[0013] Where b0-b8 are pre-measured tire fitting parameters.

[0014] In a preferred embodiment of the present invention, the step of establishing an unscented filtering framework based on the above-mentioned high-degree-of-freedom dynamic model includes: establishing an unscented filtering framework based on the high-degree-of-freedom dynamic model of the longitudinal motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the lateral motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the yaw motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the rotational motion of the above-mentioned four driving wheels, the vertical load of each tire of the above-mentioned vehicle, the expression of the above-mentioned nonlinear tire modeling, the lateral force expression of the above-mentioned tire, and the longitudinal force expression of the above-mentioned tire.

[0015] In a preferred embodiment of the present invention, based on the high degree of freedom dynamic model, the step of establishing an unscented filtering framework includes: the state equation of the unscented Kalman filtering process is: The system evolution equation of the unscented Kalman filter process is:

[0016] x k =f k (x k-1 ,u k )+W k

[0017] Where W k Represents the noise in the system evolution process; the observation equation of the unscented Kalman filter process is: Where v x ,v y represents the lateral and longitudinal velocities of the vehicle measured by the sensor; Q represents the noise in the observation process, represents the first derivative of β.

[0018] In a preferred embodiment of the present invention, based on the interacting multiple model algorithm and the unscented Kalman filter algorithm, the state parameters of the vehicle are evaluated according to the time-driven interface to obtain the state parameters of the vehicle, including: combining the model probabilities of the expressions of the linear tire longitudinal force and lateral force and the expression of the nonlinear tire modeling with the state estimate of a preset target to obtain a mixed state estimate; naming the expressions of the linear tire longitudinal force and lateral force as model i, and naming the expression of the nonlinear tire modeling as model j; wherein the model transfer matrix of the expressions of the linear tire longitudinal force and lateral force and the expression of the nonlinear tire modeling is: Among them, P is the transfer matrix of the above model, and P in the transfer matrix ij The element is the probability of the target moving from the i-th model to the j-th model. The subscript r=j of the element in the transfer matrix P is r, where r is the number of models in the preset prediction model set. The above prediction model is derived from the above model j as follows: Among them, μ j(k-1) represents the model probability of the above model j at time k-1, represents the predicted probability of the above model j, is the summation operation of the above model i from 1 to r; the expression of the mixing probability from the above model i to the above model j is: Among them, μ ij (k-1|k-1) represents the mixing probability from the above model i to the above model j; the expression for the mixed state estimation from the above model j is: in, is the state estimation of the above target, is the above-mentioned mixed state estimate of the above-mentioned model j; according to the model probability and state estimate of all filters, the state estimate of the above-mentioned target is determined to have a covariance; wherein, the mixed covariance estimate comes from the expression of model j: Where T represents the transpose of the matrix, represents the initial state of the covariance of model j, represents the covariance of model i; and determining the state parameters of the vehicle based on the covariance and the mixed state estimation.

[0019] In the second aspect, an embodiment of the present invention also provides a state parameter estimation device for an electric four-wheel drive bus, including: a model construction module for constructing a high-degree-of-freedom dynamic model of the vehicle's four driving wheels' rotational motion, lateral operation, yaw motion and longitudinal motion; a framework establishment module for establishing an unscented filtering framework based on the above-mentioned high-degree-of-freedom dynamic model; the above-mentioned unscented filtering framework reserves a time-driven interface; a state parameter evaluation module for evaluating the state parameters of the above-mentioned vehicle according to the above-mentioned time-driven interface based on the interactive multi-model algorithm and the unscented Kalman filter algorithm to obtain the state parameters of the above-mentioned vehicle.

[0020] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the electric four-wheel drive bus state parameter estimation method.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, characterized in that the computer storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the electric four-wheel drive bus state parameter estimation method.

[0022] The embodiments of the present invention have the following beneficial technical effects:

[0023] An embodiment of the present invention provides a method, device, and electronic device for estimating the state parameters of an electric four-wheel drive bus, comprising: constructing a high-degree-of-freedom dynamic model of the vehicle's four drive wheels' rotational motion, lateral movement, yaw motion, and longitudinal motion; establishing an unscented filtering framework based on the high-degree-of-freedom dynamic model; the unscented filtering framework reserves a time-driven interface; and evaluating the state parameters of the vehicle based on the time-driven interface based on an interactive multi-model algorithm and an unscented Kalman filter algorithm to obtain the state parameters of the vehicle. This method achieves accurate evaluation of vehicle state parameters by constructing a high-degree-of-freedom vehicle dynamic model and combining unscented Kalman filtering and an interactive multi-model algorithm, thereby improving the accuracy and robustness of vehicle motion control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 A schematic flow chart of a method for estimating state parameters of an electric four-wheel drive bus provided by an embodiment of the present invention;

[0026] Figure 2 A schematic structural diagram of a state parameter estimation device for an electric four-wheel drive bus provided by an embodiment of the present invention;

[0027] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0028] Icons: 31 - model building module; 32 - framework building module; 33 - state parameter evaluation module; 41 - memory; 42 - processor; 43 - bus; 44 - communication interface. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0030] As an important part of future transportation, the research on key state parameter estimation methods of smart electric vehicles has important theoretical and practical significance. However, the accuracy and robustness of current state parameter estimation are poor.

[0031] Based on this, embodiments of the present invention provide a method, device, and electronic device for estimating the state parameters of an electric four-wheel-drive bus. This method accurately assesses vehicle state parameters by constructing a high-degree-of-freedom vehicle dynamics model and combining it with an unscented Kalman filter and an interacting multiple model algorithm, thereby improving the accuracy and robustness of vehicle motion control. To facilitate understanding, a method for estimating the state parameters of an electric four-wheel-drive bus is first introduced.

[0032] Example 1

[0033] In this embodiment, Figure 1 A schematic flow chart of a method for estimating state parameters of an electric four-wheel drive bus provided by an embodiment of the present invention.

[0034] Depend on Figure 1 As can be seen, the method includes:

[0035] Step S101: constructing a high degree of freedom dynamic model of the vehicle's four driving wheels' rotational motion, lateral movement, yaw motion, and longitudinal motion.

[0036] Step S102: Based on the high-degree-of-freedom dynamics model, an unscented filtering framework is established; the unscented filtering framework reserves a time-driven interface.

[0037] Step S103: Based on the interacting multiple model algorithm and the unscented Kalman filter algorithm, according to the time drive interface, the state parameters of the vehicle are evaluated to obtain the state parameters of the vehicle.

[0038] In the embodiment of the invention, it is assumed that the road surface is flat and has no effect on the vertical movement of the wheels, thereby ignoring the wheel movement caused by the road surface dynamic load, as well as air resistance and grade resistance, and ignoring the effects of torsional vibration and yaw. That is, the state of the vehicle is the wheel movement not caused by the road surface dynamic load, air resistance and grade resistance. The step of constructing a high-degree-of-freedom dynamic model of the rotational motion, lateral operation, yaw motion and longitudinal motion of the four driving wheels of the vehicle includes: constructing a high-degree-of-freedom dynamic model of the longitudinal motion of the four driving wheels based on the following formula:

[0039]

[0040] A high degree of freedom dynamic model of the lateral motion of the four driving wheels is constructed based on the following formula:

[0041]

[0042] A high-degree-of-freedom dynamic model of the yaw motion of the four driving wheels is constructed based on the following formula:

[0043]

[0044] A high-degree-of-freedom dynamic model of the rotational motion of the four driving wheels is constructed based on the following formula:

[0045] J w ·dω ij / dt=T tij -T bij -F xij r w ;

[0046] Among them, T tij Indicates the driving torque, T bij is the braking torque, r w is the wheel radius, J w is the moment of inertia of the wheel relative to the axis of rotation; d f is the front axle track, d r is the rear axle track, F xij 、F yij 、ω ij They represent the longitudinal force, lateral force and tire speed of the four wheels of the vehicle respectively. The subscripts i=f, r represent the front axle and rear axle of the vehicle respectively. The subscripts j=r, l represent the left wheel and right wheel of the vehicle respectively. fr, fl, rr, rl represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively. m represents the total vehicle mass. s represents the unsprung mass; β and δ represent the vehicle's side slip angle and front wheel turning angle, respectively; a and b are the distances from the center of mass to the front and rear axles of the intelligent electric four-wheel drive bus when it is stationary; v x is the longitudinal acceleration of the vehicle, v y The lateral acceleration of the vehicle, represents the first derivative of the above longitudinal acceleration, represents the first derivative of the yaw rate.

[0047] Furthermore, the interaction between the tire and the ground is crucial to the performance and safety of the vehicle. The wheel dynamics equations here can reflect the impact of wheel load transfer caused by the vehicle's longitudinal and lateral angular velocity on the vertical load.

[0048] Therefore, the steps of constructing a high degree of freedom dynamic model of the vehicle's four driving wheels' rotational motion, lateral movement, yaw motion, and longitudinal motion also include:

[0049] Construct an expression for the vertical load on each tire of the vehicle above; this includes:

[0050]

[0051] Among them, F zrepresents the vertical force of the vehicle; the subscripts i = f, r represent the front axle and rear axle of the vehicle respectively, the subscripts j = q, p represent the left wheel and right wheel of the vehicle respectively, and thus the subscripts fq, fp, rq, and rp represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle respectively; m, m s 、m uf 、m ur Represents the total vehicle mass, vehicle sprung mass, and vehicle front and rear unsprung mass respectively; C φf 、C φr Respectively represent the front and rear roll stiffness of the vehicle; K φf , K φr Respectively represent the front and rear roll damping coefficients of the vehicle; V x 、V y 、ω z 、ω x They represent the vehicle's longitudinal velocity, lateral velocity, yaw rate, and sideslip rate, respectively; g represents the acceleration due to gravity; h represents the acceleration due to gravity; cg 、h uf 、h ur Respectively represent the height of the vehicle's center of mass and the front and rear wheel offset centers from the ground; respectively represent the height of the vehicle's front and rear roll centers; B f 、B r Respectively represent the front and rear track widths of the vehicle; L f 、L r Respectively represent the front and rear wheelbase of the vehicle; φ represents the roll angle, represents the differential of the above vehicle longitudinal velocity, represents the differential of the above vehicle lateral velocity;

[0052] Construct expressions for the linear tire longitudinal and lateral forces; these include:

[0053] F yi =-C y α

[0054] F x =k μ λ i F zi

[0055] Among them, F x 、F y They represent the longitudinal force and lateral force of the four wheels of the vehicle respectively; α and λ represent the tire side slip angle and slip rate respectively; C y 、k μ Indicates tire cornering stiffness and linear region μ-λ ij Image slope.

[0056] In actual operation, the "magic formula" tire model is widely used in vehicle dynamics analysis due to its high fitting accuracy and convenient modeling process.

[0057] Furthermore, the step of constructing a high degree of freedom dynamic model of the rotational motion, lateral movement, yaw motion, and longitudinal motion of the four driving wheels of the vehicle further includes: performing nonlinear tire modeling on the tires of the vehicle; the expression of the nonlinear tire modeling is:

[0058] Y=Dsin{Carctan[B(X+S h )-E(B(X+S h )-arctanB(X+S h ))]}

[0059] Where Y is the output value, which represents the lateral force or longitudinal force of the vehicle tire; D is the peak parameter, which represents the maximum amplitude of the output value; C is the curvature parameter, which represents the shape of the control output curve; arctan(·) is the inverse tangent function; B is the stiffness factor, which represents the slope of the influence curve; X is the input variable, which is the slip angle or slip rate; S h is the lateral offset, which represents the horizontal displacement of the input variable X; E is the shape factor, which is used to adjust the asymptotic behavior of the curve;

[0060] When Y is the tire lateral force, the lateral force expression of the above tire is:

[0061] F y =Dsin{Carctan[B(α+S h )-E(B(α+S h )-arctanB(α+S h ))]}+S v Where C = a0,

[0062] B=a3sin[2arctan(F z / a4)] / CD,E=a6F z +a7, where a0 to a7 are pre-measured tire fitting parameters;

[0063] When Y is the longitudinal force of the tire, the longitudinal force expression of the above tire is:

[0064] F x =Dsin{Carctan[B(s+S h )-E(B(s+S h )-arctanB(s+S h ))]}+S v

[0065] in, Where b0-b8 are pre-measured tire fitting parameters.

[0066] Furthermore, an unscented filtering framework is established and an event-driven interface is reserved, so that the intelligent electric four-wheel drive bus can complete accurate state parameter estimation even under complex driving conditions.

[0067] Here, based on the above-mentioned high-degree-of-freedom dynamic model, the steps of establishing an unscented filtering framework include: establishing an unscented filtering framework based on the high-degree-of-freedom dynamic model of the longitudinal motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the lateral motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the yaw motion of the above-mentioned four driving wheels, the high-degree-of-freedom dynamic model of the rotational motion of the above-mentioned four driving wheels, the vertical load of each tire of the above-mentioned vehicle, the expression of the above-mentioned nonlinear tire modeling, the lateral force expression of the above-mentioned tire, and the longitudinal force expression of the above-mentioned tire.

[0068] Among them, based on the above high degree of freedom dynamic model, the steps of establishing an unscented filtering framework include: the state equation of the above unscented Kalman filtering process is:

[0069]

[0070] The system evolution equation of the unscented Kalman filter process is:

[0071] x k =f k (x k-1 ,u k )+W k

[0072]

[0073] Where W k represents the noise in the system evolution process;

[0074] The observation equation of the unscented Kalman filter process is:

[0075]

[0076] Where v x ,v y represents the lateral and longitudinal velocities of the vehicle measured by the sensor; Q represents the noise in the observation process, represents the first derivative of β.

[0077] In this embodiment, the input interaction is used to combine the model probability of the above-mentioned linear tire longitudinal force and lateral force expressions and the above-mentioned nonlinear tire modeling expressions with the state estimation Get a mixed estimate

[0078] Specifically, based on the interacting multiple model algorithm and the unscented Kalman filter algorithm, according to the time-driven interface, the state parameters of the vehicle are evaluated to obtain the state parameters of the vehicle, including: combining the model probabilities of the expressions of the linear tire longitudinal force and lateral force and the expression of the nonlinear tire modeling with the state estimate of a preset target to obtain a mixed state estimate; naming the expressions of the linear tire longitudinal force and lateral force as model i, and naming the expression of the nonlinear tire modeling as model j; wherein the model transfer matrix of the expressions of the linear tire longitudinal force and lateral force and the expression of the nonlinear tire modeling is:

[0079]

[0080] Among them, P is the transfer matrix of the above model, and P in the transfer matrix ij The element is the motion transfer probability of the target from the i-th model to the j-th model. The subscript r=j of the element in the transfer matrix P, where r is the number of models in the preset prediction model set;

[0081] The above prediction model comes from the expression of the above model j:

[0082]

[0083] Among them, μ j (k-1) represents the model probability of the above model j at time k-1, represents the predicted probability of the above model j, is the summation operation from 1 to r for the above model i;

[0084] The expression of the mixing probability from the above model i to the above model j is:

[0085]

[0086] Among them, μ ij (k-1|k-1) represents the mixing probability from the above model i to the above model j;

[0087] The mixed state estimation comes from the expression of the above model j:

[0088]

[0089] in, is the state estimation of the above target, is the above mixed state estimate of the above model j;

[0090] Determine the covariance based on the model probabilities of all filters and the state estimates of the above targets;

[0091] Among them, the mixed covariance estimate comes from model j and is expressed as:

[0092]

[0093] Where T represents the transpose of the matrix, represents the initial state of the covariance of model j, represents the covariance of model i;

[0094] The state parameters of the vehicle are determined based on the covariance and the mixed state estimation.

[0095] Specifically, the state parameters of the vehicle include: the yaw rate, the sideslip angle of the center of mass, and the vehicle speed of the vehicle.

[0096] In actual operation, this application first considers that a vehicle is a complex system with multiple degrees of freedom, exhibiting strong coupling and nonlinear characteristics. The vehicle's lateral, longitudinal, and yaw motions are crucial to its stability. To effectively analyze and control these motions, a high-degree-of-freedom dynamic model is first established, encompassing the rotational motion of the four drive wheels as well as the lateral and longitudinal motions. Secondly, an unscented filtering framework is established based on the high-degree-of-freedom dynamic model. This framework provides an event-driven interface, enabling accurate state parameter estimation for the intelligent electric four-wheel drive bus under complex driving conditions. The establishment of this filtering framework lays the foundation for subsequent state estimation and control. Finally, based on the unscented filtering framework, an interactive multi-model algorithm combined with an unscented Kalman filter is used to accurately estimate the key state parameters of the intelligent electric four-wheel drive bus. By constructing an input interaction process, the system can switch between different models, further improving the accuracy and robustness of state estimation. The event-driven method for estimating key state parameters of the intelligent electric four-wheel drive bus proposed in this application improves the accuracy and reliability of vehicle state estimation and optimizes the control and safety of the intelligent electric four-wheel drive bus.

[0097] An embodiment of the present invention provides a method for estimating the state parameters of an electric four-wheel drive bus, comprising: constructing a high-degree-of-freedom dynamic model of the vehicle's four drive wheels' rotational motion, lateral movement, yaw motion, and longitudinal motion; establishing an unscented filtering framework based on the high-degree-of-freedom dynamic model; the unscented filtering framework reserves a time-driven interface; and evaluating the state parameters of the vehicle based on the time-driven interface using an interactive multi-model algorithm and an unscented Kalman filter algorithm to obtain the state parameters of the vehicle. By constructing a high-degree-of-freedom vehicle dynamic model and combining the unscented Kalman filter and the interactive multi-model algorithm, this method achieves accurate evaluation of the vehicle's state parameters, thereby improving the accuracy and robustness of vehicle motion control.

[0098] Example 2

[0099] Based on the above embodiments, Figure 2 A schematic structural diagram of a device for estimating state parameters of an electric four-wheel drive bus provided by an embodiment of the present invention.

[0100] Depend on Figure 2 As can be seen, the method includes:

[0101] The model building module 31 is used to build a high-degree-of-freedom dynamic model of the vehicle's four driving wheels' rotational motion, lateral movement, yaw motion, and longitudinal motion.

[0102] The framework establishment module 32 is used to establish an unscented filtering framework based on the high-degree-of-freedom dynamics model. The unscented filtering framework reserves a time-driven interface.

[0103] The state parameter evaluation module 33 is used to evaluate the state parameters of the vehicle based on the interacting multiple model algorithm and the unscented Kalman filter algorithm according to the time drive interface to obtain the state parameters of the vehicle.

[0104] The model building module 31 , the framework building module 32 and the state parameter evaluation module 33 are connected in sequence.

[0105] The electric four-wheel drive bus state parameter estimation device provided in the embodiment of the present invention has the same technical features as the electric four-wheel drive bus state parameter estimation method provided in the above embodiment, and therefore can solve the same technical problems and achieve the same technical effects. Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the above method embodiment, and will not be repeated here.

[0106] Example 3

[0107] This embodiment provides an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of a method for estimating state parameters of an electric four-wheel drive bus.

[0108] This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for estimating state parameters of an electric four-wheel drive bus.

[0109] See also Figure 3The structure diagram of an electronic device shown in the figure includes: a memory 41 and a processor 42. The memory 41 stores a computer program that can be run on the processor 42. When the processor executes the computer program, the steps provided by the above-mentioned electric four-wheel drive bus state parameter estimation method are implemented.

[0110] like Figure 3 As shown, the device further includes: a bus 43 and a communication interface 44, and a processor 42, a communication interface 44 and a memory 41 are connected via the bus 43; the processor 42 is used to execute executable modules stored in the memory 41, such as computer programs.

[0111] The memory 41 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage. The system network element communicates with at least one other network element via at least one communication interface 44 (which may be wired or wireless), and may utilize the Internet, a wide area network, a local area network, a metropolitan area network, or the like.

[0112] The bus 43 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0113] The memory 41 is used to store programs, and the processor 42 executes the programs after receiving execution instructions. The method performed by the electric four-wheel drive bus state parameter estimation device disclosed in any of the aforementioned embodiments of the present invention can be applied to or implemented by the processor 42. The processor 42 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 42 or software instructions. The processor 42 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processing unit (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 41, and processor 42 reads the information in memory 41 and, in conjunction with its hardware, completes the steps of the method described above.

[0114] Furthermore, an embodiment of the present invention also provides a machine-readable storage medium, which stores machine-executable instructions. When the machine-executable instructions are called and executed by the processor 42, the machine-executable instructions prompt the processor 42 to implement the above-mentioned electric four-wheel drive bus state parameter estimation method.

[0115] The electronic device and computer-readable storage medium provided by the embodiments of the present invention have the same technical features, and therefore can solve the same technical problems and achieve the same technical effects.

[0116] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0117] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A method for estimating state parameters of an electric four-wheel drive bus, characterized in that: include: Construct a high-degree-of-freedom dynamic model of the vehicle's four driving wheels' rotation, lateral movement, yaw movement, and longitudinal movement; Based on the high degree of freedom dynamics model, an unscented filtering framework is established; The traceless filtering framework reserves a time-driven interface; Based on the interacting multiple model algorithm and the unscented Kalman filter algorithm, the state parameters of the vehicle are evaluated according to the time drive interface to obtain the state parameters of the vehicle.

2. The method for estimating state parameters of an electric four-wheel drive bus according to claim 1, characterized in that: The state of the vehicle is the wheel movement not caused by road dynamic load, air resistance and slope resistance; The steps to construct a high-degree-of-freedom dynamic model of the vehicle's four driven wheels' rotational motion, lateral motion, yaw motion, and longitudinal motion include: A high degree of freedom dynamic model of the longitudinal motion of the four driving wheels is constructed based on the following formula: A high degree of freedom dynamic model of the lateral motion of the four driving wheels is constructed based on the following formula: The high degree of freedom dynamic model of the yaw motion of the four driving wheels is constructed based on the following formula: A high degree of freedom dynamic model of the rotational motion of the four driving wheels is constructed based on the following formula: J w ·dω ij / dt=T tij -T bij -F xij r w ; Among them, T tij Indicates the driving torque, T bij is the braking torque, r w is the wheel radius, J w is the moment of inertia of the wheel relative to the axis of rotation; d f is the front axle track, d r is the rear axle track, F xij 、F yij ,ω ij They represent the longitudinal force, lateral force and tire speed of the four wheels of the vehicle respectively. The subscripts i=f, r represent the front axle and rear axle of the vehicle respectively. The subscripts j=r, l represent the left wheel and right wheel of the vehicle respectively. fr, fl, rr, rl represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively. m represents the mass of the vehicle. s represents the unsuspended mass; β and δ represent the vehicle's center of mass side slip angle and front wheel turning angle, respectively; a and b are the distances from the center of mass to the front and rear axles of the intelligent electric four-wheel drive bus when it is stationary; v x is the longitudinal acceleration of the vehicle, v y The lateral acceleration of the vehicle, represents the first derivative of the longitudinal acceleration, represents the first-order derivative of the yaw rate.

3. The method for estimating state parameters of an electric four-wheel drive bus according to claim 2, characterized in that: The steps of constructing a high degree of freedom dynamic model of the rotational motion, lateral motion, yaw motion and longitudinal motion of the four driving wheels of the vehicle also include: Construct an expression for the vertical load on each tire of the vehicle; including: Among them, F z represents the vertical force of the vehicle; the subscripts i = f, r represent the front axle and rear axle of the vehicle respectively, the subscripts j = q, p represent the left wheel and right wheel of the vehicle respectively, and thus the subscripts fq, fp, rq, and rp represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle respectively; m, m s 、m uf 、m ur Respectively represent the total vehicle mass, the vehicle sprung mass, and the front and rear unsprung masses of the vehicle; C φf , C φr Respectively represent the front and rear roll stiffness of the vehicle; K φf , K φr Respectively represent the front and rear roll damping coefficients of the vehicle; V x 、V y ,ω z ,ω x They represent the vehicle longitudinal velocity, vehicle lateral velocity, vehicle yaw angular velocity and vehicle sideslip angular velocity respectively; g represents the acceleration due to gravity; h represents the acceleration due to gravity; cg 、h uf 、h ur Respectively represent the height of the vehicle's center of mass and the front and rear wheel offset centers from the ground; respectively represent the height of the front and rear roll centers of the vehicle; B f , B r Respectively represent the front and rear track widths of the vehicle; L f , L r Respectively represent the front and rear wheelbases of the vehicle; φ represents the roll angle, represents the differential of the longitudinal velocity of the vehicle, represents the differential of the lateral velocity of the vehicle; Construct expressions for the linear tire longitudinal and lateral forces; these include: F yi =-C y a F x =k μ l i F zi Among them, F x 、F y They represent the longitudinal force and lateral force of the four wheels of the vehicle respectively; α and λ represent the tire side slip angle and slip rate respectively; C y , k μ Indicates tire cornering stiffness and linear region μ-λ ij The slope of the image.

4. The method for estimating state parameters of an electric four-wheel drive bus according to claim 3, characterized in that: The steps of constructing a high degree of freedom dynamic model of the rotational motion, lateral motion, yaw motion and longitudinal motion of the four driving wheels of the vehicle also include: performing nonlinear tire modeling on tires of the vehicle; The expression of the nonlinear tire modeling is: Y=D sin{Carctan[B(X+S h )-E(B(X+S h )-arctan B(X+S h ))]} Among them, Y is the output value, which represents the lateral force or longitudinal force of the vehicle tire; D is the peak parameter, which represents the maximum amplitude of the output value; C is the curvature parameter, which represents the shape of the control output curve; arctan(·) is the inverse tangent function; B is the stiffness factor, which represents the slope of the influence curve; X is the input variable, which is the slip angle or slip rate; S h is the lateral offset, which represents the horizontal displacement of the input variable X; E is the shape factor, which is used to adjust the asymptotic behavior of the curve; When Y is the tire lateral force, the tire lateral force expression is: F y =D sin{Carctan[B(α+S h )-E(B(α+S h )-arctan B(α+S h ))]}+S v Where C = a0, B=a3 sin [2arctan(F z / a4)] / CD,E=a6F z +a7, where a0 to a7 are pre-measured tire fitting parameters; When Y is the longitudinal force of the tire, the longitudinal force expression of the tire is: F x =D sin{Carctan[B(s+S h )-E(B(s+S h )-arctan B(s+S h ))]}+S v Where C = b0, Where b0-b8 are pre-measured tire fitting parameters.

5. The method for estimating state parameters of an electric four-wheel drive bus according to claim 4, characterized in that: Based on the high degree of freedom dynamics model, the steps of establishing an unscented filtering framework include: An unscented filtering framework is established based on the high degree of freedom dynamic model of the longitudinal motion of the four driving wheels, the high degree of freedom dynamic model of the lateral motion of the four driving wheels, the high degree of freedom dynamic model of the yaw motion of the four driving wheels, the high degree of freedom dynamic model of the rotational motion of the four driving wheels, the vertical load of each tire of the vehicle, the expression of the nonlinear tire modeling, the lateral force expression of the tire and the longitudinal force expression of the tire.

6. The method for estimating state parameters of an electric four-wheel drive bus according to claim 5, characterized in that: Based on the high degree of freedom dynamics model, the steps of establishing an unscented filtering framework include: The state equation of the unscented Kalman filter process is: The system evolution equation of the unscented Kalman filter process is: x k =f k (x k-1 ,u k )+W k Where W k represents the noise in the system evolution process; The observation equation of the unscented Kalman filter process is: Where v x ,v y represents the lateral and longitudinal velocities of the vehicle measured by the sensor; Q represents the noise in the observation process, represents the first derivative of β.

7. The method for estimating state parameters of an electric four-wheel drive bus according to claim 6, characterized in that: Based on the interacting multiple model algorithm and the unscented Kalman filter algorithm, the state parameters of the vehicle are evaluated according to the time drive interface to obtain the state parameters of the vehicle, including: The model probability of the expression of the linear tire longitudinal force and the lateral force and the expression of the nonlinear tire modeling are combined with the state estimation of the preset target to obtain a mixed state estimation; The expression of the linear tire longitudinal force and lateral force is named model i, and the expression of the nonlinear tire modeling is named model j; The model transfer matrix of the expression of the linear tire longitudinal force and lateral force and the expression of the nonlinear tire modeling is: Wherein, P is the transfer matrix of the model, and P in the transfer matrix ij The element is the motion transfer probability of the target from the i-th model to the j-th model. The subscript r=j of the element in the transfer matrix P, where r is the number of models in the preset prediction model set; The prediction model comes from the expression of the model j: Among them, μ j (k-1) represents the model probability of the model j at time k-1, represents the predicted probability of the model j, is the summation operation of the model i from 1 to r; The expression of the mixing probability from the model i to the model j is: Among them, μ ij (k-1|k-1) represents the mixing probability from the model i to the model j; The mixed state estimate comes from the expression of the model j as: in, is the state estimate of the target, is the mixed state estimate of the model j; Determining a covariance based on the model probabilities of all filters and the state estimate of the target; Among them, the mixed covariance estimate comes from the expression of model j: Where T represents the transpose of the matrix, represents the initial state of the covariance of model j, represents the covariance of model i; A state parameter of the vehicle is determined based on the covariance and the mixed state estimate.

8. A state parameter estimation device for an electric four-wheel drive passenger car, characterized in that: include: Model building module, used to build a high-degree-of-freedom dynamic model of the vehicle's four driving wheel rotation, lateral movement, yaw movement, and longitudinal movement; A framework building module, used to build an untraceable filtering framework based on the high-degree-of-freedom dynamics model; the untraceable filtering framework reserves a time drive interface; The state parameter evaluation module is used to evaluate the state parameters of the vehicle based on the interacting multiple model algorithm and the unscented Kalman filter algorithm according to the time drive interface to obtain the state parameters of the vehicle.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method for estimating state parameters of an electric four-wheel drive bus according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which includes program instructions. When the program instructions are executed by the processor, the processor executes the method for estimating state parameters of an electric four-wheel drive bus as described in any one of claims 1 to 7.

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