Four-wheel-drive vehicle speed estimation method, device and equipment and storage medium

Abstract

The invention discloses a four-wheel-drive vehicle speed estimation method, device and equipment and a storage medium, and the method comprises the steps: building a kinetic model of a current four-wheel-drive vehicle, carrying out the parameter calibration of the kinetic model, and obtaining a calibrated target kinetic model; a current road adhesion coefficient estimation value is obtained, non-slip wheels of the current four-wheel-drive vehicle are determined, the weights of the non-slip wheels are corrected according to the current working condition, and the current estimated vehicle speed of the current four-wheel-drive vehicle is obtained; the speed of the current four-wheel-drive vehicle is corrected based on the MPC and the current estimated speed, the method is applied to the target dynamic model to obtain the final speed of the current four-wheel-drive vehicle, the speed is corrected by combining the digital twin technology and the MPC algorithm at the same time, error accumulation in a traditional speed estimation method is effectively reduced, and the speed estimation accuracy is improved. The method improves the accuracy and reliability of vehicle speed estimation, can adapt to different vehicle working conditions and driving environments, has high adaptability, and improves the speed and efficiency of vehicle speed estimation of the four-wheel-drive vehicle.

Description

Technical Field

[0001] The present invention relates to the field of vehicle dynamics and control technology, and in particular to a method, device, equipment and storage medium for estimating the speed of a four-wheel drive vehicle. Background Art

[0002] A vehicle's longitudinal and lateral speeds are key parameters in vehicle dynamics control. Accurately estimating speed is crucial to the performance of the vehicle's active safety systems (such as the Anti-lock Braking System (ABS) and Electronic Stability Program (ESP)) and autonomous driving systems. Traditional speed estimation methods rely primarily on hardware devices such as wheel speed sensors, accelerometers, and gyroscopes, but these methods have certain limitations. For example, wheel speed sensors provide inaccurate speed information when the tires slip, while accelerometers and gyroscopes suffer from integral drift, leading to the accumulation of speed estimation errors over time.

[0003] Digital twin technology creates virtual models of physical entities, mapping their states and behaviors in real time. In the automotive sector, digital twin technology can provide more accurate and reliable information for vehicle state estimation and control. However, there is currently no effective method for estimating the longitudinal and lateral speeds of four-wheel drive vehicles based on digital twin technology, combined with wheel speed coupling judgment under multiple operating conditions and model predictive control (MPC) algorithms. Summary of the Invention

[0004] The main purpose of the present invention is to provide a four-wheel drive vehicle speed estimation method, device, equipment and storage medium, aiming to solve the technical problem that the vehicle speed estimation algorithm in the existing technology relies on hardware devices such as wheel speed sensors, accelerometers and gyroscopes, has limitations, and easily leads to long-term accumulation of vehicle speed estimation errors and inaccurate vehicle speed prediction.

[0005] In a first aspect, the present invention provides a method for estimating the speed of a four-wheel drive vehicle, the method comprising the following steps: Establishing a dynamic model of the current four-wheel drive vehicle, calibrating parameters of the dynamic model, and obtaining a calibrated target dynamic model; Obtaining a current road surface adhesion coefficient estimate, determining a non-slipping wheel of the current four-wheel drive vehicle based on the current road surface adhesion coefficient estimate, and modifying a weight of the non-slipping wheel based on a current operating condition to obtain a current estimated vehicle speed of the current four-wheel drive vehicle; The model predictive control (MPC) and the current estimated vehicle speed are used to correct the vehicle speed of the current four-wheel drive vehicle, and the corrected vehicle speed of the current four-wheel drive vehicle is obtained by applying the target dynamics model.

[0006] Optionally, the method further comprises: The longitudinal dynamics model of the current four-wheel drive vehicle is established by the following formula:

[0007] wherein, is the vehicle mass, is the vehicle longitudinal speed, is the vehicle longitudinal total driving force or braking force, is the rolling resistance, , is the rolling resistance coefficient, is the gravity acceleration, is the road slope, is the air resistance, , is the air density, is the air resistance coefficient, is the vehicle frontal area; The lateral dynamics model of the current four-wheel drive vehicle is established by the following formula:

[0008]

[0009] wherein, is the vehicle lateral speed, is the vehicle yaw rate, is the lateral force of the front wheel, is the lateral force of the rear wheel, is the front wheel steering angle, is the vehicle moment of inertia around the z axis, is the distance from the vehicle center of mass to the front axle, is the distance from the vehicle center of mass to the rear axle. The tire dynamics model of the current four-wheel drive vehicle is established by using the magic formula tire model. The longitudinal dynamics model, the lateral dynamics model and the tire dynamics model are parameter calibrated and model calibrated to obtain the calibrated target dynamics model.

[0010] Optionally, the parameter calibration and model calibration of the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model to obtain the calibrated target dynamics model comprises: calibrating a vehicle mass, a rolling resistance coefficient, an air resistance coefficient, and a vehicle frontal area in the longitudinal dynamics model; calibrating a moment of inertia of the vehicle about the z-axis, a distance of the vehicle center of mass to the front axle, and a distance of the vehicle center of mass to the rear axle in the lateral dynamics model; calibrating a stiffness factor, a shape factor, a peak factor, and a curvature factor in the tire dynamics model; model calibrating the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model by least squares method to minimize an error function, to obtain a calibrated target dynamics model.

[0011] Optionally, the model calibrating the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model by least squares method to minimize an error function, to obtain a calibrated target dynamics model comprises: model calibrating the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model by least squares method to minimize an error function, to obtain a calibrated target dynamics model:

[0012] wherein, is an error function, is a model parameter, is a sample number, is a true value of an i-th sample, is a predicted value of the i-th sample when the model parameter is is a column vector of true values, is a column vector of predicted values, denotes a transpose operation; deriving a gradient of the error function with respect to the model parameter, and setting it to zero:

[0013] wherein, is a gradient vector of the error function with respect to the model parameter, is a transpose of a Jacobian matrix of the error function with respect to the model parameter, is a column vector of true values, is a column vector of predicted values; the minimization of the error function is achieved by an iterative algorithm as follows: ​​​​​​​​

[0014] in, For the The updated parameters after iterations, For the The updated parameters after iterations, is the number of iterations, is the Jacobian matrix of the predicted values ​​to the parameters, is the transpose of the Jacobian matrix, is a column vector of real values, For the A column vector of predicted values ​​for the iterations, starting from the initial parameter vector Start by continuously updating the parameter vector until the convergence condition is met ( ,in is the preset convergence threshold).

[0015] Optionally, obtaining a current estimated road surface adhesion coefficient, determining a non-slipping wheel of the current four-wheel drive vehicle based on the current estimated road surface adhesion coefficient, and modifying a weight of the non-slipping wheel based on a current operating condition to obtain a current estimated vehicle speed of the current four-wheel drive vehicle includes: Obtaining the current tire force of the four-wheel drive vehicle, and determining a current road adhesion coefficient estimate based on the tire force; Determining a current slip state of the four-wheel drive vehicle according to the current road adhesion coefficient estimation value, and determining a slipping wheel of the current four-wheel drive vehicle according to the slip state; The weight of the non-slip wheel of the current four-wheel drive vehicle is corrected according to the current working condition of the current four-wheel drive vehicle, and the current estimated vehicle speed of the current four-wheel drive vehicle is obtained.

[0016] Optionally, obtaining the current tire force of the four-wheel drive vehicle and determining the current road adhesion coefficient estimate according to the tire force includes: The wheel speed of each wheel is obtained by the wheel speed sensor of the current four-wheel drive vehicle, and the tire slip rate is calculated according to the wheel speed of each wheel using the following formula:

[0017] in, For the The slip rate of each tire, is the wheel speed, =1,2,3,4, respectively represent the four wheels, is the vehicle longitudinal speed; The longitudinal tire force of each wheel is obtained by the following formula:

[0018] in, is the longitudinal tire force of each wheel, is a peak factor, is a shape factor, is a stiffness factor, is a curvature factor, is a slip ratio of the i-th tire;

[0019] wherein, is a total longitudinal driving or braking force of the vehicle, is a longitudinal tire force of each wheel; obtaining the front wheel side force and the rear wheel side force of the current four-wheel drive vehicle by matrixing a lateral dynamics model of the current four-wheel drive vehicle and solving according to a least square method, and determining the lateral tire force of the current four-wheel drive vehicle according to the front wheel side force and the rear wheel side force; obtaining the vertical load of the current four-wheel drive vehicle, and obtaining a current road surface adhesion coefficient estimate value according to the longitudinal tire force, the lateral tire force and the vertical load according to calculation by the following formula:

[0020] wherein, is a current road surface adhesion coefficient estimate value, is a longitudinal tire force of each wheel, is a lateral tire force of each wheel, is a vertical load of each wheel.

[0021] Optionally, the obtaining the front wheel side force and the rear wheel side force of the current four-wheel drive vehicle by matrixing a lateral dynamics model of the current four-wheel drive vehicle and solving according to a least square method, and determining the lateral tire force of the current four-wheel drive vehicle according to the front wheel side force and the rear wheel side force, comprises: matrixing the lateral dynamics model of the current four-wheel drive vehicle:

[0022]

[0023] to obtain Y = AX, wherein:

[0024]

[0025]

[0026] solving by a least square method to obtain ​​and ; in, is the vehicle mass, is the vehicle lateral speed, is the vehicle longitudinal speed, is the vehicle yaw angular velocity, is the lateral force on the front wheel, is the lateral force on the rear wheel, is the front wheel turning angle, is the moment of inertia of the vehicle around the z-axis, is the distance from the vehicle's center of mass to the front axle, is the distance from the vehicle's center of mass to the rear axle; The lateral tire force of the current four-wheel drive vehicle is determined according to the front wheel lateral force and the rear wheel lateral force.

[0027] Optionally, determining the current slip state of the four-wheel drive vehicle according to the current road adhesion coefficient estimation value, and determining the current slipping wheel of the four-wheel drive vehicle according to the slip state includes: The wheel speed difference of adjacent wheels or diagonal wheels of the current four-wheel drive vehicle is calculated by the following formula:

[0028] in, For the wheels and The wheel speed difference, For the The wheel speed, For the wheel speed; The speed change difference between the vehicle longitudinal acceleration of the current four-wheel drive vehicle and the wheel speed change rate of each wheel is calculated by the following formula:

[0029] in, is the speed change difference between the vehicle longitudinal acceleration and the wheel speed change rate, is the vehicle longitudinal acceleration, is the rate of change of wheel speed; When the wheel speed difference exceeds a preset wheel speed difference threshold, the speed change difference exceeds a preset speed change difference threshold, and the tire force satisfies the following formula, it is determined that the current wheel is a slipping wheel of the current four-wheel drive vehicle:

[0030] in, is the estimated value of the current road adhesion coefficient, is the longitudinal tire force of each wheel, is the lateral tire force of each wheel, is the vertical load of each wheel.

[0031] Optionally, the modifying the weight of the non-slip wheel of the current four-wheel drive vehicle according to the current operating condition of the current four-wheel drive vehicle and obtaining the current estimated vehicle speed of the current four-wheel drive vehicle includes: Eliminating the slipping wheel in the current four-wheel drive vehicle and obtaining the current operating condition of the current four-wheel drive vehicle; When the current working condition is a linear acceleration working condition or a linear braking working condition, the weight of the non-slipping wheel is corrected according to the longitudinal distance between the non-slipping wheel and the center of mass by the following formula:

[0032] in, After the correction A weight without pulleys, For the The longitudinal distance between the non-pulley wheel and the center of mass; The corrected weights are normalized using the following formula:

[0033] in, For a collection without pulleys; When the current working condition is a turning condition or a lateral condition, the weight of not pulling the wheel is corrected by the following formula:

[0034] in, After the correction A weight without pulleys, For the The distance between the non-pulley wheel and the turning center, For a collection without pulleys; When the current working condition is a ramp working condition, the weight of the non-slipping wheel is corrected by the following formula:

[0035] in, After the correction A weight without pulleys, is the initial weight, is the slope influence coefficient, is the road slope; The current estimated speed of the four-wheel drive vehicle is obtained by the following formula:

[0036] in, is the current estimated vehicle speed, is a set of non-slip wheels, is a weight of the th non-slip wheel, is a wheel speed of the th non-slip wheel.

[0037] Optionally, the model predictive control (MPC) and the current estimated vehicle speed correct the vehicle speed of the current four-wheel drive vehicle, and the corrected vehicle speed is applied to the target dynamic model to obtain a final vehicle speed of the current four-wheel drive vehicle. The longitudinal dynamic model and the lateral dynamic model in the target dynamic model are discretized to obtain a state space model by the following formula:

[0038] wherein, is a state vector, is an n*n state transition matrix (n is the dimension of the state vector ), is an n*m input matrix (m is the dimension of the input vector ), is a control input vector, is a process noise, is a vehicle longitudinal speed at time t, is a vehicle lateral speed at time t, is a vehicle yaw rate at time t, is a vehicle longitudinal total driving force or braking force at time t, is a lateral force of the front wheel at time t, is a lateral force of the rear wheel at time t. The related parameters of the current four-wheel drive vehicle are constrained according to a preset road adhesion coefficient constraint condition, a preset tire force saturation constraint condition, a preset vehicle speed range constraint condition, and a preset yaw rate constraint condition. At each sampling time, the model predictive control (MPC) solves the following optimization problem:

[0039]

[0040]

[0041] wherein, is a control input at the current time (step 0), ​​​​​​is a control input for a future first step, is a control input for a future first step, is a control input for a future first step, is a prediction horizon length of the MPC, is an objective function, is a transpose of is a transpose of 、 、 is a weight matrix, is a system state for a first step, is a transpose of is a transpose of is a control input vector for a first step, is a transpose of is a transpose of is a system state at the end of the prediction horizon (a first step); obtains an optimal control input sequence of the optimization problem applies a first element in the optimal control input sequence to the target dynamics model, updates a vehicle state, and obtains a modified longitudinal vehicle speed and a modified lateral vehicle speed; calculates a final vehicle speed of the current four-wheel drive vehicle according to the modified longitudinal vehicle speed and the modified lateral vehicle speed by the following formula:

[0042] wherein, is the modified longitudinal vehicle speed, is the modified lateral vehicle speed.

[0043] In the second aspect, to achieve the above object, the application further provides a four-wheel drive vehicle speed estimation device, which comprises: a calibration module, which is configured to establish a dynamics model of a current four-wheel drive vehicle, calibrate parameters of the dynamics model, and obtain a calibrated target dynamics model; a vehicle speed estimation module, which is configured to obtain an estimated value of a current road adhesion coefficient, determine a non-slip wheel of the current four-wheel drive vehicle according to the estimated value of the current road adhesion coefficient, modify a weight of the non-slip wheel according to a current working condition, and obtain a current estimated vehicle speed of the current four-wheel drive vehicle; a correction module, which is configured to correct a vehicle speed of the current four-wheel drive vehicle based on a model predictive control (MPC) and the current estimated vehicle speed, apply to the target dynamics model, and obtain a final vehicle speed of the current four-wheel drive vehicle.

[0044] ​​​In a third aspect, to achieve the above object, the present application provides a four-wheel drive vehicle speed estimation device, comprising a memory, a processor, and a four-wheel drive vehicle speed estimation program stored in the memory and executable on the processor, the four-wheel drive vehicle speed estimation program being configured to implement the steps of the four-wheel drive vehicle speed estimation method described above.

[0045] In a fourth aspect, to achieve the above object, the present application provides a storage medium having a four-wheel drive vehicle speed estimation program stored thereon, the four-wheel drive vehicle speed estimation program being executable by a processor to implement the steps of the four-wheel drive vehicle speed estimation method described above.

[0046] The four-wheel drive vehicle speed estimation method provided by the present application can effectively reduce the error accumulation in the traditional vehicle speed estimation method, improve the accuracy of vehicle speed estimation, enhance the reliability of vehicle speed estimation, and adapt to different vehicle working conditions and driving environments, such as straight-line acceleration, straight-line braking, turning, large lateral, and slope, thereby having strong adaptability and improving the speed and efficiency of four-wheel drive vehicle speed estimation. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The device structure diagram of the hardware running environment involved in the embodiment of the present application; Figure 2 The flowchart of the first embodiment of the four-wheel drive vehicle speed estimation method of the present application; Figure 3 The flowchart of the second embodiment of the four-wheel drive vehicle speed estimation method of the present application; Figure 4 The functional module diagram of the first embodiment of the four-wheel drive vehicle speed estimation device of the present application.

[0048] The implementation, functional features, and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0049] It should be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application.

[0050] The solution of the embodiment of the present application is mainly: by establishing the dynamics model of the current four-wheel drive vehicle, calibrating the parameters of the dynamics model, obtaining the calibrated target dynamics model; obtaining the current road adhesion coefficient estimate value, determining the non-slip wheel of the current four-wheel drive vehicle according to the current road adhesion coefficient estimate value, correcting the weight of the non-slip wheel according to the current working condition, obtaining the current estimated speed of the current four-wheel drive vehicle; based on model predictive control MPC and the current estimated speed, correcting the speed of the current four-wheel drive vehicle, applying to the target dynamics model, obtaining the final speed of the current four-wheel drive vehicle, which can correct the speed by combining digital twin technology and MPC algorithm, effectively reducing the error accumulation in the traditional speed estimation method, improving the accuracy of speed estimation, enhancing the reliability of speed estimation, which can adapt to different vehicle working conditions and driving environments, such as straight acceleration, straight braking, turning, large lateral, slope, etc., has strong adaptability, improves the speed and efficiency of four-wheel drive vehicle speed estimation, solves the technical problems that the speed estimation algorithm in the prior art depends on hardware devices such as wheel speed sensor, accelerometer and gyroscope, has limitations, and is easy to cause long-time speed estimation error accumulation and inaccurate speed prediction.

[0051] Reference Figure 1 , Figure 1 The device structure diagram of the hardware running environment involved in the embodiment of the present application.

[0052] As Figure 1 shown, the device can include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection communication between these components. The user interface 1003 can include a display screen (Display), an input unit such as a keyboard (Keyboard), and an optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface). The memory 1005 can be a high-speed RAM memory, or a stable memory (Non-Volatile Memory), such as a magnetic disk memory. The memory 1005 can also be an independent storage device from the aforementioned processor 1001.

[0053] Those skilled in the art can understand that Figure 1 the device structure shown in the above description does not constitute a limitation on the device, and can include more or fewer components than the diagram, or combine certain components, or different component arrangements.

[0054] As Figure 1As shown, the memory 1005 as a storage medium may include an operating device, a network communication module, a user interface module, and a four-wheel drive vehicle speed estimation program.

[0055] The device of the present invention calls the four-wheel drive vehicle speed estimation program stored in the memory 1005 through the processor 1001 and executes the operations in the embodiment of the four-wheel drive vehicle speed estimation method described below.

[0056] Based on the above hardware structure, an embodiment of a four-wheel drive vehicle speed estimation method of the present invention is proposed.

[0057] Reference Figure 2 , Figure 2 FIG. 1 is a flow chart of a first embodiment of a method for estimating vehicle speed of a four-wheel drive vehicle according to the present invention.

[0058] In a first embodiment, the method for estimating the speed of a four-wheel drive vehicle comprises the following steps: Step S10: establishing a dynamic model of the current four-wheel drive vehicle, calibrating parameters of the dynamic model, and obtaining a calibrated target dynamic model.

[0059] It should be noted that by establishing a digital twin dynamics model that matches the four-wheel drive vehicle, the parameters of the dynamics model can be calibrated to obtain a calibrated target dynamics model.

[0060] Furthermore, the step S10 specifically includes the following steps: The longitudinal dynamics model of the current four-wheel drive vehicle is established by the following formula:

[0061] in, is the vehicle mass, is the vehicle longitudinal speed, is the total longitudinal driving force or braking force of the vehicle, is the rolling resistance, , is the rolling resistance coefficient, is the acceleration due to gravity, is the road slope, is the air resistance, , is the air density, is the air resistance coefficient, is the frontal area of ​​the vehicle; The lateral dynamics model of the current four-wheel drive vehicle is established by the following formula:

[0062]

[0063] wherein, is the vehicle lateral speed, is the vehicle yaw rate, is the front wheel lateral force, is the rear wheel lateral force, is the front wheel steering angle, is the vehicle moment of inertia around z axis, is the distance from vehicle center of mass to front axle, is the distance from vehicle center of mass to rear axle. a tire dynamics model of the current four-wheel drive vehicle is established by using a magic formula tire model; the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model are parameter calibrated and model calibrated to obtain a calibrated target dynamics model.

[0064] It can be understood that the dynamics model of the four-wheel drive vehicle is established, that is, the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model are established; the longitudinal dynamics model and the lateral dynamics model can be established by using the above formula; and for the tire dynamics model: the force characteristics of the tire can be described by using a magic formula tire model, and taking the longitudinal force as an example, the relationship between the tire longitudinal force tire slip ratio can be expressed as:

[0065] wherein, B, C, D and E are parameters of the magic formula, which are related to the structure, material and working conditions of the tire and other factors.

[0066] It should be noted that B is a stiffness factor (affecting the initial slope of the curve), C is a shape factor (controlling the shape of the curve, usually 1.2-1.4), D is a peak factor (determining the peak value of the curve), and E is a curvature factor (affecting the curvature after the peak value of the curve) In a specific implementation, for the longitudinal force model (braking force / driving force), when calculating the tire longitudinal force , the input variable is the slip ratio , which can be expressed as:

[0067] wherein, is the drift term of the longitudinal force (considering the change of vertical load and other factors), and the parameters , , , can be obtained by fitting the tire test data, and are related to the vertical load and the road friction coefficient and the like.

[0068] For the lateral force model (cornering force), when calculating the tire lateral force the input variable is the side slip angle , which can be expressed as:

[0069] where is the drift term of the lateral force, the parameters are also related to the vertical load and camber angle, etc.

[0070] Further, the step of calibrating the parameters and the model of the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model to obtain the calibrated target dynamics model specifically includes the following steps: Calibrate the vehicle mass, rolling resistance coefficient, air resistance coefficient and vehicle frontal area in the longitudinal dynamics model; Calibrate the moment of inertia of the vehicle around the z-axis, the distance from the vehicle mass center to the front axle and the distance from the vehicle mass center to the rear axle in the lateral dynamics model; Calibrate the stiffness factor, shape factor, peak factor and curvature factor in the tire dynamics model; Calibrate the model of the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model by the least square method to minimize the error function and obtain the calibrated target dynamics model.

[0071] It should be understood that by calibrating the vehicle mass, rolling resistance coefficient, air resistance coefficient and vehicle frontal area in the longitudinal dynamics model, calibrating the moment of inertia of the vehicle around the z-axis, the distance from the vehicle mass center to the front axle and the distance from the vehicle mass center to the rear axle in the lateral dynamics model, calibrating the stiffness factor, shape factor, peak factor and curvature factor in the tire dynamics model, and calibrating the model of the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model by the least square method to minimize the error function, the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model can be calibrated by the least square method to minimize the error function. The parameters and the model of the longitudinal dynamics model, the lateral dynamics model and the tire dynamics model are calibrated to obtain the calibrated target dynamics model.

[0072] It should be noted that the calibration process for calibrating the parameters of the above longitudinal dynamics model, lateral dynamics model and tire dynamics model is: 1. Longitudinal dynamics model parameter calibration includes: Vehicle mass m: calibrate this parameter to accurately reflect the actual inertia characteristics of the vehicle, because the mass of the vehicle changes when different goods are loaded, affecting the vehicle acceleration and braking performance.

[0073] Rolling resistance coefficient μr : Related to tire material, pattern, air pressure and road roughness, calibration can accurately calculate rolling resistance and describe vehicle energy loss during driving.

[0074] Air resistance coefficient C D : Reflects the influence of vehicle shape on air resistance, calibration helps to accurately calculate air resistance at high speed.

[0075] Vehicle frontal area A: Directly affects the size of air resistance, calibration makes air resistance calculation more realistic.

[0076] 2. Lateral dynamic model parameter calibration includes: Vehicle moment of inertia I around z-axis z : Related to vehicle mass distribution, accurate moment of inertia parameter is important for describing vehicle yaw motion and cornering stability.

[0077] Vehicle center of mass to front and rear axle distance l f and l r : Determine the load distribution of vehicle front and rear axles, affect tire force and vehicle handling performance, calibration can accurately simulate vehicle lateral motion dynamic response.

[0078] 3. Tire dynamics model parameter calibration (magic formula parameters) includes: Parameter D: Represents the maximum tire force, related to tire vertical load, structure and rubber properties, calibration can accurately predict the maximum driving or braking force of the tire.

[0079] Parameter C: Influences the shape of the curve, related to tire stiffness characteristics, accurate C value can describe the trend of tire force at different slip rates.

[0080] Parameter B: Related to tire stiffness, affects the initial slope of tire force change with slip rate, calibration can accurately reflect the force characteristics of the tire at small slip rate.

[0081] Parameter E: Used to adjust the curvature of the curve, related to tire nonlinear characteristics, appropriate E value can simulate the force saturation phenomenon of the tire at large slip rate.

[0082] Least squares method is used for model calibration, the goal is to minimize the error function: Model calibration is performed on the longitudinal dynamics model, lateral dynamics model and tire dynamics model by least squares method of the following formula, minimizing the error function to obtain the calibrated target dynamics model:

[0083] Where, Error function, Model parameters, Sample number, Y is the true value for the i-th sample, Y is the true value for the i-th sample, Y is the predicted value for the i-th sample when the model parameters are Y is the predicted value for the i-th sample when the model parameters are Y is the predicted value for the i-th sample when the model parameters are Y is the column vector of true values, Y is the column vector of predicted values, denotes the transpose operation; Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values,

[0084] Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values,

[0085] Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values, Y is the column vector of true values,

[0086] In step S20, an estimated value of the current road adhesion coefficient is obtained, and a non-slip wheel of the current four-wheel drive vehicle is determined according to the estimated value of the current road adhesion coefficient. The weight of the non-slip wheel is corrected according to the current working condition, and an estimated current speed of the current four-wheel drive vehicle is obtained.

[0087] It should be understood that after the model calibration, the estimation of the road adhesion coefficient can be performed, and after the current road adhesion coefficient estimation value is obtained, the slipping wheel of the current four-wheel drive vehicle can be determined according to the current road adhesion coefficient estimation value, the weight of the remaining non-slip wheel is corrected according to different working conditions, and then the current four-wheel drive vehicle speed estimation value is obtained according to the corrected weight.

[0088] Step S30, correcting the speed of the current four-wheel drive vehicle based on the model predictive control MPC and the current estimated speed, applying to the target dynamics model, obtaining the final speed of the current four-wheel drive vehicle.

[0089] It can be understood that the speed correction can be performed based on the model predictive control (Model Predictive Control, MPC), that is, the speed is corrected based on the MPC and the current estimated speed, and is applied to the target dynamics model, thereby obtaining the final speed of the current four-wheel drive vehicle.

[0090] The above scheme, by establishing the dynamics model of the current four-wheel drive vehicle, calibrating the parameters of the dynamics model, obtaining the calibrated target dynamics model; obtaining the current road adhesion coefficient estimation value, determining the non-slip wheel of the current four-wheel drive vehicle according to the current road adhesion coefficient estimation value, correcting the weight of the non-slip wheel according to the current working condition, obtaining the current estimated speed of the current four-wheel drive vehicle; based on the model predictive control MPC and the current estimated speed, the speed of the current four-wheel drive vehicle is corrected, applied to the target dynamics model, obtaining the final speed of the current four-wheel drive vehicle, which can correct the speed by combining the digital twin technology and the MPC algorithm, effectively reducing the error accumulation in the traditional speed estimation method, improving the accuracy of the speed estimation, enhancing the reliability of the speed estimation, and adapting to different vehicle working conditions and driving environments, such as straight acceleration, straight braking, turning, large lateral, slope, etc., having strong adaptability, improving the speed and efficiency of the four-wheel drive vehicle speed estimation.

[0091] Further, Figure 3 The flowchart of the second embodiment of the four-wheel drive vehicle speed estimation method of the present application is shown in Figure 3 The second embodiment of the four-wheel drive vehicle speed estimation method of the present application is proposed based on the first embodiment, and in this embodiment, the step S20 specifically includes the following steps: Step S21, obtaining the tire force of the current four-wheel drive vehicle, and determining the current road adhesion coefficient estimation value according to the tire force.

[0092] It should be noted that before calculating the current estimated value of the road surface adhesion coefficient, the current tire force of the four-wheel drive vehicle may be first obtained, and then the current estimated value of the road surface adhesion coefficient may be determined based on the tire force.

[0093] Furthermore, the step S21 specifically includes the following steps: The wheel speed of each wheel is obtained by the wheel speed sensor of the current four-wheel drive vehicle, and the tire slip rate is calculated according to the wheel speed of each wheel using the following formula:

[0094] in, For the The slip rate of each tire, is the wheel speed, =1,2,3,4, respectively represent the four wheels, is the vehicle longitudinal speed; The longitudinal tire force of each wheel is obtained by the following formula:

[0095] in, is the longitudinal tire force of each wheel, is the peak factor, is the shape factor, is the stiffness factor, is the curvature factor, For the The slip rate of each tire; The total longitudinal driving force or braking force of the vehicle is obtained by the following formula:

[0096] in, is the total longitudinal driving force or braking force of the vehicle, is the longitudinal tire force of each wheel; Matrixing the lateral dynamics model of the current four-wheel drive vehicle and solving it according to the least squares method to obtain the front wheel lateral force and the rear wheel lateral force of the current four-wheel drive vehicle, and determining the lateral tire force of the current four-wheel drive vehicle according to the front wheel lateral force and the rear wheel lateral force; The vertical load of the current four-wheel drive vehicle is obtained, and an estimated value of the current road adhesion coefficient is calculated according to the longitudinal tire force, the lateral tire force, and the vertical load using the following formula:

[0097] in, is the estimated value of the current road adhesion coefficient, is the longitudinal tire force of each wheel, is the lateral tire force of each wheel, vertical load of each wheel.

[0098] It can be understood that, according to the wheel speed of each wheel obtained by the wheel speed sensor, the slip ratio of the tire can be calculated, and in combination with the magic formula tire model, the longitudinal tire force of each wheel can be estimated, and then the total longitudinal driving force or braking force of the vehicle can be obtained.

[0099] It should be understood that, according to the lateral acceleration ay, the yaw rate ω and the steering wheel angle δ of the vehicle, in combination with the lateral dynamic model, the lateral forces F yf and F yr of the front and rear wheels are estimated by using the least square method.

[0100] Further, the step obtains the lateral forces of the front and rear wheels of the current four-wheel drive vehicle by matrixing the lateral dynamic model of the current four-wheel drive vehicle and solving according to the least square method, and determines the lateral tire force of the current four-wheel drive vehicle according to the lateral forces of the front and rear wheels, and specifically includes the following steps: The lateral dynamic model of the current four-wheel drive vehicle is:

[0101]

[0102] Matrixing is performed to obtain Y = AX, wherein:

[0103]

[0104]

[0105] Solving by the least square method , obtains and ; wherein, m is the mass of the vehicle, v is the lateral speed of the vehicle, v is the longitudinal speed of the vehicle, ω is the yaw rate of the vehicle, F is the lateral force of the front wheel, F is the lateral force of the rear wheel, δ is the steering wheel angle, Iz is the moment of inertia of the vehicle around the z axis, L is the distance from the mass center of the vehicle to the front axle, L is the distance from the mass center of the vehicle to the rear axle. The lateral tire force of the current four-wheel drive vehicle is determined according to the lateral forces of the front and rear wheels.

[0106] Correspondingly, the estimation process of the road adhesion coefficient is: Based on the estimated longitudinal tire force and vertical load (calculated from the vehicle's static load distribution and dynamic load transfer), and lateral tire forces , using the elliptical model to estimate the road adhesion coefficient ; The ellipse model expression is:

[0107] After finishing, we can get:

[0108] Take the average value of the calculation results of the four wheels as the estimated value of the current road adhesion coefficient .

[0109] Step S22: determining the current slip state of the four-wheel drive vehicle according to the current road adhesion coefficient estimation value, and determining the slipping wheel of the current four-wheel drive vehicle according to the slip state.

[0110] It can be understood that the vehicle's slip state can be judged based on the current road adhesion coefficient estimation value, and the slipping wheel can be eliminated, that is, the slip state of the current four-wheel drive vehicle is determined based on the current road adhesion coefficient estimation value, and the slipping wheel of the current four-wheel drive vehicle is determined based on the slip state.

[0111] Furthermore, the step S22 specifically includes the following steps: The wheel speed difference of adjacent wheels or diagonal wheels of the current four-wheel drive vehicle is calculated by the following formula:

[0112] in, For the wheels and The wheel speed difference, For the The wheel speed, For the wheel speed; The speed change difference between the vehicle longitudinal acceleration of the current four-wheel drive vehicle and the wheel speed change rate of each wheel is calculated by the following formula:

[0113] in, is the speed change difference between the vehicle longitudinal acceleration and the wheel speed change rate, is the vehicle longitudinal acceleration, is the rate of change of wheel speed; When the wheel speed difference exceeds a preset wheel speed difference threshold, the speed change difference exceeds a preset speed change difference threshold, and the tire force satisfies the following formula, the current wheel is determined as the slipping wheel of the current four-wheel drive vehicle:

[0114] wherein, is a current road adhesion coefficient estimation value, is a longitudinal tire force of each wheel, is a lateral tire force of each wheel, is a vertical load of each wheel.

[0115] It can be understood that the slipping wheel can be determined by wheel speed difference judgment, acceleration and wheel speed change rate comparison, tire force and adhesion limit comparison, and multiple coupling judgments. If exceeds a preset threshold , it is considered that there may be a slip; if exceeds a preset threshold , the wheel may slip; if , the wheel may slip, and through the above three judgment methods, if a wheel satisfies multiple judgment conditions at the same time, the wheel is determined as the slipping wheel and is excluded.

[0116] Step S23, according to the current working condition of the current four-wheel drive vehicle, the weight of the non-slip wheel of the current four-wheel drive vehicle is corrected, and the current estimated speed of the current four-wheel drive vehicle is obtained.

[0117] It should be understood that the weight of the non-slip wheel of the current four-wheel drive vehicle can be corrected according to the current working condition of the current four-wheel drive vehicle, so as to obtain the current estimated speed of the current four-wheel drive vehicle.

[0118] Further, the step S23 specifically includes the following steps: excluding the slipping wheel in the current four-wheel drive vehicle, obtaining the current working condition of the current four-wheel drive vehicle; when the current working condition is a straight line acceleration working condition or a straight line braking working condition, the weight of the non-slip wheel is corrected according to the longitudinal distance between the non-slip wheel and the center of mass by the following formula:

[0119] wherein, is the weight of the mth non-slip wheel after correction, is the longitudinal distance between the mth non-slip wheel and the center of mass; the corrected weight is normalized by the following formula: ​​

[0120] in, For a collection without pulleys; When the current working condition is a turning condition or a lateral condition, the weight of not pulling the wheel is corrected by the following formula:

[0121] in, After the correction A weight without pulleys, For the The distance between the non-pulley wheel and the turning center, For a collection without pulleys; When the current working condition is a ramp working condition, the weight of the non-slipping wheel is corrected by the following formula:

[0122] in, After the correction A weight without pulleys, is the initial weight, is the slope influence coefficient, is the road slope; The current estimated speed of the four-wheel drive vehicle is obtained by the following formula:

[0123] in, is the current estimated vehicle speed, For a collection without pulleys, After the correction A weight without pulleys, For the The wheel speed without slip.

[0124] It should be noted that the process of working condition identification is: according to the vehicle's driving state information (such as longitudinal acceleration a x , lateral acceleration a y , steering wheel angle δ, etc.) to identify the current working conditions, including straight-line acceleration, straight-line braking, turning, large lateral movement, and ramps.

[0125] It can be understood that the weight correction under the linear acceleration condition is: for the driving wheel, the wheel closer to the center of mass has a higher weight, because the load and driving force distribution it bears during acceleration is more stable. Let the set of non-slip wheels be S, and for the i∈S wheel, its weight w i The longitudinal distance l from the center of mass can be xi Corrected and normalized.

[0126] Weight correction under linear braking conditions: For braking wheels, the closer the wheel is to the center of mass, the higher the weight is, because the load and braking force distribution it bears during braking are more stable; the weight correction method is the same as the linear acceleration condition.

[0127] Weight correction under turning conditions: The outer wheels bear greater lateral force and load when turning, so their weight should be higher. Let the turning radius be R, and the weight w of the i∈S wheel i The distance r from the turning center can be i Correction.

[0128] Weight correction in lateral conditions, especially large lateral conditions: Similar to cornering conditions, the outer wheels have higher weights, and the weight correction method is the same as cornering conditions.

[0129] Weight correction under slope conditions: When going uphill, the weight of the driving wheel is adjusted according to the slope and driving mode; when going downhill, the weight of the braking wheel is adjusted according to the slope and braking demand; let the slope be θ, for the driving wheel or braking wheel i∈S, its weight w i It can be corrected according to the slope and then normalized.

[0130] The vehicle speed estimation process is as follows: Based on the wheel speed v without slip wi and weight w i Calculate the current estimated vehicle speed v est .

[0131] Accordingly, the step S30 specifically includes the following steps: The longitudinal dynamics model and the lateral dynamics model in the target dynamics model are discretized to obtain a state space model using the following formula:

[0132] in, is the state vector, is the n×n state transfer matrix (n is the state vector ), is an n×m input matrix (m is the input vector dimensions), is the control input vector, is the process noise, for The longitudinal speed of the vehicle at time for The lateral speed of the vehicle at the time, for The vehicle's yaw rate at time , for The total longitudinal driving force or braking force of the vehicle at the moment, for the lateral force of the front wheel at the moment, for the lateral force of the rear wheel at the moment; constraining the relevant parameters of the current four-wheel drive vehicle according to preset road surface adhesion coefficient constraint conditions, preset tire force saturation constraint conditions, preset vehicle speed range constraint conditions and preset yaw rate constraint conditions; solving the following optimization problem at each sampling moment based on model predictive control (MPC):

[0133]

[0134]

[0135] wherein, is the control input at the current moment (0th step), is the control input at the future 1st step, is the control input at the future step, is the prediction horizon length of the MPC, is the objective function, is the transpose of 、 、 is the weight matrix, is the system state at the 0th step, is the transpose of is the control input vector at the 0th step, is the transpose of is the system state at the end of the prediction horizon (the 0th step); obtains the optimal control input sequence of the optimization problem applies the first element of the optimal control input sequence to the target dynamics model, updates the vehicle state, and obtains the modified longitudinal vehicle speed and lateral vehicle speed; calculates the final vehicle speed of the current four-wheel drive vehicle according to the modified longitudinal vehicle speed and lateral vehicle speed by the following formula:

[0136] wherein, is the modified longitudinal vehicle speed, is the modified lateral vehicle speed.

[0137] ​​​​​​It should be noted that the longitudinal and lateral dynamic models are discretized to obtain a state space model, and the constraints include road adhesion coefficient constraint, tire force saturation constraint, vehicle speed range constraint, and yaw rate constraint; Among them, the road adhesion coefficient constraint: Considering the limitation of the road adhesion coefficient, the tire force must meet ( =1,2,3,4), converting it into control input constraints.

[0138] Tire force saturation constraint: There is a physical upper limit to the tire force, that is, and , =1,2,3,4, where and are the maximum longitudinal and lateral tire forces, respectively.

[0139] Speed ​​range constraint: The longitudinal and lateral speeds of the vehicle should be within a reasonable range, i.e. and , and introduce the estimated vehicle speed Constraints, such as ,in, The allowable deviation range of the estimated vehicle speed.

[0140] Yaw rate constraint: The yaw rate of the vehicle should also be within a reasonable range, that is, ,in, and are the minimum and maximum yaw angular velocity, respectively.

[0141] It is understandable that after defining the objective function J, the optimization solution can be performed through MPC, that is, at each sampling moment, the optimization problem such as the above formula is solved, and the optimal control input sequence is obtained through the constraint conditions. and its first element Applied to the vehicle dynamics model to update the vehicle status , thus obtaining the corrected longitudinal and lateral vehicle speeds and .

[0142] Based on the final vehicle speed calculation formula The final vehicle speed estimation result at the current moment can be calculated.

[0143] In specific implementation, the above solution can be realized through a four-wheel drive vehicle speed estimation system. The system mainly includes a sensor module, a digital twin model module, a tire force estimation module, a road adhesion coefficient estimation module, a slip judgment and speed estimation module, an MPC speed correction module and a communication module.

[0144] • Sensor module: Collects various state information of the vehicle, such as wheel speed, acceleration, gyroscope data, steering wheel angle, etc.

[0145] • Digital twin model module: Constructs and calibrates the digital twin model of the four-wheel drive vehicle in real time based on sensor data.

[0146] • Tire force estimation module: Estimates longitudinal and lateral tire forces based on sensor data and the digital twin model.

[0147] • Road adhesion coefficient estimation module: Estimates the current road adhesion coefficient based on tire forces and vertical loads.

[0148] • Slip determination and vehicle speed estimation module: Introduces the detection of actual four-wheel speed, couples multiple determination methods to determine the slip state, excludes the slipping wheel, corrects the weight of the non-slip wheel according to different working conditions, and calculates the current estimated vehicle speed.

[0149] • MPC vehicle speed correction module: Based on the road adhesion coefficient, estimated vehicle speed, and vehicle dynamics model, uses the MPC algorithm to correct the longitudinal and lateral vehicle speeds.

[0150] • Communication module: Transmits the vehicle speed estimation results to the active safety system and autonomous driving system of the vehicle.

[0151] The construction steps of the digital twin model module are as follows: 1. Data collection: Collects the state information of the vehicle using various sensors on the vehicle, such as wheel speed sensors, accelerometers, gyroscopes, steering wheel angle sensors, etc.

[0152] 2. Model establishment: Establishes the longitudinal dynamics model, lateral dynamics model, and tire dynamics model of the four-wheel drive vehicle based on the above theoretical formulas.

[0153] 3. Model calibration: Initialize the parameter vector .

[0154] Calculate the model prediction value .

[0155] Calculate the error function .

[0156] Calculate the Jacobian matrix .

[0157] Update the parameter vector according to the iteration formula of the Gauss-Newton method .

[0158] Repeat the above steps until the convergence condition is met.

[0159] The tire force estimation steps are as follows: 1. Longitudinal tire force estimation: Calculate tire slip ratio according to wheel speed measured by wheel speed sensor and vehicle longitudinal acceleration, estimate longitudinal tire force of each wheel combined with magic formula tire model.

[0160] 2. Lateral tire force estimation: Estimate lateral force of front and rear wheels combined with lateral dynamics model using least square method according to lateral acceleration, yaw rate and steering wheel angle of vehicle.

[0161] Road adhesion coefficient estimation steps are as follows: According to the estimated longitudinal tire force, lateral tire force and vertical load, estimate road adhesion coefficient using elliptical model, take the average value of four wheel calculation results as the estimated value of current road adhesion coefficient.

[0162] Slip state judgment and slip wheel exclusion steps are as follows: 1. Calculate the wheel speed difference value of adjacent wheels or diagonal wheels , and compare with the preset threshold .

[0163] 2. Calculate the difference value of vehicle longitudinal acceleration and wheel speed change rate of each wheel , and compare with the preset threshold .

[0164] 3. According to the estimated tire force and , and road adhesion coefficient and vertical load , judge whether it meets .

[0165] 4. Integrate the above three judgment results to determine the slip wheel and exclude it.

[0166] Non-slip wheel weight correction and vehicle speed estimation steps under different working conditions are as follows: 1. According to the driving state information of vehicle (such as longitudinal acceleration , lateral acceleration , steering wheel angle δ, etc.), identify the current working condition.

[0167] 2. According to the weight correction method of different working conditions, correct the weight of non-slip wheel and perform normalization processing.

[0168] 3. Calculate the current estimated vehicle speed according to the wheel speed of non-slip wheel and weight .

[0169] Vehicle speed correction steps based on MPC are as follows: 1. Discretize the longitudinal and lateral dynamics model to obtain a state-space model.

[0170] 2. Determine the constraints, including road adhesion coefficient constraints, tire force saturation constraints, vehicle speed range constraints (introduce estimated vehicle speed constraints), and yaw rate constraints.

[0171] 3. Define the objective function and select the appropriate weight matrix.

[0172] 4. At each sampling time, solve the MPC optimization problem to obtain the optimal control input sequence, and apply the first element to the vehicle dynamics model to update the vehicle state, obtaining the corrected longitudinal and lateral vehicle speeds.

[0173] The final vehicle speed estimation step is as follows: Calculate the square root of the sum of the squares of the corrected longitudinal and lateral vehicle speeds to obtain the final vehicle speed at the current time.

[0174] The system implementation is achieved by: 1. Sensor module: Select high-precision wheel speed sensors, accelerometers, gyroscopes, and steering wheel angle sensors, and ensure that the installation position and direction of the sensors are correct.

[0175] 2. Digital twin model module: Use high-performance processors and software platforms to realize real-time construction and calibration of the digital twin model.

[0176] 3. Tire force estimation module: Write the code for the tire force estimation algorithm and integrate it into the system.

[0177] 4. Road adhesion coefficient estimation module: Write the code for the road adhesion coefficient estimation algorithm and integrate it into the system.

[0178] 5. Slip determination and vehicle speed estimation module: Write the code for the slip determination and vehicle speed estimation algorithm and integrate it into the system.

[0179] 6. MPC vehicle speed correction module: Write the code for the MPC algorithm and integrate it into the system.

[0180] 7. Communication module: Use a reliable communication protocol (such as CAN bus) to transmit the vehicle speed estimation results to the vehicle's active safety system and autonomous driving system.

[0181] In a specific implementation, the experimental vehicle can be selected as a four-wheel drive electric vehicle, and the vehicle is equipped with wheel speed sensors, accelerometers, gyroscopes, steering wheel angle sensors and other devices; the experimental scene can be an experiment under different driving conditions (such as straight-line acceleration, straight-line braking, turning, large lateral, slope, etc.) and different road conditions (such as dry road, wet road, etc.) to collect the state information of the vehicle; of course, the vehicle and the scene can be adjusted according to the experimental requirements, and the embodiment does not limit this; the experimental results show that the method of the embodiment has significantly improved in the accuracy and reliability of longitudinal and lateral speed estimation, especially in tire slip and different conditions, and can more accurately estimate the speed.

[0182] The embodiment obtains the tire force of the current four-wheel drive vehicle, determines the current road adhesion coefficient estimation value according to the tire force, determines the slip state of the current four-wheel drive vehicle according to the current road adhesion coefficient estimation value, determines the slipping wheel of the current four-wheel drive vehicle according to the slip state, corrects the weight of the non-slip wheel of the current four-wheel drive vehicle according to the current working condition of the current four-wheel drive vehicle, and obtains the current estimated speed of the current four-wheel drive vehicle, which can effectively reduce the error accumulation in the traditional speed estimation method, improve the accuracy of speed estimation, enhance the reliability of speed estimation, adapt to different vehicle conditions and driving environments, and improve the speed and efficiency of four-wheel drive vehicle speed estimation.

[0183] Correspondingly, the application further provides a four-wheel drive vehicle speed estimation device.

[0184] Reference Figure 4 , Figure 4 The function module diagram of the first embodiment of the four-wheel drive vehicle speed estimation device of the application.

[0185] In the first embodiment of the four-wheel drive vehicle speed estimation device of the application, the four-wheel drive vehicle speed estimation device comprises: The calibration module 10 is configured to establish a dynamics model of the current four-wheel drive vehicle, calibrate parameters of the dynamics model, and obtain a calibrated target dynamics model.

[0186] The speed estimation module 20 is configured to obtain a current road adhesion coefficient estimation value, determine a non-slip wheel of the current four-wheel drive vehicle according to the current road adhesion coefficient estimation value, correct the weight of the non-slip wheel according to the current working condition, and obtain a current estimated speed of the current four-wheel drive vehicle.

[0187] The correction module 30 is configured to correct the speed of the current four-wheel drive vehicle based on model predictive control (MPC) and the current estimated speed, apply to the target dynamics model, and obtain a final speed of the current four-wheel drive vehicle.

[0188] The steps implemented by each functional module of the four-wheel drive vehicle speed estimation device can refer to the steps of the embodiments of the four-wheel drive vehicle speed estimation method, which will not be described here.

[0189] In addition, an embodiment of the present application further provides a storage medium, wherein the storage medium stores a four-wheel drive vehicle speed estimation program, and the four-wheel drive vehicle speed estimation program is executed by a processor to implement the operations in the embodiments of the four-wheel drive vehicle speed estimation method.

[0190] Those skilled in the art can understand that all or part of the steps of the methods in the above embodiments can be completed by a program instructing related hardware. The program is stored in a storage medium and includes a plurality of instructions for enabling an apparatus (which can be a single-chip microcomputer, a chip, etc.) or a processor to perform all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium is a computer-readable storage medium, including a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0191] It should be noted that, in this document, the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0192] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages or disadvantages of the embodiments.

[0193] The above only describes the preferred embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for estimating the speed of a four-wheel drive vehicle, characterized in that: The four-wheel drive vehicle speed estimation method comprises: Establishing a dynamic model of the current four-wheel drive vehicle, calibrating parameters of the dynamic model, and obtaining a calibrated target dynamic model; Obtaining a current road surface adhesion coefficient estimate, determining a non-slipping wheel of the current four-wheel drive vehicle based on the current road surface adhesion coefficient estimate, and modifying a weight of the non-slipping wheel based on a current operating condition to obtain a current estimated vehicle speed of the current four-wheel drive vehicle; The current vehicle speed of the four-wheel drive vehicle is corrected based on model predictive control (MPC) and the current estimated vehicle speed, and the correction is applied to the target dynamics model to obtain a final vehicle speed of the current four-wheel drive vehicle.

2. The four-wheel drive vehicle speed estimation method according to claim 1, wherein: The step of establishing a dynamic model of the current four-wheel drive vehicle, calibrating parameters of the dynamic model, and obtaining a calibrated target dynamic model includes: The longitudinal dynamics model of the current four-wheel drive vehicle is established by the following formula: in, is the vehicle mass, is the vehicle longitudinal speed, is the total longitudinal driving force or braking force of the vehicle, is the rolling resistance, , is the rolling resistance coefficient, is the acceleration due to gravity, is the road slope, is the air resistance, , is the air density, is the air resistance coefficient, is the frontal area of ​​the vehicle; The lateral dynamics model of the current four-wheel drive vehicle is established by the following formula: in, is the vehicle lateral speed, is the vehicle yaw angular velocity, is the lateral force on the front wheel, is the lateral force on the rear wheel, is the front wheel turning angle, is the moment of inertia of the vehicle around the z-axis, is the distance from the vehicle's center of mass to the front axle, is the distance from the vehicle's center of mass to the rear axle; A magic formula tire model is used to establish a tire dynamics model of the current four-wheel drive vehicle; Parameter calibration and model calibration are performed on the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model to obtain a calibrated target dynamics model.

3. The four-wheel drive vehicle speed estimation method according to claim 2, wherein: The performing parameter calibration and model calibration on the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model to obtain a calibrated target dynamics model includes: calibrating the vehicle mass, rolling resistance coefficient, air resistance coefficient, and vehicle frontal area in the longitudinal dynamics model; Calibrate the vehicle's moment of inertia around the z-axis, the distance from the vehicle's center of mass to the front axle, and the distance from the vehicle's center of mass to the rear axle in the lateral dynamics model; calibrating a stiffness factor, a shape factor, a peak factor, and a curvature factor in the tire dynamics model; The longitudinal dynamics model, the lateral dynamics model and the tire dynamics model are calibrated by the least square method to minimize the error function and obtain a calibrated target dynamics model.

4. The four-wheel drive vehicle speed estimation method according to claim 3, wherein: The method of calibrating the longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model by the least square method, minimizing an error function, and obtaining a calibrated target dynamics model includes: The longitudinal dynamics model, the lateral dynamics model, and the tire dynamics model are calibrated by the least square method of the following formula to minimize the error function and obtain a calibrated target dynamics model: in, is the error function, are model parameters, is the number of samples, For the The true value of the sample, The model parameters are Time The predicted value of the sample, is a column vector of real values, is the column vector of predicted values, Represents a transpose operation; right Ask about The gradient of , and set it to zero: in, for right The gradient vector of is the Jacobian matrix The transpose of is a column vector of real values, is the column vector of predicted values; The error function is minimized using the following formula using an iterative algorithm: in, For the The updated parameters after iterations, For the The updated parameters after iterations, is the number of iterations, is the Jacobian matrix of the predicted values ​​to the parameters, is the transpose of the Jacobian matrix, is a column vector of real values, For the A column vector of predicted values ​​for the iterations, starting from the initial parameter vector Start by continuously updating the parameter vector until the convergence condition is met ( ,in is the preset convergence threshold).

5. The four-wheel drive vehicle speed estimation method according to claim 1, wherein: The obtaining of a current estimated road adhesion coefficient value, determining a non-slipping wheel of the current four-wheel drive vehicle based on the current estimated road adhesion coefficient value, and modifying a weight of the non-slipping wheel based on a current operating condition to obtain a current estimated vehicle speed of the current four-wheel drive vehicle includes: Obtaining the current tire force of the four-wheel drive vehicle, and determining a current road adhesion coefficient estimate based on the tire force; Determining a current slip state of the four-wheel drive vehicle according to the current road adhesion coefficient estimation value, and determining a slipping wheel of the current four-wheel drive vehicle according to the slip state; The weight of the non-slip wheel of the current four-wheel drive vehicle is corrected according to the current working condition of the current four-wheel drive vehicle, and the current estimated vehicle speed of the current four-wheel drive vehicle is obtained.

6. The four-wheel drive vehicle speed estimation method according to claim 5, characterized in that: The obtaining of the current tire force of the four-wheel drive vehicle and determining the current road adhesion coefficient estimation value according to the tire force includes: The wheel speed of each wheel is obtained by the wheel speed sensor of the current four-wheel drive vehicle, and the tire slip rate is calculated according to the wheel speed of each wheel using the following formula: in, For the The slip rate of each tire, is the wheel speed, =1,2,3,4, respectively represent the four wheels, is the vehicle longitudinal speed; The longitudinal tire force of each wheel is obtained by the following formula: in, is the longitudinal tire force of each wheel, is the peak factor, is the shape factor, is the stiffness factor, is the curvature factor, For the The slip rate of each tire; The total longitudinal driving force or braking force of the vehicle is obtained by the following formula: in, is the total longitudinal driving force or braking force of the vehicle, is the longitudinal tire force of each wheel; Matrixing the lateral dynamics model of the current four-wheel drive vehicle and solving it according to the least squares method to obtain the front wheel lateral force and the rear wheel lateral force of the current four-wheel drive vehicle, and determining the lateral tire force of the current four-wheel drive vehicle according to the front wheel lateral force and the rear wheel lateral force; The vertical load of the current four-wheel drive vehicle is obtained, and an estimated value of the current road adhesion coefficient is calculated according to the longitudinal tire force, the lateral tire force, and the vertical load using the following formula: in, is the estimated value of the current road adhesion coefficient, is the longitudinal tire force of each wheel, is the lateral tire force of each wheel, is the vertical load of each wheel.

7. The four-wheel drive vehicle speed estimation method according to claim 6, characterized in that: The method of matrixing the lateral dynamics model of the current four-wheel drive vehicle and solving it according to the least squares method to obtain the front wheel lateral force and the rear wheel lateral force of the current four-wheel drive vehicle, and determining the lateral tire force of the current four-wheel drive vehicle according to the front wheel lateral force and the rear wheel lateral force, includes: The lateral dynamics model of the current four-wheel drive vehicle is: Perform matrix transformation to obtain Y=AX, where: Solve by least squares method ,get and ; in, is the vehicle mass, is the vehicle lateral speed, is the vehicle longitudinal speed, is the vehicle yaw angular velocity, is the lateral force on the front wheel, is the lateral force on the rear wheel, is the front wheel turning angle, is the moment of inertia of the vehicle around the z-axis, is the distance from the vehicle's center of mass to the front axle, is the distance from the vehicle's center of mass to the rear axle; The lateral tire force of the current four-wheel drive vehicle is determined according to the front wheel lateral force and the rear wheel lateral force.

8. The four-wheel drive vehicle speed estimation method according to claim 5, wherein: The determining the current slip state of the four-wheel drive vehicle according to the current road adhesion coefficient estimation value, and determining the slipping wheel of the current four-wheel drive vehicle according to the slip state, includes: The wheel speed difference of adjacent wheels or diagonal wheels of the current four-wheel drive vehicle is calculated by the following formula: in, For the wheels and The wheel speed difference, For the The wheel speed, For the wheel speed; The speed change difference between the vehicle longitudinal acceleration of the current four-wheel drive vehicle and the wheel speed change rate of each wheel is calculated by the following formula: in, is the speed change difference between the vehicle longitudinal acceleration and the wheel speed change rate, is the vehicle longitudinal acceleration, is the rate of change of wheel speed; When the wheel speed difference exceeds a preset wheel speed difference threshold, the speed change difference exceeds a preset speed change difference threshold, and the tire force satisfies the following formula, it is determined that the current wheel is a slipping wheel of the current four-wheel drive vehicle: in, is the estimated value of the current road adhesion coefficient, is the longitudinal tire force of each wheel, is the lateral tire force of each wheel, is the vertical load of each wheel.

9. The method for estimating the speed of a four-wheel drive vehicle according to claim 5, wherein: The modifying the weight of the non-slip wheel of the current four-wheel drive vehicle according to the current operating condition of the current four-wheel drive vehicle and obtaining the current estimated vehicle speed of the current four-wheel drive vehicle includes: Eliminating the slipping wheel in the current four-wheel drive vehicle and obtaining the current operating condition of the current four-wheel drive vehicle; When the current working condition is a linear acceleration working condition or a linear braking working condition, the weight of the non-slipping wheel is corrected according to the longitudinal distance between the non-slipping wheel and the center of mass by the following formula: in, After the correction A weight without pulleys, For the The longitudinal distance between the non-pulley wheel and the center of mass; The corrected weights are normalized using the following formula: in, For a collection without pulleys; When the current working condition is a turning condition or a lateral condition, the weight of not pulling the wheel is corrected by the following formula: in, After the correction A weight without pulleys, For the The distance between the non-pulley wheel and the turning center, For a collection without pulleys; When the current working condition is a ramp working condition, the weight of the non-slipping wheel is corrected by the following formula: in, After the correction A weight without pulleys, is the initial weight, is the slope influence coefficient, is the road slope; The current estimated speed of the four-wheel drive vehicle is obtained by the following formula: in, is the current estimated vehicle speed, For a collection without pulleys, After the correction A weight without pulleys, For the The wheel speed without slip.

10. The four-wheel drive vehicle speed estimation method according to claim 1, wherein: The method of correcting the current speed of the four-wheel drive vehicle based on the model predictive control (MPC) and the current estimated vehicle speed, applying the corrected speed to the target dynamics model, and obtaining the final vehicle speed of the current four-wheel drive vehicle includes: The longitudinal dynamics model and the lateral dynamics model in the target dynamics model are discretized to obtain a state space model using the following formula: in, is the state vector, is the n×n state transfer matrix (n is the state vector ), is an n×m input matrix (m is the input vector dimensions), is the control input vector, is the process noise, for The longitudinal speed of the vehicle at time for The lateral speed of the vehicle at the time, for The vehicle's yaw rate at time , for The total longitudinal driving force or braking force of the vehicle at the moment, for The lateral force on the front wheel at time , for The lateral force of the rear wheel at the moment; constraining relevant parameters of the current four-wheel drive vehicle according to a preset road adhesion coefficient constraint condition, a preset tire force saturation constraint condition, a preset vehicle speed range constraint condition, and a preset yaw rate constraint condition; Based on the model predictive control MPC, the following optimization problem is solved at each sampling time: in, is the control input at the current moment (step 0), For the control input of the next step 1, For the future The control input of the step, is the prediction time domain length of MPC, is the objective function, for The transpose of 、 、 is the weight matrix, For the The system status of the step, for The transpose of For the The control input vector of the step, for The transpose of To predict the end of the time domain ( step) of the system status; Obtain the optimal control input sequence for the optimization problem , the first element in the optimal control input sequence Applying the target dynamics model to update the vehicle state and obtain the corrected longitudinal and lateral vehicle speeds; The final speed of the current four-wheel drive vehicle is calculated according to the modified forward speed and lateral speed using the following formula: in, is the modified forward speed, is the modified lateral speed.

11. A four-wheel drive vehicle speed estimation device, characterized in that: The four-wheel drive vehicle speed estimation device comprises: A calibration module is used to establish a dynamic model of the current four-wheel drive vehicle, calibrate parameters of the dynamic model, and obtain a calibrated target dynamic model; a vehicle speed estimation module, configured to obtain an estimated value of a current road adhesion coefficient, determine a non-slipping wheel of the current four-wheel drive vehicle based on the estimated value of the current road adhesion coefficient, and modify a weight of the non-slipping wheel based on a current operating condition to obtain a current estimated vehicle speed of the current four-wheel drive vehicle; The correction module is used to correct the current speed of the four-wheel drive vehicle based on the model predictive control MPC and the current estimated speed, and apply it to the target dynamics model to obtain the final speed of the current four-wheel drive vehicle.

12. A four-wheel drive vehicle speed estimation device, characterized in that: The four-wheel drive vehicle speed estimation device includes: a memory, a processor, and a four-wheel drive vehicle speed estimation program stored in the memory and executable on the processor. The four-wheel drive vehicle speed estimation program is configured to implement the steps of the four-wheel drive vehicle speed estimation method according to any one of claims 1 to 7.

13. A storage medium, characterized in that: The storage medium stores a four-wheel drive vehicle speed estimation program, which, when executed by the processor, implements the steps of the four-wheel drive vehicle speed estimation method according to any one of claims 1 to 7.

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