Parameter acquisition method and device, medium and vehicle

Abstract

The embodiment of the invention provides a parameter acquisition method and device, a medium and a vehicle, and relates to the technical field of vehicles. According to the parameter acquisition method provided by the embodiment of the invention, the driving state information such as the three-axis acceleration, the three-axis angular velocity, the real-time vehicle speed and the output torque is acquired through the sensor equipped on the vehicle, and the algorithm such as the unscented Kalman filtering and the recursive least square with the forgetting factor is combined; the mass of the vehicle and the road gradient can be obtained with low cost, high real-time performance and high precision. The algorithms have advantages in processing nonlinear systems and dynamic changes, and can better adapt to complex working conditions, reduce interference of noise and errors and improve the vehicle quality and the acquisition precision and real-time performance of road gradient parameters especially under the complex working conditions of rapid acceleration, rapid deceleration, up and down of abrupt slopes and the like.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a parameter acquisition method, device, medium and vehicle. Background Art

[0002] In vehicle dynamics research, vehicle mass and road slope are key dynamic parameters that affect vehicle performance, and are directly related to the vehicle's acceleration performance, braking distance, energy efficiency, and passenger comfort. With the rapid development of electric vehicles and autonomous driving technology, real-time and accurate acquisition of vehicle dynamics parameters has become an important prerequisite for achieving safe, efficient, and comfortable driving.

[0003] In related technologies, the industry mainly relies on sensors already equipped on the vehicle, such as wheel speed sensors (measuring wheel speed) and acceleration sensors (measuring vehicle longitudinal acceleration), etc., to collect real-time data and solve vehicle mass and road slope by building complex dynamic equations or algorithms. The advantage of this method is low cost and strong universality, because it does not rely on additional expensive sensors, but instead utilizes the potential of the vehicle's existing hardware.

[0004] However, in order to improve the real-time acquisition of vehicle mass and road slope, the complexity of the dynamic equations or algorithms needs to be greatly reduced. These simplifications may have little effect under normal driving conditions, but under complex conditions (such as rapid acceleration, rapid deceleration, up and down steep slopes, etc.), the accuracy of parameter acquisition will be significantly reduced, affecting the accurate evaluation of vehicle performance and the effectiveness of control strategies. Summary of the invention

[0005] Based on the above problems, the present application provides a parameter acquisition method, device, medium and vehicle, which can improve the acquisition accuracy and real-time performance of vehicle mass and road slope parameters.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] In a first aspect, the present application discloses a parameter acquisition method, the method comprising:

[0008] Acquiring driving state information of the vehicle, the driving state information including three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque;

[0009] According to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed, the pitch angle of the vehicle is obtained by using an unscented Kalman filter algorithm;

[0010] Obtaining a slope angle of a road according to a pitch angle and a preset mass of the vehicle;

[0011] The actual mass of the vehicle is obtained according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

[0012] Optionally, acquiring the pitch angle of the vehicle by using an unscented Kalman filter algorithm according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed includes:

[0013] Obtain the state equation and the output equation, the state equation is as follows:

[0014]

[0015] Among them, q(k) is the unit quaternion of the current sampling time, k is the current sampling time, k-1 is the sampling time before the current sampling time, T is the sampling period, ω b is a matrix composed of three-axis angular velocities, and I is the unit matrix;

[0016] The output equation is as follows:

[0017]

[0018] Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, g is the acceleration due to gravity, a y is the longitudinal acceleration of the vehicle, q1, q2, q3, q4 are the components of the unit quaternion q;

[0019] The state equation and the output equation are calculated according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed using an unscented Kalman filter algorithm to obtain the pitch angle of the vehicle.

[0020] Optionally, the formula for obtaining the slope angle of the road is as follows:

[0021]

[0022] Among them, α is the slope angle of the road, θ * is the longitudinal inclination angle of the vehicle, △h f , △h r Respectively represent the change in front and rear suspension spring height compared to when the vehicle is stationary on a level road.

[0023] Optionally, the formula for obtaining the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor is as follows:

[0024]

[0025] in, is the real-time estimated value of the vehicle weight at time k, is the real-time estimated value of the vehicle weight at time k-1, K is the change of the forgetting factor over time or the number of iterations, y = T e i0η / r-ρC D AV 2 / 2,T e is the output torque of the driving motor, i0 is the reduction ratio of the main reducer, η is the transmission efficiency of the power system, r is the wheel radius, ρ is the air density, C D is the air resistance coefficient, A is the frontal area, V is the actual vehicle speed, E = μgcosα+gsinα+a y , g is the acceleration of gravity, α is the road slope angle, μ is the rolling friction coefficient, a y is the longitudinal acceleration of the vehicle, P(k) is the estimated value of the error covariance matrix at time k, λ m For the forgetting factor.

[0026] Optionally, after performing the step of obtaining the actual mass of the vehicle by using a recursive least squares algorithm with a forgetting factor according to the output torque and the slope angle of the road, the method further comprises:

[0027] If the root mean square error obtained according to the actual mass is greater than the first threshold, and the actual mass is greater than the vehicle's unloaded mass, then after the actual mass is updated to the vehicle's unloaded mass, it is determined again whether the root mean square error obtained according to the updated actual mass is greater than the first threshold, until the root mean square error is less than or equal to the first threshold.

[0028] Optionally, the method further comprises:

[0029] If the root mean square error obtained according to the updated actual mass is less than or equal to the first threshold, the updated actual mass is saved as the preset mass of the vehicle.

[0030] In a second aspect, the present application discloses a parameter acquisition device, the device comprising: a driving state acquisition module, a first angle acquisition module, a second angle acquisition module and an actual mass acquisition module;

[0031] The driving state acquisition module is used to acquire the driving state information of the vehicle, wherein the driving state information includes three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque;

[0032] The first angle acquisition module is used to acquire the pitch angle of the vehicle through an unscented Kalman filter algorithm according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed;

[0033] The second angle acquisition module is used to acquire the slope angle of the road according to the pitch angle and the preset mass of the vehicle;

[0034] The actual mass acquisition module is used to acquire the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

[0035] Optionally, the first angle acquisition module is specifically used to: acquire a state equation and an output equation, and the state equation is specifically as follows:

[0036]

[0037] Among them, q(k) is the unit quaternion of the current sampling time, k is the current sampling time, k-1 is the sampling time before the current sampling time, T is the sampling period, ω b is a matrix composed of three-axis angular velocities, and I is the unit matrix;

[0038] The output equation is as follows:

[0039]

[0040] Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, g is the acceleration due to gravity, a y is the longitudinal acceleration of the vehicle, q1, q2, q3, q4 are the components of the unit quaternion q;

[0041] The state equation and the output equation are calculated according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed using an unscented Kalman filter algorithm to obtain the pitch angle of the vehicle.

[0042] Optionally, the formula for obtaining the slope angle of the road is as follows:

[0043]

[0044] Among them, α is the slope angle of the road, θ * is the longitudinal inclination angle of the vehicle, △h f , △h r Respectively represent the change in front and rear suspension spring height compared to when the vehicle is stationary on a level road.

[0045] Optionally, the formula for obtaining the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor is as follows:

[0046]

[0047] in, is the real-time estimated value of the vehicle weight at time k, is the real-time estimated value of the vehicle weight at time k-1, K is the change of the forgetting factor over time or the number of iterations, y = T e i0η / r-ρC D AV 2 / 2,T e is the output torque of the driving motor, i0 is the reduction ratio of the main reducer, η is the transmission efficiency of the power system, r is the wheel radius, ρ is the air density, C D is the air resistance coefficient, A is the frontal area, V is the actual vehicle speed, E = μgcosα+gsinα+a y , g is the acceleration of gravity, α is the road slope angle, μ is the rolling friction coefficient, a y is the longitudinal acceleration of the vehicle, P(k) is the estimated value of the error covariance matrix at time k, λ m For the forgetting factor.

[0048] Optionally, the device further comprises: a convergence judgment module;

[0049] The convergence judgment module is used to update the actual mass to the vehicle's unloaded mass if the root mean square error obtained according to the actual mass is greater than a first threshold, and the actual mass is greater than the vehicle's unloaded mass, and then again judge whether the root mean square error obtained according to the updated actual mass is greater than the first threshold, until the root mean square error is less than or equal to the first threshold.

[0050] Optionally, the device further comprises: a quality preservation module;

[0051] The mass preservation module is configured to preserve the updated actual mass as the preset mass of the vehicle if a root mean square error obtained according to the updated actual mass is less than or equal to the first threshold.

[0052] In a third aspect, the present application discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parameter acquisition method described in the first aspect above is implemented.

[0053] In a fourth aspect, the present application discloses a vehicle, comprising a parameter acquisition device, wherein the parameter acquisition device is used to execute the parameter acquisition method described in the first aspect.

[0054] Compared with the prior art, this application has the following beneficial effects:

[0055] The embodiment of the present application provides a parameter acquisition method, device, medium and vehicle, the method comprising: acquiring the driving state information of the vehicle, the driving state information including three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque; acquiring the pitch angle of the vehicle according to the three-axis acceleration, three-axis angular velocity and real-time vehicle speed through the unscented Kalman filter algorithm; acquiring the slope angle of the road according to the pitch angle of the vehicle and the preset mass; acquiring the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting the recursive least squares algorithm with a forgetting factor. Therefore, the parameter acquisition method provided in the embodiment of the present application acquires driving state information such as three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque through the sensors already equipped on the vehicle, and combines the unscented Kalman filter and the recursive least squares algorithm with a forgetting factor, so as to acquire the mass of the vehicle and the slope of the road at low cost, high real-time and high precision. These algorithms have advantages in dealing with nonlinear systems and dynamic changes, especially in complex working conditions such as sudden acceleration, sudden deceleration, and up and down steep slopes. They can better adapt to these complex working conditions, reduce the interference of noise and errors, and improve the accuracy and real-time performance of obtaining vehicle quality and road slope parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 A flow chart of a parameter acquisition method provided in an embodiment of the present application;

[0058] Figure 2 A schematic diagram of a parameter acquisition method provided in an embodiment of the present application;

[0059] Figure 3 A schematic diagram of a pitch angle estimation method provided in an embodiment of the present application;

[0060] Figure 4 An uphill longitudinal force analysis diagram provided in an embodiment of the present application;

[0061] Figure 5 A single-side model diagram of a vehicle provided in an embodiment of the present application;

[0062] Figure 6 A schematic diagram of a vehicle weight estimation method provided in an embodiment of the present application;

[0063] Figure 7 A schematic diagram of a parameter acquisition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] As described above, the industry mainly relies on sensors already equipped on the vehicle, such as wheel speed sensors (measuring wheel speed) and acceleration sensors (measuring vehicle longitudinal acceleration), etc., to collect real-time data and solve vehicle mass and road slope by building complex dynamic equations or algorithms. The advantage of this method is low cost and strong universality, because it does not rely on additional expensive sensors, but instead utilizes the potential of the vehicle's existing hardware.

[0065] However, in order to improve the real-time acquisition of vehicle mass and road slope, the complexity of the dynamic equations or algorithms needs to be greatly reduced. These simplifications may have little effect under normal driving conditions, but under complex conditions (such as rapid acceleration, rapid deceleration, up and down steep slopes, etc.), the accuracy of parameter acquisition will be significantly reduced, affecting the accurate evaluation of vehicle performance and the effectiveness of control strategies.

[0066] After research, the inventors have proposed a parameter acquisition method, device, medium and vehicle. The parameter acquisition method provided in the embodiment of the present application obtains driving state information such as three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque through sensors already equipped on the vehicle, and combines algorithms such as unscented Kalman filtering and recursive least squares with forgetting factor to obtain the mass of the vehicle and the road slope at low cost, high real-time and high precision. These algorithms have advantages in processing nonlinear systems and dynamic changes, especially in complex working conditions such as rapid acceleration, rapid deceleration, and up and down steep slopes. They can better adapt to these complex working conditions, reduce the interference of noise and errors, and improve the acquisition accuracy and real-time performance of vehicle mass and road slope parameters.

[0067] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] See also Figure 1 , which is a flow chart of a parameter acquisition method provided in an embodiment of the present application. The method includes:

[0069] S101: Acquire driving status information of the vehicle, where the driving status information includes three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque.

[0070] Three-axis acceleration refers to the linear acceleration of the vehicle in three-dimensional space, along the X-axis, Y-axis and Z-axis respectively, which can be measured by the accelerometer in the IMU installed on the vehicle. Among them, the X-axis usually points in the direction of the vehicle's forward movement and measures the longitudinal acceleration of the vehicle. The Y-axis usually points in the width direction of the vehicle and measures the lateral acceleration. The Z-axis is usually perpendicular to the vehicle plane and measures the acceleration in the vertical direction.

[0071] The three-axis angular velocity refers to the speed at which the vehicle rotates around the X-axis, Y-axis, and Z-axis, which can also be measured by the gyroscope in the IMU. Among them, the X-axis represents the rotation around the vehicle's forward direction, the Y-axis represents the rotation around the vehicle's width direction, and the Z-axis represents the rotation around the vertical direction.

[0072] Real-time vehicle speed refers to the current speed of the vehicle, which is usually measured by the wheel speed sensor (WSS) or vehicle speed sensor (VSS) on the vehicle. These sensors detect the wheel speed and convert the wheel speed into the real-time speed of the vehicle by combining parameters such as wheel diameter and tire rotation.

[0073] Output torque refers to the torque value output by the drive motor in the current working state. This value is usually directly provided by the control unit (Micro Control Unit, MCU) of the drive motor. Output torque is one of the important parameters in the vehicle longitudinal dynamics model, which directly affects the acceleration performance and driving state of the vehicle. When estimating the vehicle weight, considering the output torque of the drive motor can more accurately simulate the dynamic behavior of the vehicle, thereby improving the accuracy of vehicle weight estimation.

[0074] It should be noted that the above-mentioned driving status information can be obtained through the sensors already equipped on the vehicle, without the need to install additional expensive sensors, which helps to reduce vehicle costs and improve the universality of the parameter acquisition method.

[0075] S102: Obtain the pitch angle of the vehicle according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed through an unscented Kalman filter algorithm.

[0076] First, define the unit quaternion q = [q0, q1, q2, q3] TTo represent the real-time attitude of the vehicle. The unit quaternion can describe the rotation from the vehicle coordinate system to the navigation coordinate system, and then express the vehicle's yaw angle ψ, pitch angle θ and roll angle φ. These three angles together constitute a complete attitude description of the vehicle relative to the navigation coordinate system. Among them, the yaw angle ψ is usually used to describe the direction or orientation of the vehicle, the pitch angle θ reflects the tilt state of the vehicle relative to the horizontal plane, and the roll angle φ describes the tilt degree of the left and right sides of the vehicle. Specifically, the relationship between the yaw angle ψ, the pitch angle θ, the roll angle φ and the four components q1, q2, q3, q4 of the unit quaternion q can be shown as follows:

[0077]

[0078] Secondly, the IMU installed at the center of mass of the vehicle is used to obtain the vehicle’s three-axis angular velocity ω bx ,ω by ,ω bz After that, the quaternion differential equation can be solved by the first-order Runge-Kutta method to obtain the state equation of vehicle posture estimation as follows:

[0079]

[0080] in, is the derivative of the unit quaternion, ω b is the matrix composed of three-axis angular velocity, ω bx ,ω by ,ω bz is the three-axis angular velocity of the vehicle.

[0081] Then, by discretizing formula (2), we can get the formula (3) for the change of the unit quaternion over time:

[0082]

[0083] Among them, q(k) is the unit quaternion of the current sampling time, k is the current sampling time, k-1 is the sampling time before the current sampling time, T is the sampling period, ω b is a matrix composed of three-axis angular velocities, and I is the unit matrix.

[0084] It should be noted that formula (3) is a discretized representation of the first-order approximation of formula (2), given the sampling period T and the angular velocity ω at the sampling time b , the unit quaternion of each sampling moment can be calculated to update the vehicle's posture.

[0085] In addition to angular velocity, the accelerometer in the IMU can also measure the three-axis acceleration a in the vehicle coordinate system. bx 、a by 、abz If only the longitudinal motion of the vehicle is considered, the observation equation can be obtained as follows:

[0086]

[0087] Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, a nx 、a ny 、a nz is the three-axis acceleration of the vehicle in the navigation coordinate system, and g is the gravitational acceleration.

[0088] And because of the working principle of the accelerometer in the IMU, we can know the following formula (5):

[0089] a by =a y -2(q2q3+q0q1)g(5)

[0090] Among them, a by is the longitudinal acceleration in the vehicle coordinate system, a y is the longitudinal acceleration of the vehicle, which can be obtained by differentiating the real-time vehicle speed V (which can be measured by a wheel speed meter).

[0091] Combining the above formulas (4) and (5), we can get the observation equation of the following formula (6), which converts the acceleration in the vehicle coordinate system into the acceleration in the navigation coordinate system and takes into account the influence of gravity acceleration.

[0092]

[0093] Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, g is the acceleration due to gravity, a y is the longitudinal acceleration of the vehicle, and q1, q2, q3, and q4 are the components of the unit quaternion q.

[0094] See also Figure 3 , which is a schematic diagram of a pitch angle estimation method provided by an embodiment of the present application. If x(k)=q(k)=[q0(k),q1(k),q2(k),q3(k)] T (unit quaternion q representing the real-time posture of the vehicle) as the state quantity, let z(k) = [a bx (k),a by (k),a bz (k)] T (The three-axis acceleration of the vehicle) is used as the observed quantity, and then the unit quaternion q and the three-axis angular velocity ω of the vehicle are obtained by equation (3):bx ,ω by ,ω bz The vehicle attitude estimation state equation is given by equation (6) based on the unit quaternion q and the vehicle three-axis acceleration a bx 、a by 、a bz The vehicle attitude estimation output equation is related to . Then, under the premise of using IMU and wheel speed meter to measure the vehicle's three-axis acceleration, three-axis angular velocity and actual vehicle speed, combined with the unscented Kalman filter algorithm, the unit quaternion q can be estimated in real time, and then the vehicle's pitch angle θ can be estimated. Moreover, the estimated value of this pitch angle θ is not only an important input for the subsequent road slope estimation, but also can participate in the vehicle weight estimation through the feedback mechanism, forming a closed-loop iteration to improve the overall estimation accuracy.

[0095] Therefore, by processing the three-axis acceleration, three-axis angular velocity and real-time vehicle speed, the pitch angle of the vehicle, that is, the rotation angle of the vehicle around the horizontal axis, can be estimated more accurately, which is of great significance for evaluating the stability of the vehicle, predicting vehicle behavior and improving driving safety. For example, in emergency braking or sharp turns, accurately knowing the pitch angle of the vehicle can help the driver or the autonomous driving system make a more appropriate response.

[0096] S103: Obtaining a slope angle of the road according to the pitch angle of the vehicle and a preset mass.

[0097] First, the longitudinal force analysis of the vehicle when going uphill is carried out. Figure 4 , which is an uphill longitudinal force analysis diagram provided in the embodiment of the present application. It should be noted that in order to simplify the analysis, Figure 4 The force analysis in the above figure ignores the minor factors such as the vehicle's rotational mass inertia resistance moment, rolling resistance moment and air lift. The main forces on the vehicle include: the gravity G (vertically downward) of the vehicle, the tangential reaction force of the ground (front wheel F yf 、Rear wheel F yr , enabling the vehicle to move forward on the slope), ground normal reaction force (front wheel F zf 、Rear wheel F zr , which is equal in magnitude and opposite in direction to the vertical load on the front and rear axles of the vehicle).

[0098] in addition, Figure 4 V in the figure is the actual longitudinal speed, a y is the longitudinal acceleration, L is the distance between the front and rear axles, a is the distance between the center of mass of the vehicle and the front axle, b is the distance between the center of mass of the vehicle and the rear axle, h g is the distance between the center of mass of the vehicle and the ground, α is the slope angle, m v is the vehicle mass (i.e. the vehicle mass), T ff , T frDenote the rolling resistance moment applied to the front and rear wheels, T jwf , T jwr Respectively represent the inertial resistance moment applied to the front and rear wheels, F zwf 、F zwr They respectively represent the air lift acting on the vehicle body, directly above the ground contact points of the front and rear wheels of the vehicle.

[0099] According to the force analysis, the ground normal reaction force of the front and rear wheels of the vehicle can be obtained as shown in the following formula (7):

[0100]

[0101] Among them, F zf is the ground normal reaction force of the vehicle's front wheels, F zr is the ground normal reaction force of the rear wheel of the vehicle, g is the acceleration of gravity, b is the distance between the center of mass of the vehicle and the rear axle, L is the distance between the front and rear axles, α is the slope angle, and h g is the distance between the center of mass of the vehicle and the ground, a y is the longitudinal acceleration, and a is the distance between the vehicle's center of mass and the front axle.

[0102] It can be seen from equation (7) that changes in the slope angle and longitudinal acceleration will cause load transfer between the front and rear axles of the vehicle. The more obvious the load transfer is, the more severe the slope change or the acceleration / deceleration of the vehicle is. In order to analyze the impact of this load transfer on the vehicle pitch attitude and to correct the obtained vehicle pitch angle, this application assumes that the working conditions on the left and right sides of the vehicle are the same, and establishes the following Figure 5 A simplified single-side model of a vehicle is shown.

[0103] See also Figure 5 , this figure is a single-sided model diagram of a vehicle provided in an embodiment of the present application. Figure 5 The following parameters are taken into account: △h f , △h r Respectively represent the change in the height of the front and rear suspension springs compared to when the vehicle is stationary on a horizontal road (if the spring height increases, it is a positive value, if the height decreases, it is a negative value); K f , K r are the front and rear suspension spring stiffnesses respectively; β is the rotation angle from the road surface to the vehicle body plane; θ * is the longitudinal inclination angle of the vehicle (relative to the estimated vehicle pitch angle θ, θ *＝- θ); m is the sprung mass of one side of the vehicle; F pf 、F pr are the pressures of the vehicle body on the front and rear suspensions respectively. Other parameters are Figure 4 The parameters have the same meaning.

[0104] Assuming that the wheel is a rigid body, if the influence of the ground normal reaction force caused by the vehicle suspension and wheel parts on the vehicle ground normal reaction force is excluded, and the vehicle longitudinal inclination angle θ is * Taking into account the difference in road slope angle α, the following formula (8) can be obtained to describe the load transfer between the front and rear axles and its influence on the pitch attitude of the vehicle body caused by the change of slope angle and longitudinal acceleration when the vehicle is driving on a slope:

[0105]

[0106] Among them, F pf 、F pr are the pressure of the vehicle body on the front and rear suspensions, m is the sprung mass of the vehicle on one side, g is the acceleration of gravity, b is the distance between the center of mass of the vehicle and the rear axle, θ * is the longitudinal inclination angle of the vehicle, h g is the distance between the center of mass of the vehicle and the ground, a y is the longitudinal acceleration.

[0107] Assume that when the vehicle is stationary on a horizontal road, the pressures exerted by the vehicle body on the front and rear suspensions are F and F respectively. pf0 、F pr0 , then we have the following formula (9):

[0108]

[0109] According to Hooke's law, △h f , △h r The calculation formula is shown in formula (10):

[0110]

[0111] After simplifying the above formula (10), we can get the following formula (11):

[0112]

[0113] Based on the above analysis, the calculation formula for the slope angle α of the road can be derived as follows: Formula (12): Based on this formula, the pitch angle of the electric vehicle estimated in step S102 can be corrected, so as to more accurately estimate the slope angle α of the road.

[0114]

[0115] Among them, α is the slope angle of the road, θ * is the longitudinal inclination angle of the vehicle, △h f , △h r Respectively represent the change in front and rear suspension spring height compared to when the vehicle is stationary on a level road.

[0116] In addition, it is known that m in formula (11) is the sprung mass of one side of the vehicle, and its calculation formula is as follows:

[0117] m=(m v -m t -m s ) / 2(13)

[0118] Among them, m t is the total mass of the tire, m s is the total mass of the vehicle suspension, both of which are fixed values ​​and can be obtained in advance. v is the vehicle mass (i.e. the vehicle mass), which can be calculated by first using the vehicle unloaded mass m v0 After the system completes the vehicle weight estimation, the estimated value m can be used to replace m v0 , in order to improve the estimation accuracy of road slope.

[0119] Therefore, by combining the pitch angle of the vehicle and the preset vehicle mass, the slope angle of the road can be inferred. This information is critical for applications such as navigation, route planning, and energy consumption estimation. For example, when driving in mountainous areas or complex terrain, knowing the slope of the road can help the driver choose a more appropriate gear and speed, thereby saving fuel and improving driving efficiency.

[0120] S104: According to the output torque and the slope angle of the road, the actual mass of the vehicle is obtained by adopting a recursive least squares algorithm with a forgetting factor.

[0121] First, a longitudinal dynamics model of an electric vehicle is established under the assumptions that there is no wind, no tire slip, no transmission in the power system, the driving and braking torques of the vehicle are all provided by the drive motor, and the rotational mass of the vehicle is ignored. The formula of the longitudinal dynamics model is shown in the following formula (14):

[0122]

[0123] Among them, T e is the output torque of the driving motor, i0 is the reduction ratio of the main reducer, η is the transmission efficiency of the power system, r is the wheel radius, m v is the mass of the vehicle, g is the acceleration of gravity, α is the road slope angle, μ is the rolling friction coefficient, a y is the longitudinal acceleration of the vehicle, ρ is the air density, C D is the air resistance coefficient, A is the frontal area, and V is the actual vehicle speed.

[0124] In order to perform parameter estimation, we rewrite the above model into a form that is more suitable for parameter estimation, as shown in the following formula (15):

[0125]

[0126] This formula (15) defines the system output and input. For example, we can let y = T e i0η / r-ρC D AV 2 / 2 represents the system output, E = μgcosα+gsinα+a y represents the system input, then the input and output of the system at time k are E(k) and y(k) respectively. Assume that the actual vehicle weight is m v is the parameter to be estimated, and the real-time estimated values ​​of the vehicle weight at time k and time k-1 are defined as and The actual vehicle weight m is calculated by using a recursive least squares algorithm with a forgetting factor. v Perform real-time estimation and obtain the following formula (16) for the real-time vehicle weight estimation algorithm:

[0127]

[0128] The actual vehicle weight is m v As the parameter to be estimated, it is updated in real time by recursive least squares algorithm combined with forgetting factor K. m is the forgetting factor, which can be either a constant or a time-varying parameter, ranging from 0 to 1. Its function is to give greater weight to new data and gradually forget old data to adapt to the dynamic changes of vehicle status. In the embodiment provided by the application, considering that the weight of the car can be regarded as a constant during driving, the forgetting factor is set to a value close to 1 (such as 0.99).

[0129] At the same time, in order to verify the stability and accuracy of the estimation results, the algorithm periodically calculates the most recent m n The average value of the sample results And the root mean square error (RMSE) is shown in the following formula (17):

[0130]

[0131] Among them, n m is the preset number of sampling points, T0 is the preset real-time estimation result of vehicle weight, The sampling analysis cycle, T is Real-time operation cycle, is the nearest n m indivual The average value of the sampling results, RMSE is the root mean square error.

[0132] According to the value of the root mean square error, the algorithm can determine whether the estimation process has converged. If RMSE>σ (that is, RMSE is greater than the first threshold), the real-time vehicle weight estimation algorithm has not converged yet, and the vehicle weight estimation needs to be continued and the vehicle weight estimation value needs to be appropriately adjusted to avoid unreasonable vehicle weight estimation values. In this case, in order to reduce the output value m of the vehicle weight estimator v (k) The deviation from the true value is set as follows: The real-time estimated value of the vehicle weight at time k If it is greater than the vehicle's unladen mass m v0 , then let the vehicle weight estimator output value m v (k) is equal to Otherwise, m v (k) should be the unladen mass of the vehicle m v0 On the contrary, if RMSE≤σ, it can be considered that the real-time vehicle weight estimation algorithm has converged and there is no need to continue iterating. The vehicle weight estimation value m output by the estimator is v (k) should be equal to And keep this value until the vehicle stops.

[0133] See also Figure 6 , which is a schematic diagram of a vehicle weight estimation method provided in an embodiment of the present application. v After (k), it can be input back into the slope estimator to update the vehicle weight setting value of the previous moment. In this way, by cross-iteration of the estimated values ​​of vehicle weight and slope, the estimation accuracy of both can be improved, and the design of the vehicle weight and slope estimation algorithm for electric vehicles can be completed.

[0134] Therefore, step S104 is of great significance for vehicle performance evaluation, fault diagnosis, and optimization of vehicle design and manufacturing processes. For example, during vehicle manufacturing or maintenance, accurately knowing the actual mass of the vehicle can help adjust the configuration of the suspension system, power system, etc. to achieve optimal performance and comfort.

[0135] In summary, an embodiment of the present application provides a parameter acquisition method, which includes: acquiring driving status information of a vehicle, the driving status information including three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque; acquiring the pitch angle of the vehicle through an unscented Kalman filter algorithm based on the three-axis acceleration, three-axis angular velocity and real-time vehicle speed; acquiring the slope angle of the road based on the pitch angle of the vehicle and a preset mass; acquiring the actual mass of the vehicle based on the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

[0136] See also Figure 7, which is a schematic diagram of a parameter acquisition device provided in an embodiment of the present application. The parameter acquisition device 700 includes: a driving state acquisition module 701, a first angle acquisition module 702, a second angle acquisition module 703 and an actual mass acquisition module 704;

[0137] The driving state acquisition module 701 is used to acquire the driving state information of the vehicle, and the driving state information includes three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque;

[0138] The first angle acquisition module 702 is used to acquire the pitch angle of the vehicle through an unscented Kalman filter algorithm according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed;

[0139] The second angle acquisition module 703 is used to acquire the slope angle of the road according to the pitch angle of the vehicle and the preset mass;

[0140] The actual mass acquisition module 704 is used to acquire the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

[0141] Optionally, the first angle acquisition module 702 is specifically used to: acquire a state equation and an output equation, and the state equation is specifically as follows:

[0142]

[0143] Among them, q(k) is the unit quaternion of the current sampling time, k is the current sampling time, k-1 is the sampling time before the current sampling time, T is the sampling period, ω b is a matrix composed of three-axis angular velocities, and I is the unit matrix;

[0144] The output equation is as follows:

[0145]

[0146] Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, g is the acceleration due to gravity, a y is the longitudinal acceleration of the vehicle, q1, q2, q3, q4 are the components of the unit quaternion q;

[0147] According to the three-axis acceleration, three-axis angular velocity and real-time vehicle speed, the state equation and output equation are calculated using the unscented Kalman filter algorithm to obtain the vehicle's pitch angle.

[0148] Optionally, the formula for obtaining the slope angle of the road is as follows:

[0149]

[0150] Among them, α is the slope angle of the road, θ * is the longitudinal inclination angle of the vehicle, △h f , △h r Respectively represent the change in front and rear suspension spring height compared to when the vehicle is stationary on a level road.

[0151] Optionally, according to the output torque and the slope angle of the road, by using a recursive least squares algorithm with a forgetting factor, the formula for obtaining the actual mass of the vehicle is as follows:

[0152]

[0153] in, is the real-time estimated value of the vehicle weight at time k, is the real-time estimated value of the vehicle weight at time k-1, K is the change of the forgetting factor over time or the number of iterations, y = T e i0η / r-ρC D AV 2 / 2,T e is the output torque of the driving motor, i0 is the reduction ratio of the main reducer, η is the transmission efficiency of the power system, r is the wheel radius, ρ is the air density, C D is the air resistance coefficient, A is the frontal area, V is the actual vehicle speed, E = μgcosα+gsinα+a y , g is the acceleration of gravity, α is the road slope angle, μ is the rolling friction coefficient, a y is the longitudinal acceleration of the vehicle, P(k) is the estimated value of the error covariance matrix at time k, λ m For the forgetting factor.

[0154] Optionally, the parameter acquisition device 700 further includes: a convergence judgment module;

[0155] The convergence judgment module is used to update the actual mass to the unloaded mass of the vehicle if the root mean square error obtained according to the actual mass is greater than the first threshold, and the actual mass is greater than the unloaded mass of the vehicle, and then judge again whether the root mean square error obtained according to the updated actual mass is greater than the first threshold, until the root mean square error is less than or equal to the first threshold.

[0156] Optionally, the parameter acquisition device 700 further includes: a quality preservation module;

[0157] The mass preservation module is used to preserve the updated actual mass as the preset mass of the vehicle if a root mean square error obtained according to the updated actual mass is less than or equal to a first threshold.

[0158] In summary, an embodiment of the present application provides a parameter acquisition device, which includes: acquiring driving state information of a vehicle, the driving state information including three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque; acquiring the pitch angle of the vehicle through an unscented Kalman filter algorithm based on the three-axis acceleration, three-axis angular velocity and real-time vehicle speed; acquiring the slope angle of the road based on the pitch angle of the vehicle and a preset mass; acquiring the actual mass of the vehicle based on the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

[0159] Correspondingly, the present application also discloses a vehicle, including the parameter acquisition device as described in the aforementioned embodiment.

[0160] A vehicle provided in an embodiment of the present application has the beneficial effects of the parameter acquisition device introduced above.

[0161] The "first" and "second" in the names such as "first" and "second" (if any) mentioned in the embodiments of the present application are only used as name identifiers and do not represent the first or second in order.

[0162] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0163] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments. The device and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0164] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A parameter acquisition method, characterized in that: The method comprises: Acquiring driving state information of the vehicle, the driving state information including three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque; According to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed, the pitch angle of the vehicle is obtained by using an unscented Kalman filter algorithm; Obtaining a slope angle of a road according to a pitch angle and a preset mass of the vehicle; The actual mass of the vehicle is obtained according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

2. The method according to claim 1, characterized in that The step of obtaining the pitch angle of the vehicle by using an unscented Kalman filter algorithm according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed includes: Obtain the state equation and the output equation, the state equation is as follows: Among them, q(k) is the unit quaternion of the current sampling time, k is the current sampling time, k-1 is the sampling time before the current sampling time, T is the sampling period, ω b is a matrix composed of three-axis angular velocities, and I is the unit matrix; The output equation is as follows: Among them, a bx 、a by 、a bz is the three-axis acceleration in the vehicle coordinate system, g is the acceleration due to gravity, a y is the longitudinal acceleration of the vehicle, q1, q2, q3, q4 are the components of the unit quaternion q; The state equation and the output equation are calculated according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed using an unscented Kalman filter algorithm to obtain the pitch angle of the vehicle.

3. The method according to claim 1, characterized in that The formula for obtaining the slope angle of the road is as follows: Among them, α is the slope angle of the road, θ * is the longitudinal inclination angle of the vehicle, △h f , △h r Respectively represent the change in front and rear suspension spring height compared to when the vehicle is stationary on a level road.

4. The method according to claim 1, characterized in that The formula for obtaining the actual mass of the vehicle according to the output torque and the slope angle of the road by using a recursive least squares algorithm with a forgetting factor is as follows: in, is the real-time estimated value of the vehicle weight at time k, is the real-time estimated value of the vehicle weight at time k-1, K is the change of the forgetting factor over time or the number of iterations, y = T e i0η / r-ρC D AV 2 / 2,T e is the output torque of the driving motor, i0 is the reduction ratio of the main reducer, η is the transmission efficiency of the power system, r is the wheel radius, ρ is the air density, C D is the air resistance coefficient, A is the frontal area, V is the actual vehicle speed, E = μgcosα+gsinα+a y , g is the acceleration of gravity, α is the road slope angle, μ is the rolling friction coefficient, a y is the longitudinal acceleration of the vehicle, P(k) is the estimated value of the error covariance matrix at time k, λ m For the forgetting factor.

5. The method according to claim 1, characterized in that After executing the step of obtaining the actual mass of the vehicle by using a recursive least squares algorithm with a forgetting factor according to the output torque and the slope angle of the road, the method further includes: If the root mean square error obtained according to the actual mass is greater than the first threshold, and the actual mass is greater than the vehicle's unloaded mass, then after the actual mass is updated to the vehicle's unloaded mass, it is determined again whether the root mean square error obtained according to the updated actual mass is greater than the first threshold, until the root mean square error is less than or equal to the first threshold.

6. The method according to claim 5, characterized in that The method further comprises: If the root mean square error obtained according to the updated actual mass is less than or equal to the first threshold, the updated actual mass is saved as the preset mass of the vehicle.

7. A parameter acquisition device, characterized in that: The device comprises: a driving state acquisition module, a first angle acquisition module, a second angle acquisition module and an actual mass acquisition module; The driving state acquisition module is used to acquire the driving state information of the vehicle, wherein the driving state information includes three-axis acceleration, three-axis angular velocity, real-time vehicle speed and output torque; The first angle acquisition module is used to acquire the pitch angle of the vehicle through an unscented Kalman filter algorithm according to the three-axis acceleration, the three-axis angular velocity and the real-time vehicle speed; The second angle acquisition module is used to acquire the slope angle of the road according to the pitch angle and the preset mass of the vehicle; The actual mass acquisition module is used to acquire the actual mass of the vehicle according to the output torque and the slope angle of the road by adopting a recursive least squares algorithm with a forgetting factor.

8. The device according to claim 7, characterized in that The device also includes: a convergence judgment module; The convergence judgment module is used to update the actual mass to the vehicle's unloaded mass if the root mean square error obtained according to the actual mass is greater than a first threshold, and the actual mass is greater than the vehicle's unloaded mass, and then again judge whether the root mean square error obtained according to the updated actual mass is greater than the first threshold, until the root mean square error is less than or equal to the first threshold.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the parameter acquisition method described in any one of claims 1 to 6 is implemented.

10. A vehicle, characterized in that: The vehicle comprises a parameter acquisition device, and the parameter acquisition device is used to execute the parameter acquisition method according to any one of claims 1 to 6.

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