CN116588121B - Vehicle parameter estimation method, device, medium and equipment based on vehicle information - Google Patents
Vehicle parameter estimation method, device, medium and equipment based on vehicle information Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
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Abstract
The invention discloses a vehicle parameter estimation method, a device, a medium and equipment based on vehicle information, wherein the method comprises the following steps: acquiring real-time parameters of a driving vehicle; constructing a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle; according to the nonlinear dynamics system model, analyzing the dynamics balance of the driving vehicle to construct a first state equation and a measurement equation; constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation; and estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator. The embodiment of the invention can effectively improve the accuracy of vehicle parameter estimation, the calculation efficiency and the calculation precision and the robustness and the accuracy of the vehicle parameter estimator.
Description
Technical Field
The present invention relates to the field of vehicle parameter estimation, and in particular, to a vehicle parameter estimation method, apparatus, medium and device based on vehicle information.
Background
The centroid slip angle is a very important vehicle state parameter in the stability characterization of a vehicle. The average driver is acceptable to control the centroid slip angle to within 2 °. However, due to the complexity of the vehicle system, the position of the centroid slip angle may vary with the shifting of the vehicle load and the vehicle bounce caused by road surface irregularities.
In the prior art, the measurement of the centroid slip angle is generally carried out by installing a photoelectric sensor at the centroid of a vehicle, and the photoelectric sensor estimates the centroid slip angle in real time by measuring and calculating the vehicle speed of the vehicle in the longitudinal direction and the lateral direction at each moment. But is expensive and costly, resulting in a failure to be widely used on vehicles. Meanwhile, besides a certain precision requirement on the installation position, the photoelectric sensor has calibration errors and drift errors, in addition, the sensor has higher requirements on the use of the environment, and under the condition that some environments are severe, the measurement precision of the photoelectric sensor has a large error, so that the precision of parameter values is influenced, and the effect of a related control system is further influenced.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the invention provides a vehicle parameter estimation method, device, medium and equipment based on vehicle information, which can realize accurate estimation of a mass center side deviation angle.
In order to achieve the above object, an embodiment of the present invention provides a vehicle parameter estimation method based on vehicle information, including:
Acquiring real-time parameters of a driving vehicle;
constructing a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
according to the nonlinear dynamics system model, analyzing the dynamics balance of the driving vehicle to construct a first state equation and a measurement equation;
constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation;
and estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator.
Further, the nonlinear dynamics system model includes a vehicle longitudinal motion equation, a vehicle lateral motion equation, a vehicle yaw motion equation, and a vehicle tire rotation equation.
Further, the vehicle longitudinal motion equation is constructed by the following formula (1):
the vehicle lateral motion equation is constructed by the following formula (2):
the vehicle yaw motion equation is constructed by the following formula (3):
the vehicle tire rotation equation is constructed by the following formula (4):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, V y is the vehicle lateral speed, v,/>, for the vehicle longitudinal accelerationFor vehicle lateral acceleration,The yaw acceleration is represented by delta, the steering angle of the front wheels, I z is the rotational inertia of the vehicle body, B is the wheel distance between the front axle and the rear axle, l f is the distance from the center of mass of the vehicle to the front axle, l r is the distance from the center of mass of the vehicle to the rear axle, J w is the rotational inertia of the wheels, rw is the rolling radius of the tires, and f is the rolling resistance coefficient of the tires;
F xi is the longitudinal force of the tire, F yi is the lateral force of the tire, T di is the driving torque of the tire, F zi is the vertical load of the tire, ω wi is the angular velocity of the tire, i=fl, r, rl, r, i=fl represents the front left tire, i=fr represents the front right tire, i=rl represents the rear left tire, i=rr represents the rear right tire.
Further, the analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamics system model to construct a first state equation and a measurement equation specifically includes:
Analyzing the dynamic balance of all directions of the driving vehicle based on the longitudinal motion equation of the vehicle, the lateral motion equation of the vehicle and the yaw motion equation of the vehicle by adopting the darebel principle to construct a first state equation and a measurement equation; wherein the directions include longitudinal, lateral and yaw.
Further, the first state equation is constructed by the following formula (5):
The measurement equation is constructed by the following formula (6):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, A x is the original longitudinal acceleration of the vehicle, delta is the front wheel steering angle, beta is the automobile mass center slip angle,Is centroid cornering acceleration, gamma is yaw rate,For yaw acceleration, I z is the rotational inertia of the vehicle body, l f is the distance from the vehicle mass center to the front axle, l r is the distance from the vehicle mass center to the rear axle, C f is the cornering stiffness of the front wheels of the vehicle, C r is the cornering stiffness of the rear wheels of the vehicle, and I >Is the vehicle lateral acceleration.
Further, the construction process of the unscented kalman filter parameter estimator specifically includes:
acquiring an initial value of the unscented Kalman filtering parameter estimator and a first Si gma point set;
calculating a further predicted value of the first Si gma point set based on the initial value and the first Si gma point set;
Calculating a system state quantity predicted value and a covariance matrix based on the further predicted value of the first Sigma point set and a preconfigured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value;
calculating an observed quantity of the second Sigma point set;
weighting and summing the observed quantity of the second Sigma point set to obtain an observed quantity average value;
Calculating covariance based on the observed quantity of the second Sigma point set, the observed quantity mean value and a pre-configured measurement noise covariance matrix;
calculating to obtain a Kalman gain based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, the second Sigma point set and the system state quantity predicted value;
And updating the system state quantity predicted value and the covariance matrix according to the Kalman gain until the preset updating times are reached, so as to construct and obtain the unscented Kalman filtering parameter estimator.
Further, the estimating, according to the unscented kalman filter parameter estimator, the centroid slip angle of the driving vehicle specifically includes:
Based on the unscented Kalman filter parameter estimator and a preconfigured Sage-Husa noise estimator, estimating a centroid slip angle of the driving vehicle by the following formula (7):
Wherein c is forgetting factor, M k is k time measuring noise covariance matrix, M k+1 is k+1 time measuring noise covariance matrix, y k is system observed quantity, Observed quantity mean value/>, at time k+1For the system state quantity predicted value, d k is a weighting coefficient, ε k+1 is information generated by the unscented Kalman filter parameter estimator, and P y is covariance.
The embodiment of the invention also provides a vehicle parameter estimation device based on the vehicle information, which comprises the following steps:
The real-time parameter acquisition module is used for acquiring real-time parameters of a driving vehicle;
The system model building module is used for building a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
The analysis module is used for analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamic system model so as to construct a first state equation and a measurement equation;
the estimator construction module is used for constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation;
And the estimation module is used for estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator.
The embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the vehicle parameter estimation method based on vehicle information described in any one of the above.
The embodiment of the invention also provides computer equipment, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the steps of the vehicle parameter estimation method based on the vehicle information when executing the computer program.
In summary, the invention has the following beneficial effects:
1. By constructing a nonlinear dynamics system model with seven degrees of freedom, more accurate vehicle driving information is provided, and the accuracy of vehicle parameter estimation is effectively improved;
2. the unscented Kalman filter is adopted to design the parameter estimator, so that more accurate parameter estimation can be realized, and compared with the prior art that the nonlinear function needs to be linearized, the embodiment of the invention uses unscented transformation to solve the nonlinear transfer problem of mean and covariance, and approximates the probability density distribution of the nonlinear function, and a series of determination samples are used to approximate the posterior probability density of the state, so that the linearization of the nonlinear function is avoided, and therefore, the calculation efficiency and the calculation precision can be improved;
3. the unscented Kalman filter estimator is combined with the Sage-Husa estimation algorithm to estimate the statistical characteristics of the noise, so that the problem that the noise cannot be updated in real time is solved, and the robustness and accuracy of the vehicle parameter estimator are finally improved.
Drawings
FIG. 1 is a flow chart of one embodiment of a vehicle parameter estimation method based on vehicle information provided by the present invention;
FIG. 2 is a schematic diagram of an embodiment of a vehicle parameter estimation device based on vehicle information according to the present invention;
FIG. 3 is an analytical schematic of a seven degree-of-freedom nonlinear dynamics system model in accordance with one embodiment of the present invention;
FIG. 4 is a diagram of an automobile model with three degrees of freedom, yaw, lateral and longitudinal, in accordance with one embodiment of the present invention;
FIG. 5 is a flow chart of the construction of a unscented Kalman filter parameter estimator in accordance with one embodiment of the invention;
Fig. 6 is a schematic diagram of an embodiment of a vehicle parameter estimation method based on vehicle information according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of an embodiment of a vehicle parameter estimation method based on vehicle information according to the present invention includes steps S1 to S5, specifically as follows:
s1, acquiring real-time parameters of a driving vehicle;
S2, constructing a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
s3, analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamic system model so as to construct a first state equation and a measurement equation;
s4, constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation;
and S5, estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator.
It should be noted that:
The real-time parameters include at least one of: steering wheel steering angle, vehicle longitudinal speed v x, vehicle lateral speed v y, yaw rate γ, front wheel steering angle δ, longitudinal force F xi of the tire, lateral force F yi of the tire, driving moment T di of the tire, vertical load F zi of the tire, and angular speed ω wi of the tire;
The intrinsic parameters include at least one of: the total mass m of the automobile, the wheel base B of the front axle and the rear axle, the distance l f from the center of mass of the automobile to the front axle, the distance l r from the center of mass of the automobile to the rear axle, the rolling radius rw of the tire, the rolling resistance coefficient f of the tire, the cornering stiffness C f of the front wheel of the automobile, the cornering stiffness C r of the rear wheel of the automobile, the sprung mass of the automobile, the unsprung mass of the front axle, the unsprung mass of the rear axle, the distance from the center of mass of the sprung mass to the roll center, the rotational inertia I z of the automobile body, the height h g of the center of mass of the automobile from the ground and the rotational inertia J w of the wheels;
The front wheel steering angle delta is obtained by acquiring steering wheel angles through a steering angle sensor, and the vehicle longitudinal speed v x and the vehicle lateral speed v y are acquired through a gyroscope sensor.
As an improvement of the above-described aspect, the nonlinear dynamics system model includes a vehicle longitudinal motion equation, a vehicle lateral motion equation, a vehicle yaw motion equation, and a vehicle tire rotation equation.
As an improvement of the above-mentioned scheme, the vehicle longitudinal motion equation is constructed by the following formula (1):
the vehicle lateral motion equation is constructed by the following formula (2):
the vehicle yaw motion equation is constructed by the following formula (3):
the vehicle tire rotation equation is constructed by the following formula (4):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, V y is the vehicle lateral speed, v,/>, for the vehicle longitudinal accelerationFor vehicle lateral acceleration,The yaw acceleration is represented by delta, the steering angle of the front wheels, I z is the rotational inertia of the vehicle body, B is the wheel distance between the front axle and the rear axle, l f is the distance from the center of mass of the vehicle to the front axle, l r is the distance from the center of mass of the vehicle to the rear axle, J w is the rotational inertia of the wheels, rw is the rolling radius of the tires, and f is the rolling resistance coefficient of the tires;
F xi is the longitudinal force of the tire, F yi is the lateral force of the tire, T di is the driving torque of the tire, F zi is the vertical load of the tire, ω wi is the angular velocity of the tire, i=fl, r, rl, r, i=fl represents the front left tire, i=fr represents the front right tire, i=rl represents the rear left tire, i=rr represents the rear right tire.
It should be noted that the number of the substrates,
F xfl is the longitudinal force of the front left tire, F xfr is the longitudinal force of the front right tire, F xrl is the longitudinal force of the rear left tire, F xrr is the longitudinal force of the rear right tire,
F yfl is the lateral force of the front left tire, F yfr is the lateral force of the front right tire, F yrl is the lateral force of the rear left tire, F yrr is the lateral force of the rear right tire,
T dfl is the front left tire drive torque, T dfr is the front right tire drive torque, T drl is the rear left tire drive torque, and T drr is the rear right tire drive torque;
F zfl is the vertical load of the front left tire, F zfr is the vertical load of the front right tire, F zrl is the vertical load of the rear left tire, and F zrr is the vertical load of the rear right tire.
Exemplary, referring to FIG. 3, an analytical schematic of a seven degree-of-freedom nonlinear dynamics system model is provided in accordance with one embodiment of the present invention.
Specifically, the vertical load of each tire is represented by the following formula (8):
Where m is the total mass of the automobile, a x is the longitudinal acceleration of the automobile, a y is the transverse acceleration of the automobile, h g is the height of the mass center of the automobile from the ground, g is the gravitational acceleration, B is the wheelbase of the front and rear axles, l is the distance between the front and rear axles, m w is the total mass of the tire, and F zfl、Fzfr、Fzrl、Fzrr is the vertical load of the front left, front right, rear left and rear right tires.
The tire model is represented by the following formula (9):
Wherein F x0 is the longitudinal force under the pure working condition, F x is the longitudinal force under the combined working condition, F y0 is the lateral force under the pure working condition, F y is the lateral force under the combined working condition, kappa x is the tire slip ratio, alpha y is the tire slip angle, B x、By is the tire stiffness factor, C x、Cy is the tire shape factor, D x、Dy is the tire peak factor, E x、Ey is the tire curvature factor, S vyk is the tire slip ratio lateral force, G x is the weighting function of the tire longitudinal force under the combined working condition, and G y is the weighting function of the tire lateral force under the combined working condition.
To characterize the various state quantities of the tire, the slip angle of the tire is represented by the following formula (10):
Where δ is the front wheel steering angle, γ is the yaw rate, v x、vy is the longitudinal and lateral speeds of the vehicle, B is the front-rear wheel distance, l f、lr is the distance from the front-rear axle to the center of the vehicle, and α fl、αfr、αrl、αrr is the side angles of the front left, front right, rear left, and rear right tires.
The linear velocity of the tire is represented by the following formula (11):
Where δ is the front wheel steering angle, γ is the yaw rate, v x、vy is the longitudinal and lateral speeds of the vehicle, B is the front-rear wheel distance, l f、lr is the distance from the front-rear axle to the center of the vehicle, v wfl、vwfr、vwrl、vwrr is the front left, front right, rear left, rear right tire wheel speeds.
Tire slip ratio is represented by the following formula (12):
where rw is the tire rolling radius, v wfl、vwfr、vwrl、vwrr is the front left, front right, back left, back right tire wheel speed, ω wfl、ωwfr、ωwrl、ωwrr is the front left, front right, back left, back right tire angular velocity, and λ fl、λfr、λrl、λrr is the slip ratio of the front left, front right, back left, back right tire.
As an improvement of the above-described scheme, in step S3:
The analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamics system model to construct a first state equation and a measurement equation specifically includes:
Analyzing the dynamic balance of all directions of the driving vehicle based on the longitudinal motion equation of the vehicle, the lateral motion equation of the vehicle and the yaw motion equation of the vehicle by adopting the darebel principle to construct a first state equation and a measurement equation; wherein the directions include longitudinal, lateral and yaw.
Specifically, referring to fig. 4, an automobile model diagram with yaw, lateral and longitudinal degrees of freedom according to an embodiment of the present invention is provided, assuming that a front wheel steering angle is used as an input, no consideration is given to the influence of a steering system on a vehicle, and assuming that the vehicle has only planar motion parallel to the ground, that is, no consideration is given to a roll angle of the vehicle about an x-axis, a pitch angle about a y-axis and a vertical displacement along a z-axis; the cornering characteristic of the tire is not influenced by the tangential force of the ground due to small driving force; when the lateral acceleration of the vehicle is limited to 0.4g, the tire cornering performance is linear; neglecting aerodynamic effects; the cornering characteristics of the left and right tires are not affected by load changes, and the effect of the aligning moment is not considered.
Under the assumption above, according to the darebel principle, a first state equation and a measurement equation can be constructed by analyzing the dynamic balance of the vehicle body in three directions of longitudinal, lateral and yaw.
As an improvement of the above scheme, the first state equation is constructed by the following formula (5):
The measurement equation is constructed by the following formula (6):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, A x is the original longitudinal acceleration of the vehicle, delta is the front wheel steering angle, beta is the automobile mass center slip angle,Is centroid cornering acceleration, gamma is yaw rate,For yaw acceleration, I z is the rotational inertia of the vehicle body, l f is the distance from the vehicle mass center to the front axle, l r is the distance from the vehicle mass center to the rear axle, C f is the cornering stiffness of the front wheels of the vehicle, C r is the cornering stiffness of the rear wheels of the vehicle, and I >Is the vehicle lateral acceleration.
As an improvement of the above scheme, the construction process of the unscented kalman filter parameter estimator specifically includes:
acquiring an initial value of the unscented Kalman filtering parameter estimator and a first Si gma point set;
calculating a further predicted value of the first Si gma point set based on the initial value and the first Si gma point set;
Calculating a system state quantity predicted value and a covariance matrix based on the further predicted value of the first Sigma point set and a preconfigured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value;
calculating an observed quantity of the second Sigma point set;
weighting and summing the observed quantity of the second Sigma point set to obtain an observed quantity average value;
Calculating covariance based on the observed quantity of the second Sigma point set, the observed quantity mean value and a pre-configured measurement noise covariance matrix;
calculating to obtain a Kalman gain based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, the second Sigma point set and the system state quantity predicted value;
And updating the system state quantity predicted value and the covariance matrix according to the Kalman gain until the preset updating times are reached, so as to construct and obtain the unscented Kalman filtering parameter estimator.
The unscented Kalman filter parameter estimator may comprise a second state equation and an observation equation
The second state equation is constructed by the following formula (13):
The observation equation is constructed by the following formula (14):
y(t)＝h(x(t),v(t))； (14)
exemplary, initial values of the unscented Kalman Filter parameter estimator are defined Meanwhile, the relevant parameters of the unscented Kalman filter parameter estimator are defined as the following formula (15):
Wherein w k、vk is the system noise and the measurement noise, Q k、Mk is the system noise covariance matrix and the measurement noise covariance matrix, Corresponding weight for initial mean value,Is the corresponding weight of the initial covariance,Corresponding weight of the mean value of the ith sampling point,C is a scaling coefficient, n is a state dimension, κ is a second-order scaling parameter, α is a sampling point distribution state, and β is a high-order Xiang Dongcha weight coefficient;
Acquiring a first Si gma point set, wherein the first Si gma point set is specifically 2n+1 Si gma points The following formula (16):
Wherein, For a predefined initial value, An ith column representing square root of variance matrix;
for 2n+1 Si gma points Calculating further predicted values/>, respectivelyThe following formula (17):
calculating a system state quantity predicted value and a covariance matrix, wherein the following formula (18) is adopted:
Wherein, Q k is a pre-configured system noise covariance matrix;
according to the system state quantity predicted value, a second Si gma point set is calculated The following formula (19):
calculating an observed quantity of the second Si gma point set The following formula (20):
Weighting and summing the observed quantity of the second Si gma point set to obtain an observed quantity average value The following formula (21); and
Based on the observed quantity of the second Sigma point set, the observed quantity mean value and a preconfigured measurement noise covariance matrix, calculating to obtain covariance P y, wherein the covariance P y is represented by the following formula (21):
Based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, and the system state quantity predicted value, calculating to obtain a kalman gain K k+1, where:
predicting the system state quantity by the following formula And the covariance matrix P k+1 is updated as follows (23):
Exemplary, referring to fig. 5, a flow chart of the construction of the unscented kalman filter parameter estimator according to an embodiment of the invention is provided.
As an improvement of the above-described scheme, in step S5:
The estimating, according to the unscented kalman filter parameter estimator, a centroid slip angle of the driving vehicle, specifically includes:
Based on the unscented Kalman filter parameter estimator and a preconfigured Sage-Husa noise estimator, estimating a centroid slip angle of the driving vehicle by the following formula (7):
Wherein c is forgetting factor, M k is k time measuring noise covariance matrix, M k+1 is k+1 time measuring noise covariance matrix, y k is system observed quantity, Is the observed quantity mean value at time k+1,For the system state quantity predicted value, d k is a weighting coefficient, ε k+1 is information generated by the unscented Kalman filter parameter estimator, and P y is covariance.
For example, referring to fig. 6, an embodiment of a vehicle parameter estimation method based on vehicle information provided by the present invention is shown, because observation noise is easily affected by external environmental factors and there is a large uncertainty, the Sage-Husa noise estimator estimates and adjusts the statistical characteristics of the observation noise in real time, and it can be understood that the present embodiment can effectively improve the problem that the noise cannot be updated in real time, and finally improve the robustness and accuracy of the vehicle parameter estimator.
Referring to fig. 2, a schematic structural diagram of an embodiment of a vehicle parameter estimation device based on vehicle information according to the present invention includes:
a real-time parameter acquisition module 101 for acquiring real-time parameters of a driving vehicle;
A system model construction module 102, configured to construct a nonlinear dynamics system model for characterizing a motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
an analysis module 103, configured to analyze a dynamic balance of the driving vehicle according to the nonlinear dynamics system model, so as to construct a first state equation and a measurement equation;
An estimator construction module 104, configured to construct an unscented kalman filter parameter estimator based on the first state equation and the measurement equation;
And the estimation module 105 is used for estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filter parameter estimator.
It should be noted that:
The real-time parameters include at least one of: steering wheel steering angle, vehicle longitudinal speed v x, vehicle lateral speed v y, yaw rate γ, front wheel steering angle δ, longitudinal force F xi of the tire, lateral force F yi of the tire, driving moment T di of the tire, vertical load F zi of the tire, and angular speed ω wi of the tire;
The intrinsic parameters include at least one of: the total mass m of the automobile, the wheel base B of the front axle and the rear axle, the distance l f from the center of mass of the automobile to the front axle, the distance l r from the center of mass of the automobile to the rear axle, the rolling radius rw of the tire, the rolling resistance coefficient f of the tire, the cornering stiffness C f of the front wheel of the automobile, the cornering stiffness C r of the rear wheel of the automobile, the sprung mass of the automobile, the unsprung mass of the front axle, the unsprung mass of the rear axle, the distance from the center of mass of the sprung mass to the roll center, the rotational inertia I z of the automobile body, the height h g of the center of mass of the automobile from the ground and the rotational inertia J w of the wheels;
The front wheel steering angle delta is obtained by acquiring steering wheel angles through a steering angle sensor, and the vehicle longitudinal speed v x and the vehicle lateral speed v y are acquired through a gyroscope sensor.
As an improvement of the above-described aspect, the nonlinear dynamics system model includes a vehicle longitudinal motion equation, a vehicle lateral motion equation, a vehicle yaw motion equation, and a vehicle tire rotation equation.
As an improvement of the above-mentioned scheme, the vehicle longitudinal motion equation is constructed by the following formula (1):
the vehicle lateral motion equation is constructed by the following formula (2):
the vehicle yaw motion equation is constructed by the following formula (3):
the vehicle tire rotation equation is constructed by the following formula (4):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, V y is the vehicle lateral speed, v,/>, for the vehicle longitudinal accelerationFor vehicle lateral acceleration,The yaw acceleration is represented by delta, the steering angle of the front wheels, I z is the rotational inertia of the vehicle body, B is the wheel distance between the front axle and the rear axle, l f is the distance from the center of mass of the vehicle to the front axle, l r is the distance from the center of mass of the vehicle to the rear axle, J w is the rotational inertia of the wheels, rw is the rolling radius of the tires, and f is the rolling resistance coefficient of the tires;
F xi is the longitudinal force of the tire, y yi is the lateral force of the tire, T di is the driving torque of the tire, F zi is the vertical load of the tire, ω wi is the angular velocity of the tire, i=fl, r, rl, r, i=fl represents the front left tire, i=fr represents the front right tire, i=rl represents the rear left tire, and i=rr represents the rear right tire.
It should be noted that the number of the substrates,
F xfl is the longitudinal force of the front left tire, F xfr is the longitudinal force of the front right tire, F xrl is the longitudinal force of the rear left tire, F xrr is the longitudinal force of the rear right tire,
F yfl is the lateral force of the front left tire, F yfr is the lateral force of the front right tire, F yrl is the lateral force of the rear left tire, F yrr is the lateral force of the rear right tire,
T dfl is the front left tire drive torque, T dfr is the front right tire drive torque, T drl is the rear left tire drive torque, and T drr is the rear right tire drive torque;
F zfl is the vertical load of the front left tire, F zfr is the vertical load of the front right tire, F zrl is the vertical load of the rear left tire, and F zrr is the vertical load of the rear right tire.
Exemplary, referring to FIG. 3, an analytical schematic of a seven degree-of-freedom nonlinear dynamics system model is provided in accordance with one embodiment of the present invention.
Specifically, the vertical load of each tire is represented by the following formula (8):
Where m is the total mass of the automobile, a x is the longitudinal acceleration of the automobile, a y is the transverse acceleration of the automobile, h g is the height of the mass center of the automobile from the ground, g is the gravitational acceleration, B is the wheelbase of the front and rear axles, l is the distance between the front and rear axles, m w is the total mass of the tire, and F zfl、Fzfr、Fzrl、Fzrr is the vertical load of the front left, front right, rear left and rear right tires.
The tire model is represented by the following formula (9):
Wherein F x0 is the longitudinal force under the pure working condition, F x is the longitudinal force under the combined working condition, F y0 is the lateral force under the pure working condition, F y is the lateral force under the combined working condition, kappa x is the tire slip ratio, alpha y is the tire slip angle, B x、By is the tire stiffness factor, C x、Cy is the tire shape factor, D x、Dy is the tire peak factor, E x、Ey is the tire curvature factor, S vyk is the tire slip ratio lateral force, G x is the weighting function of the tire longitudinal force under the combined working condition, and G y is the weighting function of the tire lateral force under the combined working condition.
To characterize the various state quantities of the tire, the slip angle of the tire is represented by the following formula (10):
Where δ is the front wheel steering angle, γ is the yaw rate, v x、vy is the longitudinal and lateral speeds of the vehicle, B is the front-rear wheel distance, l f、lr is the distance from the front-rear axle to the center of the vehicle, and α fl、αfr、αrl、αrr is the side angles of the front left, front right, rear left, and rear right tires.
The linear velocity of the tire is represented by the following formula (11):
Where δ is the front wheel steering angle, γ is the yaw rate, v x、vy is the longitudinal and lateral speeds of the vehicle, B is the front-rear wheel distance, l f、lr is the distance from the front-rear axle to the center of the vehicle, v wfl、vwfr、vwrl、vwrr is the front left, front right, rear left, rear right tire wheel speeds.
Tire slip ratio is represented by the following formula (12):
where rw is the tire rolling radius, v wfl、vwfr、vwrl、vwrr is the front left, front right, back left, back right tire wheel speed, ω wfl、ωwfr、ωwrl、ωwrr is the front left, front right, back left, back right tire angular velocity, and λ fl、λfr、λrl、λrr is the slip ratio of the front left, front right, back left, back right tire.
As an improvement to the above solution, the analysis module 103 is specifically configured to:
Analyzing the dynamic balance of all directions of the driving vehicle based on the longitudinal motion equation of the vehicle, the lateral motion equation of the vehicle and the yaw motion equation of the vehicle by adopting the darebel principle to construct a first state equation and a measurement equation; wherein the directions include longitudinal, lateral and yaw.
Specifically, referring to fig. 4, an automobile model diagram with yaw, lateral and longitudinal degrees of freedom according to an embodiment of the present invention is provided, assuming that a front wheel steering angle is used as an input, no consideration is given to the influence of a steering system on a vehicle, and assuming that the vehicle has only planar motion parallel to the ground, that is, no consideration is given to a roll angle of the vehicle about an x-axis, a pitch angle about a y-axis and a vertical displacement along a z-axis; the cornering characteristic of the tire is not influenced by the tangential force of the ground due to small driving force; when the lateral acceleration of the vehicle is limited to 0.4g, the tire cornering performance is linear; neglecting aerodynamic effects; the cornering characteristics of the left and right tires are not affected by load changes, and the effect of the aligning moment is not considered.
Under the assumption above, according to the darebel principle, a first state equation and a measurement equation can be constructed by analyzing the dynamic balance of the vehicle body in three directions of longitudinal, lateral and yaw.
As an improvement of the above scheme, the first state equation is constructed by the following formula (5):
The measurement equation is constructed by the following formula (6):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, A x is the original longitudinal acceleration of the vehicle, delta is the front wheel steering angle, beta is the automobile mass center slip angle,Is centroid cornering acceleration, gamma is yaw rate,For yaw acceleration, I z is the rotational inertia of the vehicle body, l f is the distance from the vehicle mass center to the front axle, l r is the distance from the vehicle mass center to the rear axle, C f is the cornering stiffness of the front wheels of the vehicle, C r is the cornering stiffness of the rear wheels of the vehicle, and I >Is the vehicle lateral acceleration.
As an improvement of the above scheme, the construction process of the unscented kalman filter parameter estimator specifically includes:
Acquiring an initial value of the unscented Kalman filter parameter estimator and a first Sigma point set;
calculating a further predicted value of the first Sigma point set based on the initial value and the first Sigma point set;
Calculating a system state quantity predicted value and a covariance matrix based on the further predicted value of the first Sigma point set and a preconfigured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value;
calculating an observed quantity of the second Sigma point set;
weighting and summing the observed quantity of the second Sigma point set to obtain an observed quantity average value;
Calculating covariance based on the observed quantity of the second Sigma point set, the observed quantity mean value and a pre-configured measurement noise covariance matrix;
calculating to obtain a Kalman gain based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, the second Sigma point set and the system state quantity predicted value;
And updating the system state quantity predicted value and the covariance matrix according to the Kalman gain until the preset updating times are reached, so as to construct and obtain the unscented Kalman filtering parameter estimator.
The unscented Kalman filter parameter estimator may comprise a second state equation and an observation equation
The second state equation is constructed by the following formula (13):
The observation equation is constructed by the following formula (14):
y(t)＝h(x(t),(t))；(14)
exemplary, initial values of the unscented Kalman Filter parameter estimator are defined Meanwhile, the relevant parameters of the unscented Kalman filter parameter estimator are defined as the following formula (15):
Wherein w k、vk is the system noise and the measurement noise, Q k、Mk is the system noise covariance matrix and the measurement noise covariance matrix, Corresponding weight for initial mean value,Is the corresponding weight of the initial covariance,Corresponding weight of the mean value of the ith sampling point,C is a scaling coefficient, n is a state dimension, κ is a second-order scaling parameter, α is a sampling point distribution state, and β is a high-order Xiang Dongcha weight coefficient;
Acquiring a first Si gma point set, wherein the first Si gma point set is specifically 2n+1 Si gma points The following formula (16):
Wherein, For a predefined initial value, An ith column representing square root of variance matrix;
for 2n+1 Si gma points Calculating further predicted values/>, respectivelyThe following formula (17):
calculating a system state quantity predicted value and a covariance matrix, wherein the following formula (18) is adopted:
Wherein, Q k is a pre-configured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value The following formula (19):
calculating an observed quantity of the second Si gma point set The following formula (20):
Weighting and summing the observed quantity of the second Si gma point set to obtain an observed quantity average value The following formula (21); and
Based on the observed quantity of the second Sigma point set, the observed quantity mean value and a preconfigured measurement noise covariance matrix, calculating to obtain covariance P y, wherein the covariance P y is represented by the following formula (21):
Based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, and the system state quantity predicted value, calculating to obtain a kalman gain K k+1, where:
predicting the system state quantity by the following formula And the covariance matrix P k+1 is updated as follows (23):
Exemplary, referring to fig. 5, a flow chart of the construction of the unscented kalman filter parameter estimator according to an embodiment of the invention is provided.
As an improvement to the above solution, the estimation module 105 is specifically configured to:
Based on the unscented Kalman filter parameter estimator and a preconfigured Sage-Husa noise estimator, estimating a centroid slip angle of the driving vehicle by the following formula (7):
Wherein c is forgetting factor, M k is k time measuring noise covariance matrix, M k+1 is k+1 time measuring noise covariance matrix, y k is system observed quantity, Is the observed quantity mean value at time k+1,For the system state quantity predicted value, d k is a weighting coefficient, ε k+1 is information generated by the unscented Kalman filter parameter estimator, and P y is covariance.
For example, referring to fig. 6, an embodiment of a vehicle parameter estimation method based on vehicle information provided by the present invention is shown, because observation noise is easily affected by external environmental factors and there is a large uncertainty, the Sage-Husa noise estimator estimates and adjusts the statistical characteristics of the observation noise in real time, and it can be understood that the present embodiment can effectively improve the problem that the noise cannot be updated in real time, and finally improve the robustness and accuracy of the vehicle parameter estimator.
The embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the vehicle parameter estimation method based on vehicle information described in any one of the above.
The embodiment of the invention also provides computer equipment, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor realizes the steps of the vehicle parameter estimation method based on the vehicle information when executing the computer program.
The computer device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a vehicle parameter estimation program based on vehicle information. The processor, when executing the computer program, implements the steps of the above-described embodiments of the vehicle parameter estimation method based on the vehicle information, such as steps S1 to S5 shown in fig. 1.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a computer device and is not limiting of the computer device, and may include more or fewer components than shown, or may combine some of the components, or different components, e.g., the computer device may also include input and output devices, network access devices, buses, etc.
The Processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is a control center of the computer device, connecting various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer program and/or modules, and the processor may implement various functions of the computer device by running or executing the computer program and/or modules stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the computer device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
In summary, the invention has the following beneficial effects:
1. By constructing a nonlinear dynamics system model with seven degrees of freedom, more accurate vehicle driving information is provided, and the accuracy of vehicle parameter estimation is effectively improved;
2. the unscented Kalman filter is adopted to design the parameter estimator, so that more accurate parameter estimation can be realized, and compared with the prior art that the nonlinear function needs to be linearized, the embodiment of the invention uses unscented transformation to solve the nonlinear transfer problem of mean and covariance, and approximates the probability density distribution of the nonlinear function, and a series of determination samples are used to approximate the posterior probability density of the state, so that the linearization of the nonlinear function is avoided, and therefore, the calculation efficiency and the calculation precision can be improved;
3. the unscented Kalman filter estimator is combined with the Sage-Husa estimation algorithm to estimate the statistical characteristics of the noise, so that the problem that the noise cannot be updated in real time is solved, and the robustness and accuracy of the vehicle parameter estimator are finally improved.
From the above description of the embodiments, it will be clear to those skilled in the art that the present invention may be implemented by means of software plus necessary hardware platforms, but may of course also be implemented entirely in hardware. With such understanding, all or part of the technical solution of the present invention contributing to the background art may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the method described in the embodiments or some parts of the embodiments of the present invention.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.
Claims (4)
1. A vehicle parameter estimation method based on vehicle information, characterized by comprising:
Acquiring real-time parameters of a driving vehicle;
constructing a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
according to the nonlinear dynamics system model, analyzing the dynamics balance of the driving vehicle to construct a first state equation and a measurement equation;
constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation;
Estimating and obtaining a centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator;
wherein the nonlinear dynamics system model comprises a vehicle longitudinal motion equation, a vehicle lateral motion equation, a vehicle yaw motion equation and a vehicle tire rotation equation;
the vehicle longitudinal motion equation is constructed by the following formula (1):
the vehicle lateral motion equation is constructed by the following formula (2):
the vehicle yaw motion equation is constructed by the following formula (3):
the vehicle tire rotation equation is constructed by the following formula (4):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, For vehicle longitudinal acceleration, v y for vehicle lateral speed,For vehicle lateral acceleration,The yaw acceleration is represented by delta, the steering angle of the front wheels, I z is the rotational inertia of the vehicle body, B is the wheel distance between the front axle and the rear axle, l f is the distance from the center of mass of the vehicle to the front axle, l r is the distance from the center of mass of the vehicle to the rear axle, J w is the rotational inertia of the wheels, rw is the rolling radius of the tires, and f is the rolling resistance coefficient of the tires;
F xi is the longitudinal force of the tire, F yi is the lateral force of the tire, T di is the driving moment of the tire, F zi is the vertical load of the tire, ω wi is the angular velocity of the tire, i=fl, fr, rl, rr, i=fl represents the front left tire, i=fr represents the front right tire, i=rl represents the rear left tire, i=rr represents the rear right tire;
The analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamics system model to construct a first state equation and a measurement equation specifically includes:
Analyzing the dynamic balance of all directions of the driving vehicle based on the longitudinal motion equation of the vehicle, the lateral motion equation of the vehicle and the yaw motion equation of the vehicle by adopting the darebel principle to construct a first state equation and a measurement equation; wherein the directions include longitudinal, lateral, and yaw;
Wherein the first state equation is constructed by the following formula (5):
The measurement equation is constructed by the following formula (6):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, A x is the original longitudinal acceleration of the vehicle, delta is the front wheel steering angle, beta is the automobile mass center slip angle,Is centroid cornering acceleration, gamma is yaw rate,For yaw acceleration, I z is the rotational inertia of the vehicle body, l f is the distance from the vehicle mass center to the front axle, l r is the distance from the vehicle mass center to the rear axle, C f is the cornering stiffness of the front wheels of the vehicle, C r is the cornering stiffness of the rear wheels of the vehicle, and I >Is the vehicle lateral acceleration;
the construction process of the unscented Kalman filter parameter estimator specifically comprises the following steps:
Acquiring an initial value of the unscented Kalman filter parameter estimator and a first Sigma point set;
calculating a further predicted value of the first Sigma point set based on the initial value and the first Sigma point set;
Calculating a system state quantity predicted value and a covariance matrix based on the further predicted value of the first Sigma point set and a preconfigured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value;
calculating an observed quantity of the second Sigma point set;
weighting and summing the observed quantity of the second Sigma point set to obtain an observed quantity average value;
Calculating covariance based on the observed quantity of the second Sigma point set, the observed quantity mean value and a pre-configured measurement noise covariance matrix;
calculating to obtain a Kalman gain based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, the second Sigma point set and the system state quantity predicted value;
Updating the system state quantity predicted value and the covariance matrix according to the Kalman gain until reaching the preset updating times to construct and obtain an unscented Kalman filtering parameter estimator;
the estimating, according to the unscented kalman filter parameter estimator, a centroid slip angle of the driving vehicle, specifically includes:
Based on the unscented Kalman filter parameter estimator and a preconfigured Sage-Husa noise estimator, estimating a centroid slip angle of the driving vehicle by the following formula (7):
Wherein c is forgetting factor, M k is k time measuring noise covariance matrix, M k+1 is k+1 time measuring noise covariance matrix, y k is system observed quantity, Is the observed quantity mean value at time k+1,For the system state quantity predicted value, d k is a weighting coefficient, ε k+1 is information generated by the unscented Kalman filter parameter estimator, and P y is covariance.
2. A vehicle parameter estimation device based on vehicle information, characterized by comprising:
The real-time parameter acquisition module is used for acquiring real-time parameters of a driving vehicle;
The system model building module is used for building a nonlinear dynamics system model for representing the motion state of the driving vehicle based on the real-time parameters and the pre-acquired intrinsic parameters of the driving vehicle;
The analysis module is used for analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamic system model so as to construct a first state equation and a measurement equation;
the estimator construction module is used for constructing an unscented Kalman filter parameter estimator based on the first state equation and the measurement equation;
the estimation module is used for estimating and obtaining the centroid slip angle of the driving vehicle according to the unscented Kalman filtering parameter estimator;
wherein the nonlinear dynamics system model comprises a vehicle longitudinal motion equation, a vehicle lateral motion equation, a vehicle yaw motion equation and a vehicle tire rotation equation;
the vehicle longitudinal motion equation is constructed by the following formula (1):
the vehicle lateral motion equation is constructed by the following formula (2):
the vehicle yaw motion equation is constructed by the following formula (3):
the vehicle tire rotation equation is constructed by the following formula (4):
where m is the total mass of the vehicle, v x is the longitudinal speed of the vehicle, For vehicle longitudinal acceleration, v y for vehicle lateral speed,For vehicle lateral acceleration,The yaw acceleration is represented by delta, the steering angle of the front wheels, I z is the rotational inertia of the vehicle body, B is the wheel distance between the front axle and the rear axle, l f is the distance from the center of mass of the vehicle to the front axle, l r is the distance from the center of mass of the vehicle to the rear axle, J w is the rotational inertia of the wheels, rw is the rolling radius of the tires, and f is the rolling resistance coefficient of the tires;
F xi is the longitudinal force of the tire, F yi is the lateral force of the tire, T di is the driving moment of the tire, F zi is the vertical load of the tire, ω wi is the angular velocity of the tire, i=fl, fr, rl, rr, i=fl represents the front left tire, i=fr represents the front right tire, i=rl represents the rear left tire, i=rr represents the rear right tire;
The analyzing the dynamic balance of the driving vehicle according to the nonlinear dynamics system model to construct a first state equation and a measurement equation specifically includes:
Analyzing the dynamic balance of all directions of the driving vehicle based on the longitudinal motion equation of the vehicle, the lateral motion equation of the vehicle and the yaw motion equation of the vehicle by adopting the darebel principle to construct a first state equation and a measurement equation; wherein the directions include longitudinal, lateral, and yaw;
Wherein the first state equation is constructed by the following formula (5):
The measurement equation is constructed by the following formula (6):
Wherein m is the total mass of the vehicle, b x is the longitudinal speed of the vehicle, A x is the original longitudinal acceleration of the vehicle, delta is the front wheel steering angle, beta is the automobile mass center slip angle,Is centroid cornering acceleration, gamma is yaw rate,For yaw acceleration, I z is the rotational inertia of the vehicle body, l f is the distance from the vehicle mass center to the front axle, l r is the distance from the vehicle mass center to the rear axle, C f is the cornering stiffness of the front wheels of the vehicle, C r is the cornering stiffness of the rear wheels of the vehicle, and I >Is the vehicle lateral acceleration;
the construction process of the unscented Kalman filter parameter estimator specifically comprises the following steps:
Acquiring an initial value of the unscented Kalman filter parameter estimator and a first Sigma point set;
calculating a further predicted value of the first Sigma point set based on the initial value and the first Sigma point set;
Calculating a system state quantity predicted value and a covariance matrix based on the further predicted value of the first Sigma point set and a preconfigured system noise covariance matrix;
calculating a second Sigma point set according to the system state quantity predicted value;
calculating an observed quantity of the second Sigma point set;
weighting and summing the observed quantity of the second Sigma point set to obtain an observed quantity average value;
Calculating covariance based on the observed quantity of the second Sigma point set, the observed quantity mean value and a pre-configured measurement noise covariance matrix;
calculating to obtain a Kalman gain based on the covariance, the observed quantity mean value, the observed quantity of the second Sigma point set, the second Sigma point set and the system state quantity predicted value;
Updating the system state quantity predicted value and the covariance matrix according to the Kalman gain until reaching the preset updating times to construct and obtain an unscented Kalman filtering parameter estimator;
the estimating, according to the unscented kalman filter parameter estimator, a centroid slip angle of the driving vehicle, specifically includes:
Based on the unscented Kalman filter parameter estimator and a preconfigured Sage-Husa noise estimator, estimating a centroid slip angle of the driving vehicle by the following formula (7):
Wherein c is forgetting factor, M k is k time measuring noise covariance matrix, M k+1 is k+1 time measuring noise covariance matrix, y k is system observed quantity, Is the observed quantity mean value at time k+1,For the system state quantity predicted value, d k is a weighting coefficient, ε k+1 is information generated by the unscented Kalman filter parameter estimator, and P y is covariance.
3. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the vehicle parameter estimation method based on vehicle information as claimed in claim 1.
4. A computer device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the vehicle parameter estimation method based on vehicle information as claimed in claim 1 when executing the computer program.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102005060219A1 (en) * | 2005-12-16 | 2007-06-21 | Ford Global Technologies, LLC, Dearborn | Estimating method for rating a friction coefficient between a road and a motor vehicle's tires uses the friction coefficient to enlarge a Kalman filter by the friction coefficient |
| CN108545081A (en) * | 2018-03-20 | 2018-09-18 | 北京理工大学 | Slip angle estimation method and system based on robust Unscented kalman filtering |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102005060219A1 (en) * | 2005-12-16 | 2007-06-21 | Ford Global Technologies, LLC, Dearborn | Estimating method for rating a friction coefficient between a road and a motor vehicle's tires uses the friction coefficient to enlarge a Kalman filter by the friction coefficient |
| CN108545081A (en) * | 2018-03-20 | 2018-09-18 | 北京理工大学 | Slip angle estimation method and system based on robust Unscented kalman filtering |
Non-Patent Citations (1)
| Title |
|---|
| 石志伟；任师通；魏民祥；查曰珩.基于自适应无迹卡尔曼滤波的汽车状态参数估计.公路与汽运.2021,(205),第8页第2栏第2段-第10页第2栏第4段，附图1-8. * |
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