CN111942399A - Vehicle speed estimation method and system based on unscented Kalman filtering - Google Patents

Vehicle speed estimation method and system based on unscented Kalman filtering Download PDF

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CN111942399A
CN111942399A CN202010693403.4A CN202010693403A CN111942399A CN 111942399 A CN111942399 A CN 111942399A CN 202010693403 A CN202010693403 A CN 202010693403A CN 111942399 A CN111942399 A CN 111942399A
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vehicle
time
unscented kalman
state
speed
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户亚威
苟斌
王成君
车顺
杜佩瑾
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Dongfeng Motor Corp
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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
    • B60W40/105—Speed
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/18—Braking system
    • B60W2510/182—Brake pressure, e.g. of fluid or between pad and disc
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/28—Wheel speed
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/40—Coefficient of friction

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  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
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Abstract

The application discloses a vehicle speed estimation method and system based on unscented Kalman filtering, and relates to the technical field of vehicle speed estimation, wherein the vehicle speed estimation method comprises the following steps: establishing a high-precision vehicle model, and acquiring vehicle parameters; establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed; and estimating the longitudinal speed and the lateral speed at the next moment by utilizing a vehicle speed estimator and combining the state quantity at the current moment, the state equation and the observation equation. According to the vehicle speed estimation method and system based on unscented Kalman filtering, the vehicle speed estimator is combined with the unscented Kalman filtering algorithm, so that the real-time longitudinal speed and the lateral speed of a vehicle can be accurately estimated in the running process of the vehicle, and the accuracy of vehicle dynamic control is improved.

Description

Vehicle speed estimation method and system based on unscented Kalman filtering
Technical Field
The application relates to the technical field of vehicle speed estimation, in particular to a vehicle speed estimation method and system based on unscented Kalman filtering.
Background
Currently, in vehicle dynamics control, vehicle speed is a vital state quantity, which is the basis for calculating the wheel slip ratio, which is the main control quantity in vehicle dynamics control. Since the cost of installing a sensor for directly measuring the vehicle speed on the vehicle is too high, the vehicle speed is generally used as a state quantity which cannot be directly obtained in actual research and development, and various estimation algorithms are adopted to estimate the vehicle speed.
In the related technology, the existing estimation algorithm comprises a maximum wheel speed method, a slope method and a synthesis method, the algorithm process is simple, the implementation is convenient, the cost is low, but the estimation precision is not high, and the method is only suitable for the traditional vehicle with low requirement on the control precision. With the development of automobile intellectualization and networking, the requirement of modern automobiles on the integrated control precision of vehicle dynamics is further improved, so that a mathematical model and a corresponding estimation algorithm for estimating the vehicle speed need to be redesigned urgently.
The method has the advantages that inevitable process errors and observation errors exist in the vehicle speed estimation process, the Kalman filter KF (Kalman Filter) is a linear estimation algorithm, the state estimation problem of a linear system can be solved only under a linear Gaussian model, and the system in practical engineering application always has nonlinearity of different degrees, for example, tires in a vehicle system are a typical highly nonlinear system, so the basic Kalman filter algorithm is not suitable for being introduced into the vehicle state estimation field, and the estimation errors are large.
Disclosure of Invention
Aiming at one of the defects in the prior art, the application aims to provide a vehicle speed estimation method and system based on unscented kalman filtering to solve the problem of low vehicle speed estimation precision in the related technology.
The application provides a vehicle speed estimation method based on unscented kalman filtering in a first aspect, which includes the steps:
establishing a high-precision vehicle model, and acquiring vehicle parameters;
establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed;
and estimating the longitudinal speed and the lateral speed at the next moment by using a vehicle speed estimator and combining the state quantity at the current moment and the state equation and the observation equation.
In some embodiments, before the obtaining the vehicle parameters, the method further includes:
the whole vehicle controller outputs the braking pressure of each wheel to the high-precision vehicle model;
the high-precision vehicle model simulates the vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters;
the whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw rate, wheel speeds of four wheels and road surface adhesion coefficients at the four wheels.
In some embodiments, after obtaining the vehicle parameters, the method further includes:
the high-precision vehicle model outputs the wheel speeds of four wheels to the whole vehicle controller.
In some embodiments, the input quantity includes wheel speeds of four wheels, the state quantity further includes a longitudinal acceleration, a lateral acceleration, a yaw rate, and road surface adhesion coefficients at the four wheels, and the observed quantity includes the longitudinal acceleration, the lateral acceleration, and the yaw rate.
In some embodiments, the estimating the longitudinal speed and the lateral speed at the next time by using the vehicle speed estimator in combination with the state quantity at the current time and the state equation and the observation equation of the unscented kalman filter specifically includes:
carrying out unscented transformation on the state quantity at the current moment to obtain a plurality of sigma points, and calculating the weight of each sigma point;
calculating a prior state estimation value, a prior error covariance and a prior observation estimation value according to the state equation and the observation equation;
and updating the next observed value and the error covariance according to the prior state estimated value, the prior error covariance and the prior observed estimated value.
In some embodiments, the state equation and the observation equation of the unscented kalman filter are:
Figure BDA0002590048260000031
wherein x isk+1Is the state vector at time k +1, xkIs the state vector at time k, zkIs the observation vector at time k, ukIs the input vector at time k, wkIs the process noise at time k, vkIs the observed noise at time k.
In some embodiments, the above equation of state is further:
Figure BDA0002590048260000032
where u (k) is the longitudinal velocity at time k,
Figure BDA0002590048260000033
is the derivative of the longitudinal velocity at time k, v (k) is the lateral velocity at time k, ax(k) Longitudinal acceleration at time k, ay(k) Lateral acceleration at time k, ωr(k) Is the yaw rate at the time k,
Figure BDA0002590048260000034
derivative of yaw rate at time k, mufl(k) Road surface adhesion coefficient of the left front wheel at time k, mufr(k) Road surface adhesion coefficient of the right front wheel at time k, murl(k) Road surface adhesion coefficient of the left rear wheel at time k, murr(k) Road surface adhesion coefficient of the right rear wheel at time k, TsThe step of time between times k and k + 1.
In some embodiments, the multiple degree of freedom vehicle dynamics model is a seven degree of freedom dynamics model related to vehicle speed.
A second aspect of the present application provides a vehicle speed estimation system based on unscented kalman filter, including:
the first modeling module is used for establishing a high-precision vehicle model and acquiring vehicle parameters through the high-precision vehicle model;
the second modeling module is used for establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed;
and the vehicle speed estimator is used for combining the state quantity at the current moment, and the state equation and the observation equation of the unscented Kalman filter to estimate the longitudinal speed and the lateral speed at the next moment.
In some embodiments, further comprising:
the vehicle control unit is used for outputting the braking pressure of each wheel to the first modeling module;
the high-precision vehicle model simulates the vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters;
the whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw rate, wheel speeds of four wheels and road surface adhesion coefficients at the four wheels.
The beneficial effect that technical scheme that this application provided brought includes:
according to the vehicle speed estimation method and system based on unscented Kalman filtering, real-time simulation is carried out on a vehicle through a high-precision vehicle model to obtain finished vehicle parameters, then through a multi-degree-of-freedom vehicle dynamics model, a state equation and an observation equation of an unscented Kalman filter are determined according to the finished vehicle parameters, input quantity, state quantity and observed quantity of the unscented Kalman filter are determined, and then a vehicle speed estimator is utilized to combine the state quantity of the current moment, the state equation and the observation equation to realize estimation of longitudinal speed and lateral speed of the next moment.
Drawings
FIG. 1 is a flow chart of a vehicle speed estimation method based on unscented Kalman filtering according to an embodiment of the present application;
FIG. 2 is a schematic diagram of vehicle speed estimation based on unscented Kalman filtering in an embodiment of the present application;
fig. 3 is a flowchart of step S3 in the embodiment of the present application;
FIG. 4 is a diagram showing a result of estimating a vehicle speed according to the embodiment of the present application;
FIG. 5 is a vehicle speed estimation error map of an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings and examples.
Referring to fig. 1, an embodiment of the present application provides a vehicle speed estimation method based on unscented kalman filtering, which includes the steps of:
s1, establishing a high-precision vehicle model and obtaining vehicle parameters. The high-precision vehicle model is based on simulation software CarSim and is used for simulating a real vehicle in real time. The high-precision vehicle model has high degree of freedom and accurate simulation result.
S2, establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter UKF (unscented Kalman Filter) according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed.
And S3, estimating the longitudinal speed and the lateral speed at the next moment by using a vehicle speed estimator and combining the state quantity at the current moment, the state equation and the observation equation.
According to the vehicle speed estimation method and system based on unscented Kalman filtering, real-time simulation is carried out on a vehicle through a high-precision vehicle model to obtain finished vehicle parameters, then through a multi-degree-of-freedom vehicle dynamics model, a state equation and an observation equation of an unscented Kalman filter are determined according to the finished vehicle parameters, input quantity, state quantity and observed quantity of the unscented Kalman filter are determined, and then a vehicle speed estimator is utilized to combine the state quantity of the current moment, the state equation and the observation equation to realize estimation of longitudinal speed and lateral speed of the next moment.
In this embodiment, the multi-degree-of-freedom vehicle dynamics model is a medium-precision vehicle model based on MATLAB/simulink, runs in the vehicle speed estimator, and is used for one-step state prediction by the vehicle speed estimator.
Preferably, the multiple-degree-of-freedom vehicle dynamics model is a seven-degree-of-freedom dynamics model related to a vehicle speed. The seven-degree-of-freedom dynamic model mainly considers longitudinal motion, transverse motion, yaw motion and rotation motion of four wheels of a vehicle body, and ignores pitching motion, rolling motion and vertical motion of the vehicle.
Further, before acquiring the vehicle parameters in step S1, the method further includes:
first, the vehicle control unit outputs the brake pressure of each wheel to the high-precision vehicle model.
And then, the high-precision vehicle model simulates the vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters.
The whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw velocity, wheel speeds of four wheels and road adhesion coefficients of the four wheels in the running process of the vehicle.
In this embodiment, after obtaining the vehicle parameters in step S1, the method further includes:
the high-precision vehicle model outputs the wheel speeds of four wheels to the whole vehicle controller.
Further, the input quantity includes wheel speeds of four wheels, the state quantity further includes a longitudinal acceleration, a lateral acceleration, a yaw rate, and road surface adhesion coefficients at the four wheels, and the observed quantity includes the longitudinal acceleration, the lateral acceleration, and the yaw rate.
Referring to fig. 2, the high-precision vehicle model outputs the longitudinal acceleration, the lateral acceleration, the yaw rate, the wheel speeds of the four wheels, and the road adhesion coefficients of the four wheels as observed values, the observed values are input into a vehicle speed estimator, the vehicle speed estimator estimates the longitudinal speed and the lateral speed at the next moment based on the multi-degree-of-freedom vehicle dynamics model, and then outputs the estimated longitudinal speed and the estimated lateral speed to a vehicle controller, and the vehicle controller outputs the braking pressure of each wheel to the high-precision vehicle model, so that a closed-loop control is formed, and the vehicle dynamics control is completed. Optionally, the high-accuracy vehicle model also outputs the steering wheel angle to a vehicle speed estimator. In this embodiment, the steering wheel angle is a default value.
In this embodiment, the nonlinear state equation and the observation equation of the unscented kalman filter are respectively:
Figure BDA0002590048260000071
wherein x isk+1Is the state vector at time k +1, xkIs the state vector at time k, zkIs the observation vector at time k, ukIs the input vector at time k, wkIs the process noise at time k, vkIs the observed noise at time k.
Referring to fig. 3, in step S3, the estimating the longitudinal speed and the lateral speed at the next time by using the vehicle speed estimator in combination with the state quantity at the current time and the state equation and the observation equation of the unscented kalman filter specifically includes:
A1. and initializing to obtain an initial state estimated value and an initial error covariance matrix.
Because the process noise comes from the inaccuracy of the dynamic model of the multi-degree-of-freedom vehicle, the observation noise comes from the inaccuracy of observationIn Kalman filtering, it is assumed that process noise w and observation noise v are both white Gaussian noise with mean value of 0, and their covariance matrixes are Q and R, i.e. E [ wwT]＝Q，E[vvT]＝R，E[wvT]＝0。
A2. And carrying out unscented transformation on the state quantity at the current moment to obtain a plurality of sigma points, and calculating the weight of each sigma point.
In this embodiment, the unscented kalman filter is based on: the idea is that the approximation of the probability density distribution of a non-linear function is easier than the approximation of the non-linear function itself. The transfer of probability density is approximated by using unscented transformation ut (unscented transformation), which needs to select (2n +1) sampling points, i.e. Sigma points, wherein the requirement of Sigma point selection is that the mean and covariance are equal to those of the original system state distribution. And then substituting the Sigma point into a state equation to obtain a propagated sample point, and finally replacing the propagated statistical characteristic of the nonlinear system with the statistical characteristic of the propagated sample point.
First, 2n +1 Sigma spots were acquired as follows:
Figure BDA0002590048260000081
wherein, Pk-1|k-1Is the error covariance of the state space at time k-1 in the system or state equation representation,
Figure BDA0002590048260000082
is the predicted value of the state at the moment k-1,
Figure BDA0002590048260000083
and
Figure BDA0002590048260000084
i and i + n Sigma points (symmetric) at time k-1, λ is a scaling factor, and λ ═ α2(n + k) -n, α, β, κ are determined from the actual state of the state space. The above selection method ensures that the 1 st and n +1, 2 nd and n +2 … n and 2n points are all around the 0 th point (i.e. the mean point),thereby meeting the requirement that the mean value and covariance of the sample points are equal to the mean value and covariance of the original system state distribution.
Then, the weights of (2n +1) Sigma points are calculated as follows:
Figure BDA0002590048260000091
wherein the content of the first and second substances,
Figure BDA0002590048260000092
is the mean weight of the 0 th Sigma point,
Figure BDA0002590048260000093
is the variance weight of the 0 th Sigma point, Wi mIs the mean weight of the ith Sigma point, Wi cIs the variance weight of the ith Sigma point.
A3. And updating time, namely calculating a prior state estimation value, a prior error covariance and a prior observation estimation value according to the state equation and the observation equation.
Firstly, carrying out predictive calculation on a Sigma point to obtain a priori state estimated value
Figure BDA0002590048260000094
And a priori state estimates of Sigma points
Figure BDA0002590048260000095
Carrying out weighted summation to obtain the prior state estimated value mean value:
Figure BDA0002590048260000096
wherein the content of the first and second substances,
Figure BDA0002590048260000097
is the average value of the prior estimation value of the state quantity at the k moment at the k-1 moment.
Then, calculating the prior error covariance according to the prior state estimation value and the prior state estimation value mean value:
Figure BDA0002590048260000098
wherein, Pk|k-1The prior error covariance for the state quantity at time k-1 is taken as the time k-1.
Finally, calculating a priori observation estimated value:
Figure BDA0002590048260000099
wherein the content of the first and second substances,
Figure BDA00025900482600000910
is a priori observed for the Sigma point at time k-1,
Figure BDA00025900482600000911
is the prior observed value mean of k-1 time to k time.
A4. And updating the next observed value and the error covariance according to the prior state estimated value, the prior error covariance and the prior observed estimated value.
First, a correction matrix is calculated:
Figure BDA0002590048260000101
wherein the content of the first and second substances,
Figure BDA0002590048260000102
in order to observe the covariance,
Figure BDA0002590048260000103
to predict observed cross variance, KkIs the kalman gain.
Then, the observation is updated:
Figure BDA0002590048260000104
wherein the content of the first and second substances,
Figure BDA0002590048260000105
is an observed value of the state quantity at the time k, namely an a posteriori estimated value.
Finally, the error covariance is updated:
Figure BDA0002590048260000106
wherein, Pk|kIs the error covariance of the observed value of the state quantity at the time k, i.e. the a posteriori error covariance.
At this time, the observed value of the state quantity at the time k is obtained
Figure BDA0002590048260000107
Including the estimated longitudinal and lateral velocities at time k. When the state quantity at the time k +1 is estimated, let k be k +1, return to step a2, perform the next round of calculation, that is, obtain the longitudinal speed and the lateral speed at the time k +1, and repeat the above calculation in a cyclic manner, that is, obtain the longitudinal speed and the lateral speed at each time.
State quantity, i.e. state vector x ═ u vax ay ωr μfl μfr μrl μrr]TThe observation vector z is [ a ]x ay ωr]TThe input quantity, i.e., the input vector u ═ ωfl ωfr ωrl ωrr]T。
Wherein u is the longitudinal velocity, v is the lateral velocity, axFor longitudinal acceleration, ayAs lateral acceleration, ωrIs yaw angular velocity, ωfl、ωfr、ωrl、ωrrRespectively the left front wheel speed, the right front wheel speed, the left rear wheel speed and the right rear wheel speed, mufl、μfr、μrl、μrrRespectively the road surface adhesion coefficient of the left front wheel and the road surface adhesion coefficient of the right front wheelRoad surface adhesion coefficient, left rear wheel road surface adhesion coefficient, right rear wheel road surface adhesion coefficient.
In this embodiment, some reasonable assumptions need to be made, the first assumed acceleration does not suddenly change, and the second assumed road adhesion coefficient does not suddenly change, so that the above equation of state further includes:
Figure BDA0002590048260000111
where u (k) is the longitudinal velocity at time k,
Figure BDA0002590048260000112
is the derivative of the longitudinal velocity at time k, v (k) is the lateral velocity at time k,
Figure BDA0002590048260000113
is the derivative of the lateral velocity at time k, ax(k) Longitudinal acceleration at time k, ay(k) Lateral acceleration at time k, ωr(k) Is the yaw rate at the time k,
Figure BDA0002590048260000114
derivative of yaw rate at time k, mufl(k) Road surface adhesion coefficient of the left front wheel at time k, mufr(k) Road surface adhesion coefficient of the right front wheel at time k, murl(k) Road surface adhesion coefficient of the left rear wheel at time k, murr(k) Road surface adhesion coefficient of the right rear wheel at time k, TsThe step of time between k and k + 1.
In the embodiment, a UKF vehicle speed estimator model and a multi-degree-of-freedom vehicle dynamic model for state estimation are built in MATLAB/simulink, and the multi-degree-of-freedom vehicle dynamic model is only used for verifying an estimation algorithm and does not relate to the control of a bottom layer execution mechanism, so that a first-order inertial System can be adopted to replace the whole brake System, vehicle model parameters of a high-precision vehicle model are set in CarSim, an Anti-lock Braking System (ABS) algorithm carried by the CarSim is adopted to carry out ABS control, a straight dry road surface with a medium adhesion coefficient is selected in a road surface setting window, the road surface adhesion coefficient is set to be 0.5, and the brake control is set to be sudden-add brake, namely, 1.5Mpa brake pressure is suddenly applied when 0.25 s; the initial braking speed was set at 70 km/h.
Referring to fig. 4, the longitudinal vehicle speed is taken as an example for verification, the high-precision vehicle model of Carsim outputs the real-time vehicle speed of the vehicle, and the estimated vehicle speed and the actual vehicle speed are basically coincident in the simulation process. Referring to fig. 5, it can be seen from the estimation error curve that the maximum value of the estimation error in the deceleration process is 1.2km/h (the error after the vehicle speed is reduced to 0 is not considered), which illustrates that the vehicle speed estimation method of the embodiment can better estimate the real-time vehicle speed of the vehicle and ensure the stability of the ABS control process.
The embodiment of the application also provides a vehicle speed estimation system based on unscented Kalman filtering, which comprises a first modeling module, a second modeling module and a vehicle speed estimator.
The first modeling module is used for establishing a high-precision vehicle model and acquiring vehicle parameters through the high-precision vehicle model.
The second modeling module is used for establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of the unscented Kalman filter according to the vehicle parameters and the multi-degree-of-freedom vehicle dynamics model, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed.
The vehicle speed estimator is used for combining the state quantity at the current moment, and the state equation and the observation equation of the unscented Kalman filter to estimate the longitudinal speed and the lateral speed at the next moment.
Further, the vehicle speed estimation system further comprises a vehicle control unit.
The vehicle control unit is used for outputting the braking pressure of each wheel to the first modeling module. And then, the high-precision vehicle model can simulate the vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters.
The whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw rate, wheel speeds of four wheels and road surface adhesion coefficients at the four wheels. Correspondingly, the input quantity includes wheel speeds of four wheels, the state quantity further includes a longitudinal acceleration, a lateral acceleration, a yaw rate, and road surface adhesion coefficients at the four wheels, and the observed quantity includes a longitudinal acceleration, a lateral acceleration, and a yaw rate.
The vehicle speed estimation system of the embodiment is suitable for the vehicle speed estimation methods, only the estimation value of the previous moment and the observation value of the current moment need to be stored in the whole estimation process, and the cyclic calculation can be carried out to obtain the longitudinal speed and the lateral speed of each moment, so that the performance requirement on the vehicle-mounted controller is reduced to the maximum extent.
The present application is not limited to the above embodiments, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present application, and such modifications and improvements are also considered to be within the scope of the present application.

Claims (10)

1. A vehicle speed estimation method based on unscented Kalman filtering is characterized by comprising the following steps:
establishing a high-precision vehicle model, and acquiring vehicle parameters;
establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed;
and estimating the longitudinal speed and the lateral speed at the next moment by utilizing a vehicle speed estimator and combining the state quantity at the current moment, the state equation and the observation equation.
2. The unscented kalman filter-based vehicle speed estimation method according to claim 1, wherein before acquiring the vehicle parameters, the method further comprises:
the vehicle control unit outputs the braking pressure of each wheel to the high-precision vehicle model;
the high-precision vehicle model simulates a vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters;
the whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw rate, wheel speeds of four wheels and road surface adhesion coefficients at the four wheels.
3. The unscented kalman filter-based vehicle speed estimation method according to claim 2, wherein after acquiring the vehicle parameters, the method further comprises:
and the high-precision vehicle model outputs the wheel speeds of four wheels to the whole vehicle controller.
4. The unscented kalman filter-based vehicle speed estimation method according to claim 2, characterized in that:
the input quantities include wheel speeds of four wheels, the state quantities further include longitudinal acceleration, lateral acceleration, yaw rate, and road surface attachment coefficients at the four wheels, and the observed quantities include longitudinal acceleration, lateral acceleration, and yaw rate.
5. The unscented kalman filter-based vehicle speed estimation method according to claim 1, wherein the estimating, by the vehicle speed estimator, the longitudinal speed and the lateral speed at the next time by combining the state quantity at the current time and the state equation and the observation equation of the unscented kalman filter specifically comprises:
carrying out unscented transformation on the state quantity at the current moment to obtain a plurality of sigma points, and calculating the weight of each sigma point;
calculating a prior state estimation value, a prior error covariance and a prior observation estimation value according to the state equation and the observation equation;
and updating the next observed value and the error covariance according to the prior state estimated value, the prior error covariance and the prior observed estimated value.
6. The unscented kalman filter-based vehicle speed estimation method according to claim 1, wherein the state equation and the observation equation of the unscented kalman filter are:
Figure FDA0002590048250000021
wherein x isk+1Is the state vector at time k +1, xkIs the state vector at time k, zkIs the observation vector at time k, ukIs the input vector at time k, wkIs the process noise at time k, vkIs the observed noise at time k.
7. The unscented kalman filter-based vehicle speed estimation method according to claim 6, wherein the state equation further is:
Figure FDA0002590048250000022
where u (k) is the longitudinal velocity at time k,
Figure FDA0002590048250000031
is the derivative of the longitudinal velocity at time k, v (k) is the lateral velocity at time k, ax(k) Longitudinal acceleration at time k, ay(k) Lateral acceleration at time k, ωr(k) Is the yaw rate at the time k,
Figure FDA0002590048250000032
derivative of yaw rate at time k, mufl(k) Road surface adhesion coefficient of the left front wheel at time k, mufr(k) Road surface adhesion coefficient of the right front wheel at time k, murl(k) Road surface adhesion coefficient of the left rear wheel at time k, murr(k) Road surface adhesion coefficient of the right rear wheel at time k, TsThe step of time between k and k + 1.
8. The unscented kalman filter-based vehicle speed estimation method according to claim 1, characterized in that: the multi-degree-of-freedom vehicle dynamic model is a seven-degree-of-freedom dynamic model related to the vehicle speed.
9. A vehicle speed estimation system based on unscented kalman filtering, comprising:
the first modeling module is used for establishing a high-precision vehicle model and acquiring vehicle parameters through the high-precision vehicle model;
the second modeling module is used for establishing a multi-degree-of-freedom vehicle dynamics model, determining a state equation and an observation equation of an unscented Kalman filter according to the vehicle parameters, and determining input quantity, state quantity and observation quantity of the unscented Kalman filter, wherein the state quantity at least comprises longitudinal speed and lateral speed;
and the vehicle speed estimator is used for combining the state quantity at the current moment, and the state equation and the observation equation of the unscented Kalman filter to estimate the longitudinal speed and the lateral speed at the next moment.
10. The unscented kalman filter-based vehicle speed estimation system according to claim 9, further comprising:
the vehicle control unit is used for outputting the braking pressure of each wheel to the first modeling module;
the high-precision vehicle model simulates a vehicle in real time according to the brake pressure of each wheel to obtain the whole vehicle parameters;
the whole vehicle parameters comprise longitudinal acceleration, lateral acceleration, yaw rate, wheel speeds of four wheels and road surface adhesion coefficients at the four wheels.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113160299A (en) * 2021-01-28 2021-07-23 西安电子科技大学 Vehicle video speed measurement method based on Kalman filtering and computer readable storage medium
CN113358371A (en) * 2021-06-24 2021-09-07 东风汽车有限公司东风日产乘用车公司 Sprung acceleration estimation method, storage medium, and electronic apparatus
CN115959140A (en) * 2023-03-16 2023-04-14 安徽蔚来智驾科技有限公司 Kalman filtering-based vehicle longitudinal resistance acquisition method and device and vehicle

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105835721A (en) * 2016-03-31 2016-08-10 电子科技大学 Four-wheel hub electric vehicle speed control method
CN107985318A (en) * 2017-11-29 2018-05-04 吉林大学 The wheel loader logitudinal centre of gravity and height of C.G. method for dynamic estimation
CN108357498A (en) * 2018-02-07 2018-08-03 北京新能源汽车股份有限公司 Vehicle state parameter determination method and device and automobile
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
CN110861651A (en) * 2019-12-02 2020-03-06 吉林大学 Method for estimating longitudinal and lateral motion states of front vehicle
CN111391822A (en) * 2020-03-27 2020-07-10 吉林大学 Automobile transverse and longitudinal stability cooperative control method under limit working condition

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105835721A (en) * 2016-03-31 2016-08-10 电子科技大学 Four-wheel hub electric vehicle speed control method
CN107985318A (en) * 2017-11-29 2018-05-04 吉林大学 The wheel loader logitudinal centre of gravity and height of C.G. method for dynamic estimation
CN108357498A (en) * 2018-02-07 2018-08-03 北京新能源汽车股份有限公司 Vehicle state parameter determination method and device and automobile
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
CN110861651A (en) * 2019-12-02 2020-03-06 吉林大学 Method for estimating longitudinal and lateral motion states of front vehicle
CN111391822A (en) * 2020-03-27 2020-07-10 吉林大学 Automobile transverse and longitudinal stability cooperative control method under limit working condition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
李静等: "公交客车纵向车速估算研究", 《工程科技II》 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113160299A (en) * 2021-01-28 2021-07-23 西安电子科技大学 Vehicle video speed measurement method based on Kalman filtering and computer readable storage medium
CN113160299B (en) * 2021-01-28 2023-04-18 西安电子科技大学 Vehicle video speed measurement method based on Kalman filtering and computer readable storage medium
CN113358371A (en) * 2021-06-24 2021-09-07 东风汽车有限公司东风日产乘用车公司 Sprung acceleration estimation method, storage medium, and electronic apparatus
CN115959140A (en) * 2023-03-16 2023-04-14 安徽蔚来智驾科技有限公司 Kalman filtering-based vehicle longitudinal resistance acquisition method and device and vehicle
CN115959140B (en) * 2023-03-16 2023-06-02 安徽蔚来智驾科技有限公司 Vehicle longitudinal resistance obtaining method and device based on Kalman filtering and vehicle

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