CN115402337A - Tire cornering stiffness identification method and device based on longitudinal dynamics model - Google Patents
Tire cornering stiffness identification method and device based on longitudinal dynamics model Download PDFInfo
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Abstract
The invention relates to a tire cornering stiffness identification method and device based on a longitudinal dynamics model, wherein the method comprises the following steps: establishing a longitudinal dynamic model according to a balance relation of acceleration resistance, road resistance, air resistance and the lateral offset force of the front axle in the longitudinal direction of the vehicle; establishing a state vector by combining the vehicle speed and the lateral deflection rigidity of the front axle, and predicting the state vector of the next period based on the longitudinal dynamic model; and filtering the predicted state vector based on an extended Kalman filtering technology, and identifying the optimal lateral deflection stiffness of the front axle. The front axle lateral deflection rigidity can be independently identified, the coupling influence between the front and rear axle lateral deflection rigidity is eliminated, and the identification precision is improved.
Description
Technical Field
The invention relates to the field of automatic driving, in particular to a tire cornering stiffness identification method and device based on a longitudinal dynamics model.
Background
At present, the identification of the cornering stiffness of a vehicle tire is mainly based on a transverse dynamic model, two state quantities of the cornering stiffness and the rotational inertia are important control parameters in a state equation of the transverse dynamic model of the vehicle, whether the data of the cornering stiffness and the rotational inertia of a vehicle body in the running process of the vehicle can be quickly and accurately obtained or not can be quickly and accurately obtained, and the effect of the transverse motion control of the intelligent vehicle is obviously influenced.
In the correlation technique, a vehicle transverse dynamic model is established, and the front and rear cornering stiffnesses of the vehicle are identified on line by adopting an iterative least square method with forgetting factors. In some techniques, the longitudinal forces of the four wheels are estimated, the additional yaw moment generated by the four forces in the transverse direction is calculated, and then the vehicle mass center slip angle estimation is carried out by adding the additional yaw moment to a traditional transverse dynamic model. Since the lateral dynamic model needs to consider lateral cornering force balance and the conservation of rotational moment of the vehicle around the Z-axis, the cornering stiffnesses of the front and rear axles must be estimated simultaneously, and the identification accuracy is low due to the coupling between the identification of the cornering stiffnesses of the front and rear wheels. Meanwhile, the moment of inertia of the vehicle around the Z axis needs to be known, and the parameter is prone to have large errors, so that the identification precision is further reduced.
Disclosure of Invention
The embodiment of the invention provides a tire cornering stiffness identification method and device based on a longitudinal dynamics model, which can be used for independently identifying the cornering stiffness of a front shaft, removing the coupling influence between the cornering stiffness of the front wheel and the cornering stiffness of the rear wheel and improving the identification precision.
The embodiment of the invention provides a tire cornering stiffness identification method based on a longitudinal dynamics model, which is characterized by comprising the following steps of:
establishing a longitudinal dynamic model according to a balance relation of acceleration resistance, road resistance, air resistance and the lateral offset force of the front axle in the longitudinal direction of the vehicle;
establishing a state vector by combining the vehicle speed and the lateral deflection rigidity of the front axle, and predicting the state vector of the next period based on the longitudinal dynamic model;
and filtering the predicted state vector based on an extended Kalman filtering technology, and identifying the optimal lateral deflection stiffness of the front axle.
In some embodiments, the building of the longitudinal dynamics model according to the balance relation among the acceleration resistance, the road resistance, the air resistance and the lateral offset force of the front axle in the longitudinal direction of the vehicle comprises the following steps:
building the longitudinal dynamics model based on a first formula, the first formula comprising:
where T is the engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of a main reducer, r is the rolling radius of a tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the frontal area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
In some embodiments, the front wheel slip angle is calculated based on a second formula, the second formula including:
wherein,
is the rate of change of the lateral distance between the vehicle and the lane line,/ f Is the distance from the center of mass of the vehicle to the front axle, and ω is the vehicle yaw rate.
In some embodiments, said combining vehicle speed and front axle cornering stiffness to create a state vector and predicting a state vector for a next cycle based on said longitudinal dynamics model, comprises the steps of:
establishing a nonlinear state prediction equation based on a third formula, the third formula comprising:
wherein x (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
estimating the front axle lateral deflection stiffness for the optimal estimation at the k-1 moment;
determining the x (k) by substituting a fourth equation into the third equation, the fourth equation comprising:
wherein,
in order to optimally estimate the acceleration of the vehicle,
the vehicle speed is estimated for the best.
In some embodiments, the identifying an optimal front axle yaw stiffness after filtering the predicted state vector based on the extended kalman filtering technique includes:
calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for iteratively updating the state vector and obtaining the optimal front axle lateral deflection stiffness.
In some embodiments, the calculating the jacobian matrix at time k after deriving the nonlinear state prediction equation comprises:
determining the Jacobian matrix based on a fifth formula, the fifth formula comprising:
wherein A (k) is the Jacobian matrix at time k.
In some embodiments, the establishing an observation equation using the real-time vehicle speed as an observed quantity and an EKF iteration equation based on the nonlinear state prediction equation, the jacobian matrix, and the observation equation includes:
establishing an EKF iteration equation based on a sixth formula, the sixth formula comprising:
K(k)＝P(k)H(k) T (H(k)P(k)H(k) T +R) -1 ，
wherein P (k) is a prediction covariance matrix at time k,
optimal estimation covariance for time k-1A difference matrix, Q being a process noise covariance matrix of the nonlinear state prediction equation, K (K) being a Kalman gain at time K, R being a measurement noise covariance matrix of a measurement sensor,
for the optimal estimated state at time k,
for an optimal estimated vehicle speed at time k,
for optimal estimation of front axle cornering stiffness at time k, z (k) is the observation vector at time k, H (k) is the observation matrix and H (k) = [ 10 ]]。
On the other hand, an embodiment of the present invention further provides a tire cornering stiffness identification device based on a longitudinal dynamics model, which is characterized in that the device includes:
the longitudinal dynamics model building module is used for building a longitudinal dynamics model according to the balance relation of the acceleration resistance, the road resistance, the air resistance and the lateral deviation force of the front axle in the longitudinal direction of the vehicle;
the state prediction module is used for establishing a state vector by combining the vehicle speed and the lateral deflection stiffness of the front axle and predicting the state vector of the next period based on the longitudinal dynamic model;
and the filtering module is used for identifying the optimal front axle lateral deflection rigidity after filtering the predicted state vector based on the extended Kalman filtering technology.
In some embodiments, the longitudinal dynamics model building module is further configured to:
building the longitudinal dynamics model based on a first formula, the first formula comprising:
wherein T is engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of the main reducer, r is the rolling radius of the tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the frontal area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
In some embodiments, the state prediction module is further to:
establishing a nonlinear state prediction equation based on a third formula, the third formula comprising:
wherein x (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
estimating the lateral deflection stiffness of the front axle for the optimal estimation at the moment k-1;
determining the x (k) by substituting a fourth equation into the third equation, the fourth equation comprising:
wherein,
in order to optimally estimate the acceleration,
estimating the vehicle speed for optimum;
the filtering module is further configured to:
calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for iteratively updating the state vector and obtaining the optimal front axle lateral deflection rigidity.
The embodiment of the invention provides a tire cornering stiffness identification method and device based on a longitudinal dynamics model. The method has the advantages that the longitudinal dynamic model is established by considering the balance relation of the acceleration resistance, the road resistance, the air resistance and the lateral offset force of the front shaft in the longitudinal direction of the vehicle, the lateral offset stiffness of the front shaft is directly calculated based on the longitudinal dynamic model, the lateral offset of the rear shaft is vertical to the longitudinal direction, the calculation of the lateral offset stiffness of the front shaft is not influenced, the coupling performance can be effectively reduced, parameters such as the rotational inertia of the vehicle around the Z shaft are not required, the calculation model is simplified, and the identification precision is effectively improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a tire cornering stiffness identification method based on a longitudinal dynamics model according to an embodiment of the present invention;
FIG. 2 is a two-degree-of-freedom automobile model provided in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a tire cornering stiffness identification device based on a longitudinal dynamics model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for identifying cornering stiffness of a tire based on a longitudinal dynamics model, which includes the steps of:
s100: establishing a longitudinal dynamic model according to a balance relation of acceleration resistance, road resistance, air resistance and the lateral deviation force of a front axle in the longitudinal direction of the vehicle;
s200: establishing a state vector by combining the vehicle speed and the lateral deflection stiffness of the front axle and predicting the state vector of the next period based on the longitudinal dynamic model;
s300: and filtering the predicted state vector based on an extended Kalman filtering technology, and then identifying the optimal front axle lateral deflection rigidity.
As shown in FIG. 2, F yf The lateral deviation of the rear axle is vertical to the longitudinal direction, so that the lateral deviation rigidity of the front axle is not influenced, the coupling performance can be effectively reduced, parameters such as rotational inertia of the vehicle around a Z axle are not required to be based, and the identification precision is effectively improved while the calculation model is simplified.
In some embodiments, S100 may establish the longitudinal dynamics model based on a first formula, and the first formula is:
where T is the engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of the main reducer, r is the rolling radius of the tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the frontal area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
Note that T, m, β can be obtained from vehicle bus data, i g ，i 0 ，r，f，C D And A can be obtained from the whole vehicle configuration of the vehicle.
In some embodiments, the front wheel side slip angle may be calculated in S100 based on a second formula, where the second formula includes:
wherein,
is the rate of change of the lateral distance between the vehicle and the lane line,/ f The distance from the center of mass of the vehicle to the front axle, and omega is the yaw velocity of the vehicle;
in addition, δ f ，ω，V x Can be obtained from the bus data of the entire vehicle,/ f Can be obtained from the overall configuration of the vehicle,
may be given by a visual sensor.
In some embodiments, the nonlinear state prediction equation may be established based on a third formula in S200, and the third formula includes:
whereinX (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
estimating the lateral deflection stiffness of the front axle for the optimal estimation at the moment k-1;
and determining said x (k) by substituting a fourth formula into the third formula, the fourth formula comprising:
wherein,
in order to optimally estimate the acceleration,
the vehicle speed is estimated for optimum.
It will be appreciated that the above-described,
and
the optimal estimated state quantities at the moment of k-1 are all the optimal estimated state quantities, and the vector formed by the corresponding vehicle speed and the lateral deflection rigidity is x (k-1).
In particular, at the initial moment of calculation, the vehicle speed Vx and the preset C are used f0 Replacement by initial value
And
and (4) participating in calculation.
x (k) is the predicted state quantity at the time k, and the third formula is to predict the state quantity at the time k by using the optimal estimated state at the time k-1.
In some embodiments, S300 includes the steps of:
s310: calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
s320: establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for carrying out state iterative update and obtaining the optimal front axle lateral deflection rigidity.
It is understood that the state (available state vector representation) mentioned in the present embodiment is a vector composed of the vehicle speed and the front wheel face stiffness, i.e., x = [ V ]) x ，C f ]Therefore, the optimal estimation state is a vector consisting of the optimal estimation vehicle speed and the optimal estimation front axle cornering stiffness. The EKF refers to a Kalman filter, an EKF iterative equation is an iterative mode using Kalman filtering technology, and the iterative mode is applied to solving the optimal estimation front axle lateral deflection rigidity.
In some embodiments, when calculating the jacobian matrix at time k after deriving the nonlinear state prediction equation in S310, the jacobian matrix may be determined based on a fifth formula, and the fifth formula includes:
wherein A (k) is the Jacobian matrix at time k.
In some embodiments, the EKF iterative equation is established in S320 based on a sixth formula, the sixth formula comprising:
K(k)＝P(k)H(k) T (H(k)P(k)H(k) T +R) -1 ，
wherein P (k) is a prediction covariance matrix at time k,
for the optimal estimated covariance matrix at time K-1, Q is the process noise covariance matrix of the nonlinear state prediction equation, K (K) is the Kalman gain at time K, R is the measurement noise covariance matrix of the measurement sensor,
for the optimal estimated state at time k,
for an optimal estimation of the vehicle speed at time k,
for optimal estimation of the front axle cornering stiffness at time k, z (k) is the observation vector at time k, H (k) is the observation matrix calculated from the observation vectors, and H (k) = [ 10 ]]。
It should be noted that Q is a process noise covariance matrix of the state equation, and may be set according to the accuracy of the nonlinear state prediction equation. In particular, when the time of the initial calculation is up, the initial prediction covariance matrix P (0) is used instead
And performing iterative computation.
K (K) represents the credibility of the prediction state and the observation state, wherein the larger the value of K (K) is, the more trusted observation state is represented, and the smaller the value of K (K) is, the more trusted prediction state is represented.
It can be understood that the iterative process combines the predicted state quantity at the time k with the observed quantity directly obtained at the time k to perform optimal estimation (the state after iterative update is the optimal estimation state), so as to obtain the optimal estimation state at the time k.
Specifically, based on the sixth formula, at the initial time of the method, we take the observed state z (0) at the initial time as the optimal estimated state
At this time, the predicted state x (1) at the next time can be calculated according to the nonlinear state prediction equation. P (0) is initially set manually and the covariance matrix P (1) is predicted. Obtaining x (1) as input at the moment 1, an observation state z (1) at the current moment and a prediction covariance matrix P (1), and combining the x (1), the z (1) and the P (1) with a sixth formula to calculate the optimal estimation state at the moment 1
And an optimal covariance matrix
So that all values are updated from time 0 to time 1. At time 2, the optimal estimation state at time 1 is used
The prediction state x (2) at the time 2 is calculated according to the nonlinear state prediction equation, and the optimal estimation covariance matrix at the time 1 is used
The prediction covariance matrix P (2) at time 2 is calculated. The x (2), P (2) and the observation state z (2) at the time 2 are taken as input and substituted into the sixth formula, so that the optimal state at the time 2 can be calculated
Iterate in this way, the optimal estimated state at time k
The second of the vectors is the optimal front cornering stiffness as determined in this embodiment.
As shown in fig. 3, an embodiment of the present invention further provides a device for identifying cornering stiffness of a tire based on a longitudinal dynamics model, which includes:
the longitudinal dynamic model building module is used for building a longitudinal dynamic model according to the balance relation of the acceleration resistance, the road resistance, the air resistance and the lateral deviation force of the front axle in the longitudinal direction of the vehicle;
the state prediction module is used for establishing a state vector by combining the vehicle speed and the lateral deflection stiffness of the front axle and predicting the state vector of the next period based on the longitudinal dynamic model;
and the filtering module is used for identifying the optimal front axle lateral deflection rigidity after filtering the predicted state vector based on the extended Kalman filtering technology.
As shown in FIG. 2, F yf The lateral deviation of the rear axle is vertical to the longitudinal direction, so that the lateral deviation rigidity of the front axle is not influenced, the coupling performance can be effectively reduced, parameters such as rotational inertia of the vehicle around a Z axle are not required to be based, and the identification precision is effectively improved while the calculation model is simplified.
In some embodiments, the longitudinal dynamics model building module is further configured to:
building the longitudinal dynamics model based on a first formula, the first formula comprising:
wherein T is engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of the main reducer, r is the rolling radius of the tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the frontal area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
In some embodiments, the front wheel slip angle may be calculated based on a second formula, and the second formula includes:
wherein,
is the rate of change of the lateral distance between the vehicle and the lane line,/ f Is the distance from the center of mass of the vehicle to the front axle, and ω is the vehicle yaw rate.
In some embodiments, the state prediction module is further to:
establishing the nonlinear state prediction equation based on a third formula, the third formula comprising:
wherein x (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
for optimal estimation of the k-1 time instantFront axle yaw stiffness;
determining the x (k) by substituting a fourth formula into a third formula, the fourth formula comprising:
wherein,
in order to optimally estimate the acceleration,
the vehicle speed is estimated for optimum.
It will be appreciated that the above-described,
and
the optimal estimated state quantities at the moment of k-1 are all the optimal estimated state quantities, and the vector formed by the corresponding vehicle speed and the lateral deflection rigidity is x (k-1).
In particular, at the initial moment of calculation, using the vehicle speed Vx and the preset value C f0 Replacement by initial value
And
and (4) participating in calculation.
x (k) is the predicted state quantity at time k, and the third formula is to predict the state quantity at time k by using the optimal estimated state at time k-1.
In some embodiments, the filtering module is further configured to:
calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for iteratively updating the state vector and obtaining the optimal front axle lateral deflection rigidity.
It is understood that the state (available state vector representation) mentioned in the present embodiment is a vector composed of the vehicle speed and the front wheel face stiffness, i.e., x = [ V ]) x ，C f ]Therefore, the optimal estimation state is a vector consisting of the optimal estimation vehicle speed and the optimal estimation front axle cornering stiffness. The EKF refers to a Kalman filter, an EKF iteration equation is an iteration mode using Kalman filtering technology, and the iteration mode is applied to solving the optimal estimation of the front axle lateral deflection rigidity.
In some embodiments, the filtering module is further configured to:
determining the Jacobian matrix based on a fifth formula comprising:
wherein A (k) is the Jacobian matrix at time k.
In some embodiments, the filtering module is further configured to:
establishing an EKF iteration equation based on a sixth equation comprising:
K(k)＝P(k)H(k) T (H(k)P(k)H(k) T +R) -1 ，
wherein P (k) is a prediction covariance matrix at time k，
For an optimal estimated covariance matrix at time K-1, Q is a process noise covariance matrix of the nonlinear state prediction equation, K (K) is a Kalman gain at time K, R is a measurement noise covariance matrix of the measurement sensor,
for the best estimated state at time k,
for an optimal estimated vehicle speed at time k,
for optimal estimation of the front axle cornering stiffness at time k, z (k) is the observation vector at time k, H (k) is the observation matrix, H (k) = [ 10 ]]。
It should be noted that Q is a process noise covariance matrix of the state equation, and can be set according to the accuracy of the nonlinear state prediction equation. Specifically, when the initial calculation time is up, the preset initial prediction covariance matrix P (0) is used to replace the initial prediction covariance matrix
And performing iterative computation.
K (K) represents the credibility of the prediction state and the observation state, wherein the larger the value of K (K) is, the more trusted observation state is represented, and the smaller the value of K (K) is, the more trusted prediction state is represented.
It can be understood that, in the iterative process, the predicted state quantity at the time k and the observed quantity directly obtained at the time k are combined to perform optimal estimation (the state after iterative update is the optimal estimation state), so as to obtain the optimal estimation state at the time k.
In a third aspect, an embodiment of the present invention further provides an apparatus, including: a memory and a processor, the memory having stored therein at least one instruction, the at least one instruction loaded and executed by the processor to implement the method of any of the method embodiments.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable storage media, which may include computer readable storage media (or non-transitory media) and communication media (or transitory media).
The term computer readable storage medium includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those skilled in the art. Computer-readable storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
It is to be noted that, in the present invention, relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A tire cornering stiffness identification method based on a longitudinal dynamics model is characterized by comprising the following steps:
establishing a longitudinal dynamic model according to a balance relation of acceleration resistance, road resistance, air resistance and the lateral offset force of the front axle in the longitudinal direction of the vehicle;
establishing a state vector by combining the vehicle speed and the lateral deflection rigidity of the front axle, and predicting the state vector of the next period based on the longitudinal dynamic model;
and filtering the predicted state vector based on an extended Kalman filtering technology, and identifying the optimal lateral deflection stiffness of the front axle.
2. The method for identifying cornering stiffness of a tire according to claim 1, wherein the longitudinal dynamical model is established based on a balance relationship between acceleration resistance, road resistance, air resistance and a cornering power of a front axle in a longitudinal direction of the vehicle, comprising the steps of:
building the longitudinal dynamics model based on a first formula, the first formula comprising:
where T is the engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of a main reducer, r is the rolling radius of a tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the windward area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
3. The method for identifying cornering stiffness of a tire based on a longitudinal dynamical model of claim 2, wherein the front wheel cornering angle is calculated based on a second formula, the second formula including:
wherein,
is the rate of change of the lateral distance between the vehicle and the lane line,/ f Is the distance from the center of mass of the vehicle to the front axle, and ω is the vehicle yaw rate.
4. The method for identifying cornering stiffness of a tire based on a longitudinal dynamical model of claim 2, wherein the step of establishing a state vector by combining a vehicle speed and a front axle cornering stiffness and predicting a state vector of a next cycle based on the longitudinal dynamical model comprises the steps of:
establishing a nonlinear state prediction equation based on a third formula, the third formula comprising:
wherein x (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
estimating the front axle lateral deflection stiffness for the optimal estimation at the k-1 moment;
determining the x (k) by substituting a fourth formula into a third formula, the fourth formula comprising:
wherein,
in order to optimally estimate the acceleration of the vehicle,
the vehicle speed is estimated for optimum.
5. The method for identifying tire cornering stiffness based on a longitudinal dynamics model according to claim 4, wherein the method for identifying the optimal front axle cornering stiffness after filtering the predicted state vector based on the extended Kalman filtering technique comprises the following steps:
calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for iteratively updating the state vector and obtaining the optimal front axle lateral deflection rigidity.
6. The method for identifying cornering stiffness according to claim 5, wherein the derivative of the nonlinear state prediction equation is used to calculate a Jacobian matrix at time k, comprising the steps of:
determining the Jacobian matrix based on a fifth formula, the fifth formula comprising:
wherein A (k) is the Jacobian matrix at time k.
7. The method for identifying cornering stiffness of a tire based on a longitudinal dynamics model according to claim 6, wherein the establishing an observation equation using a real-time vehicle speed as an observed quantity and an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation comprises the steps of:
establishing an EKF iteration equation based on a sixth equation comprising:
K(k)＝P(k)H(k) T (H(k)P(k)H(k) T +R) -1 ，
wherein P (k) is a prediction covariance matrix at time k,
for an optimal estimated covariance matrix at time K-1, Q is a process noise covariance matrix of the nonlinear state prediction equation, K (K) is a Kalman gain at time K, R is a measurement noise covariance matrix of the measurement sensor,
for the optimal estimated state at time k,
for an optimal estimated vehicle speed at time k,
for optimal estimation of front axle yaw stiffness at time k, z (k) is the observation vector at time k, H (k) is the observation matrix and H (k) = [ 10 ]]。
8. A tire cornering stiffness identification device based on a longitudinal dynamics model, characterized in that it comprises:
the longitudinal dynamic model building module is used for building a longitudinal dynamic model according to the balance relation of the acceleration resistance, the road resistance, the air resistance and the lateral deviation force of the front axle in the longitudinal direction of the vehicle;
the state prediction module is used for establishing a state vector by combining the vehicle speed and the lateral deflection stiffness of the front axle and predicting the state vector of the next period based on the longitudinal dynamic model;
and the filtering module is used for identifying the optimal front axle lateral deflection rigidity after filtering the predicted state vector based on the extended Kalman filtering technology.
9. The longitudinal dynamical model-based tire cornering stiffness identifying device of claim 8, wherein the longitudinal dynamical model building module is further configured to:
building the longitudinal dynamics model based on a first formula, the first formula comprising:
where T is the engine output torque, i g To the transmission ratio of the gearbox, i 0 Is the transmission ratio of the main reducer, r is the rolling radius of the tire, m is the mass of the whole vehicle, g is the gravity acceleration, f is the rolling resistance coefficient of the tire, beta is the road gradient, C D Is the wind resistance coefficient of the vehicle, A is the windward area of the vehicle, rho is the air density, V x Is vehicle speed, a is acceleration, C f For front axle yaw stiffness, α f Is a front wheel side slip angle, δ f Is the corner of the front wheel.
10. The longitudinal dynamical model-based tire cornering stiffness identifying device of claim 9, wherein the state prediction module is further configured to:
establishing a nonlinear state prediction equation based on a third formula, the third formula comprising:
wherein x (k) is the predicted state vector at time k,
the optimal estimated vehicle speed at the moment of k-1, the delta T is a single step operation period,
for the optimal estimated acceleration calculated by the longitudinal dynamics model at time k-1,
estimating the lateral deflection stiffness of the front axle for the optimal estimation at the moment k-1;
determining the x (k) by substituting a fourth formula into a third formula, the fourth formula comprising:
wherein,
in order to optimally estimate the acceleration,
estimating the vehicle speed for optimum;
the filtering module is further configured to:
calculating a Jacobian matrix at the k moment after the derivation of the nonlinear state prediction equation;
establishing an observation equation by taking the real-time vehicle speed as an observed quantity, and establishing an EKF iterative equation based on the nonlinear state prediction equation, the Jacobian matrix and the observation equation;
the EKF iterative equation is used for iteratively updating the state vector and obtaining the optimal front axle lateral deflection rigidity.
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