CN115946707B - Method and system for estimating tire force of all-line-control electric automobile driven by four-wheel hub motor - Google Patents
Method and system for estimating tire force of all-line-control electric automobile driven by four-wheel hub motor Download PDFInfo
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Abstract
The invention discloses a tire force estimation method and a system for a four-wheel hub motor driven full-drive electric automobile, which relate to the technical field of vehicle safety control, and the method comprises the following steps: acquiring sensing signal parameters of an in-vehicle sensor; the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data; respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters; estimating vertical tire force according to a rolling dynamics model based on a strong tracking unscented Kalman filter; estimating longitudinal tire forces according to a longitudinal vehicle model based on a classical kalman filter; based on the strong tracking unscented Kalman filter, lateral tire forces are estimated from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces. The invention utilizes the low-cost vehicle-mounted sensor information, the state feedback information of the hub motor and the linear control motor system to realize the longitudinal, lateral and vertical tire force estimation of the tire.
Description
Technical Field
The invention relates to the technical field of vehicle safety control, in particular to a method and a system for estimating tire force of a four-wheel hub motor driven full-drive electric vehicle.
Background
The mechanical states of the longitudinal, lateral and vertical tires of the vehicle are main control variables of active safety control of the vehicle, and are also important indexes for evaluating the comprehensive stability of the vehicle, and the dynamic states of the longitudinal, lateral and vertical tires of the vehicle are accurately obtained and directly related to the running stability and safety of the vehicle. The existing prior art at present is as follows:
(1) A method for estimating the lateral force of the front wheel of a distributed electrically driven vehicle includes the steps of collecting vehicle state signals, and estimating the longitudinal force and the vertical force of a tire in real time by utilizing a vehicle dynamics equation; then, the estimated longitudinal force of each wheel is transmitted to a Kalman lateral force observer in a vehicle controller together with a longitudinal acceleration signal, a lateral acceleration signal, a yaw rate signal and a steering wheel corner signal to obtain Kalman lateral force estimated values of the two front wheels and a rear axle lateral force estimated value; and finally, further processing the estimated lateral force by utilizing the vertical force of each wheel and the rotation angle difference of the front wheel to obtain a final lateral force estimated value.
(2) A steering moment and tire lateral force estimation method of a steering system comprises the following steps: 1. longitudinal force and lateral force of the vehicle are collected, and yaw moment based on the rotation center and the mass center of each tire is established according to a seven-degree-of-freedom model of the vehicle; 2. calculating the yaw moment of each tire rotation center by using an interference observer to obtain a lateral moment estimated value of each tire rotation center; 3. estimating the sum of the lateral forces of the front and rear tires by adopting a least square method; 4. respectively calculating the lateral forces of the front wheel and the rear wheel by using an empirical estimation method; 5. the lateral force of the front wheel and the rear wheel is converted into steering torque and is output to the power-assisted motor.
(3) A four-wheel drive electric automobile tire force soft measurement method comprises the following steps: the first step: acquiring the longitudinal speed, the centroid slip angle, the longitudinal acceleration, the lateral acceleration, the front wheel corner and the tire longitudinal force of the automobile; and a second step of: inputting the obtained information of the longitudinal speed, the centroid slip angle, the longitudinal acceleration, the lateral acceleration, the front wheel corner and the tire longitudinal force of the automobile to a nonlinear vehicle dynamics model, and calculating to obtain the estimated longitudinal acceleration and the estimated lateral acceleration through the vehicle dynamics model; and a third step of: and inputting the obtained longitudinal speed, centroid slip angle, longitudinal acceleration, transverse acceleration, front wheel rotation angle and tire longitudinal force information of the automobile and the longitudinal acceleration and transverse acceleration information estimated in the second step into a unscented Kalman filtering algorithm to obtain an estimated value of the tire force of the automobile based on the model.
(4) A method for estimating the lateral force of a front wheel of a distributed driving electric vehicle mainly comprises the following steps: 1. according to the vehicle state information acquired by various sensors, a sliding mode longitudinal force observer is designed based on a vehicle dynamics equation to estimate the longitudinal force of the tire in real time; 2. transmitting the estimated longitudinal force of each wheel, longitudinal acceleration signals, lateral acceleration signals, yaw rate signals and the like to a sliding mode lateral force observer to obtain a lateral force estimated value of the right front wheel; 3. the estimated lateral force is further optimized through a filtering module, so that the singular problem in the estimated lateral force value is solved, and the final estimated lateral force value of the two front wheels is output.
(5) The method is based on an interactive multi-model algorithm-volume Kalman filtering to estimate the vehicle centroid side deflection angle and the tire side force in real time, and an eight-degree-of-freedom vehicle model is established, wherein the eight-degree-of-freedom vehicle model comprises longitudinal movement, transverse movement, yaw movement, roll movement and movement of four tires, and the nonlinear vehicle model considers the influence of roll movement and load transfer in the vehicle running process; then, establishing a linear tire model and a nonlinear Dugoff tire model as a model set of an interactive multi-model; and finally, estimating the vehicle mass center slip angle and the tire lateral force.
(6) A method of tire lateral force estimation comprising the steps of: 1. a tire lateral force estimation system comprising a wheel center longitudinal speed sensor, a road surface adhesion coefficient sensor, a tire vertical force sensor, a tire side deflection angle sensor, a tire slip rate sensor and a lateral force estimation module is arranged; 2. the lateral force estimation module estimates a quasi-static lateral force value of the tire according to the collected tire slip rotation value, the tire vertical force value, the tire slip angle and the road surface adhesion coefficient value; 3. establishing a dynamic tire model according to the relation between the dynamic lateral force and the quasi-static lateral force of the tire, and correcting the quasi-static lateral force value of the tire estimated in the step 2 through the dynamic tire model according to the collected longitudinal speed of the wheel center by the lateral force estimation module to obtain a dynamic tire lateral force value; 4. and (3) sending the dynamic tire side force value obtained in the step (3) to a whole vehicle controller for controlling and monitoring the vehicle.
It can be seen that the existing vehicle tire force estimation method is mostly based on a specific tire model for estimation, and the tire model parameters are numerous, so that the model-based estimation method has the problems of poor working condition adaptability, low precision and the like; while existing solutions lack a highly reliable estimation method capable of decoupling the estimation of the longitudinal, lateral and vertical tire forces of a vehicle.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a tire force estimation method and system for a four-wheel hub motor driven full-drive electric automobile.
In order to achieve the above object, the present invention provides the following solutions:
a tire force estimation method for a four-wheel hub motor driven full-drive electric automobile comprises the following steps:
acquiring sensing signal parameters of an in-vehicle sensor; the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data;
respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters;
estimating vertical tire force according to the rolling dynamics model based on a strong tracking unscented Kalman filter;
estimating longitudinal tire forces from the longitudinal vehicle model based on a classical kalman filter;
Based on a strong tracking unscented kalman filter, a lateral tire force is estimated from the lateral vehicle model, the vertical tire force, and the longitudinal tire force.
A four-wheel hub motor driven all-drive-by-wire electric vehicle tire force estimation system, comprising:
the parameter acquisition module is used for acquiring sensing signal parameters of the in-vehicle sensor; the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data;
the model construction module is used for respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters;
the first estimation module is used for estimating the vertical tire force according to the rolling dynamics model based on a strong tracking unscented Kalman filter;
a second estimation module for estimating longitudinal tire forces from the longitudinal vehicle model based on a classical kalman filter;
a third estimation module for estimating lateral tire forces from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces based on a strong tracking unscented kalman filter.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
The invention provides a tire force estimation method and a system for a four-wheel hub motor driven full-drive electric automobile, which are used for acquiring sensing signal parameters of an in-automobile sensor, wherein the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data; respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters; estimating vertical tire force according to a rolling dynamics model based on a strong tracking unscented Kalman filter; estimating longitudinal tire forces according to a longitudinal vehicle model based on a classical kalman filter; based on the strong tracking unscented Kalman filter, lateral tire forces are estimated from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces. The invention utilizes the information of the low-cost vehicle-mounted sensor, the state feedback information of the hub motor and the linear control motor system to realize the estimation of the longitudinal, lateral and vertical tire force; the strong tracking unscented Kalman filter is used for estimating the lateral force and the vertical force of the tire, and compared with the traditional Kalman filter, the dynamic tracking performance and the convergence speed are better; the tire force estimation is carried out without using tire models with various parameters, and the estimation algorithm has high precision and good robustness.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method in an embodiment provided by the present invention;
FIG. 2 is a schematic diagram of the overall scheme in an embodiment provided by the present invention;
FIG. 3 is a schematic illustration of a quarter suspension model in a vehicle roll dynamics model in an embodiment provided by the present invention;
FIG. 4 is a schematic representation of vehicle roll dynamics in a vehicle roll dynamics model in an embodiment provided by the present invention;
FIG. 5 is a schematic diagram of a three degree of freedom vehicle model in an embodiment provided by the present invention;
FIG. 6 is a schematic view of a longitudinal tire dynamics model in an embodiment provided by the present invention;
FIG. 7 is a schematic view of a longitudinal vehicle dynamics model in an embodiment provided by 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.
The invention aims to provide a tire force estimation method and a system for a four-wheel hub motor driven full-drive electric automobile, which do not use tire models with various parameters to estimate the tire force, and have high accuracy and good robustness of an estimation algorithm.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, the present invention provides a tire force estimation method for a four-wheel hub motor driven full-drive electric vehicle, comprising: step 100: acquiring sensing signal parameters of an in-vehicle sensor; the sensor signal parameters include suspension height data, inertial measurement data, steering wheel angle data, vehicle signal data, and motor data. Step 200: and respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters. Step 300: based on a strong tracking unscented Kalman filter, vertical tire forces are estimated from the rolling dynamics model. Step 400: based on a classical kalman filter, longitudinal tire forces are estimated from the longitudinal vehicle model. Step 500: based on a strong tracking unscented kalman filter, a lateral tire force is estimated from the lateral vehicle model, the vertical tire force, and the longitudinal tire force.
Preferably, the in-vehicle sensor includes: suspension height sensor, inertial measurement unit, in-wheel motor, vehicle signal sensor and steering wheel angle sensor; the suspension height sensor is used for measuring suspension height data; the inertial measurement device is used for measuring inertial measurement data; the hub motor is used for measuring motor data; the vehicle signal sensor is used for measuring vehicle signal data; the steering wheel angle sensor is used for measuring steering wheel angle data.
As shown in fig. 2, rolling dynamics model, transverse vehicle model and longitudinal vehicle model are respectively established by taking in-vehicle sensor and vehicle signal parameters as inputs, so as to obtain expressions of vertical tire force, longitudinal tire force and lateral tire force; and then respectively selecting a strong tracking unscented Kalman filter and a classical Kalman filter to estimate the tire force. The construction method of the rolling dynamics model comprises the following steps:
a first model is constructed. Specifically, a quarter active suspension system is shown in FIG. 3. The effective tire rolling radius is neglected to change, and the rolling dynamics model, namely the expression of the first model is as follows:
wherein, 
and->
The vertical displacements on and off the spring are respectively, and the superscripts i E [ L1, L2, R1, R2 ]L1, L2, R1 and R2 represent front left, front right, rear left and rear right, respectively;
Is the actuating force of the active suspension;
A distributed sprung mass on each wheel;
And->
Stiffness and damping, respectively, for each suspension, can be obtained from the active suspension controller; g is gravity acceleration;
The height of the suspension is not deformed;
Representing a first derivative;
Representing taking the second order reciprocal.
For active suspensions, the vertical displacement of the sprung and unsprung masses can be measured by suspension height sensors. Namely, determining a second model according to the first model, wherein the expression of the second model is as follows:
Determining a third model according to the second model; specifically, tire vertical force
Consists of static and dynamic loads. The static tire force is defined by the unsprung mass distributed in each corner>
And sprung mass->
While dynamic loads are caused by lateral and longitudinal movement of the vehicle. Thus, tire vertical force +>
Can be expressed as the followingThe expression of the three models is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein, 
representing the influence of the wheel alignment parameters on the vertical force;
Representing random road incentives; / >
For vertical tyre force>
Indicating the tire normal force as a function of unsprung mass and road excitation.
A roll dynamics model is constructed. Specifically, in order to accurately predict the tire vertical force, a vehicle roll dynamics model with an active suspension system is established, as shown in fig. 4, a coordinate system with x point as the origin is established, CG represents the center of gravity of the sprung weight, and B represents the distance between the left and right tire mounting positions on the same axis;
represents the damping of a certain suspension;
Representing the stiffness of a certain suspension;
Representing the relative vertical displacement of the sprung and unsprung masses measured by a suspension height sensor on a particular suspension, O representing the center of gravity of the vehicle. To simplify the vehicle roll dynamics model, the influence of the unsprung roll angle and the lateral wind is ignored, and therefore the expression of the roll dynamics model is:
Determining a fourth model from the roll dynamics model; the expression of the fourth model is:
and determining a fifth model according to the fourth model. Specifically, compression of the suspension system on each wheel results in rolling motion of the vehicle. Therefore, the relation between the vehicle roll state and the kinematics of each suspension can be expressed as a fifth model, the expression of which is as follows:
In summary, the expression of the rolling dynamics model is:
to accurately estimate the lateral tire forces in the linear and nonlinear regions, a simplified four-wheeled lateral vehicle model is constructed, as shown in fig. 5. Wherein, 
representing the longitudinal force of the right rear wheel->
Representing the torque of the right rear wheel +.>
Indicating the speed of the right rear wheel +.>
Indicating the yaw angle of the right rear wheel +.>
Representing yaw rate of the vehicle, < >>
Representing the rotational cycle torque of the left rear wheel, +.>
Indicating the speed of the left rear wheel,/-, for example>
Representing the yaw angle of the left rear wheel +.>
Representing the torque of the left front wheel,/->
Representing the speed of the left front wheel,/-, for >
Representing the yaw angle of the left front wheel, +.>
Representing the longitudinal force of the right front wheel->
Representing the torque of the front right wheel +.>
Indicating the speed of the front right wheel,/->
Representing the yaw angle of the front right wheel, x and y in fig. 5 represent the coordinate system established in fig. 5,/->
Representing the vehicle centroid speed. Based on the above, the method for constructing the transverse vehicle model is as follows: />
Constructing a three-degree-of-freedom vehicle model; the expression of the three-degree-of-freedom vehicle model is as follows:
each individual lateral tire force corresponds to a vertical force profile, which can be given by:
wherein m is the mass of the vehicle;
is the lateral acceleration of the vehicle;
、
And->
The steering angle, yaw acceleration and moment of inertia around the z-axis of the front wheel are respectively;
And->
The front tread width and the rear tread width, respectively;
Representing the left front wheel longitudinal force;
Representing the left rear wheel longitudinal force;
Representing left front wheel lateral force;
Representing left rear wheel lateral force;
Representing the right front wheel lateral force;
Representing the right front wheel longitudinal force;
Representing the right rear wheel longitudinal force;
Represents the right rear wheel lateral force;
Representing the lateral forces of the left front wheel and the left rear wheel;
Representing the sum of the lateral forces of the right front wheel and the right rear wheel;
Representing the sum of the vertical forces of the left front wheel and the left rear wheel; / >
Representing the sum of the vertical forces of the right front wheel and the right rear wheel;
Is the vertical tire force;
And->
Representing distances from the center of gravity to the front and rear axes, respectively;
Representing a first derivative;
Indicating the yaw rate of the vehicle.
And constructing a linear transverse tire model. Specifically, in order to describe the relationship between the vehicle lateral dynamics and the front wheel steering angle, a linearized lateral tire model is established, the expression of which is:
for front and rear wheels, the tire slip angle can be calculated by:
wherein, 
and->
The cornering stiffness and the tire cornering angle of each axle are respectively marked +.>
Represents the cornering stiffness of the front and rear axle, < >>
Representing the front axle->
Representing the rear axle;
And->
The vehicle sideslip angle and the vehicle longitudinal speed respectively;
A lateral force being the front axle or rear axle tire;
Representing the slip angle of the front axle wheel;
Indicating the slip angle of the rear axle wheels.
And determining the transverse vehicle model according to the three-degree-of-freedom vehicle model and the linear transverse tire model. Specifically, ignoring longitudinal tire forces, the three degree of freedom vehicle model may be simplified as:
preferably, the construction process of the longitudinal vehicle model is as follows: in this example, a single wheel model is used, as shown in fig. 6, to describe the dynamics of each tire, wherein, 
Representing the driving torque of a wheel, F x Representing the longitudinal force of a tyre, F f Representing rolling resistance of a tyre, F z Representing the vertical force of a certain tire. Based on this, a tire dynamics model is constructed; the expression of the tire dynamics model is as follows:
Wherein, the tire slip ratio is defined as:
。
constructing a longitudinal vehicle dynamics response model; to describe the longitudinal vehicle dynamics response, as shown in fig. 7, based on this, the expression of the longitudinal vehicle dynamics response model is:
wherein m is the mass of the vehicle;
representing the longitudinal acceleration of the vehicle;
Representing the total rolling resistance of the whole vehicle;
representing the total longitudinal force of the whole vehicle; superscript i.epsilon.L 1, L2, R1, R2]L1, L2, R1 and R2 represent respectively left front, right front, left rear and right rear, superscript ++>
Represents the cornering stiffness of the front and rear axle, < >>
Representing the front axle->
Representing the rear axle;
Steering angle for front wheel; g is gravity acceleration;
、
、
、
Respectively representing total longitudinal driving force, air resistance, rolling resistance and gradient resistance; / >
Is the aerodynamic drag coefficient; a is the windward area;
Is air density; f is the rolling resistance coefficient;
Is road grade;
Is the vehicle longitudinal speed.
Determining the longitudinal vehicle model from the tire dynamics model and the longitudinal vehicle dynamics response model.
Notably, are: aerodynamic and rolling resistance can be obtained through in-situ vehicle testing, while hill resistance can be estimated. In addition, in FIG. 7,
representing the total longitudinal force of an electric vehicle tire;
Representing the total rolling resistance of an electric automobile tire;
Representing the total vertical force of an electric automobile tire;
Indicating the total driving torque of an electric automobile tire, L a =a；L b =b, R represents the radius of the electric car tire; f (F) w The total air resistance of the electric automobile is represented, and v represents the running speed of the electric automobile;
The rotational angular velocity of the electric vehicle tire is indicated. />
The suspension is a typical nonlinear system, in this embodiment, the STUKF is used to estimate the vertical tire force, the suspension compression amount and the compression rate measured by the suspension height sensor are used as observation variables, the initial state of the vertical tire force obtained by the system suspension compression amount and the compression rate and through model calculation is used as state variables, and data fusion is performed to obtain a vertical tire force estimated value which is close to the real value. Specifically, based on a strong tracking unscented kalman filter, estimating vertical tire forces from the rolling dynamics model includes:
Acquiring a nonlinear discrete space expression of a suspension system; the nonlinear discrete space expression is:
wherein, 
is a vertical state evolution function of the rolling dynamics model, which is simply called +.>
；
For the vertical observation function of the rolling dynamics model, simply called +.>
；
Is a vertical state variable;
Is a system vertical input;
is vertical process noise;
For measuring noise vertically->
Representing the vertical state quantity at the time of k-1;
Representing a system vertical measurement.
And determining a state vector at each time step according to the nonlinear discrete space expression. Specifically, for a single suspension, assuming the spring and damper have the same amount and rate of compression, it can be measured directly by the suspension height sensor. In order to improve the computational efficiency, the vertical tire force is estimated separately for each corner. Input vector
Is empty. Taking the compression amount, compression rate and tire vertical force values in the system state, the state vector in each time step can be expressed as:
wherein, 
representing the relative vertical displacement of the sprung and unsprung masses measured by the suspension height sensor;
A vertical state quantity 1 representing the time k;
A vertical state quantity 2 representing the time k;
A vertical state quantity 3 representing the time k; / >
Representing the transpose.
A nonlinear function of the vertical tire force estimate is determined from the nonlinear discrete spatial expression. In particular, since road excitation in daily travel is relatively small and the inertial force of the unsprung mass is much smaller than the vertical tire force, the effect of road excitation is ignored here. The expression of the nonlinear function of the vertical tire force estimate is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein, 
a first vertical state evolution function representing a rolling dynamics model;
A second vertical state evolution function representing a rolling dynamics model;
A third vertical state evolution function representing a rolling dynamics model;
Is a discrete time period; g is gravity acceleration;
A vertical state quantity 1 at time k-1;
A vertical state quantity 2 representing the time k-1;
Representing the damping coefficient of the ith wheel suspension;
Representing the influence of the wheel alignment parameters on the vertical force;
Indicating the i-th wheel unsprung mass. />
Measured value at time step k
It can be derived that:>
。
an observation function of the vertical tire force estimate is determined from the nonlinear discrete spatial expression. Due to observation function
Is linear, and an observation function of the vertical tire force estimate can be derived:
wherein, 
A first vertical observation function representing a rolling dynamics model;
A second vertical observation function representing a rolling dynamics model.
The vertical tire force is determined from a nonlinear function of the vertical tire force estimate and an observation function of the vertical tire force estimate.
Preferably, the estimating longitudinal tire force according to the longitudinal vehicle model based on the classical kalman filter comprises:
constructing a discrete time-varying linear control system; the expression of the time-varying linear control system is as follows:
wherein, 
is a longitudinal state vector;
Representing a longitudinal state variable at time k-1;
Is a longitudinal control input;
Is a longitudinal measurement;
And->
White noise; a ʹ and B are both state transition matrices; h= [0,1,0]Is an observation matrix;
Is a discrete time period; wheel speed->
The method comprises the steps of carrying out a first treatment on the surface of the The state vector of the longitudinal vehicle model is:
；
A vertical state vector 1 representing time k;
A vertical state vector 2 representing the time k;
A vertical state vector 3 representing the time k;
Representing a transpose; the expressions of A ʹ and B are respectively:
The method comprises the steps of carrying out a first treatment on the surface of the And carrying out data fusion according to the state vector to obtain the longitudinal tire force.
Further, in this embodiment, considering that the output torque and the wheel rotation speed of the motor may be obtained by the control unit, the longitudinal tire force estimation is performed by using a widely used classical kalman filter, the wheel rotation speed measured by the wheel speed sensor is used as an observation variable, the wheel torque is used as an input variable, and the current wheel angular speed, the angular acceleration and the calculated longitudinal force of the vehicle are used as initial values of state variables to perform data fusion, thereby obtaining a longitudinal tire force estimation value close to a true value, and the specific steps are as follows:
Consider a discrete time-varying linear control system that is:
for a single wheel, taking the wheel torque as input, and taking the angular speed, the angular acceleration and the longitudinal force of the wheel as states, the state vector can be obtained:
the state transition matrices a and B can be expressed as:
measurement value
The wheel speed is equal to the speed of the motor in the wheel:>
。
preferably, estimating lateral tire forces from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces based on a strong tracking unscented kalman filter comprises:
constructing a nonlinear discrete space equation of the tire lateral force estimation; the calculation formula of the nonlinear discrete space equation is as follows:
wherein, 
a state evolution function for said transversal vehicle model, abbreviated as +.>
，
For the observation function of the transverse vehicle model, simply called +.>
；
Representing a transverse state variable at time k;
Represents the transverse state variable at time k-1;
Representing a system lateral input;
Representing system lateral process noise;
Representing a system lateral measurement;
Representing the transverse measurement noise.
Taking the lateral acceleration, the yaw acceleration and the four lateral tire forces as system states, and acquiring state vectors; the calculation formula of the state vector is as follows:
Wherein, 
represents the 1 st lateral state variable,/-at time k>
Represents the 2 nd lateral state variable at time k,/->
Represents the 3 rd lateral state variable,/-at time k>
Represents the 4 th lateral state variable,/-at time k>
Represents the 5 th lateral state variable,/-at time k>
Represents the 6 th lateral state variable at time k.
Acquiring system transverse input; the calculation formula of the system transverse input is as follows:
wherein, 
the front wheel rotation angle at the moment k is represented;
Representing left front wheel lateral force;
Representing the right front wheel lateral force;
representing left front wheel vertical force;
Representing right front wheel vertical force;
Representing left rear wheel vertical force;
Representing right rear wheel vertical force;
Representing a system lateral input 1;
Representing a system lateral input quantity 2;
Representing a system lateral input 3;
Representing a system lateral input 4;
Representing a system lateral input 5;
Representing a system lateral input 6;
representing the system lateral input 7.
Determining a nonlinear function of the lateral tire force estimate from the nonlinear discrete space equation; the expression of the nonlinear function of the lateral tire force estimation is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein, 
a first state evolution function representing a transversal vehicle model,/->
A second state evolution function representing a transversal vehicle model,/- >
A third state evolution function representing a transversal vehicle model,/->
A fourth state evolution function representing a transversal vehicle model,/->
A fifth state evolution function representing a transversal vehicle model,/->
A sixth state evolution function representing a lateral vehicle model;
Represents the 1 st lateral state variable at time k-1;
Representation ofThe 2 nd lateral state variable at time k-1;
Represents the 3 rd lateral state variable at time k-1;
Represents the 4 th lateral state variable at time k-1;
Represents the 5 th lateral state variable at time k-1;
Represents the 6 th lateral state variable at time k-1.
Determining an observation function of the lateral tire force estimation according to the nonlinear discrete space equation; the expression of the observation function of the lateral tire force estimation is:
The lateral tire force is determined from an observation function of the lateral tire force estimate and a nonlinear function of the lateral tire force estimate.
In this embodiment, accurate lateral tire force estimation is important for vehicle lateral stability assessment. The lateral tire force estimation is to take the lateral acceleration and the yaw acceleration measured and calculated by an inertia measuring device and a steering wheel angle sensor as observation variables, the front wheel steering angle, the estimated longitudinal tire force and the vertical tire force as input variables, and the current lateral acceleration, the yaw acceleration and the four calculated lateral tire forces of the vehicle as system states for data fusion, so that a lateral tire force estimation value which is close to a real value is obtained, and the specific steps are as follows:
The nonlinear discrete space equation for the tire lateral force estimation is:
taking the lateral acceleration and yaw acceleration of the vehicle and four lateral tire forces as the system states, their state vectors can be expressed as:
the system lateral input is specifically an input vector, which is composed of a front wheel steering angle, a longitudinal tire force and a vertical tire force, and can be expressed as follows:
the steering angle of the front wheels is measured by a steering angle encoder, and the system measurement comprises lateral acceleration and yaw acceleration, and the formula is as follows:
the nonlinear function of the lateral tire force estimate can be expressed as:
observation function
Can be expressed as:
the embodiment also provides a tire force estimation system of the four-wheel hub motor driven full-drive electric automobile, which comprises a parameter acquisition module, a model construction module, a first estimation module, a second estimation module and a third estimation module.
The parameter acquisition module is used for acquiring sensing signal parameters of the in-vehicle sensor; the sensor signal parameters include suspension height data, inertial measurement data, steering wheel angle data, vehicle signal data, and motor data. And the model construction module is used for respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters. And the first estimation module is used for estimating the vertical tire force according to the rolling dynamics model based on a strong tracking unscented Kalman filter. And the second estimation module is used for estimating the longitudinal tire force according to the longitudinal vehicle model based on a classical Kalman filter. A third estimation module for estimating lateral tire forces from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces based on a strong tracking unscented kalman filter.
The beneficial effects of the invention are as follows:
(1) The invention utilizes the information of the low-cost vehicle-mounted sensor, the state feedback information of the hub motor and the linear control motor system to realize the estimation of the longitudinal, lateral and vertical tire force;
(2) The invention uses the strong tracking unscented Kalman filter to estimate the lateral force and the vertical force of the tire, and has better performance in the aspects of dynamic tracking capacity and convergence speed compared with the traditional Kalman filter;
(3) The invention does not use tire models with various parameters to estimate the tire force, and the estimation algorithm has high precision and good robustness.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The method for estimating the tire force of the four-wheel hub motor driven full-drive electric automobile is characterized by comprising the following steps of:
acquiring sensing signal parameters of an in-vehicle sensor; the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data;
respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters;
estimating vertical tire force according to the rolling dynamics model based on a strong tracking unscented Kalman filter; the construction method of the rolling dynamics model comprises the following steps:
constructing a first model: the expression of the first model is:
wherein, 
and->
The vertical displacements on and off the spring are respectively, and the superscripts i E [ L1, L2, R1, R2]L1, L2, R1 and R2 represent front left, front right, rear left and rear right, respectively;
Is the actuating force of the active suspension;
A distributed sprung mass on each wheel;
And->
Stiffness and damping for each suspension, respectively, obtained from an active suspension controller; g is gravity acceleration;
the height of the suspension is not deformed;
Representing a first derivative;
Representing a second order reciprocal;
determining a second model according to the first model, wherein the expression of the second model is as follows:
Wherein, 
relative vertical displacement of sprung and unsprung masses measured for suspension height sensor;
determining a third model according to the second model; the expression of the third model is:
wherein, 
representing the influence of the wheel alignment parameters on the vertical force;
Representing random road incentives;
For vertical tyre force>
Representing the tire vertical force as a function of unsprung mass and road excitation;
Representing the unsprung mass at each corner;
constructing a roll dynamics model; the expression of the roll dynamics model is:
wherein, 
、
and->
The roll angle, sprung mass and lateral acceleration of the vehicle respectively;
Representing the vertical distance from the center of gravity to the roll center;
Indicating that the spring mass is wound at the centre of gravityxThe moment of inertia of the shaft;
Representing roll angle stiffness, +.>
Representing the damping coefficient;lrepresenting the distance between the mounting positions of the left and right suspensions on the same shaft;
determining a fourth model from the roll dynamics model; the expression of the fourth model is:
determining a fifth model according to the fourth model; the expression of the fifth model is:
wherein, 
as a function of the amount of suspension compression and the roll angle of the vehicle,/->
Is the amount of suspension compression;
the expression of the rolling dynamics model is:
Estimating longitudinal tire forces from the longitudinal vehicle model based on a classical kalman filter;
based on a strong tracking unscented kalman filter, a lateral tire force is estimated from the lateral vehicle model, the vertical tire force, and the longitudinal tire force.
2. The four-wheel hub motor-driven all-drive-by-wire electric vehicle tire force estimation method according to claim 1, wherein the in-vehicle sensor includes: suspension height sensor, inertial measurement unit, in-wheel motor, vehicle signal sensor and steering wheel angle sensor; the suspension height sensor is used for measuring suspension height data; the inertial measurement device is used for measuring inertial measurement data; the hub motor is used for measuring motor data; the vehicle signal sensor is used for measuring vehicle signal data; the steering wheel angle sensor is used for measuring steering wheel angle data.
3. The four-wheel hub motor-driven all-drive-by-wire electric vehicle tire force estimation method according to claim 1, wherein the construction method of the lateral vehicle model is as follows:
constructing a three-degree-of-freedom vehicle model; the expression of the three-degree-of-freedom vehicle model is as follows:
wherein m is the mass of the vehicle; 
Is the lateral acceleration of the vehicle;
、
And->
The steering angle, yaw acceleration and moment of inertia around the z-axis of the front wheel are respectively;
And->
The front tread width and the rear tread width, respectively;
Representing the left front wheel longitudinal force;
representing the left rear wheel longitudinal force;
Representing left front wheel lateral force;
Representing left rear wheel lateral force;
Representing the right front wheel lateral force;
Represents the right rear wheel lateral force;
Representing the right front wheel longitudinal force;
Representing the right rear wheel longitudinal force;
Representing the lateral forces of the left front wheel and the left rear wheel;
Representing the sum of the lateral forces of the right front wheel and the right rear wheel;
Representing the sum of the vertical forces of the left front wheel and the left rear wheel;
Representing the sum of the vertical forces of the right front wheel and the right rear wheel;
And->
Representing distances from the center of gravity to the front and rear axes, respectively;
Representing a first derivative;
Representing a yaw rate of the vehicle;
constructing a linear transverse tire model; the expression of the linear transverse tire model is as follows:
wherein, 
and->
The cornering stiffness and the tire cornering angle of each axle are respectively marked +.>
Represents the cornering stiffness of the front and rear axle, < >>
Representing the front axle->
Representing the rear axle;
And->
The vehicle sideslip angle and the vehicle longitudinal speed respectively;
A lateral force being the front axle or rear axle tire; / >
Representing the slip angle of the front axle wheel;
Representing the slip angle of the rear axle wheel;
determining the lateral vehicle model from the three degree of freedom vehicle model and the linearized lateral tire model; the expression of the lateral vehicle model is:
4. the four-wheel hub motor-driven all-drive-by-wire electric vehicle tire force estimation method according to claim 1, wherein the longitudinal vehicle model construction method is as follows:
building a tire dynamics model; the expression of the tire dynamics model is as follows:
wherein, 
、
、
、
、
respectively representing the driving torque, braking torque, rolling resistance, effective radius and moment of inertia of the wheels;
Representing the longitudinal force of the tire;
Representing the rotational angular velocity of the wheel;
constructing a longitudinal vehicle dynamics response model; the expression of the longitudinal vehicle dynamics response model is:
wherein m is the mass of the vehicle;
representing the longitudinal acceleration of the vehicle;
Representing the total rolling resistance of the whole vehicle;
Representing the total longitudinal force of the whole vehicle; superscript i.epsilon.L 1, L2, R1, R2]L1, L2, R1 and R2 represent left front, right front, left rear and right rear, respectively,/->
Representing the left front wheel longitudinal force;
Representing the left rear wheel longitudinal force;
Representing left front wheel lateral force; / >
Representing left rear wheel lateral force;
Representing the right front wheel lateral force;
Represents the right rear wheel lateral force; upper energizer->
Represents the cornering stiffness of the front and rear axle, < >>
Representing the front axle->
Representing the rear axle;
Steering angle for front wheel; g is gravity acceleration;
、
、
、
Respectively representing total longitudinal driving force, air resistance, rolling resistance and gradient resistance;
Is the aerodynamic drag coefficient; a is the windward area;
Is air density;fis the rolling resistance coefficient;
Is road grade;
Is the longitudinal speed of the vehicle; />
Determining the longitudinal vehicle model from the tire dynamics model and the longitudinal vehicle dynamics response model.
5. The method for estimating tire force of a four-wheel hub motor-driven all-drive-by-wire electric vehicle according to claim 1, wherein the estimating vertical tire force based on the rolling dynamics model based on a strong tracking unscented kalman filter comprises:
acquiring a nonlinear discrete space expression of a suspension system; the nonlinear discrete space expression is:
wherein, 
is a vertical state evolution function of the rolling dynamics model, which is simply called +.>
；
For the vertical observation function of the rolling dynamics model, simply called +.>
；
Is a vertical state variable; / >
Is a system vertical input;
Is vertical process noise;
For measuring noise vertically->
Representing the vertical state quantity at the time of k-1;
Representing a system vertical measurement;
determining a state vector at each time step according to the nonlinear discrete space expression; the expression of the state vector is:
；
wherein, 
representing the relative vertical displacement of the sprung and unsprung masses measured by the suspension height sensor;
A vertical state quantity 1 representing the time k;
A vertical state quantity 2 representing the time k;
A vertical state quantity 3 representing the time k;
Representing a transpose;
determining a nonlinear function of the vertical tire force estimate from the nonlinear discrete spatial expression; the expression of the nonlinear function of the vertical tire force estimate is:
wherein, 
a first vertical state evolution function representing a rolling dynamics model;
A second vertical state evolution function representing a rolling dynamics model;
A third vertical state evolution function representing a rolling dynamics model;
Is a discrete time period; g is gravity acceleration;
A vertical state quantity 1 at time k-1;
A vertical state quantity 2 representing the time k-1;
representing the damping coefficient of the ith wheel suspension;
Representing the influence of the wheel alignment parameters on the vertical force; 
Representing the i-th wheel unsprung mass;
determining an observation function of the vertical tire force estimation according to the nonlinear discrete space expression; the expression of the observation function of the vertical tire force estimation is:
wherein, 
a first vertical observation function representing a rolling dynamics model;
A second vertical observation function representing a rolling dynamics model; />
The vertical tire force is determined from a nonlinear function of the vertical tire force estimate and an observation function of the vertical tire force estimate.
6. The four-wheel hub motor driven all-drive-by-wire electric vehicle tire force estimation method according to claim 4, wherein the estimating longitudinal tire force from the longitudinal vehicle model based on a classical kalman filter comprises:
constructing a discrete time-varying linear control system; the expression of the time-varying linear control system is as follows:
wherein, 
is a longitudinal state vector;
Representing a longitudinal state variable at time k-1;
Is a longitudinal control input;
Is a longitudinal measurement;
And->
White noise; a ʹ and B are both state transition matrices; h= [0,1,0]Is an observation matrix;
Is a discrete time period; wheel speed->
The method comprises the steps of carrying out a first treatment on the surface of the The state vector of the longitudinal vehicle model is: / >
；
A vertical state vector 1 representing time k;
A vertical state vector 2 representing the time k;
A vertical state vector 3 representing the time k;
Representing a transpose; the expressions of A ʹ and B are respectively:
and carrying out data fusion according to the state vector to obtain the longitudinal tire force.
7. The four-wheel hub motor-driven all-drive-by-wire electric vehicle tire force estimation method according to claim 3, wherein estimating lateral tire forces from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces based on a strong tracking unscented kalman filter, comprises:
constructing a nonlinear discrete space equation of the tire lateral force estimation; the calculation formula of the nonlinear discrete space equation is as follows:
wherein, 
a state evolution function for said transversal vehicle model, abbreviated as +.>
，
For the observation function of the transverse vehicle model, simply called +.>
；
Representing a transverse state variable at time k;
Represents the transverse state variable at time k-1;
Representing a system lateral input;
Representing system lateral process noise;
Representing a system lateral measurement;
Representing lateral measurement noise;
taking the lateral acceleration, the yaw acceleration and the four lateral tire forces as system states, and acquiring state vectors; the calculation formula of the state vector is as follows:
Wherein, 
represents the 1 st lateral state variable,/-at time k>
Represents the 2 nd lateral state variable at time k,/->
Represents the 3 rd lateral state variable,/-at time k>
Represents the 4 th lateral state variable,/-at time k>
Represents the 5 th lateral state variable,/-at time k>
A 6 th lateral state variable representing time k;
acquiring system transverse input; the calculation formula of the system transverse input is as follows:
wherein, 
the front wheel rotation angle at the moment k is represented;
Representing left front wheel lateral force;
Representing the right front wheel lateral force;
Representing left front wheel vertical force;
Representing right front wheel vertical force;
Representing left rear wheel vertical force;
Representing right rear wheel vertical force;
Representing a system lateral input 1;
Representing a system lateral input quantity 2;
Representing a system lateral input 3;
Representing a system lateral input 4;
Representing a system lateral input 5;
Representing a system lateral input 6;
Representing a system lateral input 7;
determining a nonlinear function of the lateral tire force estimate from the nonlinear discrete space equation; the expression of the nonlinear function of the lateral tire force estimation is:
wherein, 
a first state evolution function representing a transversal vehicle model,/->
Representing a second shape of the transverse vehicle model State evolution function (DOF)>
A third state evolution function representing a transversal vehicle model,/->
A fourth state evolution function representing a transversal vehicle model,/->
A fifth state evolution function representing a transversal vehicle model,/->
A sixth state evolution function representing a lateral vehicle model;
Represents the 1 st lateral state variable at time k-1;
Represents the 2 nd lateral state variable at time k-1;
Represents the 3 rd lateral state variable at time k-1;
Represents the 4 th lateral state variable at time k-1;
represents the 5 th lateral state variable at time k-1;
Represents the 6 th lateral state variable at time k-1;
determining an observation function of the lateral tire force estimation according to the nonlinear discrete space equation; the expression of the observation function of the lateral tire force estimation is:
wherein, 
a first observation function representing a lateral vehicle model;
A second observation function representing a lateral vehicle model;
the lateral tire force is determined from an observation function of the lateral tire force estimate and a nonlinear function of the lateral tire force estimate.
8. A four-wheel hub motor-driven all-drive-by-wire electric vehicle tire force estimation system, comprising:
the parameter acquisition module is used for acquiring sensing signal parameters of the in-vehicle sensor; the sensing signal parameters comprise suspension height data, inertia measurement data, steering wheel angle data, vehicle signal data and motor data;
The model construction module is used for respectively constructing a rolling dynamics model, a transverse vehicle model and a longitudinal vehicle model according to the sensing signal parameters; the construction method of the rolling dynamics model comprises the following steps:
constructing a first model: the expression of the first model is:
wherein, 
and->
The vertical displacements on and off the spring are respectively, and the superscripts i E [ L1, L2, R1, R2]，L1、L2, R1 and R2 represent front left, front right, rear left and rear right, respectively;
Is the actuating force of the active suspension;
A distributed sprung mass on each wheel;
And->
Stiffness and damping for each suspension, respectively, obtained from an active suspension controller; g is gravity acceleration;
the height of the suspension is not deformed;
Representing a first derivative;
Representing a second order reciprocal;
determining a second model according to the first model, wherein the expression of the second model is as follows:
wherein, 
relative vertical displacement of sprung and unsprung masses measured for suspension height sensor;
determining a third model according to the second model; the expression of the third model is:
wherein, 
representing the influence of the wheel alignment parameters on the vertical force;
Representing random road incentives;
For vertical tyre force>
Representing the tire vertical force as a function of unsprung mass and road excitation; / >
Representing the unsprung mass at each corner;
constructing a roll dynamics model; the expression of the roll dynamics model is:
wherein, 
、
and->
The roll angle, sprung mass and lateral acceleration of the vehicle respectively;
Representing the vertical distance from the center of gravity to the roll center;
Indicating that the spring mass is wound at the centre of gravityxThe moment of inertia of the shaft;
Representing roll angle stiffness, +.>
Representing the damping coefficient;lrepresenting the distance between the mounting positions of the left and right suspensions on the same shaft;
determining a fourth model from the roll dynamics model; the expression of the fourth model is:
determining a fifth model according to the fourth model; the expression of the fifth model is:
wherein, 
as a function of the amount of suspension compression and the roll angle of the vehicle,/->
Is the amount of suspension compression;
the expression of the rolling dynamics model is:
the first estimation module is used for estimating the vertical tire force according to the rolling dynamics model based on a strong tracking unscented Kalman filter;
a second estimation module for estimating longitudinal tire forces from the longitudinal vehicle model based on a classical kalman filter;
a third estimation module for estimating lateral tire forces from the lateral vehicle model, the vertical tire forces, and the longitudinal tire forces based on a strong tracking unscented kalman filter.
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