CN113247004A - Joint estimation method for vehicle mass and road transverse gradient - Google Patents

Joint estimation method for vehicle mass and road transverse gradient Download PDF

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Publication number
CN113247004A
CN113247004A CN202110652351.0A CN202110652351A CN113247004A CN 113247004 A CN113247004 A CN 113247004A CN 202110652351 A CN202110652351 A CN 202110652351A CN 113247004 A CN113247004 A CN 113247004A
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vehicle
road
lateral
mass
transverse
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宋翔
王鹏
张磊
蒋慧琳
阎舜
李丽萍
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Nanjing Xiaozhuang University
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Nanjing Xiaozhuang University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0018—Method for the design of a control system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle

Abstract

The invention discloses a method for jointly estimating vehicle mass and road transverse gradient, which is characterized in that a transverse kinematics model and a transverse dynamics model which accord with the driving characteristics of a land driving front wheel steering four-wheel vehicle are respectively established aiming at the land driving front wheel steering four-wheel vehicle, then the dynamics model and the kinematics model are fused to realize the decoupling of the vehicle mass and the road transverse gradient, and the joint estimation of the vehicle mass and the road transverse gradient is realized through a recursive least square algorithm with a forgetting factor.

Description

Joint estimation method for vehicle mass and road transverse gradient
Technical Field
The invention relates to a joint estimation method of vehicle mass and road lateral gradient.
Background
With the development of social economy, the road traffic safety problem is increasingly prominent and has become a global problem. A great amount of casualties and property loss are caused by traffic accidents every year around the world, and all countries around the world strive to reduce the occurrence of the traffic accidents. In recent years, active safety technology for automobiles has been rapidly developed. The active safety technology of automobiles can prevent accidents in the bud and actively avoid accidents, and has become one of the most important development directions of modern automobiles. The conventional active safety technology mainly comprises an anti-lock braking system (ABS), a vehicle Electronic Stability Program (ESP), a Traction Control System (TCS), an electronic control drive anti-skid system (ASR), a four-wheel steering stability control system (4WS), a lane departure early warning system (LDWS), a rollover prevention system and the like. However, the precondition for the active safety system of the automobile to effectively implement various control logics is to accurately acquire the state parameters of the automobile. As key parameters in active safety systems such as a rollover prevention system, an LDWS (laser direct horizon), an ESP (electronic stability program) and the like, the accuracy of the mass of the whole vehicle and the lateral gradient of a road surface directly influences the control effect of the active safety systems and is important reference information of the active safety systems of the vehicle, so that the real-time, accurate and low-cost measurement or estimation of the mass of the vehicle and the lateral gradient of the road has important significance on the driving safety and stability of the vehicle.
The existing research has less research on the lateral gradient of a road, but for large heavy vehicles, the rollover risk in the driving process of the vehicle is a non-negligible safety influence factor, part of the research adopts a kinematics method to independently estimate the lateral gradient of the road, and the lateral gradient of the road is estimated by simply carrying out kinematics calculation on a measurement signal of a low-cost vehicle-mounted sensor, so that the cost is low, but the method has higher requirement on the accuracy of the sensor, and the vehicle-mounted low-cost sensor has poorer accuracy and has larger measurement error due to too simple calculation processing, so that the control effect is influenced. For the estimation of the quality, the current research is mainly realized by a vehicle longitudinal dynamic model, but the longitudinal dynamic model relates to the road and environment variables such as wind resistance, road rolling coefficient and the like, and the longitudinal dynamic model is often regarded as a constant in the prior research, so that the error is large when the external environment and the road condition change. At present, some researches adopt a road transverse dynamics model to estimate the vehicle mass, but the researches usually consider that the road transverse gradient is 0, and ignore the problem of coupling between the vehicle mass and the road transverse gradient, and meanwhile, the transverse dynamics model has a variable of vehicle lateral speed which is difficult to obtain by direct measurement or only depending on the dynamics model, thereby causing larger error of a mass estimation result.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a joint estimation method of vehicle mass and road transverse gradient, which has the advantages of high precision, low cost and good real-time property and can meet the requirements of active safety measurement and control of an automobile.
The technical scheme adopted by the invention is as follows: a joint estimation method of vehicle mass and road lateral gradient is characterized in that: the invention aims at four-wheeled vehicles steered by front wheels running on land, transverse kinematics and transverse dynamics models of the vehicle running process are respectively established, further the dynamics and the kinematics models are fused to realize the decoupling of the vehicle mass and the road transverse gradient, the joint estimation of the vehicle mass and the road transverse gradient is realized through a recursive least square algorithm with a forgetting factor, the method can accurately estimate the vehicle mass and the road transverse gradient in real time under the condition of changes of external roads, environments and the like, and the specific steps comprise:
1) establishing a transverse kinematics model of a vehicle driving process
Assuming that the vehicle does plane motion, neglecting the rotation speed of the earth, and assuming that the pitch angle speed, the roll angle speed and the vertical speed of the vehicle are zero, a transverse kinematic equation of the vehicle running process can be established:
Figure BDA0003112127460000021
Figure BDA0003112127460000022
in the formula (1), vx,vyRespectively representing the longitudinal and lateral speed of the vehicle, ayIndicating the lateral acceleration, ω, of the vehiclezRepresenting the yaw rate of the vehicle, all as defined with respect to the body coordinate system, g representing the gravitational acceleration, phi representing the lateral gradient of the road, and the upper sign "·" representing the differential, e.g.
Figure BDA0003112127460000023
Represents a pair vyDifferentiation of (1);
in the formula (2), δfThe steering angle of the front wheel is shown, wherein a is the distance from the center of a wheel axle of the front wheel of the automobile to the center of mass, and b is the distance from the center of a wheel axle of the rear wheel of the automobile to the center of mass;
is obtained from the formula (1)
Figure BDA0003112127460000024
In the formula (3), considering the normal running state of the vehicle,
Figure BDA0003112127460000025
the values are small and therefore negligible, considering that in most road conditions, the lateral gradient of the road is generally small, i.e. arcsin (·) approximately,
equation (3) can be simplified as:
Figure BDA0003112127460000026
is obtained from the formula (2)
Figure BDA0003112127460000027
2) Establishing a transverse dynamics model of the driving process of an automobile
For a front-wheel steered four-wheeled vehicle traveling in a typical road traffic environment, ignoring the pitch, roll and bounce up and down motions of the vehicle, ignoring the effect of the vehicle suspension on the tire axles, a two-degree-of-freedom vehicle lateral dynamics equation can be established that considers the lateral slope of the road:
may＝2Fyf cosδf+2Fyr+mgsinφ (6)
in the formula (6), m is the mass of the vehicle, FyfIs a lateral force acting on a single front wheel, FyrIs a lateral force acting on the single rear wheel; for a vehicle traveling in a general road traffic environment, the lateral forces acting on the wheels can be generally expressed as:
Fyf＝Cαfαf，Fyr＝Cαrαr (7)
in the formula (7), Cαf、CαrRespectively representing the cornering stiffness, alpha, of the front and rear tyresf、αrRespectively representing the slip angles of the front and rear tires and can be expressed as
Figure BDA0003112127460000031
In the case of equations (7), (8) in equation (6), it is taken into account that the transverse gradient of the road is generally small, i.e. arcsin (·), given the majority of the road surface, and δ given that it is generally smallfUsually at a small angle, i.e. cos deltaf1, and after finishing, the product can be obtained:
Figure BDA0003112127460000032
namely:
Figure BDA0003112127460000033
3) recursive least squares based joint estimation of vehicle mass and lateral road grade
Expression (4) and expression (10) are expressed as parameter identification standard forms:
Figure BDA0003112127460000034
in the formula (11), k represents a discrete time,
Figure BDA0003112127460000035
representing a matrix of parameters to be estimated, wherein,
Figure BDA0003112127460000036
and
Figure BDA0003112127460000037
respectively representing the mass of the vehicle to be estimated and the lateral gradient of the road;
Figure BDA0003112127460000038
representing the system output matrix, ay_mAnd omegaz_mRespectively representing lateral acceleration and yaw rate, v, measured using low-cost MEMS (Micro-Electro-mechanical System) sensorsx_mRepresenting the longitudinal speed, delta, of the vehicle obtained by means of a vehicle speed sensorf_mIndicating the steering angle delta measured by the steering angle sensor divided by the steering gear ratio q from the steering wheel to the front wheelstTo determine the steering angle (i.e. delta) of the front wheels in real timef_m＝δ/qt)，vy_mRepresents the vehicle lateral velocity calculated in real time according to equation (5), i.e.:
Figure BDA0003112127460000041
Figure BDA0003112127460000042
representing the input regression matrix, in the inventionUpper corner markTRepresents a matrix transposition; the estimation steps for estimating the vehicle mass and the road lateral gradient in real time by using a Recursive Least Square (RLS) algorithm with a forgetting factor are as follows:
computing system output matrix y (k) and input regression matrix
Figure BDA0003112127460000043
Calculating gain matrix k (k):
Figure BDA0003112127460000044
wherein the variance matrix
Figure BDA0003112127460000045
The parameter lambda is a forgetting factor, so that the influence of old data no longer related to the model can be effectively reduced, the covariance divergence is prevented, and the value range is usually [0.9,1 ]]The invention takes 0.975; calculating a parameter matrix gamma (k) to be estimated:
Figure BDA0003112127460000046
where I is a 2 x 2 unit matrix, whereby vehicle mass and road lateral gradient can be estimated in real time.
The invention has the advantages and obvious effects that:
(1) the invention provides a vehicle mass and road transverse gradient estimation method which is low in cost, high in precision, good in real-time performance and wide in application range, and can be used for the requirement of accurate estimation of the vehicle mass and the road transverse gradient in the field of vehicle active safety control.
(2) The estimation method provided by the invention effectively avoids the defect that the common longitudinal dynamics model is greatly influenced by the environment and road conditions, and has good robustness to the change of the environment and road conditions.
(3) The method is reasonably simplified according to the driving characteristics of the vehicle, the decoupling of the vehicle mass and the road transverse gradient is effectively realized by combining the respective advantages of a vehicle kinematics model and a dynamics model, the estimation of the vehicle mass and the road transverse gradient is carried out by using a recursive least square recursion algorithm with a forgetting factor, the real-time performance of the estimation is guaranteed, and the respective defects of two methods of transverse kinematics and dynamics are overcome, namely the kinematics method has larger direct calculation error and can not obtain mass parameters, the transverse dynamics method is difficult to obtain transverse vehicle speed to complete the mass estimation and neglects the coupling problem of the vehicle mass and the road transverse gradient.
(4) The method provided by the invention can be suitable for the condition that the noise characteristic of the vehicle-mounted sensor cannot be accurately modeled, and the result can meet the requirements of precision and real-time performance of practical application.
The method of the invention aims to: the method has the advantages that proper modeling is carried out on the kinematic and dynamic processes of automobile driving, the transverse kinematic model and the transverse dynamic model are fused to realize decoupling of the mass of the automobile and the transverse gradient of the road, estimation values of the mass of the automobile and the transverse gradient of the road are obtained by using a recursive least square algorithm with a forgetting factor, the estimation values can be used for relevant control of automobile active safety, the method has the remarkable advantages of high precision, low cost, good real-time performance and the like, and belongs to the field of automobile active safety measurement and control.
The invention integrates the advantages of a kinematic method and a dynamic method, and realizes the decoupling of the automobile mass and the transverse gradient. Meanwhile, the respective defects of the two methods are fully considered, namely the kinematics method directly calculates larger errors and cannot obtain mass parameters, the longitudinal dynamics method is greatly influenced by environment and road conditions, the transverse dynamics method is difficult to obtain transverse speed to complete mass estimation and neglects the problem of coupling of the automobile mass and the transverse gradient of the road, and a real-time recursive least square algorithm with a forgetting factor is provided for estimating the automobile mass and the transverse gradient of the road.
Drawings
FIG. 1 is a block flow diagram of a method according to an embodiment of the invention.
Detailed Description
With the development of social economy, the road traffic safety problem is increasingly prominent and has become a global problem. A great amount of casualties and property loss are caused by traffic accidents every year around the world, and all countries around the world strive to reduce the occurrence of the traffic accidents. In recent years, active safety technology for automobiles has been rapidly developed. The active safety technology of automobiles can prevent accidents in the bud and actively avoid accidents, and has become one of the most important development directions of modern automobiles. The conventional active safety technology mainly comprises an anti-lock braking system (ABS), a vehicle Electronic Stability Program (ESP), a Traction Control System (TCS), an electronic control drive anti-skid system (ASR), a four-wheel steering stability control system (4WS), a lane departure early warning system (LDWS), a rollover prevention system and the like. However, the precondition for the active safety system of the automobile to effectively implement various control logics is to accurately acquire the state parameters of the automobile. As key parameters in active safety systems such as a rollover prevention system, an LDWS (laser direct horizon), an ESP (electronic stability program) and the like, the accuracy of the mass of the whole vehicle and the lateral gradient of a road surface directly influences the control effect of the active safety systems and is important reference information of the active safety systems of the vehicle, so that the real-time, accurate and low-cost measurement or estimation of the mass of the vehicle and the lateral gradient of the road has important significance on the driving safety and stability of the vehicle.
Currently, in the field of active safety of automobiles, the motion state of a vehicle is mainly measured or estimated by two methods:
the method has the advantages that the cost is low, the coupling between the vehicle mass and the road transverse gradient does not exist, but the accuracy mainly depends on the accuracy of the sensor, due to cost limitation, the vehicle-mounted sensor is mostly a low-cost sensor, the accuracy is poor, and the calculation processing is too simple, so that a large measurement error exists, and the control effect is influenced.
And the second is a dynamic method which can be used for the independent estimation or the combined estimation of the vehicle mass and the road transverse gradient and has lower cost. For the estimation of the vehicle mass, a recursion algorithm such as kalman filtering is adopted, the estimation of the vehicle mass is realized by establishing a vehicle longitudinal or transverse dynamic model, and the current research is mainly realized by adopting the vehicle longitudinal dynamic model, namely, the establishment of the whole vehicle longitudinal dynamic model is as follows:
max＝Fx-Faero-Frolling-mg sinθ (1)
in the formula (1), m represents the mass of the vehicle, axRepresenting the longitudinal acceleration of the vehicle in body coordinates, FxRepresenting the sum of longitudinal forces of front and rear tyres of the vehicle, FaeroIs the longitudinal air resistance, FrollingIs the rolling resistance of the two rear wheels: g represents gravity acceleration, and the g is 9.8m/s2And θ represents the longitudinal gradient of the road. As can be seen from the formula (1), the longitudinal dynamics model relates to road and environment variables such as wind resistance and road rolling coefficient, and the longitudinal dynamics model is often regarded as a constant in the prior research, so that the error is large when the external environment and the road condition change.
Meanwhile, some studies have already carried out preliminary studies on vehicle mass estimation by adopting a road transverse dynamic model, but these studies usually consider that the road transverse gradient is 0, and ignore the problem of coupling between the vehicle mass and the road transverse gradient, and meanwhile, the transverse dynamic model has a variable of vehicle lateral speed, which is difficult to obtain by direct measurement or only depending on the dynamic model, thereby causing a large error of the mass estimation result.
Therefore, the inventor of the invention fuses the respective advantages of the kinematic method and the dynamic method to realize the decoupling of the automobile mass and the transverse gradient. The invention considers the respective deficiency of the two methods at the same time, namely direct calculation error is bigger deficiency to the kinematics method, do not adopt the direct calculation method, but adopt the recurrence algorithm to recurrence the car quality and road lateral gradient, what is most commonly used in the recurrence algorithm is Kalman filtering algorithm, Kalman filter is the optimum state estimation filter with minimum mean square error as the criterion, it does not need to store the measured value in the past, only according to the present observed value and the estimated value of the previous moment, utilize the computer to carry on the recurrence calculation, can realize the estimation to the real-time signal, have the characteristics of small data memory space, simple algorithm, but Kalman filtering needs to model the system state model, and the quality and road lateral gradient are difficult to accurately establish the state model, therefore, the invention adopts a real-time recursion Least square (recursion Least square with forgetting factor, RLS) algorithm; aiming at the problem that the longitudinal dynamics method is greatly influenced by environment and road conditions, a transverse dynamics model method is adopted to realize quality estimation; aiming at the problems that the transverse dynamics method is difficult to obtain the transverse speed to complete the quality estimation and neglects the coupling of the automobile quality and the transverse gradient of the road, the kinematics method is combined with the transverse dynamics method to realize the estimation of the transverse speed and the decoupling of the automobile quality and the transverse gradient of the road.
In order to meet the measurement and estimation requirements of the automobile active safety control on the automobile mass and the road transverse gradient under the complex environment, firstly, the automobile is subjected to appropriate kinematic and dynamic modeling, and aiming at the application field of the invention, the invention can make the following reasonable assumptions for a front-wheel steering four-wheel vehicle (which should be the most extensive case at present, and is a typical example of a front-wheel steering car) running on a common road traffic environment:
1) assuming that the vehicle is moving in a plane, ignoring the earth's rotational speed,
2) because the longitudinal and transverse gradients of the traffic road surface on which the vehicle normally runs are smaller, the transverse gradient rate and the longitudinal gradient rate are both smaller than 20%, the pitching, rolling and up-and-down bouncing movement of the vehicle is ignored, and the pitching angle speed, the rolling inclination angle speed and the vertical speed of the vehicle are assumed to be zero
3) Neglecting the effect of the vehicle suspension on the tire axle,
4) the road surface adhesion coefficient conditions of the respective wheels are assumed to be the same.
According to the application requirements and assumptions, the invention can establish the transverse kinematic equation of the vehicle running process for the front wheel steering four-wheel automobile which is applied more currently as follows:
Figure BDA0003112127460000071
Figure BDA0003112127460000072
in the formula (2), vx,vyRespectively representing the longitudinal and lateral speed of the vehicle, ayIndicating the lateral acceleration, ω, of the vehiclezThe yaw rate of the vehicle is represented, the above definitions are all directed to a vehicle body coordinate system, g represents the gravitational acceleration, and the g is 9.8m/s in the present invention2Phi denotes the road lateral gradient and the upper sign "·" denotes the differential, e.g.
Figure BDA0003112127460000073
Represents a pair vyDifferentiation of (1);
in the formula (3), δfThe steering angle of the front wheel is represented, a is the distance from the center of a wheel axle of the front wheel of the automobile to the center of mass, b is the distance from the center of a wheel axle of the rear wheel of the automobile to the center of mass, and the values of a and b can be determined in advance according to relevant parameters of the automobile;
is obtained from the formula (2)
Figure BDA0003112127460000074
In the equation (4), considering the normal running state of the vehicle,
Figure BDA0003112127460000075
the values are small and therefore negligible, considering that in most road conditions, the lateral gradient of the road is generally small, i.e. arcsin (·) approximately,
equation (4) can be simplified as:
Figure BDA0003112127460000076
is obtained from the formula (3)
Figure BDA0003112127460000077
For a front-wheel steered four-wheeled vehicle traveling in a typical road traffic environment, ignoring the pitch, roll and bounce up and down motions of the vehicle, ignoring the effect of the vehicle suspension on the tire axles, a two-degree-of-freedom vehicle lateral dynamics equation can be established that considers the lateral slope of the road:
may＝2Fyf cosδf+2Fyr+mg sinφ (7)
in the formula (7), m is the mass of the vehicle, FyfIs a lateral force acting on a single front wheel, FyrIs a lateral force acting on the single rear wheel; for a vehicle traveling in a general road traffic environment, the lateral forces acting on the wheels can be generally expressed as:
Fyf＝Cαfαf，Fyr＝Cαrαr (8)
in the formula (8), Cαf、CαrRespectively representing the cornering stiffness of the front and rear tyres, predetermined according to vehicle-related parameters, alphaf、αrRespectively representing the slip angles of the front and rear tires and can be expressed as
Figure BDA0003112127460000078
In the case of equations (8), (9) substituted for equation (7), it is considered that the lateral gradient of the road is generally small, i.e. arcsin (·), given the majority of the road surface, and δ given that it is generally smallfUsually at a small angle, i.e. cos deltaf1, and after finishing, the product can be obtained:
Figure BDA0003112127460000081
namely:
Figure BDA0003112127460000082
in order to overcome the defects of Kalman filtering, the invention provides a method for estimating the vehicle mass and the road transverse gradient in real time based on a recursive least square algorithm with a forgetting factor, wherein the recursive least square is an iterative algorithm for unknown vectors, the minimum variance of a model error is taken as a target, for each sampling period, the unknown vectors are calculated by repeatedly iterating the existing sampling data, and the method has the characteristics of small data storage amount and simple and convenient algorithm.
Expression (5) and expression (11) are expressed as parameter identification standard forms:
Figure BDA0003112127460000083
in the formula (12), k represents a discrete time,
Figure BDA0003112127460000084
representing a matrix of parameters to be estimated, wherein,
Figure BDA0003112127460000085
and
Figure BDA0003112127460000086
respectively representing the mass of the vehicle to be estimated and the lateral gradient of the road;
Figure BDA0003112127460000087
representing the system output matrix, ay_mAnd omegaz_mRespectively representing lateral acceleration and yaw angular velocity measured by a low-cost MEMS (Micro-Electro-mechanical System) sensor, the low-cost MEMS acceleration sensor is arranged near the mass center position of the vehicle and is used for measuring the lateral acceleration along the transverse axis of a vehicle body coordinate System, the low-cost MEMS gyroscope is also arranged near the mass center position of the vehicle and is arranged along the vertical axis of the vehicle body coordinate System and is used for measuring the yaw angular velocity and vx_mIndicating vehicle obtained by a vehicle speed sensorThe vehicle longitudinal speed sensor is used for measuring the longitudinal vehicle speed, and sensors such as a Hall vehicle speed sensor or a wheel speed sensor can be adopted without limitation, but the vehicle speed measurement accuracy error is required to be less than 0.05 m/s and deltaf_mIndicating the steering angle delta measured by the steering angle sensor divided by the steering gear ratio q from the steering wheel to the front wheelstTo determine the steering angle (i.e. delta) of the front wheels in real timef_m＝δ/qt) In fact, if the vehicle is equipped with an electronic stability control or yaw stability control system, such information CAN be obtained through a CAN (Controller Area Network) bus of the vehicle.
vy_mRepresents the vehicle lateral velocity calculated in real time according to equation (5), i.e.:
Figure BDA0003112127460000091
Figure BDA0003112127460000092
representing input regression matrices, superscript in the present inventionTRepresents a matrix transposition; the estimation steps for estimating the vehicle mass and the road lateral gradient in real time by using a Recursive Least Square (RLS) algorithm with a forgetting factor are as follows:
computing system output matrix y (k) and input regression matrix
Figure BDA0003112127460000093
Calculating gain matrix k (k):
Figure BDA0003112127460000094
wherein the variance matrix
Figure BDA0003112127460000095
The parameter lambda is a forgetting factor, so that the old number which is no longer related to the model can be effectively reducedAccording to the influence, and prevents covariance from diverging, and the value is usually in the range of [0.9, 1%]The invention takes 0.975;
calculating a parameter matrix gamma (k) to be estimated:
Figure BDA0003112127460000096
where I is a 2 x 2 unit matrix, whereby vehicle mass and road lateral gradient can be estimated in real time.
The method for estimating the vehicle mass and the road transverse gradient, which is provided by the invention, has the advantages of low cost, high precision, good real-time property and wide application range, can be used for accurately estimating the vehicle mass and the road transverse gradient in the field of vehicle active safety control, can be suitable for the conditions of external environment and road condition change, and can meet the precision and real-time requirements of practical application. The method is reasonably simplified according to the driving characteristics of the vehicle, the decoupling of the mass of the vehicle and the transverse gradient of the road is effectively realized by combining the respective advantages of a vehicle kinematic model and a dynamic model, the estimation of the mass of the vehicle and the transverse gradient of the road is carried out by using a recursive least square recursion algorithm with a forgetting factor, the estimation precision is ensured, and meanwhile, the application requirements under complex working conditions are met.

Claims (1)

1. A joint estimation method of vehicle mass and road lateral gradient is characterized in that: the method comprises the steps of respectively establishing a transverse kinematics model and a transverse dynamics model of an automobile driving process aiming at four-wheeled vehicles driven by a front wheel on the land, further fusing the dynamics and the kinematics models to realize the decoupling of the vehicle mass and the road transverse gradient, and realizing the joint estimation of the vehicle mass and the road transverse gradient through a recursive least square algorithm with a forgetting factor, wherein the method can accurately estimate the vehicle mass and the road transverse gradient in real time under the condition of changes of external roads, environments and the like, and the specific steps comprise:
1) establishing a transverse kinematics model of a vehicle driving process
Assuming that the vehicle does plane motion, neglecting the rotation speed of the earth, and assuming that the pitch angle speed, the roll angle speed and the vertical speed of the vehicle are zero, a transverse kinematic equation of the vehicle running process can be established:
Figure FDA0003112127450000011
Figure FDA0003112127450000012
in the formula (1), vx,vyRespectively representing the longitudinal and lateral speed of the vehicle, ayIndicating the lateral acceleration, ω, of the vehiclezRepresenting the yaw rate of the vehicle, all as defined with respect to the body coordinate system, g representing the gravitational acceleration, phi representing the lateral gradient of the road, and the upper sign "·" representing the differential, e.g.
Figure FDA0003112127450000013
Represents a pair vyDifferentiation of (1);
in the formula (2), δfThe steering angle of the front wheel is shown, wherein a is the distance from the center of a wheel axle of the front wheel of the automobile to the center of mass, and b is the distance from the center of a wheel axle of the rear wheel of the automobile to the center of mass;
is obtained from the formula (1)
Figure FDA0003112127450000014
In the formula (3), considering the normal running state of the vehicle,
Figure FDA0003112127450000015
the values are small and therefore negligible, considering that in most road conditions, the lateral gradient of the road is generally small, i.e. arcsin (·) approximately,
equation (3) can be simplified as:
Figure FDA0003112127450000016
is obtained from the formula (2)
Figure FDA0003112127450000017
2) Establishing a transverse dynamics model of the driving process of an automobile
For a front-wheel steered four-wheeled vehicle traveling in a typical road traffic environment, ignoring the pitch, roll and bounce up and down motions of the vehicle, ignoring the effect of the vehicle suspension on the tire axles, a two-degree-of-freedom vehicle lateral dynamics equation can be established that considers the lateral slope of the road:
may＝2Fyf cosδf+2Fyr+mg sinφ (6)
in the formula (6), m is the mass of the vehicle, FyfIs a lateral force acting on a single front wheel, FyrIs a lateral force acting on the single rear wheel; for a vehicle traveling in a general road traffic environment, the lateral forces acting on the wheels can be generally expressed as:
Fyf＝Cαfαf，Fyr＝Cαrαr (7)
in the formula (7), Cαf、CαrRespectively representing the cornering stiffness, alpha, of the front and rear tyresf、αrRespectively representing the slip angles of the front and rear tires and can be expressed as
Figure FDA0003112127450000021
In the case of equations (7), (8) in equation (6), it is taken into account that the transverse gradient of the road is generally small, i.e. arcsin (·), given the majority of the road surface, and δ given that it is generally smallfUsually at a small angle, i.e. cos deltaf1, and after finishing, the product can be obtained:
Figure FDA0003112127450000022
namely:
Figure FDA0003112127450000023
3) recursive least squares based joint estimation of vehicle mass and lateral road grade
Expression (4) and expression (10) are expressed as parameter identification standard forms:
Figure FDA0003112127450000024
in the formula (11), k represents a discrete time,
Figure FDA0003112127450000025
representing a matrix of parameters to be estimated, wherein,
Figure FDA0003112127450000026
and
Figure FDA0003112127450000027
respectively representing the mass of the vehicle to be estimated and the lateral gradient of the road;
Figure FDA0003112127450000028
representing the system output matrix, ay_mAnd omegaz_mRespectively representing lateral acceleration and yaw rate, v, measured using low-cost MEMS (Micro-Electro-mechanical System) sensorsx_mRepresenting the longitudinal speed, delta, of the vehicle obtained by means of a vehicle speed sensorf_mIndicating the steering angle delta measured by the steering angle sensor divided by the steering gear ratio q from the steering wheel to the front wheelstTo determine the steering angle (i.e. delta) of the front wheels in real timef_m＝δ/qt)，vy_mRepresenting real time according to equation (5)Calculated vehicle lateral velocity, i.e.:
Figure FDA0003112127450000029
Figure FDA0003112127450000031
representing input regression matrices, superscript in the present inventionTRepresents a matrix transposition; the estimation steps for estimating the vehicle mass and the road lateral gradient in real time by using a Recursive Least Square (RLS) algorithm with a forgetting factor are as follows:
computing system output matrix y (k) and input regression matrix
Figure FDA0003112127450000032
Calculating gain matrix k (k):
Figure FDA0003112127450000033
wherein the variance matrix
Figure FDA0003112127450000034
The parameter lambda is a forgetting factor, so that the influence of old data no longer related to the model can be effectively reduced, the covariance divergence is prevented, and the value range is usually [0.9,1 ]]The invention takes 0.975;
calculating a parameter matrix gamma (k) to be estimated:
Figure FDA0003112127450000035
where I is a 2 x 2 unit matrix, whereby vehicle mass and road lateral gradient can be estimated in real time.
CN202110652351.0A 2021-06-11 2021-06-11 Joint estimation method for vehicle mass and road transverse gradient Withdrawn CN113247004A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114312808A (en) * 2022-02-15 2022-04-12 上海易巴汽车动力系统有限公司 Method for estimating weight, gradient and speed of intelligent driving vehicle
CN114347995A (en) * 2022-03-18 2022-04-15 所托(杭州)汽车智能设备有限公司 Method, device and storage medium for estimating lateral gradient of commercial vehicle
CN114485879A (en) * 2022-02-14 2022-05-13 中国第一汽车股份有限公司 Vehicle weight estimation method and system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114485879A (en) * 2022-02-14 2022-05-13 中国第一汽车股份有限公司 Vehicle weight estimation method and system
CN114312808A (en) * 2022-02-15 2022-04-12 上海易巴汽车动力系统有限公司 Method for estimating weight, gradient and speed of intelligent driving vehicle
CN114312808B (en) * 2022-02-15 2024-04-12 上海易巴汽车动力系统有限公司 Method for estimating weight, gradient and speed of intelligent driving vehicle
CN114347995A (en) * 2022-03-18 2022-04-15 所托(杭州)汽车智能设备有限公司 Method, device and storage medium for estimating lateral gradient of commercial vehicle

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