CN117302229A - Road adhesion coefficient estimation method and system - Google Patents
Road adhesion coefficient estimation method and system Download PDFInfo
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- CN117302229A CN117302229A CN202311318887.4A CN202311318887A CN117302229A CN 117302229 A CN117302229 A CN 117302229A CN 202311318887 A CN202311318887 A CN 202311318887A CN 117302229 A CN117302229 A CN 117302229A
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Classifications
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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
- B60W40/02—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
- B60W40/064—Degree of grip
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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
- 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
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
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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
- 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
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
- B60W2050/0052—Filtering, filters
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/12—Lateral speed
- B60W2520/125—Lateral acceleration
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- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
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- Mathematical Physics (AREA)
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- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention provides a road adhesion coefficient estimation method and system, and relates to the technical field of vehicles. The method of the invention comprises the following steps: the longitudinal road surface attachment coefficient of the previous scheduling period is obtained as an initial longitudinal road surface attachment coefficient, and a first longitudinal road surface attachment coefficient of the current scheduling period is determined according to the initial longitudinal road surface attachment coefficient and a tire model; acquiring a lateral road surface attachment coefficient of a previous scheduling period as an initial lateral road surface attachment coefficient, and determining a first lateral road surface attachment coefficient of the current scheduling period according to the initial lateral road surface attachment coefficient and a tire model; determining a second lateral road surface attachment coefficient of the scheduling period according to the initial lateral road surface attachment coefficient by using an extended Kalman filter; the final road surface attachment coefficient is determined from the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient, and the road surface attachment coefficient of the present scheduling period. The invention can reduce the precondition required for estimating the road adhesion coefficient.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a road adhesion coefficient estimation method and system.
Background
Road adhesion coefficient is an important external environmental parameter of a vehicle and is commonly used to characterize the roughness of a road surface. Since the forces that determine the steering stability of the vehicle are almost all from the forces exerted by the ground on the tires, and the road adhesion coefficient determines the upper limit on which the ground can provide the forces to the vehicle, estimation of the road adhesion coefficient can provide important references and boundaries for the control of the vehicle.
In the prior art, it is generally required that the maximum road adhesion coefficient is represented by calculating the road adhesion coefficient when the vehicle is in a significant slip state, so that the precondition for estimating the road adhesion coefficient is severe.
Disclosure of Invention
The problem addressed by the present invention is how to reduce the preconditions required for estimating road adhesion coefficients.
In order to solve the above problems, the present invention provides a road adhesion coefficient estimation method and system.
In a first aspect, the present invention provides a road adhesion coefficient estimation method, including:
the longitudinal road surface attachment coefficient of the previous dispatching cycle is obtained as an initial longitudinal road surface attachment coefficient, and a first longitudinal road surface attachment coefficient of the dispatching cycle is determined according to the initial longitudinal road surface attachment coefficient and a tire model;
acquiring a lateral road surface attachment coefficient of a previous scheduling period as an initial lateral road surface attachment coefficient, and determining a first lateral road surface attachment coefficient of the current scheduling period according to the initial lateral road surface attachment coefficient and the tire model;
determining a second lateral road surface attachment coefficient of the scheduling period according to the initial lateral road surface attachment coefficient by using an extended Kalman filter;
and determining a final road surface attachment coefficient according to the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient and the road surface attachment coefficient of the scheduling period.
Optionally, the determining the first longitudinal road surface adhesion coefficient of the scheduling period according to the initial longitudinal road surface adhesion coefficient and the tire model includes:
the longitudinal speed of the previous scheduling period is obtained as an initial longitudinal speed;
determining a longitudinal acceleration according to the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient and the tire model;
determining a longitudinal acceleration estimation error according to the longitudinal acceleration;
and correcting the initial longitudinal road surface attachment coefficient according to the longitudinal acceleration estimation error to determine the first longitudinal road surface attachment coefficient of the scheduling period.
Optionally, said determining the longitudinal acceleration from the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient, and the tire model includes:
determining a tire force based on the tire model from the initial longitudinal vehicle speed and the initial longitudinal road surface adhesion coefficient;
based on the tire model, the longitudinal acceleration is determined from the tire force.
Optionally, the determining a longitudinal acceleration estimation error according to the longitudinal acceleration includes:
the longitudinal acceleration is differenced from the detected longitudinal acceleration measured by the sensor to determine the longitudinal acceleration estimation error.
Optionally, said correcting said initial longitudinal road surface adhesion coefficient according to said longitudinal acceleration estimation error comprises:
determining a longitudinal road surface attachment coefficient feedback correction term according to the longitudinal acceleration estimation error and the adaptive feedback gain coefficient;
and correcting the change rate of the longitudinal road surface attachment coefficient according to the longitudinal road surface attachment coefficient feedback correction term, and integrating the corrected change rate of the longitudinal road surface attachment coefficient to determine the first longitudinal road surface attachment coefficient of the scheduling period.
Optionally, the determining, by using extended kalman filtering, the second lateral road surface adhesion coefficient of the present scheduling period according to the initial lateral road surface adhesion coefficient includes:
acquiring a lateral vehicle speed of a previous scheduling period as an initial lateral vehicle speed;
determining a lateral acceleration from the initial lateral vehicle speed, the initial lateral road surface adhesion coefficient, and the tire model;
determining a lateral acceleration estimation error from the lateral acceleration;
correcting the initial lateral vehicle speed according to the lateral acceleration estimation error to determine the final lateral vehicle speed of the scheduling period;
correcting the initial lateral road surface attachment coefficient according to the lateral acceleration estimation error to determine the middle lateral road surface attachment coefficient of the scheduling period;
and constructing an extended Kalman filter observer according to the final lateral vehicle speed, the middle lateral road surface adhesion coefficient and the yaw rate, and updating the middle lateral road surface adhesion coefficient according to the extended Kalman filter observer so as to determine the second lateral road surface adhesion coefficient of the scheduling period.
Optionally, the updating the medial lateral road surface adhesion coefficient according to the extended kalman filter observer includes:
setting updating conditions, wherein the updating conditions comprise the yaw rate, the longitudinal vehicle speed and the front and rear wheel slip angle meeting preset thresholds.
Optionally, the determining the final road surface attachment coefficient according to the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient, and using the road surface attachment coefficient of the present scheduling period includes:
activating a lateral feature calculation when the wheel slip angle exceeds a preset slip angle threshold for a first preset time and the yaw rate exceeds a preset yaw rate threshold, wherein the lateral feature calculation comprises:
if the difference value between the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient is larger than a preset difference value threshold, taking the maximum value of the second lateral road surface adhesion coefficient and the utilized road surface adhesion coefficient as the final road surface adhesion coefficient, otherwise, comparing the first lateral road surface adhesion coefficient with the second lateral road surface adhesion coefficient, comparing the minimum value of the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient with the utilized road surface adhesion coefficient, and taking the maximum value as the final road surface adhesion coefficient;
activating longitudinal feature calculation after the wheel slip rate exceeds a preset slip rate threshold for a second preset time, wherein the activating longitudinal feature calculation comprises:
and taking the maximum value of the first longitudinal road surface adhesion coefficient and the utilization road surface adhesion coefficient as the final road surface adhesion coefficient.
Optionally, the road adhesion coefficient estimation method further includes:
the road surface attachment coefficient is determined based on the longitudinal acceleration and the lateral acceleration.
In a second aspect, the present invention provides a road surface adhesion coefficient estimation system comprising a computer readable storage medium storing a computer program and a processor, the computer program implementing the above road surface adhesion coefficient estimation method when read and run by the processor.
The invention determines the first longitudinal road surface adhesion coefficient, the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient by using the tire model, fully considers the road surface adhesion coefficients obtained by various estimation modes, and can obtain the final road surface adhesion coefficient, namely the maximum road surface adhesion coefficient, under the condition of no strict condition, such as no significant skidding of a vehicle, thereby reducing the precondition required for estimating the road surface adhesion coefficient. In addition, the second lateral road surface adhesion coefficient is determined through extended Kalman filtering, so that the road surface adhesion coefficient estimated through the lateral characteristics is more accurate.
Drawings
FIG. 1 is a flow chart of a road adhesion coefficient estimation method according to an embodiment of the invention;
fig. 2 is a schematic diagram of a road adhesion coefficient estimation method according to an embodiment of the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
As shown in fig. 1, an embodiment of the present invention provides a road adhesion coefficient estimation method, including:
and acquiring a longitudinal road surface attachment coefficient of the previous scheduling period as an initial longitudinal road surface attachment coefficient, and determining a first longitudinal road surface attachment coefficient of the current scheduling period according to the initial longitudinal road surface attachment coefficient and a tire model.
Specifically, the longitudinal road surface attachment coefficient of the previous dispatching cycle is firstly obtained as the initial longitudinal road surface attachment coefficient of the dispatching cycle, and then the longitudinal road surface attachment coefficient of the dispatching cycle is determined according to the initial longitudinal road surface attachment coefficient of the dispatching cycle and the tire model.
And acquiring a lateral road surface attachment coefficient of the previous scheduling period as an initial lateral road surface attachment coefficient, and determining a first lateral road surface attachment coefficient of the current scheduling period according to the initial lateral road surface attachment coefficient and the tire model.
Wherein, the scheduling period generally refers to the time interval of measuring and evaluating the road adhesion coefficient; as environmental factors (e.g., traffic flow, weather conditions, road type, traffic safety requirements, etc.) change, different scheduling periods need to be set.
Specifically, similar to the longitudinal road surface adhesion coefficient, the specific process is described later, and will not be described here again.
And determining a second lateral road surface attachment coefficient of the scheduling period according to the initial lateral road surface attachment coefficient by using extended Kalman filtering.
Specifically, an observer is constructed to observe the lateral road surface attachment coefficient by using extended kalman filtering, so that a second lateral road surface attachment coefficient of the scheduling period is determined according to the initial lateral road surface attachment coefficient.
And determining a final road surface attachment coefficient according to the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient and the road surface attachment coefficient of the scheduling period.
And determining the final road surface attachment coefficient according to the longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient and the road surface attachment coefficient of the scheduling period. The maximum road surface adhesion coefficient can be obtained dynamically according to the model in a non-serious slipping range, the calculation threshold is reduced, and the maximum road surface adhesion coefficient is obtained in a wider range.
Wherein, the road adhesion coefficient is used for only representing the level of the acting force provided by the current road surface to the vehicle, and the maximum acting force cannot be obtained; the maximum road surface adhesion coefficient cannot be represented by the road surface adhesion coefficient, and the calculated road surface adhesion coefficient is the maximum road surface adhesion coefficient only after the vehicle uses the maximum acting force of the road surface (such as wheel slip).
Optionally, the determining the first longitudinal road surface adhesion coefficient of the scheduling period according to the initial longitudinal road surface adhesion coefficient and the tire model includes:
and acquiring the longitudinal speed of the previous scheduling period as the initial longitudinal speed.
Specifically, as shown in connection with fig. 2, when constructing the longitudinal acceleration observer, the longitudinal vehicle speed and the longitudinal road surface adhesion coefficient μ for the previous scheduling period x The current loop is initialized. If the system is initialized for the first time, the longitudinal speed and the longitudinal road adhesion coefficient are respectively assigned to 0 and 1, and if the system is not initialized, the calculation result of the last scheduling period is used as an initial value. Given that the initial is the premise of the subsequent calculation, the observer can be quickly converged, and the road surface adhesion coefficient of the longitudinal tire characteristic can be obtained.
And determining longitudinal acceleration according to the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient and the tire model.
Specifically, as shown in connection with fig. 2, by constructing the longitudinal acceleration observer, the longitudinal acceleration can be determined from the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient, and the tire model.
And determining a longitudinal acceleration estimation error according to the longitudinal acceleration.
Specifically, after the longitudinal acceleration is determined, a difference may be made with the longitudinal acceleration measured by a sensor (e.g., a gyroscope) to determine a longitudinal acceleration estimation error.
And correcting the initial longitudinal road surface attachment coefficient according to the longitudinal acceleration estimation error to determine the first longitudinal road surface attachment coefficient of the scheduling period.
Specifically, a correction term can be determined according to the longitudinal acceleration estimation error and the corresponding adaptive feedback gain coefficient, and the initial longitudinal road surface attachment coefficient is corrected by the correction term, so that the longitudinal road surface attachment coefficient of the scheduling period can be determined.
Optionally, said determining the longitudinal acceleration from the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient, and the tire model includes:
and determining tire force according to the initial longitudinal vehicle speed and the initial longitudinal road surface adhesion coefficient based on the tire model.
Specifically, based on the tire model, the longitudinal force can be obtained by the initial longitudinal vehicle speed, the wheel speed (a wheel speed sensor is required), and the initial longitudinal road surface adhesion coefficient.
Where tire force refers to the interaction force between the tire and the road, generally comprising:
(1) Vertical force (vertical load): the vertical force from the weight of the vehicle, i.e. the supporting force between the tire and the road, to which the tire is subjected, enables the vehicle to maintain contact with the road, prevents slipping and provides a suspension effect.
(2) Lateral force: the lateral force applied by the tire during turning or changing direction enables the vehicle to travel around a curve, the direction being generally perpendicular to the direction of motion of the vehicle for controlling the steering of the vehicle.
(3) Longitudinal force: refers to the force in the fore-and-aft direction that is generated by the tire during acceleration and braking, enabling the vehicle to accelerate, decelerate, and maintain speed.
(4) Rolling resistance: refers to the force generated by the bending and deformation of a tire when the tire is in contact with a road; rolling resistance is the force that the vehicle needs to overcome to maintain constant speed travel.
(5) Grip force: the grip force refers to the friction force between the tire and the road, enabling the vehicle to remain stably suspended and avoid skidding.
Based on the tire model, the longitudinal acceleration is determined from the tire force.
Specifically, based on the tire model, the longitudinal acceleration of the vehicle may be further determined from the tire force. The tire model needs to ignore the process that the tire force is reduced along with the slip rate after the tire is saturated, so that the monotone relation between the longitudinal force and the slip rate can be ensured, the potential divergence problem of an algorithm is avoided, and the calculation robustness is ensured.
Among these, tire models are mathematical models that describe the behavior and performance of tires under different conditions, and are commonly used in simulation and control system design to better predict the motion and stability of a vehicle under different road conditions.
Optionally, the determining a longitudinal acceleration estimation error according to the longitudinal acceleration includes:
the longitudinal acceleration is differenced from the detected longitudinal acceleration measured by the sensor to determine the longitudinal acceleration estimation error.
Specifically, the detected longitudinal acceleration measured by the sensor is differenced from the longitudinal acceleration obtained by the model, so that a longitudinal acceleration estimation error can be obtained, and the error can indicate whether the current estimation result is larger or smaller, and further indirectly indicate the estimation deviation of the vehicle speed and the longitudinal road surface adhesion coefficient.
Optionally, said correcting said initial longitudinal road surface adhesion coefficient according to said longitudinal acceleration estimation error comprises:
and determining a longitudinal road surface attachment coefficient feedback correction term according to the longitudinal acceleration estimation error and the adaptive feedback gain coefficient.
Specifically, two adaptive feedback gain coefficients are introduced, one is used for correcting the initial longitudinal vehicle speed, the other is used for correcting the longitudinal road surface attachment coefficient, longitudinal acceleration estimation errors are respectively applied to the adaptive feedback gain coefficients K1 and K2, and a longitudinal vehicle speed feedback correction term and a longitudinal road surface attachment coefficient feedback correction term can be obtained.
The self-adaptive feedback gain coefficient can be set by considering two conditions, one is that the absolute value of the currently estimated longitudinal acceleration is larger than the actually measured longitudinal acceleration, and the tire is in a near saturated region, at the moment, a feedback gain can be given to act on the lateral acceleration error, so that the excessive integral error can be avoided, the estimated value can be quickly adjusted when the error is excessive, and the estimated response and accuracy are increased; in the rest of the case (underestimating lateral acceleration or farther from the saturation region), an adaptive feedback gain factor can be determined based on the currently calculated tire longitudinal force and the partial derivative of the tire longitudinal force with respect to the longitudinal vehicle speed, the gain factor being between-1 and 0, the closer the tire force is to 0, the closer the gain factor is to-1, the closer the tire force is to saturation, the closer the gain factor is to 0, which helps to improve the accuracy of the estimation and avoid algorithm divergence.
And correcting the change rate of the longitudinal road surface attachment coefficient according to the longitudinal road surface attachment coefficient feedback correction term, and integrating the corrected change rate of the longitudinal road surface attachment coefficient to determine the first longitudinal road surface attachment coefficient of the scheduling period.
Specifically, the longitudinal road surface attachment coefficient change rate is corrected according to the longitudinal road surface attachment coefficient feedback correction term, and the corrected longitudinal road surface attachment coefficient change rate is integrated, so that the longitudinal road surface attachment coefficient mu of the scheduling period can be determined x 。
It should be noted here that the integration condition does not always integrate the change rate of the longitudinal road surface adhesion coefficient, and the integration is only meaningful when the vehicle exhibits a certain longitudinal dynamics. For example, the condition may be set such that the longitudinal acceleration is greater than 0.8m/s 2 And the four-wheel comprehensive slip rate is more than 8%. Compared with the prior art method in which the maximum road surface adhesion coefficient is obtained only when the wheels slip severely (for example, slip ratio exceeds 50%), the present embodiment significantly reduces the estimated threshold value, and can obtain the road surface adhesion coefficient exhibited by the longitudinal dynamic characteristics of the vehicle stably and reliably under more conditions.
Wherein the adhesion coefficient with longitudinal road surface mu x Similarly, the calculation of the first lateral road adhesion coefficient may also be determined by the above steps, which will not be described herein.
Optionally, the determining, by using extended kalman filtering, the second lateral road surface adhesion coefficient of the present scheduling period according to the initial lateral road surface adhesion coefficient includes:
and acquiring the lateral vehicle speed of the previous scheduling period as an initial lateral vehicle speed.
Specifically, as shown in connection with fig. 2, when constructing the lateral acceleration observer, the lateral vehicle speed of the last scheduling period is first acquired as the initial lateral vehicle speed.
And determining lateral acceleration according to the initial lateral vehicle speed, the initial lateral road surface adhesion coefficient and the tire model.
Specifically, similar to the longitudinal acceleration, a description thereof will be omitted.
And determining a lateral acceleration estimation error according to the lateral acceleration.
Specifically, similar to the longitudinal acceleration, a description thereof will be omitted.
And correcting the initial lateral vehicle speed according to the lateral acceleration estimation error to determine the final lateral vehicle speed of the dispatching cycle.
Specifically, as shown in connection with FIG. 2, the initial lateral vehicle speed is corrected based on the tire lateral dynamics, i.e., based on the lateral acceleration estimation error, to obtain the lateral vehicle speed
And correcting the initial lateral road surface adhesion coefficient according to the lateral acceleration estimation error to determine the middle lateral road surface adhesion coefficient of the scheduling period.
Specifically, as shown in fig. 2, the initial lateral road surface adhesion coefficient is corrected based on the tire lateral dynamics, that is, based on the lateral acceleration estimation error, to obtain the lateral road surface adhesion coefficient μ y1 。
And constructing an extended Kalman filter observer according to the final lateral vehicle speed, the middle lateral road surface adhesion coefficient and the yaw rate, and updating the middle lateral road surface adhesion coefficient according to the extended Kalman filter observer so as to determine the second lateral road surface adhesion coefficient of the scheduling period.
Specifically, as shown in connection with FIG. 2, according to the final lateral vehicle speedConstructing an extended Kalman filter observer for the intermediate lateral road surface adhesion coefficient and the yaw rate to determine a second lateral road surface adhesion coefficient mu for the scheduling period y2 The road surface adhesion coefficient estimated by the lateral characteristics can be made more accurate. Wherein yaw rate can be measured by a gyroscope.
Wherein, as shown in connection with FIG. 2, the road adhesion coefficient estimation takes into account information in multiple dimensions, including longitudinal tire characteristics, lateral tire characteristics, slip conditions, other activation conditions, etc., such as longitudinal characteristicsAnd the characteristic activation conditions (longitudinal speed) and the transverse characteristic activation conditions (slip rate and longitudinal speed) are combined with the transverse dynamic characteristics, the longitudinal dynamic characteristics, the activation conditions and the extreme working conditions, so that the estimation is more accurate while the estimation working conditions are widened. Wherein mu x 、μ y1 Sum mu y2 Road adhesion coefficients, mu, obtained in three ways, respectively y1 I.e. obtained in observation of lateral vehicle speedResult obtained by forgetting least squares method, μ x Is similar to the calculation of a lateral vehicle speed observer, mu y2 Then the adaptive Kalman filtering calculation is used, the corresponding formulas are shown in figure 2, the observability of the filter is respectively the centroid side deflection angle beta and the yaw rate omega, in addition, F yf And F yr Lateral forces of front and rear axles, J z For the moment of inertia of the vehicle, Δ represents the distributed drive additional differential torque, and m represents the vehicle mass.
Optionally, the updating the medial lateral road surface adhesion coefficient according to the extended kalman filter observer includes:
setting updating conditions, wherein the updating conditions comprise the yaw rate, the longitudinal vehicle speed and the front and rear wheel slip angle meeting preset thresholds.
Specifically, when the intermediate lateral road surface adhesion coefficient is updated to obtain the second lateral road surface adhesion coefficient, additional conditions are required, such as limitation of yaw rate, limitation of longitudinal vehicle speed, limitation of front and rear wheel slip angle, and otherwise the intermediate lateral road surface adhesion coefficient cannot be updated.
Optionally, the determining the final road surface attachment coefficient according to the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient, and using the road surface attachment coefficient of the present scheduling period includes:
activating a lateral feature calculation when the wheel slip angle exceeds a preset slip angle threshold for a first preset time and the yaw rate exceeds a preset yaw rate threshold, wherein the lateral feature calculation comprises:
and if the difference value between the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient is larger than a preset difference value threshold, taking the maximum value of the second lateral road surface adhesion coefficient and the utilized road surface adhesion coefficient as the final road surface adhesion coefficient, otherwise, comparing the first lateral road surface adhesion coefficient with the second lateral road surface adhesion coefficient, and comparing the minimum value of the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient with the utilized road surface adhesion coefficient, and taking the maximum value as the final road surface adhesion coefficient.
Specifically, the lateral feature calculation is activated when the slip angle of the wheel is large (for example, exceeds 2 °) for a certain period of time (for example, 3 calculation cycles), and the yaw rate exceeds a certain value (for example, 0.03 rad/s). If mu is y1 And mu y2 If the phase difference is large, mu is taken y2 And mu utilize The larger value of (2) is output as the final road adhesion coefficient, otherwise mu is taken y1 And mu y2 And comparing the smaller value with mu utilize Taking the larger of the two.
Activating longitudinal feature calculation after the wheel slip rate exceeds a preset slip rate threshold for a second preset time, wherein the activating longitudinal feature calculation comprises:
and taking the maximum value of the first longitudinal road surface adhesion coefficient and the utilization road surface adhesion coefficient as the final road surface adhesion coefficient.
Specifically, longitudinal feature calculation is activated when the wheel slip rate is large (e.g., 10%) for a period of time (e.g., 3 calculation cycles). The road adhesion coefficient mu can be used for the maximum period of time ut ili ze And mu x The larger value of (3) is outputted as the final road adhesion coefficient.
If the condition is not satisfied, the road surface adhesion coefficient estimation result is maintained, and if the condition is not satisfied for more than a certain time (for example, 1 s), the road surface adhesion coefficient is gradually converged to 1.
Optionally, the road adhesion coefficient estimation method further includes:
the road surface attachment coefficient is determined based on the longitudinal acceleration and the lateral acceleration.
Specifically, road surface adhesion coefficient μ is used by geometric mean calculation of longitudinal and lateral accelerations utilize Firstly, obtaining the lateral acceleration and the longitudinal acceleration of the vehicle according to a gyroscope, and then squaring the square sum of the lateral acceleration and the longitudinal acceleration to obtain a geometric average value which is used as the current road adhesion coefficient mu utilize 。
Another embodiment of the present invention provides a road surface adhesion coefficient estimation system including a computer-readable storage medium storing a computer program and a processor, the computer program implementing the above road surface adhesion coefficient estimation method when read and executed by the processor.
In particular, the system may be a computer device and may be provided on a vehicle.
Although the invention is disclosed above, the scope of the invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications will fall within the scope of the invention.
Claims (10)
1. A road adhesion coefficient estimation method, characterized by comprising:
the longitudinal road surface attachment coefficient of the previous dispatching cycle is obtained as an initial longitudinal road surface attachment coefficient, and a first longitudinal road surface attachment coefficient of the dispatching cycle is determined according to the initial longitudinal road surface attachment coefficient and a tire model;
acquiring a lateral road surface attachment coefficient of a previous scheduling period as an initial lateral road surface attachment coefficient, and determining a first lateral road surface attachment coefficient of the current scheduling period according to the initial lateral road surface attachment coefficient and the tire model;
determining a second lateral road surface attachment coefficient of the scheduling period according to the initial lateral road surface attachment coefficient by using an extended Kalman filter;
and determining a final road surface attachment coefficient according to the first longitudinal road surface attachment coefficient, the first lateral road surface attachment coefficient, the second lateral road surface attachment coefficient and the road surface attachment coefficient of the scheduling period.
2. The method of estimating road surface adhesion coefficient according to claim 1, wherein said determining the first longitudinal road surface adhesion coefficient of the present scheduling period from the initial longitudinal road surface adhesion coefficient and the tire model includes:
the longitudinal speed of the previous scheduling period is obtained as an initial longitudinal speed;
determining a longitudinal acceleration according to the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient and the tire model;
determining a longitudinal acceleration estimation error according to the longitudinal acceleration;
and correcting the initial longitudinal road surface attachment coefficient according to the longitudinal acceleration estimation error to determine the first longitudinal road surface attachment coefficient of the scheduling period.
3. The road surface adhesion coefficient estimation method according to claim 2, wherein the determining the longitudinal acceleration from the initial longitudinal vehicle speed, the initial longitudinal road surface adhesion coefficient, and the tire model includes:
determining a tire force based on the tire model from the initial longitudinal vehicle speed and the initial longitudinal road surface adhesion coefficient;
based on the tire model, the longitudinal acceleration is determined from the tire force.
4. The road surface adhesion coefficient estimation method according to claim 2, wherein the determining a longitudinal acceleration estimation error from the longitudinal acceleration includes:
the longitudinal acceleration is differenced from the detected longitudinal acceleration measured by the sensor to determine the longitudinal acceleration estimation error.
5. The road surface adhesion coefficient estimation method according to claim 2, wherein the correcting the initial longitudinal road surface adhesion coefficient according to the longitudinal acceleration estimation error includes:
determining a longitudinal road surface attachment coefficient feedback correction term according to the longitudinal acceleration estimation error and the adaptive feedback gain coefficient;
and correcting the change rate of the longitudinal road surface attachment coefficient according to the longitudinal road surface attachment coefficient feedback correction term, and integrating the corrected change rate of the longitudinal road surface attachment coefficient to determine the first longitudinal road surface attachment coefficient of the scheduling period.
6. The method of estimating road surface adhesion coefficient according to claim 1, wherein determining the second road surface adhesion coefficient of the present scheduling period from the initial road surface adhesion coefficient using extended kalman filtering includes:
acquiring a lateral vehicle speed of a previous scheduling period as an initial lateral vehicle speed;
determining a lateral acceleration from the initial lateral vehicle speed, the initial lateral road surface adhesion coefficient, and the tire model;
determining a lateral acceleration estimation error from the lateral acceleration;
correcting the initial lateral vehicle speed according to the lateral acceleration estimation error to determine the final lateral vehicle speed of the scheduling period;
correcting the initial lateral road surface attachment coefficient according to the lateral acceleration estimation error to determine the middle lateral road surface attachment coefficient of the scheduling period;
and constructing an extended Kalman filter observer according to the final lateral vehicle speed, the middle lateral road surface adhesion coefficient and the yaw rate, and updating the middle lateral road surface adhesion coefficient according to the extended Kalman filter observer so as to determine the second lateral road surface adhesion coefficient of the scheduling period.
7. The method of estimating road surface adhesion coefficient according to claim 6, wherein said updating the medial-lateral road surface adhesion coefficient according to the extended kalman filter observer includes:
setting updating conditions, wherein the updating conditions comprise the yaw rate, the longitudinal vehicle speed and the front and rear wheel slip angle meeting preset thresholds.
8. The method of estimating road surface adhesion coefficient according to claim 1, wherein the determining of the final road surface adhesion coefficient from the first longitudinal road surface adhesion coefficient, the first lateral road surface adhesion coefficient, the second lateral road surface adhesion coefficient, and the road surface adhesion coefficient according to the present scheduling period includes:
activating a lateral feature calculation when the wheel slip angle exceeds a preset slip angle threshold for a first preset time and the yaw rate exceeds a preset yaw rate threshold, wherein the lateral feature calculation comprises:
if the difference value between the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient is larger than a preset difference value threshold, taking the maximum value of the second lateral road surface adhesion coefficient and the utilized road surface adhesion coefficient as the final road surface adhesion coefficient, otherwise, comparing the first lateral road surface adhesion coefficient with the second lateral road surface adhesion coefficient, comparing the minimum value of the first lateral road surface adhesion coefficient and the second lateral road surface adhesion coefficient with the utilized road surface adhesion coefficient, and taking the maximum value as the final road surface adhesion coefficient;
activating longitudinal feature calculation after the wheel slip rate exceeds a preset slip rate threshold for a second preset time, wherein the activating longitudinal feature calculation comprises:
and taking the maximum value of the first longitudinal road surface adhesion coefficient and the utilization road surface adhesion coefficient as the final road surface adhesion coefficient.
9. The road surface adhesion coefficient estimation method according to claim 1, characterized by further comprising:
the road surface attachment coefficient is determined based on the longitudinal acceleration and the lateral acceleration.
10. A road surface adhesion coefficient estimation system comprising a computer readable storage medium storing a computer program and a processor, the computer program, when read and executed by the processor, implementing the road surface adhesion coefficient estimation method according to any one of claims 1 to 9.
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