CN109515102B - Vehicle side wind estimation method and device and vehicle - Google Patents
Vehicle side wind estimation method and device and vehicle Download PDFInfo
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- CN109515102B CN109515102B CN201710852524.7A CN201710852524A CN109515102B CN 109515102 B CN109515102 B CN 109515102B CN 201710852524 A CN201710852524 A CN 201710852524A CN 109515102 B CN109515102 B CN 109515102B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G17/00—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load
- B60G17/015—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements
- B60G17/016—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input
- B60G17/0165—Resilient suspensions having means for adjusting the spring or vibration-damper characteristics, for regulating the distance between a supporting surface and a sprung part of vehicle or for locking suspension during use to meet varying vehicular or surface conditions, e.g. due to speed or load the regulating means comprising electric or electronic elements characterised by their responsiveness, when the vehicle is travelling, to specific motion, a specific condition, or driver input to an external condition, e.g. rough road surface, side wind
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/20—Speed
- B60G2400/204—Vehicle speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/40—Steering conditions
- B60G2400/41—Steering angle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2400/00—Indexing codes relating to detected, measured or calculated conditions or factors
- B60G2400/80—Exterior conditions
- B60G2400/84—Atmospheric conditions
- B60G2400/841—Wind
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60G—VEHICLE SUSPENSION ARRANGEMENTS
- B60G2800/00—Indexing codes relating to the type of movement or to the condition of the vehicle and to the end result to be achieved by the control action
- B60G2800/20—Stationary vehicle
Abstract
The disclosure relates to a method and a device for estimating lateral wind of a vehicle and the vehicle, which can estimate the lateral wind more accurately. The method comprises the following steps: establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters, wherein N is a positive integer; respectively inputting wheel state parameters of a vehicle into the N lateral wind estimation models to obtain N response values, wherein the wheel state parameters are used for representing the motion conditions of wheels of the vehicle; determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and an actual response value output by the vehicle based on actual lateral wind; determining a lateral wind estimate from the N lateral wind parameters based on the N identification errors.
Description
Technical Field
The disclosure relates to the technical field of vehicles, in particular to a method and a device for estimating a vehicle side wind and a vehicle.
Background
With the continuous development of scientific technology, people's trip is also more and more convenient, and various cars, electric motor car etc. have become the essential vehicle in people's life, and simultaneously, people also put forward higher and higher requirement to the stability and the security of vehicle.
The vehicle is often disturbed by the lateral wind in the driving process, and particularly, when the vehicle is driven at a high speed, the lateral force generated by the lateral wind may cause the tire to deviate from the driving direction or turn to an unstable state, so that the vehicle deviates from the driving direction, and in a serious case, accidents such as sideslip and rollover may occur, and the safety of the vehicle is affected. Therefore, determination of the side wind is essential for the study of the stability of the vehicle.
However, since the lateral wind has a large uncertainty, the current research on the lateral wind is very lacking, and there is no good way to determine the lateral wind.
Disclosure of Invention
The invention aims to provide a method and a device for estimating vehicle side wind and a vehicle, which can estimate the side wind more accurately.
According to a first aspect of embodiments of the present invention, there is provided a vehicle lateral wind estimation method, including:
establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters, wherein N is a positive integer;
respectively inputting wheel state parameters of a vehicle into the N lateral wind estimation models to obtain N response values, wherein the wheel state parameters are used for representing the motion conditions of wheels of the vehicle;
determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and an actual response value output by the vehicle based on actual lateral wind;
determining a lateral wind estimate from the N lateral wind parameters based on the N identification errors.
Optionally, determining, according to the N response values and an actual response value output by the vehicle based on actual crosswind, N identification errors respectively corresponding to the N crosswind estimation models, includes:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
determining a lateral wind estimate from the N lateral wind parameters based on the N identification errors, comprising:
establishing a cost function for each of the N identification errors
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Weights to identify error steady-state values;
determining J among the N lateral wind estimation modelsiThe lateral wind estimation model corresponding to the identification error with the minimum value is a first lateral wind estimation model;
and determining the lateral wind parameter corresponding to the first lateral wind estimation model as the lateral wind estimation value.
Optionally, determining, according to the N response values and an actual response value output by the vehicle based on actual crosswind, N identification errors respectively corresponding to the N crosswind estimation models, includes:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
determining a lateral wind estimate from the N lateral wind parameters based on the N identification errors, comprising:
determining a lateral wind estimation model corresponding to the minimum value of the N identification errors as a second lateral wind estimation model in the N lateral wind estimation models;
and determining the lateral wind parameter corresponding to the second lateral wind estimation model as the lateral wind estimation value.
Optionally, before establishing, according to the set N lateral wind parameters, N lateral wind estimation models respectively corresponding to the N lateral wind parameters, the method further includes:
according to the preset N relative wind speeds, respectively setting each lateral wind parameter in the N lateral wind parameters through the following formula:
wherein, i is 1,2,3 … … N, FywiIs the ith lateral wind parameter, rho, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwTo the side angle of incoming flow, VwriThe ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed;
according to the set N lateral wind parameters, establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters, wherein the N lateral wind estimation models comprise:
aiming at each lateral wind parameter in the N lateral wind parameters, establishing a corresponding lateral wind estimation model as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzIs the moment of inertia of the vehicle around the z-axis, a is the distance from the center of mass of the vehicle to the front axle of the vehicle, b is the distance from the center of mass of the vehicle to the rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter. Optionally, the wheel state parameters include a front wheel rotation angle and a left front wheel rotation speed ω of the vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrInputting the wheel state parameters of the vehicle into the N lateral wind estimation models respectively to obtain N response values, including:
obtaining the longitudinal speed v of the vehicle according to the rotating speed of the left front wheel, the rotating speed of the right front wheel, the rotating speed of the left rear wheel and the rotating speed of the right rear wheelx；
The front wheel rotation angle and the longitudinal speed vxRespectively inputting each lateral wind estimation model of the N lateral wind estimation models to obtain N response values, wherein the N responsesThe ith response value among the response values is (a)yi,ri)。
Optionally, after determining the estimated value of the lateral wind from the N lateral wind parameters, the method further includes:
and according to the lateral wind estimated value, performing active suspension control on the vehicle so that the vertical load is redistributed at the left wheel and the right wheel when the vehicle rolls.
According to a second aspect of the embodiments of the present invention, there is provided a vehicle lateral wind estimating apparatus including:
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing N lateral wind estimation models respectively corresponding to N lateral wind parameters according to the set N lateral wind parameters, and N is a positive integer;
the obtaining module is used for respectively inputting wheel state parameters of a vehicle into the N lateral wind estimation models to obtain N response values, and the wheel state parameters are used for representing the motion conditions of wheels of the vehicle;
the first determining module is used for determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and an actual response value output by the vehicle based on actual lateral wind;
a second determining module to determine a lateral wind estimate from the N lateral wind parameters based on the N identification errors.
Optionally, the first determining module is configured to:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
the second determination module is to:
establishing a cost function for each of the N identification errors
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Weights to identify error steady-state values;
determining J among the N lateral wind estimation modelsiThe lateral wind estimation model corresponding to the identification error with the minimum value is a first lateral wind estimation model;
and determining the lateral wind parameter corresponding to the first lateral wind estimation model as the lateral wind estimation value.
Optionally, the first determining module is configured to:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
the second determination module is to:
determining a lateral wind estimation model corresponding to the minimum value of the N identification errors as a second lateral wind estimation model in the N lateral wind estimation models;
and determining the lateral wind parameter corresponding to the second lateral wind estimation model as the lateral wind estimation value.
Optionally, the apparatus further comprises:
the setting module is used for respectively setting each lateral wind parameter in the N lateral wind parameters according to the following formula before establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters and according to preset N relative wind speeds:
wherein, i is 1,2,3 … … N, FywiIs the ith lateral wind parameter, rho, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwIs the incoming flowAngle of sideslip, VwriThe ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed;
the establishing module is used for:
aiming at each lateral wind parameter in the N lateral wind parameters, establishing a corresponding lateral wind estimation model as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzIs the moment of inertia of the vehicle around the z-axis, a is the distance from the center of mass of the vehicle to the front axle of the vehicle, b is the distance from the center of mass of the vehicle to the rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter.
Optionally, the wheel state parameters include a front wheel rotation angle and a left front wheel rotation speed ω of the vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrThe obtaining module is configured to:
according to the rotating speed omega of the left front wheelflThe right front wheel rotational speed ωfrThe rotation speed omega of the left rear wheelrlAnd the right rear wheel rotational speed omegarrObtaining a longitudinal speed v of said vehiclex；
The front wheel rotation angle and the longitudinal speed vxRespectively inputting each of the N lateral wind estimation models to obtain N response values, wherein the ith response value of the N response values is (a)yi,ri)。
Optionally, the apparatus further comprises:
a control module for, after determining a lateral wind estimate from the N lateral wind parameters, controlling an active suspension of the vehicle based on the lateral wind estimate such that vertical loads are redistributed at left and right wheels when the vehicle is rolling.
According to a third aspect of the embodiments of the present invention, there is provided a vehicle including the vehicle lateral wind estimating apparatus of the second aspect.
By the technical scheme, different lateral wind estimation models can be established according to different set lateral wind parameters, then response values output by the lateral wind estimation models are compared with actual response values of the vehicle, the response value closest to the actual response value of the vehicle is found out through identification errors, and then the lateral wind estimation value is determined from the different set lateral wind parameters. Therefore, by establishing a nonlinear vehicle model and setting a multi-model switching mode, the lateral wind borne by the vehicle can be estimated accurately, a foundation is laid for the research on the influence of the lateral wind on the static and dynamic characteristics of the vehicle, the control and the research on the stability of the vehicle body of the vehicle are facilitated, and the stability and the safety of the vehicle are improved.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method of estimating a vehicle lateral wind according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating a method of estimating a vehicle lateral wind according to an exemplary embodiment;
FIG. 3 is a block diagram illustrating a vehicle lateral wind estimation device according to an exemplary embodiment;
FIG. 4 is another block diagram of a vehicle lateral wind estimation device, shown in accordance with an exemplary embodiment;
FIG. 5 is another block diagram illustrating a vehicle lateral wind estimation device according to an exemplary embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart illustrating a vehicle lateral wind estimation method according to an exemplary embodiment, which may be applied to a vehicle, as shown in fig. 1, including the following steps.
Step S11: and establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters, wherein N is a positive integer.
Step S12: and respectively inputting the wheel state parameters of the vehicle into the N lateral wind estimation models to obtain N response values, wherein the wheel state parameters are used for representing the motion state of the wheels of the vehicle.
Step S13: and determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and the actual response value output by the vehicle based on the actual lateral wind.
Step S14: and determining a lateral wind estimated value from the N lateral wind parameters according to the N identification errors.
Referring to fig. 2, fig. 2 is a schematic diagram of a vehicle lateral wind estimation method provided by an embodiment of the present disclosure, a lateral wind estimation model 1 to a lateral wind estimation model N correspond to N lateral wind parameters, that is, each lateral wind parameter corresponds to a lateral wind estimation model, a wheel state parameter is input into each lateral wind estimation model to obtain N response values, the N response values are compared with actual response values output by a vehicle, N identification errors can be obtained, an optimal lateral wind estimation model is found through the identification errors, and a lateral wind parameter corresponding to the optimal lateral wind estimation model is used as a lateral wind estimation value. Therefore, by establishing a nonlinear vehicle model and setting a multi-model switching mode, the lateral wind borne by the vehicle can be estimated accurately, a foundation is laid for the research on the influence of the lateral wind on the static and dynamic characteristics of the vehicle, the control and the research on the stability of the vehicle body of the vehicle are facilitated, and the stability and the safety of the vehicle are improved.
Optionally, please continue to refer to fig. 2, after the estimated value of the lateral wind is obtained, the active suspension of the vehicle may be controlled according to the estimated value of the lateral wind, so that when the vehicle rolls, the vertical load is redistributed at the left and right wheels, thereby reducing the possibility of the vehicle rolling over and improving the stability of the vehicle. Of course, the present disclosure is not limited to controlling the active suspension, and may also perform operations such as adjusting the braking system, the driving wheel steering angle, etc. of the vehicle, such as redistributing the left and right braking forces of the vehicle to prevent the vehicle from rolling over, slipping, etc., as long as the stability of the vehicle can be improved by the estimated value of the lateral wind. The lateral wind estimation method provided by the disclosure can estimate the current lateral wind of the vehicle in real time, and further adjust the stability of the vehicle according to the estimated value of the lateral wind in real time, thereby being beneficial to improving the safety of the vehicle.
In the disclosed embodiment, the lateral wind parameter may be a lateral force F equivalent to a lateral windyw. Assuming that the lateral forces belong to a finite set, i.e. Fyw∈FwThe N lateral forces (N lateral wind parameters) may be N characteristic points contained in the set, i.e. N characteristic points
The value of N is not limited in this disclosure, but takes into account FwIf too many feature points are included, the calculation efficiency of the algorithm is reduced, and conversely, if the number of the feature points is too muchToo few will affect the algorithm accuracy, and this disclosure takes 11 feature points, i.e. N equals 11 as an example.
Optionally, before the N lateral wind estimation models respectively corresponding to the N lateral wind parameters are established according to the set N lateral wind parameters, each lateral wind parameter of the N lateral wind parameters may be respectively set according to the preset N relative wind speeds by using the following formula:
wherein, i is 1,2,3 … … N, FywiFor the ith lateral wind parameter, ρ, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwTo the side angle of incoming flow, VwriAnd the ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed. Coefficient of lateral force CyIs the incoming flow slip angle βwFor convenience of calculation, e.g. C can be takeny(βw) 2, etc.
In the formula, VwriAny relative wind speed VwrCan be determined by the following formula, in which VwWind speed, V vehicle speed, θwIs the wind direction angle, which is the angle between the absolute wind direction and the longitudinal axis of the vehicle.
For example, according to the classification of tropical cyclone strength, when N is 11, under different wind levels, the lateral force F of each characteristic point is obtainedywi(lateral wind parameters) are shown in Table 1 below.
TABLE 1 wind speed Range and lateral force FywiParameter value of each feature point
The wind speed upper and lower limits corresponding to each wind level are set values, the wind speed of the characteristic point is the relative wind speed of the characteristic point, and the relative wind speed is used for representing the speed vector difference between the wind speed and the vehicle speed.
Optionally, the selection principle of the feature points is as follows: and when the lateral wind in different directions is received, a point which is overlapped in the lateral windward side of the vehicle is taken as a characteristic point for calculating the lateral wind. Generally, a middle point of the side surface of the vehicle is taken as a characteristic point, and the wind speed of the characteristic point (namely the wind speed of the characteristic point) when different levels of lateral wind are tested, and the lateral force of the characteristic point under the wind speed are also called as equivalent lateral force applied to the vehicle. When the lateral wind suffered by the vehicle is calculated, the equivalent lateral force suffered by the vehicle is estimated by taking the characteristic points as reference points for estimating the lateral wind, and the size of the lateral wind suffered by the vehicle is further determined.
Since the influence of the lateral wind on the vehicle depends on the combined action of the wind speed and the vehicle speed due to the influence of the vehicle speed during the high-speed running of the vehicle, the wind speed of the characteristic point in the table is taken as the relative wind speed of the characteristic point, and the relative wind speed is used for representing the speed vector difference between the wind speed and the vehicle speed.
Optionally, for each lateral wind parameter of the N lateral wind parameters, a corresponding lateral wind estimation model is established as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzFor rotation of the vehicle about the z-axisInertia, a is a distance from a center of mass of the vehicle to a front axle of the vehicle, b is a distance from the center of mass of the vehicle to a rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter.
Through the method, a lateral wind estimation model can be further obtained by combining the determination of lateral wind parameters, and a better lateral wind estimation mode is provided.
Optionally, the wheel state parameters include a front wheel rotation angle and a left front wheel rotation speed ω of the vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrCan be based on the left front wheel rotating speed omegaflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrObtaining a longitudinal speed v of the vehiclexThen the front wheel turning angle and the longitudinal speed vxRespectively inputting each of the N lateral wind estimation models to obtain N response values, wherein the ith response value of the N response values is (a)yi,ri)。
Longitudinal speed v of the vehiclexCan pass through the left front wheel rotating speed omegaflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlRight rear wheel rotation speed omegarrAnd (6) calculating.
To determine the dynamic response (a) of each modelyi,ri) Considering the effect of the lateral wind but neglecting the yaw moment caused by the lateral wind, the differential equations of motion of the ith lateral wind estimation model, namely equations (1) and (2) in the above model, can be obtained.
The wheel state parameters can be acquired by a sensor arranged on the vehicle, and the vehicle state parameters are obtainedThe number is input into each lateral wind estimation model, and r can be obtained by the formula (1) and the formula (2)i(t) a is obtained by the formula (3)yi(t)。
Actual response value (a) of vehicleyR) can be detected by sensors arranged on the vehicle, ayIs the lateral acceleration and r is the yaw rate.
By the mode, the response value of each lateral wind estimation model can be well obtained, the comparison between the response value output by the lateral wind estimation model and the actual response value of the vehicle is facilitated, and the lateral wind is more accurately estimated.
Optionally, for each response value in the N response values, the difference between the response value and the actual response value may be determined as the identification error of the lateral wind estimation model corresponding to the response value, and then for each identification error in the N identification errors, a cost function may be established
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Is the weight to identify the error steady state value. Determining J in N lateral wind estimation modelsiAnd determining the lateral wind parameter corresponding to the first lateral wind estimation model as a lateral wind estimation value.
That is, the actual vehicle response value is (a)yR), the response value of the ith lateral wind estimation model is (a)yi,ri) Then a recognition error e can be determinedi(t) is
Because there may be uncertainty in practical applicationA prime (e.g., a non-linear factor) and a random error (e.g., a measurement error), and thus, the cost function is established by considering both a transient value and a steady-state value of the identification error. Where p is1To identify the weight of the error transient, ρ2To identify the weight of the error steady-state value, ρ1＞0、ρ2> 0, for example, ρ may be set1And rho21:1, or 1:2, etc. Find out a cost function JiAnd determining the lateral force corresponding to the first lateral wind estimation model as the lateral wind estimation value. In this way, the further determined estimate of the lateral wind may be made more accurate.
Optionally, the identification error may also be directly used to find the lateral wind estimated value, and similarly, for each response value of the N response values, the difference between the response value and the actual response value may be determined as the identification error of the lateral wind estimation model corresponding to the response value, so that in the N lateral wind estimation models, the lateral wind estimation model corresponding to the minimum value of the N identification errors may be determined as the second lateral wind estimation model, and the lateral wind parameter corresponding to the second lateral wind estimation model may be determined as the lateral wind estimated value.
That is, the lateral wind estimation model (the second lateral wind estimation model) with the minimum identification error is directly found, and then the lateral force corresponding to the second lateral wind estimation model is the lateral wind estimation value to be determined. Therefore, the lateral wind estimated value can be conveniently and quickly obtained.
Referring to fig. 3, based on the same inventive concept, an embodiment of the present disclosure provides a vehicle lateral wind estimating apparatus 300, where the apparatus 300 may include:
the establishing module 301 is configured to establish, according to N set lateral wind parameters, N lateral wind estimation models respectively corresponding to the N lateral wind parameters, where N is a positive integer;
an obtaining module 302, configured to input wheel state parameters of a vehicle into the N lateral wind estimation models respectively, so as to obtain N response values, where the wheel state parameters are used to characterize motion conditions of wheels of the vehicle;
a first determining module 303, configured to determine, according to the N response values and an actual response value output by the vehicle based on actual crosswind, N identification errors respectively corresponding to the N crosswind estimation models;
a second determining module 304, configured to determine a lateral wind estimate from the N lateral wind parameters according to the N recognition errors.
Optionally, the first determining module 303 is configured to:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
the second determining module 304 is configured to:
establishing a cost function for each of the N identification errors
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Weights to identify error steady-state values;
determining J among the N lateral wind estimation modelsiThe lateral wind estimation model corresponding to the identification error with the minimum value is a first lateral wind estimation model;
and determining the lateral wind parameter corresponding to the first lateral wind estimation model as the lateral wind estimation value.
Optionally, the first determining module 303 is configured to:
determining a difference value between each of the N response values and the actual response value as an identification error of a lateral wind estimation model corresponding to the response value;
the second determining module 304 is configured to:
determining a lateral wind estimation model corresponding to the minimum value of the N identification errors as a second lateral wind estimation model in the N lateral wind estimation models;
and determining the lateral wind parameter corresponding to the second lateral wind estimation model as the lateral wind estimation value.
Optionally, referring to fig. 4, the apparatus 300 further includes:
a setting module 305, configured to, before establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters, respectively set each lateral wind parameter of the N lateral wind parameters according to preset N relative wind speeds by using the following formula:
wherein, i is 1,2,3 … … N, FywiIs the ith lateral wind parameter, rho, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwTo the side angle of incoming flow, VwriThe ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed;
the establishing module 301 is configured to:
aiming at each lateral wind parameter in the N lateral wind parameters, establishing a corresponding lateral wind estimation model as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzIs the moment of inertia of the vehicle around the z-axis, a is the distance from the center of mass of the vehicle to the front axle of the vehicle, b is the distance from the center of mass of the vehicle to the rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter.
Optionally, the wheel state parameters include a front wheel rotation angle and a left front wheel rotation speed ω of the vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrThe obtaining module 302 is configured to:
according to the rotating speed omega of the left front wheelflThe right front wheel rotational speed ωfrThe rotation speed omega of the left rear wheelrlAnd the right rear wheel rotational speed omegarrObtaining a longitudinal speed v of said vehiclex；
The front wheel rotation angle and the longitudinal speed vxRespectively inputting each of the N lateral wind estimation models to obtain N response values, wherein the ith response value of the N response values is (a)yi,ri)。
Optionally, referring to fig. 5, the apparatus 300 further includes:
a control module 306 configured to control an active suspension of the vehicle based on the estimated lateral wind value after determining an estimated lateral wind value from the N lateral wind parameters such that a vertical load is redistributed to left and right wheels when the vehicle is rolling.
Based on the same inventive concept, the disclosed embodiments provide a vehicle including a vehicle lateral wind estimating apparatus shown in any one of fig. 3 to 5.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (11)
1. A method of estimating a vehicle lateral wind, comprising:
establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters, wherein N is a positive integer;
respectively inputting wheel state parameters of a vehicle into the N lateral wind estimation models to obtain N response values, wherein the wheel state parameters are used for representing the motion conditions of wheels of the vehicle;
determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and an actual response value output by the vehicle based on actual lateral wind;
determining a lateral wind estimation value from the N lateral wind parameters according to the N identification errors;
wherein said determining a lateral wind estimate from said N lateral wind parameters based on said N identification errors comprises:
establishing a cost function for each of the N identification errors
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Weights to identify error steady-state values; determining J among the N lateral wind estimation modelsiThe lateral wind estimation model corresponding to the identification error with the minimum value is a first lateral wind estimation model; determining a lateral wind parameter corresponding to the first lateral wind estimation model as the lateral wind estimation value; or comprises the following steps:
determining a lateral wind estimation model corresponding to the minimum value of the N identification errors as a second lateral wind estimation model in the N lateral wind estimation models; and determining the lateral wind parameter corresponding to the second lateral wind estimation model as the lateral wind estimation value.
2. The vehicle crosswind estimation method according to claim 1, wherein determining N recognition errors respectively corresponding to the N crosswind estimation models from the N response values and an actual response value output by the vehicle based on an actual crosswind comprises:
and determining the difference between each of the N response values and the actual response value as the identification error of the lateral wind estimation model corresponding to the response value.
3. The vehicle lateral wind estimation method according to claim 1, before establishing N lateral wind estimation models respectively corresponding to N lateral wind parameters according to the set N lateral wind parameters, further comprising:
according to the preset N relative wind speeds, respectively setting each lateral wind parameter in the N lateral wind parameters through the following formula:
wherein, i is 1,2,3 … … N, FywiIs the ith lateral wind parameter, rho, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwFor the inflow yaw angle, the lateral wind parameter is the lateral force equivalent to the lateral wind, the lateral force is set to belong to a finite set, the N lateral wind parameters are N characteristic points contained in the set, VwriThe ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed;
according to the set N lateral wind parameters, establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters, wherein the N lateral wind estimation models comprise:
aiming at each lateral wind parameter in the N lateral wind parameters, establishing a corresponding lateral wind estimation model as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzIs the moment of inertia of the vehicle around the z-axis, a is the distance from the center of mass of the vehicle to the front axle of the vehicle, b is the distance from the center of mass of the vehicle to the rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter.
4. The vehicle side wind estimation method according to claim 3, wherein the wheel state parameters include a front wheel rotation angle, a left front wheel rotation speed ω, of the vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrInputting the wheel state parameters of the vehicle into the N lateral wind estimation models respectively to obtain N response values, including:
obtaining the longitudinal speed v of the vehicle according to the rotating speed of the left front wheel, the rotating speed of the right front wheel, the rotating speed of the left rear wheel and the rotating speed of the right rear wheelx；
The front wheel rotation angle and the longitudinal speed vxRespectively inputting each of the N lateral wind estimation models to obtain N response values, wherein the ith response value of the N response values is (a)yi,ri)。
5. The vehicle lateral wind estimation method according to any one of claims 1-4, further comprising, after determining a lateral wind estimate from the N lateral wind parameters:
controlling an active suspension of the vehicle based on the estimated lateral wind such that vertical loads are redistributed at left and right wheels as the vehicle rolls.
6. A vehicle lateral wind estimating apparatus, characterized by comprising:
the system comprises an establishing module, a calculating module and a calculating module, wherein the establishing module is used for establishing N lateral wind estimation models respectively corresponding to N lateral wind parameters according to the set N lateral wind parameters, and N is a positive integer;
the obtaining module is used for respectively inputting wheel state parameters of a vehicle into the N lateral wind estimation models to obtain N response values, and the wheel state parameters are used for representing the motion conditions of wheels of the vehicle;
the first determining module is used for determining N identification errors respectively corresponding to the N lateral wind estimation models according to the N response values and an actual response value output by the vehicle based on actual lateral wind;
a second determining module, configured to determine a lateral wind estimation value from the N lateral wind parameters according to the N identification errors;
wherein the second determination module is to:
establishing a cost function for each of the N identification errors
Wherein i is 1,2,3 … … N, ei(t) is a transient value of the identification error of the ith lateral wind estimation model,
estimating a steady state value, ρ, of a model identification error for the ith crosswind1To identify the weight of the error transient, ρ2Weights to identify error steady-state values; determining J among the N lateral wind estimation modelsiThe lateral wind estimation model corresponding to the identification error with the minimum value is a first lateral wind estimation model; determining a lateral wind parameter corresponding to the first lateral wind estimation model as the lateral wind estimation value; or for:
determining a lateral wind estimation model corresponding to the minimum value of the N identification errors as a second lateral wind estimation model in the N lateral wind estimation models; and determining the lateral wind parameter corresponding to the second lateral wind estimation model as the lateral wind estimation value.
7. The vehicle lateral wind estimation device of claim 6, wherein the first determination module is configured to:
and determining the difference between each of the N response values and the actual response value as the identification error of the lateral wind estimation model corresponding to the response value.
8. The vehicle lateral wind estimation device according to claim 6, characterized in that the device further comprises:
the setting module is used for respectively setting each lateral wind parameter in the N lateral wind parameters according to the following formula before establishing N lateral wind estimation models respectively corresponding to the N lateral wind parameters according to the set N lateral wind parameters and according to preset N relative wind speeds:
wherein, i is 1,2,3 … … N, FywiIs the ith lateral wind parameter, rho, of the N lateral wind parametersaFor the density of the incoming side wind stream, ALIs the projected area of the lateral windward side of the vehicle, CyAs lateral force coefficient, βwFor the inflow yaw angle, the lateral wind parameter is the lateral force equivalent to the lateral wind, the lateral force is set to belong to a finite set, the N lateral wind parameters are N characteristic points contained in the set, VwriThe ith relative wind speed in the wind speeds of the N characteristic points is used for representing the speed vector difference between the wind speed and the vehicle speed;
the establishing module is used for:
aiming at each lateral wind parameter in the N lateral wind parameters, establishing a corresponding lateral wind estimation model as follows:
wherein v isyiIs the lateral speed corresponding to the ith lateral wind parameter, m is the total vehicle mass of the vehicle, FxfIs a front axle longitudinal force of the vehicle, FyfIs a front axle lateral force of the vehicle, FyrIs the rear axle lateral force of the vehicle, is the front wheel angle, riYaw rate response, v, for the ith lateral wind parameterxIs the longitudinal speed of the vehicle, IzIs the moment of inertia of the vehicle around the z-axis, a is the distance from the center of mass of the vehicle to the front axle of the vehicle, b is the distance from the center of mass of the vehicle to the rear axle of the vehicle, twFor the track width of two front wheels or two rear wheels of said vehicle, FxflIs a longitudinal force of a left front wheel of the vehicle, FxfrIs the longitudinal force of the right front wheel of the vehicle, FyflIs a left front wheel side force of the vehicle, FyfrIs a right front wheel side force of the vehicle, FxrlIs a longitudinal force of the left rear wheel of the vehicle, FxrrIs the right rear wheel longitudinal force of the vehicle, ayiThe lateral acceleration response corresponding to the ith lateral wind parameter.
9. The vehicle lateral wind estimating device according to claim 8, wherein said wheel state parameter includes a front wheel rotation angle, a left front wheel rotation speed ω, of said vehicleflRight front wheel rotational speed omegafrLeft rear wheel rotation speed omegarlAnd the right rear wheel rotational speed omegarrThe obtaining module is configured to:
according to the rotating speed omega of the left front wheelflThe right front wheel rotational speed ωfrThe rotation speed omega of the left rear wheelrlAnd the right rear wheel rotational speed omegarrObtaining a longitudinal speed v of said vehiclex；
The front wheel rotation angle and the longitudinal speed vxRespectively inputting each of the N lateral wind estimation models to obtain N response values, wherein the ith response value of the N response values is (a)yi,ri)。
10. The vehicle lateral wind estimating apparatus according to any one of claims 6 to 9, characterized by further comprising:
a control module for controlling an active suspension of the vehicle based on the estimated lateral wind value after determining an estimated lateral wind value from the N lateral wind parameters such that a vertical load is redistributed at left and right wheels when the vehicle is rolling.
11. A vehicle characterized by comprising the vehicle lateral wind estimating device according to any one of claims 6 to 10.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080015754A1 (en) * | 2006-07-14 | 2008-01-17 | Hac Aleksander B | System for estimating and compensating for lateral disturbances using controlled steering and braking |
| CN103619691A (en) * | 2011-06-16 | 2014-03-05 | 戴姆勒股份公司 | Method for operating a side wind assistant for a vehicle and side wind assistant for a vehicle |
| CN104331611A (en) * | 2014-10-24 | 2015-02-04 | 武汉理工大学 | Road vehicle running danger state early warning method and system under strong lateral wind effect |
| CN104627194A (en) * | 2013-11-07 | 2015-05-20 | 庞巴迪运输有限公司 | Crosswind stabilisation method and associated rail vehicle |
-
2017
- 2017-09-19 CN CN201710852524.7A patent/CN109515102B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080015754A1 (en) * | 2006-07-14 | 2008-01-17 | Hac Aleksander B | System for estimating and compensating for lateral disturbances using controlled steering and braking |
| CN103619691A (en) * | 2011-06-16 | 2014-03-05 | 戴姆勒股份公司 | Method for operating a side wind assistant for a vehicle and side wind assistant for a vehicle |
| CN104627194A (en) * | 2013-11-07 | 2015-05-20 | 庞巴迪运输有限公司 | Crosswind stabilisation method and associated rail vehicle |
| CN104331611A (en) * | 2014-10-24 | 2015-02-04 | 武汉理工大学 | Road vehicle running danger state early warning method and system under strong lateral wind effect |
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