CN113002549B - Vehicle state estimation method, device, equipment and storage medium - Google Patents

Vehicle state estimation method, device, equipment and storage medium Download PDF

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CN113002549B
CN113002549B CN202110562137.6A CN202110562137A CN113002549B CN 113002549 B CN113002549 B CN 113002549B CN 202110562137 A CN202110562137 A CN 202110562137A CN 113002549 B CN113002549 B CN 113002549B
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vehicle
mass
state estimation
road surface
estimation
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CN113002549A (en
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徐显杰
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Tianjin Soterea Automotive Technology Co Ltd
Zhejiang Suoto Ruian Technology Group Co Ltd
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Tianjin Soterea Automotive Technology Co Ltd
Zhejiang Suoto Ruian Technology Group Co Ltd
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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
    • B60W40/076—Slope angle of the road
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
    • B60W40/13—Load or weight
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • 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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  • Automation & Control Theory (AREA)
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  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention provides a vehicle state estimation method, which comprises the following steps: s1: establishing a vehicle longitudinal dynamic model containing braking force parameters, acquiring the air resistance, rolling resistance and vehicle rotating mass conversion coefficient of a vehicle, and introducing the air resistance, rolling resistance and vehicle rotating mass conversion coefficient into the vehicle longitudinal dynamic model; s2: obtaining the output torque of an engine and the pressure of a brake pressure regulating valve of a vehicle; s3: and (5) introducing the engine output torque and the brake pressure regulating valve pressure into the vehicle longitudinal dynamic model in the step (S1), estimating the mass of the whole vehicle according to the vehicle longitudinal dynamic model, and correcting the estimated mass value by adopting a least square method. The invention also discloses a vehicle state estimation device, equipment and a storage medium. The invention estimates the mass and the road surface slope angle based on the estimation method combining the extended Kalman filtering and the least square method, the mass error is 2-3%, and the error of the estimation result of the road surface slope angle in the braking process is within +/-0.1 degree.

Description

Vehicle state estimation method, device, equipment and storage medium
Technical Field
The invention belongs to the technical field of automobile control, particularly relates to a commercial vehicle control technology, and particularly provides a vehicle state estimation method, a vehicle state estimation device and a storage medium.
Background
In the current urban public transport, the total mass of the bus is obviously changed due to the change of the number of passengers in the bus after the bus enters and exits the station. In some cities, the road surface slope angle is continuously changed due to complex terrains. In addition, with the intensive research on the braking performance of the vehicle, the mass of the vehicle and the slope angle of the road surface are found to have great influence on the braking effect. Due to the lack of such vehicle state information, problems such as insufficient braking force or unstable deceleration may result. The existing gradient sensor GPS sensor and the like have poor estimation effects on the quality and the gradient due to low precision. Part of the estimation algorithm requires an acceleration sensor, which is costly and has large errors. And on downhill sections, the estimation of the vehicle mass is subject to errors due to the intervention of the vehicle braking force.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a vehicle state estimation method, a device, equipment and a storage medium, the estimation method based on the combination of Extended Kalman Filtering (EKF) and least square method (RLS) estimates the quality, the accuracy of the estimation result is high, and the estimation error of the whole vehicle quality is 2-3%; meanwhile, the slope angle of the road surface can be estimated.
The technical scheme adopted by the invention is as follows: a vehicle state estimation method comprising the steps of:
s1: establishing a vehicle longitudinal dynamic model containing braking force parameters, acquiring the air resistance, rolling resistance and vehicle rotating mass conversion coefficient of a vehicle, and introducing the air resistance, rolling resistance and vehicle rotating mass conversion coefficient into the vehicle longitudinal dynamic model;
s2: obtaining the output torque of an engine and the pressure of a brake pressure regulating valve of a vehicle;
s3: and (5) introducing the engine output torque and the brake pressure regulating valve pressure into the vehicle longitudinal dynamic model in the step (S1), estimating the mass of the whole vehicle according to the vehicle longitudinal dynamic model, and correcting the estimated mass value by adopting a least square method.
In step S3, the estimating the mass of the entire vehicle according to the vehicle longitudinal dynamics model includes: and estimating the whole vehicle mass and the road surface slope angle information by using extended Kalman filtering according to the vehicle longitudinal dynamics model.
The essence of the vehicle system parameter identification method using the extended Kalman filtering is that measurable observed data and unknown data to be estimated are fused, the vehicle mass and the road surface gradient angle are used as state components of a state vector, the vehicle mass and the road surface gradient angle at the current moment are estimated according to the estimation result at the previous moment, simultaneously, measurable speed parameters are measured to obtain an observation variable, and finally, the vehicle mass and the road surface gradient angle at the current moment are obtained by comparing and correcting the measured speed (obtained by measuring a wheel speed sensor) and the estimated speed.
In step S3, the estimated quality value is corrected using a recursive least square method with a forgetting factor.
And correcting the quality parameters by adopting a least square method, obtaining an estimation error of the estimation model at the current moment by comparing the actual system output of the vehicle with the estimated output of the vehicle, and finally reducing the estimation error by continuously updating the parameters of the estimation model, wherein the final estimation model output is close to the actual system output. The quality and the road surface gradient angle of the vehicle are changed continuously in the urban operation process due to the change of the getting on and off of the passengers of the commercial vehicle and the change of the driving road surface. After the vehicle state changes, some existing vehicle mass old data have a large influence on the changed vehicle mass estimation result, so that a forgetting factor parameter needs to be introduced to correct the estimation result.
The vehicle longitudinal dynamics model is:
Figure 732891DEST_PATH_IMAGE001
in the formula (I), the compound is shown in the specification,
Figure 774665DEST_PATH_IMAGE002
is a driving force of the vehicle,
Figure 616720DEST_PATH_IMAGE003
in order to be the rolling resistance,
Figure 11929DEST_PATH_IMAGE004
in order to provide the slope resistance,
Figure 888618DEST_PATH_IMAGE005
in order to be the air resistance,
Figure 711080DEST_PATH_IMAGE006
in order to be a braking force,
Figure 978114DEST_PATH_IMAGE007
in the case of acceleration or deceleration, for example,
Figure 239331DEST_PATH_IMAGE008
the value of the conversion coefficient of the rotating mass of the vehicle is related to the rotational inertia of the flywheel and each wheel of the engine and the transmission ratio of the transmission system, and m is the mass of the vehicle.
The driving force is obtained by transmitting engine torque to a driving wheel through a transmission system, and the specific expression is as follows:
Figure 970526DEST_PATH_IMAGE009
in the formula
Figure 26207DEST_PATH_IMAGE010
Is the engine output torque, with the unit of N · m;
Figure 842853DEST_PATH_IMAGE011
the transmission ratio of the main speed reducer is set,
Figure 642182DEST_PATH_IMAGE012
in order to achieve the transmission ratio of the gearbox,
Figure 165567DEST_PATH_IMAGE013
for transmission system mechanical efficiency;
Figure 720045DEST_PATH_IMAGE014
is the rolling radius of the wheel, and the unit is m;
the slope resistance represents the component force of the gravity along the slope direction when the automobile goes up and down the slope, and the specific expression is as follows:
Figure 86305DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 361428DEST_PATH_IMAGE016
is a road surface slope angle;
the air resistance only considers the stress of the vehicle running under the windless condition, and the specific expression is as follows:
Figure 67216DEST_PATH_IMAGE017
in the formula (I), the compound is shown in the specification,
Figure 776284DEST_PATH_IMAGE018
in order to be the air resistance coefficient,
Figure 567522DEST_PATH_IMAGE019
is the frontal area in
Figure 708654DEST_PATH_IMAGE020
；
Figure 206631DEST_PATH_IMAGE021
Is the air density in
Figure 40595DEST_PATH_IMAGE022
；
Figure 991233DEST_PATH_IMAGE023
The unit is the longitudinal running speed of the vehicle and is m/s;
the rolling resistance is the resistance between the tire and the ground, and the specific expression is as follows:
Figure 936056DEST_PATH_IMAGE024
in the formula (I), the compound is shown in the specification,
Figure 288540DEST_PATH_IMAGE025
is the rolling resistance coefficient of the vehicle;
the braking force represents the braking force generated in the air braking process of the vehicle, and the specific expression is as follows:
Figure 293405DEST_PATH_IMAGE026
in the formula (I), the compound is shown in the specification,
Figure 105241DEST_PATH_IMAGE027
is a torque conversion coefficient with the unit of N x m/pa,
Figure 335977DEST_PATH_IMAGE028
the magnitude of the pressure in the valve is regulated for brake pressure.
And defining the state vector of the extended Kalman filtering system as a speed v, a mass m and a road surface gradient angle i:
Figure 808547DEST_PATH_IMAGE029
T
the speed v of the vehicle is measured by the wheel speed sensor, and the mass and gradient derivatives over time may be approximately zero, negligible, and the differential equation may be expressed as:
Figure 984313DEST_PATH_IMAGE030
the observation matrix of the system is
Figure 909544DEST_PATH_IMAGE031
。
A vehicle state estimation device comprises a vehicle state estimation module, a vehicle state estimation module and a vehicle state estimation module, wherein the vehicle state estimation module is used for acquiring air resistance, rolling resistance, a vehicle rotating mass conversion coefficient, engine output torque and brake pressure regulating valve pressure of a vehicle, introducing the air resistance, the rolling resistance, the vehicle rotating mass conversion coefficient, the engine output torque and the brake pressure regulating valve pressure into a vehicle longitudinal dynamics model, and estimating the whole vehicle mass and road surface slope angle information by using extended Kalman filtering;
and the whole vehicle mass correction module is used for correcting the whole vehicle mass value estimated by the vehicle state estimation module by adopting a least square method.
A vehicle state estimation device comprising: the vehicle state estimation method comprises a memory, a processor and a vehicle state estimation program stored on the memory and capable of running on the processor, wherein the vehicle state estimation program realizes the steps of the vehicle state estimation method when being executed by the processor.
A computer-readable storage medium having a vehicle state estimation program stored thereon, the vehicle state estimation program, when executed by a computer processor, implementing the steps of the vehicle state estimation method described above.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the air resistance, the rolling resistance, the rotating mass conversion coefficient of the vehicle, the output torque of the engine of the vehicle and the pressure of the brake pressure regulating valve are led into the longitudinal dynamic model of the vehicle, the mass of the whole vehicle is estimated, and the estimated mass value is corrected by adopting a least square method, so that the estimation error of the mass of the whole vehicle is reduced;
2. the vehicle longitudinal dynamics model estimates the whole vehicle mass and the road surface slope angle information by using extended Kalman filtering, the whole vehicle mass and the road surface slope angle are used as state components of state vectors, the whole vehicle mass and the road surface slope angle at the current moment are estimated according to the estimation result at the previous moment, measurable speed parameters are measured at the same time to obtain an observation variable, and finally the measured quantity and the estimated quantity of the speed are compared and corrected to obtain the whole vehicle mass and the road surface slope angle at the current moment;
3. correcting the estimated mass value by adopting a recursive least square method with a forgetting factor, and fully considering that some existing vehicle mass old data have great influence on the changed vehicle mass estimation result after the vehicle state changes, so that a forgetting factor parameter needs to be introduced to correct the estimation result and control the whole vehicle mass estimation error to be 2-3%;
4. the longitudinal dynamics model used by the invention considers the influence of the braking working condition on the slope estimation, can correct related parameters during braking, and increases the estimation accuracy and real-time performance;
5. the vehicle state estimation device can automatically estimate the whole vehicle mass and the road surface slope angle information through a vehicle state estimation algorithm, the estimation result is accurate, the error of the road surface slope angle estimation result in the braking process is within +/-0.1 degree, the whole vehicle mass estimation error is 2-3 percent, and an acceleration sensor and a slope sensor are not adopted, so that the vehicle cost can be reduced.
6. The vehicle state estimating apparatus stores a vehicle state estimating program, and may operate a vehicle state estimating method on a processor to estimate a mass of the entire vehicle and road surface gradient angle information.
7. The computer-readable storage medium stores a vehicle state estimation program executable by a computer processor to implement a vehicle state estimation method for estimating vehicle mass and road surface slope angle information.
Drawings
FIG. 1 is a logic diagram of an embodiment of the present invention;
FIG. 2 is a diagram of the evaluation results of the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Example one
An embodiment of the present invention provides a vehicle state estimation method, as shown in fig. 1, including the steps of:
s1: establishing a vehicle longitudinal dynamics model containing braking force parameters according to a vehicle dynamics principle, acquiring air resistance, rolling resistance and a vehicle rotating mass conversion coefficient of a vehicle, and introducing the air resistance, the rolling resistance and the vehicle rotating mass conversion coefficient into the vehicle longitudinal dynamics model;
the vehicle longitudinal dynamics model is:
Figure 196169DEST_PATH_IMAGE032
in the formula (I), the compound is shown in the specification,
Figure 257666DEST_PATH_IMAGE002
is a driving force of the vehicle,
Figure 604334DEST_PATH_IMAGE003
in order to be the rolling resistance,
Figure 16860DEST_PATH_IMAGE004
in order to provide the slope resistance,
Figure 107176DEST_PATH_IMAGE005
in order to be the air resistance,
Figure 288759DEST_PATH_IMAGE006
in order to be a braking force,
Figure 540748DEST_PATH_IMAGE007
in the case of acceleration or deceleration, for example,
Figure 440571DEST_PATH_IMAGE008
the vehicle rotating mass conversion coefficient is, and m is the vehicle mass.
The driving force is obtained by transmitting engine torque to a driving wheel through a transmission system, and the specific expression is as follows:
Figure 899077DEST_PATH_IMAGE033
in the formula
Figure 325379DEST_PATH_IMAGE010
Is the engine output torque, with the unit of N · m;
Figure 685953DEST_PATH_IMAGE011
the transmission ratio of the main speed reducer is set,
Figure 135389DEST_PATH_IMAGE012
in order to achieve the transmission ratio of the gearbox,
Figure 567508DEST_PATH_IMAGE013
for transmission system mechanical efficiency;
Figure 458103DEST_PATH_IMAGE014
is the rolling radius of the wheel, and the unit is m;
the slope resistance represents the component force of the gravity along the slope direction when the automobile goes up and down the slope, and the specific expression is as follows:
Figure 379792DEST_PATH_IMAGE034
in the formula (I), the compound is shown in the specification,
Figure 50944DEST_PATH_IMAGE016
is a road surface slope angle;
the air resistance only considers the stress of the vehicle running under the windless condition, and the specific expression is as follows:
Figure 224437DEST_PATH_IMAGE035
in the formula (I), the compound is shown in the specification,
Figure 297435DEST_PATH_IMAGE018
in order to be the air resistance coefficient,
Figure 265391DEST_PATH_IMAGE019
is the frontal area in
Figure 423840DEST_PATH_IMAGE020
；
Figure 135444DEST_PATH_IMAGE021
Is the air density in
Figure 62949DEST_PATH_IMAGE022
；
Figure 201806DEST_PATH_IMAGE023
The unit is the longitudinal running speed of the vehicle and is m/s;
the rolling resistance is the resistance between the tire and the ground, and the specific expression is as follows:
Figure 847551DEST_PATH_IMAGE024
in the formula (I), the compound is shown in the specification,
Figure 97267DEST_PATH_IMAGE025
is the rolling resistance coefficient of the vehicle;
the braking force represents the braking force generated in the air braking process of the vehicle, and the specific expression is as follows:
Figure 82540DEST_PATH_IMAGE036
in the formula (I), the compound is shown in the specification,
Figure 454616DEST_PATH_IMAGE027
is a torque conversion coefficient with the unit of N x m/pa,
Figure 525340DEST_PATH_IMAGE028
the magnitude of the pressure in the valve is regulated for brake pressure.
The vehicle longitudinal dynamics model may be expressed as:
Figure 641063DEST_PATH_IMAGE037
。
s2: parameter information of the output torque of the engine and the pressure of the brake pressure regulating valve is obtained through a CAN bus of the vehicle;
s3: and importing the acquired parameter information into a vehicle longitudinal dynamics model, estimating the vehicle mass and the road surface slope angle information by using extended Kalman filtering, and correcting the estimated mass value by adopting a least square method. The specific calculation method is as follows:
suppose that the calculated noise and the measured noise of the system are respectively
Figure 480843DEST_PATH_IMAGE038
，
Figure 23820DEST_PATH_IMAGE039
The white gaussian noises are independent white gaussian noises with zero mean value. The state space expression for an EKF system may be expressed as:
Figure 316261DEST_PATH_IMAGE040
at this time, the process of the present invention,
Figure 235676DEST_PATH_IMAGE041
is the observation matrix of the system.
And defining the state vector of the extended Kalman filtering system as a speed v, a mass m and a road surface gradient angle i:
Figure 929962DEST_PATH_IMAGE029
T。
estimating the quality and the road surface gradient angle according to an extended Kalman filtering algorithm: the extended kalman filter includes two calculation processes: time updates and measurement updates.
The equation for the time update can be expressed as:
Figure 643840DEST_PATH_IMAGE042
at this time, the process of the present invention,
Figure 689157DEST_PATH_IMAGE043
the representation represents the covariance of the prior error,
Figure 146683DEST_PATH_IMAGE044
a matrix of the solution process is represented,
Figure 695476DEST_PATH_IMAGE045
a-priori estimates of the state variables are represented,
Figure 783518DEST_PATH_IMAGE046
the error covariance at the last time is represented,
Figure 112868DEST_PATH_IMAGE047
the model noise was predicted at the last time.
In the present estimation algorithm, one obtains:
Figure 374085DEST_PATH_IMAGE048
in the formula:
Figure 839701DEST_PATH_IMAGE049
，
Figure 160961DEST_PATH_IMAGE050
，
Figure 915291DEST_PATH_IMAGE051
。
the measurement update of the system can be expressed as:
Figure 714619DEST_PATH_IMAGE052
Figure 300322DEST_PATH_IMAGE053
Figure 792483DEST_PATH_IMAGE054
in the formula
Figure 768529DEST_PATH_IMAGE055
The method is expressed in terms of the kalman gain,
Figure 371549DEST_PATH_IMAGE056
the a-posteriori estimates of the state variables are represented,
Figure 15020DEST_PATH_IMAGE057
the covariance of the a posteriori error is expressed,
Figure 412503DEST_PATH_IMAGE058
representing an identity matrix.
The speed of the vehicle is measured by the wheel speed sensor and the derivative of mass and gradient over time may be approximated to zero over a sampling interval (0.1 second) and the differential equation may be expressed as:
Figure 203741DEST_PATH_IMAGE030
the observation matrix of the system is
Figure 282556DEST_PATH_IMAGE031
。
The mass parameters are corrected by a least square method, and the recursive format of the vehicle mass can be represented as follows:
Figure 967484DEST_PATH_IMAGE059
in the formula
Figure 660502DEST_PATH_IMAGE060
，
Figure 876720DEST_PATH_IMAGE061
Respectively, system input and observable data vector, gain matrix
Figure 759225DEST_PATH_IMAGE062
Sum error covariance matrix
Figure 298660DEST_PATH_IMAGE063
Can be expressed as:
Figure 303525DEST_PATH_IMAGE064
Figure 803777DEST_PATH_IMAGE065
in the formula
Figure 224394DEST_PATH_IMAGE066
Is the forgetting factor of the least square method model.
As shown in fig. 2, the vehicle experienced two mass changes over a measured time of 400 seconds, and at each mass experienced a different grade of acceleration and deceleration. The specific speed of the vehicle is shown in fig. 2 (a).
The actual value of the vehicle mass, the mass estimation value calculated by the extended Kalman filtering system and the mass estimation value corrected by the least square method are shown in fig. 2 (b), the error of the mass estimation by using the extended Kalman filtering algorithm alone is 5-6%, and the error is reduced to 2-3% after the correction by the least square method. The estimation result of the road surface gradient angle (°) is shown in fig. 2 (c), and the error of the estimation result is within ± 0.03 degrees. The embodiment considers the influence of the braking working condition on the gradient estimation, and has higher stability and precision by adopting a proper hybrid algorithm.
Example two
A vehicle state estimation device comprises a vehicle state estimation module, a vehicle state estimation module and a vehicle state estimation module, wherein the vehicle state estimation module is used for acquiring air resistance, rolling resistance, a vehicle rotating mass conversion coefficient, engine output torque and brake pressure regulating valve pressure of a vehicle, introducing the air resistance, the rolling resistance, the vehicle rotating mass conversion coefficient, the engine output torque and the brake pressure regulating valve pressure into a vehicle longitudinal dynamics model, and estimating the whole vehicle mass and road surface slope angle information by using extended Kalman filtering;
and the whole vehicle mass correction module is used for correcting the whole vehicle mass value estimated by the vehicle state estimation module by adopting a least square method.
The concrete operation of the vehicle state estimating apparatus includes the steps of:
s1: establishing a vehicle longitudinal dynamics model containing braking force parameters according to a vehicle dynamics principle, acquiring air resistance, rolling resistance and a vehicle rotating mass conversion coefficient of a vehicle, and introducing the air resistance, the rolling resistance and the vehicle rotating mass conversion coefficient into the vehicle longitudinal dynamics model;
the vehicle longitudinal dynamics model is:
Figure 759280DEST_PATH_IMAGE067
in the formula (I), the compound is shown in the specification,
Figure 607150DEST_PATH_IMAGE002
is a driving force of the vehicle,
Figure 594698DEST_PATH_IMAGE003
in order to be the rolling resistance,
Figure 819006DEST_PATH_IMAGE004
in order to provide the slope resistance,
Figure 146082DEST_PATH_IMAGE005
in order to be the air resistance,
Figure 492750DEST_PATH_IMAGE006
in order to be a braking force,
Figure 905277DEST_PATH_IMAGE007
in the case of acceleration or deceleration, for example,
Figure 730013DEST_PATH_IMAGE008
the vehicle rotating mass conversion coefficient is, and m is the vehicle mass.
The driving force is obtained by transmitting engine torque to a driving wheel through a transmission system, and the specific expression is as follows:
Figure 911596DEST_PATH_IMAGE068
in the formula
Figure 429165DEST_PATH_IMAGE010
Is the engine output torque, with the unit of N · m;
Figure 328988DEST_PATH_IMAGE011
the transmission ratio of the main speed reducer is set,
Figure 895098DEST_PATH_IMAGE012
in order to achieve the transmission ratio of the gearbox,
Figure 993504DEST_PATH_IMAGE013
for transmission system mechanical efficiency;
Figure 619658DEST_PATH_IMAGE014
is the rolling radius of the wheel, and the unit is m;
the slope resistance represents the component force of the gravity along the slope direction when the automobile goes up and down the slope, and the specific expression is as follows:
Figure 69094DEST_PATH_IMAGE069
in the formula (I), the compound is shown in the specification,
Figure 259070DEST_PATH_IMAGE016
is a road surface slope angle;
the air resistance only considers the stress of the vehicle running under the windless condition, and the specific expression is as follows:
Figure 415245DEST_PATH_IMAGE070
in the formula (I), the compound is shown in the specification,
Figure 212300DEST_PATH_IMAGE018
in order to be the air resistance coefficient,
Figure 883452DEST_PATH_IMAGE019
is the frontal area in
Figure 56945DEST_PATH_IMAGE020
；
Figure 129943DEST_PATH_IMAGE021
Is the air density in
Figure 97899DEST_PATH_IMAGE022
；
Figure 256348DEST_PATH_IMAGE023
The unit is the longitudinal running speed of the vehicle and is m/s;
the rolling resistance is the resistance between the tire and the ground, and the specific expression is as follows:
Figure 827007DEST_PATH_IMAGE071
in the formula (I), the compound is shown in the specification,
Figure 957774DEST_PATH_IMAGE025
is the rolling resistance coefficient of the vehicle;
the braking force represents the braking force generated in the air braking process of the vehicle, and the specific expression is as follows:
Figure 158948DEST_PATH_IMAGE036
in the formula (I), the compound is shown in the specification,
Figure 742376DEST_PATH_IMAGE027
is a torque conversion coefficient with the unit of N x m/pa,
Figure 116725DEST_PATH_IMAGE028
the magnitude of the pressure in the valve is regulated for brake pressure.
The vehicle longitudinal dynamics model may be expressed as:
Figure 164316DEST_PATH_IMAGE037
。
s2: parameter information of the output torque of the engine and the pressure of the brake pressure regulating valve is obtained through a CAN bus of the vehicle;
s3: and importing the acquired parameter information into a vehicle longitudinal dynamics model, estimating the vehicle mass and the road surface slope angle information by using extended Kalman filtering, and correcting the estimated mass value by adopting a least square method. The specific calculation method is as follows:
and defining the state vector of the extended Kalman filtering system as a speed v, a mass m and a road surface gradient angle i:
Figure 474074DEST_PATH_IMAGE029
T
the speed of the vehicle is measured by the wheel speed sensor and the derivative of mass and gradient over time may be approximated to zero over a sampling interval (0.1 second) and the differential equation may be expressed as:
Figure 607116DEST_PATH_IMAGE030
the observation matrix of the system is
Figure 660522DEST_PATH_IMAGE031
。
The mass parameters are corrected by a least square method, and the recursive format of the vehicle mass can be represented as follows:
Figure 562619DEST_PATH_IMAGE059
in the formula
Figure 43279DEST_PATH_IMAGE060
，
Figure 335720DEST_PATH_IMAGE061
Respectively, system input and observable data vector, gain matrix
Figure 314522DEST_PATH_IMAGE062
Sum error covariance matrix
Figure 8808DEST_PATH_IMAGE063
Can be expressed as:
Figure 722686DEST_PATH_IMAGE072
Figure 830320DEST_PATH_IMAGE073
in the formula
Figure 287846DEST_PATH_IMAGE066
Is the forgetting factor of the least square method model.
EXAMPLE III
A vehicle state estimation device comprising: the vehicle state estimation method comprises a memory, a processor and a vehicle state estimation program stored on the memory and capable of running on the processor, wherein the vehicle state estimation program realizes the steps of the vehicle state estimation method when being executed by the processor.
Example four
A computer-readable storage medium having a vehicle state estimation program stored thereon, the vehicle state estimation program, when executed by a computer processor, implementing the steps of the vehicle state estimation method described above.
The present invention has been described in detail with reference to the embodiments, but the description is only illustrative of the present invention and should not be construed as limiting the scope of the present invention. The scope of the invention is defined by the claims. The technical solutions of the present invention or those skilled in the art, based on the teaching of the technical solutions of the present invention, should be considered to be within the scope of the present invention, and all equivalent changes and modifications made within the scope of the present invention or equivalent technical solutions designed to achieve the above technical effects are also within the scope of the present invention.

Claims (8)

1. A vehicle state estimation method, characterized by comprising:
establishing a vehicle longitudinal dynamic model containing braking force parameters, and introducing the acquired air resistance, rolling resistance and vehicle rotating mass conversion coefficient of the vehicle into the vehicle longitudinal dynamic model;
the vehicle longitudinal dynamics model is as follows:
Figure 206185DEST_PATH_IMAGE002
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE003
is a driving force of the vehicle,
Figure 787339DEST_PATH_IMAGE004
in order to be the rolling resistance,
Figure DEST_PATH_IMAGE005
in order to provide the slope resistance,
Figure 60189DEST_PATH_IMAGE006
in order to be the air resistance,
Figure DEST_PATH_IMAGE007
in order to be a braking force,
Figure 726794DEST_PATH_IMAGE008
in the case of acceleration or deceleration, for example,
Figure DEST_PATH_IMAGE009
the coefficient is converted for the rotational mass of the vehicle,
Figure 805608DEST_PATH_IMAGE010
is the vehicle mass;
obtaining the output torque of an engine and the pressure of a brake pressure regulating valve of a vehicle;
introducing the output torque of the engine and the pressure of the brake pressure regulating valve into the vehicle longitudinal dynamic model, estimating the mass of the whole vehicle according to the vehicle longitudinal dynamic model, and correcting the estimated mass value by adopting a least square method;
the estimation of the mass of the whole vehicle according to the vehicle longitudinal dynamics model comprises the following steps:
estimating the whole vehicle mass and the road surface slope angle information by using extended Kalman filtering according to the vehicle longitudinal dynamics model: taking the vehicle mass and the road surface slope angle as state components of the state vector, estimating the vehicle mass and the road surface slope angle at the current moment according to an estimation result of the state component at the previous moment, measuring measurable vehicle speed parameters to obtain an observation variable, and finally comparing and correcting the measured quantity and the estimated quantity of the speed to obtain the vehicle mass and the road surface slope angle at the current moment;
and defining the state vector of the extended Kalman filtering system as a speed v, a mass m and a road surface gradient angle i:
Figure DEST_PATH_IMAGE011
T
the speed v of the vehicle is measured by the wheel speed sensor and, over a sampling interval, the differential equation can be expressed as:
Figure 731015DEST_PATH_IMAGE012
the observation matrix of the system is
Figure DEST_PATH_IMAGE013
。
2. The vehicle state estimation method according to claim 1, wherein the correcting the estimated quality value using the least square method includes:
and correcting the estimated quality value by adopting a recursive least square method with a forgetting factor.
3. The vehicle state estimation method according to claim 2, wherein the correcting the estimated quality value using a recursive least square method with a forgetting factor includes:
the estimation error of the estimation model at the current moment is obtained by comparing the actual system output of the vehicle with the estimated output of the vehicle, and the estimation error is finally reduced by continuously updating the parameters of the estimation model, and the final output of the estimation model is close to the actual system output.
4. The vehicle state estimation method according to claim 1,
Figure DEST_PATH_IMAGE015
in the formula (I), the compound is shown in the specification,
Figure 440345DEST_PATH_IMAGE016
is a torque conversion coefficient with the unit of N x m/pa,
Figure DEST_PATH_IMAGE017
the pressure in the valve is regulated and controlled for the brake pressure,
Figure 328666DEST_PATH_IMAGE018
is the rolling radius of the wheel in m.
5. The vehicle state estimation method according to claim 1, characterized in that: the sampling interval is equal to 0.1 second.
6. A vehicle state estimation device characterized in that: the system comprises a vehicle state estimation module, a vehicle speed estimation module and a vehicle speed estimation module, wherein the vehicle state estimation module is used for acquiring air resistance, rolling resistance, a vehicle rotating mass conversion coefficient, engine output torque and brake pressure regulating valve pressure of a vehicle, importing the air resistance, rolling resistance, vehicle rotating mass conversion coefficient, engine output torque and brake pressure regulating valve pressure into a vehicle longitudinal dynamics model, and estimating the whole vehicle mass and road surface slope angle information by using extended Kalman filtering;
estimating the whole vehicle mass and the road surface slope angle information by using extended Kalman filtering according to the vehicle longitudinal dynamics model: taking the vehicle mass and the road surface slope angle as state components of the state vector, estimating the vehicle mass and the road surface slope angle at the current moment according to an estimation result of the state component at the previous moment, measuring measurable vehicle speed parameters to obtain an observation variable, and finally comparing and correcting the measured quantity and the estimated quantity of the speed to obtain the vehicle mass and the road surface slope angle at the current moment;
and the whole vehicle mass correction module is used for correcting the whole vehicle mass value estimated by the vehicle state estimation module by adopting a least square method.
7. A vehicle state estimation device, characterized by comprising: a memory, a processor, and a vehicle state estimation program stored on the memory and executable on the processor, the vehicle state estimation program when executed by the processor implementing the steps of the vehicle state estimation method of any of claims 1-5.
8. A computer-readable storage medium, characterized in that a vehicle state estimation program is stored thereon, which when executed by a computer processor, implements the steps of the vehicle state estimation method according to any one of claims 1 to 5.
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