CN110395266B - Estimation method for decoupling mass change of bus and road gradient - Google Patents

Estimation method for decoupling mass change of bus and road gradient Download PDF

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CN110395266B
CN110395266B CN201910749846.8A CN201910749846A CN110395266B CN 110395266 B CN110395266 B CN 110395266B CN 201910749846 A CN201910749846 A CN 201910749846A CN 110395266 B CN110395266 B CN 110395266B
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estimation
mass
vehicle
acceleration
road gradient
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CN110395266A (en
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苏亮
熊璐
刘志伟
冷搏
龚刚
宋光吉
黄玲
陈超
朱武喜
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Xiamen King Long United Automotive Industry 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/30—Wheel torque

Abstract

The invention discloses an estimation method for decoupling mass change of a bus and road gradient, relates to the technical field of intelligent transportation, and carries out decoupling estimation on mass and road slope by considering acceleration in sections. The slope is subjected to table lookup according to GPS positioning or a preset position of the station, and slope information of the station is obtained; starting a quality estimation algorithm through judging a vehicle door opening and closing signal and acceleration; sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method and carrying out normalization processing; if the vehicle speed is less than the set threshold value, the algorithm is stopped and the estimation result is output, otherwise, the output is continued until the vehicle stops running. For the estimation of the road gradient, under the condition that the quality outputs an estimation result, the method adopts a fusion algorithm based on a dynamics method with least square of forgetting factors and a kinematics method based on an acceleration sensor, thereby ensuring the online estimation precision.

Description

Estimation method for decoupling mass change of bus and road gradient
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to an estimation method for decoupling mass change of a bus and road gradient.
Background
The mass of the whole vehicle and the gradient of a road are important parameters in a vehicle dynamic model, and the mass and the gradient can be accurately estimated in real time, so that the dynamic property and the economical efficiency of the vehicle can be effectively improved. The driving and braking control strategy is adjusted timely according to the change of the whole vehicle mass, so that the dynamic property and the economical efficiency can be improved to the maximum extent, the control feeling of a driver on the vehicle is enhanced, and the driving smoothness is improved. The accurate estimation of the road gradient can more accurately calculate the axle load transfer of the vehicle, and the minimum output torque required under the current working condition is calculated according to the axle load transfer, so that the driving feeling is improved, and meanwhile, the dynamic property and the economical efficiency can be improved. However, if the mass and the gradient are estimated by a dynamic method, the mass and the gradient are strongly coupled, so that it is necessary to explore a decoupling estimation algorithm of the mass and the gradient.
Disclosure of Invention
The invention provides an estimation method for decoupling mass change of a bus and road gradient, and mainly aims to solve the problems in the prior art.
In order to solve the technical problems, the invention adopts the following technical scheme:
an estimation method for decoupling mass change of a bus and road gradient comprises the following steps: (1) acquiring gradient information of a station; (2) judging whether the current working condition of the vehicle meets the enabling condition of using a quality estimation algorithm; (3) if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, if the mass variance is smaller than the set threshold, terminating the mass estimation algorithm and outputting an estimation result, and if the mass variance is smaller than the set threshold, continuously outputting the estimation result until the vehicle stops running; (4) and under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
Further, the enabling conditions in the step (2) are: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
Further, the torque threshold F is 1000Nm, and the acceleration threshold a is 0.5m/s2And the time threshold t is 0.3 s.
Further, in the step (2), the estimation formula of the vehicle mass is as follows:
Figure 423275DEST_PATH_IMAGE002
in the formula, m is the number of sampling points, asensor,xRepresenting the acceleration sensor measuring a gravitational acceleration component along a measuring axis, FxThe resultant force of the longitudinal forces of the wheels is obtained;
Figure 605995DEST_PATH_IMAGE003
，μris rolling resistance coefficient, M is vehicle mass, theta is gradient angle, rho is air density, CdIs the wind resistance coefficient, A is the windward area, vxThe longitudinal speed of the whole vehicle.
Further, in the step (4), the estimation formula of the road gradient is:
Figure 307103DEST_PATH_IMAGE004
wherein τ is a time constant; s is a weighting coefficient;
Figure 436733DEST_PATH_IMAGE005
an estimated value of a slope angle based on a kinematic method; (ii) a Wherein a issensor,xRepresenting the acceleration sensor measuring a gravitational acceleration component, v, along a measuring axisxThe longitudinal speed of the whole vehicle is adopted;
Figure 47843DEST_PATH_IMAGE006
the road surface gradient angle estimation value is based on a dynamic method; wherein
Figure 11251DEST_PATH_IMAGE007
，μrIs a rolling resistance coefficient, M is wholeAnd the vehicle mass theta is a slope angle.
From the above description of the structure of the present invention, compared with the prior art, the present invention has the following advantages:
the invention carries out decoupling estimation on the mass and the road ramp by considering the acceleration in a subsection way. The slope is subjected to table lookup according to GPS positioning or a preset position of the station, and slope information of the station is obtained; starting a quality estimation algorithm through judging a vehicle door opening and closing signal and acceleration; sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method and carrying out normalization processing; if the vehicle speed is less than the set threshold value, the algorithm is stopped and the estimation result is output, otherwise, the output is continued until the vehicle stops running. For the estimation of the road gradient, under the condition that the quality outputs an estimation result, the method adopts a fusion algorithm based on a dynamics method with least square of forgetting factors and a kinematics method based on an acceleration sensor, thereby ensuring the online estimation precision.
Drawings
FIG. 1 is a flow chart of a mass and road grade estimation algorithm.
Fig. 2 is a schematic view of the road surface gradient.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
Vehicle quality identification
As shown in fig. 1 and 2, the longitudinal force analysis is performed on the vehicle:
Figure 75022DEST_PATH_IMAGE009
。
wherein the content of the first and second substances,F xin order to obtain the resultant force of the longitudinal forces of the wheels,F gin order to provide the resistance for climbing the slope,F win order to be the air resistance,T ifor the drive torque of each wheel，J wIs the moment of inertia of the wheel, wiAs the angular velocity of each wheel of the vehicle,Ris the wheel radius, μrIs the rolling resistance coefficient, theta is the slope angle,C din order to obtain the wind resistance coefficient,Athe area of the wind-facing surface is,v xthe longitudinal speed of the whole vehicle is set,Mthe weight of the whole vehicle is measured,ρis the air density.
Is finished to obtain
Figure 929715DEST_PATH_IMAGE010
。
And because the acceleration sensor can measure the gravity acceleration component along the measuring axis, the measured acceleration is as follows:
Figure 333014DEST_PATH_IMAGE011
。
so that there are
Figure 857536DEST_PATH_IMAGE013
。
Wherein order
Figure 283970DEST_PATH_IMAGE014
。
Then there is
Figure 552140DEST_PATH_IMAGE015
。
Estimating by least square method
Figure 809946DEST_PATH_IMAGE016
。
M is the number of sampling points, and M is obtained by the least square method to satisfy the following formula to obtain the minimum value, namely
Figure 161162DEST_PATH_IMAGE018
。
The formula of the estimated value of the vehicle mass is solved as follows:
Figure 137208DEST_PATH_IMAGE019
。
considering that the above formula contains a resistance variable, and the resistance is related to the road gradient, the road gradient needs to be determined in advance to estimate the vehicle mass. That is to say, the mass of the whole vehicle and the gradient of the road have a strong coupling relation. Considering the influence of acceleration on estimation error, a decoupling estimation method of mass and gradient based on acceleration segmentation is provided, namely, when the acceleration is lower than a certain small acceleration, the estimation error of mass is larger, and conversely, the estimation precision is much higher.
The research object of the invention is a bus which runs under the special working condition, and the bus has a fixed running route, fixed road conditions and bus stops, so that offline table lookup can be carried out through GPS positioning or the preset position of the stop to obtain the slope information of the stop and obtain the slope of the road; and then, starting a mass estimation module by judging a vehicle door opening and closing signal, longitudinal acceleration and driving torque, sampling the driving torque, vehicle speed and vehicle acceleration within a period of time, estimating the mass of the whole vehicle by adopting a least square method, normalizing the mass variance, terminating the mass estimation algorithm and outputting an estimation result if the mass variance is converged to be less than a set threshold value, and otherwise, continuously outputting until the vehicle stops running.
In the starting process of the vehicle, the acceleration is inevitably increased to a larger value from 0, and the estimation of the mass is started under the larger acceleration, so that in the initial stage (namely the acceleration is less than or equal to the acceleration threshold value a), the half-load mass of the whole vehicle can be used as a predicted value, and the overlarge or undersize deviation of the true mass can not be caused; and when the acceleration is larger than the acceleration threshold value a, the estimation value formula of the whole vehicle mass is used for estimation.
Specifically, a recursive least square method with a forgetting factor is adopted for estimation, that is, an estimation value of a last sampling moment is corrected by using a measurement value of a current sampling moment. The algorithm is described as follows:
Figure 209069DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 259065DEST_PATH_IMAGE021
。
after the start of the quality estimation, every othertsThe quality estimate is sampled in seconds. Taking the last in time series when calculatingnThe variance of the estimated value is found from the data of the sampling points, and the variance is normalized:
Figure 328652DEST_PATH_IMAGE022
wherein:
Figure 854311DEST_PATH_IMAGE023
as an estimate of mass (time of sampling)tsSecond)；
Figure 667547DEST_PATH_IMAGE024
Is the most recentnMean of the mass estimates of the individual samples. When the algorithm is not converged, the quality estimation value is output in real time to reduce the use of the predicted valueMThe resulting error; when the value of the variance σ is smaller than σ0When the vehicle is stopped, the algorithm is considered to be stable, estimation is stopped, the estimated value at the time is used as the vehicle quality and is input into other control systems and identification algorithms, and the algorithm resets the output value to the calibration value again until the vehicle stops and opens the door next timeM。
Therefore, whether the current working condition of the vehicle meets the enabling condition of the use quality estimation algorithm needs to be judged firstly; and if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, terminating the mass estimation algorithm and outputting an estimation result if the mass variance is converged to be smaller than the set threshold, and otherwise, continuously outputting until the vehicle stops running. The enabling conditions are: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
Preferably, the threshold torque F is 1000Nm and the threshold acceleration a is 0.5m/s2The time threshold t is 0.3s, even if the enabling conditions are: under the condition that the vehicle door is closed, the driving torque is more than 1000Nm or the longitudinal acceleration of the whole vehicle is more than 0.5m/s2。
Thus, when the door is closed and the acceleration of the vehicle is less than 0.5m/s2The half-load mass of the whole vehicle can be used as a predicted value; and when the enabling condition is met and the retention time of the enabling condition is more than 0.3s, starting the mass estimation module, and estimating the mass by using the estimation value formula of the vehicle mass by using the mass estimation module.
(4) And under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
Second, road slope estimation
(1) Kinetic method
As shown in fig. 2, using a vehicle dynamics model, let
Figure 89825DEST_PATH_IMAGE025
Then
Figure 392630DEST_PATH_IMAGE026
Wherein the content of the first and second substances,ythe driving force is a longitudinal driving force and can be accurately obtained through feedback signals of the distributed driving motor;uas a function of mass and velocity, available with known mass;bthe mass is known as a function of mass and grade angle, so the grade angle value is obtained.
Since the slope is time-varying, it is possible to reduce the time required for the slope to be constantbThe values are estimated using a least squares method with a forgetting factor.
Figure 77690DEST_PATH_IMAGE028
The time of each moment can be estimated by the above formulabThe value is further used for obtaining a road surface slope angle estimated value theta based on a dynamic method by using the following formula d 。
Figure 632299DEST_PATH_IMAGE029
(2) Kinematic method
The acceleration sensor is fixed on the vehicle body and measures the valuea x In addition to the running acceleration of the vehicle itself, the road surface gradient is also affected. Slope angle estimation value theta based on kinematics method k ：
Figure 719204DEST_PATH_IMAGE030
(3) Fusion method
As shown in fig. 1, the kinetic method relies more on the accuracy of the model parameters, whereas the kinematic method is more susceptible to the quality of the sensor signal. Therefore, the invention adopts a weighted average method to fuse the two methods, and finally obtains the estimated value theta of the subtended slope angle.
Figure 458490DEST_PATH_IMAGE032
Wherein τ is a time constant, preferably of the order of 10-2A constant of (d); s is a weighting coefficient.
In conclusion, the invention performs a decoupling estimation of the mass and the road slope by taking the acceleration into account in sections. The slope is subjected to table lookup according to GPS positioning or a preset position of the station, and slope information of the station is obtained; starting a quality estimation algorithm through judging a vehicle door opening and closing signal and acceleration; sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method and carrying out normalization processing; if the vehicle speed is less than the set threshold value, the algorithm is stopped and the estimation result is output, otherwise, the output is continued until the vehicle stops running. For the estimation of the road gradient, under the condition that the quality outputs an estimation result, the method adopts a fusion algorithm based on a dynamics method with least square of forgetting factors and a kinematics method based on an acceleration sensor, thereby ensuring the online estimation precision.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.

Claims (5)

1. An estimation method for decoupling mass change of a bus and road gradient is characterized by comprising the following steps: (1) acquiring gradient information of a station; (2) judging whether the current working condition of the vehicle meets the enabling condition of using a quality estimation algorithm; (3) if the enabling condition is met and the holding time is longer than the time threshold t, sampling the driving torque, the vehicle speed and the acceleration within a period of time, estimating the mass of the whole vehicle by using a least square method, normalizing the mass variance, if the mass variance is smaller than the set threshold, terminating the mass estimation algorithm and outputting an estimation result, and if the mass variance is smaller than the set threshold, continuously outputting the estimation result until the vehicle stops running; (4) and under the condition of the output quality estimation result, estimating the road gradient by adopting a road gradient estimation algorithm based on the combination of a dynamics method with least square forgetting factor and a kinematics method based on an acceleration sensor.
2. An estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: the enabling conditions in the step (2) are as follows: when the vehicle door is closed, the driving torque is larger than a torque threshold value F or the longitudinal acceleration of the whole vehicle is larger than an acceleration threshold value a.
3. An estimation method as claimed in claim 2 regarding the decoupling of mass change of the bus and road gradient, characterized in that: the torque threshold valueF is 1000Nm, and the acceleration threshold value a is 0.5m/s2And the time threshold t is 0.3 s.
4. An estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: in the step (2), the estimation formula of the vehicle mass is as follows:
Figure DEST_PATH_IMAGE002
in the formula, m is the number of sampling points, asensor,xRepresenting the acceleration sensor measuring a gravitational acceleration component along a measuring axis, FxThe resultant force of the longitudinal forces of the wheels is obtained;
Figure DEST_PATH_IMAGE004
，μris rolling resistance coefficient, M is vehicle mass, theta is gradient angle, rho is air density, CdIs the wind resistance coefficient, A is the windward area, vxThe longitudinal speed of the whole vehicle.
5. An estimation method as claimed in claim 1 regarding the decoupling of mass change of the bus and road gradient, characterized in that: in the step (4), the estimation formula of the road gradient is as follows:
Figure DEST_PATH_IMAGE006
wherein τ is a time constant; s is a weighting coefficient;
Figure DEST_PATH_IMAGE008
an estimated value of a slope angle based on a kinematic method; wherein a issensor,xRepresenting the acceleration sensor measuring a gravitational acceleration component, v, along a measuring axisxThe longitudinal speed of the whole vehicle is adopted;
Figure DEST_PATH_IMAGE010
the road surface gradient angle estimation value is based on a dynamic method; wherein
Figure DEST_PATH_IMAGE012
，μrThe coefficient of rolling resistance is M is the mass of the whole vehicle, and theta is the slope angle.
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