CN107117178A - Consider the vehicle mass method of estimation of gearshift and road grade factor - Google Patents
Consider the vehicle mass method of estimation of gearshift and road grade factor Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
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Abstract
The invention discloses a kind of vehicle mass method of estimation for considering gearshift and road grade factor, comprise the following steps：Step 1：Gathered data：Step 11：Utilize data acquisition device collection vehicle transport condition data；Step 12：With reference to the intrinsic parameter of vehicle and vehicle running state data, calculating, which obtains model, needs parameter；Step 2：Based on acquired vehicle running state data, longitudinal vehicle dynamic model is set up；Step 3：Set up the vehicle mass estimation model for considering gearshift factor；Step 4：Mechanical efficiency of power transmission estimation model is set up, and the vehicle mass estimation model of the consideration gearshift factor proposed with step 3 is collectively formed while considering that the vehicle mass of gearshift and road grade factor estimates model；Step 5：Determine the use condition of the real-time estimating system of vehicle mass；Step 6：The vehicle running state data and correlation model parameters that collect are inputted into the real-time estimating system of vehicle mass, real-time vehicle quality is estimated.
Description
Technical field
It is specifically a kind of vehicle matter for considering gearshift and road grade factor the present invention relates to vehicle mass method of estimation
Amount estimation method.
Background technology
The real-time estimation of vehicle dynamic model parameter is the basis of wagon control, and vehicle mass parameter is vehicle power
The important parameter in model is learned, the accurate parameter of vehicle mass estimation in real time can effectively improve the property of active safety systems of vehicles
Can, such as electronic stabilizing control system (ESP) relies on vehicle mass parameter and calculates side slip angle and yaw angle in real time, so as to realize
The control of situation is slid to lateral direction of car.Meanwhile, there are some researches show according to the real-time change situation of mass parameter adjustment gearshift rule
Rule can be automobile fuel saving about 2.62%-4.86%.Therefore, accurate estimation vehicle mass parameter for improve vehicle safety and
Fuel economy is respectively provided with significance.
In existing literature, the method about vehicle mass parameter Estimation can be divided into two classes, and a class is sensor-based
Quality estimation method, this kind of method is needed to install corresponding sensor additional in the car, and car is also improved while taking interior space
Production cost, it is difficult to meet practical application request；Another kind of is the vehicle mass estimation side based on vehicle CAN bus data
Method, this method is based on longitudinal vehicle dynamic model, without additional sensor, with preferable application portability.However, vehicle
Unavoidably there is certain gradient in the road of actual travel, in longitudinal vehicle dynamic model, and the gradient and vehicle mass are deposited
In certain coupled relation, if not considering the influence of road grade in quality estimation process, quality estimation will necessarily be reduced
Precision；Meanwhile, and because vehicle needs often to shift gears in normal driving process, and the dynamics state of shift process vehicle
Longitudinal direction of car kinetic description can not be used, and the correction coefficient of rotating mass of gearshift fore-aft vehicle Longitudinal Dynamic Model can occur
Change, so that causing gearshift behavior to occur rear vehicle quality estimation results has larger deviation, have a strong impact on vehicle mass and estimate
Result is counted, and then reduces the accuracy of vehicle mass estimation.
The content of the invention
In view of this, in order to solve the deficiencies in the prior art, consider to change it is an object of the invention to provide one kind
The vehicle mass method of estimation of gear and road grade factor.
To reach above-mentioned purpose, the present invention provides following technical scheme：
A kind of vehicle mass method of estimation for considering gearshift and road grade factor, comprises the following steps：
Step 1：Gathered data
Step 11：Utilize data acquisition device collection vehicle transport condition data；
Step 12：With reference to the intrinsic parameter of vehicle and vehicle running state data, calculating, which obtains model, needs parameter；
Step 2：Based on acquired vehicle running state data, longitudinal vehicle dynamic model is set up, different shelves are obtained
The lower torque in position and speed relation；
Step 3：Based on longitudinal vehicle dynamic model, the vehicle mass estimation model for considering gearshift factor is set up；
Step 4：Based on longitudinal vehicle dynamic model, mechanical efficiency of power transmission estimation model is set up, and propose with step 3
Consideration gearshift factor vehicle mass estimation model collectively form and meanwhile consider gearshift and road grade factor vehicle mass
Estimate model；
Step 5：Determine the use condition of the real-time estimating system of vehicle mass；
Step 6：By the vehicle running state data and correlation model parameters that collect input vehicle mass estimate in real time be
System, estimates real-time vehicle quality.
Further, in the step 11, vehicle running state data include motor torque Tq, speed v, engine speed
n；
In the step 12, the intrinsic parameter of vehicle includes tire rolling radius r, final driver ratio i0, road roll
Resistance coefficient f, mechanical efficiency of power transmission η, vehicle air resistance coefficient Cd, the positive front face area A of vehicle；
Parameter needed for model includes vehicle acceleration a and transmission ratio ig；
Wherein, acceleration a can be obtained by speed v to time difference, and the accelerometer at kth moment is shown as：
T is the sampling period of data acquisition device；
Transmission ratio igComputational methods be：
Further, longitudinal vehicle dynamic model is：
Ft=Ff+Fi+Faero+Fj
Wherein, FtFor vehicle drive force, and
FfFor rolling resistance, and Ff=mgf；
FiFor grade resistance, and Fi=mgi；
FaeroFor air drag, and
FjFor acceleration resistance, and Fj=ma；
Wherein, TqRepresent motor torque；igRepresent transmission ratio；i0Represent final driver ratio；η represents to pass
Dynamic is efficiency；R represents radius of wheel；M represents vehicle mass；F represents coefficient of rolling resistance；CdRepresent the air drag system of vehicle
Number；A represents front face area；V represents car speed；I is road grade；
After substitution, torque and speed relation under different gears are obtained：
Further, the step 3 comprises the following steps：
Step 31：Derive the least squares formalism of longitudinal vehicle dynamic model；
Step 32：Set up the weighted least-squares recurrence estimation model with many forgetting factors；
Step 33：Set up the weighted least-squares recursion quality estimation model with many forgetting factors.
Further, in the step 31, longitudinal vehicle dynamic model is changed into following least squares formalism：
Wherein, a represents acceleration；δ represents correction coefficient of rotating mass.
Further, in the step 32, if the input/output relation of system can be described as following least squares formalism：
Z (k)=hT(k)θ+n(k)
Wherein, z (k) is the output of system, and h (k) is Observable data vector, and n (k) is white noise, and θ is ginseng to be estimated
Number；
When there is two parameters to be estimated in model, defining criterion function is：
Wherein Λ (i) is weighting function, λ1And λ2Respectively two model parameter θs to be estimated1And θ2Corresponding forgetting factor；
Using sequence { z (k) } and { h (k) }, minimization criterion function, that is, to θ derivations, can try to achieve the least square of parameter θ
EstimateThe estimates of parameters at kth moment can be expressed as：
Above-mentioned estimated result is converted into recursive form, the weighted least-squares recurrence estimation mould with many forgetting factors is obtained
Type is as follows：
Wherein：
Further, in the step 33, the longitudinal vehicle dynamic model least squares formalism in step 31 is applied to
Least Square Recurrence estimation model in step 32, then have：
The recursion shape of the weighted least-squares quality estimation model with many forgetting factors can be obtained by substituting the above to least square
Formula is：
Wherein, λ1And λ2The respectively two corresponding forgetting factors of parameter m and δ m to be estimated, span for [0,1), and
λ1＞ λ2；
Λ (k) is the weighted factor of data, to ensure that the vehicle running state data under the conditions of non-gearshift can be occupied more
High data weighting, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before gearshift is 1, is changed
Weighted factor after gear is represented by：
Further, the step 4 comprises the following steps：
Step 41：Set up mechanical efficiency of power transmission estimation model；
It will derive that corresponding least squares formalism is applied to many forgetting factors based on longitudinal vehicle dynamic model
In weighted least square model, wherein：
Derive that corresponding least squares formalism is based on longitudinal vehicle dynamic model：
If：
The estimation model that then can obtain mechanical efficiency of power transmission is as follows：
Wherein：
Step 42：Model is estimated based on mechanical efficiency of power transmission and considers that the vehicle mass of gearshift factor estimates model, altogether
With the vehicle mass estimation model for building consideration gearshift simultaneously and road grade factor.
Further, the method for determining the use condition of the real-time estimating system of vehicle mass is
When meeting following condition simultaneously, quality estimation system starts：
1) in vehicle launch and operating range more than 300m；
2) car speed is more than 20km/h；
3) vehicle acceleration be on the occasion of；
4) accelerator open degree is more than 0；
5) vehicle is not in on-position；
6) vehicle is not in gearshift condition；
7) steering wheel angle is not more than 30 °；
8) the current driving road segment gradient is less than 2 °；
9) automobile gear level is not neutral；
When meeting following any condition, quality estimation system temporary close：
1) vehicle is in on-position；
2) steering wheel for vehicle corner is less than 90 °；
3) automobile gear level is neutral；
4) vehicle is in dead ship condition.
Further, in the step 6, estimation real-time vehicle quality mainly includes the three below stage：
1) startup stage：
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and the weighting with many forgetting factors is set
The initial value of information gain matrix in Least Square Recurrence quality estimation model：
P (0)=a2I
Wherein a is fully big positive number；
The vehicle running state data at current time are subsequently based on, the estimation of the first inferior quality is proceeded by, obtains m and δ m's
First time estimate；
2) operation phase
It is minimum that the vehicle running state data and m the and δ m of last moment estimate that current time is updated bring weighting into
Two multiply in recursion quality estimation model, carry out new round quality estimation, and repeat this step, obtain real-time quality evaluation value；
3) stop phase
When the estimate of vehicle mass is converged within zone of reasonableness, stop carrying out recurrence estimation, but still receive vehicle
Transport condition data, when occurring so as to fortuitous event, quality estimation can be carried out again.
The beneficial effects of the present invention are：
The present invention considers the vehicle mass method of estimation of gearshift and road grade factor, makes vehicle straight and with the gradient
Road surface on, result in ideal quality estimation results；Simultaneously consider gearshift factor bring gearratio mutation and
The change of gearshift fore-aft vehicle correction coefficient of rotating mass, using the weighted least-squares recurrence estimation side with many forgetting factors
Method, realizes the real-time estimation to vehicle mass；Vehicle matter can be estimated in real time, exactly i.e. in normally travel and vehicle shift
Amount, is reducing the accuracy that vehicle mass estimation is also improved while gearshift factor estimates the influence caused for quality and Shandong
Rod.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carried out
Explanation：
Fig. 1 considers the flow chart of the vehicle mass method of estimation of gearshift and road grade factor for the present invention；
Fig. 2 is consideration gearshift and the vehicle mass estimation model of road grade factor；
Fig. 3 is longitudinal vehicle dynamic model stress diagram.
Embodiment
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings, so that those skilled in the art can be with
It is better understood from the present invention and can be practiced, but illustrated embodiment is not as a limitation of the invention.
The present embodiment considers the vehicle mass method of estimation of gearshift and road grade factor, comprises the following steps：
Step 1：Gathered data
Step 11：Utilize OpenXC data acquisition device collection vehicle transport condition datas
Specifically, the present embodiment utilizes OpenXC data acquisition device collection vehicle transport condition datas, and vehicle is travelled
Status data includes motor torque Tq, speed v, engine speed n, steering wheel angle φw, accelerator open degree, brake signal, shelves
Position information etc., and the Real-time Road gradient i drawn is calculated based on data acquisition device built-in sensors；Come from by Bluetooth receptions
Real-time vehicle transport condition data on vehicle CAN bus is simultaneously stored on mobile data processing terminal.
Step 12：With reference to the intrinsic parameter of vehicle and vehicle running state data, calculating, which obtains model, needs parameter
Specifically, the intrinsic parameter of the vehicle of the present embodiment includes tire rolling radius r, final driver ratio i0, road
Coefficient of rolling resistance f, mechanical efficiency of power transmission η, vehicle air resistance coefficient Cd, the positive front face area A of vehicle etc.；Needed for model
Parameter includes vehicle acceleration a and transmission ratio ig。
Wherein, acceleration a can be obtained by speed v to time difference, and the accelerometer at kth moment is shown as：
T is the sampling period of OpenXC data acquisition devices；
Transmission ratio igComputational methods be：
Step 2：Based on acquired vehicle running state data, longitudinal vehicle dynamic model is set up, different shelves are obtained
The lower torque in position and speed relation.Specifically, longitudinal vehicle dynamic model is：
Ft=Ff+Fi+Faero+Fj
Wherein, FtFor vehicle drive force, and
FfFor rolling resistance, and Ff=mgf；
FiFor grade resistance, and Fi=mgi；
FaeroFor air drag, and
FjFor acceleration resistance, and Fj=ma；
Wherein, TqRepresent motor torque；igRepresent transmission ratio；i0Represent final driver ratio；η represents to pass
Dynamic is efficiency；R represents radius of wheel；M represents vehicle mass；F represents coefficient of rolling resistance；CdRepresent the air drag system of vehicle
Number；A represents front face area；V represents car speed；I is road grade；
After substitution, torque and speed relation under different gears are obtained：
Step 3：Based on longitudinal vehicle dynamic model, the vehicle mass estimation model for considering gearshift factor is set up
Step 31：Derive the least squares formalism of longitudinal vehicle dynamic model
The target of the present embodiment is to estimate vehicle mass in real time in the condition for considering gearshift factor, and before vehicle shift
Afterwards, the gyrating mass conversion of vehicle is that δ numbers can occur more significantly to change, influence vehicle mass estimation.It is based only upon vehicle CAN
The data of bus can not calculate the correction coefficient of rotating mass of vehicle in real time, to ensure still to be able to relatively be defined under the conditions of gearshift
True vehicle mass estimate is, it is necessary to which the correction coefficient of rotating mass δ and vehicle mass m of vehicle are together estimated as vehicle mass
Count the parameter to be estimated of model.Therefore, longitudinal vehicle dynamic model is changed into following least squares formalism：
Wherein, a represents acceleration；δ represents correction coefficient of rotating mass.
Step 32：Set up the weighted least-squares recurrence estimation model with many forgetting factors
After vehicle start-up, vehicle mass m is almost unchanged, is a slow variable；And correction coefficient of rotating mass δ can be with
Quick change occurs for the factors such as gearshift, is a fast variable.The estimation mould existed jointly for this fast variable of guarantee and slow variable
The estimate of slow variable can fast and accurately restrain in type, while the estimated result of fast variable can possess preferable tracking
Performance is, it is necessary to be the different forgetting factor of different parameter settings to be estimated, with the accuracy of lift scheme estimated result.
The motion state due to vehicle in shift process can not be used in longitudinal direction of car kinetic description, shift process and produced again
Raw vehicle running state data can increase the error of vehicle mass estimated result, in order to suppress the mistake produced during this
Difference, need to use method of weighting, weaken the weight of vehicle running state data in shift process, quality estimation method was being shifted gears
Accurate quality estimation results can be still exported in journey.
If the input/output relation of system can be described as following least squares formalism：
Z (k)=hT(k)θ+n(k)
Wherein, z (k) is the output of system, and h (k) is Observable data vector, and n (k) is white noise, and θ is ginseng to be estimated
Number；
When there is two parameters to be estimated in model, defining criterion function is：
Wherein Λ (i) is weighting function, λ1And λ2Respectively two model parameter θs to be estimated1And θ2Corresponding forgetting factor；
Using sequence { z (k) } and { h (k) }, minimization criterion function, that is, to θ derivations, can try to achieve the least square of parameter θ
EstimateThe estimates of parameters at kth moment can be expressed as：
In order to ensure vehicle mass estimating system can real-time update estimated result, above-mentioned estimated result need to be converted into and passed
Form is pushed away, the weighted least-squares recurrence estimation model with many forgetting factors is obtained as follows：
Wherein：
Step 33：Set up the weighted least-squares recursion quality estimation model with many forgetting factors
The least square that longitudinal vehicle dynamic model least squares formalism in step 31 is applied in step 32 is passed
Meter model is estimated, then is had：
The recursion shape of the weighted least-squares quality estimation model with many forgetting factors can be obtained by substituting the above to least square
Formula is：
Wherein, λ1And λ2The respectively two corresponding forgetting factors of parameter m and δ m to be estimated, span for [0,1), examine
δ m are considered for fast variable, to ensure that δ m estimated result has preferable tracking performance, there should be λ1＞ λ2, λ under default situations1Take
For 0.95, λ2It is taken as 0.5.
Λ (k) is the weighted factor of data, to ensure that the vehicle running state data under the conditions of non-gearshift can be occupied more
High data weighting, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before gearshift is 1, is changed
Weighted factor after gear is represented by：
Step 4：Based on longitudinal vehicle dynamic model, mechanical efficiency of power transmission estimation model is set up, and propose with step 3
Consideration gearshift factor vehicle mass estimation model collectively form and meanwhile consider gearshift and road grade factor vehicle mass
Estimate model；
Step 41：Set up mechanical efficiency of power transmission estimation model
When with vehicle mass estimation is carried out on acclive road surface, rely solely on and provided accurately for quality estimation model
Road grade information, still can not accurately carry out quality estimation.This is mainly due to the change of road grade, system of vehicle transmission
It is that efficiency eta is also being continually changing, and the parameter can not be obtained by vehicle CAN bus or external sensor, it is therefore necessary to set up
The estimation model of mechanical efficiency of power transmission can be obtained in real time, and is applied to the weighted least-squares with many forgetting factors passs
Push away in quality estimation model, so that it is guaranteed that degree of accuracy when vehicle estimates quality on the road surface for exist certain slope.
It is primarily based on longitudinal vehicle dynamic model and derives corresponding least squares formalism
By above formula as can be seen that mechanical efficiency of power transmission η and vehicle mass m are considered as ginseng to be estimated by least squares formalism
Number, wherein vehicle mass is almost unchanged in the process of moving, belongs to slow variable；Mechanical efficiency of power transmission is with vehicle transmission system
The change of working condition and quickly change, belong to fast variable.Accordingly, it would be desirable to for vehicle mass assign a larger forgetting because
Son, to ensure the continuous and stability of estimation；A less forgetting factor is assigned for mechanical efficiency of power transmission, to ensure estimation
As a result there is good tracking performance.Due to being possible to shift gears in estimation procedure, travelled to suppress vehicle during gearshift
The harmful effect that status data is caused to estimated result, it is also desirable to add adding as gearshift factor quality estimation model is considered
Weight factor Λ (k), so as to ensure the stability and continuity of estimated result.
The least squares formalism that longitudinal vehicle dynamic model is converted to is applied to the weighting with many forgetting factors most
A young waiter in a wineshop or an inn multiplies in estimation model, if：
The estimation model that then can obtain mechanical efficiency of power transmission is as follows：
Wherein：
Step 42：Model is estimated based on mechanical efficiency of power transmission and considers that the vehicle mass of gearshift factor estimates model, altogether
With the vehicle mass estimation model for building consideration gearshift simultaneously and road grade factor.
The weighted least-squares recursion quality estimation model for many forgetting factors of band that step 3 is obtained is imitated with vehicle transmission system
Rate models coupling constitutes the vehicle mass estimation model for considering gearshift and road grade factor together, just, as shown in Figure 2.
Vehicle mass estimation start before, according to the actual conditions of vehicle, preset drive line efficiency initial value and
The initial value of correction coefficient of rotating mass.When quality estimation starts, the vehicle running state that OpenXC data acquisition devices are provided
Data simultaneously many forgetting factors of input tape weighted least-squares recursion quality estimation model and vehicle transmission system efficiency Model it
In.Vehicle running state data complete the weighting with many forgetting factors most jointly with drive line efficiency initial value as mode input
A young waiter in a wineshop or an inn multiplies the first time estimation of recursion quality estimation model, while vehicle transmission system efficiency estimation model also will be based on vehicle traveling
The initial value of status data and correction coefficient of rotating mass, completes to estimate for the first time.The gyrating mass that then first time is obtained is changed
The estimate for calculating coefficient and mechanical efficiency of power transmission substitutes its default initial value respectively, and as mode input, two are carried out respectively
Second of estimation of model.Then proceed to the correction coefficient of rotating mass and mechanical efficiency of power transmission for exporting last moment model
Estimate substitutes original mode input, and next round recurrence estimation is together carried out with newest vehicle running state data, repeats
This process, until obtaining the convergence of quality estimation curve.
Consider that the vehicle mass estimation model of gearshift and road grade factor overcomes the gearratio that shift process brings and dashed forward
Change problem, and by estimating that the method for vehicle transmission system efficiency ensure that vehicle estimates the essence of vehicle mass on the road of slope in real time
Degree, more accurately quality estimation is still resulted under conditions of driver has gearshift behavior and road has the gradient
As a result.
Step 5：Determine the use condition of the real-time estimating system of vehicle mass
When meeting following condition simultaneously, quality estimation system starts：
1) in vehicle launch and operating range more than 300m；
2) car speed is more than 20km/h；
3) vehicle acceleration be on the occasion of；
4) accelerator open degree is more than 0；
5) vehicle is not in on-position；
6) vehicle is not in gearshift condition；
7) steering wheel angle is not more than 30 °；
8) the current driving road segment gradient is less than 2 °；
9) automobile gear level is not neutral；
When meeting following any condition, quality estimation system temporary close：
1) vehicle is in on-position；
2) steering wheel for vehicle corner is less than 90 °；
3) automobile gear level is neutral；
4) vehicle is in dead ship condition.
The quality estimation method of the present embodiment is generally used for estimating the vehicle mass under current stroke, after vehicle stall,
Vehicle mass estimating system can remove historical data, and re-start quality estimation in next stroke.
Step 6：By the vehicle running state data and correlation model parameters that collect input vehicle mass estimate in real time be
System, estimates real-time vehicle quality.The present embodiment estimation real-time vehicle quality mainly includes the three below stage：
1) startup stage：
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and the weighting with many forgetting factors is set
The initial value of information gain matrix in Least Square Recurrence quality estimation model：
P (0)=a2I
Wherein a is fully big positive number, and default value is taken as 1000000；
The vehicle running state data at current time are subsequently based on, the estimation of the first inferior quality is proceeded by, obtains m and δ m's
First time estimate；
2) operation phase
It is minimum that the vehicle running state data and m the and δ m of last moment estimate that current time is updated bring weighting into
Two multiply in recursion quality estimation model, carry out new round quality estimation, and repeat this step, obtain real-time quality evaluation value；
3) stop phase
When the estimate of vehicle mass is converged within zone of reasonableness, stop carrying out recurrence estimation, but still receive vehicle
Transport condition data, when occurring so as to fortuitous event, quality estimation can be carried out again.
After vehicle stall, stopping carrying out the historical data in recurrence estimation, quality estimation system will also reset, and quality is estimated
Meter systems are out of service.
The present embodiment considers the vehicle mass method of estimation of gearshift and road grade factor, makes vehicle straight and with slope
On the road surface of degree, ideal quality estimation results are resulted in；The gearratio mutation that gearshift factor is brought is considered simultaneously
With gearshift fore-aft vehicle correction coefficient of rotating mass change, using the weighted least-squares recurrence estimation with many forgetting factors
Method, realizes the real-time estimation to vehicle mass；I.e. in normally travel and vehicle shift can in real time, estimate vehicle exactly
Quality, reduce gearshift factor for quality estimate cause influence while also improve vehicle mass estimation accuracy and
Robustness.
Embodiment described above is only the preferred embodiment to absolutely prove the present invention and being lifted, protection model of the invention
Enclose not limited to this.Equivalent substitute or conversion that those skilled in the art are made on the basis of the present invention, in the present invention
Protection domain within.Protection scope of the present invention is defined by claims.
Claims (10)
1. a kind of vehicle mass method of estimation for considering gearshift and road grade factor, it is characterised in that：Comprise the following steps：
Step 1：Gathered data
Step 11：Utilize data acquisition device collection vehicle transport condition data；
Step 12：With reference to the intrinsic parameter of vehicle and vehicle running state data, calculating, which obtains model, needs parameter；
Step 2：Based on acquired vehicle running state data, longitudinal vehicle dynamic model is set up, is obtained under different gears
Torque and speed relation；
Step 3：Based on longitudinal vehicle dynamic model, the vehicle mass estimation model for considering gearshift factor is set up；
Step 4：Based on longitudinal vehicle dynamic model, set up mechanical efficiency of power transmission estimation model, and with step 3 propose examine
The vehicle mass estimation model for considering gearshift factor is collectively formed while considering that the vehicle mass of gearshift and road grade factor is estimated
Model；
Step 5：Determine the use condition of the real-time estimating system of vehicle mass；
Step 6：The vehicle running state data and correlation model parameters that collect are inputted into the real-time estimating system of vehicle mass, estimated
Count real-time vehicle quality.
2. the vehicle mass method of estimation according to claim 1 for considering gearshift and road grade factor, it is characterised in that：
In the step 11, vehicle running state data include motor torque Tq, speed v, engine speed n；
In the step 12, the intrinsic parameter of vehicle includes tire rolling radius r, final driver ratio i0, road rolling resistance system
Number f, mechanical efficiency of power transmission η, vehicle air resistance coefficient Cd, the positive front face area A of vehicle；
Parameter needed for model includes vehicle acceleration a and transmission ratio ig；
Wherein, acceleration a can be obtained by speed v to time difference, and the accelerometer at kth moment is shown as：
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Transmission ratio igComputational methods be：
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3. the vehicle mass method of estimation according to claim 1 for considering gearshift and road grade factor, it is characterised in that：
Longitudinal vehicle dynamic model is：
Ft=Ff+Fi+Faero+Fj
Wherein, FtFor vehicle drive force, and
FfFor rolling resistance, and Ff=mgf；
FiFor grade resistance, and Fi=mgi；
FaeroFor air drag, and
FjFor acceleration resistance, and Fj=ma；
Wherein, TqRepresent motor torque；igRepresent transmission ratio；i0Represent final driver ratio；η represents power train
Efficiency；R represents radius of wheel；M represents vehicle mass；F represents coefficient of rolling resistance；CdRepresent the coefficient of air resistance of vehicle；A
Represent front face area；V represents car speed；I is road grade；
After substitution, torque and speed relation under different gears are obtained：
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<mi>i</mi>
<mi>g</mi>
</msub>
<msub>
<mi>i</mi>
<mn>0</mn>
</msub>
<mi>&eta;</mi>
</mrow>
<mi>r</mi>
</mfrac>
<mo>=</mo>
<mi>m</mi>
<mi>g</mi>
<mi>f</mi>
<mo>+</mo>
<mi>m</mi>
<mi>g</mi>
<mi>i</mi>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mi>d</mi>
</msub>
<msup>
<mi>Av</mi>
<mn>2</mn>
</msup>
</mrow>
<mn>21.15</mn>
</mfrac>
<mo>+</mo>
<mi>m</mi>
<mi>a</mi>
<mo>.</mo>
</mrow>
4. the vehicle mass method of estimation according to claim 3 for considering gearshift and road grade factor, it is characterised in that：
The step 3 comprises the following steps：
Step 31：Derive the least squares formalism of longitudinal vehicle dynamic model；
Step 32：Set up the weighted least-squares recurrence estimation model with many forgetting factors；
Step 33：Set up the weighted least-squares recursion quality estimation model with many forgetting factors.
5. the vehicle mass method of estimation according to claim 4 for considering gearshift and road grade factor, it is characterised in that：
In the step 31, longitudinal vehicle dynamic model is changed into following least squares formalism：
<mrow>
<mfrac>
<mrow>
<msub>
<mi>T</mi>
<mi>q</mi>
</msub>
<msub>
<mi>i</mi>
<mi>g</mi>
</msub>
<msub>
<mi>i</mi>
<mn>0</mn>
</msub>
<mi>&eta;</mi>
</mrow>
<mi>r</mi>
</mfrac>
<mo>-</mo>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mi>d</mi>
</msub>
<msup>
<mi>Av</mi>
<mn>2</mn>
</msup>
</mrow>
<mn>21.15</mn>
</mfrac>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>g</mi>
<mi>f</mi>
<mo>+</mo>
<mi>g</mi>
<mi>i</mi>
</mrow>
</mtd>
<mtd>
<mi>a</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>m</mi>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>m</mi>
<mi>&delta;</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, a represents acceleration；δ represents correction coefficient of rotating mass.
6. the vehicle mass method of estimation according to claim 5 for considering gearshift and road grade factor, it is characterised in that：
In the step 32, if the input/output relation of system can be described as following least squares formalism：
Z (k)=hT(k)θ+n(k)
Wherein, z (k) is the output of system, and h (k) is Observable data vector, and n (k) is white noise, and θ is parameter to be estimated；
When there is two parameters to be estimated in model, defining criterion function is：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>V</mi>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>,</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>,</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>&lambda;</mi>
<mn>1</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<msup>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>&lambda;</mi>
<mn>2</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<msup>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Wherein Λ (i) is weighting function, λ1And λ2Respectively two model parameter θs to be estimated1And θ2Corresponding forgetting factor；
Using sequence { z (k) } and { h (k) }, minimization criterion function, that is, to θ derivations, can try to achieve the least-squares estimation of parameter θ
ValueThe estimates of parameters at kth moment can be expressed as：
<mrow>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msubsup>
<mi>&lambda;</mi>
<mn>1</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>&lambda;</mi>
<mn>1</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
<msubsup>
<mi>&lambda;</mi>
<mn>2</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mrow>
<mo>(</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>k</mi>
</munderover>
<mi>&Lambda;</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>&lambda;</mi>
<mn>2</mn>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mi>i</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Above-mentioned estimated result is converted into recursive form, the weighted least-squares recurrence estimation model with many forgetting factors is obtained such as
Under：
<mrow>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mrow>
<mo>(</mo>
<mi>z</mi>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>-</mo>
<msup>
<mi>h</mi>
<mi>T</mi>
</msup>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mover>
<mi>&theta;</mi>
<mo>^</mo>
</mover>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein：
<mrow>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<msub>
<mi>&lambda;</mi>
<mn>1</mn>
</msub>
</mfrac>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<msub>
<mi>&lambda;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
</mfrac>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&lambda;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&lambda;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
2
<mrow>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mi>I</mi>
<mo>-</mo>
<msub>
<mi>K</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<msubsup>
<mi>h</mi>
<mn>1</mn>
<mi>T</mi>
</msubsup>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mfrac>
<mn>1</mn>
<msub>
<mi>&lambda;</mi>
<mn>1</mn>
</msub>
</mfrac>
</mrow>
<mrow>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mi>I</mi>
<mo>-</mo>
<msub>
<mi>K</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<msubsup>
<mi>h</mi>
<mn>2</mn>
<mi>T</mi>
</msubsup>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mfrac>
<mn>1</mn>
<msub>
<mi>&lambda;</mi>
<mn>2</mn>
</msub>
</mfrac>
</mrow>
<mrow>
<msub>
<mi>K</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<msub>
<mi>&lambda;</mi>
<mn>1</mn>
</msub>
<mrow>
<mi>&Lambda;</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>+</mo>
<msubsup>
<mi>h</mi>
<mn>1</mn>
<mi>T</mi>
</msubsup>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<msub>
<mi>P</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
<msub>
<mi>h</mi>
<mn>1</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
</mrow>
<mrow>
<msub>
<mi>K</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<msub>
<mi>&lambda;</mi>
<mn>2</mn>
</msub>
<mrow>
<mi>&Lambda;</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
<mo>+</mo>
<msubsup>
<mi>h</mi>
<mn>2</mn>
<mi>T</mi>
</msubsup>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<msub>
<mi>P</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
<msub>
<mi>h</mi>
<mn>2</mn>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>.</mo>
</mrow>
7. the vehicle mass method of estimation according to claim 6 for considering gearshift and road grade factor, it is characterised in that：
In the step 33, the longitudinal vehicle dynamic model least squares formalism in step 31 is applied to the most young waiter in a wineshop or an inn in step 32
Multiply recurrence estimation model, then have：
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Substitute the above to least square can the recursive form of the weighted least-squares quality estimation model with many forgetting factors is：
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Wherein, λ1And λ2The respectively two corresponding forgetting factors of parameter m and δ m to be estimated, span for [0,1), and λ1＞
λ2；
Λ (k) is the weighted factor of data, to ensure that the vehicle running state data under the conditions of non-gearshift can occupy higher
Data weighting, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before gearshift is 1, after gearshift
Weighted factor be represented by：
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8. the vehicle mass method of estimation according to claim 7 for considering gearshift and road grade factor, it is characterised in that：
The step 4 comprises the following steps：
Step 41：Set up mechanical efficiency of power transmission estimation model；
It will derive that corresponding least squares formalism is applied to the weighting with many forgetting factors based on longitudinal vehicle dynamic model
In least-squares estimation model, wherein：
Derive that corresponding least squares formalism is based on longitudinal vehicle dynamic model：
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</mrow>
If：
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</mfenced>
</mrow>
The estimation model that then can obtain mechanical efficiency of power transmission is as follows：
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<mover>
<mi>&theta;</mi>
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</mover>
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Step 42：Model is estimated based on mechanical efficiency of power transmission and considers that the vehicle mass of gearshift factor estimates model, common structure
Build while considering the vehicle mass estimation model of gearshift and road grade factor.
9. the vehicle mass method of estimation according to claim 1 for considering gearshift and road grade factor, it is characterised in that：
The method for determining the use condition of the real-time estimating system of vehicle mass is
When meeting following condition simultaneously, quality estimation system starts：
1) in vehicle launch and operating range more than 300m；
2) car speed is more than 20km/h；
3) vehicle acceleration be on the occasion of；
4) accelerator open degree is more than 0；
5) vehicle is not in on-position；
6) vehicle is not in gearshift condition；
7) steering wheel angle is not more than 30 °；
8) the current driving road segment gradient is less than 2 °；
9) automobile gear level is not neutral；
When meeting following any condition, quality estimation system temporary close：
1) vehicle is in on-position；
2) steering wheel for vehicle corner is less than 90 °；
3) automobile gear level is neutral；
4) vehicle is in dead ship condition.
10. the vehicle mass method of estimation according to claim 1 for considering gearshift and road grade factor, its feature exists
In：In the step 6, estimation real-time vehicle quality mainly includes the three below stage：
1) startup stage：
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and sets the weighting with many forgetting factors minimum
Two multiply the initial value of information gain matrix in recursion quality estimation model：
P (0)=a2I
Wherein a is fully big positive number；
The vehicle running state data at current time are subsequently based on, the estimation of the first inferior quality is proceeded by, obtains the first of m and δ m
Secondary estimate；
2) operation phase
The vehicle running state data and m the and δ m of last moment estimate that current time is updated bring weighted least-squares into
In recursion quality estimation model, new round quality estimation is carried out, and repeats this step, real-time quality evaluation value is obtained；
3) stop phase
When the estimate of vehicle mass is converged within zone of reasonableness, stop carrying out recurrence estimation, but still receive vehicle traveling
Status data, when occurring so as to fortuitous event, quality estimation can be carried out again.
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Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107826124A (en) * | 2017-11-02 | 2018-03-23 | 潍柴动力股份有限公司 | A kind of vehicle descending reminding method and system based on engine braking |
| CN108189843A (en) * | 2017-12-28 | 2018-06-22 | 天津清智科技有限公司 | A kind of evaluation method of vehicle weight |
| CN110727994A (en) * | 2019-10-28 | 2020-01-24 | 吉林大学 | Parameter decoupling electric automobile mass and gradient estimation method |
| CN111311782A (en) * | 2020-02-12 | 2020-06-19 | 北京经纬恒润科技有限公司 | Load estimation method and device |
| CN111497859A (en) * | 2020-06-30 | 2020-08-07 | 北京主线科技有限公司 | Vehicle longitudinal control method combining weight parameter identification |
| CN111579037A (en) * | 2020-04-29 | 2020-08-25 | 北理新源(佛山)信息科技有限公司 | Method and system for detecting vehicle overload |
| CN112379348A (en) * | 2020-11-17 | 2021-02-19 | 安徽四创电子股份有限公司 | Multi-model estimation target maneuvering identification method based on Kalman filtering |
| CN112590803A (en) * | 2020-12-16 | 2021-04-02 | 北理慧动（常熟）车辆科技有限公司 | Online estimation method for finished vehicle mass of single-shaft parallel hybrid power commercial vehicle |
| CN112926140A (en) * | 2021-03-25 | 2021-06-08 | 交通运输部公路科学研究所 | Freight vehicle quality estimation method based on vehicle-road cooperation and TBOX |
| CN113392518A (en) * | 2021-06-08 | 2021-09-14 | 阿波罗智联(北京)科技有限公司 | Method and device for estimating vehicle weight |
| WO2021223334A1 (en) * | 2020-05-06 | 2021-11-11 | 北京理工大学 | Method for iterative joint estimation of vehicle mass and road gradient on the basis of mmrls and sh-stf |
| CN114564791A (en) * | 2022-02-24 | 2022-05-31 | 广东工业大学 | Bus total weight measurement method based on vehicle operation data |
| CN114954494A (en) * | 2022-06-14 | 2022-08-30 | 广西玉柴机器股份有限公司 | Heavy commercial vehicle load rapid estimation method |
| CN114987510A (en) * | 2022-06-17 | 2022-09-02 | 东风悦享科技有限公司 | Method and device for on-line estimation of quality parameters of automatic driving vehicle |
| WO2024138355A1 (en) * | 2022-12-27 | 2024-07-04 | 采埃孚商用车系统(青岛)有限公司 | Method, apparatus, and electronic device for estimating vehicle mass, and computer-readable medium and vehicle |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102486400A (en) * | 2010-12-06 | 2012-06-06 | 罗伯特·博世有限公司 | Vehicle mass identification method and device |
| CN102951158A (en) * | 2012-11-02 | 2013-03-06 | 浙江吉利汽车研究院有限公司杭州分公司 | Vehicle mass estimation method |
| CN103264669A (en) * | 2013-05-31 | 2013-08-28 | 吉林大学 | Heavy vehicle weight real-time identification method based on CAN information and function principle |
| EP2862772A2 (en) * | 2013-10-18 | 2015-04-22 | MAN Truck & Bus AG | Method for checking the loading state of a semitrailer or trailer of a commercial vehicle |
-
2017
- 2017-05-23 CN CN201710369680.8A patent/CN107117178B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102486400A (en) * | 2010-12-06 | 2012-06-06 | 罗伯特·博世有限公司 | Vehicle mass identification method and device |
| CN102951158A (en) * | 2012-11-02 | 2013-03-06 | 浙江吉利汽车研究院有限公司杭州分公司 | Vehicle mass estimation method |
| CN103264669A (en) * | 2013-05-31 | 2013-08-28 | 吉林大学 | Heavy vehicle weight real-time identification method based on CAN information and function principle |
| EP2862772A2 (en) * | 2013-10-18 | 2015-04-22 | MAN Truck & Bus AG | Method for checking the loading state of a semitrailer or trailer of a commercial vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 冯源等: "基于分段递推最小二乘估计的汽车质量辨识试验", 《同济大学学报(自然科学版)》 * |
| 许嘉等: "参数估计算法在汽车动态称重中的应用", 《自动化技术与应用》 * |
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|---|---|---|---|---|
| CN107826124A (en) * | 2017-11-02 | 2018-03-23 | 潍柴动力股份有限公司 | A kind of vehicle descending reminding method and system based on engine braking |
| CN108189843A (en) * | 2017-12-28 | 2018-06-22 | 天津清智科技有限公司 | A kind of evaluation method of vehicle weight |
| CN110727994A (en) * | 2019-10-28 | 2020-01-24 | 吉林大学 | Parameter decoupling electric automobile mass and gradient estimation method |
| CN111311782A (en) * | 2020-02-12 | 2020-06-19 | 北京经纬恒润科技有限公司 | Load estimation method and device |
| CN111579037B (en) * | 2020-04-29 | 2021-06-04 | 北理新源(佛山)信息科技有限公司 | Method and system for detecting vehicle overload |
| CN111579037A (en) * | 2020-04-29 | 2020-08-25 | 北理新源(佛山)信息科技有限公司 | Method and system for detecting vehicle overload |
| WO2021223334A1 (en) * | 2020-05-06 | 2021-11-11 | 北京理工大学 | Method for iterative joint estimation of vehicle mass and road gradient on the basis of mmrls and sh-stf |
| CN111497859A (en) * | 2020-06-30 | 2020-08-07 | 北京主线科技有限公司 | Vehicle longitudinal control method combining weight parameter identification |
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| CN112379348B (en) * | 2020-11-17 | 2024-04-09 | 安徽四创电子股份有限公司 | Multi-model estimation target maneuvering recognition method based on Kalman filtering |
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| CN113392518A (en) * | 2021-06-08 | 2021-09-14 | 阿波罗智联(北京)科技有限公司 | Method and device for estimating vehicle weight |
| WO2022257310A1 (en) * | 2021-06-08 | 2022-12-15 | 阿波罗智联(北京)科技有限公司 | Method and apparatus for estimating weight of vehicle |
| CN113392518B (en) * | 2021-06-08 | 2024-05-17 | 阿波罗智联(北京)科技有限公司 | Method and apparatus for estimating vehicle weight |
| CN114564791B (en) * | 2022-02-24 | 2022-10-28 | 广东工业大学 | Bus total weight measurement method based on vehicle operation data |
| CN114564791A (en) * | 2022-02-24 | 2022-05-31 | 广东工业大学 | Bus total weight measurement method based on vehicle operation data |
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| CN114954494B (en) * | 2022-06-14 | 2024-03-26 | 广西玉柴机器股份有限公司 | Heavy commercial vehicle load rapid estimation method |
| CN114987510A (en) * | 2022-06-17 | 2022-09-02 | 东风悦享科技有限公司 | Method and device for on-line estimation of quality parameters of automatic driving vehicle |
| WO2024138355A1 (en) * | 2022-12-27 | 2024-07-04 | 采埃孚商用车系统(青岛)有限公司 | Method, apparatus, and electronic device for estimating vehicle mass, and computer-readable medium and vehicle |
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