CN107117178B - Consider the vehicle mass estimation method of shift and road grade factor - Google Patents
Consider the vehicle mass estimation method of shift and road grade factor Download PDFInfo
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- CN107117178B CN107117178B CN201710369680.8A CN201710369680A CN107117178B CN 107117178 B CN107117178 B CN 107117178B CN 201710369680 A CN201710369680 A CN 201710369680A CN 107117178 B CN107117178 B CN 107117178B
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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
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Abstract
The invention discloses a kind of vehicle mass estimation methods for considering shift and road grade factor, include the following steps: step 1: acquisition data: step 11: acquiring vehicle transport condition data using data acquisition device；Step 12: in conjunction with the intrinsic parameter of vehicle and vehicle running state data, parameter needed for model is calculated；Step 2: being based on acquired vehicle running state data, establish longitudinal vehicle dynamic model；Step 3: establishing and consider that the vehicle mass of shift factor estimates model；Step 4: the vehicle mass estimation model of the considerations of establishing mechanical efficiency of power transmission estimation model, and proposing with step 3 shift factor collectively forms considers that the vehicle mass of shift and road grade factor estimates model simultaneously；Step 5: determining the use condition of vehicle mass real-time estimation system；Step 6: collected vehicle running state data and correlation model parameters being inputted into vehicle mass real-time estimation system, estimate real-time vehicle quality.
Description
Technical field
The present invention relates to vehicle mass estimation methods, are specifically a kind of vehicle matter for considering shift and road grade factor
Amount estimation method.
Background technique
The real-time estimation of vehicle dynamic model parameter is the basis of vehicle 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, side slip angle and yaw angle are calculated in real time as electronic stabilizing control system (ESP) relies on vehicle mass parameter, to realize
Control to lateral direction of car sliding situation.Meanwhile some researches show that adjust shift rule according to the real-time change situation of mass parameter
Rule can be automobile fuel saving about 2.62%-4.86%.Therefore, accurately estimation vehicle mass parameter for improve vehicle safety and
Fuel economy all has significance.
In existing literature, the method in relation to vehicle mass parameter Estimation can be divided into two classes, and one kind is sensor-based
Quality estimation method, such methods need to install additional corresponding sensor in the car, also improve vehicle while occupying 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 are based on longitudinal vehicle dynamic model, are not necessarily to additional sensor, have preferable application portability.However, vehicle
Unavoidably there is certain gradient in the road of actual travel, in longitudinal vehicle dynamic model, 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 since 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 for fore-aft vehicle Longitudinal Dynamic Model of shifting gears can occur
Change, rear vehicle quality estimation results occur so as to cause shift behavior, and there are biggish deviations, seriously affect vehicle mass and estimate
Meter as a result, reduce the accuracy of vehicle mass estimation in turn.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of considerations to change in order to solve the deficiencies in the prior art
The vehicle mass estimation method of gear and road grade factor.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of vehicle mass estimation method considering shift and road grade factor, includes the following steps:
Step 1: acquisition data
Step 11: acquiring vehicle transport condition data using data acquisition device；
Step 12: in conjunction with the intrinsic parameter of vehicle and vehicle running state data, parameter needed for model is calculated；
Step 2: being based on acquired vehicle running state data, establish longitudinal vehicle dynamic model, obtain different shelves
The lower torque in position and speed relationship；
Step 3: being based on longitudinal vehicle dynamic model, establish and consider that the vehicle mass of shift factor estimates model；
Step 4: being based on longitudinal vehicle dynamic model, establish mechanical efficiency of power transmission estimation model, and propose with step 3
The considerations of shift factor vehicle mass estimation model collectively form while considering shift and the vehicle mass of road grade factor
Estimate model；
Step 5: determining the use condition of vehicle mass real-time estimation system；
Step 6: collected vehicle running state data and correlation model parameters are inputted into vehicle mass real-time estimation system
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, vehicle forward direction front face area A；
Parameter needed for model includes vehicle acceleration a and transmission ratio ig；
Wherein, acceleration a can obtain time difference by speed v, and the accelerometer at kth moment is shown as:
T is the sampling period of data acquisition device；
Transmission ratio igCalculation method are as follows:
Further, longitudinal vehicle dynamic model are as follows:
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, TqIndicate motor torque；igIndicate transmission ratio；i0Indicate final driver ratio；η indicates to pass
Dynamic is efficiency；R indicates radius of wheel；M indicates vehicle mass；F indicates coefficient of rolling resistance；CdIndicate the air drag system of vehicle
Number；A indicates front face area；V indicates car speed；I is road grade；
After substitution, torque and speed relationship under different stalls are obtained:
Further, the step 3 includes the following steps:
Step 31: deriving the least squares formalism of longitudinal vehicle dynamic model；
Step 32: establishing the weighted least-squares recurrence estimation model with more forgetting factors；
Step 33: establishing the weighted least-squares recursion quality estimation model with more forgetting factors.
Further, in the step 31, longitudinal vehicle dynamic model is converted to following least squares formalism:
Wherein, a indicates acceleration；δ indicates 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 are when two parameters to be estimated, defining criterion function in model are as follows:
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 can acquire the least square of parameter θ that is, to θ derivation
Estimated valueThe estimates of parameters at kth moment can indicate are as follows:
Recursive form is converted by above-mentioned estimated result, obtains the weighted least-squares recurrence estimation mould with more forgetting factors
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 in step 32 estimates model, then has:
The recursion shape of the weighted least-squares quality estimation model with more forgetting factors can be obtained by substituting the above to least square
Formula are as follows:
Wherein, λ1And λ2Respectively two corresponding forgetting factors of parameter m and δ m to be estimated, value range be [0,1), and
λ1> λ2；
Λ (k) is the weighted factor of data, to guarantee that the vehicle running state data under the conditions of non-shift can occupy more
High data weighting, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before shift is 1, is changed
Weighted factor after gear may be expressed as:
Further, the step 4 includes the following steps:
Step 41: establishing mechanical efficiency of power transmission estimation model；
It will derive that corresponding least squares formalism is applied to more forgetting factors based on longitudinal vehicle dynamic model
In weighted least square model, in which:
Corresponding least squares formalism is derived based on longitudinal vehicle dynamic model are as follows:
If:
The estimation model that mechanical efficiency of power transmission then can be obtained is as follows:
Wherein:
Step 42: based on mechanical efficiency of power transmission estimation model and considering that the vehicle mass of shift factor estimates model, altogether
With building while considering that the vehicle mass of shift and road grade factor estimates model.
Further, the method for determining the use condition of vehicle mass real-time estimation system is
When meeting the following conditions simultaneously, quality estimation system starting:
1) in vehicle launch, simultaneously operating range is more than 300m；
2) car speed is greater than 20km/h；
3) vehicle acceleration is positive value；
4) accelerator open degree is greater 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 gear；
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 gear；
4) vehicle is in dead ship condition.
Further, in the step 6, estimation real-time vehicle quality mainly includes following three phases:
1) startup stage:
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and the weighting with more forgetting factors is set
The initial value of information gain matrix in Least Square Recurrence quality estimation model:
P (0)=a2I
Wherein a is sufficiently big positive number；
The then vehicle running state data based on current time start to carry out the first inferior quality estimation, obtain m and δ m's
First time estimated value；
2) operation phase
It is minimum to bring the estimated value of vehicle running state data and m the and δ m of last moment that current time updates into weighting
Two multiply in recursion quality estimation model, carry out the estimation of new round quality, and repeat this step, obtain real-time quality evaluation value；
3) stop phase
When the estimated value of vehicle mass is converged within zone of reasonableness, stopping carries out recurrence estimation, but still receives vehicle
Transport condition data when occurring so as to fortuitous event, can carry out quality estimation again.
The beneficial effects of the present invention are:
The present invention considers the vehicle mass estimation method of shift and road grade factor, makes vehicle straight and with the gradient
Road surface on, can obtain ideal quality estimation results；Simultaneously consider shift factor bring transmission ratio mutation and
The variation of shift fore-aft vehicle correction coefficient of rotating mass, using the weighted least-squares recurrence estimation side for having more forgetting factors
Method realizes the real-time estimation to vehicle mass；Vehicle matter can in real time, be accurately estimated in normally travel and vehicle shift
Amount also improves accuracy and the Shandong of vehicle mass estimation while reducing shift factor for influencing caused by quality estimation
Stick.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the flow chart for the vehicle mass estimation method that the present invention considers shift and road grade factor；
Fig. 2 is the vehicle mass estimation model for considering shift and road grade factor；
Fig. 3 is longitudinal vehicle dynamic model stress diagram.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings and specific examples, so that those skilled in the art can be with
It better understands 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 estimation method of shift and road grade factor, includes the following steps:
Step 1: acquisition data
Step 11: acquiring vehicle transport condition data using OpenXC data acquisition device
Specifically, the present embodiment acquires vehicle transport condition data, and vehicle driving using OpenXC data acquisition device
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 obtained is calculated based on data acquisition device built-in sensors；It is come from by bluetooth reception
Real-time vehicle transport condition data on vehicle CAN bus is simultaneously stored on mobile data processing terminal.
Step 12: in conjunction with the intrinsic parameter of vehicle and vehicle running state data, parameter needed for model is calculated
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, vehicle forward direction front face area A etc.；Needed for model
Parameter includes vehicle acceleration a and transmission ratio ig。
Wherein, acceleration a can obtain time difference by speed v, and the accelerometer at kth moment is shown as:
T is the sampling period of OpenXC data acquisition device；
Transmission ratio igCalculation method are as follows:
Step 2: being based on acquired vehicle running state data, establish longitudinal vehicle dynamic model, obtain different shelves
The lower torque in position and speed relationship.Specifically, longitudinal vehicle dynamic model are as follows:
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, TqIndicate motor torque；igIndicate transmission ratio；i0Indicate final driver ratio；η indicates to pass
Dynamic is efficiency；R indicates radius of wheel；M indicates vehicle mass；F indicates coefficient of rolling resistance；CdIndicate the air drag system of vehicle
Number；A indicates front face area；V indicates car speed；I is road grade；
After substitution, torque and speed relationship under different stalls are obtained:
Step 3: being based on longitudinal vehicle dynamic model, establish and consider that the vehicle mass of shift factor estimates model
Step 31: deriving the least squares formalism of longitudinal vehicle dynamic model
The target of the present embodiment is the condition real-time estimation vehicle mass in consideration shift factor, and before vehicle shift
Afterwards, the gyrating mass conversion of vehicle is that δ number can occur more significantly to change, and influences 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 guarantee still to be able to obtain compared with subject under the conditions of shift
True vehicle mass estimated value needs together to estimate the correction coefficient of rotating mass δ of vehicle and vehicle mass m as vehicle mass
Count the parameter to be estimated of model.Therefore, longitudinal vehicle dynamic model is converted to following least squares formalism:
Wherein, a indicates acceleration；δ indicates correction coefficient of rotating mass.
Step 32: establishing the weighted least-squares recurrence estimation model with more 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
Quickly variation occurs for the factors such as shift, is a fast variable.To guarantee this fast variable and the common existing estimation mould of slow variable
The estimated value of slow variable can fast and accurately restrain in type, while the estimated result of fast variable can have preferable tracking
Performance needs the forgetting factor different for 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 use longitudinal direction of car kinetic description again, produce in shift process
Raw vehicle running state data will increase the error of vehicle mass estimated result, in order to inhibit the mistake generated during this
Difference need to use method of weighting, weaken the weight of vehicle running state data in shift process, quality estimation method be made to shift 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 are when two parameters to be estimated, defining criterion function in model are as follows:
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 can acquire the least square of parameter θ that is, to θ derivation
Estimated valueThe estimates of parameters at kth moment can indicate are as follows:
In order to guarantee vehicle mass estimating system can real-time update estimated result, above-mentioned estimated result need to be converted to and passed
Form is pushed away, it is as follows to obtain the weighted least-squares recurrence estimation model with more forgetting factors:
Wherein:
Step 33: establishing the weighted least-squares recursion quality estimation model with more forgetting factors
Longitudinal vehicle dynamic model least squares formalism in step 31 is applied to the least square in step 32 to pass
Meter model is estimated, then is had:
The recursion shape of the weighted least-squares quality estimation model with more forgetting factors can be obtained by substituting the above to least square
Formula are as follows:
Wherein, λ1And λ2Respectively two corresponding forgetting factors of parameter m and δ m to be estimated, value range be [0,1), examine
Considering δ m is fast variable, to guarantee that the estimated result of δ m has preferable tracking performance, Ying You λ1> λ2, λ under default situations1It takes
It is 0.95, λ2It is taken as 0.5.
Λ (k) is the weighted factor of data, to guarantee that the vehicle running state data under the conditions of non-shift can occupy more
High data weighting, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before shift is 1, is changed
Weighted factor after gear may be expressed as:
Step 4: being based on longitudinal vehicle dynamic model, establish mechanical efficiency of power transmission estimation model, and propose with step 3
The considerations of shift factor vehicle mass estimation model collectively form while considering shift and the vehicle mass of road grade factor
Estimate model；
Step 41: establishing mechanical efficiency of power transmission estimation model
When with vehicle mass estimation is carried out on acclive road surface, relies solely on and provided accurately for quality estimation model
Road slope information still can not accurately carry out quality estimation.This is mainly due to the variation with road grade, system of vehicle transmission
It is that efficiency eta is also constantly changing, and the parameter can not be obtained by vehicle CAN bus or external sensor, it is therefore necessary to establish
The estimation model of mechanical efficiency of power transmission can be obtained in real time, and is applied to the weighted least-squares with more forgetting factors and is passed
It pushes away in quality estimation model, so that it is guaranteed that accuracy of the vehicle when estimating quality on the road surface there are 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 variation of working condition and quickly change, belong to fast variable.Therefore, it is necessary to for vehicle mass assign a biggish forgetting because
Son, to guarantee the continuous and stability of estimation；A lesser forgetting factor is assigned for mechanical efficiency of power transmission, to guarantee to estimate
As a result there is good tracking performance.Due to being possible to shift gears in estimation procedure, vehicle driving when in order to inhibit shift
Status data adverse effect caused by estimated result, it is also desirable to be added and consider adding as shift factor quality estimation model
Weight factor Λ (k), to guarantee 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 more forgetting factors most
Small two multiply in estimation model, if:
The estimation model that mechanical efficiency of power transmission then can be obtained is as follows:
Wherein:
Step 42: based on mechanical efficiency of power transmission estimation model and considering that the vehicle mass of shift factor estimates model, altogether
With building while considering that the vehicle mass of shift and road grade factor estimates model.
The weighted least-squares recursion quality estimation model with more forgetting factors and vehicle transmission system that step 3 is obtained are imitated
Rate models coupling together, just constitutes the vehicle mass estimation model for considering shift and road grade factor, as shown in Figure 2.
Before vehicle mass estimation starts, 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 of OpenXC data acquisition device offer
Data simultaneously the more 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 more forgetting factors most as mode input with drive line efficiency initial value jointly
Small two multiply the first time estimation of recursion quality estimation model, while vehicle transmission system efficiency estimation model will also be based on vehicle driving
The initial value of status data and correction coefficient of rotating mass is completed to estimate for the first time.Then the gyrating mass that first time obtains is changed
The estimated value for calculating coefficient and mechanical efficiency of power transmission substitutes its preset initial value respectively, as mode input, carries out two respectively
Second of estimation of model.Then proceed to the correction coefficient of rotating mass for exporting last moment model and mechanical efficiency of power transmission
Estimated value substitutes original mode input, and next round recurrence estimation is carried out together with newest vehicle running state data, repeats
This process, until obtaining the convergence of quality estimation curve.
Consider that the vehicle mass of shift and road grade factor estimates that model overcomes shift process bring transmission ratio and dashes forward
Change problem, and ensure that vehicle estimates the essence of vehicle mass on the slope by the method for real-time estimation vehicle transmission system efficiency
Degree, in driver, there are shift behavior and road, there are more accurately quality estimation can be still obtained under conditions of the gradient
As a result.
Step 5: determining the use condition of vehicle mass real-time estimation system
When meeting the following conditions simultaneously, quality estimation system starting:
1) in vehicle launch, simultaneously operating range is more than 300m；
2) car speed is greater than 20km/h；
3) vehicle acceleration is positive value；
4) accelerator open degree is greater 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 gear；
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 gear；
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: collected vehicle running state data and correlation model parameters are inputted into vehicle mass real-time estimation system
System estimates real-time vehicle quality.The present embodiment estimates that real-time vehicle quality mainly includes following three phases:
1) startup stage:
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and the weighting with more forgetting factors is set
The initial value of information gain matrix in Least Square Recurrence quality estimation model:
P (0)=a2I
Wherein a is sufficiently big positive number, and default value is taken as 1000000；
The then vehicle running state data based on current time start to carry out the first inferior quality estimation, obtain m and δ m's
First time estimated value；
2) operation phase
It is minimum to bring the estimated value of vehicle running state data and m the and δ m of last moment that current time updates into weighting
Two multiply in recursion quality estimation model, carry out the estimation of new round quality, and repeat this step, obtain real-time quality evaluation value；
3) stop phase
When the estimated value of vehicle mass is converged within zone of reasonableness, stopping carries out recurrence estimation, but still receives vehicle
Transport condition data when occurring so as to fortuitous event, can carry out quality estimation again.
After vehicle stall, stop carrying out recurrence estimation, the historical data in quality estimation system will also be reset, and quality is estimated
Meter systems are out of service.
The present embodiment considers the vehicle mass estimation method of shift and road grade factor, makes vehicle straight and with slope
On the road surface of degree, ideal quality estimation results can be obtained；Consider that shift factor bring transmission ratio is mutated simultaneously
With shift fore-aft vehicle correction coefficient of rotating mass variation, using have more forgetting factors weighted least-squares recurrence estimation
Method realizes the real-time estimation to vehicle mass；I.e. in normally travel and vehicle shift can in real time, accurately estimate vehicle
Quality, reduce shift factor for quality estimation caused by influence while also improve vehicle mass estimation accuracy and
Robustness.
Embodiment described above is only to absolutely prove preferred embodiment that is of the invention and being lifted, protection model of the invention
It encloses without being limited thereto.Those skilled in the art's made equivalent substitute or transformation on the basis of the present invention, in the present invention
Protection scope within.Protection scope of the present invention is subject to claims.
Claims (10)
1. a kind of vehicle mass estimation method for considering shift and road grade factor, characterized by the following steps:
Step 1: acquisition data
Step 11: acquiring vehicle transport condition data using data acquisition device；
Step 12: in conjunction with the intrinsic parameter of vehicle and vehicle running state data, ginseng needed for vehicle mass estimation model is calculated
Number；
Step 2: being based on acquired vehicle running state data, establish longitudinal vehicle dynamic model, obtain under different stalls
Torque and speed relationship；
Step 3: being based on longitudinal vehicle dynamic model, establish and consider that the vehicle mass of shift factor estimates model；
Step 4: be based on longitudinal vehicle dynamic model, establish consider road grade factor vehicle mass estimate model, and with
The vehicle mass estimation model of the considerations of step 3 proposes shift factor collectively forms while considering shift and road grade factor
Vehicle mass estimates model；
Step 5: the use condition of vehicle mass real-time estimation system is determined according to the vehicle mass estimation model in step 4；
Step 6: collected vehicle running state data and vehicle mass estimation model parameter input vehicle mass are estimated in real time
Meter systems estimate real-time vehicle quality.
2. the vehicle mass estimation method according to claim 1 for considering shift 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, vehicle forward direction front face area A；
Parameter needed for model includes vehicle acceleration a and transmission ratio ig；
Wherein, acceleration a can obtain time difference by speed v, and the accelerometer at kth moment is shown as:
T is the sampling period of data acquisition device；
Transmission ratio igCalculation method are as follows:
3. the vehicle mass estimation method according to claim 1 for considering shift and road grade factor, it is characterised in that:
Longitudinal vehicle dynamic model are as follows:
Ft=Ff+Fi+Faero+Fj
Wherein, FtFor vehicle drive force, and
FfFor rolling resistance, and Ft=mgf；
FiFor grade resistance, and Fi=mgi；
FaeroFor air drag, and
FjFor acceleration resistance, and Fj=ma；
Wherein, TqIndicate motor torque；igIndicate transmission ratio；i0Indicate final driver ratio；η indicates power train
Efficiency；R indicates radius of wheel；M indicates vehicle mass；F indicates coefficient of rolling resistance；CdIndicate the coefficient of air resistance of vehicle；A
Indicate front face area；V indicates car speed；I is road grade；G is acceleration of gravity, and a is pickup；
After substitution, torque and speed relationship under different stalls are obtained:
4. the vehicle mass estimation method according to claim 3 for considering shift and road grade factor, it is characterised in that:
The step 3 includes the following steps:
Step 31: deriving the least squares formalism of longitudinal vehicle dynamic model；
Step 32: establishing the weighted least-squares recurrence estimation model with more forgetting factors；
Step 33: establishing the weighted least-squares recursion vehicle mass with more forgetting factors and estimate model.
5. the vehicle mass estimation method according to claim 4 for considering shift and road grade factor, it is characterised in that:
In the step 31, longitudinal vehicle dynamic model is converted to following least squares formalism:
Wherein, a indicates acceleration；δ indicates correction coefficient of rotating mass.
6. the vehicle mass estimation method according to claim 5 for considering shift 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, and T is
The sampling period of data acquisition device；
When there are when two parameters to be estimated, defining criterion function in model are as follows:
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 can acquire the least-squares estimation of parameter θ that is, to θ derivation
ValueThe estimates of parameters at kth moment can indicate are as follows:
Recursive form is converted by above-mentioned estimated result, obtains the weighted least-squares recurrence estimation model with more forgetting factors such as
Under:
Wherein:
7. the vehicle mass estimation method according to claim 6 for considering shift 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 minimum two in step 32
Multiply recurrence estimation model, then have:
The shape of the weighted least-squares recursion vehicle mass estimation model with more forgetting factors can be obtained by substituting the above to least square
Formula are as follows:
Wherein, λ1And λ2Respectively two corresponding forgetting factors of parameter m and δ m to be estimated, value range be [0,1), and λ1>
λ2, T is the sampling period of data acquisition device；
A (k) is the weighted factor of data, to guarantee that the vehicle running state data under the conditions of non-shift can occupy higher number
According to weight, if current time is t (k), tgearFor the last shift time, the weighted factor perseverance before shift is 1, after shift
Weighted factor may be expressed as:
8. the vehicle mass estimation method according to claim 7 for considering shift and road grade factor, it is characterised in that:
The step 4 includes the following steps:
Step 41: establishing and consider that the vehicle mass of road grade factor estimates model；
It will derive that corresponding least squares formalism is applied to the weighting with more forgetting factors based on longitudinal vehicle dynamic model
In least-squares estimation model, in which:
Corresponding least squares formalism is derived based on longitudinal vehicle dynamic model are as follows:
If:
The estimation model that mechanical efficiency of power transmission then can be obtained is as follows:
Wherein:
Step 42: based on the vehicle mass estimation model for considering road grade factor and considering that the vehicle mass of shift factor is estimated
Model, common building while the vehicle mass estimation model for considering shift and road grade factor.
9. the vehicle mass estimation method according to claim 1 for considering shift and road grade factor, it is characterised in that:
The method for determining the use condition of vehicle mass real-time estimation system is
When meeting the following conditions simultaneously, the starting of vehicle mass real-time estimation system:
1) in vehicle launch, simultaneously operating range is more than 300m；
2) car speed is greater than 20km/h；
3) vehicle acceleration is positive value；
4) accelerator open degree is greater 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 gear；
When meeting following any condition, vehicle mass real-time 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 gear；
4) vehicle is in dead ship condition.
10. the vehicle mass estimation method according to claim 1 for considering shift and road grade factor, feature exist
In: in the step 6, estimation real-time vehicle quality mainly includes following three phases:
1) startup stage:
The estimation initial value of quality estimation model parameter to be estimated is disposed as 0, and it is minimum that the weighting with more forgetting factors is arranged
Two multiply the initial value of information gain matrix in recursion vehicle mass estimation model:
P (0)=a2I
Wherein a is sufficiently big positive number；
The then vehicle running state data based on current time start to carry out the first inferior quality estimation, obtain the first of m and δ m
Secondary estimated value, m and δ m are two parameters to be estimated；I is unit matrix；
2) operation phase
Bring the estimated value of vehicle running state data and m the and δ m of last moment that current time updates into weighted least-squares
In recursion quality estimation model, the estimation of new round quality is carried out, and repeat this step, obtain real-time quality evaluation value；
3) stop phase
When the estimated value of vehicle mass is converged within zone of reasonableness, stopping carries out recurrence estimation, but still receives vehicle driving
Status data when occurring so as to fortuitous event, can carry out quality estimation again.
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