CN111259493B - Vehicle emission model modeling method suitable for intelligent network vehicle emission control - Google Patents

Vehicle emission model modeling method suitable for intelligent network vehicle emission control Download PDF

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CN111259493B
CN111259493B CN202010085509.6A CN202010085509A CN111259493B CN 111259493 B CN111259493 B CN 111259493B CN 202010085509 A CN202010085509 A CN 202010085509A CN 111259493 B CN111259493 B CN 111259493B
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刘迪
胡云峰
张辉
宫洵
高金武
郭洪艳
陈虹
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Jilin University
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Abstract

A vehicle emission model modeling method suitable for intelligent networked vehicle emission control belongs to the technical field of intelligent control. The invention aims to relate a vehicle and an engine by driving power, and is a vehicle emission model modeling method suitable for intelligent networking vehicle emission control, which is suitable for controlling different control targets such as oil consumption, emission and the like of the vehicle in an intelligent networking environment. The invention comprises an engine rotating speed module, a vehicle power module, a fuel consumption rate module and an emission module. The invention relates to a vehicle and an engine through driving power according to the vehicle state from the perspective of an upper vehicle, establishes an engine fuel consumption rate model according to the power, and then fits a linear model of the fuel consumption rate and the emission according to the data. The model established by the modeling method is simple and high in accuracy, and is suitable for controlling different control targets such as oil consumption and emission of the vehicle in an intelligent networking environment.

Description

Vehicle emission model modeling method suitable for intelligent network vehicle emission control
Technical Field
The invention belongs to the technical field of intelligent control.
Background
The vehicle intelligent network (internet of vehicles) is a huge interactive network formed by information such as vehicle position, speed and route. The vehicle can complete the collection of self environment and state information through devices such as a GPS, an RFID, a sensor, a camera image processing device and the like; through the internet technology, all vehicles can transmit and gather various information of the vehicles to the central processing unit; through computer technology, the information of these vehicles can be analyzed and processed, thus calculate the best route of different vehicles, and report road conditions in time, arrange signal lamp cycle. The intelligent network contact information plays an important role in improving the traffic efficiency of roads, reducing the energy consumption and pollution of automobiles and reducing traffic accidents. In the aspect of improving road efficiency, the intelligent traffic technology can reduce traffic jam by about 60 percent, improve short-distance transportation efficiency by about 70 percent and improve the traffic capacity of the existing road network by 2 to 3 times; in the aspect of reducing traffic accidents, the vehicle safety accident rate can be reduced by 20 percent compared with the prior art, and the death rate caused by the traffic accidents is reduced by 30 to 70 percent every year; in the aspect of reducing the energy consumption and pollution of the automobile, the average speed is improved through intelligent traffic control, the fuel consumption and the exhaust emission are reduced, and the oil consumption of the automobile can be reduced by about 15 percent. However, when the intelligent traffic information is applied to plan and control the vehicle, the traditional control-oriented vehicle emission model stays at the level of the engine, the externally-presented states of the vehicle are speed, acceleration and gear, and the control of the vehicle in the intelligent networking environment is also speed or acceleration control, so that the traditional modeling mode lacks the combination of a bottom-layer engine and an upper-layer vehicle, and is not suitable for the optimal control and use of the vehicle in the intelligent networking environment. In addition, the traditional emission modeling is a partial differential equation of chemical reaction and thermal energy reaction, and a strong nonlinear and multivariable coupling relation exists, so that the method is not suitable for subsequent controller design or optimization planning.
Disclosure of Invention
The invention aims to relate a vehicle and an engine by driving power, and is a vehicle emission model modeling method suitable for intelligent networking vehicle emission control, which is suitable for controlling different control targets such as oil consumption, emission and the like of the vehicle in an intelligent networking environment.
The method comprises the following steps:
engine speed module
Current engine speed
Figure BDA0002381136830000011
Wherein, V eng Is the engine speed r tire Is the radius of a vehicle tire, I g Is a gear ratio of the vehicle, I 0 For differential amplification ratio, v car Is the vehicle speed;
② vehicle power module
Figure BDA0002381136830000012
Wherein, W tract For the vehicle drive power Ma as the vehicle mass, v car Is vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D w For the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D r Is the rolling resistance coefficient;
obtaining the final required power of the engine:
Figure BDA0002381136830000013
wherein W is engine power, ε is transmission efficiency, W acc The power required to drive the accessories;
fuel consumption rate module
Theoretical specific fuel consumption of engine
Figure BDA0002381136830000021
Wherein the content of the first and second substances,
Figure BDA0002381136830000022
FR is calculated specific fuel consumption, G is engine friction coefficient, G 0 To send outInitial coefficient of friction, V, of the motor eng Is engine speed, Di is engine displacement, psi is engine indicated efficiency, b 1 And C is a coefficient;
the specific fuel consumption was corrected using a one-time line fit:
Efuel＝a fuel ·FR+b fuel (5)
wherein Efuel is the true fuel consumption rate, a fuel And b fuel Is a fitting parameter;
mean square error of
Figure BDA0002381136830000023
Wherein m is the number of the selected fitting data, R fuel Minimum value of (A) is to satisfy
Figure BDA0002381136830000024
Figure BDA0002381136830000025
And (3) obtaining an equation satisfied by the fitting curve through sorting:
Figure BDA0002381136830000026
Figure BDA0002381136830000027
or
Figure BDA0002381136830000028
Obtained by the elimination method or the claimer method
Figure BDA0002381136830000031
Figure BDA0002381136830000032
Fourthly, discharge module
NO x The emission formula is:
ENO x ＝a NOx ·FR+b NOx (10)
ENO x is NO x Discharge amount of a NOx And b NOx Is a linear fit to identify the parameters, the mean square error is
Figure BDA0002381136830000033
R NOx Minimum value of (A) is to satisfy
Figure BDA0002381136830000034
Figure BDA0002381136830000035
And (3) obtaining an equation satisfied by the fitting curve through sorting:
Figure BDA0002381136830000036
Figure BDA0002381136830000037
or
Figure BDA0002381136830000038
Obtained by the elimination method or the claimer method
Figure BDA0002381136830000041
Figure BDA0002381136830000042
The invention relates to a vehicle and an engine through driving power according to the vehicle state from the perspective of an upper vehicle, establishes an engine fuel consumption rate model according to the power, and then fits a linear model of the fuel consumption rate and the emission according to the data. The model established by the modeling method is simple and high in accuracy, and is suitable for controlling different control targets such as oil consumption and emission of the vehicle in an intelligent networking environment.
Drawings
FIG. 1 is a model block diagram;
FIG. 2 illustrates vehicle speed under NEDC conditions;
FIG. 3 is NEDC operating mode NO x Comparing the curves;
FIG. 4 shows the NEDC operating mode NO x Fitting effect;
FIG. 5 is a graph of fuel consumption versus NEDC operating conditions;
FIG. 6 is a fuel consumption rate fit effect for the NEDC operating conditions;
FIG. 7 illustrates UDDS operating vehicle speed;
FIG. 8 shows UDDS operating mode NO x Comparing the curves;
FIG. 9 shows UDDS operating mode NO x Fitting effect;
FIG. 10 is a UDDS operating mode specific fuel consumption versus curve;
FIG. 11 is a UDDS operating mode specific fuel consumption fit effect;
FIG. 12 is WLTC operating vehicle speed;
FIG. 13 shows WLTC condition NO x Comparing the curves;
FIG. 14 is WLTC condition NO x Fitting effect;
FIG. 15 is a graph of fuel consumption versus WLTC operating conditions;
FIG. 16 is the effect of the WLTC operating mode specific fuel consumption fit.
Detailed Description
The method comprises the following steps:
1.1. engine rotating speed calculating module
And obtaining the vehicle gear information in real time, obtaining the gear ratio under the gear according to the current gear, and then calculating the current engine speed according to the formula (1).
Figure BDA0002381136830000051
Wherein, V eng Is the engine speed r tire Is the radius of a vehicle tire, I g Is a gear ratio of the vehicle, I 0 For differential amplification ratio, v car Is the vehicle speed.
1.2. Vehicle power calculation module
After the rotating speed of the engine is obtained, the driving power demand of the vehicle is calculated according to a formula (2), according to Newton's law, the acceleration demand force is Ma.Ac, the force for driving the vehicle to overcome the gradient of the road is Ma.g.sin omega, and the force for driving the vehicle to overcome the wind resistance demand is Ma.g.sin omega
Figure BDA0002381136830000052
The force required to drive the vehicle to overcome rolling resistance is Ma g D r Cos ω, from force versus power: obtaining the required power by power/speed, and obtaining a vehicle driving power calculation formula shown in (2):
Figure BDA0002381136830000053
wherein, W tract For the vehicle drive power Ma as the vehicle mass, v car Is vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D w For the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D r Is the rolling resistance coefficient.
After the driving power required by the vehicle is obtained, because the transmission efficiency exists when the engine power is transmitted to the vehicle, and the work done by the engine can drive accessories such as an air conditioner and the like simultaneously in the running process of the vehicle, the final required power of the engine is obtained according to the following formula:
Figure BDA0002381136830000054
wherein W is engine power, ε is transmission efficiency, W acc Power required to drive accessories such as air conditioners and the like.
1.3. Fuel consumption rate calculation module
After the engine speed and the engine power are obtained, the theoretical fuel consumption rate of the engine can be calculated according to the following semi-empirical formula commonly used in engineering
Figure BDA0002381136830000055
Wherein G ═ G 0 ·[1+C·(V eng -V 0 )]，
Figure BDA0002381136830000056
FR is calculated specific fuel consumption, G is engine friction coefficient, G 0 Is the initial coefficient of friction, V, of the engine eng Is engine speed, Di is engine displacement, psi is engine indicated efficiency, b 1 The coefficient and C are obtained through identification, the two coefficients are different according to different working conditions and vehicles and engines, so that the coefficients need to be obtained through identification and calculation of an MATLAB system according to real-time collected data, the fuel consumption rate collected in a period of time can be used as FR, and b suitable for the working conditions is calculated 1 And C, after calculation, the model can be used for modeling subsequent models from vehicle speed to oil consumption or emission under the working condition.
Since the transmission efficiency of the GT software engine model cannot obtain an accurate value, the FR obtained according to empirical formula (4) deviates from the true fuel consumption rate, so the fuel consumption rate is corrected using a one-time line fitting:
Efuel＝a fuel ·FR+b fuel (5)
wherein Efuel is the true fuel consumption rate, a fuel And b fuel Are fitting parameters.
The solution process is as follows:
mean square error of
Figure BDA0002381136830000061
Wherein m is the number of the selected fitting data, if the fitting effect is the best, the mean square error is required to be the minimum, and R is the minimum value according to the calculus theory fuel Minimum value of (A) is to satisfy
Figure BDA0002381136830000062
Figure BDA0002381136830000063
And (3) obtaining an equation satisfied by a fitting curve through sorting:
Figure BDA0002381136830000064
Figure BDA0002381136830000065
or
Figure BDA0002381136830000066
Weighting (3) as a normal equation of a fitting curve, and solving the equation by a null method or a Cramer method
Figure BDA0002381136830000071
Figure BDA0002381136830000072
1.4. Emission calculation module
NO x The emission calculation formula is:
ENO x ＝a NOx ·FR+b NOx (10)
ENO x is NO x Discharge amount of a NOx And b NOx Is a linear fit to identify the parameters, the mean square error is
Figure BDA0002381136830000073
If the fitting effect is best, the mean square error is required to be the minimum value, and according to the calculus theory, R NOx Minimum value of (A) is to satisfy
Figure BDA0002381136830000074
Figure BDA0002381136830000075
And (3) obtaining an equation satisfied by the fitting curve through sorting:
Figure BDA0002381136830000076
Figure BDA0002381136830000077
or
Figure BDA0002381136830000078
Weighting (3) as a normal equation of a fitting curve, and solving the equation by a null method or a Cramer method
Figure BDA0002381136830000081
Figure BDA0002381136830000082
Two simulation curves and analysis
Three representative working conditions, namely a NEDC working condition, a UDDS working condition and a WLTC working condition are selected, and working condition curves are shown in figures 2, 7 and 12. Respectively collecting data in GT-power, modeling according to the method provided by the invention to obtain model data, comparing and analyzing the model data with the original data, wherein the comparison curve and the data analysis curve are shown in the attached drawings 2 All exceed 0.8, meet the requirement for model precision when the controller is designed, thus proving the effectiveness of the method provided by the invention.
TABLE 1 model parameters
(symbol) Name (R) Numerical value Unit of
Ma Mass of the vehicle 40000 kg
D w Coefficient of wind resistance 0.006 -
Ar Frontal area of vehicle 10 m 2
ρ Density of air 1.292 kg/m 3
g Acceleration of gravity 9.82 m/s 2
D r Coefficient of rolling resistance 0.5 -
r tire Radius of vehicle tyre 0.5 m
G Coefficient of friction of engine 0.02 -
Di Displacement of engine 12.7 L
ψ Indicating efficiency of engine 0.5 -
ε Transmission efficiency 1 -
I 0 Differential amplification ratio 3 -
TABLE 2 model parameters
Figure BDA0002381136830000091
。

Claims (1)

1. A vehicle emission model modeling method suitable for intelligent online vehicle emission control is characterized in that: the method comprises the following steps:
engine speed module
Current engine speed
Figure FDA0003693114770000011
Wherein, V eng Is the engine speed, r tire Is the radius of a vehicle tire, I g Is a gear ratio of the vehicle, I 0 For differential amplification ratio, v car Is the vehicle speed;
② vehicle power module
Figure FDA0003693114770000012
Wherein, W tract For vehicle drive power, Ma for vehicle mass, v car Is vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D w For the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D r Is the rolling resistance coefficient;
obtaining the final required power of the engine:
Figure FDA0003693114770000013
wherein W is engine power, ε is transmission efficiency, W acc The power required to drive the accessories;
fuel consumption rate module
Theoretical specific fuel consumption of engine
Figure FDA0003693114770000014
Wherein G ═ G 0 ·[1+C·(V eng -V 0 )]，
Figure FDA0003693114770000015
FR is calculated fuel consumption, G is engineCoefficient of friction, G 0 Is the initial coefficient of friction, V, of the engine eng Is engine speed, Di is engine displacement, psi is engine indicated efficiency, b 1 And C is a coefficient;
the specific fuel consumption was corrected using a one-time line fit:
Efuel＝a fuel ·FR+b fuel (5)
wherein Efuel is the true fuel consumption rate, a fuel And b fuel Is a fitting parameter;
mean square error of
Figure FDA0003693114770000016
Wherein m is the number of the selected fitting data, R fuel Minimum value of (A) is to satisfy
Figure FDA0003693114770000021
Figure FDA0003693114770000022
And (3) obtaining an equation satisfied by the fitting curve through sorting:
Figure FDA0003693114770000023
Figure FDA0003693114770000024
or
Figure FDA0003693114770000025
Obtained by the elimination method or the claimer method
Figure FDA0003693114770000026
Figure FDA0003693114770000027
Discharge module
NO x The emission formula is:
ENO x ＝a NOx ·FR+b NOx (10)
ENO x is NO x Discharge amount of (a) NOx And b NOx Is a linear fit to identify the parameters, the mean square error is
Figure FDA0003693114770000028
R NOx Minimum value of (A) is to satisfy
Figure FDA0003693114770000031
Figure FDA0003693114770000032
And (3) obtaining an equation satisfied by the fitting curve through sorting:
Figure FDA0003693114770000033
Figure FDA0003693114770000034
or
Figure FDA0003693114770000035
Obtained by the elimination method or the claimer method
Figure FDA0003693114770000036
Figure FDA0003693114770000037
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