CN111259493A - Vehicle emission model modeling method suitable for intelligent network vehicle emission control - Google Patents

Abstract

The invention relates to a vehicle emission model modeling method suitable for intelligent networked vehicle emission control, belonging to the technical field of intelligent control, and aiming at linking a vehicle and an engine through driving power, and the vehicle emission model modeling method suitable for intelligent networked vehicle emission control is suitable for controlling different control targets of oil consumption, emission and the like of the vehicle in an intelligent networked 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:

① Engine speed Module

Current engine speed

Wherein, V_engIs the engine speed r_tireIs the radius of a vehicle tire, I_gIs a gear ratio of the vehicle, I₀For differential amplification ratio, v_carIs the vehicle speed;

② vehicle power module

Wherein, W_tractFor the vehicle drive power Ma as the vehicle mass, v_carIs vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D_wFor the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D_rIs the rolling resistance coefficient;

obtaining the final required power of the engine:

wherein W is engine power, ε is transmission efficiency, W_accThe power required to drive the accessories;

③ fuel consumption rate module

Theoretical specific fuel consumption of engine

Wherein the content of the first and second substances,

FR is calculated specific fuel consumption, G is engine friction coefficient, G₀Is the initial coefficient of friction, V, of the engine_engIs engine speed, Di is engine displacement, psi is engine indicated efficiency, b₁And 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_fuelAnd b_fuelIs a fitting parameter;

mean square error of

Wherein m is the number of the selected fitting data, R_fuelMinimum value of (A) is to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Obtained by the elimination method or the claimer method

④ discharge module

NO_xThe emission formula is:

ENO_x＝a_NOx·FR+b_NOx(10)

ENO_xis NO_xDischarge amount of a_NOxAnd b_NOxIs a linear fit to identify the parameters, the mean square error is

R_NOxMinimum value of (A) is to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Obtained by the elimination method or the claimer method

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_xComparing the curves;

FIG. 4 shows the NEDC operating mode NO_xFitting 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_xComparing the curves;

FIG. 9 shows UDDS operating mode NO_xFitting 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_xComparing the curves;

FIG. 14 is WLTC condition NO_xFitting 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).

Wherein, V_engIs the engine speed r_tireIs the radius of a vehicle tire, I_gIs a gear ratio of the vehicle, I₀For differential amplification ratio, v_carIs 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

The force required to drive the vehicle against rolling resistance is Ma g D_rCos ω, from force versus power: obtaining the required power by power/speed, and obtaining a vehicle driving power calculation formula shown in (2):

wherein, W_tractFor the vehicle drive power Ma as the vehicle mass, v_carIs vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D_wFor the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D_rIs 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:

wherein W is engine power, ε is transmission efficiency, W_accPower 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

Wherein G ═ G₀·[1+C·(V_eng-V₀)]，

FR is calculated specific fuel consumption, G is engine friction coefficient, G₀Is the initial coefficient of friction, V, of the engine_engIs engine speed, Di is engine displacement, psi is engine indicated efficiency, b₁The 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 calculated₁And 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_fuelAnd b_fuelAre fitting parameters.

The solution process is as follows:

mean square error of

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_fuelMinimum value of (A) is to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Weighting (3) as a normal equation of a fitting curve, and solving the equation by a null method or a Cramer method

1.4. Emission calculation module

NO_xThe emission calculation formula is:

ENO_x＝a_NOx·FR+b_NOx(10)

ENO_xis NO_xRow of (2)Discharge amount, a_NOxAnd b_NOxIs a linear fit to identify the parameters, the mean square error is

If the fitting effect is best, the mean square error is required to be the minimum value, and according to the calculus theory, R_NOxMinimum value of (A) is to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Weighting (3) as a normal equation of a fitting curve, and solving the equation by a null method or a Cramer method

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, and obtaining the modelThe model data is compared and analyzed with the original data, and the comparison curve and the data analysis curve are shown in the attached drawings²All 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
Dw	Coefficient of wind resistance	0.006	-
				Ar	Frontal area of vehicle	10	m2
ρ	Density of air	1.292	kg/m3
				g	Acceleration of gravity	9.82	m/s2
Dr	Coefficient of rolling resistance	0.5	-
				rtire	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	-
I0	Differential amplification ratio	3	-

TABLE 2 model parameters

。

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:

① Engine speed Module

Current engine speed

Wherein, V_engIs the engine speed r_tireIs the radius of a vehicle tire, I_gIs a gear ratio of the vehicle, I₀For differential amplification ratio, v_carIs the vehicle speed;

② vehicle power module

Wherein, W_tractFor the vehicle drive power Ma as the vehicle mass, v_carIs vehicle speed, Ac is vehicle acceleration, g is gravitational acceleration, ω is road slope, D_wFor the drag coefficient, Ar is the frontal area of the vehicle, ρ is the air density, D_rIs the rolling resistance coefficient;

obtaining the final required power of the engine:

wherein W is engine power, ε is transmission efficiency, W_accThe power required to drive the accessories;

③ fuel consumption rate module

Theoretical specific fuel consumption of engine

Wherein G ═ G₀·[1+C·(V_eng-V₀)]，

FR is calculated specific fuel consumption, G is engine friction coefficient, G₀Is the initial coefficient of friction, V, of the engine_engIs engine speed, Di is engine displacement, psi is engine indicated efficiency, b₁And 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_fuelAnd b_fuelIs a fitting parameter;

mean square error of

Wherein m is the number of the selected fitting data, R_fuelOf (2)Small value to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Obtained by the elimination method or the claimer method

④ discharge module

NO_xThe emission formula is:

ENO_x＝a_NOx·FR+b_NOx(10)

ENO_xis NO_xDischarge amount of a_NOxAnd b_NOxIs a linear fit to identify the parameters, the mean square error is

R_NOxMinimum of (2)The value is to satisfy

And (3) obtaining an equation satisfied by the fitting curve through sorting:

or

Obtained by the elimination method or the claimer method

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