CN114475605B - Double-layer prediction control method for energy conservation of heavy truck based on internet connection information - Google Patents
Double-layer prediction control method for energy conservation of heavy truck based on internet connection information Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000004134 energy conservation Methods 0.000 title claims abstract description 28
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- 229910021529 ammonia Inorganic materials 0.000 claims abstract description 44
- 238000010531 catalytic reduction reaction Methods 0.000 claims abstract description 40
- 238000006722 reduction reaction Methods 0.000 claims abstract description 27
- 230000009467 reduction Effects 0.000 claims abstract description 26
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- 238000005265 energy consumption Methods 0.000 claims description 15
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- 229910000069 nitrogen hydride Inorganic materials 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 10
- OWIKHYCFFJSOEH-UHFFFAOYSA-N Isocyanic acid Chemical compound N=C=O OWIKHYCFFJSOEH-UHFFFAOYSA-N 0.000 claims description 9
- MWUXSHHQAYIFBG-UHFFFAOYSA-N Nitric oxide Chemical compound O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 9
- 239000007789 gas Substances 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 7
- 239000002355 dual-layer Substances 0.000 claims description 7
- XLJMAIOERFSOGZ-UHFFFAOYSA-N anhydrous cyanic acid Natural products OC#N XLJMAIOERFSOGZ-UHFFFAOYSA-N 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 6
- 238000003795 desorption Methods 0.000 claims description 4
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- 229910002089 NOx Inorganic materials 0.000 claims description 3
- 230000004913 activation Effects 0.000 claims description 3
- 238000006460 hydrolysis reaction Methods 0.000 claims description 3
- 230000009103 reabsorption Effects 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
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- 238000005457 optimization Methods 0.000 abstract description 18
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 description 7
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
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- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
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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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
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- 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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- B60W2050/0028—Mathematical models, e.g. for simulation
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- 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
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0037—Mathematical models of vehicle sub-units
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- B60W2520/10—Longitudinal speed
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Abstract
The invention discloses a double-layer predictive control method for energy conservation of a heavy truck based on internet connection information. The method comprises upper-layer energy-saving prediction control and lower-layer selective reduction prediction control, wherein the upper-layer energy-saving prediction control comprises the steps of acquiring host vehicle information, front vehicle information and front road information, and performing real-time driving control on a vehicle according to the target vehicle speed, the target rotating speed and the target torque based on the host vehicle information, the front vehicle information and the target rotating speed in a front road information prediction control time domain; the lower-layer selective reduction predictive control includes controlling the selective catalytic reduction system in real time according to the ammonia coverage based on the ammonia coverage in the target rotational speed and target torque predictive control time domain. The invention introduces the movement information of the front vehicle and the geographical information of the front road, and solves the problems of energy optimization and NOx emission optimization of the heavy truck under multiple scenes under the framework of model predictive control.
Description
Technical Field
The invention belongs to the technical field of energy conservation and emission reduction control of heavy trucks, and particularly relates to a double-layer prediction control method for energy conservation of heavy trucks based on internet connection information.
Background
For heavy trucks, the pollution emission of a diesel heavy truck is approximately equal to the pollution emission of 306 passenger cars, so energy conservation and emission reduction are the perpetual subjects of the heavy trucks, and with the rapid development of control technology, sensing technology and information technology, the environment of the internet of vehicles provides unprecedented huge opportunities for high energy efficiency and low carbonization emission of automobiles.
Currently, for heavy trucks that travel on highways most of the time, the control method is essentially focused on four aspects from the aspect of energy-saving control: driver behavior, road density assessment, route planning and speed control, with road related information and air resistance being the most influential factors. In terms of road related information, global optimization, model predictive control, and transient control are commonly used for economical driving strategies. Because of the lack of an energy efficiency optimization general algorithm which contains multi-source networking information and can run in real time, the industry generally adopts global optimization, namely, an optimal solution of the whole time domain is obtained through a dynamic programming method and the like, and the method has the defects of huge calculation load and uncertainty in processing.
Optimizing the combustion process and aftertreatment system is two major research directions in the reduction of emissions from heavy trucks, including high pressure injection, alternative fuels, controlling compression ignition reactions, and the like, in terms of emissions reduction control. However, improvements in combustion chambers are difficult to achieve and costly, so adding an aftertreatment system appears to be a necessary trend. Common aftertreatment configurations for heavy trucks today include diesel oxidation catalysts, diesel particulate filters, and selective catalytic reduction systems. Specifically, the selective catalytic reduction technique is to reduce engine NOx emissions by injecting urea solution, however, the performance of urea-based SCR systems is largely dependent on the dosing strategy of the urea solution. Excessive urea solution will cause slip of NH3 in the exhaust gas, and insufficient dosage will cause too low conversion efficiency of NOx, so it is a challenge to build a controller that can achieve both high conversion efficiency of NOx and low slip of ammonia, and can be controlled in real time.
Under the current intelligent traffic system and the car networking environment, the vehicle can fully sense and understand complex traffic environment, road geographic information and the like, and on the basis of the complex traffic environment, the car energy-saving control technology can not only acquire the self state of the vehicle, but also acquire the road (such as gradient, curvature, speed limit and the like) and traffic (such as congestion condition, traffic light position, time sequence and the like) information of the vehicle. In summary, under the large background of intelligent traffic and automobile intelligence, the energy-saving and emission-reduction control technology of the heavy truck based on more intelligent road information and optimization methods has become one of the hot research directions in the field of energy conservation and emission reduction.
Disclosure of Invention
The invention aims to solve the defects in the background technology, and provides a double-layer predictive control method for energy conservation of a heavy truck based on networking information, so that the total energy consumption and NOx emission of the heavy truck are minimized, and the economy and the emission of the vehicle are improved.
The technical scheme adopted by the invention is as follows: the double-layer predictive control method for energy conservation of heavy trucks based on network connection information comprises upper-layer energy conservation predictive control and lower-layer selective reduction predictive control,
the upper energy-saving predictive control comprises the steps of obtaining information of a vehicle, information of a front vehicle and information of a front road, predicting a target speed, a target rotating speed and a target torque in a control time domain based on the information of the vehicle, the information of the front vehicle and the information of the front road, and performing real-time driving control on the vehicle according to the target speed, the target rotating speed and the target torque;
the lower-layer selective reduction predictive control includes controlling the selective catalytic reduction system in real time according to the ammonia coverage based on the ammonia coverage in the target rotational speed and target torque predictive control time domain.
Further, the process of predicting the target vehicle speed, the target rotational speed and the target torque in the control time domain is as follows:
and constructing a vehicle longitudinal dynamics model and a fuel consumption model, establishing a driving force objective function, and solving the objective function according to the vehicle longitudinal dynamics model and the fuel consumption model through the Pontrisian maximum principle to obtain a target vehicle speed, a target engine speed and a target engine torque.
Further, the longitudinal dynamics model is:
wherein k represents the time of k,is vehicle acceleration; v (k) is the driving speed, +.>Is the running speed; delta is the conversion coefficient of rotating mass, m is the mass of the truck, F t (k) For driving force, F b (k) For braking force, C D Is wind resistance coefficient, A is windward area of vehicle, ρ is air density, g is gravitational acceleration, f is wheel rolling resistance coefficient, and α (k) isRoad grade.
Further, the fuel consumption model is:
n e，min (k)≤n e (k)≤n e，min (k)
T e，min (n e (k))≤T e (k)≤T e，max (n e (k))
υ min (k)≤υ(k)≤υ max (k)
wherein Q is t (k) Fuel consumption rate for the vehicle; iota (iota) i，j For fitting coefficients, i and j are corresponding orders and are pure numbers respectively; k represents the moment of time k,is n e (k) To the power j>Is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) Engine torque at time k; r is (r) w For the radius of the wheel, I 0 Is the main speed reduction ratio, I g (k) For transmission ratio at time k, n e，min (k)、n e，max (k) Respectively an engine speed lower limit value and an engine speed upper limit value, T e，min (n e (k))、T e，max (n e (k) V) are the lower limit value of the engine torque and the upper limit value of the engine speed, respectively min (k)、v max (k) The lower limit value and the upper limit value of the running speed are respectively.
Further, the driving force objective function is:
L[x(k)，u(k)，k]＝Q t (k)Δt+κ 1 (υ(k)-υ ref ) 2
wherein J is an objective function for predicting energy conservation,for terminal penalty term, L [ x (k), u (k), k]As an incremental function of fuel consumption and vehicle speed tracking, x (N) =v (N) is the terminal moment vehicle speed, N is the prediction horizon, k represents k moment, x (k) =v (k) is the travel speed, u (k) is the engine torque, Q t (k) Δt is the energy consumption index, κ 1 ，κ 2 Respectively the weight coefficient, and upsilon (k) is the running speed, upsilon ref Is the reference vehicle speed.
Further, the process of predicting the ammonia coverage in the control time domain is:
and constructing an original NOx emission model of the engine and a dynamic model of a selective catalytic reduction system, establishing a catalytic reduction objective function, and solving the catalytic reduction objective function according to the original NOx emission model of the engine, the dynamic model of the selective catalytic reduction system, the target rotating speed and the target torque through a Pontrisis maximum principle to obtain the ammonia coverage rate.
Further, the raw NOx emission model is:
wherein RawNO x (k) For original emission of NOx, k represents k time, i and j are respectively corresponding orders, omega i，j In order to fit the coefficients of the coefficients,is n e (k) To the power of j of (2),/>is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) The engine torque at time k.
Further, the dynamic model of the selective catalytic reduction system is as follows:
wherein r is 1 Is the hydrolysis reaction rate of isocyanic acid, r 2 For ammonia gas reabsorption rate, r 3 Is the ammonia desorption rate, r 4 For NOx catalytic reduction reaction rate, r 5 For ammoxidation reaction rate, k i As a frequency factor of the relevant reaction,for ammonia coverage, E i Reaction activation energy for the related reaction, c HNCO Is of isocyanic acid concentration->Is the concentration of gaseous ammonia, T is the temperature of the SCR bed, m 1 To correct the parameters c NO Is the concentration of nitrogen oxide, theta cirl Is critical ammonia coverage.
Further, it is characterized in that: the catalytic reduction objective function is
M[x(k)，u(k)，k]＝p 1 x 1 (k)Δt+p 2 x 2 (k)Δt+p 3 u(k)Δt
u min (k)≤u(k)≤u max (k)
Δu min (k)≤Δu(k)≤Δu max (k)
Wherein P is an objective function of predicted emissions, M [ x (k), u (k), k]The increment function of NOx, NH3 and ammonia coverage rate in the exhaust emission is adopted, and k represents k time; x (k) is a system state quantity including a tailstock NOx concentration and an NH3 concentration; u (k) is ammonia coverage, N is prediction time domain, x 1 (k) For NOx concentration in the tail gas at time k, x 2 (k) For the concentration of NH3 in the tail gas at the moment k, p 1 ，p 2 ，p 3 Respectively weighting coefficients, delta t is sampling time, u min (k)、u max (k) The ammonia coverage is lower and upper, respectively, Δu (k) is the ammonia coverage variation, Δu min (k)、Δu max (k) The ammonia coverage rate change lower limit and upper limit, respectively.
Further, it is characterized in that: the process of acquiring the vehicle information, the front vehicle information and the front road information comprises the following steps:
acquiring current longitudinal speed and position information of the vehicle through CAN communication, a sensor and a high-precision map of a vehicle-mounted system;
acquiring front road information through a high-precision map;
and acquiring longitudinal speed of the front vehicle, current position of the front vehicle and time sequence information of the front traffic light through a V2X communication technology in the camera and the network system.
The beneficial effects of the invention are as follows: according to the invention, by utilizing the driving state information, the movement information of the front vehicle and the geographical information of the front road in the vehicle networking environment, based on the external power characteristics and the energy consumption characteristics of the heavy truck, the factors such as energy consumption, drivability, commute time, vehicle tracking and emission performance are comprehensively considered, corresponding objective functions and solving modes are respectively provided in an upper-layer predictive energy-saving controller and a lower-layer selective catalytic reduction predictive control, and the working states of the whole vehicle and the selective catalytic reduction system are optimized, so that the total energy consumption and NOx emission of the heavy truck are minimum, and the economy and the emission of the vehicle are improved.
Drawings
FIG. 1 is a diagram of a dual-layer predictive control framework for energy conservation and emission reduction of a heavy truck in a networked information environment.
FIG. 2 is a graph comparing energy saving effects of a dual-layer predictive controller in a typical scenario.
FIG. 3 is a graph comparing NOx abatement effects of a dual layer predictive controller in a typical scenario.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings. The description of these embodiments is provided to assist understanding of the present invention, but is not intended to limit the present invention. In addition, technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
As shown in fig. 1, the invention provides a double-layer prediction control method for energy conservation of a heavy truck based on internet connection information, which comprises an information collection module and a double-layer prediction control module (an energy conservation prediction control module and a selective catalytic reduction prediction control module);
the information collection module is used for collecting the state of the vehicle, the information of the vehicles in front and the road in front in real time and providing data support for the double-layer predictive control frame, and the specific method is as follows:
s101: acquiring the current longitudinal speed and position information of the vehicle through CAN communication of a vehicle-mounted system, a sensor and a high-precision map carried by the vehicle-mounted system;
s102: acquiring front road information such as road gradient, road curvature, road speed limit and the like through a high-precision map carried by the vehicle;
s103: acquiring longitudinal speed of a front vehicle, current position of the front vehicle and front traffic light time sequence information through a camera and a V2X communication technology in a network system;
the double-layer prediction control module utilizes the collected traffic flow state information to synchronously optimize the energy conservation and emission reduction of the heavy truck, and the specific method is as follows:
s201: the double-layer predictive control comprises two parts, wherein the upper layer is energy-saving predictive control, and the lower layer is selective reduction predictive control;
s202: in the upper-layer energy-saving prediction control, the speed, the target rotation speed and the target torque of an engine are optimized by utilizing the road and traffic information in front, the dynamic behavior of the vehicle in a future period is predicted, and the speed of the vehicle in the future period is optimized by controlling the driving force of the engine of the vehicle, so that the vehicle meets the aims of low energy consumption, good drivability and short commute time;
s203: in the lower selective reduction prediction control, the target rotating speed and the target torque of an upper engine are utilized to optimize the post-treatment state of the engine, the working state of the selective catalytic reduction system in future once time is predicted, and the optimal working state of the selective catalytic reduction system in future once time is selected, so that the aim of reducing NOx emission is fulfilled.
The double-layer predictive control can utilize the front vehicles and roads, consider scene constraint, a dynamics/energy consumption model and an engine aftertreatment system model, on one hand, carry out vehicle speed planning to enable the vehicle speed planning to meet the targets of low energy consumption, good drivability, short commute time and good vehicle tracking performance, and on the other hand, carry out selective reduction technology optimization to achieve the target of reducing NOx emission.
In step S202, the upper-layer energy-saving prediction control includes acquiring host vehicle information, front vehicle information, and front road information, predicting a target vehicle speed, a target rotational speed, and a target torque in a control time domain based on the host vehicle information, the front vehicle information, and the front road information, and performing real-time driving control on the vehicle according to the target vehicle speed, the target rotational speed, and the target torque; the process of predicting the target vehicle speed, the target rotating speed and the target torque in the control time domain is as follows: and constructing a vehicle longitudinal dynamics model and a fuel consumption model, establishing a driving force objective function, and solving the objective function according to the vehicle longitudinal dynamics model and the fuel consumption model through the Pontrisian maximum principle to obtain a target vehicle speed, a target engine speed and a target engine torque.
In step 203, the lower-layer selective reduction prediction control includes controlling the selective catalytic reduction system in real time according to the ammonia coverage rate based on the ammonia coverage rate in the target rotational speed and target torque prediction control time domain, so as to obtain the optimal working state of the selective catalytic reduction system. The process of predicting the ammonia coverage rate in the control time domain comprises the following steps: and constructing an original NOx emission model of the engine and a dynamic model of a selective catalytic reduction system, establishing a catalytic reduction objective function, and solving the catalytic reduction objective function according to the original NOx emission model of the engine, the dynamic model of the selective catalytic reduction system, the target rotating speed and the target torque through a Pontrisis maximum principle to obtain the ammonia coverage rate.
The double-layer predictive control method can utilize the front vehicles and roads and consider scene constraint, a dynamics/energy consumption model and an engine aftertreatment system model, so that on one hand, vehicle speed planning is performed, and the targets of low energy consumption, good drivability, short commute time and good vehicle tracking performance are met, and on the other hand, selective reduction technology optimization is performed, so that the NOx emission reduction target is realized.
The method for acquiring the front vehicle and the road information of the vehicle according to the information of the internet of vehicles comprises the following steps: the front vehicle information is acquired by a vehicle Global Positioning System (GPS), a Geographic Information System (GIS) and camera data: speed v of the vehicle h Speed v of front vehicle p And the relative distance deltas between the front and rear vehicles. The following variables are defined: relative speed of front and rear vehicle Δv=v p -v h . The front road information is obtained from a Global Positioning System (GPS) and high-precision map data, and the front road information that can be obtained includes: road grade, road curvature, road speed limit, etc.
In the upper energy-saving predictive controller, the vehicle speed optimization and engine speed and engine torque optimization processes are as follows:
1) Establishing a vehicle dynamics model and an energy consumption model
The vehicle dynamics equations considered in predicting the energy conservation optimization problem are described as follows.
Wherein k represents the time of k,is vehicle acceleration; v (k) is the driving speed, +.>Is the running speed; delta is the conversion coefficient of rotating mass, m is the mass of the truck, F t (k) For driving force, F b (k) For braking force, C D The wind resistance coefficient is the windward area of the vehicle, ρ is the air density, g is the gravitational acceleration, f is the wheel rolling resistance coefficient, and α (k) is the road gradient.
Further, the vehicle longitudinal acceleration, driving force, and resistance may be expressed as:
wherein eta is t For transmission mechanical efficiency, I 0 T is the main speed reduction ratio g (k) For transmission gear, r w For driving the radius of the wheel, T e (k) Is the engine torque.
A polynomial fit to the fuel consumption rate of a heavy truck engine is used to describe the fuel consumption model of the engine as described in the following.
Further, for a truck driveline, the relationship between vehicle longitudinal speed, transmission gear ratio, engine speed may be expressed as:
in which Q t (k) Fuel consumption rate for the vehicle; iota (iota) i，j For fitting coefficients, i and j are corresponding orders and are pure numbers respectively; k represents the moment of time k,is n e (k) To the power j>Is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) Engine torque at time k; r is (r) w For the radius of the wheel, I 0 Is the main speed reduction ratio, I g (k) Is the transmission ratio at time k.
2) Constructing a predictive energy-saving optimization problem and solving
For heavy trucks, the upper controller aims to find the optimal vehicle speed to reduce energy consumption, and the optimal driving force F is found through an optimal control strategy t The basic form of the objective function is selected as described in the following formula.
Wherein,
L[x(k)，u(k)，k]＝Q t (k)Δt+κ 1 (υ(k)-υ ref ) 2
where J is an objective function for predicting energy conservation,for terminal penalty term, L [ x (k), u (k), k]As an incremental function of fuel consumption and vehicle speed tracking, x (N) =v (N) is the terminal moment vehicle speed, N is the prediction horizon, k represents k moment, x (k) =v (k) is the travel speed, u (k) is the engine torque, Q t (k) Δt is the energy consumption index, κ 1 ，κ 2 Respectively the weight coefficient, and upsilon (k) is the running speed, upsilon ref Is the reference vehicle speed.
The objective function comprises three indexes, namely an energy consumption index Q t (k) Δt aims to minimize fuel consumption, and the speed tracking index (v (k) -v ref ) 2 Aiming at tracking a reference vehicle speed to avoid excessive speed tracking errors, a terminal penalty termIt is intended to ensure that the vehicle speed reaches around the reference vehicle speed at the terminal moment.
For the optimization problem described above, there are some constraints on the vehicle, such as maximum and minimum vehicle speed, engine speed range, and engine torque capacity, due to system dynamics or system limitations, as follows:
n e，min (k)≤n e (k)≤n e，min (k)
T e，min (n e (k))≤T e (k)≤T e，max (n e (k))
υ min (k)≤υ(k)≤υ max (k)
wherein n is e，min (k) And n e，max (k) Representing minimum and maximum engine speeds, T e，min (n e (k) And T) e，max (n e (k) Represents the minimum torque and the maximum torque, v determined by the engine speed at time k min (k) And v max (k) Representing minimum and maximum vehicle speeds determined according to driving scenarios (mainly, reference vehicle speed and speed limit information).
Construction toolAfter the optimization problem with dynamic characteristics, the Pontrisia maximum value principle is applied to solve the problem, so that the optimal vehicle speed v can be obtained * (k) Engine target torque T e * (k) And the engine target rotation speed n e * (k)。
In the lower layer predictive emissions control, the selective catalytic reduction system state optimization process is as follows:
1) Establishing a model of a selective catalytic reduction system
Firstly, by taking the engine speed and the engine torque as inputs, adopting linear polynomial curve fitting to establish an original NOx emission model of the engine:
in RawNO x (k) For original emission of NOx, k represents k time, i and j are respectively corresponding orders, omega i，j In order to fit the coefficients of the coefficients,is n e (k) To the power j>Is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) The engine torque at time k.
Then, a selective catalytic reduction system model is established through chemical reactions such as urea pyrolysis, ammonia re-adsorption and desorption, NOx catalytic reduction, ammonia oxidation and the like:
wherein r is 1 Is the hydrolysis reaction rate of isocyanic acid, r 2 For ammonia gas reabsorption rate, r 3 Is the ammonia desorption rate, r 4 For NOx catalytic reduction reaction rate, r 5 For ammoxidation reaction rate, k i As a frequency factor of the relevant reaction,for ammonia coverage, E i Reaction activation energy for the related reaction, c HNCO Is of isocyanic acid concentration->Is the concentration of gaseous ammonia, T is the temperature of the SCR bed, m 1 To correct the parameters c NO Is the concentration of nitrogen oxide, theta cirl Is critical ammonia coverage.
In order to build a control-oriented model, some assumptions need to be made and a continuous stirred tank is used as the model framework, with the following kinetic equations:
wherein EF is v Represents the discharge flow, θ max Indicating maximum coverage of ammonia.
2) Construction and solving of selective catalytic reduction predictive control optimization problem
In a selective catalytic reduction system, the control objective includes two parts: high NOx conversion efficiency and low ammonia slip, the objective function is selected as follows:
wherein,
M[x(k)，u(k)，k]＝p 1 x 1 (k)Δt+p 2 x 2 (k)Δt+p 3 u(k)Δt
where P is the objective function of predicted emissions, M [ x (k), u (k), k]The increment function of NOx, NH3 and ammonia coverage rate in the exhaust emission is adopted, and k represents k time; x (k) is a system state quantity including a tailstock NOx concentration and an NH3 concentration; u (k) is ammonia coverage, N is prediction time domain, x 1 (k) For NOx concentration in the tail gas at time k, x 2 (k) For the concentration of NH3 in the tail gas at the moment k, p 1 ，p 2 ，p 3 Respectively, weighting coefficients, Δt is the sampling time. In the objective function, the first index aims at improving the NOx conversion efficiency and reducing the NOx emission in the tail gas; the second index aims at reducing NH3 emissions in the exhaust gas, which will also reduce urea consumption; the last indicator with control variables is aimed at reducing ammonia coverage in the selective catalytic reduction system.
There are also some constraints in the optimization problem. Wherein the controlled variable has a true physical meaning, its value being between 0 and 1. Furthermore, the actuator of the SCR system has saturation constraints and deltau is also limited, summarized as follows:
u min (k)≤u(k)≤u max (k)
Δu min (k)≤Δu(k)≤Δu max (k)
the upper layer predictive energy-saving controller obtains the engine torque T e * (k) And engine speed n e * (k)After the optimization curve of (2), transmitting the information to an original NOx emission model of the engine and a dynamic model of the selective catalytic reduction system, and solving according to the Pontrian maximum principle on the basis of the information to obtain the optimal working state of the selective catalytic reduction system.
According to the double-layer prediction control method for energy conservation of the heavy truck based on the internet connection information, simulation result verification under typical working conditions is carried out, the speed information of a front vehicle is assumed to be known under the typical working conditions, and the aim is to save energy as much as possible while tracking the target speed on the vehicle.
The energy-saving simulation results are shown in fig. 2, and the numerical results are shown in table 1.
Table 1 illustrates specific numerical values of energy conservation and emission reduction of double-layer predictive controller in typical scene
As can be seen from the data in the graph and the table, in the typical scene, compared with the traditional algorithm which only considers the speed tracking performance, the algorithm can effectively track the speed of the front vehicle and simultaneously reduce the oil consumption by 14.7 percent.
The emission reduction simulation results are shown in fig. 3, and the numerical results are shown in table 1. As can be seen from the data in the figures and tables, in this typical scenario, the algorithm of the present invention can reduce NOx emissions by 28.9% compared to the conventional emission reduction algorithm, while guaranteeing constant urea consumption.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The double-layer prediction control method for energy conservation of the heavy truck based on the internet connection information is characterized by comprising the following steps of: comprises an upper energy-saving predictive control and a lower selective reduction predictive control,
the upper energy-saving predictive control comprises the steps of obtaining information of a vehicle, information of a front vehicle and information of a front road, predicting a target speed, a target rotating speed and a target torque in a control time domain based on the information of the vehicle, the information of the front vehicle and the information of the front road, and performing real-time driving control on the vehicle according to the target speed, the target rotating speed and the target torque;
the lower selective reduction predictive control comprises the step of controlling the selective catalytic reduction system in real time according to the ammonia coverage rate based on the ammonia coverage rate in the target rotating speed and target torque predictive control time domain;
the process of predicting the ammonia coverage in the control time domain is as follows:
constructing an original NOx emission model of an engine and a dynamic model of a selective catalytic reduction system, establishing a catalytic reduction objective function, and solving the catalytic reduction objective function according to the original NOx emission model of the engine, the dynamic model of the selective catalytic reduction system, a target rotating speed and a target torque through a Pontrisis maximum principle to obtain ammonia coverage rate;
the dynamic model of the selective catalytic reduction system is as follows:
wherein r is 1 Is the hydrolysis reaction rate of isocyanic acid, r 2 For ammonia gas reabsorption rate, r 3 Is the ammonia desorption rate, r 4 For NOx catalytic reduction reaction rate, r 5 For ammoxidation reaction rate, k i As a frequency factor of the relevant reaction,for ammonia coverage, E i Reaction activation energy for the related reaction, c HNCO Is of isocyanic acid concentration->Is the concentration of gaseous ammonia, T is the temperature of the SCR bed, m 1 To correct the parameters c NO Is the concentration of nitrogen oxide, theta cirl Is critical ammonia coverage.
2. The method for double-layer predictive control of energy conservation of heavy trucks based on internet connection information according to claim 1, wherein the process of predicting the target vehicle speed, target rotational speed and target torque in the control time domain is as follows:
and constructing a vehicle longitudinal dynamics model and a fuel consumption model, establishing a driving force objective function, and solving the objective function according to the vehicle longitudinal dynamics model and the fuel consumption model through the Pontrisian maximum principle to obtain a target vehicle speed, a target engine speed and a target engine torque.
3. The method for controlling energy conservation double-layer prediction of heavy trucks based on internet connection information according to claim 2, wherein the longitudinal dynamics model is as follows:
wherein k represents the time of k,is vehicle acceleration; v (k) is the driving speed, +.>Is the running speed; delta is the conversion coefficient of rotating mass, m is the mass of the truck, F t (k) For driving force, F b (k) For braking force, C D The wind resistance coefficient is the windward area of the vehicle, ρ is the air density, g is the gravitational acceleration, f is the wheel rolling resistance coefficient, and α (k) is the road gradient.
4. The dual-layer predictive control method for energy conservation of a heavy truck based on internet connection information according to claim 2, wherein the fuel consumption model is:
n e,min (k)≤n e (k)≤n e,min (k)
T e,min (n e (k))≤T e (k)≤T e,max (n e (k))
v min (k)≤v(k)≤v max (k)
wherein Q is t (k) Fuel consumption rate for the vehicle; iota (iota) i，j For fitting coefficients, i and j are corresponding orders and are pure numbers respectively; k represents the moment of time k,is n e (k) To the power j>Is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) Engine torque at time k; r is (r) w For the radius of the wheel, I 0 Is the main speed reduction ratio, I g (k) For transmission ratio at time k, n e,min (k)、n e,max (k) Respectively an engine speed lower limit value and an engine speed upper limit value, T e,min (n e (k))、T e,max (n e (k) V) are the lower limit value of the engine torque and the upper limit value of the engine speed, respectively min (k)、v max (k) The lower limit value and the upper limit value of the running speed are respectively.
5. The bi-layer predictive control method for heavy truck energy conservation based on internet connection information of claim 2, wherein the driving force objective function is:
L[x(k),u(k),k]＝Q t (k)Δt+κ 1 (v(k)-v ref ) 2
wherein J is an objective function for predicting energy conservation,for terminal penalty term, L [ x (k), u (k), k]As an incremental function of fuel consumption and vehicle speed tracking, x (N) =v (N) is the terminal time vehicle speed, N is the prediction horizon, k represents k time, x (k) =v (k) is the travel speed, u (k) is the engine torque, Q t (k) Deltat is an energy consumption index，κ 1 ，κ 2 Respectively the weight coefficient, v (k) is the running speed, v ref Is the reference vehicle speed.
6. The dual-layer predictive control method for energy conservation of heavy trucks based on internet connection information of claim 1, wherein the method comprises the following steps: the raw NOx emission model is:
wherein RawNO x (k) For original emission of NOx, k represents k time, i and j are respectively corresponding orders, omega i，j In order to fit the coefficients of the coefficients,is n e (k) To the power of j, T e i (k) Is T e (k) To the power of i, n e (k) For the engine speed at time k, T e (k) The engine torque at time k.
7. The dual-layer predictive control method for energy conservation of heavy trucks based on internet connection information of claim 1, wherein the method comprises the following steps: the catalytic reduction objective function is
M[x(k)，u(k)，k]＝p 1 x 1 (k)Δt+p 2 x 2 (k)p 2 x 2 (k)Δt+p 3 u(k)Δt
u min (k)≤u(k)≤u max (k)
Δu min (k)≤Δu(k)≤Δu max (k)
Wherein P is an objective function of predicted emissions, M [ x (k), u (k), k]The increment function of NOx, NH3 and ammonia coverage rate in the exhaust emission is adopted, and k represents k time; x (k) is a system state quantity including tailpipe NOx concentrationAnd NH3 concentration; u (k) is ammonia coverage, N is prediction time domain, x 1 (k) For NOx concentration in the tail gas at time k, x 2 (k) For the concentration of NH3 in the tail gas at the moment k, p 1 ，p 2 ，p 3 Respectively weighting coefficients, delta t is sampling time, u min (k)、u max (k) The ammonia coverage is lower and upper, respectively, Δu (k) is the ammonia coverage variation, Δu min (k)、Δu max (k) The ammonia coverage rate change lower limit and upper limit, respectively.
8. The dual-layer predictive control method for energy conservation of heavy trucks based on internet connection information of claim 1, wherein the method comprises the following steps: the process of acquiring the vehicle information, the front vehicle information and the front road information comprises the following steps:
acquiring current longitudinal speed and position information of the vehicle through CAN communication, a sensor and a high-precision map of a vehicle-mounted system;
acquiring front road information through a high-precision map;
and acquiring longitudinal speed of the front vehicle, current position of the front vehicle and time sequence information of the front traffic light through a V2X communication technology in the camera and the network system.
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