CN108583576B - Economic vehicle speed forward-looking optimization method - Google Patents

Economic vehicle speed forward-looking optimization method Download PDF

Info

Publication number
CN108583576B
CN108583576B CN201810174883.6A CN201810174883A CN108583576B CN 108583576 B CN108583576 B CN 108583576B CN 201810174883 A CN201810174883 A CN 201810174883A CN 108583576 B CN108583576 B CN 108583576B
Authority
CN
China
Prior art keywords
vehicle
speed
optimization
max
sampling
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810174883.6A
Other languages
Chinese (zh)
Other versions
CN108583576A (en
Inventor
周健豪
孙静
盛雪爽
丁一
何龙强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN201810174883.6A priority Critical patent/CN108583576B/en
Publication of CN108583576A publication Critical patent/CN108583576A/en
Application granted granted Critical
Publication of CN108583576B publication Critical patent/CN108583576B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • B60W30/188—Controlling power parameters of the driveline, e.g. determining the required power
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B60W2050/0039—Mathematical models of vehicle sub-units of the propulsion unit
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0638—Engine speed
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/06—Combustion engines, Gas turbines
    • B60W2510/0657—Engine torque
    • 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
    • B60W2510/00—Input parameters relating to a particular sub-units
    • B60W2510/10—Change speed gearings
    • B60W2510/1005—Transmission ratio engaged
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2554/00—Input parameters relating to objects
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Control Of Vehicle Engines Or Engines For Specific Uses (AREA)

Abstract

The invention discloses an economical vehicle speed forward-looking optimization method, which comprises the following steps: firstly, designing a global speed optimization control scheme by establishing an engine fuel consumption model of a vehicle and a longitudinal dynamics model of the vehicle, and obtaining an optimized speed curve of the vehicle by taking fuel economy and vehicle transportation efficiency as constraint conditions based on a distribution estimation algorithm (EDA); then, a local speed optimization control scheme is designed based on Model Predictive Control (MPC), and the obtained optimized speed curve is continuously subjected to real-time rolling optimization by taking fuel economy, vehicle transportation efficiency and a safety distance between the vehicle and a front vehicle as constraint conditions. The method can improve the fuel economy of the vehicle; the method uses a distribution estimation algorithm, so that the optimization process can be accelerated; the method can optimize the speed of the vehicle in real time according to the current traffic condition.

Description

Economic vehicle speed forward-looking optimization method
Technical Field
The invention relates to a method for improving the fuel economy of vehicles on a highway, in particular to an economical vehicle speed forward-looking optimization method.
Background
With the rapid development of the automobile industry, the number of automobiles increases at a high speed, so that the energy consumption is directly increased, and simultaneously, a large amount of harmful tail gas and particulate matters are discharged, so that the automobile industry is facing huge energy conservation and emission reduction pressure. Much research has been devoted to reducing the fuel consumption of vehicles and improving their fuel economy. In order to further reduce the fuel consumption of the highway vehicles, the speed of the highway vehicles is optimized by the invention.
Currently, much research has been done on the problem of optimizing vehicle speed with the goal of improving vehicle fuel economy. In early optimization control, an optimized speed curve is obtained by establishing a vehicle model to solve an objective function, and the objective function can be solved by methods such as dynamic programming and quadratic programming, but because the vehicle is a complex nonlinear system and has complex constraints, the calculation time is long, and the vehicle speed cannot be rapidly optimized in real time in the face of real-time changing traffic conditions. In order to optimize the vehicle speed in real time in a short time, many control methods aim to combine model predictive control with various optimization algorithms, but most of the control methods are optimized by taking time as a step unit, and when traffic is congested, a controller is still repeatedly performing ineffective optimization and simultaneously influencing the subsequent optimization period. The vehicles are mostly used for various transports, the driving routes and the starting points of the driving are mostly determined, and therefore, a two-stage controller with displacement as a step unit is proposed: before the vehicle starts, a first stage obtains an optimized speed curve based on a distribution estimation algorithm (EDA); in the driving process of the vehicle, the second stage optimizes the speed in real time based on a model predictive control theory (MPC) according to the real-time change of the traffic condition, thereby improving the fuel economy of the vehicle.
Disclosure of Invention
In order to solve the problems that the existing speed optimization controller is long in calculation time and cannot achieve real-time optimization, the invention provides a vehicle speed optimization method, which can improve the fuel economy of a vehicle, accelerate the optimization process and shorten the calculation time.
In order to achieve the purpose, the invention adopts the following technical scheme:
step one, establishing an engine fuel consumption model of a vehicle: establishing an engine fuel consumption model based on a Willans line model of the engine, wherein the engine fuel consumption model is used for expressing the relation between the fuel consumption rate and the engine torque and the engine rotating speed;
step two, establishing a longitudinal dynamic model of the vehicle;
designing a global speed optimization controller based on a distribution estimation algorithm;
and fourthly, designing a local speed optimization controller based on a model predictive control theory.
Preferably, the fuel consumption model of the engine established in the first step is as follows:
b＝f(Te,n0)＝(α1n0+α2)Te+β1n0+β2(1)
wherein the content of the first and second substances,b is the fuel consumption rate; t iseEngine torque in units of N ∙ m; n is0α is the engine speed in r/min1、α2、β1、β2Is a constant determined from engine specific performance parameters.
Since the actual output of the vehicle is the vehicle speed while the vehicle is running, the engine speed n is replaced by the vehicle speed v0The engine fuel consumption model can be obtained as follows:
Figure GDA0001851863740000021
wherein igIs the transmission ratio of the transmission; i.e. i0The transmission ratio of the main speed reducer is set; r is the wheel radius in m; and v is the running speed of the vehicle and has the unit of km/h.
Preferably, the vehicle longitudinal dynamic model established in the second step is:
Figure GDA0001851863740000022
wherein M is the mass of the vehicle in kg ηtThe transmission efficiency of the whole vehicle transmission system is improved; cDIs the air resistance coefficient; a is the frontal area in m2(ii) a Rho is the air density in kg/m3(ii) a g is the acceleration of gravity in m/s2(ii) a f is a rolling resistance coefficient; θ (t) is road grade in rad; fbAnd (t) is the magnitude of braking force.
Preferably, the objective function of the step three global speed optimization controller is as follows:
Figure GDA0001851863740000023
s.t.
Temin≤Te(k)≤Temax (5)
0≤Fb(k)≤Fbmax (6)
vlimitmin≤v(k)≤vlimitmax (7)
v(0)＝v(n)＝0 (8)
where n is the nth (last) sampling location; k represents the kth sampling position; Δ tkDriving time of the vehicle from the sampling position k to k + 1;
Figure GDA0001851863740000031
is the upper limit value of the velocity at the sampling position k + 1; q. q.s1、q2Is a weighting factor.
The equation (4) is an objective function of the global speed optimization controller, the first term on the right of the equation represents the fuel economy, and the second term on the right of the equation represents the vehicle transportation efficiency;
the equation (5) is a constraint on the engine torque during the optimization, Temin and Temax is the minimum and maximum values that the engine torque can reach, respectively, in units of N ∙ m;
the said equation (6) is the constraint of braking force during braking, Fbmax is the maximum braking force that the vehicle can achieve, in units of N;
the expression (7) is a constraint on the speed of the vehicle during running, vlimitmin and vlimitmax represents a lower limit value and an upper limit value of a running speed specified by a road regulation at the sampling position k respectively;
equation (8) is a constraint on the initial speed and the final speed of the vehicle over the entire trip, both the initial speed and the final speed being 0.
In the solving process of the objective function, a distribution estimation algorithm is adopted.
Preferably, the objective function of the step four local area speed optimization controller is:
Figure GDA0001851863740000032
s.t.
Temin≤Te(l)≤Temax (10)
0≤Fb(l)≤Fbmax (11)
vlimitmin≤v(l)≤vlimitmax (12)
wherein m is the step length of local optimization within the kth step length of global optimization; v. of*(l) Taking the smaller one of the vehicle speed at the position l and the vehicle speed limit value at the position l obtained by global optimization; ssafe(l) Is a safe distance from the front vehicle; s*(l) Is the estimated inter-vehicle distance from the preceding vehicle. The terms 1-3 on the right side of the equation of equation (9) represent fuel economy, vehicle transportation efficiency, and safe distance to the leading vehicle, respectively.
The constraints of the expressions (10) to (12) are the same as the constraints of the expressions (5) to (7).
The method adopts a model prediction control theory when the rolling optimization is carried out on the fuel economy, the vehicle transportation efficiency and the safety distance between the vehicle and the front vehicle; in the process of solving the objective function, a distribution estimation algorithm is adopted.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention adopts staged speed optimization control, the global optimization of the first stage starts to be optimized before the vehicle starts, the local optimization control of the second stage has smaller sampling distance, can carry out real-time optimization on the speed aiming at the change of the traffic condition, has better real-time performance, and can improve the fuel economy of the vehicle.
2. When the objective function of the controller is solved, the invention uses a distribution estimation algorithm: a probability model is established by sampling and selecting excellent samples to generate a new population for solving, so that the calculation time is greatly shortened.
3. When the controller designed by the invention is used for sampling, the step length units are all displacement rather than time, so that an invalid optimization period caused by traffic congestion is greatly eliminated, and the optimization process is accelerated.
Drawings
FIG. 1 is a schematic diagram of an overall speed optimization controller;
FIG. 2 is a schematic drawing of a sample;
FIG. 3 is a flow chart of a solution of a distribution estimation algorithm;
fig. 4 is a schematic diagram of a speed curve.
Detailed Description
The invention is further explained below with reference to the drawings.
FIG. 1 illustrates an overall speed optimizer that takes a vehicle dynamics model, an engine model, and predicted traffic conditions as inputs and obtains a global optimized speed curve based on a distributed estimation algorithm; the local area speed optimization controller takes the obtained speed optimization curve and real-time traffic conditions as input, and roll optimizes the fuel economy of the vehicle, the transportation efficiency of the vehicle and the driving safety based on the model prediction control theory.
The invention provides a vehicle speed optimization method which mainly comprises the following steps:
step one, establishing an engine fuel consumption model of a vehicle: based on a Willans line model of the engine, an engine fuel consumption model is established to express the relationship between the fuel consumption rate, the engine torque and the engine speed.
The model of engine fuel consumption is:
b＝f(Te,n0)＝(α1n0+α2)Te+β1n0+β2(1)
wherein b is the fuel consumption rate; t iseEngine torque in units of N ∙ m; n is0α is the engine speed in r/min1、α2、β1、β2Is a constant determined from engine specific performance parameters.
Since the actual output of the vehicle is the vehicle speed while the vehicle is running, the engine speed n is replaced by the vehicle speed v0The engine fuel consumption model can be obtained as follows:
Figure GDA0001851863740000051
wherein igIs the transmission ratio of the transmission; i.e. i0The transmission ratio of the main speed reducer is set; r is the wheel radius in m; v is the vehicle runningAnd the driving speed is km/h.
Step two, establishing a longitudinal dynamic model of the vehicle: in order to facilitate the analysis and control of a vehicle system, the axle load transfer of the front axle and the rear axle of the automobile is ignored, a vehicle longitudinal dynamic model is established according to Newton's second law, and the longitudinal dynamic equation is as follows:
Figure GDA0001851863740000052
wherein, FtIs the driving force, in units of N; fwIs the air resistance, in units of N; ffIs rolling resistance in units of N; fiIs slope resistance in units of N; fbThe unit is N for braking force.
F is to bet、Fw、Ff、FiRespectively substituting the formula (2), and obtaining a longitudinal dynamic model of the vehicle as follows:
Figure GDA0001851863740000053
wherein M is the mass of the vehicle in kg ηtThe transmission efficiency of the whole vehicle transmission system is improved; cDIs the air resistance coefficient; a is the frontal area in m2(ii) a Rho is the air density in kg/m3(ii) a g is the acceleration of gravity in m/s2(ii) a f is a rolling resistance coefficient; θ (t) is road grade in rad; fbAnd (t) is the magnitude of braking force.
The engine fuel consumption model and the vehicle longitudinal dynamics model are both functions related to the time t, and the sampling unit of the invention is displacement, so that the conversion relation between the time and the displacement needs to be obtained:
by
Figure GDA0001851863740000054
It is possible to obtain:
Figure GDA0001851863740000055
Figure GDA0001851863740000056
Figure GDA0001851863740000057
Figure GDA0001851863740000061
Figure GDA0001851863740000062
by working up formula (5), one can obtain:
Figure GDA0001851863740000063
Figure GDA0001851863740000064
Figure GDA0001851863740000065
Figure GDA0001851863740000066
where v (k) represents the magnitude of the vehicle speed at the k-th sampling position, kkDenotes the distance between the k-1 th sampling position and the k-th sampling position, and k1＝k2＝…＝kkFig. 2 shows a concrete representation of the sampling position and the sampling distance.
The velocity magnitude at the next position k +1 is:
Figure GDA0001851863740000067
the formula (7) is multiplied by v (k) + v (k +1) at both ends, and the formula (4) is substituted into the arrangement:
Figure GDA0001851863740000068
step three, designing a global speed optimization controller based on a distribution estimation algorithm: based on the established engine fuel consumption model and the vehicle longitudinal dynamics model, in order to balance the relation between the fuel economy and the vehicle speed, a weighting factor q is introduced1、q2Establishing an objective function of the global speed optimization controller:
Figure GDA0001851863740000069
s.t.
Temin≤Te(k)≤Temax (10)
0≤Fb(k)≤Fbmax (11)
vlimitmin≤v(k)≤vlimitmax (12)
v(0)＝v(n)＝0 (13)
where n is the nth (last) sampling location; k represents the kth sampling position; Δ tkFor the driving time of the vehicle from the sampling position k to k +1,
Figure GDA0001851863740000071
Figure GDA0001851863740000072
is the upper limit value of the velocity at the sampling position k + 1; q. q.s1、q2Is a weighting factor. Equation (10) is a constraint on engine torque during optimization, Temin and Temax is the minimum and maximum values that the engine torque can reach, respectively; equation (11) is a constraint on the braking force during braking, Fbmax is the maximum braking force that the vehicle can achieve; equation (12) is a constraint on the speed of the vehicle during travel, vlimitmin and vlimitmax represents the lower limit of the travel speed defined by the road code at the sampling position k andan upper limit value; equation (13) is a constraint on the initial and final speeds of the vehicle over the course, both the initial and final speeds being 0.
In the solving process of the objective function, a distribution estimation algorithm is adopted, and fig. 3 is a solving flow chart of the distribution estimation algorithm: (1) initializing a population, randomly generating a population meeting the requirements of the population size, randomly generating a predefined sample in the vehicle domain when randomly generating the predefined sample because the invention is directed to vehicles, and generating each sample by n pairs of control inputs (T) in the global optimization process because there are n sampling positions in totale、Fb) Composition is carried out; (2) selecting a sample with a smaller objective function value as an excellent sample; (3) calculating the average value and standard deviation of the excellent samples, updating the probability matrix, and establishing a probability model; (4) randomly sampling the probability model to obtain a new sample; (5) and (3) judging whether the termination condition of the algorithm is met, if so, outputting the optimal speed curve, and if not, returning to the step (2) until the optimal speed curve is output.
Designing a local speed optimization controller based on a model predictive control theory: the method is used for carrying out real-time rolling optimization on the fuel economy, the transportation efficiency and the safety of the vehicle based on a model prediction control theory, and the control input is (T)e、Fb) The state variable is v, and the objective function of the local velocity optimization controller is:
Figure GDA0001851863740000073
s.t.
Temin≤Te(l)≤Temax (15)
0≤Fb(l)≤Fbmax (16)
vlimitmin≤v(l)≤vlimitmax (17)
wherein m is the step length of the local optimization within the kth step length of the global optimization, and fig. 4 shows a relationship curve between the displacement as a sampling unit and the speed; v. of*(l) The vehicle speed at the position l and the position l obtained by global optimization are takenThe lesser of the vehicle speed limits; ssafe(l) Is a safe distance from the front vehicle; s*(l) Is the estimated inter-vehicle distance from the preceding vehicle.
The constraints of equations (15) to (17) are the same as the constraints of equations (10) to (12).
The model prediction control theory is adopted when the rolling optimization is carried out on the fuel economy, the vehicle speed and the safety; in the process of solving the objective function, a distribution estimation algorithm is adopted.
Safety distance s between the vehicle and the front vehicle at position lsafe(l) The method is obtained according to real-time traffic conditions and an empirical formula:
ssafe(l)＝k0×v(l)+sstop(18)
wherein k is0Is a safe proportionality coefficient; v (l) the speed optimized for the vehicle at l; sstopIs the expected safe distance from the front workshop when the vehicle stops, and the unit is m.
Calculating the estimated distance s between the position l and the preceding vehicle*(l) The method mainly comprises the following steps:
1. estimating the acceleration of the front vehicle at the sampling position k:
Figure GDA0001851863740000081
wherein the content of the first and second substances,
Figure GDA0001851863740000082
the estimated acceleration of the front vehicle at the sampling position k is obtained; a isq(l | k-1) represents the actual magnitude of the forward vehicle acceleration at the l control horizon within the k-1 th step size; and p (l) is a weighting factor, the closer to the sampling position k, the larger the weighting factor is obtained, namely p (m | k-1) ≧ p (m-1| k), and so on, so as to obtain a more accurate acceleration estimation value.
2. The speed of the leading vehicle at the next sampling location is estimated:
Figure GDA0001851863740000083
wherein the content of the first and second substances,
Figure GDA0001851863740000084
the estimated speed of the front vehicle at the position of the kth sampling period l is obtained; v. ofq(0| k) is the actual speed of the lead vehicle at the sampling location k;
Figure GDA0001851863740000085
c represents the c small period in the kth large period, and 1 < c is less than or equal to m.
3. When the vehicle runs from the position l-1 to the position l in the kth sampling period, estimating the position of the front vehicle:
Figure GDA0001851863740000091
4. estimated vehicle distance s between the preceding vehicle and the position l of the k-th sampling period*(l | k) is:
Figure GDA0001851863740000092
wherein L (L | k) is the position of the own vehicle; sq(0| k) is the actual inter-vehicle distance from the leading vehicle at the end of the k-1 cycle.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (7)

1. The vehicle economical vehicle speed forward-looking optimization method is characterized by comprising the following steps of:
step 1: establishing an engine fuel consumption model and a longitudinal dynamics model of a vehicle;
step 2: designing a global speed optimization controller before a vehicle starts, wherein the input of the global speed optimization controller is the engine fuel consumption model, the longitudinal dynamics model and the estimated traffic condition, and the output is an optimized speed curve of the whole travel; the traffic condition comprises driving time and distance between sampling positions in the vehicle journey, a speed upper limit value at the sampling positions, a lower limit value and an upper limit value of the driving speed;
and step 3: and designing a local speed optimization controller after the vehicle starts, wherein the input of the local speed optimization controller is the optimized speed curve of the whole travel and the real-time traffic condition, and the output of the local speed optimization controller is the actual speed curve of the local travel.
2. The vehicle economy speed look-ahead optimization method of claim 1, wherein the engine fuel consumption model and the longitudinal dynamics model are both displaced in step units at the time of sampling.
3. The vehicle economy vehicle speed look-ahead optimization method of claim 2, wherein the engine fuel consumption model is:
Figure FDA0002391245270000011
wherein k is a sampling position in the vehicle travel; b (k) is specific fuel consumption; t ise(k) Is the engine torque; n is0α is the engine speed1、α2、β1、β2Is a constant determined from the engine specific performance parameter; i.e. igIs the transmission ratio of the transmission; i.e. i0The transmission ratio of the main speed reducer is set; r is the wheel radius; v (k) is vehicle travel speed;
the longitudinal dynamics model is as follows:
Figure FDA0002391245270000012
wherein M is the mass of the vehicle ηtThe transmission efficiency of the whole vehicle transmission system is improved; CD is the air resistance coefficient; a is the windward area; rho is airDensity; g is the acceleration of gravity; f is a rolling resistance coefficient; θ (k) is the road slope; fb(k) Is the magnitude of the braking force.
4. A vehicle economy vehicle speed look-ahead optimization method as claimed in claim 3, wherein the global speed optimization controller objective function is:
Figure FDA0002391245270000013
s.t.
Temin≤Te(k)≤Temax (4)
0≤Fb(k)≤Fbmax (5)
vlimitmin≤v(k)≤vlimitmax (6)
v(0)＝v(n)＝0 (7)
wherein n represents that n sampling positions are selected in the vehicle journey; k represents the kth sampling position; Δ tkFor the driving time of the vehicle from the sampling position k to k +1,
Figure FDA0002391245270000021
Δ s represents the distance between adjacent sampling locations; v (k +1) is the upper limit value of the velocity at the sampling position k + 1; q. q.s1、q2Is a weight factor; t isemin and Temax is the minimum and maximum values that the engine torque can reach, respectively; fbmax is the maximum braking force that the vehicle can achieve; vlimitmin and vlimitmax represent the lower limit value and the upper limit value of the running speed at the sampling position k, respectively.
5. The vehicle economy speed look-ahead optimization method of claim 4, wherein the objective function of the local speed optimization controller is:
Figure FDA0002391245270000022
s.t.
Temin≤Te(l)≤Temax (9)
0≤Fb(l)≤Fbmax (10)
vlimitmin≤v(l)≤vlimitmax (11)
wherein m is the step length of local optimization within the kth step length of global optimization; v. of*(l) Taking the smaller one of the vehicle speed at the position l and the vehicle speed limit value at the position l obtained by global optimization; ssafe(l) Is a safe distance from the front vehicle; s*(l) Is the estimated inter-vehicle distance between the vehicle and the front vehicle; q. q.s3、q4、q5Is a weighting factor.
6. The vehicle-economy vehicle speed look-ahead optimization method of claim 5, wherein the solution of the objective functions of the global speed optimization controller and the local speed optimization controller both use a distributed estimation algorithm.
7. The vehicle-economy vehicle speed look-ahead optimization method of claim 5, wherein a model predictive control theory is used to roll optimize an objective function of the local speed optimizer controller.
CN201810174883.6A 2018-03-02 2018-03-02 Economic vehicle speed forward-looking optimization method Active CN108583576B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810174883.6A CN108583576B (en) 2018-03-02 2018-03-02 Economic vehicle speed forward-looking optimization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810174883.6A CN108583576B (en) 2018-03-02 2018-03-02 Economic vehicle speed forward-looking optimization method

Publications (2)

Publication Number Publication Date
CN108583576A CN108583576A (en) 2018-09-28
CN108583576B true CN108583576B (en) 2020-07-07

Family

ID=63625614

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810174883.6A Active CN108583576B (en) 2018-03-02 2018-03-02 Economic vehicle speed forward-looking optimization method

Country Status (1)

Country Link
CN (1) CN108583576B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111791887A (en) * 2020-07-03 2020-10-20 北京理工大学 Vehicle energy-saving driving method based on layered vehicle speed planning

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109910890B (en) * 2019-03-19 2020-11-27 吉林大学 Truck prediction energy-saving system based on road terrain information and control method
CN110778670B (en) * 2019-11-01 2021-09-17 长春工业大学 Comprehensive optimization control strategy for continuously variable transmission based on model predictive control
CN113291310A (en) * 2020-02-24 2021-08-24 中寰卫星导航通信有限公司 Vehicle travel control method and apparatus
CN111645685B (en) * 2020-06-05 2022-02-08 上海元城汽车技术有限公司 Economic vehicle speed planning method and device, storage medium and equipment
EP4211526A1 (en) 2020-10-22 2023-07-19 embotech AG Real-time, energy-efficient computations of optimal road vehicle velocity profiles
CN112498355B (en) * 2020-11-02 2022-11-25 浙江吉利控股集团有限公司 Speed planning method and device
CN114516342B (en) * 2020-11-19 2024-05-03 上海汽车集团股份有限公司 Vehicle control method and device and vehicle
CN112525210B (en) * 2020-11-24 2022-09-16 同济大学 Energy-saving-oriented global path and speed joint optimization method for electric automobile
CN113173174B (en) * 2021-06-09 2023-02-28 苏州智加科技有限公司 Method, device and equipment for determining vehicle running speed
CN113561993B (en) * 2021-08-13 2023-02-07 苏州智加科技有限公司 Vehicle speed planning method and device and electronic equipment
CN114212094B (en) * 2021-11-29 2023-09-29 东风商用车有限公司 Vehicle acceleration management method based on MPC control
CN115257756B (en) * 2022-08-09 2024-05-31 东风柳州汽车有限公司 Method and device for controlling lane change of vehicle based on optimal oil consumption

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3012783B1 (en) * 2013-11-05 2015-10-23 Renault Sas METHOD AND SYSTEM FOR CONTROLLING THE SPEED OF A VEHICLE
GB201420988D0 (en) * 2014-11-26 2015-01-07 Tomtom Telematics Bv Apparatus and method for vehicle economy improvement
CN107117170B (en) * 2017-04-28 2019-04-09 吉林大学 A kind of real-time prediction cruise control system driven based on economy
CN107161154B (en) * 2017-05-18 2019-07-05 重庆大学 Consider the economic pace acquisition methods of gear

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111791887A (en) * 2020-07-03 2020-10-20 北京理工大学 Vehicle energy-saving driving method based on layered vehicle speed planning

Also Published As

Publication number Publication date
CN108583576A (en) 2018-09-28

Similar Documents

Publication Publication Date Title
CN108583576B (en) Economic vehicle speed forward-looking optimization method
CN111439260B (en) Network-connected commercial diesel vehicle cruise running optimization control system oriented to individual requirements
CN109733378B (en) Offline optimized online predicted torque distribution method
KR101607248B1 (en) Method and module for controlling a vehicle&#39;s speed based on rules and/or costs
CN110893853B (en) Vehicle control method and system based on front gradient information
CN103496368A (en) Automobile cooperative type self-adaptive cruise control system and method with learning ability
SE534752C2 (en) Method and module in connection with cruise control
CN113788021A (en) Adaptive following cruise control method combined with preceding vehicle speed prediction
CN113269963B (en) Internet vehicle signal lamp control intersection economic passing method based on reinforcement learning
CN111703418B (en) Multi-vehicle distributed cooperative collision avoidance method and device based on vehicle-to-vehicle communication
CN112477846B (en) Intelligent networking electric automobile queue control method giving consideration to stability and energy conservation
CN113593275A (en) Intersection internet automatic driving method based on bus signal priority
DE102017129018A1 (en) Method for operating a motor vehicle
CN109064760B (en) Data-driven intelligent robust vehicle speed real-time planning method and system
Kaku et al. Model predictive control for ecological vehicle synchronized driving considering varying aerodynamic drag and road shape information
CN112731806B (en) Intelligent networking automobile random model prediction control real-time optimization method
CN113276829A (en) Vehicle running energy-saving optimization weight-changing method based on working condition prediction
CN114537420B (en) Urban bus rapid transit energy-saving driving control method based on dynamic planning
CN111591324A (en) Heavy-load train energy consumption optimization method based on gray wolf optimization algorithm
CN109131349B (en) Method for inhibiting road traffic ghost from being blocked
CN113232651A (en) Fuel automobile ecological self-adaptive cruise control method based on ADHDP
Wu et al. Research on predictive cruise control of electric vehicle based on time-varying model prediction
Luo et al. Energy-efficient feedback control strategy of vehicle platoon on highway with varying slopes
Nie et al. An Improved Velocity Forecasts Method Considering the Speed and Following Distance of Two Vehicles
CN115257756B (en) Method and device for controlling lane change of vehicle based on optimal oil consumption

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant