CN110182215B - Automobile economical cruise control method and device - Google Patents

Automobile economical cruise control method and device Download PDF

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CN110182215B
CN110182215B CN201910437853.4A CN201910437853A CN110182215B CN 110182215 B CN110182215 B CN 110182215B CN 201910437853 A CN201910437853 A CN 201910437853A CN 110182215 B CN110182215 B CN 110182215B
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vehicle
time
speed
vehicle speed
road
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CN110182215A (en
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周健豪
何龙强
赵万忠
孙静
薛四伍
丁一
宋廷伦
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Nanjing University of Aeronautics and Astronautics
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—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
    • 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
    • B60W2050/0034—Multiple-track, 2D vehicle model, e.g. four-wheel model
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/15—Road slope
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/40—Coefficient of friction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention discloses an automobile economical cruise control method and device, wherein the control method comprises the following steps: establishing a vehicle fuel consumption model and a vehicle longitudinal dynamics model, establishing a vehicle speed optimization target, and solving an economical cruise vehicle speed; the driving computer estimates the road adhesion coefficient, and determines the corresponding economical cruising speed according to the estimated current road adhesion coefficient; converting the economical cruising speed corresponding to the discrete position into an economical cruising speed corresponding to the discrete time; the invention adopts the model prediction controller to track and control the vehicle speed, and ensures that the vehicle can give consideration to the driving safety and the fuel economy under the complex road surface with the real-time change of the road gradient and the road adhesion coefficient.

Description

Automobile economical cruise control method and device
Technical Field
The invention relates to control of automobile economy cruise under complex road conditions, and mainly considers the influence of road gradient, road adhesion coefficient and change thereof on the fuel economy and driving safety of an automobile.
Background
The intelligent automobile based on the internet is a trend of the development of the automobile industry at present, and the optimal decision is made mainly by acquiring a large amount of vehicle and road information and through various optimization algorithms, so that the driving quality of the vehicle is improved. In recent years, a series of researches for planning the economic speed of the vehicle by utilizing environmental information based on the internet intelligent vehicle are developed at home and abroad. Scholars such as Matthew Barth at riverside school of California university in America study the economic vehicle speed when a vehicle passes through a traffic light, and the vehicle carries out vehicle speed planning by acquiring traffic light phase transformation information in advance, so that unnecessary acceleration and deceleration when the vehicle passes through a traffic light intersection is avoided, and the fuel economy of the vehicle is improved. Scholars such as M.A.S.Kamal university of Kyushu, Japan plan the vehicle speed during the process of ascending and descending the slope based on a model prediction algorithm, and effectively reduce the fuel consumption of the vehicle by reducing braking and sudden acceleration. The invention discloses a safe vehicle speed calculation method for a curve based on vehicle-road cooperation by students such as zhu summit, which is the university of wuhan theory, in the invention patent CN105118316B, the vehicle speed optimization of a vehicle running on the curve is completed, and a method for accurately calculating the safe vehicle speed of the vehicle when the vehicle enters the curve is established. Xin scholars in patent CN104200656B propose a vehicle speed optimization method based on traffic signal information, which avoids rapid acceleration and rapid deceleration and long-time idling when vehicles pass through a traffic intersection and controls the vehicle speed within a certain range, thereby improving the fuel economy of vehicles.
At present, aiming at speed optimization control of road information, traffic lights, curves, road slopes and other information are mainly considered at home and abroad, and influence of road condition change on a vehicle speed optimization process is not considered, wherein the most typical road surface change is road surface adhesion rate mutation caused by weather, such as road surface ponding, icing and the like caused by sudden rain and snow, according to statistics of relevant data, the traffic accident occurrence rate in winter is obviously higher than the average level all year round, the accident occurrence rate in the month of severe weather is 5 times of that in the month of severe weather, and the accident occurrence probability in the day of ice and snow is 12 times of that in a clear day, so that vehicle speed is optimized under the condition that the road condition change is not considered, and adverse influence is generated on fuel economy and driving safety.
The invention content is as follows:
in order to solve the existing problems, the invention provides the cruise control method for the economy of the automobile, which considers the road gradient and the road adhesion condition, so that the driving safety of the automobile can be ensured and the optimal fuel economy can be achieved under the conditions of hilly terrain with variable gradient and road surface adhesion conditions.
In order to achieve the purpose, the invention adopts the technical scheme that: an automobile economical cruise control method comprises the following steps:
step 1, establishing a vehicle fuel consumption model by adopting a polynomial fitting method, wherein a mathematical expression of the model is as follows:
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (1)
in the formula, mfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a fitting coefficient;
step 2, establishing a vehicle longitudinal dynamic model based on discrete positions, wherein the mathematical expression of the model is as follows:
Figure GDA0002902570040000021
in the formula(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk) Is a driving force; fb(sk) Is braking force; fg(sk) Is the ramp resistance; fr(sk) Is rolling friction resistance; fa(sk) Is the air resistance;
step 3, constructing an objective function and constraint, wherein the mathematical expressions are respectively expressed as formulas (3) and (4):
Figure GDA0002902570040000022
in the formula (I), the compound is shown in the specification,
Figure GDA0002902570040000023
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to both fuel economy and driving timeliness,
the constraint expression is:
Figure GDA0002902570040000024
in the formula, vminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively; etabIn order to achieve a high braking efficiency,
Figure GDA0002902570040000025
is the road surface adhesion coefficient; a isbmaxIs the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, α(s)k) Is a slope angle;
step 4, dividing the continuously changed road adhesion coefficients according to intervals, taking the lower limit of the adhesion coefficients of the intervals as a calculation basis, and sequentially calculating the corresponding optimal vehicle speed sequence under different road adhesion conditions
Figure GDA0002902570040000026
n is the number of the road adhesion coefficient division areas, the continuously-changed adhesion coefficients are calculated in a grading manner, so that the calculation difficulty is reduced, and the lower limit value of the adhesion coefficient area is used as the calculation basis to ensure the vehicle speed safety of the vehicle in the adhesion coefficient area;
and 5, converting the economical cruise speed corresponding to the discrete position in the step 4 into a position corresponding to the discrete time, an economical cruise speed and an acceleration by adopting the formulas (5) to (7):
Figure GDA0002902570040000027
Figure GDA0002902570040000031
Figure GDA0002902570040000032
in the formula (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure GDA0002902570040000033
an economic cruise speed corresponding to the discrete position;
Figure GDA0002902570040000034
and
Figure GDA0002902570040000035
respectively corresponding positions, economical cruising speeds and accelerations of discrete time;
step 6, establishing a vehicle state space model, constructing an objective function and a constraint expression, and performing vehicle speed tracking control by adopting a model prediction controller, wherein the state space model is as follows:
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (8)
in the formula (I), the compound is shown in the specification,
Figure GDA0002902570040000036
is a state variable, namely the speed and position of the vehicle;
Figure GDA0002902570040000037
Figure GDA0002902570040000038
the objective function is:
Figure GDA0002902570040000039
in the formula, Qv、RaWeighting coefficients of vehicle speed tracking error and acceleration fluctuation respectively,
a(ti|tk) Is tkTime tiPredicted acceleration value at time, i ═ kp-1，
The constraint expression is:
Figure GDA00029025700400000310
in the formula (I), the compound is shown in the specification,
Figure GDA00029025700400000311
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
Preferably, the system comprises an information acquisition module, an optimization calculation module and a real-time control module, wherein the information acquisition module acquires the current position of the vehicle and road gradient information of a limited distance to the vehicle, acquires a sensor and vehicle state information, and the real-time control module realizes tracking control of the economical cruise vehicle speed on the premise of ensuring driving safety.
Preferably, the information acquisition module acquires the real-time position of the vehicle through a global positioning system GPS signal receiver, and performs travel planning according to the departure place and the destination input by the driver; acquiring road gradient information of a limited distance ahead through a Geographic Information System (GIS), wherein the road gradient information also comprises position information corresponding to the road gradient information; acquiring real-time vehicle speed through a vehicle speed sensor; acquiring real-time spring load through a load sensor; and acquiring the real-time wheel rotating speed through a wheel speed sensor.
Preferably, the optimization calculation module calculates the economical cruising speed sequence corresponding to the positions under different road adhesion coefficients by adopting the formulas (1) to (4), estimates the road adhesion coefficient in real time by adopting a Dugoff tire model in combination with the speed, the spring load mass, the wheel speed and the vehicle state information, determines the economical cruising speed corresponding to the current road adhesion coefficient according to the current road adhesion coefficient,
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (1)
in the formula, mfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a fitting coefficient;
Figure GDA0002902570040000041
in the formula(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk) Is a driving force; fb(sk) Is braking force; fg(sk) Is the ramp resistance; fr(sk) Is rolling friction resistance; fa(sk) Is the air resistance;
Figure GDA0002902570040000042
in the formula (I), the compound is shown in the specification,
Figure GDA0002902570040000043
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to both fuel economy and driving timeliness,
Figure GDA0002902570040000044
in the formula, vminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively; etabIn order to achieve a high braking efficiency,
Figure GDA0002902570040000045
is the road surface adhesion coefficient; a isbmaxIs the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, α(s)k) Is a slope angle.
Preferably, the real-time control module takes the economical cruising speed as a reference, combines the information of a geographic information system GIS and a global positioning system GPS, adopts the formulas (5) to (7) to convert the economical cruising speed corresponding to the position into the economical cruising speed corresponding to the time,
Figure GDA0002902570040000051
Figure GDA0002902570040000052
Figure GDA0002902570040000053
in the formula (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure GDA0002902570040000054
an economic cruise speed corresponding to the discrete position;
Figure GDA0002902570040000055
and
Figure GDA0002902570040000056
respectively, discrete time corresponding position, economical cruise speed and acceleration.
Preferably, the model prediction controller is adopted for vehicle speed tracking control, and a real-time control module is combined to control the acceleration of the vehicle through coordinating a driving system and a braking system, so that the economical cruise vehicle speed tracking control is realized; the state space model adopted by the controller is shown as a formula (8), the objective function is shown as a formula (9), the constraint is shown as a formula (10),
the state space model is:
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (8)
in the formula (I), the compound is shown in the specification,
Figure GDA0002902570040000057
is a state variable, namely the speed and position of the vehicle;
Figure GDA0002902570040000058
Figure GDA0002902570040000059
the objective function is:
Figure GDA00029025700400000510
wherein Q isv、RaWeighting coefficients of vehicle speed tracking error and acceleration fluctuation respectively,
a(ti|tk) Is tkTime tiPredicted acceleration value at time, i ═ kp-1，
The constraint expression is:
Figure GDA00029025700400000511
wherein the content of the first and second substances,
Figure GDA00029025700400000512
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
And solving the nonlinear optimization problem containing the constraint in real time, wherein the obtained economical cruise speed corresponding to the discrete position not only comprises speed information, but also comprises corresponding vehicle position information.
The speed cruise control method provided by the invention adopts a speed optimization mode based on the position, and the invalid optimization period caused by traffic congestion is greatly eliminated. The vehicle does not need to optimize the speed curve again after passing through the congested road section, the calculation difficulty of a driving computer is reduced, the calculation time is saved, and the fuel economy and the driving safety of the vehicle are improved.
The continuously changed attachment coefficients are calculated in a grading manner, so that the calculation difficulty is reduced, and the scheme has implementability; the lower limit value of the adhesion coefficient interval is used as a calculation basis, so that the vehicle speed safety of the vehicle in the adhesion coefficient interval is ensured. Meanwhile, when the road surface adhesion coefficient changes suddenly, the vehicle only needs to select from the existing optimal speed curves, the time is not needed to be spent on recalculating the vehicle optimal speed curve corresponding to the current road surface, and the vehicle can rapidly complete the speed switching process. The method avoids the situation that the vehicle needs to recalculate the optimal speed and runs at an unreasonable speed for a long time after the road adhesion coefficient is suddenly changed, ensures the driving safety of the vehicle and improves the fuel economy of the vehicle.
The control device applying the control method comprises an information acquisition module, an optimization calculation module and a real-time control module, wherein the information acquisition module acquires road gradient information of a current position and a limited distance to the vehicle, acquires a sensor and vehicle state information, and the real-time control module realizes tracking control of the economical cruise vehicle speed on the premise of ensuring driving safety.
Preferably, the information acquisition module acquires the real-time position of the vehicle through a global positioning system GPS signal receiver, and performs travel planning according to the departure place and the destination input by the driver; acquiring road gradient information of a limited distance ahead through a Geographic Information System (GIS), wherein the information also comprises corresponding position information; acquiring real-time vehicle speed through a vehicle speed sensor; acquiring real-time spring load through a load sensor; and acquiring the real-time wheel rotating speed through a wheel speed sensor.
The optimization calculation module calculates the economical cruise vehicle speed sequence corresponding to the positions under different road adhesion coefficients by adopting the formulas (11) to (14):
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (11)
wherein m isfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6In order to be a coefficient of fit,
Figure GDA0002902570040000061
wherein, -(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk) Is a driving force; fb(sk) Is braking force; fg(sk) Is the ramp resistance; fr(sk) Is rolling friction resistance; fa(sk) Is the air resistance;
Figure GDA0002902570040000062
wherein the content of the first and second substances,
Figure GDA0002902570040000071
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to both fuel economy and driving timeliness,
Figure GDA0002902570040000072
wherein v isminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively; etabIn order to achieve a high braking efficiency,
Figure GDA0002902570040000073
is the road surface adhesion coefficient; a isbmaxIs the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance;
the method comprises the following steps of adopting a Dugoff tire model to combine vehicle speed, spring load mass, wheel speed and vehicle state information to estimate a real-time road adhesion coefficient, and determining an economical cruising vehicle speed corresponding to the current road adhesion coefficient:
the vehicle cruise control method provided by the invention fully considers the road gradient and the road adhesion coefficient change in the driving process, and calculates the optimal vehicle speed of the vehicle on the basis of the road gradient and the road adhesion coefficient change, so that the vehicle can adapt to the road gradient to drive at the speed which is most beneficial to the vehicle economy; meanwhile, the vehicle can be controlled to run at the speed within the safety range under various road adhesion coefficients, and the safety of the vehicle under the complex road adhesion environment is ensured.
Preferably, the real-time control module takes the economical cruise vehicle speed as a reference, combines information of a Geographic Information System (GIS) and a Global Positioning System (GPS), and adopts equations (15) to (17) to convert the economical cruise vehicle speed corresponding to the position into the economical cruise vehicle speed corresponding to the time:
Figure GDA0002902570040000074
Figure GDA0002902570040000075
Figure GDA0002902570040000076
wherein, - (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure GDA0002902570040000077
an economic cruise speed corresponding to the discrete position;
Figure GDA0002902570040000078
and
Figure GDA0002902570040000079
respectively corresponding positions, economical cruising speeds and accelerations of discrete time;
preferably, the model prediction controller is adopted for vehicle speed tracking control, and a real-time control module is combined to control the acceleration of the vehicle through coordinating a driving system and a braking system, so that the economical cruise vehicle speed tracking control is realized; the state space model adopted by the controller is shown as an equation (18), the objective function is shown as an equation (19), and the constraint is shown as an equation (20):
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (18)
wherein the content of the first and second substances,
Figure GDA0002902570040000081
is a state variable, namely the speed and position of the vehicle;
Figure GDA0002902570040000082
Figure GDA0002902570040000083
Figure GDA0002902570040000084
wherein Q isv、RaWeighting coefficients of vehicle speed tracking error and acceleration fluctuation respectively,
Figure GDA0002902570040000085
wherein the content of the first and second substances,
Figure GDA0002902570040000086
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
According to the vehicle cruise control method, the model prediction controller is adopted to control the vehicle state, the control system is simple in mechanism, and meanwhile, the anti-interference performance is high, so that the reliability of the vehicle control process is ensured, and the fuel economy and the safety of the vehicle cruise process are effectively improved.
Description of the drawings:
FIG. 1 is a flow chart of a vehicle economy cruise optimization and control method;
FIG. 2 is a graph comparing fuel consumption for a vehicle at constant speed cruising with economical cruising under different road surfaces;
FIG. 3 is a schematic representation of road adhesion coefficients;
FIG. 4 is a graph of optimized vehicle speed versus road safe vehicle speed based on good road surfaces;
FIG. 5 is a graph comparing an optimized vehicle speed based on a wet road surface to a road safe vehicle speed;
FIG. 6 is a graph showing a comparison of an actual control vehicle speed of a vehicle with a road safety vehicle speed based on a road adhesion coefficient change result;
fig. 7 is a schematic structural diagram of the vehicle economy cruise optimization control apparatus.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
Example one
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A vehicle economical cruise control method according to an embodiment of the present invention will be described below with reference to the accompanying drawings, and first, the proposed vehicle economical cruise control method will be described with reference to the accompanying drawings, including the steps of: step 1, establishing a vehicle fuel consumption model and a vehicle longitudinal dynamics model, establishing a vehicle speed optimization target, and solving an economic cruise vehicle speed; step 2, a traveling computer carries out pavement adhesion coefficient estimation, and determines a corresponding economical cruising speed according to the estimated adhesion coefficient of the current pavement; step 3, converting the economical cruising speed corresponding to the discrete position into an economical cruising speed corresponding to the discrete time; and 4, carrying out vehicle speed tracking control by adopting a model prediction controller.
As shown in fig. 1, solving for the economical cruise vehicle speed comprises the steps of:
step one, establishing a vehicle fuel consumption model;
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (1)
wherein m isfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a resultant coefficient. The fuel consumption model is obtained by fitting a multiple term expression related to the torque and the rotating speed of the engine, and the fitting result is shown in the attached figure 3;
it should be noted that the fuel consumption model is suitable for traditional fuel (gas) engines such as gasoline engines, diesel engines, natural gas engines and the like.
Step two, establishing a vehicle longitudinal dynamic model corresponding to the position:
Figure GDA0002902570040000091
wherein, -(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk)＝ηmPe(sk)/v(sk) As a driving force, Pe(sk)＝f(Te,ωe) Is the engine power, ηmFor transmission system mechanical efficiency;
Figure GDA0002902570040000092
is braking force, ηbIn order to achieve a high braking efficiency,
Figure GDA0002902570040000093
alpha(s) as a road surface adhesion coefficientk) Is a slope angle; fg(sk)＝mgsinα(sk) Is the ramp resistance; fr(sk)＝mgfcosα(sk) Is rolling friction resistance, and f is a rolling resistance coefficient; fa(sk)＝0.5CDρAv(sk)2As air resistance, CDIs the air resistance coefficient, A is the frontal area, and ρ is the air density.
Constructing an optimized objective function and a constraint expression, and solving the economical cruise speed;
the economic cruise vehicle speed optimization objective function is as follows:
Figure GDA0002902570040000094
wherein the content of the first and second substances,
Figure GDA0002902570040000095
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to fuel economy and driving timeliness.
The constraint expression is:
Figure GDA0002902570040000101
wherein v isminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively;
Figure GDA0002902570040000102
is the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, set to 50 m;
solving the nonlinear optimization problem containing the constraint in real time to obtain the economical cruise speed corresponding to the discrete position, wherein the economical cruise speed not only comprises speed information, but also comprises corresponding vehicle position information;
dividing continuously-changed road adhesion coefficients according to intervals, taking the lower limit of the adhesion coefficients of the intervals as a calculation basis, and sequentially calculating the corresponding optimal vehicle speed sequence under different road adhesion conditions
Figure GDA0002902570040000103
The continuously changed attachment coefficients are calculated in a grading manner, so that the calculation difficulty is reduced, and the scheme has implementability; the lower limit value of the adhesion coefficient interval is used as a calculation basis, so that the vehicle speed safety of the vehicle in the adhesion coefficient interval is ensured.
The road surface adhesion coefficient is divided into five sections: 0.05-0.2, 0.2-E0.4, 0.4-0.6, 0.6-0.8, 0.8-1, and calculating the economical cruise speed sequence with road surface coefficients of 0.05, 0.2, 0.4, 0.6 and 0.8
Figure GDA0002902570040000104
As shown in fig. 1, the method for controlling the economical cruising speed of the automobile comprises the following steps:
estimating a road adhesion coefficient according to a sensor and vehicle state information, and determining a corresponding economical cruise vehicle speed according to the current road adhesion coefficient;
estimating the vehicle road surface adhesion coefficient in real time by adopting a Kalman filtering method based on a Dugoff tire model;
according to the current road adhesion coefficient
Figure GDA0002902570040000105
And selecting the corresponding vehicle speed from the economical cruising vehicle speed sequence as the reference vehicle speed.
Converting the economical cruising speed corresponding to the discrete position into an economical cruising speed corresponding to the discrete time;
converting the economical cruise speed corresponding to the discrete position into a position, an economical cruise speed and an acceleration corresponding to the discrete time by adopting the formulas (5) to (7) so as to conveniently adopt a model prediction controller to carry out speed tracking control;
Figure GDA0002902570040000106
Figure GDA0002902570040000107
Figure GDA0002902570040000108
wherein, - (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure GDA0002902570040000111
calculating an economical cruising speed corresponding to the discrete position by the method of claim 3;
Figure GDA0002902570040000112
and
Figure GDA0002902570040000113
respectively, discrete time corresponding position, economical cruise speed and acceleration. Discrete velocity points are fitted to a continuous velocity curve by interpolation, as shown in figure 4.
Step three, establishing a vehicle state space model, constructing a target function and a constraint expression, and performing vehicle speed tracking control by adopting a model prediction controller; the vehicle state space model is as follows:
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (8)
wherein the content of the first and second substances,
Figure GDA0002902570040000114
is a state variable, namely the speed and position of the vehicle;
Figure GDA0002902570040000115
Figure GDA0002902570040000116
the objective function is:
Figure GDA0002902570040000117
wherein Q isv、RaThe weight coefficients of the vehicle speed tracking error and the acceleration fluctuation are respectively.
The constraint expression is:
Figure GDA0002902570040000118
wherein the content of the first and second substances,
Figure GDA0002902570040000119
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
In order to verify the effect of the whole optimization control, the simulation verification is performed on the whole optimization control process by combining Matlab and the specialized vehicle simulation software Trucksim in the embodiment.
As shown in fig. 2, the fuel consumption of the vehicle running on three different roads respectively at constant speed cruising and in the optimized mode of the invention, the fuel consumption of the vehicle running according to the optimized speed curve on the left side, and the fuel consumption of the vehicle running on the right side through the corresponding road at constant speed cruising at 20 m/s; simulation results show that compared with the traditional constant-speed cruising, the economical cruising provided by the invention on three different roads reduces the fuel consumption by 9.3%, 14% and 8% respectively; the vehicle speed optimization method provided by the invention can effectively improve the vehicle fuel economy, and proves the effectiveness of the designed control method in the vehicle running process speed optimization.
It should be noted that, in order to save the calculation time and improve the optimization timeliness of the economical cruise, the total travel is divided into a plurality of parts, the vehicle speed optimization is carried out in a successive optimization mode, the vehicle speed optimization is carried out on the travel with limited distance each time, and the vehicle speed optimization of the next limited distance is carried out after the optimization is finished.
FIG. 3 is a road adhesion coefficient condition, as shown in FIG. 3, where the adhesion coefficient is reduced due to weather conditions for some road segments; FIG. 4 is an economical cruise vehicle speed curve versus safe vehicle speed based on a road adhesion coefficient of 1, i.e., good road optimization; FIG. 5 is an economical cruise vehicle speed curve versus safe vehicle speed based on a road adhesion coefficient of 0.5, i.e., wet road optimization; FIG. 6 is an economical cruise vehicle speed curve and safe vehicle speed resulting from the optimization control strategy proposed by the present invention. The analytical curve can be found: the vehicle speed obtained based on the single road adhesion coefficient optimization has the possibility of exceeding the safe vehicle speed on a low adhesion road section, and the vehicle is easy to drift or sideslip; although the speed of a vehicle is guaranteed to be within a safe range in a high-adhesion road section, the overall speed of the vehicle is low, the difference between the overall speed of the vehicle and the economical cruising speed of the vehicle is large, the transportation time is increased, and the improvement of the fuel economy of the vehicle is not facilitated. The method avoids the defects of the first two optimization modes, the obtained economical cruise speed can adapt to the change of road environment, the speed is adjusted according to the change of the road adhesion coefficient, the safety of the vehicle is ensured, meanwhile, the timeliness of the driving process is ensured, and the fuel economy of the vehicle is improved.
Example two
As shown in fig. 7, the economical cruise control device for the automobile comprises an information acquisition module, an optimization calculation module and a real-time control module, wherein the information acquisition module acquires the current position of the automobile and the road gradient information of the limited distance to the automobile, and acquires the sensor and the state information of the automobile, and the real-time control module realizes the tracking control of the economical cruise speed on the premise of ensuring the driving safety.
Further, the information acquisition module acquires the real-time position of the vehicle through a Global Positioning System (GPS) signal receiver and carries out travel planning according to a departure place and a destination input by a driver;
acquiring road gradient information of a limited distance ahead through a Geographic Information System (GIS), wherein the information also comprises corresponding position information;
acquiring real-time vehicle speed through a vehicle speed sensor;
acquiring real-time spring load through a load sensor; and
and acquiring the real-time wheel rotating speed through a wheel speed sensor.
Further, the optimization calculation module calculates the economical cruise vehicle speed sequence corresponding to the positions under different road adhesion coefficients by adopting the following formulas (11) to (14):
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (11)
wherein m isfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a resultant coefficient. The fuel consumption model is obtained by fitting a multiple term expression related to the torque and the rotating speed of the engine, and the fitting result is shown in the attached figure 3;
Figure GDA0002902570040000121
wherein, -(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk)＝ηmPe(sk)/v(sk) As a driving force, Pe(sk)＝f(Te,ωe) Is the engine power, ηmFor transmission system mechanical efficiency;
Figure GDA0002902570040000131
is braking force, ηbIn order to achieve a high braking efficiency,
Figure GDA0002902570040000132
alpha(s) as a road surface adhesion coefficientk) Is a slope angle; fg(sk)＝mgsinα(sk) Is the ramp resistance; fr(sk)＝mgfcosα(sk) Is rolling friction resistance, and f is a rolling resistance coefficient; fa(sk)＝0.5CDρAv(sk)2As air resistance, CDIs the air resistance coefficient, A is the frontal area, and ρ is the air density.
Figure GDA0002902570040000133
Wherein the content of the first and second substances,
Figure GDA0002902570040000134
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to fuel economy and driving timeliness.
Figure GDA0002902570040000135
Wherein v isminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively;
Figure GDA0002902570040000136
is the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, set to 50 m;
estimating the real-time road surface adhesion coefficient by adopting a Dugoff tire model in combination with the vehicle speed, the spring load mass, the wheel speed, the vehicle state information and the like;
determining an economical cruising speed corresponding to the current road adhesion coefficient; when the road adhesion coefficient changes, vehicle speed switching is performed.
Further, the real-time control module takes the economical cruise vehicle speed as a reference, combines information of a Geographic Information System (GIS) and a Global Positioning System (GPS), and adopts formulas (15) to (17) to convert the economical cruise vehicle speed corresponding to the position into the economical cruise vehicle speed corresponding to the time:
Figure GDA0002902570040000137
Figure GDA0002902570040000138
Figure GDA0002902570040000141
wherein, - (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure GDA0002902570040000142
calculating an economical cruising speed corresponding to the discrete position by the method of claim 3;
Figure GDA0002902570040000143
and
Figure GDA0002902570040000144
respectively, discrete time corresponding position, economical cruise speed and acceleration.
Further, a model predictive controller is adopted for vehicle speed tracking control, a state space model adopted by the controller is shown as an equation (18), an objective function is shown as an equation (19), and a constraint is shown as an equation (20).
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (18)
Wherein the content of the first and second substances,
Figure GDA0002902570040000145
is a state variable, namely the speed and position of the vehicle;
Figure GDA0002902570040000146
Figure GDA0002902570040000147
Figure GDA0002902570040000148
wherein Q isv、RaThe weight coefficients of the vehicle speed tracking error and the acceleration fluctuation are respectively.
Figure GDA0002902570040000149
Wherein the content of the first and second substances,
Figure GDA00029025700400001410
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
Furthermore, the real-time control module controls the acceleration of the vehicle by coordinating the driving and braking systems, so that the economical cruise vehicle speed tracking control is realized.
In the description herein, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (6)

1. An automobile economical cruise control method comprises the following steps:
step 1, establishing a vehicle fuel consumption model by adopting a polynomial fitting method, wherein a mathematical expression of the model is as follows:
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (1)
in the formula, mfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a fitting coefficient;
step 2, establishing a vehicle longitudinal dynamic model based on discrete positions, wherein the mathematical expression of the model is as follows:
Figure FDA0002902570030000011
in the formula(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk) Is a driving force; fb(sk) Is braking force; fg(sk) Is the ramp resistance; fr(sk) Is rolling friction resistance; fa(sk) Is the air resistance;
step 3, constructing an objective function and constraint, wherein the mathematical expressions are respectively expressed as formulas (3) and (4):
Figure FDA0002902570030000012
in the formula (I), the compound is shown in the specification,
Figure FDA0002902570030000013
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to both fuel economy and driving timeliness,
the constraint expression is:
Figure FDA0002902570030000014
in the formula, vminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively; etabIn order to achieve a high braking efficiency,
Figure FDA0002902570030000015
is the road surface adhesion coefficient; a isbmaxIs the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, α(s)k) Is a slope angle;
step 4, dividing the continuously changed road adhesion coefficients according to intervals, taking the lower limit of the adhesion coefficients of the intervals as a calculation basis, and sequentially calculating the corresponding optimal vehicle speed sequence under different road adhesion conditions
Figure FDA0002902570030000016
n is the number of the road adhesion coefficient division areas, the continuously-changed adhesion coefficients are calculated in a grading manner, so that the calculation difficulty is reduced, and the lower limit value of the adhesion coefficient area is used as the calculation basis to ensure the vehicle speed safety of the vehicle in the adhesion coefficient area;
and 5, converting the economical cruise speed corresponding to the discrete position in the step 4 into a position corresponding to the discrete time, an economical cruise speed and an acceleration by adopting the formulas (5) to (7):
Figure FDA0002902570030000021
Figure FDA0002902570030000022
Figure FDA0002902570030000023
in the formula (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure FDA0002902570030000024
an economic cruise speed corresponding to the discrete position;
Figure FDA0002902570030000025
and
Figure FDA0002902570030000026
respectively corresponding positions, economical cruising speeds and accelerations of discrete time;
step 6, establishing a vehicle state space model, constructing an objective function and a constraint expression, and performing vehicle speed tracking control by adopting a model prediction controller, wherein the state space model is as follows:
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (8)
in the formula (I), the compound is shown in the specification,
Figure FDA0002902570030000027
is a state variable, namely the speed and position of the vehicle;
Figure FDA0002902570030000028
Figure FDA0002902570030000029
the objective function is:
Figure FDA00029025700300000210
in the formula, Qv、RaWeighting coefficients of vehicle speed tracking error and acceleration fluctuation respectively,
a(ti|tk) Is tkTime tiPredicted acceleration value at time, i ═ kp-1，
The constraint expression is:
Figure FDA00029025700300000211
in the formula (I), the compound is shown in the specification,
Figure FDA00029025700300000212
is the maximum acceleration of the vehicle; a (t)i|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) M is the maximum deceleration of the vehicle, a (t)i|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
2. The control device applied to the control method according to claim 1 comprises an information acquisition module, an optimization calculation module and a real-time control module, wherein the information acquisition module acquires road gradient information of a current position and a limited distance to the vehicle, acquires a sensor and vehicle state information, and the real-time control module realizes tracking control of the economical cruise vehicle speed on the premise of ensuring driving safety.
3. The control device of claim 2, wherein the information acquisition module acquires the real-time position of the vehicle through a Global Positioning System (GPS) signal receiver and performs travel planning according to a departure place and a destination input by a driver; acquiring road gradient information of a limited distance ahead through a Geographic Information System (GIS), wherein the road gradient information also comprises position information corresponding to the road gradient information; acquiring real-time vehicle speed through a vehicle speed sensor; acquiring real-time spring load through a load sensor; and acquiring the real-time wheel rotating speed through a wheel speed sensor.
4. The control device according to claim 2 or 3, wherein the optimization calculation module calculates the economical cruise vehicle speed sequence corresponding to the positions under different road adhesion coefficients by using the formulas (1) to (4), performs real-time road adhesion coefficient estimation by using a Dugoff tire model in combination with vehicle speed, sprung mass, wheel speed and vehicle state information, determines the economical cruise vehicle speed corresponding to the current road adhesion coefficient,
mf＝η1+η2ωe+η3Te+η4ωeTe+η5ωe 2Te+η6ωe 3 (1)
in the formula, mfIs specific to fuel consumption, omegaeIs the engine speed, TeIs engine torque, η1，η2……η6Is a fitting coefficient;
Figure FDA0002902570030000031
in the formula(s)k) Is a position skThe corresponding parameters; Δ s is a predetermined fixed value, position sk-1To a position skThe distance of (d); m is the sprung mass; v(s)k) Is the vehicle speed; fe(sk) Is a driving force; fb(sk) Is braking force; fg(sk) Is the ramp resistance; fr(sk) Is rolling friction resistance; fa(sk) Is the air resistance;
Figure FDA0002902570030000032
in the formula (I), the compound is shown in the specification,
Figure FDA0002902570030000033
presetting an average vehicle speed for a driver; gamma is a weight coefficient, so that the optimization process gives consideration to both fuel economy and driving timeliness,
Figure FDA0002902570030000034
in the formula, vminAnd vmaxRespectively representing the lower limit and the upper limit of the speed of the running road; t iseminAnd TemaxMinimum and maximum engine torque; omegaeminAnd ωemaxLowest and highest engine speeds, respectively; etabIn order to achieve a high braking efficiency,
Figure FDA0002902570030000041
is the road surface adhesion coefficient; a isbmaxIs the maximum braking deceleration; Γ is the sum of driver reaction time and brake response time; sbmaxIs the maximum braking distance, α(s)k) Is a slope angle.
5. The control device according to claim 4, wherein the real-time control module converts the economical cruise vehicle speed corresponding to the position into the economical cruise vehicle speed corresponding to the time by using the formulas (5) to (7) with the economical cruise vehicle speed as a reference and combining the Geographic Information System (GIS) and the Global Positioning System (GPS) information,
Figure FDA0002902570030000042
Figure FDA0002902570030000043
Figure FDA0002902570030000044
in the formula (t)i|tk) Is tkTime tiThe predicted value of time, i ═ k.,. k + Np-1；ΔtMPCSampling time for the MPC controller;
Figure FDA0002902570030000045
an economic cruise speed corresponding to the discrete position;
Figure FDA0002902570030000046
and
Figure FDA0002902570030000047
respectively, discrete time corresponding position, economical cruise speed and acceleration.
6. The control device of claim 5, wherein the vehicle speed tracking control is realized by adopting a model predictive controller and combining a real-time control module to control the acceleration of the vehicle by coordinating a driving system and a braking system; the state space model adopted by the controller is shown as a formula (8), the objective function is shown as a formula (9), the constraint is shown as a formula (10),
the state space model is:
x(ti+1|tk)＝Ax(ti|tk)+Ba(ti|tk) (8)
in the formula (I), the compound is shown in the specification,
Figure FDA0002902570030000048
as state variables, i.e. vehiclesSpeed and position;
Figure FDA0002902570030000049
Figure FDA00029025700300000410
the objective function is:
Figure FDA00029025700300000411
wherein Q isv、RaWeighting coefficients of vehicle speed tracking error and acceleration fluctuation respectively,
a(ti|tk) Is tkTime tiPredicted acceleration value at time, i ═ kp-1，
The constraint expression is:
Figure FDA0002902570030000051
wherein the content of the first and second substances,
Figure FDA0002902570030000052
is the maximum acceleration of the vehicle;a(ti|tk)＝-(Fb(ti|tk)+Fg(ti|tk)+Fa(ti|tk)+Fr(ti|tk) Is the maximum deceleration of the vehicle is/m),a(ti|tk) Dynamically adjusted under the influence of the road adhesion coefficient.
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