CN104249736A - Hybrid electric vehicle energy-saving predictive control method based on platoons - Google Patents

Hybrid electric vehicle energy-saving predictive control method based on platoons Download PDF

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CN104249736A
CN104249736A CN201410420797.0A CN201410420797A CN104249736A CN 104249736 A CN104249736 A CN 104249736A CN 201410420797 A CN201410420797 A CN 201410420797A CN 104249736 A CN104249736 A CN 104249736A
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vehicle
optimal control
control
car
speed
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CN104249736B (en
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余开江
胡治国
许孝卓
张宏伟
王莉
杨俊起
荆鹏辉
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Henan University of Technology
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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
    • 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
    • B60W40/02—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 ambient conditions
    • B60W40/04—Traffic conditions
    • 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
    • B60W40/107—Longitudinal acceleration
    • 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
    • B60W2510/24—Energy storage means
    • B60W2510/242—Energy storage means for electrical energy
    • B60W2510/244—Charge state
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • 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
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4041—Position
    • 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
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/801—Lateral distance
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/65—Data transmitted between vehicles

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Hybrid Electric Vehicles (AREA)

Abstract

The invention discloses a hybrid electric vehicle energy-saving predictive control method based on platoons. The method includes acquiring real-time traffic information of the own vehicle and front vehicle from a global positioning system and an intelligent traffic system as system input; establishing a hybrid electric vehicle mathematical model serving as future vehicle state predication basis; defining the hybrid electric vehicle platoon optimal control problem, and providing a function equation to figure out an optimal control value; feeding back the optimal control in real time, figuring out the optimal control value, after the safe distance is met, acquiring the information to adjust and optimize hybrid electric vehicle energy flowing online according to the global positioning system, a radar, the intelligent traffic system and a vehicle communication system, and acquiring the optimal performance of the hybrid electric vehicles. A planetary gear mechanism serves as an electronic variable transmission, an engine is in the optimal operating point constantly, the front vehicle driving state is predicated through the road traffic information, the hybrid electric vehicle energy flowing is adjusted online, the purpose of energy saving and emission reduction is achieved; the method is different from a traditional fixed time headway control method, and a novel way is provided for improving the performance of a central controller of a hybrid electric vehicle management system.

Description

The energy-conservation forecast Control Algorithm of hybrid vehicle based on platoon driving
Technical field
The present invention relates to the energy-conservation forecast Control Algorithm of a kind of hybrid vehicle based on platoon driving, particularly a kind of method for controlling hybrid power vehicle of real-time optimum.
Background technology
Increasing rapidly of global energy and environment situation increasingly serious, particularly automobile pollution, promotes the development of new-energy automobile and intelligent transportation system.For transport solution blocks up, ecological deterioration and the large problem of traffic accident three, the present invention proposes the energy-conservation forecast Control Algorithm of hybrid vehicle based on platoon driving.Vehicle platoon travels technology and refers to multiple vehicle with less vehicle headway with the technology of a platoon driving.This technology greatly can improve the aerodynamic characteristic of vehicle periphery, reduces its air resistance, strengthens traffic safety, and effectively can improve the fuel economy of vehicle.On the other hand, compared with orthodox car, hybrid vehicle has the redundancy of battery and the driving of fuel oil dual system, uses this redundancy that actuating device operation point can be regulated to optimal location, thus realizes target for energy-saving and emission-reduction.The main flow of expectation future automobile will be this hybrid vehicle.Because hybrid vehicle can reclaim the regenerating braking energy of escort vehicle deceleration generation; Utilize the redundancy of drive system (driving engine and motor) to optimize actuating device operation point, therefore can greatly play energy-saving and emission-reduction effectiveness.But optimal working point is with the characteristic of driving engine, the motoring condition of surrounding vehicles, the change of road traffic condition and time changing.And rotate system's (driving engine and motor) and have the rotational speed and torque limit, battery has the state-of-charge limit, exceeds these limit very large for the performance impact of vehicle key components and parts.Therefore, the effects of energy saving and emission reduction of hybrid vehicle depends on its energy management strategies (meeting constraint condition) to a great extent.And its gordian technique is the real-time optimization in energy management central controller, to realizing the commercialization of control policy, industrialization.
The control policy of Energy Management System for Hybrid Electric Vehicle is technological core and the design difficulty of its research and development.The control policy proposed at present roughly can be divided into 4 classes: numerical value optimal control, resolves optimal control, instantaneous self correlation and heuristic control.The Typical Representative of numerical value optimal control is dynamic programming and Model Predictive Control.The Typical Representative of resolving optimal control is Pang Te lia king minimal principle control policy.The Typical Representative of instantaneous self correlation is the minimum control policy of instantaneous equivalent oil consumption.The Typical Representative of heuristic control strategy is rule-based control policy.Traditional area-wide optimal control method dynamic programming and Pang Te lia king minimal principle control method, because following all work informations known in advance by needs, cannot realize real-time optimum.Traditional rule-based control policy cannot maximize by implementation efficiency.General feed-forward type controls (assuming that car speed pattern is certain) cannot realize real-time optimum.Traditional instantaneous self correlation parameter affects too large by Shape Of Things To Come working conditions change, cannot meet controller performance.
Since early 1990s, the research and development of countries in the world to hybrid vehicle and intelligent transportation system give great attention, and achieve some great achievement and progress.1997, in the intelligent transportation system exhibition sponsored by US Department of Transportation, illustrate the platoon driving technology be made up of 8 cars.Toyota Motor company achieved the mass production of hybrid vehicle in 1997, within 2012, achieved the mass production of plug-in hybrid-power automobile.US President Barack Obama announces advanced battery of future generation and plug-in hybrid-power automobile plan for 2009.At home, national Eleventh Five-Year Plan 863 Program set up energy-conservation with new-energy automobile major project.The present inventor does one's doctorate period in Kyushu University, has grasped the C/GMRES fast resolution of Model Predictive Control method that Japanese enterprises and university generally adopt and the quick proposition of Japanese scholars great mound.The combination of these two kinds of methods solves the actual application problem of this advanced method of Model Predictive Control.
In this context, improve efficiency of energy utilization, reduce automobile has become current automobile industry development top priority to the pollution of environment and enhancing safety traffic.Meanwhile, utilize traffic information, improve the reality need that actuating device efficiency also becomes current social development further.In order to solve the problem, need to develop a kind of based on platoon driving can the hybrid vehicle model predictive control method of industrialization, thus realize target for energy-saving and emission-reduction.
Summary of the invention
For the problems referred to above, the object of the present invention is to provide a kind of hybrid vehicle model prediction method based on platoon driving can carrying out real-time estimate to Shape Of Things To Come operating mode, to reach energy-saving and emission-reduction to greatest extent, make it to become industrialization hybrid vehicle energy management central controller.
For achieving the above object, the present invention takes following technical scheme: the energy-conservation forecast Control Algorithm of a kind of hybrid vehicle based on platoon driving, and the first step is information acquisition, and second step is vehicle modeling, 3rd step is formulistic control policy, and the 4th step is online optimal control; It is characterized in that: comprise the following steps:
1) information acquisition:
Front truck and car's location information is gathered, as real-time vehicle feedback of status by global positioning system; Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device; Gather traffic signal information by intelligent transportation system and load-and-vehicle communication system, real-time road condition information and from car and front vehicle speed, acceleration information, for intellectual traffic control; The battery information gathered is utilized to estimate storage battery charge state by Kalman filter.
2) vehicle modeling:
Planetary gear type parallel-serial hybrid power automobile comprises 5 Larger Dynamic parts, and they are driving engines, storage battery, 2 electricity generation and electromotion all-in-ones and wheel.Planetary wheel, as the effect of the existing speed coupler of distributing means for power supply, has again the effect of electronics buncher.According to vehicle mechanical coupling and electron coupled relation, row write system dynamics equation, to kinetics equation decoupling zero, and the state-space model of final acquisition system, shown in (1):
x = [ p 1 v 1 w 1 SOC 1 p 2 v 2 w 2 SOC 2 ] u = [ u 1 u 2 P batt 1 P batt 1 ] x = f ( x , u ) f ( x , u ) = v 1 w 1 - 1 2 ρ C D 1 A 1 v 1 2 / m 1 - 9.8 μ - 9.8 sin θ 1 kp * ( u 1 - w 1 ) - V max 1 - V max 1 2 - 4 P batt 1 R batt 1 2 R batt 1 Q batt 1 v 2 w 2 - 1 2 ρ C D 2 A 2 v 2 2 / m 2 - 9.8 μ - 9.8 sin θ 2 kp * ( u 2 - w 2 ) - V max 2 - V max 2 2 - 4 P batt 2 R batt 2 2 R batt 2 Q batt 2 - - - ( 1 )
In formula, x is quantity of state, and u is controlling quantity.Parameter p 1, v 1, w 1and SOC 1for car's location, speed, consider driving acceleration/accel and the storage battery charge state of delay.Parameter p 2, v 2, w 2and SOC 2for the position of front truck, speed, consider driving acceleration/accel and the storage battery charge state of delay.Parameter u 1, u 2, P batt1and P batt2for the driving acceleration/accel from car, the driving acceleration/accel of front truck, from the charge-discharge electric power of car storage battery and the charge-discharge electric power of front truck storage battery.Parameter ρ, C d1, C d2, A 1, A 2, m 1, m 2, g, μ, θ 1and θ 2be density of air, from car aerodynamic drag factor, front truck aerodynamic drag factor, from car wind area, front truck wind area, from car quality, front truck quality, acceleration due to gravity, coefficient of rolling resistance, from car road grade and front truck road grade.V oC, R battand Q battbattery open-circuit voltage, internal resistance and capacity.
The fuel economy evaluation of vehicle adopts Wei Lanshi linear model, shown in (2):
.m f(t)＝.m f(P req(t)-P batt(t))≈c f(P req(t)-P batt(t)) (2)
M in formula ffor fuel consumption rate.Parameter P reqfor vehicle needs power.C ffor constant parameter.
3) formulistic control policy:
Step based on the hybrid vehicle energy management model prediction optimal control policy of platoon driving is: first detect from car and front truck state, comprise position, speed and acceleration information, secondly use the math modeling set up and formulistic control policy to solve optimal control problem, finally apply first controlling quantity of the optimal control sequence of trying to achieve in system; Because Model Predictive Control is interval optimal control, so its optimal control amount of trying to achieve is quantity is the sequence of forecast interval divided by the sampling interval.First controlling quantity of optimal control sequence and existing condition closest, so adopt it to be used as actual controlling quantity.
The groundwork of Model Predictive Control is: in each sampling instant, according to forecast model, the following cost function of system is predicted, by being optimized the performance figure in future anticipation interval, and carry out feedback compensation according to the output of actual measurement object, control policy design is converted into optimizing process, control sequence is obtained by the optimization problem solving corresponding forecast interval, and first of sequence controlling quantity is acted on system, realize controlled reset, afterwards in next sampling instant, forecast interval is pushed forward, constantly repeats this process.It comprises three parts in summary: forecast model, rolling optimization and controlled reset.The real-time optimistic control to system can be realized by the prediction inputted system in future.
The characteristic of this control policy has 2 points.The first, along with auto navigation, digital map, the development of inter-vehicle communication technology and intelligent transportation system, utilizes road traffic condition, carries out optimization to hybrid vehicle velocity mode.Second, when there is a vehicle in front, the control method of traditional fixing following distance now or main flow, the control policy that vehicle headway floats more than minimum value, improve the degree of freedom of changes in vehicle speed, the raising of Fuel Economy for Hybrid Electric Vehicles is had may.Above-mentioned two large characteristics have corresponding embodiment, for hybrid vehicle system performance provides larger possibility in evaluation function in control policy design.
Forecast model is being discussed in upper part.
Optimal control problem definition is such as formula shown in (3):
min imize J = ∫ t t + T L ( x ( τ | t ) , u ( τ | t ) ) dτ subject to P batt 1 min ≤ P batt 1 ( τ | t ) ≤ P batt 1 max u 1 min ≤ u 1 ( τ | t ) ≤ u 1 max P batt 2 min ≤ P batt 2 ( τ | t ) ≤ P batt 2 max u 2 min ≤ u 2 ( τ | t ) ≤ u 2 max - - - ( 3 )
In formula, T is forecast interval.Parameter P batt1min, P batt1max, P batt2min, P batt2max, u 1max, u 1min, u 2maxand u 2minfor control quantity constraint.
Evaluation function definition is such as formula shown in (4):
L = w x L x + w y L y + w z L z + w d L d + w e L e + w f L f + w r L r L x = ( w 1 - 1 2 ρ C D 1 A 1 v 1 2 / m 1 - 9.8 * μ ) 2 / 2 + ( w 2 - 1 2 ρ C D 2 A 2 v 2 2 / m 1 - 9.8 * μ ) 2 / 2 L y = ( v 1 - v d ) 2 / 2 + ( v 2 - v d ) 2 / 2 L z = 0.0874 * ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) / ( 1 + e - 0.5 * ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) ) + 0.0874 * ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) / ( 1 + e - 0.5 * ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) ) L d = ( SOC 1 - SOC d ) 2 + ( SOC 2 - SOC d ) 2 L e = ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) 2 / 2 + ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) 2 / 2 L f = ( - ln [ SOC 1 - 0.6 ] - ln [ 0.8 - SOC 1 ] ) + ( - ln [ SOC 2 - 0.6 ] - ln [ 0.8 - SOC 2 ] ) L r = - ln ( d - d d ) - - - ( 4 )
SOC in formula dit is target storage battery charge state.V dbe vehicle target speed, its value is the optimum constant-speed fuel economy speed of vehicle.W x, w y, w z, w d, w e, w f, and w rit is weight coefficient.D dfor minimum spaces of vehicles, evaluation function arranges and makes it float more than minimum spaces of vehicles, thus increases control freedom degree, improves Vehicle Economy.Barrier function retrains for the treatment of state of the system.
4) online optimal control:
For ensureing the real-time optimal performance of system, the numerical value fast solution method based on Hamilton's equation is used to solve above-mentioned optimal control problem.Because the limited iteration several times of its need just can calculate the optimal solution of numerical value equation, the on-line performance of this method is fine.And because it is based on Hamilton's equation, the stability of this solution can be guaranteed.Solution specifically, uses minimal principle that optimal control problem is converted into two-point boundary value problem, and adopt partial area matching to solve when processing the relevant simultaneous differential equation of Hamiltonian function and Algebraic Equation set, this is a kind of GMRES solution.
In each sampling instant, first, front vehicle position is measured, from truck position, front vehicle speed, from vehicle speed, front truck acceleration/accel, from car acceleration/accel, front truck storage battery charge state and from real-time status signals such as car storage battery charge states, secondly, global positioning system is utilized to unify the state of the following certain interval vehicle of intelligent transportation system prediction and surrounding environment, again, according to the auto model set up and optimal control problem, above-mentioned numerical value fast resolution is utilized to solve optimal control sequence in forecast interval.First controlling quantity of the optimal control sequence in applied forcasting interval is in vehicle.Afterwards in next sampling instant, forecast interval is pushed forward, so move in circles, realize online optimal control.
The present invention is owing to taking above technical scheme, and it has the following advantages:
1) along with auto navigation, the development of digital map, utilizes road traffic condition, carries out optimization to hybrid vehicle velocity mode.Be different from orthodox method and suppose the given known situation of velocity mode.
2), when there is a vehicle in front, the control method of traditional fixing following distance now or main flow.Applicator proposes the control policy that vehicle headway floats more than minimum value, improves the degree of freedom of changes in vehicle speed, and the raising of Fuel Economy for Hybrid Electric Vehicles is had may.
3) propose the hybrid vehicle centralized control model based on platoon driving, the modelling for hybrid vehicle platoon driving provides general universal method opinion and instructs.
Use this method can increase substantially Fuel Economy for Hybrid Electric Vehicles, emission behavior and safety performance.
Accompanying drawing explanation
Fig. 1 is planetary gear type parallel-serial hybrid power automobile driving system structural representation of the present invention.
In Fig. 1: 1, driving engine; 2, power distributor; 3, electrical generator; 4, storage battery; 5, inverter; 6 electrical motors; 7, main reduction gear.
Fig. 2 is the energy-conservation forecast Control Algorithm diagram of circuit of hybrid vehicle based on car-following model.
Fig. 3 is the energy-conservation predictive controller constructional drawing of hybrid vehicle based on car-following model.
Detailed description of the invention
The specific embodiment of the present invention is described in detail below in conjunction with technical scheme and accompanying drawing.
As shown in the figure, the invention discloses the energy-conservation forecast Control Algorithm of a kind of hybrid vehicle based on platoon driving, comprise the following steps: from global positioning system unify intelligent transportation system obtain input as system from car and front car traffic information in real time; Set up the foundation of hybrid vehicle math modeling as prediction Shape Of Things To Come state; Definition hybrid vehicle platoon driving optimal control problem, provides the functional equation solving optimal control amount; Real-time Feedback optimal control, solves optimal control amount.The present invention is when meeting safe spacing, adopt the energy-conservation forecast Control Algorithm of a kind of hybrid vehicle based on platoon driving, according to global positioning system, radar, the information on-line tuning that intelligent transportation system and load-and-vehicle communication system obtain optimizes hybrid vehicle energy flow, and then can obtain hybrid vehicle system optimal performance.The method uses sun and planet gear as electronics buncher, makes driving engine work in its best operating point all the time.Meanwhile, use traffic information, vehicle travelling state before prediction, on-line tuning hybrid vehicle energy flow, reaches the target of energy-saving and emission-reduction.In addition, the present invention is different from traditional fixing time headway control method, can be applicable to the real-time control of actual vehicle, for Energy Management System for Hybrid Electric Vehicle central controller performance provides a kind of new way.
Fig. 1 is planetary gear type parallel hybrid power-driven system structural representation of the present invention, mainly comprises: driving engine 1; Power distributor 2; Electrical generator 3; Storage battery 4; Inverter 5; Electrical motor 6; Main reduction gear 7.Fig. 1 is the constructional drawing of the research object of this patent control method.This constructional drawing analysis system machinery and electrical couplings relation is used in vehicle modeling process.Comprise hybrid vehicle in constructional drawing and comprise 5 Larger Dynamic parts.They are driving engines, storage battery, 2 electricity generation and electromotion all-in-ones and wheel.Electrical motor is connected with wheel by main reduction gear, transmission system power.Planetary wheel, as the effect of the existing speed coupler of distributing means for power supply, has again the effect of electronics buncher.Planetary wheel mechanical couplings driving engine and 2 electricity generation and electromotion all-in-ones.Inverter electrical couplings storage battery and 2 electricity generation and electromotion all-in-ones.By obtaining independently 3DOF system model to system mechanics coupling and electrical couplings decoupling zero.Control method of the present invention is system software, Figure 1 shows that system hardware.
Fig. 2 is the process disclosing whole control method.The information gathered is as the input of system model.Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device.Traffic signal information and real-time road condition information is gathered, for intellectual traffic control by intelligent transportation system.The battery information gathered is utilized to estimate storage battery charge state by Kalman filter.Vehicle is modeled as formulistic Model Predictive Control strategy and provides the model predicted required for Shape Of Things To Come state.The functional equation that formulism control policy provides needs to solve for online optimal control.
Fig. 3 is the whole process of the concrete control method of the present invention.Inquired about the road grade obtaining vehicle position by vehicle location by global positioning system.Target storage battery charge state producer produces target storage battery charge state according to road grade.Front vehicles position, speed and traffic information is obtained by intelligent transportation system.The vehicle-state measured, road grade information, front vehicles position and speed and traffic information, target storage battery charge state, target vehicle velocity input model predictive controller, model predictive controller, according to Vehicular system model, solves optimal control problem, obtain optimal control amount, and act on vehicle.
Embodiment:
Be described for planetary gear type parallel hybrid power-driven system, as shown in Figure 1; The inventive method first step is information acquisition, and second step is vehicle modeling, and the 3rd step is formulistic control policy, and the 4th step is online optimal control.
As shown in Figure 2, concrete control method comprises the following steps the party's ratio juris:
1) information acquisition:
Front truck and car's location information is gathered, as real-time vehicle feedback of status by global positioning system.Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device.Gather traffic signal information by intelligent transportation system and load-and-vehicle communication system, real-time road condition information and from car and front vehicle speed, acceleration information, for intellectual traffic control.Storage battery 4 information gathered is utilized to estimate storage battery charge state by Kalman filter.
2) vehicle modeling:
Planetary gear type parallel-serial hybrid power automobile comprises 5 Larger Dynamic parts.They are driving engines 1, storage battery 4, electrical generator 3, electrical motor 6 and wheel.Power distributor 2, as the effect of the existing speed coupler of distributing means for power supply, has again the effect of electronics buncher.According to vehicle mechanical coupling and electron coupled relation, can arrange and write system dynamics equation.To kinetics equation decoupling zero, finally can obtain the state-space model of system, shown in (1).
x = [ p 1 v 1 w 1 SOC 1 p 2 v 2 w 2 SOC 2 ] u = [ u 1 u 2 P batt 1 P batt 1 ] x = f ( x , u ) f ( x , u ) = v 1 w 1 - 1 2 ρ C D 1 A 1 v 1 2 / m 1 - 9.8 μ - 9.8 sin θ 1 kp * ( u 1 - w 1 ) - V max 1 - V max 1 2 - 4 P batt 1 R batt 1 2 R batt 1 Q batt 1 v 2 w 2 - 1 2 ρ C D 2 A 2 v 2 2 / m 2 - 9.8 μ - 9.8 sin θ 2 kp * ( u 2 - w 2 ) - V max 2 - V max 2 2 - 4 P batt 2 R batt 2 2 R batt 2 Q batt 2 - - - ( 1 )
In formula, x is quantity of state, and u is controlling quantity.Parameter p 1, v 1, w 1and SOC 1for car's location, speed, consider driving acceleration/accel and storage battery 4 state-of-charge of delay.Parameter p 2, v 2, w 2and SOC 2for the position of front truck, speed, consider driving acceleration/accel and storage battery 4 state-of-charge of delay.Parameter u 1, u 2, P batt1and P batt2for the driving acceleration/accel from car, the driving acceleration/accel of front truck, from the charge-discharge electric power of car storage battery 4 and the charge-discharge electric power of front truck storage battery 4.Parameter ρ, C d1, C d2, A 1, A 2, m 1, m 2, g, μ, θ 1and θ 2be density of air, from car aerodynamic drag factor, front truck aerodynamic drag factor, from car wind area, front truck wind area, from car quality, front truck quality, acceleration due to gravity, coefficient of rolling resistance, from car road grade and front truck road grade.V oC, R battand Q battstorage battery 4 open circuit voltage, internal resistance and capacity.
The fuel economy evaluation of vehicle adopts Wei Lanshi linear model, shown in (2):
.m f(t)＝.m f(P req(t)-P batt(t))≈c f(P req(t)-P batt(t)) (2)
M in formula ffor fuel consumption rate.Parameter P reqfor vehicle needs power.C ffor constant parameter.
3) formulistic control policy:
Step based on the hybrid vehicle energy management model prediction optimal control policy of platoon driving is: first detect from car and front truck state, comprise position, speed and acceleration information, secondly use the math modeling set up and formulistic control policy to solve optimal control problem, finally apply first controlling quantity of the optimal control sequence of trying to achieve in system.Because Model Predictive Control is interval optimal control, so its optimal control amount of trying to achieve is quantity is the sequence of forecast interval divided by the sampling interval.First controlling quantity of optimal control sequence and existing condition closest, adopt it to be used as actual controlling quantity so general.
The groundwork of Model Predictive Control is: in each sampling instant, according to forecast model, the following cost function of system is predicted, by being optimized the performance figure in future anticipation interval, and carry out feedback compensation according to the output of actual measurement object, control policy design is converted into optimizing process, control sequence is obtained by the optimization problem solving corresponding forecast interval, and first of sequence controlling quantity is acted on system, realize controlled reset, afterwards in next sampling instant, forecast interval is pushed forward, constantly repeats this process.It comprises three parts in summary: forecast model, rolling optimization and controlled reset.The real-time optimistic control to system can be realized by the prediction inputted system in future.
The characteristic of this control policy has 2 points.The first, along with auto navigation, digital map, the development of inter-vehicle communication technology and intelligent transportation system, utilizes road traffic condition, carries out optimization to hybrid vehicle velocity mode.Second, when there is a vehicle in front, the control algorithm of traditional fixing following distance now or main flow, the control policy that vehicle headway floats more than minimum value, improve the degree of freedom of changes in vehicle speed, the raising of Fuel Economy for Hybrid Electric Vehicles is had may.Above-mentioned two large characteristics have corresponding embodiment, for hybrid vehicle system performance provides larger possibility in evaluation function in control policy design.
Forecast model is being discussed in upper part.
Optimal control problem definition is such as formula shown in (3):
min imize J = ∫ t t + T L ( x ( τ | t ) , u ( τ | t ) ) dτ subject to P batt 1 min ≤ P batt 1 ( τ | t ) ≤ P batt 1 max u 1 min ≤ u 1 ( τ | t ) ≤ u 1 max P batt 2 min ≤ P batt 2 ( τ | t ) ≤ P batt 2 max u 2 min ≤ u 2 ( τ | t ) ≤ u 2 max - - - ( 3 )
In formula, T is forecast interval.Parameter P batt1min, P batt1max, P batt2min, P batt2max, u 1max, u 1min, u 2maxand u 2minfor control quantity constraint.
Evaluation function definition is such as formula shown in (4).
L = w x L x + w y L y + w z L z + w d L d + w e L e + w f L f + w r L r L x = ( w 1 - 1 2 ρ C D 1 A 1 v 1 2 / m 1 - 9.8 * μ ) 2 / 2 + ( w 2 - 1 2 ρ C D 2 A 2 v 2 2 / m 1 - 9.8 * μ ) 2 / 2 L y = ( v 1 - v d ) 2 / 2 + ( v 2 - v d ) 2 / 2 L z = 0.0874 * ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) / ( 1 + e - 0.5 * ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) ) + 0.0874 * ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) / ( 1 + e - 0.5 * ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) ) L d = ( SOC 1 - SOC d ) 2 + ( SOC 2 - SOC d ) 2 L e = ( m 1 * w 1 * v 1 / 1000 - P batt 1 ) 2 / 2 + ( m 2 * w 2 * v 2 / 1000 - P batt 2 ) 2 / 2 L f = ( - ln [ SOC 1 - 0.6 ] - ln [ 0.8 - SOC 1 ] ) + ( - ln [ SOC 2 - 0.6 ] - ln [ 0.8 - SOC 2 ] ) L r = - ln ( d - d d ) - - - ( 4 )
SOC in formula dit is target storage battery 4 state-of-charge.V dbe vehicle target speed, its value is the optimum constant-speed fuel economy speed of vehicle.W x, w y, w z, w d, w e, w f, and w rit is weight coefficient.D dfor minimum spaces of vehicles, evaluation function arranges and makes it float more than minimum spaces of vehicles, thus increases control freedom degree, improves Vehicle Economy.Barrier function is for the treatment of state of the system constraint etc.
4) online optimal control:
For ensureing the real-time optimal performance of system, the numerical value fast solution method based on Hamilton's equation is used to solve above-mentioned optimal control problem.Because the limited iteration several times of its need just can calculate the optimal solution of numerical value equation, the on-line performance of this method is fine.And because it is based on Hamilton's equation, the stability of this solution can be guaranteed.Solution specifically, uses minimal principle that optimal control problem is converted into two-point boundary value problem, and adopt partial area matching to solve when processing the relevant simultaneous differential equation of Hamiltonian function and Algebraic Equation set, this is a kind of GMRES solution.
In each sampling instant, first, front vehicle position is measured, from truck position, front vehicle speed, from vehicle speed, front truck acceleration/accel, from car acceleration/accel, front truck storage battery 4 state-of-charge and from real-time status signals such as car storage battery 4 state-of-charges, secondly, global positioning system is utilized to unify the state of the following certain interval vehicle of intelligent transportation system prediction and surrounding environment, again, according to the auto model set up and optimal control problem, above-mentioned numerical value fast resolution is utilized to solve optimal control sequence in forecast interval.First controlling quantity of the optimal control sequence in applied forcasting interval is in vehicle.Afterwards in next sampling instant, forecast interval is pushed forward, so move in circles, realize online optimal control.
The present invention is equally applicable to other form mixed power automobile driving systems, and concrete modeling method is consistent with control process and planetary gear type parallel-serial hybrid power automobile driving system, does not repeat them here.

Claims (1)

1., based on the energy-conservation forecast Control Algorithm of hybrid vehicle of platoon driving, it is characterized in that:
The first step is information acquisition, and second step is vehicle modeling, and the 3rd step is formulistic control policy, and the 4th step is online optimal control; Specifically comprise the following steps:
1) information acquisition:
Front truck and car's location information is gathered, as real-time vehicle feedback of status by global positioning system; Front vehicles speed is gathered, for tracing control by trailer-mounted radar speed measuring device; Gather traffic signal information by intelligent transportation system and load-and-vehicle communication system, real-time road condition information and from car and front vehicle speed, acceleration information, for intellectual traffic control; The battery information gathered is utilized to estimate storage battery charge state by Kalman filter;
2) vehicle modeling:
Planetary gear type parallel-serial hybrid power automobile comprises 5 Larger Dynamic parts, and they are driving engines, storage battery, 2 electricity generation and electromotion all-in-ones and wheel, and planetary wheel, as the effect of the existing speed coupler of distributing means for power supply, has again the effect of electronics buncher; According to vehicle mechanical coupling and electron coupled relation, row write system dynamics equation, to kinetics equation decoupling zero, and the state-space model of final acquisition system, shown in (1):
In formula, x is quantity of state, and u is controlling quantity; Parameter p 1, v 1, w 1and SOC 1for car's location, speed, consider driving acceleration/accel and the storage battery charge state of delay; Parameter p 2, v 2, w 2and SOC 2for the position of front truck, speed, consider driving acceleration/accel and the storage battery charge state of delay; Parameter u 1, u 2, P batt1and P batt2for the driving acceleration/accel from car, the driving acceleration/accel of front truck, from the charge-discharge electric power of car storage battery and the charge-discharge electric power of front truck storage battery; Parameter ρ, C d1, C d2, A 1, A 2, m 1, m 2, g, μ, θ 1and θ 2density of air, from car aerodynamic drag factor, front truck aerodynamic drag factor, from car wind area, front truck wind area, from car quality, front truck quality, acceleration due to gravity, coefficient of rolling resistance, from car road grade and front truck road grade; V oC, R battand Q battbattery open-circuit voltage, internal resistance and capacity;
The fuel economy evaluation of vehicle adopts Wei Lanshi linear model, shown in (2):
.m f(t)＝.m f(P req(t)-P batt(t))≈c f(P req(t)-P batt(t)) (2)
M in formula ffor fuel consumption rate, parameter P reqfor vehicle needs power, c ffor constant parameter;
3) formulistic control policy:
Step based on the hybrid vehicle energy management model prediction optimal control policy of platoon driving is: first detect from car and front truck state, comprise position, speed and acceleration information, secondly use the math modeling set up and formulistic control policy to solve optimal control problem, finally apply first controlling quantity of the optimal control sequence of trying to achieve in system; Because Model Predictive Control is interval optimal control, so its optimal control amount of trying to achieve is quantity is the sequence of forecast interval divided by the sampling interval, first controlling quantity of optimal control sequence and existing condition closest, so adopt it to be used as actual controlling quantity;
In each sampling instant, according to forecast model, the following cost function of system is predicted, by being optimized the performance figure in future anticipation interval, and carry out feedback compensation according to the output of actual measurement object, control policy design is converted into optimizing process, control sequence is obtained by the optimization problem solving corresponding forecast interval, and first of sequence controlling quantity is acted on system, realize controlled reset, afterwards in next sampling instant, forecast interval is pushed forward, constantly repeats this process;
It comprises three parts in summary: forecast model, rolling optimization and controlled reset, realizes the real-time optimistic control to system by the prediction inputted system in future;
Optimal control problem definition is such as formula shown in (3):
In formula, T is forecast interval, parameter P batt1min, P batt1max, P batt2min, P batt2max, u 1max, u 1min, u 2maxand u 2min is control quantity constraint,
Evaluation function definition is such as formula shown in (4):
SOC in formula dtarget storage battery charge state, v dbe vehicle target speed, its value is the optimum constant-speed fuel economy speed of vehicle, w x, w y, w z, w d, w e, w f, and w rweight coefficient, d dfor minimum spaces of vehicles, evaluation function arranges and makes it float more than minimum spaces of vehicles, thus increases control freedom degree, and improve Vehicle Economy, barrier function retrains for the treatment of state of the system;
4) online optimal control:
For ensureing the real-time optimal performance of system, the numerical value fast solution method based on Hamilton's equation is used to solve above-mentioned optimal control problem, because the limited iteration several times of its need just calculates the optimal solution of numerical value equation, solution specifically, use minimal principle that optimal control problem is converted into two-point boundary value problem, adopt partial area matching to solve when processing the relevant simultaneous differential equation of Hamiltonian function and Algebraic Equation set, this is a kind of GMRES solution;
In each sampling instant, first, front vehicle position is measured, from truck position, front vehicle speed, from vehicle speed, front truck acceleration/accel, from car acceleration/accel, front truck storage battery charge state and from car storage battery charge state real-time status signal, secondly, global positioning system is utilized to unify the state of the following certain interval vehicle of intelligent transportation system prediction and surrounding environment, again, according to the auto model set up and optimal control problem, above-mentioned numerical value fast resolution is utilized to solve optimal control sequence in forecast interval; First controlling quantity of the optimal control sequence in applied forcasting interval is in vehicle; Afterwards in next sampling instant, forecast interval is pushed forward, so move in circles, realize online optimal control.
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