CN105501216B - The layering energy management control method of hybrid vehicle based on car networking - Google Patents
The layering energy management control method of hybrid vehicle based on car networking Download PDFInfo
- Publication number
- CN105501216B CN105501216B CN201610052624.7A CN201610052624A CN105501216B CN 105501216 B CN105501216 B CN 105501216B CN 201610052624 A CN201610052624 A CN 201610052624A CN 105501216 B CN105501216 B CN 105501216B
- Authority
- CN
- China
- Prior art keywords
- msub
- mrow
- mtd
- mtr
- msubsup
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- 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
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
-
- 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
- B60W2556/00—Input parameters relating to data
- B60W2556/45—External transmission of data to or from the vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2720/00—Output or target parameters relating to overall vehicle dynamics
- B60W2720/10—Longitudinal speed
Abstract
The invention discloses a kind of layering energy management control method of hybrid vehicle based on car networking, top level control device solves the scope of target vehicle speed using traffic lights timing and sets the target vehicle speed of hybrid vehicle as the upper limit of this vehicle speed range, proposes the optimal objective speed sequence in a kind of accelerated model prediction algorithm prediction preset time window.The present invention has advantages below compared with prior art：A kind of layering energy management control method prediction optimal objective speed of hybrid vehicle based on car networking of the present invention, based on traffic lights timing, effectively avoid hybrid vehicle red parking or collision, optimal objective speed sequence is obtained using accelerated model prediction, ensure to reduce the time cost of model prediction on the premise of prediction data precision and Fuel Economy for Hybrid Electric Vehicles, the effective execution efficiency for improving program.
Description
Technical field
Car networking is based on the present invention relates to a kind of hybrid vehicle energy management control method, more particularly to one kind
Hybrid vehicle layering energy management control method.
Background technology
The energy management control method of hybrid vehicle directly affects dynamic property, economy, comfortableness and the row of vehicle
Put, be the emphasis and difficult point of field of hybrid electric vehicles research.At present, have been carried out commercial application is rule-based control
Method processed, however, rule-based control method relies on expertise, does not possess good adaptability for working condition, therefore, scholars
Control method of the primary study based on optimization.
Control method based on optimization mainly includes two kinds of global optimization and instantaneous optimization.In global optimization approach, move
State planning (dynamic programming, DP), quadratic programming (quadratic programming, QP) and classic variation
The methods such as method (classical variational method, VP) are required for known state of cyclic operation, and in automobile actual travel
During, state of cyclic operation is unknown.Wherein, the Dynamic Programming based on the reverse optimizing of bellman principle is that search capability is best
Global optimization approach, but program structure is sufficiently complex, and online optimizing needs to use model prediction (model
Predictive control, MPC) algorithm acquisition state of cyclic operation, the time cost of calculating is added, real vehicle application is unsatisfactory for
It is required that, and research shows that traditional model prediction algorithm is only used in the dynamical system of " slow enough ", for on-line optimization
Hybrid power system and do not apply to.In order to save the time of program operation, scholars have studied the wink based on minimal principle
When optimized algorithm, such as equivalent fuel consumption minimum principle (equivalent consumption minimization
Strategy, ECMS) and Pang Teyajin minimal principles (Pontryagin ' s minimum strategy, PMP) etc., these
Algorithm can obtain approximate globally optimal solution and need not known state of cyclic operation.However, relative to the fortune of vehicle-mounted single-chip microcomputer
Calculation ability, these algorithms can not still realize real-time control.The object of current hybrid vehicle energy management research is
One car, i.e. energy management optimized algorithm can only ensure that the economy of single vehicle is optimal, dynamic for many mixing in traffic system
Power automobile energy management optimization problem, there is no related report at present.In addition, currently for single hybrid vehicle energy
Management and optimization control algolithm do not consider traffic signals influence and car and car between influence each other, energy now
Management optimal solution has a certain distance with the optimal solution under actual conditions.
With continuing to develop for intelligent transportation system, many mixing in intelligent transportation system are solved using car networking technology dynamic
The real-time optimal control of power automobile is possibly realized.Hybrid vehicle layering energy management control method based on car networking, can
New thinking is provided with the real-time power management for many hybrid vehicles of solution and the problem of optimization.
The content of the invention
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of hybrid vehicle based on car networking
It is layered energy management control method.
In order to solve the above-mentioned technical problem, the present invention is adopted the following technical scheme that：A kind of hybrid power based on car networking
The layering energy management control method of automobile, comprises the following steps：
Step (1), based on car networking environment, hybrid vehicle passes through Dedicated Short Range Communications (DSRC), radio frequency identification
(RFID), bluetooth, ZIGBEE or WI-FI enter that driving communicates with car and car and traffic implementation communicate；
Step (2), top level control device mathematical modeling mathematical modeling is set up, comprised the following steps：
(21) vehicle Longitudinal Dynamic Model is set up
(22) traffic lights timing is utilized, the scope of target vehicle speed is obtained and sets the target vehicle speed of hybrid vehicle
For the upper limit of this vehicle speed range；
(23) target vehicle speed based on hybrid vehicle, preset time window is predicted using accelerated model prediction algorithm
Optimal objective speed sequence；
(24) optimal objective speed sequence feeds back to the driver of each car by being wirelessly transferred form, driver according to
Optimal objective speed sequence is accelerated or braked；
Step (3), lower floor's controller obtain optimal turn of current time engine and motor according to acceleration or braking information
Square or power distribution, and optimum torque or power distribution instruction are sent to engine controller, motor control by being wirelessly transferred
Device processed, gearbox control and electrokinetic cell controller；
Step (4), each power part controller control corresponding power part to perform phase according to the control instruction received
The output operation of pass, and the reality output of power part is fed back into lower floor's controller progress Closed-cycle correction.
As the further optimization of such scheme, the vehicle Longitudinal Dynamic Model set up in step (21), such as formula
(1)：
In formula (1), xiFor the state vector of i-th car；siFor the position of i-th car, stated with coordinate；viFor i-th car
Speed, unit is m/s；uiFor the control variable of i-th car, that is, any time unit mass tractive force or brake force,
Unit is N/kg；MiThe quality of i-th car, unit is kg；CDFor control resistance coefficient；ρaFor atmospheric density, unit is kg/m3；
AfiFor the front face area of i-th car, unit is m2；μ is coefficient of rolling resistance；θ is the gradient；
As the further optimization of such scheme, in step (22), using traffic lights timing, target vehicle speed is obtained
The upper and lower bound and sets target speed of scope are the upper limit of this scope, such as formula (2)：
In formula (2), vilFor the lower limit of target vehicle speed, unit is m/s；vihFor the upper limit of target vehicle speed, unit is m/s；dia
(td) for the position s of i-th cariWith traffic lights a distance, unit is m；KwFor the cycle-index of signal lamp, round numbers；
tg、trRespectively the duration of red light and green light, unit is s；tcFor the time in a traffic lights cycle, unit is s；tdFor
The time of running car, unit is s；vimaxThe maximum of speed is sailed for i-th hybrid electric vehicle, unit is m/s；viobj
For hybrid vehicle target vehicle speed, unit is m/s；
As the further optimization of such scheme, according to the scope of target vehicle speed, the control for obtaining i-th car of t becomes
Measure ui(t) constraints, as shown in formula (3), controls variable ui(t) constraints, target vehicle speed v are metiobjIt is limited at
[vil, vih] in the range of, hybrid vehicle can avoid red parking；
In formula (3), uimin、uimaxThe minimum and maximum value of variable is respectively controlled, unit is N/kg；δ t are material calculation,
Unit is s；ai(t) it is the longitudinal acceleration of i-th car of current time, unit is m/s2；ui(t) it is the control of i-th car of t
Variable processed, unit is N/kg；
The Forecasting Methodology of the optimal objective speed sequence of hybrid vehicle based on car networking, comprises the following steps：
Step4A：The majorized function of accelerated model prediction is set up, and using the excellent of Novel Algorithm solving model prediction
Change function, the majorized function of the accelerated model prediction is such as represented with formula (5)：
In formula (4), T is given time window, and unit is s；SijFor the distance of i-th car and jth car, unit is m；
αi(i=1,2,3) is weight coefficient, uid(t) it is preferable control variable, unit is N/kg；；siAnd s (t)j(t) it is respectively i-th
The position of car and jth car in time t, uses coordinate representation；thFor the interval time of front and rear two car set in advance, unit is
s；S0For safe distance set in advance, unit is m；viminFor the minimum value of automobile driving speed, unit is m/s；N is fleet
The quantity of middle hybrid vehicle；
Step4B：The Longitudinal Dynamic Model of hybrid vehicle is converted into the hybrid vehicle longitudinal direction of linear forms
Kinetic model, as shown in formula (6),
In formula (6), Ai(xi) it is the parameter matrix related to state；BiFor a constant column matrix；K is time step, single
Position is s；
Step4C：The majorized function of model prediction described in step4A is changed into the model prediction of quadratic programming form
Majorized function, as shown in formula (7),
In formula (7), yiFor the state variable comprising target vehicle speed and preferable control variable；QiFor diagonal matrix；yiobjFor state
The desired value of variable, Pi、qi、Ci、biIt is the coefficient matrix related to state variable；
Step4D:The Lagrange for obtaining the majorized function of the model prediction of the quadratic programming form described in step4C is solved
Formula, as shown in formula (8),
In formula (8), λiAnd υiFor Lagrange multiplier；
Step4D1：Single order Ku En-Plutarch (KKT) optimal conditions equation of Lagrangian solution formula is obtained, such as formula (9)
It is shown,
In formula (9), siFor slack variable；γiAnd SiRespectively Lagrange multiplier νiWith slack variable siLeading diagonal member
The column vector of element composition；E is unit column vector；
Step4D2：Single order Ku En-Plutarch optimal conditions equation, the iteration of Newton iteration method is solved using Newton iteration method
Shown in equation such as formula (10)：
In formula (10), [Δ yi Δλi Δνi Δsi]TFor the direction of search of the Newton iteration method optimized variable；βiFor
The iteration step length of the Newton iteration method, the iteration step length can ensure Lagrange multiplier and slack variable be on the occasion of；
Shown in the solution equation such as formula (11) of the direction of search of Newton iteration method optimized variable,
In formula (11), Ryi、Rλi、Rυi、RsiThe residual error of position Kuhn-Tucker condition；
The solution equation of the direction of search of optimized variable in Newton iteration method is reduced to letter by Step4D3 by linear change
Change equation, such as shown in (12),
The direction of search of formula (12) is [Δ yi Δλi]T；WiFor the transformation matrix related to slack variable；
Step4D4：Using the searcher of the equation solution reduced equation based on cholesky (Cholesky) factorization
To, the equation obtained based on cholesky (Cholesky) factorization, as shown in formula (13),
Step4E is according to the iterative equation (10) of the Newton iteration method and Step4D4 cholesky (Cholesky)
The equation (13) that factorization is obtained solves optimum state variable yi；
Step4F is according to optimum state variable yiSolve the optimal objective speed sequence of preset time window.
Compared with prior art, the layering energy management control for a kind of hybrid vehicle based on car networking that the present invention is provided
Method processed has the beneficial effect that：
1st, the layering energy management control method of a kind of hybrid vehicle based on car networking of the invention, by car networking
Technology is incorporated into the energy management of hybrid vehicle, from the angle of macroscopic view, is communicated by truck traffic and car with signal lamp, controls
The state and time interval of traffic lights processed, with the minimum target of gross energy of each hybrid electric vehicle consumption in traffic flow, are obtained
To the speed sequence that each car is optimal.The control method of the present invention can solve one hybrid power vapour of field of traffic of macroscopic view
Successive vehicles, i.e., the problem of many hybrid vehicle fuel consumptions are optimal.
2nd, a kind of layering energy management control method of hybrid vehicle based on car networking of the invention is joined using car
Network technology, take into full account and real-time Transmission driver driving intention, true pavement conditions, the gradient, automobile load and car with
Influencing each other between car, while fuel consumption is reduced, can avoid the collision between vehicle, improve its driving safety
Property.
3rd, the layering energy management control method of a kind of hybrid vehicle based on car networking of the invention, point of design
Layer controller architecture effectively improves the execution efficiency of program.Top level control device, which is based on traffic lights timing, effectively to be kept away
Exempt from hybrid vehicle red parking.The data communicated using real-time truck traffic and car with signal lamp, using accelerated model
Prediction obtains optimal objective speed sequence, it is ensured that the time cost of model prediction is reduced on the premise of prediction data precision, is easy to reality
When control.
4th, a kind of layering energy management control method of hybrid vehicle based on car networking of the invention, it is to avoid mixing
Power vehicle red parking, predicts that (F-MPC) realizes the optimal speed prediction and fuel-economy close with MPC using accelerated model
Property, and the relative calculating time of each time step be reduced to 7.2 by the 100 of MPC；WL-ECMS energy pipes proposed by the present invention
Reason control method realizes that good speed is followed, and fuel consumption per hundred kilometers is suitable with ECMS, and during the relative calculating of each time step
Between be reduced to 1.48 by the 100 of ECMS.The research method of the present invention carries to solve mixed power vehicle real-time power management and optimization
For new thinking.
Brief description of the drawings
Fig. 1 is a kind of schematic diagram of the layering energy management control of hybrid vehicle based on car networking of the present invention.
Fig. 2 (a)-Fig. 2 (j) is a kind of hybrid power based on car networking of-No. ten cars of a car using the present invention respectively
The vehicle speed trajectory schematic diagram of the layering energy management control method of automobile.
Fig. 3 (a)-Fig. 3 (j) is-No. ten cars of a car respectively in control rule-based, based on WL-ECMS, based on ECMS
Electrokinetic cell SOC tracks schematic diagram under system strategy.
The path curves schematic diagram that it is-No. ten cars of a car that Fig. 4, which is,.
Embodiment
Embodiments of the invention are elaborated below, the present embodiment is carried out lower premised on technical solution of the present invention
Implement, give detailed embodiment and specific operating process, but protection scope of the present invention is not limited to following implementations
Example.
Fig. 1 is a kind of schematic diagram of the layering energy management control of hybrid vehicle based on car networking of the present invention.
A kind of layering energy management control method of hybrid vehicle based on car networking, comprises the following steps
Step (1), based on car networking environment, hybrid vehicle is entered by radio frequency identification, bluetooth, ZIGBEE or WI-FI
Driving communicates with car and car is implemented to communicate with traffic；
Under car networking environment, automobile passes through Dedicated Short Range Communications (dedicated short range
Communication, DSRC), it is radio frequency identification (radio frequency identification devices, RFID), blue
The wireless communications method such as tooth (Bluetooth), ZIGBEE, Wi-Fi or Cellular Networks (cellular network) realizes Che Chetong
Letter and car communicate with means of transportation.Each car is equipped with communication module, can receive in certain distance from front truck, after
The signal of car, traffic lights and other sender units.
Step (2), top level control device calculate the higher limit and lower limit of target vehicle speed based on traffic lights timing.According to
Communicated the information obtained based on truck traffic and car with signal lamp, using accelerated model prediction algorithm, solves optimal objective car
The optimal speed calculated is simultaneously sent to driver by speed, and driver is accelerated or braked according to optimal objective speed.
Top level control device mathematical modeling is set up, is comprised the following steps：
(21) vehicle Longitudinal Dynamic Model is set up
(22) traffic lights timing is utilized, the scope of target vehicle speed is obtained and sets the target vehicle speed of hybrid vehicle
For the upper limit of this vehicle speed range；
Traffic lights timing refers to the opportunity that the phase and each phase of traffic lights occur, and phase refers to traffic
The state of signal lamp, i.e. red light or green light (do not consider amber light) in this preferred embodiment, the opportunity of signal lamp refers to red light or green
At the time of lamp persistently occurs and duration.
(23) target vehicle speed based on hybrid vehicle, preset time window is predicted using accelerated model prediction algorithm
Optimal objective speed sequence；
(24) optimal objective speed sequence feeds back to the driver of each car by being wirelessly transferred form, driver according to
Optimal objective speed sequence is accelerated or braked.
Step (3), lower floor's controller obtain optimal turn of current time engine and motor according to acceleration or braking information
Square or power distribution, and optimum torque or power distribution instruction are sent to engine controller, motor control by being wirelessly transferred
Device processed, gearbox control and electrokinetic cell controller；
Step (4), each power part controller control corresponding power part to perform phase according to the control instruction received
The output operation of pass, and the reality output of power part is fed back into lower floor's controller progress Closed-cycle correction.
Wherein, Longitudinal Dynamic Model integrated in top level control device, such as formula (1) are obtained in step (21)：
In formula (1), xiFor the state vector of i-th car；siFor the position of i-th car, stated with coordinate；viFor i-th car
Speed, unit is m/s；uiFor the control variable of i-th car, that is, any time unit mass tractive force or brake force,
Unit is N/kg；MiThe quality of i-th car, unit is kg；CDFor control resistance coefficient；ρaFor atmospheric density, unit is kg/m3；
AfiFor the front face area of i-th car, unit is m2；μ is coefficient of rolling resistance；θ is the gradient；
The Longitudinal Dynamic Model of hybrid vehicle is mainly used in solving the vapour of the state variable, i.e. any time of automobile
Truck position and its speed, in the case where other conditions are constant, the position of automobile and speed are by its tractive force or brake force
Determine, for any given vehicle traction or brake force, automobile can be solved by Newton's second law
Speed, the distance of its form can be solved to rate integrating, i.e. its position.
Using traffic lights timing, the upper and lower bound and sets target speed for obtaining the scope of target vehicle speed are this model
The upper limit enclosed, such as formula (2)：
In formula (2), vilFor the lower limit of target vehicle speed, unit is m/s；vihFor the upper limit of target vehicle speed, unit is m/s；dia
(td) for the position s of i-th cariWith traffic lights a distance, unit is m；KwFor the cycle-index of signal lamp, round numbers；
tg、trRespectively the duration of red light and green light, unit is s；tcFor the time in a traffic lights cycle, unit is s；tdFor
The time of running car, unit is s；vimaxThe maximum of speed is sailed for i-th hybrid electric vehicle, unit is m/s；viobj
For hybrid vehicle target vehicle speed, unit is m/s；
Above-mentioned target vehicle speed viobjScope solve be based on traffic lights timing, using the timing of traffic signals,
It can fully avoid hybrid vehicle from running into red light when by traffic lights, that is, avoid hybrid vehicle red light from stopping, so that
Reduce its fuel consumption.
According to the scope of target vehicle speed, the control variable u of i-th car of t is obtainedi(t) constraints, such as formula (3)
It is shown, control variable ui(t) constraints, target vehicle speed v are metiobjIt is limited at [vil, vih] in the range of, hybrid power
Automobile can avoid red parking；
In formula (3), uimin、uimaxThe minimum and maximum value of variable is respectively controlled, unit is N/kg；δ t are material calculation,
Unit is s；ai(t) it is the longitudinal acceleration of i-th car of current time, unit is m/s2；ui(t) it is the control of i-th car of t
Variable processed, unit is N/kg；
In this formula (3), the scope of the longitudinal acceleration at the current time of i-th car can by vehicle speed range function table
Show, and longitudinal acceleration is also the function for controlling variable, therefore, it can obtain controlling by vehicle speed range the constraints of variable.
Red light and green light are referred to as traffic signal light condition, and the state of traffic lights can be represented with formula (4),
In the solution formula of the signal lamp state,That represent is tdDivided by tcResulting remainder.
The invention also discloses a kind of prediction of the optimal objective speed sequence of hybrid vehicle based on car networking
Method.The layering energy management control method of hybrid vehicle based on car networking, is obtained based on traffic lights timing
Hybrid vehicle target vehicle speed, the optimal objective in preset time window is predicted using accelerated model prediction algorithm (F-MPC)
Speed sequence, specifically includes following steps：
Step4A：The majorized function of accelerated model prediction is set up, and using the excellent of Novel Algorithm solving model prediction
Change function, the majorized function of the accelerated model prediction is such as represented with formula (5)：
In formula (5), T is given time window, and unit is s；SijFor the distance of i-th car and jth car, unit is m；
αi(i=1,2,3) is weight coefficient, uid(t) it is preferable control variable, unit is N/kg；；siAnd s (t)j(t) it is respectively i-th
The position of car and jth car in time t, uses coordinate representation；thFor the interval time of front and rear two car set in advance, unit is
s；S0For safe distance set in advance, unit is m；viminFor the minimum value of automobile driving speed, unit is m/s；N is fleet
The quantity of middle hybrid vehicle；
It is relative between the optimal objective speed and actual vehicle speed of hybrid vehicle and difference, car and the car of target vehicle speed
Distance and the difference of actual control variable and preferable control variable have a relation, thus optimal speed solution be appreciated that with
Constant, control variable is kept to be controlled with preferable close to the relative distance between target vehicle speed, car and car with problem, i.e. actual vehicle speed
Variable is close to being, the reality output speed of hybrid vehicle is exactly optimal speed, therefore the majorized function of accelerated model prediction
It can be represented with above-mentioned formula (5).
Step4B：The Longitudinal Dynamic Model of hybrid vehicle is converted into the hybrid vehicle longitudinal direction of linear forms
Kinetic model, as shown in formula (6),
In formula (6), Ai(xi) it is the parameter matrix related to state；BiFor a constant column matrix；K is time step, single
Position is s；
Step4C：The majorized function of model prediction described in step4A is changed into the model prediction of quadratic programming form
Majorized function, as shown in formula (7),
In formula (7), yiFor the state variable comprising target vehicle speed and preferable control variable；QiFor diagonal matrix；yiobjFor state
The desired value of variable, Pi、qi、Ci、biIt is the coefficient matrix related to state variable；
Step4D:The Lagrange for obtaining the majorized function of the model prediction of the quadratic programming form described in step4C is solved
Formula, as shown in formula (8),
In formula (8), λiAnd υiFor Lagrange multiplier；
Step4D1：Single order Ku En-Plutarch (KKT) optimal conditions equation of Lagrangian solution formula is obtained, such as formula (9)
It is shown,
In formula (9), siFor slack variable；γiAnd SiRespectively Lagrange multiplier νiWith slack variable siLeading diagonal member
The column vector of element composition；E is unit column vector；
Step4D2：Single order Ku En-Plutarch optimal conditions equation, the iteration of Newton iteration method is solved using Newton iteration method
Shown in equation such as formula (10)：
In formula (10), [Δ yi Δλi Δνi Δsi]TFor the direction of search of the Newton iteration method optimized variable；βiFor
The iteration step length of the Newton iteration method, the iteration step length can ensure Lagrange multiplier and slack variable be on the occasion of；
Shown in the solution equation such as formula (11) of the direction of search of Newton iteration method optimized variable,
In formula (11), Ryi、Rλi、Rυi、RsiThe residual error of position Kuhn-Tucker condition；
The solution equation of the direction of search of optimized variable in Newton iteration method is reduced to letter by Step4D3 by linear change
Change equation, such as shown in (12),
The direction of search of formula (12) is [Δ yi Δλi]T；WiFor the transformation matrix related to slack variable；
Step4D4：Using the searcher of the equation solution reduced equation based on cholesky (Cholesky) factorization
To, the equation obtained based on cholesky (Cholesky) factorization, as shown in formula (13),
Step4E is according to the iterative equation (10) of the Newton iteration method and Step4D4 cholesky (Cholesky)
The equation (13) that factorization is obtained solves optimum state variable yi；
Step4F is according to optimum state variable yiSolve the optimal objective speed sequence of preset time window.
Lower floor's controller is accelerated and braking information using the driver received, obtains the optimal of current time power part
Torque or power distribution, are then sent to power part controller by optimum control instruction.Each power part controller is according to control
System instruction control power part performs associative operation, and its reality output is fed back into lower floor's controller progress Closed-cycle correction.
The optimal speed that lower floor's controller is obtained according to top level control device, carries out the energy management of hybrid vehicle.
In order to realize that the approximate real-time optimal energy of hybrid vehicle is managed, the present embodiment uses a kind of ECMS of simplification
Control method, i.e. WL-ECMS.WL-ECMS general principle is the Willans Line models using engine and motor, will be according to
Rely in interpolation and the ECMS searching method approximate regulations tabled look-up, so that the ECMS algorithms based on search are reduced to being based on rule
Then similar algorithm, greatly reduces the complexity of program to save the time cost of calculating.
Shown in engine Willans-Line models such as formula (14).
Pef=aePem+be (14)
In formula (14), PefAnd PemRespectively fuel oil combustion power and effective engine power, unit is W；aeAnd beRespectively
It is the function of engine speed to represent the inverse of indicated efficiency of engine and the regression coefficient of friction loss.
Shown in the Willans-Line models such as formula (15) of motor.
In formula (15), Pme、PmmRespectively electrokinetic cell power and motor effective power, unit is W；am1And bm1Respectively
Motor effective power is more than regression coefficient when zero；am2And bm2Regression coefficient during for motor effective power less than zero.am1、bm1、
am2、bm2It is the function of motor speed.
In any sampling instant, the hybrid vehicle drive pattern energy management control strategy based on Willans Line
It can be represented with formula (16), the energy management control strategy based on ECMS is reduced to and rule-based similar control plan by the formula
Slightly.
In formula (16),For the optimal effective output of motor, unit is W；WithRespectively motor and engine
Optimal power output, unit is W；PreqPower is asked for driver, unit is W.
Electric-only mode and hybrid mode are equilibrium mode, and the drive pattern of hybrid vehicle is in pure electronic mould
When switching between formula and hybrid mode, the critical point of driver's request power is electric-only mode demand power with mixing
The equal point of dynamic mode demand power.When vehicle demand power is less than critical power, the drive pattern of hybrid vehicle
For electric-only mode；Conversely, being then hybrid mode.
The layering energy management control method of a kind of hybrid vehicle based on car networking of the present invention, using the U.S. gram
(this preferred embodiment uses computing module for HP DL580 to the large server Palmetto of the gloomy university of lime, and processor is 24
Core Intel Xeon7542, RAM are 505G) to calculate the optimal objective speed of top level control device, and the speed after optimization is protected
Matlab data formats are saved as, for the offline hardware-in-the-loop test of lower floor's energy management control method, test platform is
DSPACE, test period is 450s.
In test program, setting in fleet has the hybrid electric vehicle of 10 same models and all on same track；Vapour
The initial position of car is [121 109 98 85 70 60 45.5666 30.2293 15.9196 0.8724], and unit is m；Just
Beginning speed is [14.5 16 15 16.3 16.7 12.08 13.0047 14.1788 10.373012.0473], and unit is m/
s；The time window of model prediction is 6s, and the step-length of calculating is 0.5s；Setting signal lamp quantity is 15, and the red light duration is
40s, the green light duration is 15s, and the distance of two traffic lights is 500m；The maximum of automobile, minimum speed difference are set
For 20m/s and 0.The complete vehicle curb weight for setting each car is 1750kg, and front face area is 2.36m2, coefficient of air resistance is
0.32, coefficient of rolling resistance is 0.015, and road grade is 0, and engine power is 103kW, and the rated power of motor is 40kW,
Peak power is 80kW.[b is set0,b1,b2,b3]=[0.1569,0.0245, -7.415 × 10-4,5.975×10-5], [c0,
c1,c2]=[0.07224,0.09681,1.075 × 10-3]。
Referring to Fig. 2 (a)-Fig. 2 (j), Fig. 2 (a)-Fig. 2 (j) is a kind of base of-No. ten cars of a car using the present invention respectively
In the vehicle speed trajectory schematic diagram of the layering energy management control method of the hybrid vehicle of car networking.Top level control is included in figure
Device uses F-MPC and MPC optimal speed to contrast and car when top level control for F-MPC and lower floor's controller is WL-ECMS
Speed follows curve.From Fig. 2 (a)-Fig. 2 (j), using F-MPC top level control device optimum prediction speed with using MPC most
Excellent prediction speed is roughly the same.In addition, for a time step, the relative calculating time of top level control device is dropped by the 100 of MPC
It is low to the 7.2 of F-MPC, calculate time cost and be greatly lowered.Demonstrating the F-MPC of the present embodiment proposition can be greatly reduced
The control effect close with MPC is realized on the basis of calculating time, realization control in real time.Song is followed by the speed of lower floor's controller
Line understands that lower floor's controller based on WL-ECMS follows speed and the optimum prediction speed of top level control device to keep one substantially
Cause, illustrate that lower floor's controller based on WL-ECMS can ensure that good speed is followed, realize the basic energy of hybrid vehicle
Buret is managed.
Referring to Fig. 3 (a)-Fig. 3 (j), Fig. 3 (a)-Fig. 3 (j) be respectively car-ten it is rule-based-, based on WL-
Electrokinetic cell SOC tracks schematic diagram under ECMS, the control strategy strategy based on ECMS.As seen from the figure, in the case where three kinds different
Under coating control method, electrokinetic cell SOC fluctuation range is remained in rational scope, illustrates three kinds of lower floor's energy managements
Control method can realize the equilibrium of power battery for hybrid electric vehicle, realize the basic energy management of hybrid vehicle.It is right
SOC tracks under the lower coating control method more different than in each subgraph understand that generally speaking, coating control method is followed successively by base instantly
In rule, based on WL-ECMS, based on ECMS when, electrokinetic cell SOC fluctuation ranges are sequentially reduced, and coating control method is successively instantly
During for WL-ECMS with based on ECMS, SOC fluctuation range is suitable.Illustrate that the lower coating control method based on WL-ECMS can be realized
With based on the suitable balancing energy control effects of ECMS, and the control effect of the two is superior to rule-based control method.This
Outside, for a time step, with respect to the calculating time by 100 based on ECMS be reduced to based on WL-ECMS 1.48, during calculating
Between cost be greatly lowered.
Referring to Fig. 4, the path curves schematic diagram that it is-No. ten cars of a car that Fig. 4, which is,.In figure, the reality parallel with transverse axis
Horizontal line represents that the white space in the middle of red time window, two red time windows represents green time window.As seen from the figure,
Under the testing program that the present embodiment is set, what is run into when hybrid vehicle is by traffic lights is green light.Therefore, originally
Embodiment patent of the present invention based on the top level control device of car networking can effectively avoid hybrid vehicle red parking.Separately
Outside, the geometric locus of ten cars does not have intersection point, illustrate the present embodiment based on the top level control device of car networking can be effective
Hybrid vehicle is avoided to collide.
The hybrid vehicle fuel consumption per hundred kilometers of table 1
Table 1 is respectively fuel consumption per hundred kilometers of ten hybrid vehicles under different the upper and lower control methods.By table 1
Understand, when top level control device uses F-MPC or MPC, average fuel consumption per hundred kilometers being averaged relative to ECMS based on WL-ECMS
Fuel consumption per hundred kilometers rate of rise is only 2.26% or 3.57%.When lower floor's controller uses WL-ECMS or ECMS, based on F-
MPC average fuel consumption per hundred kilometers is only 0.96% or 2.25% relative to average hundred kilometers of rates of rise based on MPC.
It can be obtained by this preferred embodiment, a kind of hybrid vehicle based on car networking of the invention of the invention
Layering energy management control method：Top level control device is based on F-MPC with being kept substantially based on the obtained optimal vehicle speed trajectories of MPC
Unanimously, for a time step, relative calculating time of top level control device is reduced to the 7.2 of F-MPC by the 100 of MPC, and on
Layer controller differs smaller using F-MPC with using fuel consumption per hundred kilometers during MPC.Power when lower floor's controller uses WL-ECMS
Battery SOC is fluctuated in rational scope, and speed tracking error is smaller, the close hundred kilometers of oil based on ECMS of fuel consumption per hundred kilometers
Consumption.For a time step, the relative calculating time based on WL-ECMS lower floors energy management control method can be by ECMS's
100 are reduced to 1.48.By a kind of layering energy management control method of hybrid vehicle based on car networking of the present invention,
Optimal objective speed is solved, hybrid vehicle can effectively avoid red parking when by traffic lights.
The layering energy management control method of a kind of hybrid vehicle based on car networking of the present invention, based on F-MPC
Predict that the top level control device of optimal speed and lower floor's controller based on WL-ECMS can be run in significantly reduction program
On the premise of time, realization control in real time, it is ensured that the good fuel economy of hybrid vehicle.One kind of the present invention is based on car
The layering energy management control method of the hybrid vehicle of networking, the real-time power for solution hybrid vehicle is managed and excellent
Change and new thinking is provided.
It is obvious to a person skilled in the art that the invention is not restricted to the details of above-mentioned one exemplary embodiment, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, embodiment all should be regarded as exemplary, and be nonrestrictive, the scope of the present invention is by appended power
Profit is required rather than described above is limited, it is intended that all in the implication and scope of the equivalency of claim by falling
Change is included in the present invention.Any reference in claim should not be considered as to the claim involved by limitation.
Moreover, it will be appreciated that although the present specification is described in terms of embodiments, not each embodiment is only wrapped
Containing an independent technical scheme, this narrating mode of specification is only that for clarity, those skilled in the art should
Using specification as an entirety, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
It may be appreciated other embodiment.
Claims (6)
1. the layering energy management control method of a kind of hybrid vehicle based on car networking, it is characterised in that including as follows
Step：
Step (1), based on car networking environment, hybrid vehicle passes through Dedicated Short Range Communications (DSRC), radio frequency identification
(RFID), bluetooth, ZIGBEE or WI-FI enter that driving communicates with car and car and traffic implementation communicate；
Step (2), top level control device mathematical modeling is set up, comprised the following steps：
(21) vehicle Longitudinal Dynamic Model is set up；
(22) traffic lights timing is utilized, the scope of target vehicle speed is obtained and sets the target vehicle speed of hybrid vehicle as this
The upper limit of vehicle speed range；
(23) target vehicle speed based on hybrid vehicle, the optimal of preset time window is predicted using accelerated model prediction algorithm
Target vehicle speed sequence；
(24) optimal objective speed sequence is fed back to the driver of each car by being wirelessly transferred form, driver is according to optimal
Target vehicle speed sequence is accelerated or braked；
2. the layering energy management control method of the hybrid vehicle according to claim 1 based on car networking：Also wrap
Include following step：
Step (3), lower floor's controller according to accelerating or braking information, obtain current time engine and motor optimum torque or
Power distribution, and by optimum torque or power distribution instruction by be wirelessly transferred be sent to engine controller, electric machine controller,
Gearbox control and electrokinetic cell controller；
Step (4), each power part controller control corresponding power part to perform correlation according to the control instruction received
Output operation, and the reality output of power part is fed back into lower floor's controller progress Closed-cycle correction.
3. the layering energy management control method of the hybrid vehicle according to claim 1 or 2 based on car networking, its
It is characterised by, the vehicle Longitudinal Dynamic Model set up in step (21), such as formula (1)：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>&CenterDot;</mo>
</mover>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<msub>
<mi>M</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<msub>
<mi>C</mi>
<mi>D</mi>
</msub>
<msub>
<mi>&rho;</mi>
<mi>a</mi>
</msub>
<msub>
<mi>A</mi>
<mrow>
<mi>f</mi>
<mi>i</mi>
</mrow>
</msub>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<mi>&mu;</mi>
<mi>g</mi>
<mo>-</mo>
<mi>g</mi>
<mi>&theta;</mi>
<mo>+</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mo>&lsqb;</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (1), xiFor the state vector of i-th car；siFor the position of i-th car, stated with coordinate；viFor the speed of i-th car
Degree, unit is m/s；uiFor the control variable of i-th car, that is, any time unit mass tractive force or brake force, unit
For N/kg；MiThe quality of i-th car, unit is kg；CDFor control resistance coefficient；ρaFor atmospheric density, unit is kg/m3；AfiFor
The front face area of i-th car, unit is m2；μ is coefficient of rolling resistance；θ is the gradient；
4. the layering energy management control method of the hybrid vehicle according to claim 1 or 2 based on car networking, its
It is characterised by：In step (22), using traffic lights timing, obtain the upper and lower bound of the scope of target vehicle speed and set mesh
Mark the upper limit that speed is this scope, such as formula (2)：
In formula (2), vilFor the lower limit of target vehicle speed, unit is m/s；vihFor the upper limit of target vehicle speed, unit is m/s；dia(td)
For the position s of i-th cariWith traffic lights a distance, unit is m；KwFor the cycle-index of signal lamp, round numbers；tg、tr
Respectively the duration of red light and green light, unit is s；tcFor the time in a traffic lights cycle, unit is s；tdFor garage
The time sailed, unit is s；vimaxThe maximum of speed is sailed for i-th hybrid electric vehicle, unit is m/s；viobjFor mixing
Power vehicle target vehicle speed, unit is m/s；
5. the layering energy management control method of the hybrid vehicle according to claim 4 based on car networking, it is special
Levy and be：According to the scope of target vehicle speed, the control variable u of i-th car of t is obtainedi(t) constraints, such as formula (3) institute
Show, control variable ui(t) constraints, target vehicle speed v are metiobjIt is limited at [vil, vih] in the range of, hybrid power vapour
Car can avoid red parking；
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>&delta;</mi>
<mi>t</mi>
</mrow>
</mfrac>
<mo>&rsqb;</mo>
<mo>&le;</mo>
<msub>
<mi>a</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>h</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mi>&delta;</mi>
<mi>t</mi>
</mrow>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>a</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<msub>
<mi>M</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<msub>
<mi>C</mi>
<mi>D</mi>
</msub>
<msub>
<mi>&rho;</mi>
<mi>a</mi>
</msub>
<msub>
<mi>A</mi>
<mrow>
<mi>f</mi>
<mi>i</mi>
</mrow>
</msub>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>&mu;</mi>
<mi>g</mi>
<mo>-</mo>
<mi>g</mi>
<mi>&theta;</mi>
<mo>+</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>h</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (3), uimin、uimaxThe minimum and maximum value of variable is respectively controlled, unit is N/kg；δ t are material calculation, unit
For s；ai(t) it is the longitudinal acceleration of i-th car of current time, unit is m/s2；ui(t) become for the control of i-th car of t
Amount, unit is N/kg；
6. the layering energy management control method of the hybrid vehicle according to claim 4 based on car networking, it is special
Levy and be：Comprise the following steps：
Step4A：The majorized function of accelerated model prediction is set up, and using the optimization letter of Novel Algorithm solving model prediction
Number, the majorized function of the accelerated model prediction, is such as represented with formula (5)：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<munder>
<mi>min</mi>
<mrow>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</munder>
<mo>{</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>t</mi>
<mo>=</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>+</mo>
<mi>T</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<msub>
<mi>&alpha;</mi>
<mn>1</mn>
</msub>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msub>
<mi>&alpha;</mi>
<mn>2</mn>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<msub>
<mi>S</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msub>
<mi>&alpha;</mi>
<mn>3</mn>
</msub>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>}</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>S</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>S</mi>
<mn>0</mn>
</msub>
<mo>+</mo>
<msub>
<mi>t</mi>
<mi>h</mi>
</msub>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>&lsqb;</mo>
<msub>
<mi>s</mi>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<msub>
<mi>M</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<msub>
<mi>C</mi>
<mi>D</mi>
</msub>
<msub>
<mi>&rho;</mi>
<mi>a</mi>
</msub>
<msub>
<mi>A</mi>
<mrow>
<mi>f</mi>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<mi>&mu;</mi>
<mi>g</mi>
<mo>+</mo>
<mi>g</mi>
<mi>&theta;</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>l</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mi>h</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>t</mi>
<mi>d</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (5), T is given time window, and unit is s；SijFor the distance of i-th car and jth car, unit is m；αi(i
=1,2,3) it is weight coefficient, uid(t) it is preferable control variable, unit is N/kg；siAnd s (t)j(t) be respectively i-th car and
Position of the jth car in time t, uses coordinate representation；thFor the interval time of front and rear two car set in advance, unit is s；S0
For safe distance set in advance, unit is m；viminFor the minimum value of automobile driving speed, unit is m/s；N is mixed in fleet
Close the quantity of power vehicle；
Step4B：The Longitudinal Dynamic Model of hybrid vehicle is converted into the hybrid vehicle Longitudinal of linear forms
Model is learned, as shown in formula (6),
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>A</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>B</mi>
<mi>i</mi>
</msub>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>A</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<msub>
<mi>M</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
<msub>
<mi>C</mi>
<mi>D</mi>
</msub>
<msub>
<mi>&rho;</mi>
<mi>a</mi>
</msub>
<msub>
<mi>A</mi>
<mrow>
<mi>f</mi>
<mi>i</mi>
</mrow>
</msub>
<msubsup>
<mi>v</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mo>-</mo>
<mi>&mu;</mi>
<mi>g</mi>
<mo>-</mo>
<mi>g</mi>
<mi>&theta;</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>1</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (6), Ai(xi) it is the parameter matrix related to state；BiFor a constant column matrix；K is time step, and unit is
s；
Step4C：The majorized function of accelerated model prediction described in step4A is changed into the model prediction of quadratic programming form
Majorized function, as shown in formula (7),
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>&lsqb;</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mi>T</mi>
</msup>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>&le;</mo>
<msub>
<mi>q</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>b</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msup>
<mrow>
<mo>&lsqb;</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>...</mo>
<mo>,</mo>
<msub>
<mi>u</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>T</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>T</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (7), yiFor the state variable comprising target vehicle speed and preferable control variable；QiFor diagonal matrix；yiobjFor state variable
Desired value, Pi、qi、Ci、biIt is the coefficient matrix related to state variable；
Step4D:The Lagrange for obtaining the majorized function of the model prediction of the quadratic programming form described in step4C solves public affairs
Formula, as shown in formula (8),
<mrow>
<msub>
<mi>L</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mi>T</mi>
</msup>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>b</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>q</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (8), λiAnd υiFor Lagrange multiplier；
Step4D1：Single order Ku En-Plutarch (KKT) optimal conditions equation of Lagrangian solution formula is obtained, as shown in formula (9),
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mi>o</mi>
<mi>b</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>C</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>q</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>b</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&gamma;</mi>
<mi>i</mi>
</msub>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mi>e</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>q</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&gamma;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mi>d</mi>
<mi>i</mi>
<mi>a</mi>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mi>d</mi>
<mi>i</mi>
<mi>a</mi>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (9), siFor slack variable；γiAnd SiRespectively Lagrange multiplier νiWith slack variable siThe elements in a main diagonal group
Into column vector；E is unit column vector；
Step4D2：Single order Ku En-Plutarch optimal conditions equation, the iterative equation of Newton iteration method is solved using Newton iteration method
As shown in formula (10)：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>+</mo>
<msub>
<mi>&beta;</mi>
<mi>i</mi>
</msub>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;y</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;&lambda;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;v</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;s</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (10), [Δ yi Δλi Δνi Δsi]TFor the direction of search of the Newton iteration method optimized variable；βiTo be described
The iteration step length of Newton iteration method, the iteration step length can ensure Lagrange multiplier and slack variable be on the occasion of；
Shown in the solution equation such as formula (11) of the direction of search of Newton iteration method optimized variable,
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
</mtd>
<mtd>
<msubsup>
<mi>C</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>&Delta;</mi>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;&lambda;</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;v</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;s</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<msub>
<mi>F</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>-</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>R</mi>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>R</mi>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>R</mi>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>R</mi>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula (11), Ryi、Rλi、Rυi、RsiThe residual error of position Kuhn-Tucker condition；
The solution equation of the direction of search of optimized variable in Newton iteration method is reduced to simplification side by Step4D3 by linear change
Journey, such as shown in (12),
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
<mtd>
<msubsup>
<mi>C</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;y</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&Delta;&lambda;</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>R</mi>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>R</mi>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</msub>
<mo>-</mo>
<msubsup>
<mi>&gamma;</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>R</mi>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>R</mi>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>&gamma;</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>R</mi>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</msub>
<mo>=</mo>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>W</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
The direction of search of formula (12) is [Δ yi Δλi]T；WiFor the transformation matrix related to slack variable；
Step4D4：Using the direction of search of the equation solution reduced equation based on cholesky (Cholesky) factorization, base
The equation obtained in cholesky (Cholesky) factorization, as shown in formula (13),
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<msubsup>
<mi>C</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>&Delta;&lambda;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msup>
<mo>&lsqb;</mo>
<msub>
<mi>R</mi>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<msub>
<mi>R</mi>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</msub>
<mo>-</mo>
<msubsup>
<mi>&gamma;</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>R</mi>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</msub>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<msub>
<mi>R</mi>
<msub>
<mi>&lambda;</mi>
<mi>i</mi>
</msub>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>(</mo>
<msub>
<mi>Q</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
<msub>
<mi>&Delta;y</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>y</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>P</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msubsup>
<mi>W</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>2</mn>
</mrow>
</msubsup>
<mo>(</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>v</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<msubsup>
<mi>&gamma;</mi>
<mi>i</mi>
<mrow>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>R</mi>
<msub>
<mi>s</mi>
<mi>i</mi>
</msub>
</msub>
<mo>)</mo>
<mo>-</mo>
<msubsup>
<mi>C</mi>
<mi>i</mi>
<mi>T</mi>
</msubsup>
<msub>
<mi>&Delta;&lambda;</mi>
<mi>i</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
Step4E is according to the iterative equation (10) of the Newton iteration method and Step4D4 cholesky (Cholesky) factor
Decompose obtained equation (13) and solve optimum state variable yi；
Step4F is according to optimum state variable yiSolve the optimal objective speed sequence of preset time window.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610052624.7A CN105501216B (en) | 2016-01-25 | 2016-01-25 | The layering energy management control method of hybrid vehicle based on car networking |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610052624.7A CN105501216B (en) | 2016-01-25 | 2016-01-25 | The layering energy management control method of hybrid vehicle based on car networking |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105501216A CN105501216A (en) | 2016-04-20 |
| CN105501216B true CN105501216B (en) | 2017-11-07 |
Family
ID=55709644
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610052624.7A Active CN105501216B (en) | 2016-01-25 | 2016-01-25 | The layering energy management control method of hybrid vehicle based on car networking |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105501216B (en) |
Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106502098B (en) * | 2016-11-19 | 2019-03-01 | 合肥工业大学 | A kind of optimal speed closed loop quick predict control method and device based on car networking |
| DE102017201177A1 (en) * | 2017-01-25 | 2018-07-26 | Robert Bosch Gmbh | Method and device for braking a vehicle |
| CN107577234B (en) * | 2017-09-21 | 2021-01-15 | 合肥工业大学 | Automobile fuel economy control method for driver in-loop |
| CN107885932B (en) * | 2017-11-07 | 2020-10-09 | 长春工业大学 | Automobile emergency collision avoidance layered control method considering man-machine harmony |
| CN110012039B (en) * | 2018-01-04 | 2021-01-05 | 华北电力大学 | ADMM-based task allocation and power control method in Internet of vehicles |
| CN110691349B (en) * | 2018-07-04 | 2021-02-05 | 青岛农业大学 | Adaptive control method for safe application-oriented combined power and competition window in Internet of vehicles |
| CN109291925B (en) * | 2018-09-20 | 2020-08-18 | 厦门大学 | Energy-saving intelligent network-connection hybrid electric vehicle following control method |
| CN109348532B (en) * | 2018-10-26 | 2021-07-09 | 南京航空航天大学 | Cognitive Internet of vehicles efficient combined resource allocation method based on asymmetric relay transmission |
| CN109484334A (en) * | 2018-11-07 | 2019-03-19 | 佛山科学技术学院 | A kind of running speed recommended method and its system |
| CN109624986B (en) * | 2019-03-01 | 2021-01-15 | 吉林大学 | Driving style learning cruise control system and method based on mode switching |
| CN110264757B (en) * | 2019-05-21 | 2021-10-26 | 吉林大学 | Intelligent networking automobile layered speed planning method based on continuous signal lamp information |
| CN110696815B (en) * | 2019-11-21 | 2020-10-09 | 北京理工大学 | Prediction energy management method of network-connected hybrid electric vehicle |
| CN111152780B (en) * | 2020-01-08 | 2021-06-25 | 吉林大学 | Vehicle global energy management method based on 'information layer-substance layer-energy layer' framework |
| CN111619545B (en) * | 2020-05-08 | 2021-10-01 | 北京航空航天大学 | Hybrid electric vehicle energy management method based on traffic information |
| CN111891110B (en) * | 2020-05-15 | 2022-02-01 | 吉林大学 | Intelligent network-connected hybrid electric vehicle energy-heat integrated real-time management system |
| CN111768616B (en) * | 2020-05-15 | 2022-04-08 | 重庆大学 | Vehicle fleet consistency control method based on vehicle-road cooperation in mixed traffic scene |
| CN112907937B (en) * | 2021-02-03 | 2022-10-14 | 湖南大学 | Hybrid vehicle queue control method and system considering rear vehicle information |
| CN113232651B (en) * | 2021-02-08 | 2022-07-22 | 杭州电子科技大学 | Fuel automobile ecological self-adaptive cruise control method based on ADHDP |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1903629A (en) * | 2006-08-09 | 2007-01-31 | 吉林省卧龙科技发展有限责任公司 | Random energy management method of bienergy source power automobile |
| CN1983240A (en) * | 2005-12-14 | 2007-06-20 | 通用汽车环球科技运作公司 | Method for assessing models of vehicle driving style or vehicle usage model detector |
| CN102717797A (en) * | 2012-06-14 | 2012-10-10 | 北京理工大学 | Energy management method and system of hybrid vehicle |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8332347B2 (en) * | 2006-07-28 | 2012-12-11 | Persistent Systems Limited | System and method for inferring a network of associations |
| FR2975795B1 (en) * | 2011-05-25 | 2014-05-09 | Commissariat Energie Atomique | METHOD FOR MANAGING THE ENERGY CONSUMED BY A MOBILE SYSTEM, IN PARTICULAR A MOTOR VEHICLE, ON-BOARD DEVICE IMPLEMENTING SUCH A METHOD |
| US9676382B2 (en) * | 2014-04-17 | 2017-06-13 | Palo Alto Research Center Incorporated | Systems and methods for hybrid vehicles with a high degree of hybridization |
-
2016
- 2016-01-25 CN CN201610052624.7A patent/CN105501216B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1983240A (en) * | 2005-12-14 | 2007-06-20 | 通用汽车环球科技运作公司 | Method for assessing models of vehicle driving style or vehicle usage model detector |
| CN1903629A (en) * | 2006-08-09 | 2007-01-31 | 吉林省卧龙科技发展有限责任公司 | Random energy management method of bienergy source power automobile |
| CN102717797A (en) * | 2012-06-14 | 2012-10-10 | 北京理工大学 | Energy management method and system of hybrid vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105501216A (en) | 2016-04-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105501216B (en) | The layering energy management control method of hybrid vehicle based on car networking | |
| CN105759753B (en) | The energy management optimal control method of hybrid vehicle based on V2X | |
| Hu et al. | Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios | |
| He et al. | An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle | |
| Sciarretta et al. | Energy-efficient driving of road vehicles | |
| CN105946857B (en) | Parallel plug-in hybrid electric vehicle (PHEV) energy management method based on intelligent transportation system | |
| CN106476806B (en) | Cooperating type self-adaption cruise system algorithm based on traffic information | |
| Nie et al. | Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model | |
| Yang et al. | An efficient vehicle-following predictive energy management strategy for PHEV based on improved sequential quadratic programming algorithm | |
| Luo et al. | Green light optimal speed advisory for hybrid electric vehicles | |
| Cheng et al. | Longitudinal autonomous driving based on game theory for intelligent hybrid electric vehicles with connectivity | |
| CN110135632A (en) | PHEV adaptive optimal energy management method based on routing information | |
| CN104249736B (en) | The energy-conservation forecast Control Algorithm of hybrid vehicle based on platoon driving | |
| Olin et al. | Reducing fuel consumption by using information from connected and automated vehicle modules to optimize propulsion system control | |
| CN106502098A (en) | A kind of optimum speed closed loop fast prediction control method and device based on car networking | |
| CN107577234A (en) | A kind of automobile fuel ecomomy control method of driver in ring | |
| CN104590247A (en) | Hybrid electric vehicle energy conservation predictive control method based on traffic signal lamp information | |
| CN105083276A (en) | Hybrid electric vehicle energy-saving predication control method based on decentralized control | |
| Ouddah et al. | From offline to adaptive online energy management strategy of hybrid vehicle using Pontryagin’s minimum principle | |
| CN106997675A (en) | Target vehicle speed Forecasting Methodology based on Dynamic Programming | |
| Liu et al. | Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach | |
| Wang et al. | Research on speed optimization strategy of hybrid electric vehicle queue based on particle swarm optimization | |
| CN112614340A (en) | Method and device for enabling branch vehicles to converge into main road, electronic equipment and storage medium | |
| CN111959492A (en) | HEV energy management hierarchical control method considering lane change behavior in networking environment | |
| Tang et al. | Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |