CN109334654A - A kind of parallel hybrid electric vehicle energy management method with gearbox-gear control - Google Patents

A kind of parallel hybrid electric vehicle energy management method with gearbox-gear control Download PDF

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CN109334654A
CN109334654A CN201811107090.9A CN201811107090A CN109334654A CN 109334654 A CN109334654 A CN 109334654A CN 201811107090 A CN201811107090 A CN 201811107090A CN 109334654 A CN109334654 A CN 109334654A
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model
motor
gearbox
gear
ref
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汪少华
张晟
孙晓强
施德华
余铖铨
张弛
惠易佳
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Jiangsu University
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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
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/06—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of combustion engines
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/04—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units
    • B60W10/08—Conjoint control of vehicle sub-units of different type or different function including control of propulsion units including control of electric propulsion units, e.g. motors or generators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/10—Conjoint control of vehicle sub-units of different type or different function including control of change-speed gearings
    • B60W10/11—Stepped gearings
    • 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
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/30—Control strategies involving selection of transmission gear ratio
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/06—Combustion engines, Gas turbines
    • B60W2710/0644—Engine speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/08—Electric propulsion units
    • B60W2710/081—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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/10—Change speed gearings
    • B60W2710/1005—Transmission ratio engaged
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/62—Hybrid vehicles

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)

Abstract

The invention discloses a kind of parallel hybrid electric vehicle energy management methods with gearbox-gear control, it is related to hybrid vehicle field of energy management, the development law that this method passes through battery charge state change rate and engine fuel consumption rate under each gear of analysis, and combine integrating mixed logic dynamic modeling method, establish the parallel hybrid electric vehicle Energy Management System model including the discrete gear information of gearbox, it is further introduced into Model Predictive Control thought, motor torque is defined as continuous control variable, gearbox-gear is defined as discrete control variable, optimization problem is converted into MINLP model problem to be solved, the strategy can optimize the torque distribution of each power source, decision goes out the best gear of gearbox simultaneously.The advantages that present invention is directed to such parallel hybrid electric vehicle energy management problem with gearbox, has with strong points, and practicability is high, and effect of optimization is good, highly integrated.

Description

A kind of parallel hybrid electric vehicle energy management method with gearbox-gear control
Technical field
The present invention relates to parallel hybrid electric vehicle field of energy management more particularly to a kind of band gearbox-gear to control Energy management method.
Background technique
For parallel hybrid electric vehicle because structure is simple, fuel economy is superior, has been increasingly used in volume production Vehicle, more integrated energy management strategies are to realize its wider popularization to further decrease fuel consumption for design Key.
Parallel hybrid electric vehicle structure is simple, is easy to from conventional fuel oil automobile improvement, therefore can contract significantly Subtract research and development cost, dynamical system mainly includes the power parts such as engine, motor, battery and gearbox.In general, gearbox is rich Rich gear can increase the probability that vehicle is run in High Load Rate region, be conducive to improve fuel economy, but mixing In dynamical system, system is corresponding in different system requirements torques and revolving speed under different stalls, and engine fuel is caused to consume It is not quite similar with battery charge state change procedure.Therefore, in the present system, there is only as engine fuel consumption rate and electricity Continuous variable as the state-of-charge rate of pond, and include that gearbox-gear switches this kind of discrete event, the wherein shape of continuous variable State is updated to continuous process, and on the one hand discrete event determines the update rule of continuous variable, at the same its development law also by The driving of given threshold is broken through in evolutionary process to continuous variable.From the above analysis as can be seen that the parallel connection comprising gearbox Formula Energy Management System for Hybrid Electric Vehicle has apparent Hybrid dynamics feature, is typical hybrid system.
In recent years, Hybrid System Theory is gradually applied to Practical Project, at abroad, Hybrid System Theory successfully solves The engineering problems such as automobile electric gasoline throttle nonlinear Control, traction control, at home, Hybrid System Theory are planted in deep liquid stream The control of training Experiment Greenhouse Temperature, Semi-active air suspension control etc. also achieve successful application.Hybrid system and its control Theory has been acknowledged as having important finger to the solution of the complex engineerings technical problems such as Automation of Manufacturing Process, robot control Lead meaning.
In currently used hybrid vehicle energy management strategies, although rule-based energy management strategies do not depend on In accurate mathematical model, but it controls superiority and inferiority and directly depends on expertise, and is not applied for multi-state；Global optimum calculates Method, which needs to predict entire operating condition, just can be carried out global optimizing, have significant limitation；Although instantaneous optimization control strategy can be with On-line implement, but be difficult to obtain optimal solution, and computationally intensive, to limit its popularization and application on hybrid vehicle. More it is essential that the emphasis of above-mentioned energy management strategies is all to obtain the mode of control rule, research pair is but had ignored As the existing some complex characteristics of namely Energy Management System for Hybrid Electric Vehicle itself, thus it is difficult to effectively solve above-mentioned Strategy applies existing some confinement problems on hybrid vehicle.
Summary of the invention
Existing on existing hybrid vehicle in order to overcome the problems, such as, the present invention proposes a kind of comprising gearbox-gear control The parallel hybrid electric vehicle energy management method of system realizes the energy efficient management of parallel hybrid electric vehicle.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of parallel hybrid electric vehicle energy management method with gearbox-gear control, main includes following step It is rapid:
Calculate with amendment demand torque: be based on vehicle travel resistance equation, calculate the torque of vehicle demand, and further combined with PI is to demand torque dynamic corrections；
Piecewise affine: being based on piecewise affine technology, consumes curved surface to engine fuel and power of motor curved surface carries out two dimension Piecewise affine carries out one-dimensional point to charge electricity condition change rate, motor torque restricted model and motor torque restricted model Section is affine；
Determine battery charge state change rate and engine fuel consumption rate under each discrete gear；
Establish Energy Management System model: being established using mixed logical dynamics includes the discrete gear information of gearbox Parallel hybrid electric vehicle Energy Management System model；
Design a model predictive control strategy: being based on Model Predictive Control thought, will include the discrete gear information of gearbox Energy Management System mixed logical dynamics optimize distribution as prediction model, to the output torque in different dynamic source, The best gearbox-gear at decision current time simultaneously.
Further, it is described calculating with amendment demand torque) in vehicle demand torque calculation formula:
Treq=(+0.5 ρ C of mgfcos αdAvref 2+mgsinα+maref)R+Kp(vref-vact)+Ki∫(vref-vact)dt
Wherein, TreqFor vehicle demand torque, m is complete vehicle quality, and g is acceleration of gravity, and f is coefficient of rolling resistance, and α is Road grade, ρ are atmospheric density, CdFor coefficient of air resistance, A front face area, vrefReference speed, arefReference acceleration, vact Actual speed, KpFor proportionality coefficient, KiFor integral coefficient.
3. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, special Sign is, the piecewise affine) in piecewise affine method it is as follows:
It establishes model: engine consumption model, power of motor model, battery model, engine is obtained by platform experiment Torque restricted model, motor torque restricted model；
For engine consumption model, power of motor model carries out two-dimentional piecewise affine by multiple planes:
Engine consumption model: fuel_i=giTe+jiωe+li (Te,ωe)∈Xi；
Power of motor model: Pbatt_ii=aiiTM+biiωM+cii (TM,ωM)∈Ωii；
Wherein, fuel_iFor engine fuel consumption rate in i-th of fitted area, Pbatt_iiFor in i-th i fitted area The power of battery, Te, TMRespectively motor torque and motor torque, ωe, ωMRespectively engine speed and motor speed, gi, ji, li, aii, bii, ciiFor fitting coefficient, Xi, ΩiiFor fit Plane domain；Subscript i, ii is fitting number；
It is one-dimensional by the progress of a plurality of straight line for battery model, motor torque restricted model and motor torque restricted model Piecewise affine:
Battery model:
Motor torque restricted model: Te_if=mifωe+nif ωe∈Ae_if；
Motor torque restricted model: TM_id=pidωM+qid ωM∈AM_id；
Wherein,For battery charge state change rate in i-th ii fitted area, PbattFor the power of battery, Te_ifFor The torque capacity of engine, minimum torque 0, T in i-th f fitted areaM_idMost for motor in i-th d fitted area Big torque, minimum torque are the opposite number of torque capacity, riii, mif, nif, pid, qidFor fitting coefficient, Λiii, Ae_if, AM_id For fitting a straight line domain, subscript iii, if, id are fitting number.Further, gearbox is five shift automatic variable speeds in step (3) Case, there are five drive shift, transmission ratio λ altogetheris(i=1,2,3,4,5).
Further, each shelves battery charge state change rate and engine fuel consumption rate are as follows in step (3):
When gearbox is in one grade:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ1TM+Te=Treq/(fdλ1), engine demand rotational speed omegae=fdvrefλ1/Rw, motor demand rotational speed omegaM=itrsfdvrefλ1/ Rw；
When gearbox is in two grades:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ2TM+Te=Treq/(fdλ2), engine demand rotational speed omegae=fdvrefλ2/Rw, motor demand rotational speed omegaM=itrsfdvrefλ2/ Rw；
When gearbox is in third gear:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ3TM+Te=Treq/(fdλ3), engine demand rotational speed omegae=fdvrefλ3/Rw, motor demand rotational speed omegaM=itrsfdvrefλ3/ Rw；
When gearbox is in fourth gear:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ4TM+Te=Treq/(fdλ4), engine demand rotational speed omegae=fdvrefλ4/Rw, motor demand rotational speed omegaM=itrsfdvrefλ4/ Rw；
When gearbox is in five grades:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ5TM+Te=Treq/(fdλ5), engine demand rotational speed omegae=fdvrefλ5/Rw, motor demand rotational speed omegaM=itrsfdvrefλ5/ Rw；
Further, being established in step (4) based on integrating mixed logic dynamic includes the parallel connection of the discrete gear information of gearbox Formula Energy Management System for Hybrid Electric Vehicle mixing dynamic model includes the following steps:
A. using motor torque as continuous control variable, with gearbox-gear for discrete control variable, it is based on propositional logic pair Engine fuel consumption rate and battery charge state variation under each gear of parallel hybrid electric vehicle and each gear Rate is described；
B. logical variable is combined to form unified state space equation of the parallel hybrid electric vehicle under all gears；
C. auxiliary continuous variable is introduced, converts integrating mixed logic dynamic canonical form for unified state space equation；
D. MIXED INTEGER linear inequality is converted by propositional logic and relevant operation rule, forms integrating mixed logic dynamic rule Inequation under norm form.
Further, the Model Predictive Control in step 5 mainly comprises the steps that
A, for statistical analysis to history road condition data, velocity band is divided into N number of subset, calculates current time each subset The probability for jumping to each subset of subsequent time obtains a step Markov method transfer matrix, such as following formula:
B, objective function J=(SOC-SOC is setref)2+fuel2, SOCrefFor reference battery state, previous item is pair in formula Charge and discharge electric equilibrium is considered, and latter is considered to oil consumption；
C, prediction model is that the parallel hybrid electric vehicle Energy Management System mixing comprising gearbox-gear information is patrolled Dynamic model is collected, the control output of system is defined as the selection of gearbox-gear and the torque of motor, in the process to electricity Pond, motor and engine operating range are constrained；
D, the optimization problem of the parallel hybrid electric vehicle Energy Management System controlled comprising gearbox-gear is converted For a MINLP model problem；
E, line solver is carried out to the MINLP model problem in conjunction with software GLPK, CPLEX, lpsolve etc., To obtain system model predictions control law.
The invention has the advantages that establishing the parallel connection comprising the discrete gear information of gearbox based on integrating mixed logic dynamic Formula Energy Management System for Hybrid Electric Vehicle model, has sufficiently grasped hybrid characteristic existing for system, on this basis, further Model Predictive Control is carried out for built Hybrid dynamics model, to optimize point to the output torque of engine and motor Match, while decision goes out the best gear at parallel hybrid electric vehicle current time, the control strategy is highly integrated, realizes pair The efficient management of parallel hybrid electric vehicle energy, and merged the control to gearbox.
Detailed description of the invention
Fig. 1 is parallel hybrid electric vehicle energy management strategies overview flow chart
Fig. 2 is parallel hybrid electric vehicle structure chart
Fig. 3 is engine consumption piecewise affine model
Fig. 4 is power of motor piecewise affine model
Fig. 5 is battery charge state piecewise affine model
Fig. 6 is that motor torque constrains piecewise affine model
Fig. 7 is that motor torque constrains piecewise affine model
Fig. 8 is parallel hybrid electric vehicle Model Predictive Control schematic diagram
Fig. 9 is that parallel hybrid electric vehicle gearbox-gear switches schematic diagram
Appended drawing reference is as follows:
1- engine；2- clutch；3- motor；4- battery；5- gearbox；6- wheel；7- main reducing gear.
Specific embodiment
As shown in Figure 1 and Figure 2, a kind of band parallel hybrid electric vehicle energy management method of the invention, specifically include as Lower step:
(1) parallel hybrid electric vehicle structure is analyzed, in this configuration, motor 3 passes through gear and engine 1 Output shaft is coupled, and the decoupling of 3 torque of 1 torque of engine and motor may be implemented, and gearbox 5 is located at engine 1 and motor 3 Rear portion, arrangement is simple, which had both been able to achieve 1 torque of engine and the optimization of 3 torque of motor distributes, simultaneously also It is integrated with the control of 5 gear of gearbox.
(2) torque of vehicle demand can be obtained by the basis of vehicle travel resistance equation, introducing PI dynamic adjustment:
Treq=(+0.5 ρ C of mgfcos αdAvref 2+mgsinα+maref)R+Kp(vref-vact)+Ki∫(vref-vact)dt
(3) to meet modeling demand, the non-linear components in hybrid power system will carry out piecewise affine, be divided into one-dimensional point The affine and two-dimentional piecewise affine of section, the specific steps are as follows:
1. carrying out two-dimentional piecewise affine by multiple planes for engine consumption model, power of motor model:
It is illustrated in figure 3 engine consumption piecewise affine model, engine consumption curved surface is intended by two planes It closes, fit equation can be expressed as: fuel_i=giTe+jiωe+li(Te,ωe)∈Xi；
As can be seen, the engine consumption piecewise affine model obtained with this method is corresponding with its non-thread from the figure Property model is very close, illustrates this patent method significant effect, can be used for parallel hybrid electric vehicle energy management method Design.
It is illustrated in figure 4 power of motor piecewise affine model, power of motor curved surface is fitted by four planes, Fit equation can be expressed as: Pbatt_ii=aiiTM+biiωM+cii (TM,ωM)∈Ωi；
As can be seen, the power of motor piecewise affine model obtained with this method is corresponding with its non-linear from the figure Model is very close, illustrates this patent method significant effect, can be used for setting for parallel hybrid electric vehicle energy management method Meter.
Wherein, fuel_iFor engine fuel consumption rate in i-th of fitted area, Pbatt_iiFor in i-th i fitted area The power of battery, Te, TMRespectively motor torque and motor torque, ωe, ωMRespectively engine speed and motor speed, gi, ji, li, aii, bii, ciiFor fitting coefficient, Xi, ΩiiFor fit Plane domain, subscript i, ii are fitting number；
2. carrying out one-dimensional piecewise affine by a plurality of straight line for battery model, engine and motor torque restricted model:
It is illustrated in figure 5 battery piecewise affine model, battery charge state change rate curve is carried out by two straight lines Fitting, fit equation can be expressed as:As can be seen, being obtained with this method from the figure The corresponding nonlinear model of battery piecewise affine model is very close, illustrates this patent method significant effect, can be used for simultaneously The design of connection formula hybrid vehicle energy management method.It is illustrated in figure 6 engine constraint piecewise affine model, passes through three Straight line is fitted engine torque capacity curve, and fit equation can be expressed as: Te_if=mifωe+nifωe∈Ae_if；From As can be seen, constraining the corresponding nonlinear model of piecewise affine model with the motor torque that this method obtains on the figure It is very close, illustrate this patent method significant effect, can be used for the design of parallel hybrid electric vehicle energy management method.
It is illustrated in figure 7 motor constraint piecewise affine model, motor torque capacity curve is intended by seven straight lines It closes, fit equation can be expressed as: TM_id=pidωM+qidωM∈AM_id；
Wherein,For battery charge state change rate in i-th ii fitted area, PbattFor the power of battery, Te_ifFor The torque capacity of engine, minimum torque 0, T in i-th f fitted areaM_idMost for motor in i-th d fitted area Big torque, minimum torque are the opposite number of torque capacity, riii, mif, nif, pid, qidFor fitting coefficient, Λiii, Ae_if, AM_id For fitting a straight line domain, subscript iii, if, id are fitting number.As can be seen, the motor obtained with this method from the figure Torque match value and actual value it is very close, this method can manage the energy management problem of parallel type vehicle very well.
In conjunction with attached drawing 8 and 9, (4) determine the battery charge state change rate and engine of hybrid power system under each gear Fuel consumption rate, specific as follows:
1. gearbox is in one grade:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ1TM+Te=Treq/(fdλ1), engine demand rotational speed omegae=fdvrefλ1/Rw, motor demand rotational speed omegaM=itrsfdvrefλ1/ Rw；
2. gearbox is in two grades:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ2TM+Te=Treq/(fdλ2), engine demand rotational speed omegae=fdvrefλ2/Rw, motor demand rotational speed omegaM=itrsfdvrefλ2/ Rw；
3. gearbox is in third gear:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ3TM+Te=Treq/(fdλ3), engine demand rotational speed omegae=fdvrefλ3/Rw, motor demand rotational speed omegaM=itrsfdvrefλ3/ Rw；
4. gearbox is in fourth gear:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), Middle λ4TM+Te=Treq/(fdλ4), engine demand rotational speed omegae=fdvrefλ4/Rw, motor demand rotational speed omegaM=itrsfdvrefλ4/ Rw；
5. gearbox is in five grades:
Battery charge state change rate approximate expression isFuel consumption rate fuel=g (Te,ωe), In
λ5TM+Te=Treq/(fdλ5), engine demand rotational speed omegae=fdvrefλ5/Rw, motor demand rotational speed omegaM= itrsfdvrefλ5/Rw。
(5) the parallel hybrid electric vehicle energy management system comprising discrete gear information is established based on integrating mixed logic dynamic System model, specifically comprises the following steps:
1., with gearbox-gear for discrete control variable, the torque of engine is logical using motor torque as continuous control variable It crosses motor torque to be indicated, based on propositional logic to the hair under each gear of parallel hybrid electric vehicle and each gear Motivation fuel consumption rate and battery charge state change rate are described, specific as follows:
One grade:Wherein, δ1For the logical variable of introducing, t1Represent gear 1；
Two grades:Wherein, δ2For the logical variable of introducing, t2Represent gear 2；
Third gear:Wherein, δ3For the logical variable of introducing, t3Represent gear 3；
Fourth gear:Wherein, δ4For the logical variable of introducing, t4Represent gear 4；
Five grades:Wherein, δ5For the logical variable of introducing, t5Represent gear 5；
2. since the non-linear components in system have carried out piecewise affine, yet by propositional logic under each gear The continuous variables such as fuel consumption rate and battery charge state change rate are described；
3. forming unified state space equation of the parallel hybrid electric vehicle under all gears in conjunction with logical variable；
4. introducing auxiliary continuous variable, integrating mixed logic dynamic canonical form is converted by unified state space equation；
5. converting MIXED INTEGER linear inequality for propositional logic and relevant operation rule, integrating mixed logic dynamic rule are formed Inequation under norm form.
(6) the parallel hybrid electric vehicle Energy Management System Model Predictive Control comprising gearbox-gear control, tool Body the following steps are included:
1. it is for statistical analysis to history road condition data, velocity band is divided into N number of subset, calculates current time each subset It jumps
To the probability of each subset of subsequent time, a step Markov method transfer matrix is obtained, such as following formula:
2. objective function J=(SOC-SOC is arrangedref)2+fuel2, SOC is battery charge state, SOCrefFor reference battery State, previous item is considered to charge and discharge electric equilibrium in formula, and latter is considered to oil consumption, model prediction principle such as Fig. 8 institute Show, prediction model is dynamic for the parallel hybrid electric vehicle Energy Management System mixed logic comprising the discrete gear information of gearbox The control output of system is defined as the selection of gearbox-gear and the torque of motor, in the process to battery, electricity by states model The range of operation of machine and engine is constrained.According to objective function, the difference of prediction model output and reference locus is changed into MINLP model problem, and Optimal Control output sequence is planned on finite time-domain, and only export first control It is applied to system, planning is re-started according to the feedback of controlled device at next moment, realizes online rolling optimization.
3. including the parallel of gearbox-gear control by the description of the line solvers such as software GLPK, CPLEX, lpsolve Energy Management System for Hybrid Electric Vehicle optimization MINLP model problem, be illustrated in figure 9 each gear of gearbox it Between handoff relation, by the solution to the above problem, this method can be solved simultaneously under discrete control variable gear and the gear Best continuous control variable motor torque, decision process is simple and efficient.
4. carrying out performance simulation to the hybrid model Predictive control law sought, the tune of control parameter is carried out for simulation result It is whole, until obtaining optimal control effect.
Wherein, PI is (proportion, integral) Chinese name proportional integration.
The embodiment is a preferred embodiment of the present invention, but present invention is not limited to the embodiments described above, not In the case where substantive content of the invention, any conspicuous improvement that those skilled in the art can make, replacement Or modification all belongs to the scope of protection of the present invention.

Claims (6)

1. a kind of parallel hybrid electric vehicle energy management method with gearbox-gear control, which is characterized in that including such as Lower step:
It calculates with amendment demand torque: being based on vehicle travel resistance equation, calculate the torque of vehicle demand, and further combined with PI tune Section is to vehicle demand torque dynamic corrections；
Piecewise affine: being based on piecewise affine technology, consumes curved surface to engine fuel and power of motor curved surface carries out two-dimentional segmentation It is affine, it is imitative that one-dimensional segmentation is carried out to charge electricity condition change rate, motor torque restricted model and motor torque restricted model It penetrates；
Determine battery charge state change rate and engine fuel consumption rate under each discrete gear；
It establishes Energy Management System model: establishing the parallel connection including the discrete gear information of gearbox using mixed logical dynamics Formula Energy Management System for Hybrid Electric Vehicle model；
Design a model predictive control strategy: Model Predictive Control thought is based on, by the energy including the discrete gear information of gearbox Management system mixed logical dynamics optimize distribution as prediction model, to the output torque in different dynamic source, simultaneously The best gearbox-gear at decision current time.
2. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, feature exist In the calculating and the vehicle demand torque calculation formula in amendment demand torque:
Treq=(+0.5 ρ C of mgfcos αdAvref 2+mgsinα+maref)R+Kp(vref-vact)+Ki∫(vref-vact)dt
Wherein, TreqFor vehicle demand torque, m is complete vehicle quality, and g is acceleration of gravity, and f is coefficient of rolling resistance, and α is road The gradient, ρ are atmospheric density, CdFor coefficient of air resistance, A front face area, vrefReference speed, arefReference acceleration, vactIt is practical Speed, KpFor proportionality coefficient, KiFor integral coefficient.
3. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, feature exist In the piecewise affine) in piecewise affine method it is as follows:
It establishes model: engine consumption model, power of motor model, battery model, motor torque is obtained by platform experiment Restricted model, motor torque restricted model；
For engine consumption model, power of motor model carries out two-dimentional piecewise affine by several planes:
Engine consumption model: fuel_i=giTe+jiωe+li(Te,ωe)∈Xi；
Power of motor model: Pbatt_ii=aiiTM+biiωM+cii(TM,ωM)∈Ωii；
Wherein, fuel_iFor engine fuel consumption rate in i-th of fitted area, Pbatt_iiFor battery in i-th i fitted area Power, Te, TMRespectively motor torque and motor torque, ωe, ωMRespectively engine speed and motor speed, gi, ji, li, aii, bii, ciiFor fitting coefficient, Xi, ΩiiFor fit Plane domain, subscript i, ii is fitting number；
One-dimensional segmentation is carried out by several straight lines for battery model, motor torque restricted model and motor torque restricted model It is affine:
Battery model:
Motor torque restricted model: Te_if=mifωe+nifωe∈Ae_if；
Motor torque restricted model: TM_id=pidωM+qidωM∈AM_id；
Wherein,For battery charge state change rate in i-th ii fitted area, PbattFor the power of battery, Te_ifIt is the i-th f The torque capacity of engine in fitted area, minimum torque 0, TM_idTurn for the maximum of motor in i-th d fitted area Square, minimum torque are the opposite number of torque capacity, riii, mif, nif, pid, qidFor fitting coefficient, Λiii, Ae_if, AM_idIt is quasi- Straight line domain is closed, subscript iii, if, id are fitting number.
4. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, feature exist In gearbox is five shift automatic variable speed casees, transmission ratio λis(is=1,2,3,4,5), battery charge state changes under each gear Rate and engine fuel consumption rate are as follows:
When gearbox is in one grade:
Battery charge state change rate calculates functionFuel consumption rate fuel=g (Te,ωe), wherein λ1TM+Te=Treq/(fdλ1), engine speed ωe=fdvrefλ1/Rw, motor demand rotational speed omegaM=itrsfdvrefλ1/Rw, f (TM, Te) it is that battery charge state change rate calculates function, g (Te,ωe) it is that fuel consumption rate calculates function, fdFor final ratio, vref Reference speed, RwFor radius of wheel, λ1For one grade of transmission ratio, itrsTransmission ratio between motor and transmission input；ωeFor Engine speed, ωMFor motor speed；Te, TMRespectively motor torque and motor torque
When gearbox is in two grades:
Battery charge state change rate isFuel consumption rate fuel=g (Te,ωe), wherein λ2TM+Te= Treq/(fdλ2), engine demand rotational speed omegae=fdvrefλ2/Rw, motor demand rotational speed omegaM=itrsfdvrefλ2/Rw, f (TM, Te) Function, g (T are calculated for battery charge state change ratee,ωe) it is that fuel consumption rate calculates function, fdFor final ratio, RwFor vehicle Take turns radius, λ2For two grades of transmission ratios, itrsTransmission ratio between motor and transmission input；
When gearbox is in third gear:
Battery charge state change rate isFuel consumption rate fuel=g (Te,ωe), wherein λ3TM+Te= Treq/(fdλ3), engine demand rotational speed omegae=fdvrefλ3/Rw, motor demand rotational speed omegaM=itrsfdvrefλ3/Rw, f (TM, Te) Function, g (T are calculated for battery charge state change ratee,ωe) it is that fuel consumption rate calculates function, fdFor final ratio, RwFor vehicle Take turns radius, λ3For third gear transmission ratio, itrsTransmission ratio between motor and transmission input；
When gearbox is in fourth gear:
Battery charge state change rate isFuel consumption rate fuel=g (Te,ωe), wherein λ4TM+Te= Treq/(fdλ4), engine demand rotational speed omegae=fdvrefλ4/Rw, motor demand rotational speed omegaM=itrsfdvrefλ4/Rw, f (TM, Te) Function, g (T are calculated for battery charge state change ratee,ωe) it is that fuel consumption rate calculates function, fdFor final ratio, RwFor vehicle Take turns radius, λ4For fourth gear transmission ratio, itrsTransmission ratio between motor and transmission input；
When gearbox is in five grades:
Battery charge state change rate isFuel consumption rate fuel=g (Te,ωe), wherein λ5TM+Te= Treq/(fdλ5), engine demand rotational speed omegae=fdvrefλ5/Rw, motor demand rotational speed omegaM=itrsfdvrefλ5/Rw, f (TM, Te) Function, g (T are calculated for battery charge state change ratee,ωe) it is that fuel consumption rate calculates function, fdFor final ratio, RwFor vehicle Take turns radius, λ5For five grades of transmission ratios, itrsTransmission ratio between motor and transmission input.
5. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, feature exist In described to establish management system model) in mixed logical dynamics the following steps are included:
Using motor torque as continuous control variable, with gearbox-gear for discrete control variable, based on propositional logic to parallel connection Battery charge state change rate and fuel consumption under the gear of formula hybrid vehicle and each gear are described；
Unified state space equation of the parallel hybrid electric vehicle under all gears is formed in conjunction with logical variable；Further draw Enter auxiliary logic variable and continuous variable, converts unified state space equation to the standard type of integrating mixed logic dynamic；
MIXED INTEGER inequality is converted by propositional logic, heuristic proposition and operation constraint, forms integrating mixed logic dynamic mark Inequation under pseudotype.
6. the parallel hybrid electric vehicle energy management method according to claim 1 with gearbox-gear control, feature exist In described to establish prediction model) in Model Predictive Control mainly comprise the steps that
History road condition data is statisticallyd analyze, Markov method transfer matrix is obtained, the speed in one section of time domain is carried out pre- It surveys；
Objective function J=(SOC-SOC is setref)2+fuel2, SOC is battery charge state, SOCrefFor the charged shape of reference battery State, previous item (SOC-SOC in formularef)2It is to be considered to charge and discharge electric equilibrium, latter fuel2It is to be considered to oil consumption；
Prediction model is parallel hybrid electric vehicle Energy Management System mixed logical dynamics, and the control of system is exported Be defined as the selection of gearbox-gear and the torque of motor, in the process to the range of operation of battery, motor and engine into Row constraint；
MINLP model problem is converted by the optimization problem of parallel hybrid electric vehicle Energy Management System；In conjunction with Software GLPK or CPLEX or lpsolve carries out line solver to the MINLP model problem, to obtain and be Model Predictive Control of uniting rule.
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