CN109733378A - Optimize the torque distribution method predicted on line under a kind of line - Google Patents
Optimize the torque distribution method predicted on line under a kind of line Download PDFInfo
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- CN109733378A CN109733378A CN201811558512.4A CN201811558512A CN109733378A CN 109733378 A CN109733378 A CN 109733378A CN 201811558512 A CN201811558512 A CN 201811558512A CN 109733378 A CN109733378 A CN 109733378A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- 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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Abstract
The invention discloses optimize the torque distribution method predicted on line under a kind of line, when driver will drive vehicle to a certain destination, pilot model is manually selected first, then destination is inputted, vehicle control device is according to the pilot model of selection, stroke is optimized for the learning outcome of the driver, and according to the beginning and end planning path of stroke, traffic information is obtained by onboard navigation system using selected traveling terminal, in conjunction with traffic information obtained and the pilot model and SOC value of battery of selection, preliminary torque distribution optimization is carried out using the dynamic programming method；Then on the basis of preliminary torque distributes optimum results, dynamic realtime optimum control is carried out again using Model Predictive Control.Further real-time optimistic control is carried out on the basis of having preliminary optimal control, alleviates the calculation amount of dynamic optimization, while realizing the real-time online control of Fuel Economy for Hybrid Electric Vehicles.
Description
Technical field
The invention belongs to field of hybrid electric vehicles, and in particular to torque distribution method in one mode switching.
Background technique
In recent years, with the rapid development of auto industry, car ownership continues to increase, the pressure of environment and energy crisis
Power is increasingly sharpened, and popularizing new-energy automobile technology is that China solves the problems, such as one of effective way of environmental energy.Mixing is dynamic
Power automobile, from orthodox car to the key link of pure electric automobile transition, has both internal combustion engine and electric power storage as in new energy technology
Two kinds of pond onboard power source makes it have the opposite better fuel consumption and emission performance of traditional vehicle and opposite pure electric vehicle vapour
It the advantages of vehicle longer continual mileage, shows huge application potential, becomes the hot spot that countries in the world automotive field is competitively studied.
And good integrated vehicle control tactics or method are the key that realize the high economy of hybrid vehicle and low emission, it is whole for improving
Vehicle performance reduces cost and has great importance.
Hybrid vehicle items technology has all tended to mature at present, in terms of integrated vehicle control tactics, patrols because rule-based
Volume thresholding control strategy is simple and easy, practical and have preferable robustness, is widely used in existing hybrid vehicle
In the control strategy of pattern switching.But this strategy does not consider the dynamic change and motor, battery, power train effect of actual road conditions
The influence of the factors such as rate, thus it is also just unable to reach global optimal and vehicle fuel economy highest, so in control strategy
Aspect also needs further perfect.And one section of driving habit and future from the point of view of control effect, for specific driver
The traveling road conditions of time, minimum with global oil consumption or the maximum global optimization control method of efficiency can be considered as hybrid power system
It unites ideal or most fuel-economizing potentiality optimization methods, but needs to acquire the driving habit of driver in advance, and pass through hand
Section obtains the traffic information in following a period of time, but calculation amount is huge, cannot be directly used to real vehicle real-time control, but can be with
As the evaluation criterion in the energy management design phase, some necessary reference informations are provided for real-time control.
Summary of the invention
The invention proposes a kind of control methods of hybrid power torque distribution, can need for different driver's torques
It asks and carries out torque distribution.Driver's driving habit is obtained by the method for iterative learning, drives wish acquisition control acceleration
The acceleration of pedal and brake pedal obtains the torque-demand or power demand of vehicle；Torque-demand for driver or
Power demand has very strong randomness, and the present invention describes the demand of driver using Markov Chain, obtains driver control
Torque transferred probability under system；When driver will drive vehicle to a certain destination, pilot model is manually selected first, so
After input destination, vehicle control device (vehicle-mounted ECU can be used) is directed to the study of the driver according to the pilot model of selection
As a result stroke is optimized, and according to the beginning and end planning path of stroke, and according to selected traveling terminal by
Onboard navigation system (GPS/GIS etc.) obtains traffic information: level road, descending, or goes up a slope, or traffic congestion etc., in conjunction with being obtained
The pilot model and SOC value of battery of the information and selection that obtain utilize dynamic programming method to carry out preliminary torque distribution optimization；
Then on the basis of preliminary torque distributes optimum results, dynamic realtime optimum control is carried out using Model Predictive Control,
Have and carry out real-time optimistic control on the basis of preliminary optimal control, alleviates the calculation amount of dynamic optimization, while realizing mixing
The real-time online of power vehicle fuel economy controls.
Beneficial effects of the present invention:
1, the power output that engine, motor A, B can be controlled according to power, the torque-demand of driver, meets and drives
Member's operating habit.
2, path planning is carried out by onboard navigation system, and segregation reasons is carried out according to traffic information, obtained preliminary excellent
Change calculates online as a result, it is possible to greatly reduce, and improves the real-time of control.
3, traffic information is acquired in driving process in real time, carries out suboptimization again, real time correction is carried out to offline optimization result,
Optimal control result is obtained, ensure that hybrid vehicle optimal fuel economy.
Detailed description of the invention
Fig. 1 is control module logic diagram.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings.
Step1: the operator torque request modeling based on Markov model
According to current status information, prediction model and exogenous disturbances (speed demand, torque in following a period of time
Demand or power demand etc.) predict that the output of controlled device is the key that model predictive controller design.Driver's driving procedure
It is middle by according to the aperture of the factor controllings accelerator pedals and brake pedal such as itself driving wish, driving habit and external environment,
So that the torque-demand or power demand of vehicle have very strong randomness.In view of the randomness of practical driving cycles, the present invention
The torque-demand of driver in prediction model is described based on Markov Chain.Markov model is established, from current state xiTurn
Move on to the state x of subsequent timejProbability Pij(1), have:
Pij(1)=Pr (Xk+1=xj|Xk=xi)
In formula, PijRepresent a step transition probability of system；XkAnd Xk+1Respectively indicate the shape of current time and subsequent time
State；Pr is conditional probability.Torque-demand model of the driver in prediction time domain is established using Markov model, then driver
It is only related with the torque-demand at current time in the torque-demand of subsequent time.According to the possibility driving cycle of vehicle, vehicle is determined
The maximum value and minimum value of demand torque, and continuous numerical discretization is turned to in value range the set of limited value,
State as event development process.
In formula, N indicates the number that continuous demand torque is discrete between minimum value and maximum value, correspondingly indicates shape
The number of state.At any one time, system is transferred to the transition probability of a certain state of subsequent time from some current possible state
(a step transition probability) set constitutes state transition probability matrix Pij(1), it may be assumed that
Therefore, if known k moment operator demand's torque Treq(k), using nearest neighbour method, quantified to some discretization
Torque conditions Treqp(k).According to the Markov characteristic of driving behavior, can successively acquire each in the following finite time-domain
Driver's input torque at a moment.Wherein, building state transition probability matrix is crucial.The present invention uses standard cycle operating condition
Or the data of collected city state of cyclic operation, each moment under state of cyclic operation is acquired based on dynamics of vehicle equilibrium equation
Treq
In formula: m is automobile gross mass, and g is acceleration of gravity, and α is inclination angle, and f is road surface coefficient of rolling friction, ρ
For atmospheric density, CDFor coefficient of air resistance, A is vehicle front face area, and u is speed, and R is vehicle wheel roll radius.
Then quantified to discrete state point, by be calculated between different conditions to all data
A step transition probability.
Step2: traffic information obtains
Dynamic Programming needs the driving cycle of known vehicle following a period of time, and then driving cycle is combined to carry out the overall situation most
Excellent control calculating can obtain optimal control strategy, it is therefore desirable to by vehicle mounted guidance after driver selects traveling terminal
System (GPS/GIS etc.) obtains traffic information: level road, descending, or goes up a slope, or traffic congestion etc..Hybrid vehicle according to
The operating mode (charging operating condition or electric discharge operating condition) of road conditions and battery SOC adjustment hybrid vehicle, with most effective operation
Mode passes through the following stretch line.When the front road conditions that onboard navigation system (GPS/GIS etc.) is obtained are the same as the road conditions at current time
It is identical, and road conditions preferably, battery SOC it is higher when, vehicle keeps electric-only mode, i.e., corresponding electric discharge operating condition；In front of detecting
Road conditions change, need it is high-power, motor provide underpower or battery SOC be lower than threshold value, engine participate in driving, and
According to driver's power demand feature (anxious acceleration or slow acceleration), engine power is controlled, i.e., corresponding charging operating condition.
By the driving habit of combining road condition information and driver, the switching of mode is carried out, so that it is dynamic to reach reduction mixing
Oil consumption and discharge when power automobile actual motion, are greatly reduced the target of hybrid vehicle fuel consumption.
Step3: offline optimization is carried out with Dynamic Programming
Optimization aim is to be distributed by the torque of optimization engine and motor, is in the output torque of engine and starts
Ji Di oil consumption area reduces oil consumption and discharge when hybrid vehicle actual motion, and hybrid vehicle combustion is greatly reduced
Oil consumption.
According to work information, optimal solution is carried out to optimization aim using Dynamic Programming, founding mathematical models:
In formula: J is optimization object function, t0And tfRespectively indicate the starting and ending time of test emulation road conditions, h (SOC
(tf)) indicate between the SOC value and SOC reference value of any sampling instant deviation penalty coefficient,Engine
Fuel consumption adds the equivalent fuel consumption of the charged variable quantity of battery.
Target according to energy management strategies is that the value of performance functional is minimum, and meets the system state equation formula of system
With the constraint equation of system.
System state equation formula:
Y=g (x, u, v)
In formula, the state variable x=SOC of system, system control variablesSystem can survey input quantity
System output quantity
System restriction equation are as follows:
Te、weThe respectively torque of engine and revolving speed, T0、n0Operator demand's torque and revolving speed,For the fuel oil of automobile
Consumption rate, PbattFor cell output, wmA、wmBFor motor A, B revolving speed, TmA、TmBFor motor A, B torque, subscript m in, max
Minimum value, maximum value for corresponding amount.
Traffic information on one section of the future prediction route according to provided by onboard navigation system (GPS/GIS etc.): level road,
Descending, or go up a slope, or traffic congestion etc..Tentatively optimized before traveling with Dynamic Programming, when obtaining one section following
Between optimum results control running car.
Step4: model prediction dynamic controls
The initial optimization result of offline dynamic optimization is used for the control of controller, is theoretic optimum control, for
Actual road conditions information be it is continually changing, the result of offline optimization may be no longer optimal, needs to be adjusted control result.Road
Condition only needs to be adjusted on original optimum results after changing, and greatly reduces calculation amount.It is specific as follows: with combustion
The minimum direct torque that hybrid vehicle is used for offline optimization result of the battery SOC for the purpose of stable of oil consumption.Exist simultaneously
Onboard navigation system (GPS/GIS etc.) constantly acquires real-time traffic information and information of vehicles in driving process, with offline dynamic
The data of planning are as reference.By entire driving cycle, several segments, mould are divided into according to the sampling time of model predictive controller
The quantity of state of type predictive controller is current motor torque, revolving speed, and control input quantity is demand torque and the vehicle of driver
Speed, the real-time road condition information and information of vehicles of onboard navigation system (GPS/GIS etc.) acquisition are inputted as measurable interference volume
Model predictive controller, the output of controller are real-time vehicle fuel consumption rate, cell output, motor A, B revolving speed, electricity
The correction value of machine A, B torque.Performance model forecast Control Algorithm carry out the calculating of optimum control signal, calculated result be all based on from
The improvement of line optimum results.Since model prediction is all real-time perfoming, so being not required to the entire work information of to master, it is only necessary to
The traffic information in prediction time domain is obtained by onboard navigation system (GPS/GIS etc.).By model prediction to offline excellent
The rolling optimization and real time correction for changing result, further obtain real-time optimistic control.
The series of detailed descriptions listed above only for feasible embodiment of the invention specifically
Protection scope bright, that they are not intended to limit the invention, it is all without departing from equivalent implementations made by technical spirit of the present invention
Or change should all be included in the protection scope of the present invention.
Claims (9)
1. optimizing the torque distribution method predicted on line under a kind of line, which comprises the steps of:
Step 1, operator torque request model is established, turning for driver's subsequent time is obtained according to driver's current demand torque
Square；
Step 2, traffic information is obtained, according to the operating mode of traffic information and battery SOC adjustment hybrid vehicle；
Step 3, offline optimization torque distribution is carried out using Dynamic Programming；
Step 4, the optimum results of step 3 are carried out with model prediction dynamic control adjustment again, exports actual torque distribution.
2. optimizing the torque distribution method predicted on line under a kind of line according to claim 1, which is characterized in that the step
In rapid 1, operator torque request model is established using Markov model.
3. optimizing the torque distribution method predicted on line under a kind of line according to claim 2, which is characterized in that described to adopt
It is with the specific method of Markov model, if from current state xiIt is transferred to the state x of subsequent timejProbability Pij(1), then
Have:
Pij(1)=Pr (Xk+1=xj|Xk=xi)
In formula, PijRepresent a step transition probability of system；XkAnd Xk+1Respectively indicate the state of current time and subsequent time；Pr
For conditional probability；
Above-mentioned to establish torque-demand model of the driver in prediction time domain using Markov model, analysis show that driver exists
The torque-demand of subsequent time is only related with the torque-demand at current time；According to the driving cycle of vehicle, vehicle demand is determined
The maximum value and minimum value of torque, and continuous numerical discretization is turned to in value range the set of limited value, as thing
The state of part development process, it may be assumed that
In formula, N indicates the number that continuous demand torque is discrete between minimum value and maximum value, correspondingly indicates state
Number；At any one time, system is transferred to the transition probability sets of a certain state of subsequent time from some current possible state
Constitute state transition probability matrix Pij(1), it may be assumed that
If known k moment operator demand's torque Treq(k), using nearest neighbour method, quantified the torque conditions to some discretization
Treqp(k), according to the Markov characteristic of driving behavior, driving in the following finite time-domain each moment can successively be acquired
The person's of sailing input torque.
4. optimizing the torque distribution method predicted on line under a kind of line according to claim 1, which is characterized in that the step
In rapid 2, the acquisition traffic information utilizes onboard navigation system GPS or GIS, and the road conditions include level road, descending, upward slope or stifled
Vehicle etc..
5. optimizing the torque distribution method predicted on line under a kind of line according to claim 4, which is characterized in that the step
In rapid 2, charging operating condition and electric discharge operating condition are divided into according to the operating mode of traffic information and battery SOC adjustment hybrid vehicle,
It is specific: when onboard navigation system obtain front road conditions it is identical with the road conditions at current time, and road conditions preferably, battery SOC compared with
Gao Shi, vehicle keep electric-only mode, i.e. electric discharge operating condition；When detecting that front road conditions change, need high-power, motor mentions
The underpower or battery SOC of confession are lower than threshold value, and engine participates in driving, and is anxious add according to driver's power demand feature
Speed or slow acceleration control engine power, i.e. charging operating condition.
6. optimizing the torque distribution method predicted on line under a kind of line according to claim 1, which is characterized in that the step
Rapid 3 specific implementation includes:
According to work information, optimal solution is carried out to optimization aim using Dynamic Programming, founding mathematical models:
In formula: J is optimization object function, t0And tfRespectively indicate the starting and ending time of test emulation road conditions, h (SOC (tf))
Indicate the penalty coefficient of the deviation between the SOC value and SOC reference value of any sampling instant,The fuel oil of engine
Consumption adds the equivalent fuel consumption of the charged variable quantity of battery.
Target according to energy management strategies is that the value of performance functional is minimum, and meets the system state equation formula of system and be
The constraint equation of system.
7. optimizing the torque distribution method predicted on line under a kind of line according to claim 6, which is characterized in that the system
System equation of state are as follows:
Y=g (x, u, v)
In formula, the state variable x=SOC of system, system control variablesSystem can survey input quantitySystem
Output quantity
The system restriction equation are as follows:
Te、weThe respectively torque of engine and revolving speed, T0、n0Operator demand's torque and revolving speed,For the fuel consumption of automobile
Rate, PbattFor cell output, wmA、wmBFor motor A, B revolving speed, TmA、TmBFor motor A, B torque, subscript m in, max is phase
Minimum value, the maximum value that should be measured.
8. optimizing the torque distribution method predicted on line under a kind of line according to claim 1, which is characterized in that the step
Rapid 4 specific implementation includes that the offline optimization result for the purpose of fuel consumption is minimum and battery SOC is stablized is used for hybrid power vapour
The control of vehicle, while onboard navigation system constantly acquires real-time traffic information and information of vehicles in the process of moving, with offline
The data of Dynamic Programming as reference, then entire actual state of cyclic operation is divided into it is several apart from subequal segment,
The quantity of state of model predictive controller is set as current motor torque, revolving speed, and the demand that control input quantity is set as driver turns
Square and speed, the real-time road condition information and information of vehicles of onboard navigation system acquisition are pre- as measurable interference volume input model
Controller is surveyed, the output of controller is fuel consumption rate, cell output, motor A, B revolving speed, motor A, B torque of automobile
Correction value.
9. optimizing the torque distribution method predicted on line under a kind of line according to claim 1-8, feature exists
In the specifically used process of the method: when driver will drive vehicle to a certain destination, manually selecting driver first
Model, then inputs destination, vehicle control device according to the pilot model of selection, for the driver learning outcome to row
Journey optimizes, and according to the beginning and end planning path of stroke, using selected traveling terminal by vehicle mounted guidance system
System obtains traffic information, in conjunction with traffic information obtained and the pilot model and SOC value of battery of selection, is moved using described
State planing method carries out preliminary torque distribution optimization；Then pre- using model on the basis of preliminary torque distributes optimum results
Observing and controlling system carries out dynamic realtime optimum control again.
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| CN110562239A (en) * | 2019-08-28 | 2019-12-13 | 武汉理工大学 | Variable-domain optimal energy management control method and device based on demand power prediction |
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| CN112193232A (en) * | 2020-09-23 | 2021-01-08 | 江苏大学 | Self-adaptive energy management system and method for hybrid electric vehicle |
| CN112810504A (en) * | 2021-01-20 | 2021-05-18 | 同济大学 | Fuel cell automobile energy management method based on nonlinear prediction model control |
| CN112977154A (en) * | 2019-12-17 | 2021-06-18 | 联合汽车电子有限公司 | Electric energy management method and system based on driver operation behavior prediction and automobile |
| CN113093708A (en) * | 2021-04-06 | 2021-07-09 | 哈尔滨理工大学 | Multi-signal fusion hub motor automobile torque distribution system and prospective control method |
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| CN113859214A (en) * | 2021-09-28 | 2021-12-31 | 清华大学 | Method and device for controlling dynamic energy efficiency of engine of hybrid power system |
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| CN113093708B (en) * | 2021-04-06 | 2023-03-21 | 哈尔滨理工大学 | Multi-signal fusion hub motor automobile torque distribution test system and prospective control method |
| CN113093708A (en) * | 2021-04-06 | 2021-07-09 | 哈尔滨理工大学 | Multi-signal fusion hub motor automobile torque distribution system and prospective control method |
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| CN113799760A (en) * | 2021-09-14 | 2021-12-17 | 上汽通用五菱汽车股份有限公司 | Automobile energy management method, device, equipment and computer readable storage medium |
| CN113859214A (en) * | 2021-09-28 | 2021-12-31 | 清华大学 | Method and device for controlling dynamic energy efficiency of engine of hybrid power system |
| CN113911103A (en) * | 2021-12-14 | 2022-01-11 | 北京理工大学 | Hybrid power tracked vehicle speed and energy collaborative optimization method and system |
| CN113911103B (en) * | 2021-12-14 | 2022-03-15 | 北京理工大学 | Hybrid power tracked vehicle speed and energy collaborative optimization method and system |
| CN117002475A (en) * | 2023-09-20 | 2023-11-07 | 中汽研汽车检验中心(昆明)有限公司 | Energy optimization control method and device for hybrid electric vehicle, electronic equipment and storage medium |
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