CN105539423B - The hybrid electric vehicle torque distribution control method and system of combining environmental temperature protection battery - Google Patents
The hybrid electric vehicle torque distribution control method and system of combining environmental temperature protection battery Download PDFInfo
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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
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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
- 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
- B60W10/119—Conjoint control of vehicle sub-units of different type or different function including control of all-wheel-driveline means, e.g. transfer gears or clutches for dividing torque between front and rear axle
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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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/24—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means
- B60W10/26—Conjoint control of vehicle sub-units of different type or different function including control of energy storage means for electrical energy, e.g. batteries or capacitors
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/24—Energy storage means
- B60W2510/242—Energy storage means for electrical energy
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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
- B60W2510/00—Input parameters relating to a particular sub-units
- B60W2510/24—Energy storage means
- B60W2510/242—Energy storage means for electrical energy
- B60W2510/246—Temperature
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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
- B60W2710/00—Output or target parameters relating to a particular sub-units
- B60W2710/06—Combustion engines, Gas turbines
- B60W2710/0666—Engine torque
-
- 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
- B60W2710/083—Torque
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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
Abstract
The invention discloses the hybrid electric vehicle torque distribution control method and system of combining environmental temperature protection battery, including the 1) ambient temperature information of collection vehicle their location；2) according to ambient temperature information and battery equivalent circuit model, battery actual temperature is calculated；The SoC values of battery are calculated by battery dump energy and battery maximum electricity；3) according to the actual temperature of the SoC values and battery, neural network control method calculation optimization control parameter is utilized；4) online minimum equivalent oil consumption strategy modeling, establishes the target function type and constraint formula of equivalent fuel consumption, and solve corresponding motor torque and motor torque during equivalent fuel consumption minimum；5) engine torque value and motor torque value that vehicle power train and dynamics module are drawn by step 4) carry out torque distribution and send control command driving vehicle.By consideration of the temperature to battery behavior, power-driven system is controlled, protection galvanic action is played, there is practical significance.
Description
Technical field
The present invention relates to a kind of forecast Control Algorithm of battery of hybrid vehicle protection, more particularly to a kind of combining environmental temperature
The hybrid electric vehicle torque distribution control method and system of degree protection battery.
Background technology
Era development so far, compared to traditional internal combustion engine for vehicle (ICE) and pure electric automobile (EV), hybrid vehicle
More excellent fuel economy and disposal of pollutants performance can be realized under the premise of certain course continuation mileage is ensured.As electric power drives
Dynamic key technology further develops, and the ratio in hybrid vehicle shared by power transmission system increases increasingly, such as：Plug-in
Formula hybrid vehicle (PHEV), and battery is even more widely to be paid close attention to as the power resources in power transmission system.
Energy management control develops for a long time with optimisation strategy as the important research field that hybrid vehicle is studied.In early days
Energy management control uses didactic method, the tactful form of gained is turned into boolean or fuzzy rule, nowadays these sides
Method is still all using, and is improved in nearest research.The domestic research to this field at present still belongs to the starting stage.
2013, Tsing-Hua University leaf dawn analyzed dynamic programming (DP), Pang Teyajin minimize principle (PMP) and etc. oil consumption minimum
Tactful (ECMS) this several energy management strategies, the fuel economy of very close global optimization can be obtained by demonstrating ECMS.And
In the feasibility that ECMS strategies are demonstrated on hybrid power passenger car.In the same year, Jilin University Zhou Wenbin, calculated using fuzzy neural network
Method optimizes distribution to motor torque and motor torque, devises the energy control strategy based on fuzzy logic algorithm.
2015, Tongji University's Xu Guoqing etc. proposed to carry out the most optimal sorting of hybrid vehicle torque by the prediction to transport information
Match somebody with somebody.
Battery is one of the important component in hybrid electric vehicle power source, the energy pipe of passing hybrid vehicle
Usually consider battery electric quantity surplus in reason strategy study and ignore the influence of temperature, so conventional research can not be from full-vehicle control
Battery system on Developing Tactics for the vehicle under low temperature or very high temperature environment provides protection.
The content of the invention
In order to solve the above problems, the invention provides a kind of hybrid electric vehicle torque of combining environmental temperature protection battery
Control method and system are distributed, battery is provided in this field of protection at different ambient temperatures with making up existing control method
Blank, according to different environment temperatures, the motor torque of the online motor vehicle driven by mixed power of adjustment in real time, to realize control battery
Load, protect the purpose of Vehicular battery.To achieve the above object, the present invention takes following technical scheme：
The hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery, comprises the following steps：
1) ambient temperature information of collection vehicle their location；
2) according to ambient temperature information and battery equivalent circuit model, battery actual temperature is calculated；Remained by battery
The SoC values of battery are calculated in remaining electricity and battery maximum electricity；
3) according to the actual temperature of the SoC values and battery, neural network control method calculation optimization control parameter is utilized
f；
4) online minimum equivalent oil consumption strategy modeling, establishes the target function type and constraint formula of equivalent fuel consumption, and solve
Corresponding motor torque and motor torque during equivalent fuel consumption minimum；
5) engine torque value and motor torque value that vehicle power train and dynamics module are drawn by step 4) are carried out
Torque distributes and sends control command driving vehicle.
Preferably, the method for collection environment temperature is realized by weather forecasting software in step 1), the weather
Software includes：Mobile phone weather forecast software, vehicle-mounted computer weather module software, weather forecast software in GPS navigation device.
Preferably, the expression formula that battery actual temperature is calculated in step 2) is：
In formula, I represents battery current, R, R0Series connection and resistance value in parallel, T respectively in battery equivalent circuitbodyIt is electricity
Pond actual temperature, TambIt is environment temperature, mcIt is single battery thermal capacity, hA is coefficient of heat transfer.
Preferably, the expression formula that battery SoC values are calculated in step 2) is：
In formula, QmaxIt is the maximum electricity of battery, Q (t) is the remaining electricity of battery t, and I (τ) is unit interval battery
Electric current.
Preferably, the neutral net described in step 3) uses single neuron control structure；The reality of the step 3)
Now specifically comprise the following steps：
3-1), two input quantities of neutral net are calculated：x1And x (SoC)2(temp)；
3-2), according to 3-1) in two input quantities optimal control parameter f=x is calculated1(SoC)·w1+x2
(temp)·w2。
Preferably, the step 3-1) in calculate x1(SoC) process includes：
A, by the battery SoC values read from battery management system by formulaHandle
To xSoC, and make xSoCBetween section [- 1,1]；Wherein, SoC represents battery remaining power value, also referred to as battery charge state, SoCH
Represent highest battery remaining power value, SoCLRepresent minimum battery remaining power value；
B, constructed fuctionAnd make x1(SoC) between section [0,1]；
The step 3-1) in calculate x2(temp) process includes：
C, characterisitic function f (temp) of the battery capacity with battery actual temperature change is obtained by testing；
D, formula is pressed to functional value f (temp)It is normalized, obtains
To second input value x as artificial neural network2(temp), and x is made2(temp) value is between section [0,1].
Preferably, the step 3-2) in w1=0.5, w2=0.5.
Preferably, the target function type established in step 4) is：
The constraint formula of foundation is：
Wherein, J (t) is the object function for representing the equivalent fuel consumption of vehicle,Represent that instantaneous equivalent fuel oil disappears
Consumption, mice(τ) is the fuel that engine is consumed, mem(τ) and mgene(τ) is the equivalent fuel oil of motor and generator respectively
Consumption, T and ω represent torque and rotating speed respectively, and i represents engine, one kind in motor and generator.Ti min(ωi) represent
Minimum torque, Ti max(ωi) torque capacity is represented,Represent minimum speed,Represent maximum (top) speed.SoC is remaining battery
Electricity, SoCminRepresent minimum battery remaining power, SoCmaxRepresent largest battery residual capacity, Pbatt(t) work(of battery t
Rate,Battery minimum power is represented,Represent battery peak power.
Preferably, the step 4) also includes establishing motor equivalent fuel consumption functional expression：
Wherein,Factor γ values depend on electric motor operation state, ηchAnd ηdchIt is respectively electronic
The energy conversion efficiency of motor, T during machine charge and dischargemAnd ωmIt is torque and the rotating speed of motor respectively, f is optimization control
Parameter processed, obtain, QLHVRepresent low heat value；
Also include establishing motor torque functional expression：Treq=Tm+Tice, wherein TreqIt is to be obtained by accelerator pedal signal
Current demand torque.
The invention also provides the hybrid electric vehicle torque distribution control system of combining environmental temperature protection battery, including temperature
Spend information acquisition module, battery parameter computing module, control process module and power train and dynamics module；The temperature letter
Acquisition module connection battery parameter computing module is ceased, the battery parameter computing module connects control process module, the control
Interconnected between processing module and the power train and dynamics module；
The temperature information acquisition module is used for the temperature of collection vehicle local environment and gives the temperature value of environment to institute
Battery parameter computing module is stated, the temperature information acquisition module is realized by weather software；
The battery parameter computing module includes battery actual temperature computation model and battery allowance appraising model, the electricity
Pond actual temperature computation model is used to obtain battery actual temperature, and battery allowance appraising model is used to obtain battery allowance value, institute
State battery parameter computing module and give obtained battery actual temperature and battery allowance value to the control process module；
The control process module includes optimal control parameter processing module and torque distribution control Optimized model module；Institute
State optimal control parameter processing module and use artificial neural-network control structure, for more than the battery actual temperature and battery
Value is handled to obtain optimal control parameter f, and the effect of the torque distribution control Optimized model module is the value knot using f
Build vertical equivalent fuel consumption object function jointly and obtain motor torque and motor torque value, the control process module will be sent out
Motivation torque and motor torque value give the power train and dynamics module；
The power train and dynamics module carry out torque distribution and drive according to motor torque and motor torque value
Motor-car.
Beneficial effects of the present invention：
1) with smart mobile phone, the development of various weather forecast softwares, using weather software, collection vehicle local environment
Real time temperature information is more convenient.
2) improve equivalent fuel consumption and minimize Policy model, design control parameter influenced by ambient temperature, make hybrid power
Vehicle electric motor and battery operated mode are possibly realized by temperature change adjustment.
3), quantum chemical method temperature and SoC carrying capacity theoretical using artificial neural-network control are proposed, and establishes rule and asks
Solve control parameter f.To wait oil consumption minimum Policy model to provide a kind of method reality for determining optimal control parameter f after optimization
Example.
4) in actual use by adding consideration of the temperature to battery behavior, control power-driven system uses part
Volume, play a part of protecting battery and extend battery life.By the importing of real time temperature weather information, make optimization more actual
Meaning.
Brief description of the drawings
Fig. 1 is certain plug-in hybrid SUV powertrain arrangement schematic diagrames.
Fig. 2 is battery equivalent circuit diagram；
Fig. 3 is single artificial neural network algorithm schematic diagram.
Fig. 4 is the torque distribution control theory diagram of the battery of hybrid vehicle protection based on ambient temperature information.
The oil consumption minimum strategy implement algorithm logic figures such as Fig. 5 is.
Fig. 6 is that certain ferric phosphate lithium cell capacity varies with temperature performance plot.
Embodiment
The invention will be further described with specific embodiment below in conjunction with the accompanying drawings.
Fig. 1 show the powertrain arrangement schematic diagram of certain plug-in hybrid, using engine precursor motor rear-guard
4 wheel driven Multifunctional bicycle (SUV) structure type, engine crosses semiaxis driving front-wheel by torque-converters and gearbox；It is electric afterwards
Machine is battery powered, and is slowed down by gear train and is driven trailing wheel after increasing torsion；Preceding generator is charged the battery by driven by engine；With this
Meanwhile rear motor can carry out Brake energy recovery in braking procedure, charge the battery.
The present invention illustrates so that the oil consumption such as this plug-in hybrid electric vehicle minimize policy controlling system as an example, this hair
The torque distribution control theory diagram of bright proposition as shown in figure 4, including temperature information acquisition module, battery parameter computing module,
Control process module and power train and dynamics module；The temperature information acquisition module connects battery parameter computing module,
The battery parameter computing module connects control process module, the control process module and the power train and dynamics module
Between interconnect；The temperature information acquisition module is used for the temperature of collection vehicle local environment and gives the temperature value of environment
The battery parameter computing module, the temperature information acquisition module are realized by weather forecasting software；The battery parameter meter
Calculating module includes battery actual temperature computation model and battery allowance appraising model, and the battery actual temperature computation model is used for
Battery actual temperature is obtained, battery allowance appraising model is used to obtain battery allowance value, and the battery parameter computing module is incited somebody to action
To battery actual temperature and battery allowance value give the control process module；The control process module includes optimal control
Parameter processing module and torque distribution control Optimized model module；The optimal control parameter processing module uses ANN
Network control structure, for being handled to obtain optimal control parameter f to the battery actual temperature and battery allowance value, described turn
The effect of square distribution control Optimized model module is the value using f, and engine is obtained with reference to the equivalent fuel consumption object function of foundation
Torque value and motor torque value, the control process module will obtain engine torque value and motor torque value give it is described
Power train and dynamics module；The power train and dynamics module are turned according to engine torque value and motor torque value
Square is distributed to drive vehicle.
At the same time, the invention also provides the torque distribution control method based on system shown in Figure 4, including：The first step
Gathered for ambient temperature information, second step calculates for battery behavior parameter, and the 3rd step is to be calculated using neural network control method
Optimal control parameter, the 4th step model for online equivalent fuel consumption minimum control strategy, and the 5th step is output engine and motor
Torque distribution.It is implemented as follows described：
Step 1) ambient temperature information gathers：Gathered by weather forecasting software line locality meteorological observation department and stored car
The ambient temperature information of their location.Including：Mobile phone weather forecast software, vehicle-mounted computer weather module software, GPS navigation dress
Put middle weather forecast software etc..
Step 2) battery behavior parameter calculates：Real-time ambient temperature information is read by Vehicular battery management system, with reference to
Battery equivalent circuit model (as shown in Figure 2), battery actual temperature T is calculated by formula (1)body.Meanwhile by remaining battery electricity
The SoC values of battery are calculated in amount and battery maximum electricity.
In formula (1), I represents battery current, R, R0The resistance of series connection and parallel connection in battery equivalent circuit respectively shown in Fig. 2
Value, TbodyIt is battery actual temperature, TambIt is environment temperature, mcIt is single battery thermal capacity, hA is coefficient of heat transfer.
In formula (2), QmaxIt is the maximum electricity of battery, Q (t) is the remaining electricity of battery t, and I (τ) is the unit interval
Battery current.
Step 3) uses neural network control method calculation optimization control parameter：It is actual to combine battery SoC values and battery
Two parameters of temperature, the comprehensive torque that determines motor and should be assigned to, the present invention are determined using neural network control method
Optimal control parameter f in object function.Because input quantity is less, therefore single neuron control structure (as shown in Figure 3) is used,
Be advantageous to the efficiency that control is implemented.Solve two input quantities of neutral net first for this：x1And x (SoC)2(temp)。
The SoC values for the battery being calculated are read from battery management system, is handled by formula (3), makes intermediate treatment
Value xSoCBetween section [- 1,1]；To meet to punish (reduction) power-driven system under SoC low values, electricity is encouraged under SoC high level
Power-driven system, by formula (4) constructed fuction x1(SoC) first input value x of artificial neural network, is made1(SoC) between section
[0,1]。
In formula (3)-(4), SoC represents battery remaining power value, also referred to as battery charge state, SoCHRepresent highest battery
Remaining capacity value, SoCLRepresent minimum battery remaining power value.
Second input value x of artificial neural network2(temp) it is, that a function related to battery actual temperature is defeated
Enter.It is mainly derived from the experimental data of actual use on-vehicle battery.Battery capacity can be obtained with the actual temperature of battery by experiment
The characterisitic function f (temp) of change is spent, is illustrated in figure 6 certain ferric phosphate lithium cell characteristic that experiment measures, a) is -20 DEG C -25
DEG C when battery capacity with battery actual temperature change curve；B) be 25 DEG C -60 DEG C when battery capacity with battery actual temperature become
The curve of change.F (temp) has reacted the change actually occurred with temperature change battery behavior, and the higher explanation battery of functional value is special
Property it is more preferable, power-driven system is encouraged when battery behavior ideal, otherwise punish (reductions) its use.To functional value f
(temp) it is normalized by formula (6), obtains second input value x as artificial neural network2(temp), between area
Between [0,1].
The solution rule of artificial neural network is finally defined with formula (7), because two influence factors of input belong to arranged side by side
Characteristic, define weight coefficient w1=w2=0.5, optimal control parameter f is obtained, and f value is between [0,1] section.
F=x1(SoC)·w1+x2(temp)·w2 (7)
Online minimum equivalent oil consumption strategy (online-ECMS) Target Modeling of step 4)：Online equivalent fuel consumption controller is real
Apply strategy and with vehicle connection diagram as shown in figure 5, online equivalent fuel consumption controller reads current demand torque from vehicle,
Using demand torque and maximum motor torque as the upper limit, optimizing is carried out by oil consumption minimum target to motor torque, most backward vehicle is defeated
Go out motor torque and motor torque that optimization obtains.
The main target of online equivalent fuel consumption strategy is to find to draw optimization torque with the minimum object solving of equivalent fuel consumption
Distribute (including motor torque and motor torque), while meet some equatioies and inequality constraints.With reference to equivalent fuel consumption most
Smallization Policy model, obtain the target function type (8) and constraint formula (9) of the minimum strategy of the online equivalent fuel consumption of this system.
In formula (8)-(9), J (t) is the object function for representing the equivalent fuel consumption of vehicle,Represent instantaneous equivalent
Fuel consumption, mice(τ) is the fuel that engine is consumed, mem(τ) and mgene(τ) be respectively motor and generator etc.
Imitate fuel consumption, T and ω represent torque and rotating speed respectively, and i represents engine, one kind in motor and generator.Ti min
(ωi) represent minimum torque, Ti max(ωi) torque capacity is represented,Represent minimum speed,Represent maximum (top) speed.SoC is
Battery dump energy, SoCminRepresent minimum battery remaining power, SoCmaxRepresent largest battery residual capacity, Pbatt(t) battery t
The power at moment,Battery minimum power is represented,Represent battery peak power.
Wherein, the functional expression (10) of the equivalent fuel consumption of motor is as follows：
In formula, ηchAnd ηdchThe energy conversion efficiency of motor, T respectively in charge and discharge processmAnd ωmIt is motor
Torque and rotating speed, f are the optimal control parameters that step 3) obtains, QLHVRepresent low heat value.Factor γ values depend on motor work
Make state, such as charged state in Brake energy recovery, i.e. electric power will be stored in battery and be not used, and equivalent fuel oil disappears
It is negative to consume, and oil consumption is just during driven.
Generator is always negative so shown in the function of equivalent fuel consumption such as formula (5) due to only playing charging.
In formula (12), ηgeneFor generator efficiency, TgeneAnd ωgeneTorque and rotating speed for generator.
By taking above-mentioned 4 wheel driven structure as an example, current demand torque T is can obtain by accelerator pedal signal etc.req, demand turn
Square distributes to the torque T of motormWith the torque T of engineiceMeet formula (13).
Treq=Tm+Tice (13)
Motor torque T further can be obtained by formula (13)ice。
The engine torque value and motor torque value that step 5) vehicle power train and dynamics module are drawn by step 4)
Carry out torque distribution and send control command driving vehicle.
The present disclosure applies equally to other tandems, parallel and series parallel type hybrid vehicle energy management control system
The oil consumption minimum policy controlling systems such as system, specific modeling method and control process and hybrid vehicle described herein are consistent,
It is not repeated to describe herein.
Described above to be used only for describing technical scheme, the protection domain being not intended to limit the present invention should
Work as understanding, on the premise of without prejudice to substantive content of the present invention and principle, change, equivalent substitution etc. falls within this
In the protection domain of invention.
Claims (6)
1. the hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery, it is characterised in that including following step
Suddenly：
1) ambient temperature information of collection vehicle their location；
2) according to ambient temperature information and battery equivalent circuit model, battery actual temperature is calculated；By remaining battery electricity
The SoC values of battery are calculated in amount and battery maximum electricity；
Calculate battery actual temperature expression formula be：
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In formula, I represents battery current, R, R0Series connection and resistance value in parallel, T respectively in battery equivalent circuitbodyIt is battery reality
Border temperature, TambIt is environment temperature, mcIt is single battery thermal capacity, hA is coefficient of heat transfer；
3) according to the actual temperature of the SoC values and battery, neural network control method calculation optimization control parameter f, institute are utilized
The neutral net stated uses single neuron control structure；
3-1), two input quantities of neutral net are calculated：x1And x (SoC)2(temp)；
Calculate x1(SoC) process includes：
A, by the battery SoC values read from battery management system by formulaProcessing obtains
xSoC, and make xSoCBetween section [- 1,1]；Wherein, SoC represents battery remaining power value, also referred to as battery charge state, SoCHGeneration
Table highest battery remaining power value, SoCLRepresent minimum battery remaining power value；
B, constructed fuctionAnd make x1(SoC) between section [0,1]；
Calculate x2(temp) process includes：
C, characterisitic function f (temp) of the battery capacity with battery actual temperature change is obtained by testing；
D, formula is pressed to functional value f (temp)It is normalized, obtains conduct
Second input value x of artificial neural network2(temp), and x is made2(temp) value is between section [0,1]；
3-2), according to 3-1) in two input quantities optimal control parameter f=x is calculated1(SoC)·w1+x2(temp)·w2；
4) online minimum equivalent oil consumption strategy modeling, establishes the target function type and constraint formula of equivalent fuel consumption, and solve equivalent
Corresponding motor torque and motor torque during oil consumption minimum；
The target function type of foundation is：
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The constraint formula of foundation is：
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Wherein, J (t) is the object function for representing the equivalent fuel consumption of vehicle,Instantaneous equivalent fuel consumption is represented,
mice(τ) is the fuel that engine is consumed, mem(τ) and mgene(τ) is the equivalent fuel consumption of motor and generator respectively, T
Torque and rotating speed are represented respectively with ω, and i represents engine, one kind in motor and generator.Ti min(ωi) represent minimum turn
Square, Ti max(ωi) torque capacity is represented,Represent minimum speed,Represent maximum (top) speed.SoC is battery dump energy,
SoCminRepresent minimum battery remaining power, SoCmaxRepresent largest battery residual capacity, Pbatt(t) power of battery t,Battery minimum power is represented,Represent battery peak power；
5) engine torque value and motor torque value that vehicle power train and dynamics module are drawn by step 4) carry out torque
Distribute and send control command driving vehicle.
2. the hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery according to claim 1, its
It is characterised by, the method for collection environment temperature realizes that the weather software includes by weather forecasting software in step 1)：
Mobile phone weather forecast software, vehicle-mounted computer weather module software, weather forecast software in GPS navigation device.
3. the hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery according to claim 1, its
It is characterised by, the expression formula that battery SoC values are calculated in step 2) is：
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In formula, QmaxIt is the maximum electricity of battery, Q (t) is the remaining electricity of battery t, and I (τ) is unit interval battery electricity
Stream.
4. the hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery according to claim 1, its
Be characterised by, the step 3-2) in w1=0.5, w2=0.5.
5. the hybrid electric vehicle torque distribution control method of combining environmental temperature protection battery according to claim 1, its
It is characterised by, the step 4) also includes establishing motor equivalent fuel consumption functional expression：
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Wherein,Factor γ values depend on electric motor operation state, ηchAnd ηdchRespectively motor fill,
The energy conversion efficiency of motor, T in discharge processmAnd ωmIt is torque and the rotating speed of motor respectively, f is optimal control ginseng
Number, obtain, QLHVRepresent low heat value；
Also include establishing motor torque functional expression：Treq=Tm+Tice, wherein TreqBe obtained by accelerator pedal signal it is current
Demand torque.
6. the hybrid electric vehicle torque distribution control system of combining environmental temperature protection battery, it is characterised in that believe including temperature
Cease acquisition module, battery parameter computing module, control process module and power train and dynamics module；The temperature information is adopted
Collect module connection battery parameter computing module, the battery parameter computing module connects control process module, the control process
Interconnected between module and the power train and dynamics module；
The temperature information acquisition module is used for the temperature of collection vehicle local environment and gives the temperature value of environment to the electricity
Pond parameter calculating module, the temperature information acquisition module are realized by weather software；
The battery parameter computing module includes battery actual temperature computation model and battery allowance appraising model, and the battery is real
Border temperature calculation models are used to obtain battery actual temperature, and battery allowance appraising model is used to obtain battery allowance value, the electricity
Pond parameter calculating module gives obtained battery actual temperature and battery allowance value to the control process module；
The control process module includes optimal control parameter processing module and torque distribution control Optimized model module；It is described excellent
Change control parameter processing module and use artificial neural-network control structure, for the battery actual temperature and battery allowance value
Handled to obtain optimal control parameter f, the effect of the torque distribution control Optimized model module is to combine to build using f value
Vertical equivalent fuel consumption object function obtains motor torque and motor torque value, and the control process module will obtain engine
Torque and motor torque value give the power train and dynamics module；
The power train and dynamics module carry out torque distribution and driving car according to motor torque and motor torque value
；
Calculate battery actual temperature expression formula be：In formula, I represents battery
Electric current, R, R0Series connection and resistance value in parallel, T respectively in battery equivalent circuitbodyIt is battery actual temperature, TambIt is environment temperature
Degree, mcIt is single battery thermal capacity, hA is coefficient of heat transfer；
Calculation optimization control parameter f process is：1) two input quantities of neutral net are calculated：x1And x (SoC)2(temp)；Calculate x1
(SoC) process includes：A, by the battery SoC values read from battery management system by formula
Processing obtains xSoC, and make xSoCBetween section [- 1,1]；Wherein, SoC represents battery remaining power value, also referred to as battery charge shape
State, SoCHRepresent highest battery remaining power value, SoCLRepresent minimum battery remaining power value；B, constructed fuctionAnd make x1(SoC) between section [0,1]；Calculate x2(temp) process includes：C, obtained by testing
Battery capacity with battery actual temperature change characterisitic function f (temp)；D, formula is pressed to functional value f (temp)It is normalized, obtains second input as artificial neural network
Value x2(temp), and x is made2(temp) value is between section [0,1]；2) optimization control is calculated according to two input quantities in 1)
Parameter f=x processed1(SoC)·w1+x2(temp)·w2；
The target function type of foundation is：
The constraint formula of foundation is：Wherein, J (t) is to represent the equivalent fuel consumption of vehicle
Object function,Represent instantaneous equivalent fuel consumption, mice(τ) is the fuel that engine is consumed, mem(τ) and
mgene(τ) is the equivalent fuel consumption of motor and generator respectively, and T and ω represent torque and rotating speed respectively, and i represents to start
Machine, one kind in motor and generator.Ti min(ωi) represent minimum torque, Ti max(ωi) torque capacity is represented,Represent
Minimum speed,Represent maximum (top) speed.SoC is battery dump energy, SoCminRepresent minimum battery remaining power, SoCmaxTable
Show largest battery residual capacity, Pbatt(t) power of battery t,Battery minimum power is represented,Represent battery most
It is high-power.
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| CN106100477B (en) * | 2016-07-14 | 2019-04-05 | 广州汽车集团股份有限公司 | Automobile current generator control method and device |
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| CN112849119B (en) * | 2019-11-12 | 2022-06-24 | 上海汽车变速器有限公司 | Multivariable torque optimizing control distribution method for engine and motor of hybrid electric vehicle |
| CN112373457B (en) * | 2020-05-15 | 2021-09-28 | 吉林大学 | Energy and heat integrated model of hybrid electric vehicle for energy-saving control |
| CN114103921B (en) * | 2020-08-27 | 2023-12-12 | 比亚迪股份有限公司 | Vehicle protection control method, device and readable storage medium |
| CN113602221B (en) * | 2021-08-25 | 2023-10-31 | 中国第一汽车股份有限公司 | Load power supply control method, device, equipment and storage medium |
| CN116238476A (en) * | 2021-12-07 | 2023-06-09 | 广州汽车集团股份有限公司 | Vehicle system energy control method, device, controller, vehicle and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2796698B2 (en) * | 1995-02-02 | 1998-09-10 | 株式会社エクォス・リサーチ | Hybrid vehicle |
| CN101086522A (en) * | 2006-06-07 | 2007-12-12 | 通用汽车环球科技运作公司 | Method and apparatus for determining the effect of temperature upon life expectancy of an electric energy storage device |
| CN101367342A (en) * | 2007-06-20 | 2009-02-18 | 福特环球技术公司 | Negative driveline torque control incorporating transmission state selection for a hybrid vehicle |
| CN102765388A (en) * | 2012-07-03 | 2012-11-07 | 清华大学 | Vehicle control method based on multi-information integration |
| CN103395375A (en) * | 2013-08-09 | 2013-11-20 | 清华大学 | Electric automobile driving range optimization method based on battery pack heating |
-
2015
- 2015-12-25 CN CN201510996693.9A patent/CN105539423B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2796698B2 (en) * | 1995-02-02 | 1998-09-10 | 株式会社エクォス・リサーチ | Hybrid vehicle |
| CN101086522A (en) * | 2006-06-07 | 2007-12-12 | 通用汽车环球科技运作公司 | Method and apparatus for determining the effect of temperature upon life expectancy of an electric energy storage device |
| CN101367342A (en) * | 2007-06-20 | 2009-02-18 | 福特环球技术公司 | Negative driveline torque control incorporating transmission state selection for a hybrid vehicle |
| CN102765388A (en) * | 2012-07-03 | 2012-11-07 | 清华大学 | Vehicle control method based on multi-information integration |
| CN103395375A (en) * | 2013-08-09 | 2013-11-20 | 清华大学 | Electric automobile driving range optimization method based on battery pack heating |
Non-Patent Citations (2)
| Title |
|---|
| A novel energy management method for series plug-in hybrid electric vehicles;zheng chen等;《Elsevier Applied Energy》;20150204;第145卷;第172-179页 * |
| Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt;Simona Onori等;《Elsevier Applied Energy》;20150121;第147卷;第224-234页 * |
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