CN108215747B - The torque optimization method of bi-motor arrangement and convex optimized algorithm based on pure electric automobile - Google Patents
The torque optimization method of bi-motor arrangement and convex optimized algorithm based on pure electric automobile Download PDFInfo
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- CN108215747B CN108215747B CN201810002393.8A CN201810002393A CN108215747B CN 108215747 B CN108215747 B CN 108215747B CN 201810002393 A CN201810002393 A CN 201810002393A CN 108215747 B CN108215747 B CN 108215747B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- B60K1/00—Arrangement or mounting of electrical propulsion units
- B60K1/02—Arrangement or mounting of electrical propulsion units comprising more than one electric motor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
Abstract
The torque optimization method of the present invention relates to a kind of bi-motor arrangement and convex optimized algorithm based on pure electric automobile, this method comprise the following steps: S1: according to the parameter of automobile, establishing the Longitudinal Dynamic Model of automobile；S2: selecting the state of cyclic operation of automobile, according to selected state of cyclic operation, calculates the demand torque T of automobiledem(k), demand power Pdem(k), greatest requirements torque TDem, maxWith maximum demanded power PDem, max；S3: under the premise of assuming that the capacity of automobile batteries meets dynamic property demand, according to TDem, maxAnd PDem, maxValue, select the motor size and battery size of automobile；S4: convex optimization processing is carried out by motor and battery of the convex optimized algorithm to automobile；S5: each component working condition of car transmissions is constrained；S6: cost objective function is determined.The method of the present invention selects bi-motor arrangement, compensates for the single motor arrangement ineffective disadvantage of motor in electric automobile, while the optimization algorithm calculating time of the invention is fast, as a result accurately.
Description
Technical field
The invention belongs to technical field of new energy, it is related to a kind of bi-motor arrangement based on pure electric automobile and convex excellent
Change the torque optimization method of algorithm.
Background technique
More and more with environmental pollution caused by the growing tension of Global Oil resource and the exhaust emissions of orthodox car
Seriously, each state is promoted all to support to develop pure electric automobile energetically.Compared with orthodox car or hybrid vehicle, pure electric vehicle vapour
Vehicle has the outstanding advantages of zero emission and pollution.
The transmission system of pure electric automobile is usually made of power battery, driving motor, gearbox, with motor control skill
The maturation of art has electric car using no speed changer structure, directly controls the revolving speed of motor to realize the variation of car speed.
The prior art mostly uses a power battery and a driving motor or a power battery two identical driving motor structures
At.And four motor-driven electric vehicles are seldom used on real vehicle since complicated mechanical-electric coupling controls.Pure electric vehicle at present
Electric efficiency, maximum can achieve 95% even higher, and for the pure electric automobile of single motor, demand torque Tdem is direct
It is provided by a motor, in order to meet dynamic property requirement, can generally select the motor of a larger size, export torque capacity
It is larger with peak power output.Although such power train arrangement is simple, motor works in low efficiency region mostly, and
It is more to the electricity waste of battery.In order to solve the problems, such as that motor work in low efficiency region, is developed by scholar using two
The identical motor of the smaller performance of relative size, mean allocation demand torque, by making two motors work in high efficient area simultaneously,
And then the efficiency of power train is improved, it, can be to avoid because a motor job be low although two mutually coordinated work of same motor
Imitate area, although however torque mean allocation strategy it is easily controllable, do not ensure that its control strategy is optimal.Above
Two kinds of arragement constructions, all can prevent the electricity of battery from making full use of, however in order to guarantee the dynamic property of vehicle, and have to
The size for increasing battery, leads to the higher cost of pure electric automobile, hinders the development of electric car.
Dynamic Programming (DP, Dynamic Programming) algorithm can guarantee that its is optimal in optimization algorithm at present
Solution be global optimum, but the calculatings time of DP with control variable increase exponentially trend growth, computation burden are larger.Have
The electric vehicle of bi-motor structure uses the strategy of torque mean allocation, although this control strategy simply can save the time,
It is not ensure that the distribution of torque is global optimum.
Summary of the invention
In view of this, the bi-motor arrangement and convex optimization that the purpose of the present invention is to provide a kind of based on pure electric automobile are calculated
The torque optimization method of method realizes that in allowable error, optimal solution is globally optimal solution, and it is few to calculate the time, as a result accurately
Purpose.
In order to achieve the above objectives, the invention provides the following technical scheme:
The torque optimization method of bi-motor arrangement and convex optimized algorithm based on pure electric automobile, this method include following step
It is rapid:
S1: according to the parameter of automobile, the Longitudinal Dynamic Model of automobile is established；
S2: selecting the state of cyclic operation of automobile, according to selected state of cyclic operation, calculates the demand torque T of automobiledem(k)、
Demand power Pdem(k), greatest requirements torque TDem, maxWith maximum demanded power PDem, max；
S3: under the premise of assuming that the capacity of automobile batteries meets dynamic property demand, according to TDem, maxAnd PDem, maxValue,
Select the motor size and battery size of automobile；
S4: convex optimization processing is carried out by motor and battery of the convex optimized algorithm to automobile；
S5: each component working condition of car transmissions is constrained；
S6: cost objective function is determined.
Further, in step S1, the Longitudinal Dynamic Model of automobile is established are as follows:
Wherein, Ft(k) vehicle traction is indicated,Indicate air drag when running car, cdFor air resistance
Force coefficient, AfFor the front face area of automobile, ρ is atmospheric density, and v is automobile driving speed, and k represents the running car moment, and g attaches most importance to
Power acceleration, crFor the coefficient of rolling resistance of road, β is road grade, acceleration when a is running car, mtotIndicate automobile
Quality.
Further, the demand torque T of automobile is calculated in step S2dem(k), demand power Pdem(k), greatest requirements torque
TDem, maxWith maximum demanded power PDem, maxAre as follows:
Pdem(k)=Ft(k)*v(k)
Tdem(k)=Ft(k)*rwheel
Tdem,max=max (Tdem(k))
PDem, max=max (Pdem(k))
Wherein, FtIt (k) is the tractive force of k moment automobile, v (k) is the speed of k moment automobile, rwheelFor the wheel of automobile
Radius.
Further, the motor size and battery size that automobile is selected in step S3 meet:
TEM2, max> TEM1, max
PEM2, max> PEM1, max
TEM1, max+TEM2, max≥TDem, max
PBat, max≥PDem, max
Wherein, TEM2, maxFor the maximum output torque of automobile back wheel motor, TEM1, maxFor the maximum output of vehicle front motor
Torque, PEM2, maxFor the peak power output of automobile back wheel motor, PEM1, maxFor the peak power output of vehicle front motor,
PBat, maxFor the peak power output of battery.
Further, convex optimization processing described in step S4 are as follows:
VOC(k)=b0*SOC(k)+b1
Wherein, PEMi, lossIt (k) is the wasted power of k moment motor, aij(i=1,2, j=1,2,3) is power loss
Coefficient, VOCFor the open-circuit voltage of battery, TEMi(k) (i=1,2) is output torque of the front and back turbin generator at the k moment, b0, b1It is quasi-
The coefficient of cell voltage is closed, is constant value, SOC (k) is state-of-charge of the automobile batteries at the k moment.
Further, step S5 constrains each component working condition of car transmissions specifically:
TEMi(k)∈[TENi, min, TEMi, max
Pbat(k)∈[PBat, min, PBat, max]*sbat
Ebat∈[SOCmin, 5OCmax]*Voc*Q*sbat
sbat∈[sBat, min, sBat, max]
Wherein TEMi(k) output torque for turbin generator before and after automobile at the k moment, PbatIt (k) is function of the battery at the k moment
Rate, EbatFor the storage electricity of battery, PBat, min, PBat, maxThe respectively minimum value and maximum value of the power of battery, SOCmin, SOCmax
The respectively minimum value and maximum value of battery charge state, VocFor the open-circuit voltage of battery, Q is the capacity of battery, sbatFor battery
Size factor, sBat, min, sBat, maxThe respectively minimum value and maximum value of battery size coefficient.
Further, cost objective function in step S6 are as follows:
Jcost=min costbat+∫Pbatdt
costbat=wb*sbat
Wherein, costbatFor the cost of battery, wbFor the cost coefficient of battery.
The beneficial effects of the present invention are:
1, selection bi-motor arrangement compensates for the single motor arrangement ineffective disadvantage of motor in electric automobile.
2, when selecting motor size, the motor for selecting two sizes different, compared with two same motor arrangements
Compared with reducing two motors and work at the same time time in low efficiency region.
3, torque uses the algorithm of optimum allocation, so that two motors may be simultaneously operated in high efficient district, improves energy benefit
Use efficiency.
4, the size of power battery and motor size can be made to match, saves integral vehicle cost.
5, the convex optimized algorithm calculating time is fast, as a result accurately.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the vehicle drive system structure figure of the method for the present invention；
Fig. 2 is the efficiency chart of the selected small machine of the method for the present invention；
Fig. 3 is the efficiency chart of the selected big motor of the method for the present invention；
Fig. 4 is the power flow and torque flow of automobile of the present invention in motion.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
Implementation of the invention can be realized by pure electric coach model, as shown in Figure 1, the electronic vehicle model is equipped with two
A driving motor, before being arranged on bridge and rear axle, rear axle uses larger-size motor 2, maximum output torque and defeated
Bridge motor 1 before power is all larger than out, the efficiency chart of two motors is as shown in Figure 2 and Figure 3, since original dimension is different, therefore two electricity
The high efficiency region of machine is also different, and wherein 1 high efficient area of motor is in low torque (400N*m-800N*m), high speed area, and electric
Machine 2 then in high torque (HT) (600N*m-1000N*m), middle rotary speed area.Since there are two motor drivens, therefore the dynamic property of the vehicle
It can satisfy, additionally due to the efficiency chart of two motors is different, so with optimal control algorithm by demand torque TdemDistribute to two
A motor, torque distribution principle is specifically in Fig. 4.
The specific steps of the present invention are as follows:
S1: first having to establish car kinetic model according to automobile parameter, at the k moment, the tractive force of automobile are as follows:
Wherein,Air drag when for running car, mtotgcrCos (β (k)) is rolling resistance, mtotgsin
(β (k)) is grade resistance, mtotA (k) is acceleration resistance.Ft(k) vehicle traction, c are indicateddFor coefficient of air resistance, AfFor vapour
The front face area of vehicle, ρ are atmospheric density, and v is automobile driving speed, and k represents the running car moment, and g is acceleration of gravity, crFor
The coefficient of rolling resistance of road, β are road grade, acceleration when a is running car, mtotIndicate the quality of automobile.
S2: one state of cyclic operation of random selection calculates the demand torque T of automobile according to the state of cyclic operation of selectiondem(k)、
Demand power Pdem(k), greatest requirements torque Tdem,maxWith maximum demanded power Pdem,max；
Pdem(k)=Ft(k)*v(k)
Tdem(k)=Ft(k)*rwheel
Tdem,max=max (Tdem(k))
Pdem,max=max (Pdem(k))
Wherein, FtIt (k) is the tractive force of k moment automobile, v (k) is the speed of k moment automobile, rwheelFor the wheel of automobile
Radius.
S3: assuming that the capacity of battery meets dynamic property demand, according to Tdem,maxAnd Pdem,maxValue, select suitable motor
And battery size, make its satisfaction:
TEM2, max> TEM1, max
PEM2, max> PEM1, max
TEM1, max+TEM2, max≥TDem, max
PBat, max≥PDem, max
Wherein, TEM2, maxFor the maximum output torque of automobile back wheel motor (motor 2), TEM1, maxFor vehicle front motor (electricity
Machine 1) maximum output torque, PEM2, maxFor the peak power output of electric motor of automobile 2, PEM1, maxFor the maximum output of electric motor of automobile 1
Power, PBat, maxFor the peak power output of battery.
In addition, to meet always in state of cyclic operation and keep motor 1 identical with the revolving speed of motor 2:
ωEM1=ωEM2
S4: convex optimization processing is carried out by motor and battery of the convex optimized algorithm to automobile, the present invention is used motor function
The mode of rate loss quadratic fit is expressed, in addition, the voltage of battery and state-of-charge can be expressed with linear relational expression,
Steps are as follows for the done convexification of the present invention:
VOC(k)=b0*SOC(k)+b1
Wherein, PEMi, lossIt (k) is the wasted power of k moment motor, aij(i=1,2, j=1,2,3) is power loss
Coefficient, VOCFor the open-circuit voltage of battery, TEMi(k) (i=1,2) is output torque of the front and back turbin generator at the k moment, b0, b1It is quasi-
The coefficient of cell voltage is closed, is constant value, SOC (k) is state-of-charge of the automobile batteries at the k moment.
S5: each component working condition of vehicle power train is constrained:
TEMi(k)∈[TENi, min, TEMi, max
Pbat(k)∈[PBat, min,PBat, max]*sbat
Ebat∈[SOCmin, SOCmax]*VOC*Q*sbat
sbat∈[sBat, min, sBat, max]
TENi, min, TEMi, maxRespectively indicate the minimum output torque and maximum output torque of front and back turbin generator, PbatIt (k) is electricity
Power of the pond at the k moment, EbatFor the storage electricity of battery, PBat, min, PBat, maxThe respectively minimum value and maximum of the power of battery
Value, SOCmin, SOCmaxThe respectively minimum value and maximum value of battery charge state, VocFor the open-circuit voltage of battery, Q is battery
Capacity, sbatFor the size factor of battery, sBat, min, sBat, maxThe respectively minimum value and maximum value of battery size coefficient.
S6: cost objective function J is determinedcost, the objective function that the method for the present invention determines is not only in state of cyclic operation
Energy consumption has further included the cost of battery, therefore objective function can not only guarantee energy consumption minimum, and guarantee battery
Size is in reasonable range:
Jcost=min costbat+∫Pbatdt
CoStbat=wb*Sbat
Wherein costbat,wbFor the cost and cost coefficient of battery.
By data, discretization is solved in time domain, and objective function is converted are as follows:
Δ t is sampling time interval, and N is sampling number.
Finally, it is stated that preferred embodiment above is only to illustrate the technical solution of invention rather than limits, although passing through
Above preferred embodiment is described in detail the present invention, however, those skilled in the art should understand that, can be in shape
Various changes are made in formula and to it in details, without departing from claims of the present invention limited range.
Claims (6)
1. the torque optimization method of bi-motor arrangement and convex optimized algorithm based on pure electric automobile, it is characterised in that: this method
It comprises the following steps:
S1: according to the parameter of automobile, the Longitudinal Dynamic Model of automobile is established；
S2: selecting the state of cyclic operation of automobile, according to selected state of cyclic operation, calculates the demand torque T of automobiledem(k), demand
Power Pdem(k), greatest requirements torque Tdem,maxWith maximum demanded power Pdem,max；
S3: under the premise of assuming that the capacity of automobile batteries meets dynamic property demand, according to Tdem,maxAnd Pdem,maxValue, selection
The motor size and battery size of automobile；
S4: convex optimization processing is carried out by motor and battery of the convex optimized algorithm to automobile；
S5: each component working condition of car transmissions is constrained；
S6: cost objective function is determined；
In step S1, the Longitudinal Dynamic Model of automobile is established are as follows:
Wherein, Ft(k) vehicle traction is indicated,Indicate air drag when running car, cdFor air drag system
Number, AfFor the front face area of automobile, ρ is atmospheric density, and v is automobile driving speed, and k represents the running car moment, and g adds for gravity
Speed, crFor the coefficient of rolling resistance of road, β is road grade, acceleration when a is running car, mtotIndicate the matter of automobile
Amount.
2. the torque optimization side of the bi-motor arrangement and convex optimized algorithm according to claim 1 based on pure electric automobile
Method, it is characterised in that: the demand torque T of automobile is calculated in step S2dem(k), demand power Pdem(k), greatest requirements torque
Tdem,maxWith maximum demanded power Pdem,maxAre as follows:
Pdem(k)=Ft(k)*v(k)
Tdem(k)=Ft(k)*rwheel
Tdem,max=max (Tdem(k))
PDem, max=max (Pdem(k))
Wherein, FtIt (k) is the tractive force of k moment automobile, v (k) is the speed of k moment automobile, rwheelFor the radius of wheel of automobile.
3. the torque optimization side of the bi-motor arrangement and convex optimized algorithm according to claim 2 based on pure electric automobile
Method, it is characterised in that: the motor size of automobile and battery size is selected to meet in step S3:
TEM2,max>TEM1,max
PEM2,max>PEM1,max
TEM1,max+TEM2,max≥Tdem,max
Pbat,max≥Pdem,max
Wherein, TEM2,maxFor the maximum output torque of automobile back wheel motor, TEM1,maxTurn for the maximum output of vehicle front motor
Square, PEM2,maxFor the peak power output of automobile back wheel motor, PEM1,maxFor the peak power output of vehicle front motor,
Pbat,maxFor the peak power output of battery.
4. the torque optimization side of the bi-motor arrangement and convex optimized algorithm according to claim 3 based on pure electric automobile
Method, it is characterised in that: convex optimization processing described in step S4 are as follows:
VOC(k)=b0*SOC(k)+b1
Wherein, PEMi,lossIt (k) is the wasted power of k moment motor, aij(i=1,2, j=1,2,3) is the coefficient of power loss,
VOCFor the open-circuit voltage of battery, TEMi(k) (i=1,2) is output torque of the front and back turbin generator at the k moment, b0,b1For fitting electricity
The coefficient of cell voltage, is constant value, and SOC (k) is state-of-charge of the automobile batteries at the k moment.
5. the torque optimization side of the bi-motor arrangement and convex optimized algorithm according to claim 4 based on pure electric automobile
Method, it is characterised in that: step S5 constrains each component working condition of car transmissions specifically:
TEMi(k)∈[TENi,min,TEMi.max]
Pbat(k)∈[Pbat,min,Pbat.max]*Sbat
Ebat∈[SOCmin,SOCmin]*Voc*Q*sbat
sbat∈[sbat,min,sbat,max]
Wherein TEMi(k) output torque for turbin generator before and after automobile at the k moment, PbatIt (k) is power of the battery at the k moment, Ebat
For the storage electricity of battery, Pbat,min,Pbat.maxThe respectively minimum value and maximum value of the power of battery, SOCmin,SOCmaxRespectively
The minimum value and maximum value of battery charge state, VocFor the open-circuit voltage of battery, Q is the capacity of battery, sbatFor the size of battery
Coefficient, sbat,min,sbat,maxThe respectively minimum value and maximum value of battery size coefficient.
6. the torque optimization side of the bi-motor arrangement and convex optimized algorithm according to claim 5 based on pure electric automobile
Method, it is characterised in that: cost objective function in step S6 are as follows:
Jcost=min costbat+∫Pbatdt
costbat=wb*sbat
Wherein, costbatFor the cost of battery, wbFor the cost coefficient of battery.
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| CN109299567B (en) * | 2018-10-22 | 2023-01-03 | 重庆大学 | Energy-saving-oriented design optimization method for main transmission system of numerically controlled lathe |
| CN110203075B (en) * | 2019-05-31 | 2022-08-05 | 武汉理工大学 | Four-wheel hub motor vehicle system power matching method |
| CN110936824B (en) * | 2019-12-09 | 2021-06-04 | 江西理工大学 | Electric automobile double-motor control method based on self-adaptive dynamic planning |
| CN111209633B (en) * | 2020-01-09 | 2024-04-09 | 重庆大学 | Evaluation and parameter optimization method for power transmission system of plug-in hybrid electric vehicle |
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