CN105867168A - Control allocation model establishing method and device for electric automobile - Google Patents

Abstract

The invention discloses a control allocation model establishing method and device for an electric automobile. The method comprises the following steps of acquiring virtual control vectors, a control efficiency matrix or control input vectors, wherein the dimensionality of the control input vectors is larger than that of the virtual control vectors. According to the technical scheme, by the step of acquiring the virtual control parameters, the control input parameters and the like, a mapping model between control torque allocation and energy consumption is established, and the control allocation model establishing problem of the electric automobile is solved.

Description

Control of Electric Vehicles distribution method for establishing model and device

Technical field

The present invention designs electric automobile torque control field, particularly relates to a kind of Control of Electric Vehicles distribution model foundation side Method and device.

Background technology

Overdrive system (over-actuated systems) refers to that the quantity of effective driver is more than degree of freedom in system System.Many physical systems, such as aircraft, boats and ships and earth's surface vehicle, broadly fall into overdrive system.The spy of overdrive system Point is the reliability utilizing unnecessary driver to improve system, performance and reconfigurability (reconfigurability).

Such as aircraft, owing to the quantity of the control rudder face (driver) of general aircraft is more than the degree of freedom of airplane motion, So the rudder face combination that aircraft to realize employing required for the change of a certain attitude/state has multiple probability.Different rudder faces Although combination can realize required attitude/state change, but the deflection of their complexity, rudder face, degree of stability are the most not Equally.How under conditions of system physical limits (physical constraints), search out in each moment of flight Optimum (according to the degree of stability set, complexity, deflection index) rudder face/drive combination, is the one of flight control system One of individual subject matter.Solve that the one of this problem is feasible and potential method is to control distribution method (control Allocation, hereinafter referred to as CA or control distribution).

Earth's surface vehicle (such as automobile) is the same with aircraft can be considered as overdrive system.In this overdrive system of automobile In, driver is exactly four wheels contacted with ground or the motor driving wheel, and automobile relies on the effect of wheel and ground Power realizes the change of attitude.(subtract in slowing down in turning, keep straight on, turning, keeping straight on to realize some common attitudes vibration Speed), which wheel distribution how many moments of torsion automobile needs to, and this problem also has the most different solutions.This problem is for electricity Motor-car is more more complicated than common automobile, because: 1. a lot of electric motor cars have the independent ability to the different moment of torsion of four wheels distribution；2. The driver (such as taking turns sub-motor) of electric motor car, except the mode of operation of power consumption, is also equipped with antipodal another kind of production capacity Mode of operation (regenerative braking).For this kind of electric motor car, CA method is a kind of effective and that the scope of application is the widest driver instruction The solving method of (input of motor) optimum allocation, it is possible to adapt to different configurations electric motor car (wheel that can independently drive is different, The wheel having the ability of regenerative braking is different, and the kinetic characteristic (dynamics) of wheel, motor and automobile is the most equal).

Summary of the invention

For this reason, it may be necessary to provide a kind of Controlling model method for building up for electric automobile, solve electric automobile model and set up Problem.

For achieving the above object, a kind of Control of Electric Vehicles distribution method for establishing model is inventor provided, including as follows Step, obtains virtual controlling vector, control efficiency matrix or controls input vector；The dimension of described control input vector is more than void Intend controlling the dimension of vector；

According to described control efficiency matrix, control input vector, virtual controlling Vector operation control distribution model J；

Min J=| | W_υ(Bu-υ_d)||+λP_c (2)

s.t.u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency square Battle array, u ∈ R^pFor controlling input vector, W_v, λ be parameters optimization.

Further, the electric power of described driving means consumption determines by the following method:

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.

Further, when the driver of electric automobile exists many drive patterns, described control input vector is multiple driving The control input vector superposition of dynamic device, described control distribution model becomes:

Min J=| | W_υ(B_a[u^T u'^T]^T-u_d)||+λP_c (5)

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；Drive The electric power of dynamic device consumption determines by the following method:

P_oiAnd η_oiRepresent output and and efficiency, and the P of driver under the first drive pattern respectively_iiAnd η_iiThen Represent input power and the efficiency of driver under the second drive pattern respectively.

A kind of Control of Electric Vehicles distribution model sets up device, including acquisition module, model computation module, described acquisition mould Block is used for obtaining virtual controlling vector, control efficiency matrix or controlling input vector；The dimension of described control input vector is more than The dimension of virtual controlling vector；

Described model computation module is for according to described control efficiency matrix, control input vector, virtual controlling vectormeter Calculate and control distribution model J；

Min J=| | W_υ(B_u-υ_d)||+λP_c (22)

s.t.u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency square Battle array, u ∈ R^pFor controlling input vector, W_v, λ be parameters optimization.

Further, the electric power of described driving means consumption determines by the following method:

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.

Further, described acquisition module is additionally operable to when the driver of electric automobile exists many drive patterns, described control Input vector processed is designated as the control input vector superposition of multiple driver, and described control distribution model becomes:

Min J=| | W_υ(B_a[u^T u'^T]^T-υ_d)||+λP_c (5)

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；Drive The electric power of dynamic device consumption determines by the following method:

P_oiAnd η_oiRepresent output and and efficiency, and the P of driver under the first drive pattern respectively_iiAnd η_iiThen Represent input power and the efficiency of driver under the second drive pattern respectively.

Being different from prior art, technique scheme, by obtaining virtual controlling parameter, controls the steps such as input parameter, builds The vertical mapping model controlled between moment of torsion distribution and energy consumption, solves the problem that Control of Electric Vehicles distribution model is set up.

Accompanying drawing explanation

Fig. 1 is the Control of Electric Vehicles distribution method for establishing model flow chart described in present invention embodiment；

Fig. 2 is that apparatus module figure set up by the Control of Electric Vehicles distribution model described in present invention embodiment；

Fig. 3 is the Control of Electric Vehicles efficiency distribution method flow chart described in present invention embodiment；

Fig. 4 is the Control of Electric Vehicles efficiency distributor module map described in present invention embodiment；

Fig. 5 is the fitting experimental data figure described in the specific embodiment of the invention；

Fig. 6 is that the total power consumption described in the specific embodiment of the invention is with change in torque figure；

Fig. 7 is the local minimum schematic diagram of the moment of torsion described in the specific embodiment of the invention；

Fig. 8 is the global minimum schematic diagram described in the specific embodiment of the invention；

Fig. 9 is the power consumption global minimum solution described in the specific embodiment of the invention；

Figure 10 is the vehicle tracking control cage composition described in the specific embodiment of the invention；

Figure 11 is the monotype test simulation figure described in the specific embodiment of the invention；

Figure 12 is the double mode test simulation figure described in the specific embodiment of the invention.

Description of reference numerals:

200, acquisition module；

202, model computation module；

400, model acquisition module；

402, function computation module.

Detailed description of the invention

By describing the technology contents of technical scheme, structural feature in detail, being realized purpose and effect, below in conjunction with concrete real Execute example and coordinate accompanying drawing to be explained in detail.

1. background

Be mostly based on CA fly control derivation algorithm it is considered that for the optimization of control surface deflection amplitude rather than for rudder The catabiotic optimization in face.In majorized function, the deflection amplitude to rudder face is punished, to minimize the purpose of control surface deflection. But the deflection amplitude of rudder face not necessarily with the energy consumption efficiency positive correlation of associated drive.For the overdrive system of high energy consumption, Such as electric automobile, the emphasis of optimization should be placed in the consumption of energy.

For energy efficiency CA problem, it may be considered that with a kind of particularly nonlinear programming, because representing system energy The the second optimization item consumed contains multinomial and/or Fraction Functions.By using this characteristic, energy efficiency is controlled to divide The nonlinear programming program joined, the classical eigenvalue under the conditions of can being converted into based on KKT (Karush-Kuhn-Tucker) Problem, and these problems are independently of initial condition.First the most significant all of eigenvalue is obtained (eigenvalue), these eigenvalues are the locally optimal solution of CA.Then, by simply comparing and getting rid of, from all of office Portion's optimal solution finds globally optimal solution.

Therefore, in some embodiments herein below, we will introduce:

(1) propose a kind of very to electric motor car, for the purpose of optimizing energy efficiency control distribution model, this model is clear and definite Gear efficiency and actuating device operational mode are included in the control distribution of excessive drive system；

(2) propose one to control to distribute nonconvex property optimization problem fast being associated for the energy efficiency solved and propose Speed and the algorithm of the overall situation.

2. general thought

Third chapter single-mode and double mode under energy efficiency control distribution model design；

Chapter 4, introduction solves the algorithm of the CA model global minimum that a chapter proposes；

Chapter 5, performance parameter based on a Practical electric car sample car and experimental data, with concrete numerical value case explanation How to seek the solution of CA problem under the conditions of KKT, it was demonstrated that the effectiveness of the global optimization method proposed in three, four chapters；

Chapter 6, in, we can verify us by controlling simulation based on the lengthwise movement starting electric car built-in to wheel The optimized algorithm proposed is better than the active set optimized algorithm of standard.

3. energy efficiency controls distribution model

Under 3.1 driver single-modes, energy efficiency is optimized for the control distribution modelling of target

The dynamical system of one excessive driving can represent with following formula:

Wherein, system state vector is with x ∈ RⁿExpress, y ∈ R^mFor system output vector, v_d∈R^mFor virtual controlling Vector, B ∈ R^m×pFor control efficiency matrix, u ∈ R^pFor controlling input vector.Here n, m, p are the dimension of vector, pass through Formula 1 is it is found that virtual controlling vector is drawn by the change of system state vector, and this method can obtain system shape from step State vector starts, and obtains virtual controlling vector according to system state vector x, obtains further according to virtual controlling vector and controls input arrow Amount.It should be noted that work as v_dWhen being non-linear relation with u, the method obtaining control efficiency matrix B is: when each is sampled Carve, by affine maps is carried out Local approximation with linearisation.For excessive drive system, p ＞ m always sets up, because The quantity of driving means is greater than dummy pilot signal number and controlled system output quantity.Thus, general u is the most only One solves, it is common that solve R with CA^p→R^mOptimization mapping problems.

Therefore, in a kind of Control of Electric Vehicles shown in we Fig. 1 distributes method for establishing model, comprise the steps, S100 obtains virtual controlling vector, control efficiency matrix or controls input vector；The dimension of described control input vector is more than void Intend controlling the dimension of vector；

S102 controls distribution model J according to described control efficiency matrix, control input vector, virtual controlling Vector operation；

Min J=| | W_υ(B_u-υ_d)||+λP_c (22)

s.t.u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency square Battle array, u ∈ R^pFor controlling input vector, W_v, λ be parameters optimization.

For each driving means only one of which drive pattern and the excessive drivetrain of a corresponding energy efficiency equation For system, energy efficiency controls distribution and can express with (2):

Wherein, P_cIt it is the instantaneous total electricity of all driving means consumption.The up-and-down boundary vector of driving means amplitude is respectively With the u of componentwise_maxAnd u_minRepresent.The setting on this magnitude border can also take driving means into consideration based on given system Rate limit in system sample time and under different formulas.One little positive parameter lambda can be used to balance and reduces CA mistake and electricity Relation between power consumption.Further, parameter lambda is the least, and the CA mistake obtained is the fewest.Little CA mistake may be not significantly affected by Whole system controls performance.Further, the sane controller of a high-level can help to overcome/alleviate at hybrid optimization equation The impact that the limited CA mistake of middle generation is brought.

Further, the electric power of described driving means consumption determines by the following method:

Efficiency equation based on corresponding driving means, the power consumption of system can be power or the moment of torsion of driving means The equation of value:

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.Therefore, P_oi(u_i) compare η_oi(u_i) mark represent the energy resource consumption P of i-th driver_ci.Therefore, energy efficiency CA under single-mode Represent with formula (2) (3).Said method solves the electric automobile running status mapping problems to nonlinear control system, solves The problem that Control of Electric Vehicles of having determined distribution model is set up.

The 3.2 double mode lower energy efficiencies of driver are optimized for the control distribution modelling of target

When the driver in system has double-mode (such as consume the energy and obtain energy source gain) and two patterns pair When answering respective efficiency equation, the control assignment problem of energy resource consumption is the most increasingly complex.For this double mode driver, its Act on virtual controlling, magnitude/rate constraint, efficiency, energy resource consumption or gain characteristic all may under two kinds of operational modes It is different.In order to solve the energy efficiency CA problem under this pattern, the model selection of driver is also required to be included into CA group In conjunction.Accordingly, it is capable to the combination of source efficiency CA not only clearly to send magnitude instruction, also each redundant drive is sent and run Mode instruction.This point is not accomplished in the CA combination of standard at present.In this article, we introduce a virtual drive concept For tackling this problem.Originally in formula (1), expression to excessive drive system can be expanded into following formula:

We introduce a virtual drive and control input vector u^/∈R^q, 1≤q≤p carrys out p driver in expression system In have q driver to have double-mode, thus double mode driver also can be included in energy efficiency CA combination in.This Sample, CA not only sends the magnitude index of driver, also sends the instruction of driver operational mode, thus solve described previously asking Topic.The matrix B increased_a=[B B_q]∈R^m×(p+q)It it is the new control efficiency matrix under new system.It should be noted that matrix B_qIt is Determined by double mode driver.

Also include step S104 when there is many drive patterns in the driver of electric automobile, described control input vector is many The control input vector superposition of individual driver, described control distribution model becomes:

Min J=| | W_υ(B_a[u^T u'^T]^T-υ_d)||+λP_c(5)

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；

Energy efficiency CA combination (2) is revised as the form of (5) the most accordingly.

Owing to any one specific double mode driver can be only in two kinds of operational modes in any given moment A certain kind, the 3rd condition added in constraint ensure that by energy efficiency CA, to a driving means, only can distribute To its a kind of operational mode.In electric car, electric motor can work with driving mode (consuming airborne energy), or with again Raw braking mode work (electromotor manufactures electric power from vehicle kinergety).

In formula (5), total power consumption P_cInstantaneous electricity including the double-mode corresponding to each driving means Power consumes and gain.Such as, if u represents driver energy consumption patterns, and u^/Represent driver energy gain mode, then Total energy resource consumption of all of driver in different modes can be expressed with following formula:

In formula (6), P_oiAnd η_oiRepresent output and and the effect of driver under energy consumption patterns respectively Rate, and P_iiAnd η_iiRepresent input power and the efficiency of driver under energy gain mode the most respectively.Therefore, double mode energy Source efficiency CA problem can be stated with formula (5) and (6).Said method is by entering the control input vector of many drive patterns Row is integrated, and solves and controls the problem that distribution model is set up under the many driving conditions of electric automobile.

Shown in Fig. 2 a kind of Control of Electric Vehicles distribute model set up in apparatus module figure, including acquisition module 200, Model computation module 202, described acquisition module 200 is used for obtaining virtual controlling vector, control efficiency matrix or controlling input vowing Amount；The dimension of described control input vector is more than the dimension of virtual controlling vector；

Described model computation module 202 is for according to described control efficiency matrix, control input vector, virtual controlling vector Calculate and control distribution model J；

Min J=| | W_υ(Bu-υ_d)||+λP_c (2)

s.t.u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency square Battle array, u ∈ R^pFor controlling input vector, W_v, λ be parameters optimization.Designed by said apparatus, it is possible to that sets up that vehicle moves is virtual Control the vector mapping to control input, solve the problem that Control of Electric Vehicles distribution model is set up.

Further, the electric power of described driving means consumption determines by the following method:

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.

In some further embodiment, described acquisition module 200 is additionally operable to exist in the driver of electric automobile drive more During dynamic model formula, described control input vector is designated as the control input vector superposition of multiple driver, and described control distribution model becomes For:

Min J=| | W_υ(B_a[u^T u'^T]^T-υ_d)||+λP_c (5)

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；Drive The electric power of dynamic device consumption determines by the following method:

P_oiAnd η_oiRepresent output and and efficiency, and the P of driver under the first drive pattern respectively_iiAnd η_iiThen Represent input power and the efficiency of driver under the second drive pattern respectively.Said apparatus is by by many drive patterns Control input vector to integrate, solve and under the many driving conditions of electric automobile, control the problem that distribution model is set up.

4. energy efficiency based on KKT condition controls distribution global optimization approach

Be no matter monotype or double mode under energy efficiency CA problem can be by the nonlinear optimization of a standard Method, active set algorithm solves.But this active set algorithm does not ensures that global optimization, and it is for initial condition Selection very sensitive.Based on KKT condition, what we used this overall situation of subordinate arranges unrelated optimized algorithm with initial condition Solve energy efficiency CA problem.

(1) the KKT condition under monotype and energy efficiency CA problem algorithm

In the embodiment that Fig. 3 shows, for the control efficiency distribution method of a kind of electric automobile, comprise the steps, S300 obtains control efficiency distribution model, and described control efficiency distribution model is function J (u) controlling input vector u, controls effect Rate distribution model i.e. can express the function controlling input with efficiency corresponding relation, and the method for building up of control efficiency distribution model is such as Above-mentioned, the most only discuss how control efficiency distribution solution to model method obtains the control input vector of optimum.

Also include step S302, definition Lagrangian:

Wherein, non-negative vectorλ∈R^pWithIt it is Lagrange's multiplier；

By above-mentioned Lagrange's multiplier L to u derivation:

Step S304 is by Lagrange's multiplierλ ^*OrWhen being equal to 0, derivation obtains controlling the border of input vector u value Minimum, by lagrangian multiplier^*WithWhen being all set to be not zero, control minimum u of input vector^*Equal to boundary value u_max Or u_min；Above-mentioned minimum is compared, thus draws global minimum.

Specifically, in order to try to achieve the algorithm under monotype, formula (2) (3) is combined by we, is modified as following formula:

Square correction to CA mistake is for the ease of follow-up Lagrangian derivation.P_O(u),η_O(u)∈R^pIt is defeated respectively Go out the vector form of power and efficiency.

Define following Lagrangian:

In formula (8), non-negative vectorλ∈R^pWithIt it is Lagrange's multiplier.Based on KKT condition, corresponding to spy Determine Lagrange's multiplierλ ^*WithOptimal solution u^*Meet following condition:

Remarks 1: in general, in order to be able to exist local minimum, KKT condition must is fulfilled for.At target/expenditure letter When number and constraint set are convexity, they can also be used to fully describe globally optimal solution.Although the expenditure letter in formula (7) Number is not convex, and further checking can get rid of maximum and local minimum.Optimum control u^*Calculating can be by drawing Ge Lang multiplierλ ^*WithValue be classified.Work as Lagrange's multiplierλ ^*And/orDuring equal to 0, we can be by solving generation Number equation (9), obtains controlling the border of u value.For energy efficiency CA under specific form, the second optimization item is a series of mark Sum, denominator and the molecule of this mark represent with the lower order polynomial expressions function of power and efficiency so that (9) easily solve.When Lagrange's multiplierλ ^*WithWhen either or both of which is all not zero, based on KKT condition, control optimal solution u^*Necessarily equal to limit Dividing value u_maxAnd/or u_min.Therefore, the local minimum of some including boundary value can be obtained by above-mentioned two classifying step Arrive.Finally, condition based on minimal power consumption, can compare in above-mentioned local minimum, thus draw overall situation pole Little value.Generally speaking, algorithm false code is as follows:

Input:

●v_dSystem model and higher controller is come from B.

●P_O(u),η_O(u),u_maxAnd u_minCome from driver model and experimental calibration.

● λ and W_vFor in order to try to achieve optimal solution and adjustable parameter.

Step:

1) orderSolve algebraic equation (9), and obtain the local minimum u of all non-trivial^*。

2) orderTherefore u is drawn^*=u_imin, andOtherCan be by solving In formula (9), remaining algebraic equation obtains, similar step 1.

3) it is similar to step 2, orderTherefore u is drawn^*=u_imaxAndOtherPermissible Obtained by remaining algebraic equation in solution formula (9), similar step 1.

4) to all u drawn in above three step^*, calculate total power consumption (second in formula (7) of its correspondence ), and draw global optimum u by contrast^*。

Pass through said method, it is possible to obtain the area-wide optimal control input ginseng minimum so that electric automobile torque distribution power consumption Number, solves the problem that electric automobile CA controls.

(2) the KKT condition under double mode and energy efficiency CA problem algorithm

Being similar to monotype, formula (5) (6) is combined and makes a little amendment, to show that to meet double mode formula as follows:

Wherein, P_i(u^/),η_i(u^/)∈R^qIt is respectively input power function and the vector form of efficiency.

Therefore in the specific embodiment described in this trifle, step is also included, when the driver of electric automobile exists many During drive pattern, described Lagrangian becomes,

Wherein, non-negative vectorλ∈R^pWithAndλ ^/∈R^pWithBeing Lagrange's multiplier, u and u ' is The control input vector of one drive pattern and the control input vector of the second drive pattern；

Successively by Lagrange's multiplierλ ^*、WithOne or more be set to zero, obtain multiple local minimum Value, compares in multiple local minimums, obtains global minimum.

In formula (11), non-negative vectorλ∈R^pWithAndλ ^/∈R^pWithIt it is Lagrange's multiplier. Based on KKT condition, corresponding specific Lagrange's multiplierλ ^*、WithOptimal solution u^*WithMeet following condition:

u^*-u_min>=0, u_max-u^*>=0, u'^*-u'_min>=0,

u'_max-u'^*>=0, λ >=0,λ ^*>=0, λ '^*>=0,λ' ^*≥0.

Remarks 2: similarly, although based on KKT condition (this is to obtain necessary to local minimum), the most not Can guarantee that non-convex consumes the globally optimal solution of equation, it is possible to do further detection and get rid of maximum and local minimum Value.About controlling u^*WithCalculating can pass through Lagrange's multiplierλ ^*、WithValue classify.When drawing Ge Lang multiplierλ ^*And/orDuring equal to 0, Solving Algebraic Equation (12) can be passed through and calculate controlling value u in border^*( ).The second optimization item in formula (10) contains multinomial and mark sum, its denominator and molecule can be by the power of low order Express with the polynomial equation of efficiency, so that equation (12) is easily solved.It addition, supplementary conditionCertainly Determine u^*WithCan not be non-zero simultaneously, such that it is able to be further used on equation (12), make u^*WithEqual to 0, To reduce amount of calculation.Work as Lagrange's multiplierλ ^*≠ 0 and/or(And/or), controlling value u^*( ) be necessary for equal to boundary value.Therefore, some local minimums (including boundary value) can be drawn by above-mentioned two step classification.? After, compare in these local minimums, according to the condition of minimal power consumption, global minimum can be selected.Similar In monotype CA, the false code form of double mode CA algorithm is as follows:

Input:

●v_dAnd B_a=[B B_q] come from system model and higher controller.

●P_o(u),_i(u^/),η_o(u),_i(u^/),u_min, _maxWithCome from driving

Device model and experimental calibration.

● λ and W_vFor in order to try to achieve optimal solution and adjustable parameter.

Step:

1) orderSolve algebraic equation (12), and obtain the local minimum of all non-trivial u^*WithIt should be noted that can be by using supplementary conditionSimplify the calculating of (12), i.e. by by whole Individual process is divided into u_i=0,And u_i≠0,Solve.

2) orderTherefore drawAndAnd pass through supplementary conditionOrderOtherWithRemaining algebraic equation in solution formula (12) can be passed through obtain, similar Step 1.

3) it is similar to step 2, orderTherefore u is drawn^*=u_imaxAndAnd pass through supplementary conditionOrderOtherCan be obtained by remaining algebraic equation in solution formula (12) , similar step 1.

4) useWithRepeat step 2) and 3), obtain u^*With

5) all u just drawn from above-mentioned steps^*WithCalculate total power consumption (Section 2 in formula (10)), By comparison, find out the u of global optimum^*With

It should be noted that along with the increase of the unnecessary driver of system, the amount of calculation of algorithm and difficulty also can synchronize to rise. It addition, drive efficiency equation characteristic also influences whether complexity based on KKT energy efficiency CA algorithm.

By above-mentioned steps, solve the control efficiency optimization problem of electric automobile under many drive patterns, reached to save The effects driving Control of Electric Vehicles energy consumption more.

4.3 in the embodiment shown in fig. 4, for the control efficiency distributor of a kind of electric automobile, obtains including model Module 400, function computation module 402；

Described model acquisition module 400 is used for obtaining control efficiency distribution model, and described control efficiency distribution model is control Function J (u) of input vector u processed,

Described function computation module 402 is used for defining Lagrangian:

Wherein, non-negative vectorλ∈R^pWithIt it is Lagrange's multiplier；

Described function computation module 402 is additionally operable to above-mentioned Lagrange's multiplier L u derivation:

Specifically for, Lagrange's multiplierλ ^*OrWhen being equal to 0, derivation obtains controlling the border of input vector u value Minimum, by Lagrange's multiplierλ ^*WithWhen being all set to be not zero, control minimum u of input vector^*Equal to boundary value u_max Or u_min；Above-mentioned minimum is compared, thus draws global minimum.

Apparatus of the present invention input parameter by the area-wide optimal control obtained so that electric automobile torque distribution power consumption is minimum, Solve the problem that electric automobile CA controls.

Further, when the driver of electric automobile exists many drive patterns, described Lagrangian becomes,

Wherein, non-negative vectorWithAndλ ^/∈R^pWithIt is Lagrange's multiplier, u and u ' It is control input vector and the control input vector of the second drive pattern of the first drive pattern；

Described function computation module 402 is additionally operable to successively by Lagrange's multiplierλ ^*、WithOne or more It is set to zero, obtains multiple local minimum, compare in multiple local minimums, obtain global minimum.

Said apparatus preferably solves many driving Control of Electric Vehicles optimization problems.

5. concrete case based on electric motor car model

In this chapter, our energy efficiency based on KKT condition to being described above controls allocation algorithm and provides numerical value Case (include monotype and double mode).These examples, be from wheel built-in motor and experimental data draw it was confirmed Assume and previously described formula.

The delivery efficiency equation η of wheel built-in brushless DC (BLDC) electromotor₀U () and controller thereof can be by Fig. 5 Experimental data carry out matching.We have employed two linear equation and come rising part and the falling portion of matching measured data of experiment Point:

Wherein, a₁₁,b₁₁,a₁₂,b₁₂It is all coefficient, arranges in the following table:

TABLE I

IN-WHEEL MOTOR EFFICIENCY FuNCTION PARAMETERS

Symbol	Values
		a11	0.0372
b11	0.122
		a12	-0.0025
b12	0.9181

A lot of reason is had to make piecewise linear function (13) become Efficiency Fit function: first, to allow for amount of calculation. It can be seen that KKT condition makes algebraic equation or eigenvalue problem have optimal solution from previous formula (9) or (12).? Simple Efficiency Fit equation can make amount of calculation the lowest.We are it will be seen that such segment processing makes global optimum below Solution can obtain.Second, it is the DC characteristic due to brushless DC motor.Piecewise linear function (13) can fully be described along with electromotor The raising and lowering trend that moment of torsion increases and produces.Finally, although what engine speed also can be slight has influence on engine efficiency, This point can be found out by the experimental data figure from Fig. 5, and efficiency curve is all in the range of a engine rotary speed the biggest Similar.It addition, for CA, owing to sample time is short, can reasonably assume that the rotary speed of electromotor is at each Instantaneous is all constant.

The power consumption of wheel built-in motor can represent with following formula:

P_O(u)=u ω₀ (14)

Wherein, ω₀Being a given rotary speed, u represents engine torque.In the case of without loss of generality, Two wheel built-in BLDC electromotors are considered as to control for the vehicular longitudinal velocity in the case of straight-line travelling.We will not Same conversion coefficient efficiency curve in Fig. 5, in order to for two electromotors and two kinds of operational modes structure efficiency function. Under monotype drives, control efficiency matrix is B=[1 1]^T.Under double mode driving, control efficiency matrix is B_a=[1 1 1 1]^T. the boundary value of driving means: be u under driving mode_min=0Nm and u_max=100Nm, under braking mode beWith

(1) energy efficiency CA under single-mode

In this mode, engine rotary speed is set to ω by us₀=400rpm, i.e. tire effective radius are 0.3 meter Vehicle with 50km/h speed per hour run.Penalty coefficient λ is set to 0.001, and weight matrix is set to unit matrix.Second The conversion coefficient of wheel built-in motor efficiency is set to 0.9.

In order to try to achieve nontrivial solution, equation (14) is substituted into (9), and makesCan obtain following formula:

Owing to efficiency function (13) is piecewise linear, so the equation in (15) must be by combining different efficiency letters Number solves.Substantially, it is possible to obtain four couple of two algebraic equations.In order to solve two algebraic equations of two variablees, Such as (15), owing to (13) and (14) have employed simple efficiency and power expression, by variable to multinomial to finding The root of formula.The problem that the multinomial obtained may be considered that the band multinomial characteristic of a classical eigenvalue.Therefore, optimization is asked Topic translates into four (two to) eigenvalue problems, i.e. solves optimumWithCompared with trivial solution (boundary value of driver) Relatively, global minimum can finally be obtained.In order to embody the real global minimum that we draw, the sky of total power consumption Between/surface (i.e. in formula (7) second optimization item) may refer to Fig. 6.

As seen from Figure 6, total power consumption typically rises along with the increase of engine torque.But, due to surface Non-convex characteristic, be difficult to find out global minimum by criteria optimization algorithm.By the piecewise fitting of the efficiency function of driver, Space/the surface of non-convex can essentially be divided into four partsEach part its each as defined in the range of It it is convexity.Therefore, in each region, KKT condition (being equivalent to an eigenvalue problem) can be passed through and find correspondence Global minimum.Then, the simple comparison to the boundary value of these global minimums and zones of different, can obtain real Global minimum in whole nonconvex property power consumption spaces.Five lines from the teeth outwards represent corresponding to different v_d= u₁+u₂Different virtual controlling values, they are the cross curves between non-convex power consumption surface and vertical plane.These are bent Line defines under different dummy pilot signals, does not consider the disaggregation of problem (7) in the case of minimum power.In view of not Same virtual controlling v_d, we can clearly observe that from Fig. 7 moment of torsion is distributedWithGlobal minimum.

Fig. 7, by inserting the efficiency in (13) and (14) and horse-power formula, illustrates power consumption optimization problem (7).Figure In every curve represent the different dummy pilot signals from 10,20,40,60 to 80Nm.On every curve, corresponding void Intend controlling the distribution sum equal on two electromotors.As it is shown in fig. 7, the place marking numeral represents globe optimum.To the greatest extent Manage these global minimums to be also not quite similar on different non-convex curves, the algorithm based on KKT that we prefer that, actually By eigenvalue problem and simply compare with boundary value, have found all of solution.If the active set algorithm quilt of a standard Being used for solving nonlinear optimal problem, due to the improper selection of initial condition, global minimum is possibly cannot be found, and only Local minizing point can be obtained.

(2) double mode energy efficiency CA problem

Under double mode case, we are about engine rotary speed ω₀, weight matrix W_vSelection with penalty coefficient λ is all Consistent with monotype.Assume that two wheel built-in motor have identical efficiency attributes, the running efficiency of rear end electromotor Conversion coefficient is set to 0.9.Further, the regenerative braking efficiency of electromotor is generally low than running efficiency.Therefore, each electromotor Regenerative braking efficiency conversion coefficient is set to 0.9 times of the running efficiency of correspondence.

Formula (14) is substituted into (12), and makes all of nontrivial solutionUnder can obtaining Formula:

Although above formula lists four algebraic equations and two supplementary equations, due to two supplementary equations soWithOrWithFour equations set up the most simultaneously.So, two supplementary equations can use to reduce calculating step by step Amount, because only that two algebraic equations solve simultaneously.Similar with monotype, we, by the step of approximation, divide by inserting Section linear efficiency equation (13) carrys out solving equation (16), to obtain local minimum.Then, by comparing local minimum peace All solutions (boundary value), can obtain global minimum, refer to Fig. 8.

As shown in Figure 8, the power consumption space under double mode more presents nonconvex property than monotype.Due to each virtual Driver has two piecewise linearity efficiency portion, total non-convex power space to include 16 convexity regionsSo And, for certain specific virtual controlling value, only part convexity region is related.Fig. 8 presents and works as v_dDuring≤20Nm, can To obtain seven convexity planes.Therefore, it is similar to monotype, if the global minimum of simple comparison zones of different and border Value can be obtained by whole non-convex global minimum spatially.Four lines in plane represent different virtual controlling values, Fig. 9 clearly presentsWithGlobal minimum under moment of torsion distribution.

Fig. 9 illustrates the efficiency by substituting into (13) (14) and power expression, the power of the optimization problem solved (10) Consume.Every curve in upper figure represents from 4 respectively, the different virtual controlling values of 8,10 to 20Nm.On every curve, right The virtual controlling answered is equal to the distribution sum of two electromotors.The digitized representation that marks in upper figure globe optimum.Although this A little global minimums are all different on the non-convex curve of each bar, and algorithm based on KKT condition is all found out them accurately.

6. the control of Virtual Sample Vehicle and feedback model

Compared with traditional vehicle drive system framework, the built-in independent electric car driving electromotor of wheel is had to provide Preferably control the advantages such as convenient.Therefore, relevant efficiency and control assignment problem are the most highly significant.But it is noted that one As the CA combination of property be different from the power management method of hybrid electric vehicle.

The Organization Chart of vehicular longitudinal velocity tracing control such as Figure 10, so conveniently pushes away with us for common active set method The algorithm based on KKT condition recommended.We want and the actual car speed measured uses V respectively_xrAnd V_xRepresent.High-rise The speed tracking control device of level is proportional integral (PI) controller (can replace to other controllers that function is identical in reality), He can control allotter for energy efficiency provides virtual controlling value v.We use previously described new algorithm enterprising at CarSim Row simulated operation.

(1) monotype simulation

From the simulation experiment data result figure of Figure 11 it can be seen that active set method and algorithm based on KKT condition all may be used To provide virtual controlling value to travel by design for vehicle.But, the two differ primarily in that amount of calculation and the overall situation are The acquisition of the figure of merit.From the point of view of moment of torsion scattergram, active set method is only able to find the local minimum from the 23rd second to the 28th second. In other times section, find the selection of global minimum initial conditions to be depend heavilyed on active set.It addition, equally Simulated environment under, algorithm based on KKT condition has only spent 4.5 seconds and has just run the simulation being over 30 seconds, and active set method flower 90 seconds.

(2) simulation under double mode

In dual-mode, different drive pattern situations (for travelling, rear end electromotor is brake to Herba Plantaginis end electromotor) is only Just can occur virtual value is the least when, during because virtual value is big, CA algorithm can enter monotype driving, and (two electromotors are all It is driving mode).Such distribution is largely depending on the efficiency curve of electromotor.When virtual controlling is the least, Double mode controller will not distribute to the little traveling moment of torsion of two drivers with inefficient interval, but can be higher with one Efficiency interval autocommand increase and travel moment of torsion, simultaneously equally produce regenerative braking torque with a higher efficiency interval, Reach identical virtual controlling effect by this way.Substantially, two high efficiency big travelings and regenerative braking torque, The little traveling moment of torsion inefficient compared to two, can consume less power.In electric car practice, wheel is built-in to be started The regenerative braking efficiency of machine will be generally less than its running efficiency.But, in some cases, the wheel of such as vehicle front is built-in When electromotor is made mistakes, the regenerative braking efficiency of the wheel built-in motor of far-end is likely larger than imitates equal to the traveling of front end electromotor Rate, so such traveling and control for brake distribution distribution will occur.

Along with vehicle accelerates, its required virtual controlling value also can rise, and CA can be automatically converted to the single of single driver Pattern distribution (front end electromotor is driving mode, and far-end electromotor is had a rest), transfers the single distributed mode of two drivers subsequently to Formula (two electromotors are all driving modes).When virtual controlling level is reduced to certain degree, energy efficiency CA can return to list The single-mode distribution of one driver.

As shown in figure 12, active set method and algorithm based on KKT condition can provide virtual controlling value for vehicle Travel by design.Similar with single-mode, the main distinction of the two is still the acquisition of amount of calculation and global optimum. Result of the comparison, the algorithm that we prefer that is better than active set method.

It should be noted that in this article, the relational terms of such as first and second or the like is used merely to a reality Body or operation separate with another entity or operating space, and deposit between not necessarily requiring or imply these entities or operating Relation or order in any this reality.And, term " includes ", " comprising " or its any other variant are intended to Comprising of nonexcludability, so that include that the process of a series of key element, method, article or terminal unit not only include those Key element, but also include other key elements being not expressly set out, or also include for this process, method, article or end The key element that end equipment is intrinsic.In the case of there is no more restriction, statement " including ... " or " comprising ... " limit Key element, it is not excluded that there is also other key element in including the process of described key element, method, article or terminal unit.This Outward, in this article, " be more than ", " being less than ", " exceeding " etc. are interpreted as not including this number；More than " ", " below ", " within " etc. understand For including this number.

Those skilled in the art are it should be appreciated that the various embodiments described above can be provided as method, device or computer program product Product.These embodiments can use complete hardware embodiment, complete software implementation or combine software and hardware in terms of embodiment Form.All or part of step in the method that the various embodiments described above relate to can instruct relevant hardware by program Completing, described program can be stored in the storage medium that computer equipment can read, and is used for performing the various embodiments described above side All or part of step described in method.Described computer equipment, includes but not limited to: personal computer, server, general-purpose computations Machine, special-purpose computer, the network equipment, embedded device, programmable device, intelligent mobile terminal, intelligent home device, Wearable Smart machine, vehicle intelligent equipment etc.；Described storage medium, includes but not limited to: RAM, ROM, magnetic disc, tape, CD, sudden strain of a muscle Deposit, the storage of USB flash disk, portable hard drive, storage card, memory stick, the webserver, network cloud storage etc..

The various embodiments described above are with reference to according to the method described in embodiment, equipment (system) and computer program Flow chart and/or block diagram describe.It should be understood that can every by computer program instructions flowchart and/or block diagram Flow process in one flow process and/or square frame and flow chart and/or block diagram and/or the combination of square frame.These computers can be provided Programmed instruction to the processor of computer equipment to produce a machine so that the finger performed by the processor of computer equipment Order produces for realizing specifying in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame The device of function.

These computer program instructions may be alternatively stored in the computer that computer equipment can be guided to work in a specific way and set In standby readable memory so that the instruction being stored in this computer equipment readable memory produces the manufacture including command device Product, this command device realizes at one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame middle finger Fixed function.

These computer program instructions also can be loaded on computer equipment so that performs a series of on a computing device Operating procedure is to produce computer implemented process, thus the instruction performed on a computing device provides for realizing in flow process The step of the function specified in one flow process of figure or multiple flow process and/or one square frame of block diagram or multiple square frame.

Although being described the various embodiments described above, but those skilled in the art once know basic wound The property made concept, then can make other change and amendment to these embodiments, so the foregoing is only embodiments of the invention, Not thereby the scope of patent protection of the present invention, every equivalent structure utilizing description of the invention and accompanying drawing content to be made are limited Or equivalence flow process conversion, or directly or indirectly it is used in other relevant technical fields, the most in like manner it is included in the patent of the present invention Within protection domain.

Claims (6)

1. a Control of Electric Vehicles distribution method for establishing model, it is characterised in that comprise the steps, obtain virtual controlling and vow Amount, control efficiency matrix or control input vector；The dimension of described control input vector is more than the dimension of virtual controlling vector；

According to described control efficiency matrix, control input vector, virtual controlling Vector operation control distribution model J；

Min J=| | W_v(Bu-v_d)||+λP_c (2)

s.t. u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency matrix, u ∈R^pFor controlling input vector, W_v, λ be parameters optimization.

Control of Electric Vehicles the most according to claim 1 distribution method for establishing model, it is characterised in that described driving means The electric power consumed determines by the following method:

$P_c=\underset{i=1}{\overset{p}{\Σ}}{P}_{ci}\left(u_i\right)=\underset{i=1}{\overset{p}{\Σ}}\frac{P_{oi}\left(u_i\right)}{{\mathrm{\η}}_{oi}\left(u_i\right)}.---\left(3\right)$

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.

Control of Electric Vehicles the most according to claim 1 distribution method for establishing model, it is characterised in that when electric automobile When driver exists many drive patterns, described control input vector is the control input vector superposition of multiple driver, described control System distribution model becomes:

Min J=| | W_v(B_a[u^T u′^T]^T-v_d)||+λP_c (5)

$s.t.\{\begin{array}{c}{u}_{\min }\≤u\≤u_{\max }\\ u_{\min }^{\′}\≤u\≤u_{\max }^{\′}\\ u_i{u}_i^{\′}=0,i=1,...,q\end{array}.$

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；Drive dress The electric power putting consumption determines by the following method:

$P_c=\underset{i=1}{\overset{p}{\Σ}}\frac{P_{oi}\left(u_i\right)}{{\mathrm{\η}}_{oi}\left(u_i\right)}-\underset{i=1}{\overset{q}{\Σ}}{P}_{ii}\left(u_i^{\′}\right){\mathrm{\η}}_{ii}\left(u_i^{\′}\right).---\left(6\right)$

P_oiAnd η_oiRepresent output and and efficiency, and the P of driver under the first drive pattern respectively_iiAnd η_iiThen distinguish Represent input power and the efficiency of driver under the second drive pattern.

4. device set up by a Control of Electric Vehicles distribution model, it is characterised in that include acquisition module, model computation module, Described acquisition module is used for obtaining virtual controlling vector, control efficiency matrix or controlling input vector；Described control input vector Dimension more than the dimension of virtual controlling vector；

Described model computation module is for according to described control efficiency matrix, control input vector, virtual controlling Vector operation control System distribution model J；

Min J=| | W_v(Bu-v_d)||+λP_c (2)

s.t. u_min≤u≤u_max

Wherein P_cIt is the electric power of described driving means consumption, v_d∈R^mFor virtual controlling vector, B ∈ R^m×pFor control efficiency matrix, u ∈R^pFor controlling input vector, W_v, λ be parameters optimization.

Device set up by Control of Electric Vehicles the most according to claim 4 distribution model, it is characterised in that described driving means The electric power consumed determines by the following method:

$P_c=\underset{i=1}{\overset{p}{\Σ}}{P}_{ci}\left(u_i\right)=\underset{i=1}{\overset{p}{\Σ}}\frac{P_{oi}\left(u_i\right)}{{\mathrm{\η}}_{oi}\left(u_i\right)}.---\left(3\right)$

Wherein, P_oi(u_i) and η_oi(u_i) be respectively i-th driving means output electric power equation and efficiency equation.

Device set up by Control of Electric Vehicles the most according to claim 4 distribution model, it is characterised in that described acquisition module Being additionally operable to when the driver of electric automobile exists many drive patterns, described control input vector is designated as the control of multiple driver Input vector superposition, described control distribution model becomes:

Min J=| | W_v(B_a[u^T u′^T]^T-v_d)||+λP_c (5)

$s.t.\left\{\begin{array}{c}{u}_{\min }\≤u\≤u_{\max }\\ u_{\min }^{\′}\≤u\≤u_{\max }^{\′}\\ u_i{u}_i^{\′}=0,i=1,...,q\end{array}\right..$

Wherein, u^TIt is the control input vector of the first drive pattern, u '^TIt it is the control input vector of the second drive pattern；Drive dress The electric power putting consumption determines by the following method:

$P_c=\underset{i=1}{\overset{p}{\Σ}}\frac{P_{oi}\left(u_i\right)}{{\mathrm{\η}}_{oi}\left(u_i\right)}-\underset{i=1}{\overset{q}{\Σ}}{P}_{ii}\left(u_i^{\′}\right){\mathrm{\η}}_{ii}\left(u_i^{\′}\right).---\left(6\right)$

P_oiAnd η_oiRepresent output and and efficiency, and the P of driver under the first drive pattern respectively_iiAnd η_iiThen distinguish Represent input power and the efficiency of driver under the second drive pattern.