CN106130125B - Electric car fuzzy sliding mode feedback charge controller and its feedback charge control method - Google Patents
Electric car fuzzy sliding mode feedback charge controller and its feedback charge control method Download PDFInfo
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- CN106130125B CN106130125B CN201610614853.3A CN201610614853A CN106130125B CN 106130125 B CN106130125 B CN 106130125B CN 201610614853 A CN201610614853 A CN 201610614853A CN 106130125 B CN106130125 B CN 106130125B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/14—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from dynamo-electric generators driven at varying speed, e.g. on vehicle
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J2207/20—Charging or discharging characterised by the power electronics converter
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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
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention discloses a kind of electric car fuzzy sliding mode feedback charge controller and its feedback charge control methods.Electric car fuzzy sliding mode feedback charge controller of the present invention; including storage battery charge controller; the output end of storage battery charge controller is sequentially connected driving/isolation circuit, regenerative braking charging system, sensing acquisition circuit, and sensing acquisition circuit finally connects back to storage battery charge controller.Feedback charge control method of the present invention includes system model stage when establishing DC/DC converter feedback charging, predicted value stage, the fuzzy sliding mode charge controller comprehensive design stage for establishing feedback charging lower DC/DC converter T-S fuzzy model stage, calculating converter duty ratio.Control method of the present invention has very strong robustness, can recycle more braking energies, can effectively improve the course continuation mileage number of electric car；Controller architecture of the present invention is simple, low in cost, high reliablity.
Description
Technical field
The invention belongs to electric car charge control technical fields, and in particular to a kind of two-way DC/DC change for electric vehicle
The T-S fuzzy variable structure feedback charge control method and Fuzzy Sliding Model Controller of parallel operation.
Background technique
Pure electric automobile is a kind of green traffic tool of zero emission, zero pollution, in national new-energy automobile support policy
Support under, increasingly by the welcome of consumer.But bring is continuous due to by battery capacity deficiency and charging modes inconvenience
The mileage that navigates is insufficient, then seriously restricts the development of pure electric automobile.In the case where battery technology bottleneck is difficult to substantive breakthroughs,
Feedback charging based on energy regeneration has obtained widely as a kind of technology for effectively extending pure electric automobile course continuation mileage number
Research.
Technically, the realization of feedback charging is it needs to be determined that power converter topologies structure, design charge control method.
When feedback is charged, since back-emf amplitude is generally below battery open-circuit voltage, the main work of DC/DC converter in electric car
It is used as booster converter and output voltage is promoted to sufficiently high level；And when normal driving, by the energy in battery with
Desired size is supplied to motor so that vehicle driving is advanced.Currently, when not needing to design individual control circuit, greatly
Most automobile-used DC/DC converters can not achieve two-way flow of the energy between battery and motor；Minority is able to achieve two-way flow
Converter lead to that structure is complicated, increased costs due to needing more circuit element.
And on charge control method, it largely remains in and needs the Traditional control of mathematical models technical.So
And it relies solely on traditional control means and is difficult to obtain higher control performance.This is because：On the one hand, include motor, become
The charging system of parallel operation, battery and car body has complicated electromechanical Coupled Dynamics feature, in electric car operation, motor
The factors such as parameter, traveling road conditions, payload size, driving mode and cell voltage are all variations, it is difficult to establish its accurate number
Learn model.On the other hand, most charge controls are all based on small-signal analysis method, i.e., at some stable operating point into
Row linearization process, it is difficult to meet linearity requirements of the system under a wide range of speed, global working environment.At the same time, electric
Electrical automobile is under complicated driving cycle, because of temperature rise, vehicle parameter uncertainty, uncertain input voltage, output loading variation
Equivalent load resistance variation with when battery charging, causes traditional controller performance to decline, control system lacks robustness.
Therefore, the feedback charge control side that design structure is simple, control precision is high, stability is strong and high efficiency of energy utilizes
Method, it is significant to the safety, stability and the course continuation mileage number that improve electric automobile during traveling.
Summary of the invention
The first purpose of this invention is to provide a kind of electric car fuzzy sliding mode feedback charge controller, the electronic vapour
Vehicle fuzzy sliding mode feedback charge controller has the advantages that control precision height, strong robustness, structure are simple, so as to effectively mention
The course continuation mileage number of high electric car.
Above-mentioned purpose of the invention is realized by the following technical solutions：The electric car fuzzy sliding mode feedback is filled
Electric controller, it includes storage battery charge controller, and the output end of storage battery charge controller is sequentially connected driving/isolation electricity
Road, regenerative braking charging system, sensing acquisition circuit, sensing acquisition circuit finally connect back to storage battery charge controller.
Specifically, the storage battery charge controller is realized by digital signal processor DSP (TMS320F2812).
Specifically, the regenerative braking charging system includes a battery, four current sensor SA1~SA4, two
Voltage sensor SV1, SV2, storage capacitor C, two-way DC/DC converter and star-like connection dc motor；Battery it is upper
End series-connected current sensors SA1 posterior end parallel connection is connected to voltage sensor SV1；Storage capacitor C is in parallel with voltage sensor SV2,
Again with release can resistance R connect after it is in parallel with voltage sensor SV1；Two-way DC/DC converter is made of six power tube T1~T6,
Upper bridge arm be T1, T3 and T5, lower bridge arm T4, T2 and T6, every bridging connect midpoint distinguish series-connected current sensors SA2, SA3, SA4
It is connect afterwards with the phase winding of dc motor.
Specifically, driving/the isolation circuit includes photoelectric isolating device U1 and drive control circuit U2, photoisolator
Part U1 uses photoelectric coupling chip 4N25, drive control circuit U2 to use chip I R2122S.
Specifically, the sensing acquisition circuit includes voltage, current sensor and two channel operation amplifier U4, U5, electricity
Pressure, current sensor use HAS200-P, and operational amplifier uses LF353.
Second object of the present invention is to provide returning based on above-mentioned electric car fuzzy sliding mode feedback charge controller
Present charge control method, this method include establish DC/DC converter feedback charging when the system model stage, establish feedback charging
Under the DC/DC converter T-S fuzzy model stage, calculate predicted value stage of converter duty ratio, fuzzy sliding mode charge control
The device comprehensive design stage；
(1) described in establish DC/DC converter feedback charging when the system model stage the step of it is as follows：
(1) system model of charging circuit is calculated；
Under feedback charging situation, using with the same set of DC/DC translation circuit in the case of normal driving；Using six pipe full-bridges
Pulse width modulation, is not necessarily to extra circuits unit；Mathematical model is analyzed by taking A phase and B phase as an example, opens in switch, turn off
In the case of two kinds, research control target to charge the battery voltage, electric current when system mathematical model, with output be battery charge
Voltage y=Vo(t) illustrate for；Writ state variable x=[iL vc]T, the system model of circuit provides by following equations：
Matrix under opening, turning off in formula is respectively
CON=[0 RbR-1],
COFF=[RbRcR-1 RbR-1], fON=fOFF=RcR-1vb；Coefficient a1=-(2Rm+Rs+Rd)/2Lm,
a2=-1/CR, a3=-(Rm+Rd)/Lm-RcRb/(2LmR), a4=Rb/(2LmR), g1=-a2vb,
g2=-Rc/(2LmR)vb；Symbol Lm is winding inductance, RmIt is winding resistance, RsAnd RdIt is power switch respectively and continuous
Flow the conducting resistance of diode, C and RcIt is battery DC side capacitors capacitor and dead resistance, R respectivelybIt is battery equivalent internal resistance,
R=Rc+Rb, vcIt is the voltage drop on capacitor, vbIndicate cell emf, ibFor the feedback charging current for flowing through battery, eabFor
Two phase winding counter electromotive force, voFor output voltage；
Output equation in circuit model is charging voltage equation, and when output is charging current, output equation is rewritten
For io=-R-1vc+R-1vb；
(2) space State Average Model is calculated；
On circuit model both sides respectively multiplied by PWM duty cycle d (t) and d'(t)=1-d (t), and handling averagely is carried out,
Acquiring space State Average Model is：
Matrix in formula xm、ymIt is the state variable average value in single PWM cycle respectively
And output voltage average value；For giving duty ratioIt enablesIt can acquireThe state for locating quiescent point becomes
Measuring steady-state value is：
In formulaWithRespectively indicate xmAnd ymSteady-state value；Due in operating pointThere are the dry of small signal at place
It disturbs, then the instantaneous value of variable can be written as：Wherein d (t), x
(t), y (t) is variable instantaneous value,For small signal disturbance；
(3) the small signal of state space and integration control model are calculated；
Steady-state variable and transient state variable are isolated using small signal disturbance analysis method, ignores the secondary or more of disturbance quantity
Higher order term, acquiring state space small-signal model is：
Matrix in formula
For State space averaging and state space two kinds of models of small signal, duty ratio stable state at operating point is all relied on
ValueFor the zero steady track error for realizing output voltage, integrating state variable is introduced：xe=∫ edt=∫ (yr-ym) dt,
Tracking error e=y in formular-ym, yrIt is expected output voltage；State space small-signal model is rewritten as following integration control mould
Type：
In formulaFor augmented state variable, control input is duty ratio transient valueMatrix
In vehicle travel process, with the variation of operating point, the duty ratio of converterIt changes therewith, so as to cause
The zero pole point and amplitude-frequency response of state space small-signal model transmission function change, thus state space small-signal model is
The nonlinear function of duty ratio, charge control system are a Nonlinear Uncertain Systems；
(2) the step of DC-DC converter T-S fuzzy model established under feedback charging is as follows：
(1) T-S fuzzy model；Nonlinear system is approached using T-S fuzzy technology, for i-th of operating point, using as follows
IF-THEN rule describes nonlinear state space small-signal model：
I-th model rule：If z1It (t) is F1 i, and z2(t) it is..., and zn(t) it isSo：
In formulaFor fuzzy set, z (t)=[z1,…,zn] it is former piece variable,It is shape
State error,It is control input, AiAnd BiFor to set matrix, regular number i=1,2 ..., r；Fuzzy weighting valueFi j[zjIt (t)] >=0 is zj(t) right under i-th fuzzy rule
The degree of membership answered, and haveBased on single-point fuzzification, product inference and weighted average anti fuzzy method, global mould
Fuzzy model is：
(2) after the various interference and uncertainty that are subject to when considering charging, Parameter uncertainties fuzzy model is：
Δ A in formulaiWith Δ BiFor the matching uncertainties of parameter,Indicate input and load disturbance；Assuming that：(i)
There are certainty function ΜA(t), ΜB(t) and Μw(t) make Δ Ai=BiΜA(t), Δ Bi=BiΜB(t) andSet up；(ii) system control matrix is unsatisfactory for B1=B2=...=Br；Rewrite Parameter uncertainties mould
Type is as follows：
In formula： External disturbanceIt there will necessarily be one
Know normal number ηBSo that 0≤| | ΜB||≤ηB<1, and continuous positive function ηA、ηwSo that It can then calculateThe upper bound of norm is
(3) determination of T-S model；[0,1] between the global work area of vehicle driving-cycle is divided into 7 sub-spaces, point
It is not：[0,0.2], [0.2,0.3], [0.3,0.4], [0.4,0.5], [0.5,0.6], [0.6,0.7], [0.7,1]；Each
A steady operation point is chosen in subspace, determines 7 stable operating points as the following formula：
D in formulaiFor the subspace upper bound, diFor subspace lower bound；It is obtained at above-mentioned steady operation point using T-S modeling method
7 linear submodels are described as following T-S fuzzy rule：
I-th object-rule：Ifis Fi, then
F in formulai(i=1~7) are fuzzy sets,
(3) the step of predicted value stage for calculating converter duty ratio is as follows：
Output voltage-input voltage transfer ratioIt is the nonlinear function of duty ratio, due to having strong non-linear spy
Property and parameter uncertainty, predict duty ratio using T-S fuzzy close method；Prediction process is as follows：Firstly, by transfer ratio area
Between be divided into 12 subinterval (S1,S2,…,Sn), each subinterval SiDefine an affine function；Then, this affine letter is utilized
Number calculates the duty ratio predicted value on each subintervalFinally, will using T-S technologyIt joins together, calculates
Global duty cycle
T-S fallout predictor is the affine function of a single-input single-output, is inputted as α=yr/eabAnd meet 1≤α≤M, wherein M
For transfer ratio functionMaximum value；Enable fiIt isiThe output (1≤i≤12) in a subinterval, form are：fi(α)=ai
α+bi, wherein ai, biFor constant；Then desired output voltage is yrWhen, T-S fallout predictor fuzzy rule is：
I-th fallout predictor rule：If α is SiSo fi(α)=aiα+bi；
S in formulaiFor i-th of fuzzy set, f is exported to the affine function on each subintervaliCarry out center is average, weights
Anti fuzzy method, then global fuzzy output beμ in formulai(α) is α in fuzzy son
Collect SiOn subordinating degree function, and have
(4) the step of fuzzy sliding mode charge controller comprehensive design stage is as follows：
(1) Integral Sliding Mode diverter surface is designed based on sliding mode control theory and Lyapunov method：
Constant λ in formula>0 is integral gain, sliding formwork coefficient S ∈ Rm×n, the selection needs of sliding mode control theory requirement coefficient S
Ensure the presence of Equivalent Sliding Mode control amount, i.e. matrixIt must be reversible；For this purpose, being based on Lyapunov
Method provides the calculation method of coefficient S and sliding-mode surface；
Based on Lyapunov method, if following linear inequalities
There are feasible solution (Q, α, β, μ), then designing sliding formwork coefficient is S=(BTQ-1B)-1BTQ-1, wherein invertible matrix Q ∈ Rn ×n, decision variable α, β, μ ∈ R, K are the orthogonal complement matrix of matrix B, λBIt is matrix BTThe minimal eigenvalue of B, and meet λBI≤
BTB, the transposition of mark " * " representing matrix corresponding position element；The advantages of choosing Integral Sliding Mode face is to can guarantee closed-loop control system
The stable state charging voltage error of system is zero；
(2) it is designed as based on sliding mode control theory and Lyapunov method design control law, Lyapunov function：V2=σTσ
The derivative of >=0, Lyapunov function against time is：
In order to protect
CardDesign following fuzzy sliding mode tracking control rule：
I-th control rule：IfisSo
In formula, ξ=ηB+τ+ηBτ, sliding formwork handoff gain Constant εi>0, sgn (σ) is sign function；Designing global fuzzy sliding mode charge controller is：
Based on Lyapunov stability law, at this timeIt demonstrates when using the overall situation
Fuzzy sliding mode charge controller when, control system feedback charging voltage error will be asymptotically convergent to zero.
Compared with the prior art and controller, the advantages of control method of the present invention and Fuzzy Sliding Model Controller, is embodied in as follows
Several points：
(1) present invention is filled due to using the feedback that T-S method constitutes motor in electric automobile, DC/DC converter, battery
Electric system carries out obscurity model building, so that the overall situation under vehicle complexity operating condition, large-scale linearization modeling can be realized, and model
Accuracy is high.
(2) present invention solves Traditional control side under a variety of driving cycles due to using fuzzy sliding mode variable structure control
Method is because when vehicle parameter is uncertain, speed changes caused uncertain input voltage, output loading variation and battery charging
Equivalent load resistance variation caused by controller performance decline, feedback charge control method of the invention have very strong robust
Property, more braking energies can be recycled.
(3) present invention does not need to change existing electric vehicle controller in structure, additional hard without increasing
Part circuit is realized and is only relied under one group of six pipe full bridge PWM control, two-way flow of the energy between motor and battery, structure letter
Single, low in cost, high reliablity.
Detailed description of the invention
Fig. 1 is the theory structure block diagram of fuzzy sliding mode feedback charging method of the embodiment of the present invention.
Fig. 2 is the circuit diagram of battery regenerative braking charging system 3 in Fig. 1.
Fig. 3 is equivalent circuit diagram when switch T4 is opened in Fig. 2.
Fig. 4 is equivalent circuit diagram when switch T4 is turned off in Fig. 2.
Fig. 5 is the subordinating degree function figure of Global fuzzy model in Fig. 1.
Fig. 6 is the linear world model's illustrative view of functional configuration of T-S of regenerative braking charging system 3 in Fig. 1.
Fig. 7 is the subordinating degree function figure of fuzzy predictor in Fig. 1.
Fig. 8 is pwm signal driving/isolation circuit 4 circuit diagram in Fig. 1.
Fig. 9 is the circuit diagram of sensing acquisition circuit 2 in Fig. 1.
Figure 10 is the flow chart of feedback charge control method of the embodiment of the present invention.
Figure 11~Figure 13 is the step response curve figure of the method for the present invention charge control effect.
Figure 14~Figure 16 is the sinusoidal response curve graph of the method for the present invention charge control effect.
In figure, 1 is the storage battery charge controller based on DSP, and 2 be sensing acquisition circuit, and 3 be regenerative braking Charging
System, 4 be driving/isolation circuit.
Specific embodiment
Fig. 1 to Figure 16 and specific embodiment charge to the method for the present invention and fuzzy sliding mode feedback with reference to the accompanying drawings of the specification
Controller is described in detail.
The present embodiment method includes：It establishes system model stage when DC/DC converter feedback is charged, establish feedback charging
Under the DC/DC converter T-S fuzzy model stage, calculate predicted value stage of converter duty ratio, fuzzy sliding mode charge control
The device comprehensive design stage.
Specifically follow the steps below：
One, system model when DC/DC converter feedback charging is established
Under feedback charging situation, the present embodiment is used and the same set of DC/DC translation circuit in the case of normal driving.Adopt
The DC/DC translation circuit that (PWM) is modulated with six pipe full-bridge pulse widths, without extra circuits unit.With small signal point
Analysis method be means, determine motor, DC/DC converter, battery composition system energy regeneration feedback charging under desired electrical
Road model.When feedback is charged, mathematical model is analyzed by taking A phase and B phase as an example, at this time counter electromotive force eabIt is equivalent to voltage source, is switched
T4 makees periodical pulsewidth modulation, other switches are turned off.When speed is constant, back-emf eabAmplitude maintain it is constant.It is switching
In the case of T4 is opened, is turned off two kinds, research control target to charge the battery voltage, electric current when system mathematical model.
1, during T4 is opened：
Referring to Fig. 3, T4 conducting at this time, other switches are disconnected, and winding inductance Lm absorbs the energy discharged from counter electromotive force,
So that winding terminal voltage rises, electric current iLSwitch T4 and sustained diode 2 are flowed through, as shown in Figure 1.In a PWM cycle TsIt is interior,
According to Kirchhoff's law, to control target to charge the battery for voltage, circuit state equation is：
Wherein Lm is winding inductance, RmIt is winding resistance, RsAnd RdIt is the electric conduction of power switch and freewheeling diode respectively
Resistance, C and RcIt is battery DC side capacitors capacitor and dead resistance, R respectivelybIt is battery equivalent internal resistance, R=Rc+Rb, vcIt is capacitor
Voltage drop on device, vbIndicate cell emf, ibFor the feedback charging current for flowing through battery, eabIt is anti-electronic for two phase windings
Gesture, voFor output voltage.Output equation in formula (1) is charging voltage equation, when output is charging current, output equation
I can be readily rewritten aso=-R-1vc+R-1vb。
2, during T4 shutdown：
Referring to fig. 4, when winding terminal voltage rise enough to height, turn off all switches, start to battery charge.Electric current iLStream
Sustained diode 1 and D2 are crossed, thus brshless DC motor energy feeding telegram in reply pond, as shown in Figure 2.Likewise, circuit state side
Cheng Wei：
Writ state variable x=[iL vc]T, export as battery charging voltage y=vo(t), by state equation and output equation
(1), (2) are converted into following form：
Matrix under opening, turning off in formula is respectively CON=[0 RbR-1], COFF=[RbRcR-1 RbR-1], fON
=fOFF=RcR-1vb；Coefficient a1=-(2Rm+Rs+Rd)/2Lm, a2=-1/CR, a3=-(Rm+Rd)/Lm-RcRb/(2LmR), a4=
Rb/(2LmR), g1=-a2vb, g2=-Rc/(2LmR)vb。
On equation (3) both sides respectively multiplied by the duty ratio d (t) of PWM modulation and d'(t)=1-d (t), and average
Processing, acquiring space State Average Model is：
Matrix in formula xm、ymState variable average value and output voltage average value in a respectively PWM cycle.For
Some given duty ratioIt enablesQuiescent point can be acquiredThe state variable steady-state value at place is
In formulaWithRespectively indicate xmAnd ymSteady-state value.Due in operating pointPlace there are small signal interference,
Then the instantaneous value of variable can be written as：
Wherein d (t), x (t), the instantaneous value that y (t) is variable,For the small signal disturbance amount of variable.
The steady-state value and instantaneous value that variable is isolated using small signal disturbance analysis method, ignore the secondary of disturbance quantity and with
Upper higher order term, acquiring state space small-signal model is：
Matrix in formula(7) two kinds of models of formula (4) and formula are established a capital really dependent on duty ratio stable state
Value After calculating, then calculate operating point steady-state valueFor the zero steady track error for realizing output voltage, introduce
Following integrating state variable：
xe=∫ edt=∫ (yr-ym)·dt (8)
Tracking error e=y in formular-ym, yrIt is expected output voltage.Then state space small-signal model (7) be rewritten as
Lower integral Controlling model：
In formulaFor augmented state variable, control input is duty ratio instantaneous valueMatrix
In vehicle travel process, with the variation of operating point, converter duty ratioValue changes therewith, so as to cause
The pole, a Right-half-plant zero and amplitude-frequency response of formula (7) transmission function change, thus state space small-signal model
It is the nonlinear function of duty ratio, integration control model is a uncertain nonlinear system.
Two, the DC/DC converter T-S fuzzy model under feedback charging is established
In the case where electric car speed and load wide variation, the small signal mode of the state space of feedback charging system
Type is the nonlinear function of duty ratio, and classical control theory is difficult to that this strong nonlinearity is effectively treated to control performance bring not
Benefit influences.Takagi-Sugeno (hereinafter referred to as T-S) fuzzy technology is that a kind of pair of nonlinear system carries out the effective of obscurity model building
Means, key Design thought be by nonlinear system by non-linear subordinating degree function approximate description be local linear subsystem
Smooth weighted sum, being theoretically proved this is approximately consistent asymptotic expansion, and its stability analysis can be by Lyapunov
Method directly proves.
The present embodiment chooses N number of stable operating point, and being divided between the global work area of vehicle driving-cycle, N number of son is empty
Between, N number of linear submodel at equalization point is obtained using T-S modeling method, by Parameter Perturbation, input voltage and output loading
Variation, equivalent load resistance changing factor are all regarded as system interference, then go out global linearization using calculated with weighted average method
Model.
1. theoretical according to T-S fuzzy close, the integration control model of DC/DC converter feedback charging system can be by T-S
Fuzzy system is infinitely approached, and for i-th of operating point, describes state space small-signal model using following IF-THEN rule
Linear submodel：
I-th model rule:If z1(t) it isAnd z2(t) it is..., and zn(t) it isSo
In formulaFor fuzzy set, z (t)=[z1,…,zn] it is former piece variable,It is state error,It is control input, AiAnd BiFor to set matrix, regular number i=1,2 ..., r.
Ambiguity in definition weight hi[z (t)] can be abbreviated as hi(z)：
F in formulai j[zjIt (t)] >=0 is zj(t) the corresponding degree of membership under i-th fuzzy rule, and have
Based on single-point fuzzification, product inference and weighted average anti fuzzy method, can be calculated entirely by formula (10) and formula (11)
Office fuzzy model be：
2. system (12) is further after considering the various interference being subject to when electric car described previously charging and uncertainty
It is rewritten as：
Δ A in formulaiWith Δ BiFor matched parameter uncertainty,Indicate input and load disturbance.
Assuming that：(i) there are certainty function ΜA(t), ΜB(t) and Μw(t) make Δ Ai=BiΜA(t), Δ Bi=Bi
ΜB(t) andSet up；(ii) system control matrix is unsatisfactory for B1=B2=...=Br, for i=1,
2,…,r.Then system (9) is rewritten into：
Matrix in formula
Then meetExternal disturbance functionIt there will necessarily be known to one
Normal number ηBSo that 0≤| | ΜB||≤ηB<1 and two continuous positive function ηA, ηwSo that Function can then be calculatedThe norm upper bound is
The determination of 3.T-S model.N=7 is enabled in the present embodiment, and [0,1] between the global work area of vehicle driving-cycle is drawn
It is divided into 7 sub-spaces, respectively：[0,0.2], [0.2,0.3], [0.3,0.4], [0.4,0.5], [0.5,0.6], [0.6,
0.7], [0.7,1].It is corresponding that a steady operation point is chosen in every sub-spaces, i.e., 7 steady operations are determined as the following formula
Point：D in formulaiFor the subspace upper bound, diFor subspace lower bound.It is modeled using T-S
Method obtains 7 linear submodels at above-mentioned steady operation point, is described as following T-S fuzzy rule：
I-th object-rule：Ifis Fi, then
F in formulai(i=1~7) are fuzzy sets,
In the present invention, the subordinating degree function of Global fuzzy model is as shown in figure 5, Fig. 6 is the T-S of regenerative braking charging system
Linear world model functional structure.
Three, the predicted value of converter duty ratio is calculated
Output voltage-input voltage transfer ratioIt is the nonlinear function of duty ratio, i.e.,：
Then give expectation charging voltage yr, the duty ratio of PWM modulation can be calculated by the equationDue to transfer ratio letter
NumberWith strong nonlinear characteristic, and there are parameter uncertainties, and the present embodiment is using T-S fuzzy close method come pre-
Survey duty ratio.Prediction process is as follows：Firstly, being 12 subintervals by transfer ratio interval division, each subinterval defines one and imitates
Penetrate function；Then, the duty ratio predicted value on each subinterval is calculated using this affine functionFinally, utilizing T-S skill
Art will be on all subintervalsIt joins together, calculates global duty ratio
T-S fallout predictor is substantially a single-input single-output process, and output function is affine function, enables fiIt is i-th
The output function (1≤i≤12) in subinterval, form is as follows：
fi(α)=aiα+bi (15)
A in formulai, biFor constant, α=yr/eabInput and satisfaction 1≤α≤M for T-S fallout predictor, wherein M is transfer ratio letter
NumberMaximum value.Transfer ratio value interval [1, M] is divided into 12 subintervals：(S1,S2,…,Sn)。
Then desired output voltage is yrWhen, T-S fallout predictor fuzzy rule is：
I-th fallout predictor rule：If α is Si
So fi(α)=aiα+bi
S in formulaiFor i-th of fuzzy set, i=1,2 ... 12, the affine function output on each subinterval carried out
The heart is average, weights anti fuzzy method, then global fuzzy output is
α is former piece variable, μ in formulai(α) is α in fuzzy subset SiOn subordinating degree function, and haveIn the present invention, the degree of membership letter of fuzzy predictor
Number is as shown in Figure 7.
Four, fuzzy sliding mode feedback charge controller comprehensive design
In the case where electric car speed and load wide variation, in feedback charging system there are it is a plurality of types of not
Certainty, to bring adverse effect to the control performance of charging system.Firstly, since winding back-emf and electric car power generation
Machine revolving speed is proportional, and automobile torque and the variation of speed are the severe jammings in feedback charge control system；Secondly, the parameter of electric machine
There is certain uncertainty under different temperature rises with winding electrical variable；Finally, although the load resistance of battery is being cut
Changing in interval can be considered constant, but the equivalent load resistance of battery will be with the variation of charging voltage and battery charge (SOC)
And change, so that there are the disturbances of biggish load resistance.In the present embodiment, the T-S Fuzzy Sliding Model Controller of robust is designed, with
Overcome above-mentioned probabilistic influence, realizes high performance constant voltage, current feedback charging.
Using sliding moding structure technology, the sliding-mode surface of the present embodiment design is chosen, is asked using linear matrix inequality approach
Sliding formwork coefficient and controller gain are solved, realizes T-S Design of Fuzzy sliding mode controller.The design of sliding mode controller is set including sliding-mode surface
Two meter, design of control law steps.
1. designing integral form obscures sliding-mode surface：
In formulaFor sliding-mode surface, constant λ>0 is integral gain, sliding formwork coefficient S ∈ Rm×n, Sliding mode variable structure control reason
By requiring the selection of S to be necessary to ensure that the presence of Equivalent Sliding Mode control amount, i.e. matrixIt must be reversible.For
This, theorem 1 gives the calculation method and existence proof of S and sliding-mode surface.The present embodiment chooses the reason of Integral Sliding Mode face, is
It can guarantee the advantages of stable state charging voltage error of closed-loop control system is zero using it.
Theorem 1：For the fuzzy system (14) of transducer status space small-signal model, the integral form such as formula (17) is designed
Fuzzy sliding mode face.Make (18) if there is feasible solution (Q, α, β, μ), the linear inequality (LMIs) in (19) and (20) formula at
Vertical, then designing sliding formwork coefficient is S=(BTQ-1B)-1BTQ-1。
Invertible matrix Q ∈ R in formulan×n, decision variable α, β, μ ∈ R, K are the orthogonal complement matrix of matrix B, λBIt is matrix BTB
Minimal eigenvalue, and meet λBI≤BTB, the transposition of mark " * " representing matrix corresponding position element.
It proves:The existence of sliding formwork coefficient S is proved first.According to Schur theorem, by linear inequality (18), (19) and
(20) it can must release
Order matrix G=BTQ-1, S=(GB)-1GQ,It is easy to derive τ ∈ R1×1；By formula (12), haveWithIt sets up.Following inequality can be then released to set up
Again depending on Schur theorem, by linear inequality (18), (19) are derived
0<β-1I<Q<α I, 0<α-1I<Q-1<βI (23)
Then have：
By formula (24) and inequality (19), and notice inequality(a, b are nonnegative number in formula), can obtain
Can be obtained by equation (25) | | τ | |<1, to demonstrate matrixIt is nonsingular, matrix
It is reversible.This shows that sliding-mode surface and equivalent control amount exist when choosing sliding formwork coefficient by theorem 1, once system mode enters
Sliding-mode surfaceInterior, the dynamic of fuzzy system (14) is equivalent to the sliding formwork movement of depression of order, is
The state trajectory of system also goes to zero asymptotic.
It is defined as follows linear transformation T, system mode x is decomposed into the sliding formwork state of m rank and the depression of order shape of (n-m) rank
State：
T in formula1∈R(n-m)×n, T2∈Rm×n, matrix K meets BTK=0, KTK=I, be easy to calculate the inverse of linear transformation is
T-1=[Q-1K B].After linear transformation, new state, which is calculated, is：
By formula (27), it is known thatFor sliding formwork state, z1For reduced order state, and the dynamic side of state z
Cheng Wei：
T can be calculated by formula (26)1B=(KTQ-1K)-1KTB=0 and T2B=I is enabled according to sliding mode control theoryThen calculating equivalent control amount is：
By the equivalent control amount in formula (29) in people's formula (28), can obtain：
In formula：
Carry out sliding formwork in analysis mode (30) below to moveStability.Define Lyapunov function
Wherein matrix P=KTQK>0.To function V1Derivation obtains：
Due toFormula (31) are substituted into, can be obtained：
W=Kz in formula1∈Rn×1.By inequality (22), (24) andIt can obtain：
Define vectorV ∈ R in formula(n-m)×nFor any vector, then qiIt can
It is rewritten as
By formula (34), can obtain：
In formulaFor any vector v, enableB=qi, W=μ2Ω-1, can obtain：
By inequality (25), (26) can be calculated
Again depending on Schur theorem, inequality (36) is equivalent to linear inequality (37)：
This demonstrate that the integral form that the present embodiment defines obscures the existence of sliding-mode surface and equivalent control amount, and sliding formwork moves
It is asymptotically stable.
2. design of control law.
Controller design process is design control law in next step, in the present embodiment, designs following fuzzy sliding mode tracking control rule
Then：The
I item control rule：Ifis
So
In formula, ξ=ηB+τ+ηBτ, sliding formwork handoff gainMeet constraint condition：
Constant εi>0, sgn (σ) is sign function.
The global fuzzy sliding mode tracking control of design, which is restrained, is
Based on Lyapunov stability law, theorem 2 is demonstrated when using controller (41), control system feedback charging
Voltage error will be asymptotically convergent to zero.
Theorem 2：For the fuzzy system (14) of transducer status space small-signal model, the integral designed using formula (17)
Pattern pastes sliding-mode surface, and when using controller (41), system trajectory will be driven on sliding-mode surface, and system will be asymptotic problem
's.
It proves：It is proved based on sliding mode control theory and Lyapunov method, Lyapunov function is designed as：V2=σTσ >=0,
The derivative of Lyapunov function against time is：By inequalityWith controller formula
(41) it substitutes into, can be obtained after being computed：
Due to εi>0, it is obtained by formula (42)This shows as σ (t) ≠ 0, under the action of controller (41), system shape
State will be driven into design integral form obscure sliding-mode surface on, that is, enter sliding formwork move, closed-loop system be it is asymptotically stable,
Feedback charging voltage error will level off to zero.
Fig. 1 is the theory structure block diagram of the present embodiment electric car fuzzy sliding mode feedback charge control method.The present embodiment
The structure of fuzzy sliding mode feedback charge controller, including storage battery charge controller 1, the output end of storage battery charge controller 1
It is sequentially connected driving/isolation circuit 4, regenerative braking charging system 3, sensing acquisition circuit 2, sensing acquisition circuit 2 finally connects
Return storage battery charge controller 1.
On the one hand battery provides driving energy as power source for electric automobile during traveling, on the other hand store electronic vapour
Feedback energy when vehicle regenerative braking；Storage battery charge controller and battery, two-way DC/DC converter, sensing circuit and electricity
It is electrically connected between machine using cable bus；By controlling the power switch tube of two-way DC/DC converter, battery charging electricity is realized
The tracing control of pressure, electric current makes feedback energy keep relative constant and smooth, recycles the braking energy of electric car；Battery
Charge controller is using battery charging voltage and electric current as control target, charging security energy with higher and electrical energy
Efficiency of transmission, burst feedback energy when well-tolerated electric vehicle brake, to extend the mileage travelled of electric car.
Storage battery charge controller 1 is constituted by dsp processor control circuit board is integrated, and dsp processor passes through sensing acquisition
2 collection voltages of circuit and current signal and pedal signal；Dsp processor is modulated from PWM1~PWM6 pin output pulse width
(PWM) signal, overdrived/isolation circuit 4 control two-way DC converter (DC/DC) six power tubes switch motion；
External interrupt pin 1 (XINT1) the response brake pedal of DSP steps on movement, 2/3 stroke before pedal, it is expected that the electricity that charges
Pressure, electric current and pedal-displacement are linear；In rear 1/3 stroke of pedal, to protect battery not overcharged, it is expected that charging
Voltage, electric current no longer increase with pedal-displacement and are increased, and are set to maximum charging voltage, electric current, and keep constant.
It further include fuzzy predictor, space State Average Model, global mould in the functional structure of storage battery charge controller 1
The modules such as fuzzy model and fuzzy sliding mode charge controller are completed by dsp software algorithm；DSP obtains pedal via interrupt mode
The displacement of braking and desired charging voltage, electric current, the steady-state value of PWM modulation wave duty ratio is estimated using fuzzy predictorThen, on the one hand accumulator voltage is combined to calculate state-space model, obtains state steady-state valueOn the other hand it combines
State variable measured value xmWith tracking error xe, Global fuzzy model is calculated, state variable small signal value is obtainedFinally, by
The control method of the present embodiment calculates duty ratio instantaneous valueWith steady-state valueIt is added, it is defeated from PWM1~PWM6 pin of DSP
Modulation waveform out controls six pipe switch motions of two-way DC/DC converter through overdriving with isolation circuit 4, carries out constant pressure, constant current
Charging.
The circuit structure for the regenerative braking charging system that the present embodiment is related to is shown in Fig. 2, including a battery, four electric currents
Sensor SA1~SA4, two voltage sensor SV1~SV2, storage capacitor C, two-way DC/DC converter and star-like connection it is straight
Galvanic electricity motivation；The upper end posterior end the series-connected current sensors SA1 parallel connection of battery is connected to voltage sensor SV1；Storage capacitor C with
Voltage sensor SV2 is in parallel, then in parallel with voltage sensor SV1 after energy resistance R connects with releasing；Two-way DC/DC converter is by six
A power tube T1~T6 composition, upper bridge arm are T1, T3 and T5, lower bridge arm T4, T2 and T6, and every bridging connects midpoint and distinguishes series electrical
It is connect after flow sensor SA2~SA4 with the phase winding of motor.
When electric car works in regenerative braking operating condition, battery charging, two-way DC/DC converter works in reversed boosting
State, upper bridge arm power tube S1, S3 and S5 chopping modulation, flows to battery after feeding braking energy back is boosted and fills for it at this time
Electricity；Each bridge of two-way DC/DC converter staggeredly works within a period, is respectively connected 120 degree phases, i.e. S1 and S4 work 0~
120 degree, S3 and S2 work at 120~240 degree, and S5 and S6 work at 240~360 degree；To realize high performance perseverance when making charging
Pressure charging, takes Fuzzy Sliding Model Controller, and six switching tubes for controlling two-way DC/DC converter carry out independent pulse width tune
System.
Fig. 8 is the circuit diagram of driving/isolation circuit 4, and driving/isolation circuit 4 is by photoelectric isolating device U1 and drive control
Circuit is constituted, and photoelectric isolating device U1 uses photoelectric coupling chip 4N25, drive control circuit U2 to use chip I R2122S, DSP
The modulated signal of processor pin PWM1~PWM6 output controls the two-way each function of DC/DC converter after driving/isolation circuit
The switch motion of rate pipe.
Fig. 9 is sensing acquisition circuit diagram, by voltage, current sensor and two channel operation amplifier U4, U5 groups on circuit
At voltage, current sensor realize that operational amplifier is realized using LF353 using HAS200-P；Operational amplifier U5B acquisition
Hall voltage signal, current signal, and be sent into dsp processor via external interrupt ADCINT0~ADCINT5 pin.
The electric car fuzzy sliding mode feedback charge controller that the present embodiment is related to is realized to battery constant pressure, constant-current charge
The method of control is as shown in Figure 10, includes the following steps in process：
(1) storage battery charge controller power supply, automatic initial survey are connected, and enters state to be charged；
(2) storage battery charge controller is initialized, parameter matrix A is inputtedi、Bi、EiAnd Di, and calculate matrix
(3) judge whether battery needs to charge by DSP with interrupt mode response brake pedal signal；If desired it charges,
Then desired charging voltage, current value y are calculated according to brake pedal displacementr；Otherwise, turn (2) step；
(4) judge that LMI (18)~(20) feasible solution whether there is by theorem 1, enter if it exists in next step；Otherwise, turn
(2) step；
(5) steady-state value of PWM modulation wave duty ratio is estimated by fuzzy predictor
(6) byState-space model is calculated with accumulator voltage, obtains state steady-state value
(7) bonding state variable measurements xmWith tracking error xe, Global fuzzy model is calculated, it is small to obtain state variable
Signal value
(8) duty ratio instantaneous value is calculated by control method of the inventionWith steady-state valueIt is added, from the PWM mouth of DSP
Modulation waveform is exported, controls six pipe switch motions of two-way DC/DC converter with isolation circuit through overdriving, carries out constant pressure, perseverance
Current charge.
Figure 11~Figure 13 is the step response curve figure of the present embodiment method charge control effect.Wherein accumulator internal resistance Rb
It was doubled at 0.015 second by nominal value, returns to nominal value within 0.025 second；The corresponding back-emf value e of speedabIncreased at 0.03 second+
20%, -20% was reduced at 0.035 second；The desired value and y of charging voltagerFor under step signal, Figure 11, Figure 12, Figure 13 give respectively
Charging voltage aircraft pursuit course, the duty ratio curve of winding current curve and controller output are gone out；As seen from the figure, the rank of this method
The response time is jumped less than 0.015 second, steady track error can overcome accumulator parameter perturbation to bring well less than 0.76%
Performance decline.
Figure 14~Figure 16 is the sinusoidal response curve graph of the present embodiment method charge control effect.At this point, in consecutive variations
Desired output voltage under, the steady operation point of DC/DC converter is also in corresponding change.Wherein accumulator internal resistance was at 0.015 second
It is doubled by nominal value, returns to nominal value within 0.025 second；The corresponding back-emf value of speed increased+20% at 0.03 second,
Reduce -20% within 0.035 second；The desired value of charging voltage is under step signal, and charging electricity is set forth in Figure 14, Figure 15, Figure 16
Press aircraft pursuit course, the duty ratio curve of winding current curve and controller output；As seen from the figure, this method can overcome vehicle well
Speed variation, desired signal transformation bring performance decline, and all have at different steady operation points that steady track error is small, Shandong
The strong feature of stick.
By emulation and actual tests, the charging performance meet demand of the present embodiment method and controller, charging effect is enabled
People is satisfied, the braking energy of electric car can be recycled well, to effectively extend battery-operated service life and garage
Sail mileage number.
Claims (1)
1. a kind of feedback charge control method of electric car fuzzy sliding mode feedback charge controller, the electric car mould being based on
Paste sliding formwork feedback charge controller, it includes storage battery charge controller (1), the output end of storage battery charge controller (1) according to
Secondary connection driving/isolation circuit (4), regenerative braking charging system (3), sensing acquisition circuit (2), sensing acquisition circuit (2) is most
After connect back to storage battery charge controller (1)；The regenerative braking charging system (3) includes a battery, four electric currents biographies
Sensor SA1~SA4, two voltage sensor SV1, SV2, storage capacitor C, two-way DC/AC converter and star-like connection it is brushless
Dc motor；The upper end posterior end the series-connected current sensors SA1 parallel connection of battery is connected to voltage sensor SV1；Storage capacitor C
It is in parallel with voltage sensor SV2 then in parallel with voltage sensor SV1 after energy resistance R connects with releasing；Two-way DC/AC converter by
Six power tube T1~T6 composition, upper bridge arm are T1, T3 and T5, lower bridge arm T4, T2 and T6, and every bridging connects midpoint and connects respectively
It is connect after current sensor SA2, SA3, SA4 with the phase winding of dc motor；Driving/the isolation circuit (4) includes photoelectricity
Isolating device U1 and drive control circuit U2, photoelectric isolating device U1 use photoelectric coupling chip 4N25, drive control circuit U2
Using chip I R2122S；The sensing acquisition circuit (2) include voltage, current sensor and two channel operation amplifier U4,
U5, voltage, current sensor use HAS200-P, and operational amplifier uses LF353；
It is characterized in that：It include establish DC/DC converter feedback charging when the system model stage, establish feedback charging under
DC/DC converter T-S fuzzy model stage, the predicted value stage for calculating converter duty ratio, fuzzy sliding mode charge controller are comprehensive
Close the design phase；
(1) described in establish DC/DC converter feedback charging when the system model stage the step of it is as follows：
(1) system model of charging circuit is calculated；
Under feedback charging situation, using with the same set of DC/DC translation circuit in the case of normal driving；Using six pipe full-bridge pulses
Width modulated is not necessarily to extra circuits unit；Mathematical model is analyzed by taking A phase and B phase as an example, opens in switch, turn off two kinds
In the case of, research control target to charge the battery voltage, electric current when system mathematical model, to export as battery charging voltage
Y=vo(t) illustrate for；Writ state variable x=[iL vc]T, the system model of circuit provides by following equations：
Matrix under opening, turning off in formula is respectively CON=[0 RbR-1], COFF=[RbRcR-1 RbR-1], fON=fOFF=RcR-1vb；Coefficient a1=-(2Rm+Rs+Rd)/2Lm, a2=-1/CR, a3=-(Rm+Rd)/Lm-RcRb/
(2LmR), a4=Rb/(2LmR), g1=-a2vb, g2=-Rc/(2LmR)vb；Symbol Lm is winding inductance, RmIt is winding resistance, Rs
And RdIt is the conducting resistance of power switch and freewheeling diode, C and R respectivelycIt is battery DC side capacitors capacitor and parasitism respectively
Resistance, RbIt is battery equivalent internal resistance, R=Rc+Rb, vcIt is the voltage drop on capacitor, vbIndicate cell emf, ibTo flow through electricity
The feedback charging current in pond, eabFor two phase winding counter electromotive force, voFor output voltage；
Output equation in circuit model is charging voltage equation, and when output is charging current, output equation is rewritten as io
=-R-1vc+R-1vb；
(2) space State Average Model is calculated；
On circuit model both sides respectively multiplied by PWM duty cycle d (t) and d'(t)=1-d (t), and handling averagely is carried out, it acquires
Space State Average Model is：
Matrix in formula xm、ymIt is that state variable in single PWM cycle is average respectively
Value and output voltage average value；For giving duty ratioIt enablesIt can acquireLocate the state of quiescent point
Variable steady-state value is：
In formulaWithRespectively indicate xmAnd ymSteady-state value；Due in operating pointPlace then becomes there are the interference of small signal
The instantaneous value of amount can be written as：Wherein d (t), x (t), y (t)
For variable instantaneous value,For small signal disturbance；
(3) the small signal of state space and integration control model are calculated；
Steady-state variable and transient state variable are isolated using small signal disturbance analysis method, ignores the secondary and above high-order of disturbance quantity
, acquiring state space small-signal model is：
Matrix in formula
For State space averaging and state space two kinds of models of small signal, duty ratio steady-state value at operating point is all relied on
For the zero steady track error for realizing output voltage, integrating state variable is introduced：xe=∫ edt=∫ (yr-ym) dt, in formula
Tracking error e=yr-ym, yrIt is expected output voltage；State space small-signal model is rewritten as following integration control model：
In formulaFor augmented state variable, control input is duty ratio transient valueMatrix
In vehicle travel process, with the variation of operating point, the duty ratio of converterIt changes therewith, so as to cause state
The zero pole point and amplitude-frequency response of space small-signal model transmission function change, thus state space small-signal model is duty
The nonlinear function of ratio, charge control system are a Nonlinear Uncertain Systems；
(2) the step of DC-DC converter T-S fuzzy model established under feedback charging is as follows：
(1) T-S fuzzy model；Nonlinear system is approached using T-S fuzzy technology, for i-th of operating point, using following IF-
THEN rule describes nonlinear state space small-signal model：
I-th model rule：If z1It (t) is F1 i, and z2(t) it is..., and zn(t) it isSo：
In formulaFor fuzzy set, z (t)=[z1,…,zn] it is former piece variable,It is state error,It is control input, AiAnd BiFor to set matrix, regular number i=1,2 ..., r；Fuzzy weighting valueFi j[zjIt (t)] >=0 is zj(t) right under i-th fuzzy rule
The degree of membership answered, and haveBased on single-point fuzzification, product inference and weighted average anti fuzzy method, global mould
Fuzzy model is：
(2) after the various interference and uncertainty that are subject to when considering charging, Parameter uncertainties fuzzy model is：
Δ A in formulaiWith Δ BiFor the matching uncertainties of parameter,Indicate input and load disturbance；Assuming that：(i) exist
Certainty function ΜA(t), ΜB(t) and Μw(t) make Δ Ai=BiΜA(t), Δ Bi=BiΜB(t) andSet up；(ii) system control matrix is unsatisfactory for B1=B2=...=Br；Rewrite Parameter uncertainties mould
Type is as follows：
In formula： External disturbanceIt there will necessarily be one
Know normal number ηBSo that 0≤| | ΜB||≤ηB< 1, and continuous positive function ηA、ηwSo that It can then calculateThe upper bound of norm is
(3) determination of T-S model；[0,1] between the global work area of vehicle driving-cycle is divided into 7 sub-spaces, respectively：
[0,0.2], [0.2,0.3], [0.3,0.4], [0.4,0.5], [0.5,0.6], [0.6,0.7], [0.7,1]；It is empty in every height
Between one steady operation point of middle selection, as the following formula determine 7 stable operating points：
D in formulaiFor the subspace upper bound, diFor subspace lower bound；7 at above-mentioned steady operation point are obtained using T-S modeling method
A linear submodel is described as following T-S fuzzy rule：
I-th object-rule：Ifis Fi, thenF in formulai(i=1~7) are fuzzy
Set,
(3) the step of predicted value stage for calculating converter duty ratio is as follows：
Output voltage-input voltage transfer ratioThe nonlinear function of duty ratio, due to strong nonlinear characteristic and
Parameter uncertainty predicts duty ratio using T-S fuzzy close method；Prediction process is as follows：Firstly, transfer ratio section is drawn
It is divided into 12 subinterval (S1,S2,…,Sn), each subinterval SiDefine an affine function；Then, this affine function meter is utilized
Calculate the duty ratio predicted value on each subintervalFinally, will using T-S technologyIt joins together, calculates complete
Office's duty ratio
T-S fallout predictor is the affine function of a single-input single-output, is inputted as α=yr/eabAnd meet 1≤α≤M, wherein M is to pass
It is defeated to compare functionMaximum value；Enable fiFor the output (1≤i≤12) in i-th of subinterval, form is：fi(α)=aiα+
bi, wherein ai, biFor constant；Then desired output voltage is yrWhen, T-S fallout predictor fuzzy rule is：
I-th fallout predictor rule：If α is SiSo fi(α)=aiα+bi；
S in formulaiFor i-th of fuzzy set, f is exported to the affine function on each subintervaliCarry out center is average, weights reverse
It is gelatinized, then global fuzzy output isμ in formulai(α) is α in fuzzy subset
SiOn subordinating degree function, and have
(4) the step of fuzzy sliding mode charge controller comprehensive design stage is as follows：
(1) Integral Sliding Mode diverter surface is designed based on sliding mode control theory and Lyapunov method：
Constant λ in formula>0 is integral gain, sliding formwork coefficient S ∈ Rm×n, sliding mode control theory require coefficient S selection be necessary to ensure that
The presence of Equivalent Sliding Mode control amount, i.e. matrixIt must be reversible；For this purpose, being based on Lyapunov method
Provide the calculation method of coefficient S and sliding-mode surface；
Based on Lyapunov method, if following linear inequalities
There are feasible solution (Q, α, β, μ), then designing sliding formwork coefficient is S=(BTQ-1B)-1BTQ-1, wherein invertible matrix Q ∈ Rn×n, certainly
Plan variable α, β, μ ∈ R, K are the orthogonal complement matrix of matrix B, λBIt is matrix BTThe minimal eigenvalue of B, and meet λBI≤BTB, note
The transposition of number " * " representing matrix corresponding position element；The advantages of choosing Integral Sliding Mode face is can guarantee closed-loop control system steady
State charging voltage error is zero；
(2) it is designed as based on sliding mode control theory and Lyapunov method design control law, Lyapunov function：V2=σTσ >=0,
The derivative of Lyapunov function against time is：
In order to guaranteeDesign following fuzzy sliding mode tracking control rule：
I-th control rule：IfSo
In formula, ξ=ηB+τ+ηBτ, sliding formwork handoff gain Constant εi> 0, sgn (σ) are sign function；Designing global fuzzy sliding mode charge controller is：
Based on Lyapunov stability law, at this timeIt demonstrates and is obscured when using global
When sliding formwork charge controller, control system feedback charging voltage error will be asymptotically convergent to zero.
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