CN106338264B - The method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG position sensors - Google Patents
The method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG position sensors Download PDFInfo
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- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
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Abstract
The present invention discloses a kind of method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG position sensors, the electric energy exported by accumulator provides current value i after power inverter for switching magnetic-resistance BSG, real-time magnetic linkage value ψ and current value i are inputted into wavelet neural network position estimator, the rotor position angle of wavelet neural network position estimator output estimationBy the rotor position angle of estimationWith actual rotor position angle θ as input signal input fault diagnostic module, fault diagnosis module pairWith θ residual error R is obtained as residual noise reductioni, by residual error RiWith the threshold value T of settingiIt compares and carrys out failure judgement type；The collecting sample of input is trained using wavelet neural network algorithm, make full use of quick self study, height robustness and the fault-tolerant ability of the good Time-Frequency Localization characteristic of wavelet transformation and neural network, it is compared with traditional neural network, convergence rate is speeded, and accuracy rate rises.
Description
Technical field
The invention belongs to technical field of hybrid power, specifically hybrid vehicle belt driven type starting-generating all-in-one machine
The fault diagnosis technology of (hereinafter referred to as BSG) position sensor.
Background technology
Hybrid vehicle has outstanding advantages of high efficiency, low stain, and generator is the key that hybrid vehicle portion
One of part, it is desirable that power generation function is efficient, reliablely and stablely works.Traditional automobile starter and generator is separated two portions
Part, and BSG rolls into one starter and generator, when automobile start moment, BSG quickly drags engine as starter and arrives
Idling speed, when automobile normal running or deceleration, BSG charges as generator to automobile power source and electrical equipment.BSG takes
For orthodox car generator, engine design not only can be simplified, reduce car weight, and fuel consumption and dirt can be reduced
Dye discharge.Currently, BSG is mostly mixed excitation claw-pole motor, magneto and induction machine, however for mixed excitation claw-pole electricity
Machine, obtains that high torque (HT) is more difficult and complex rotor structure in low speed, is unfavorable for high-speed cruising；For magneto, due to existing
Permanent-magnet material, so of high cost and under high temperature and high magnetic field environments stability is difficult to ensure；For induction machine, speed governing
Performance is poor, is not easy to be precisely controlled, more demanding to the control system of motor.Switched reluctance machines are with its jail simple in structure
Gu, at low cost and high reliability, be suitable for high-speed cruising and adverse circumstances, used by BSG, referred to as switching magnetic-resistance
BSG。
For switching magnetic-resistance BSG, to detect its rotor-position, position sensor, position sensing are had mounted thereto
Device exports commutation information.Position sensor is the key that the correct commutations of switching magnetic-resistance BSG, if position signal breaks down, electricity
The phase change logic of machine will get muddled, and the reduction of motor torque fan-out capability, motor speed is caused to decline or be zero.Therefore, it is
Improve switching magnetic-resistance BSG systems reliability, make motor correct rotor-position export commutation information, to position sensor into
Row fast accurate earth fault checkout and diagnosis is necessary.
The fault type of position sensor includes：Entirely ineffective failure, accuracy decline, droop failure and drift bias
Failure.First two fault type is easier discovery and can be with timely processing, but droop failure and drift bias failure are
Do not allow detectable failure, a series of problems that can not be estimated can be caused during failure occurs, keep control system long-term
It cannot normally play a role.
In existing position sensor fault diagnosis technology, the mostly method for diagnosing faults based on analytic modell analytical model, such side
Method generates residual error using neural network failure observer, then is analyzed residual error and diagnosed fault.However it is based on analytic modell analytical model
Method for diagnosing faults need to establish the mathematical expression of system model, and this is for serious nonlinear switched Reluctance Motor Control
It is almost difficult to realize for system, and neural network failure observer needs sample of the system under various malfunctions to carry out
Training, however during practical control, fault sample extremely lacks.
Wavelet neural network is the neural network model built based on wavelet transformation, it can absorb wavelet transformation when
Domain and frequency domain have many advantages, such as good local characteristic, and neural network can be utilized to have adaptive, self study, robustness,
So that wavelet neural network is able to extensive use in pattern-recognition, nonlinear science, fault diagnosis etc..
Existing flux measurement technology mostly uses indirect measurement scheme, i.e., by measuring the voltage and current of certain phase winding
Calculating flux linkage characteristic is connect, flux linkage calculation formula is：ψ in formulai(t) it is a certain
The magnetic linkage of phase, ui、ii、RiThe voltage, electric current and resistance of a certain phase are indicated respectively.However this method is difficult to meet real-time online inspection
Survey the practical engineering application of magnetic linkage.
Invention content
In response to the problems existing in the prior art, the purpose of the present invention is to provide a kind of hybrid vehicle switching magnetic-resistance BSG
The method for diagnosing faults of position sensor, real-time, quick, accurately diagnostic position sensor failure, is switching magnetic-resistance BSG systems
The work of system high efficient and reliable provides safeguard.
The technical solution that the method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG position sensors of the present invention uses
It is：Position sensor detects and exports the actual rotor position angle θ of switching magnetic-resistance BSG, by the electric energy of accumulator output through work(
Current value i is provided for switching magnetic-resistance BSG after rate converter, it is further comprising the steps of：
A, to being serially connected in the output end of magnetic linkage acquisition module after wavelet neural network position estimator off-line training, using magnetic
Chain acquisition module obtains the real-time magnetic linkage value ψ of switching magnetic-resistance BSG；
B, wavelet neural network position estimation is inputted using the real-time magnetic linkage value ψ and current value i as input signal
Device, wavelet neural network position estimator export the rotor position angle of estimation after handling ψ and i
C, by the rotor position angle of estimationWith actual rotor position angle θ mould is diagnosed as input signal input fault
Block, fault diagnosis module pairWith θ residual error R is obtained as residual noise reductioni, by residual error RiWith the threshold value T of settingiIt compares to judge event
Hinder type.
Further, in step C, if Ri≤Ti, then judge that position sensor works normally；If Ri＞ Ti, then judge position
Sensor is faulty.
If residual error RiAt a time tiOccur being more than threshold value T lateriAnd RiIt is worth color constancy, then judges position sensor
Moment t occurs in failureiDroop failure has occurred；If residual error RiAt a time tiOccur being more than threshold value T lateriAnd RiWith
TiThe increasing phenomenon of difference, then judge position sensor failure occur moment tiDrift bias failure has occurred.
Further, in step A, the method to wavelet neural network position estimator off-line training is：First obtain switch
The relation curve of magnetic linkage-electric current-rotor position angle under magnetic resistance BSG normal operating conditions, composition original training set { ia, ib, ic,
id, ψa, ψb, ψc, ψd, θa, θb, θc, θd, to original training set off-line training；ia, ib, ic, idIndicate switching magnetic-resistance BSG's respectively
A, the phase current of B, C, D phase, ψa, ψb, ψc, ψdThe magnetic linkage of A, B, C, D phase of switching magnetic-resistance BSG, θ are indicated respectivelya, θb, θc, θdPoint
Not Biao Shi switching magnetic-resistance BSG A, B, C, D phase position angle.
The beneficial effects of the invention are as follows：
1, the present invention is trained the collecting sample of input using wavelet neural network algorithm, makes full use of wavelet transformation
Quick self study, height robustness and the fault-tolerant ability of good Time-Frequency Localization characteristic and neural network, with tradition god
It is compared through network, convergence rate is speeded, and accuracy rate rises.Meanwhile only needing the normal shape of system in training wavelet neural network
Collecting sample under state overcomes the problem that position sensor fault sample lacks.
2, magnetic linkage is directly measured using the Magnetic Sensor on switching magnetic-resistance BSG and is obtained using magnetic linkage acquisition module
Real-time magnetic linkage value, can rapidly and accurately input real time data online, highly practical, have extensive engineering application value.
3, the present invention is using the position angle of wavelet neural network position estimator output and by position sensor output
Actual rotor position angle carries out residual noise reduction, and the threshold comparison of obtained remaining precision and setting carrys out failure judgement type.It should
Method control principle is simple, and only needs to realize by software programming, is not necessarily to other hardware devices, at low cost, is easy to engineering
It realizes.
Description of the drawings
Fig. 1 is the position sensor fault diagnosis system block diagram of hybrid vehicle switching magnetic-resistance BSG；
Fig. 2 is the flow chart of the position sensor method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG of the present invention；
Fig. 3 is the flux linkage characteristic curve graph of a certain phases of switched reluctance machines BSG in Fig. 1；
Fig. 4 is that droop fault graph occurs for switching magnetic-resistance BSG in Fig. 1；
Fig. 5 is that drift bias fault graph occurs for switching magnetic-resistance BSG in Fig. 1.
In Fig. 1：1. measurand；2. rotor-position signal is estimated and fault diagnosis module；3. accumulator；4. power conversion
Device；5. engine；6. clutch；7. speed changer；11. switching magnetic-resistance BSG；12. position sensor；21. wavelet neural network position
Set estimator；22. magnetic linkage acquisition module；23. fault diagnosis module；24. Magnetic Sensor.
Specific implementation mode
As shown in Figure 1, switching magnetic-resistance BSG11 and position sensor 12 form the measurand 1 of fault diagnosis, position sensing
Device 12 is mounted on switching magnetic-resistance BSG11, the rotor-position of detection switch magnetic resistance BSG11, output actual rotor position angle θ letters
Number to rotor-position signal estimation and fault diagnosis module 2.Rotor-position signal estimation and fault diagnosis module 2 are connected on tested
Behind the position sensor 12 of object 1, the actual rotor position angle θ signals that position sensor 12 exports are received, to diagnose position
Set the fault type of sensor 12.
Switching magnetic-resistance BSG11 is separately connected engine 5 and power inverter 4, and engine 5 passes through clutch 6 and speed changer 7
It is connected, power inverter 4 is DC/DC converters, connection automobile power source accumulator 3.When automobile start moment, switching magnetic-resistance
BSG11 works as starter, and the electric energy that accumulator 3 exports at this time carries after the effect of power inverter 4 for switching magnetic-resistance BSG11
For suitable current value i, Simultaneous Switching magnetic resistance BSG11 is that engine 5 provides rotary power ω, and rotary power ω is again via clutch
Device 6 and the starting of 7 rear-drive automobile of speed changer；When automobile normal running or deceleration, switching magnetic-resistance BSG11 is as generator work
Make, switching magnetic-resistance BSG11 receives the rotary power ω of the offer of engine 5 and generates electricity at this time, and obtained current value i becomes via power
Parallel operation 4 is that accumulator 3 provides electric energy after acting on.
Rotor-position signal is estimated and fault diagnosis module 2 obtains mould by wavelet neural network position estimator 21, magnetic linkage
Block 22, fault diagnosis module 23 and Magnetic Sensor 24 form.
Wavelet neural network position estimator 21 is trained using the method for off-line training.Switching magnetic-resistance BSG11 is logical
It crosses electromagnetism farm software ANSOFT and establishes finite element model, obtain magnetic linkage-electric current-under switching magnetic-resistance BSG11 normal operating conditions
Thus the relation curve of rotor position angle, relation curve as shown in Figure 3 form original training set { ia, ib, ic, id, ψa,
ψb, ψc, ψd, θa, θb, θc, θd, wherein ia, ib, ic, idThe phase current of A, B, C, D phase of switching magnetic-resistance BSG11, ψ are indicated respectivelya,
ψb, ψc, ψdThe magnetic linkage of A, B, C, D phase of switching magnetic-resistance BSG11, θ are indicated respectivelya, θb, θc, θdSwitching magnetic-resistance BSG11 is indicated respectively
A, B, C, D phase position angle, original training set is trained using the method for off-line training.Wavelet neural network position
The wavelet neural network of estimator 21 uses four-layer structure, respectively includes input layer, output layer and hidden layer, hidden layer is selected as two
Layer structure, the determination method of number of nodes are：The number of nodes for choosing two hidden layers first is all 8, then uses and gradually increases
Method and gradually pruning method are gradually added and are deleted the node number of each hidden layer by experiment, finally obtain the section of 2 layers of hidden layer
Points are respectively 10 and 8.In addition Mexican hat (sombrero) wavelet functions is used to swash as the neuron of hidden layer node
Function is encouraged, which is represented by：H (x) is Mexican hat wavelet function in formula, and x is that the time is normal
Amount.After determining the structure of wavelet neural network, wavelet neural network is trained using Powell (Bao Weier) algorithm,
Powell algorithms can be expressed as：Mk+1=Mk-[KTK+μI]-1KTD, wherein Mk+1For the wavelet neural when iterations are k+1
The vector that the entirety of the weight coefficient of network is formed；MkFor the entirety of the weight coefficient of wavelet neural network when iterations are k
The vector formed；The vector that M is made of the entirety of the weight coefficient of wavelet neural network；μ=104For quality coefficient；K is to work as
Preceding iterations；K is the Jacobian matrix of the first derivative of the error of wavelet neural network weight coefficient；KTFor wavelet neural network
Jacobi's transposed matrix of the first derivative of the error of weight coefficient；T is matrix transposition；I indicates unit matrix；D is wavelet neural
Error of the network for weight coefficient.The calculation formula of each element is in Jacobian matrix K：
Wherein i=1,2,3 ..., m, m are input variable number；J=1,2,3 ..., n, n are input variable number；Kij(Mk+1) it is k+1
The function of moment Jacobian matrix K, fi(Mk) be k moment wavelet neural network desired output and reality output difference, fi(Mk+1)
For the difference of k+1 moment wavelet neural network desired output and reality output,For the weight coefficient of k moment wavelet neural networks
The vector that entirety is formed,The vector being made of the entirety of the weight coefficient of k+1 moment wavelet neural networks.
The output end of Magnetic Sensor 24 is connected with the input terminal of magnetic linkage acquisition module 22, trained wavelet neural network
Position estimator 21 is serially connected in the output end of magnetic linkage acquisition module 22.Magnetic Sensor 24 is mounted on switching magnetic-resistance BSG11, directly
The real-time magnetic linkages of switching magnetic-resistance BSG11 are measured, the real-time magnetic linkage value ψ of switching magnetic-resistance BSG11 is obtained using magnetic linkage acquisition module 22.
Using the magnetic linkage value ψ that magnetic linkage acquisition module 22 exports and the current value i that power inverter 4 exports as trained Wavelet Neural Network
The input signal of network position estimator 21, magnetic linkage value ψ and current value i are defeated after the processing of wavelet neural network position estimator 21
Go out the rotor position angle of estimation
Fault diagnosis module 23 is by the rotor position angle of estimationThe actual rotor position angle exported with position sensor 12
θ is spent as input signal, and by the rotor position angle of estimationResidual noise reduction is carried out with actual rotor position angle θ, will be obtained
Residual error RiWith the threshold value T of settingiIt compares and carrys out failure judgement type, referring to Fig. 2.
Fault diagnosis module 23 defines residual errorF represents R in formulaiWithFunctional relation, by RiWith certainly
Determine the threshold value T of 12 normal condition of position sensor and malfunctioniIt is compared.If Ri≤Ti, then may determine that position sensor
12 normal works；If Ri＞ Ti, then may determine that position sensor 12 is faulty.Referring to Fig. 4, if residual error RiAt a time ti
Occur being more than threshold value T lateriAnd RiIt is worth color constancy, then may determine that in failure moment t occurs for position sensor 12iIt has occurred
Droop failure.Referring to Fig. 5, if residual error RiAt a time tiOccur being more than threshold value T lateriAnd RiWith TiDifference increasingly
Big phenomenon then judges that in failure moment t occurs for position sensor 12iDrift bias failure has occurred.For different faults type
Fault alarm is set, to diagnose fault type real-time.
According to the above, the present invention can be realized.To those skilled in the art in the spirit without departing substantially from the present invention
Other changes and modifications with being made in the case of protection domain, are still included within the scope of the present invention.
Claims (4)
1. a kind of method for diagnosing faults of hybrid vehicle switching magnetic-resistance BSG position sensors, position sensor detection is simultaneously defeated
The actual rotor position angle θ for going out switching magnetic-resistance BSG is switching magnetic-resistance after power inverter by the electric energy of accumulator output
BSG provides current value i, it is characterized in that further comprising the steps of：
A, it to being serially connected in the output end of magnetic linkage acquisition module after wavelet neural network position estimator off-line training, is obtained using magnetic linkage
Modulus block obtains the real-time magnetic linkage value ψ of switching magnetic-resistance BSG；
Method to wavelet neural network position estimator off-line training is：First obtain under switching magnetic-resistance BSG normal operating conditions
Magnetic linkage-electric current-rotor position angle relation curve, composition original training set { ia, ib, ic, id, ψa, ψb, ψc, ψd, θa, θb, θc,
θd, to original training set off-line training；ia, ib, ic, idThe phase current of A, B, C, D phase of switching magnetic-resistance BSG, ψ are indicated respectivelya,
ψb, ψc, ψdThe magnetic linkage of A, B, C, D phase of switching magnetic-resistance BSG, θ are indicated respectivelya, θb, θc, θdIndicate respectively switching magnetic-resistance BSG A,
B, the position angle of C, D phase；The wavelet neural network of wavelet neural network position estimator includes input layer, output layer and implies
Layer, hidden layer is double-layer structure, in addition uses wavelet function h (x)=(1-x2)e-x/2Neuron as hidden layer node encourages
Function, x are time constant,
B, wavelet neural network position estimator is inputted using the real-time magnetic linkage value ψ and current value i as input signal, it is small
Wave neural network position estimator exports the rotor position angle of estimation after handling ψ and i
C, by the rotor position angle of estimationWith actual rotor position angle θ as input signal input fault diagnostic module, therefore
Hinder diagnostic module pairWith θ residual error R is obtained as residual noise reductioni, by residual error RiWith the threshold value T of settingiIt compares and carrys out failure judgement class
Type.
2. method for diagnosing faults according to claim 1, it is characterized in that：In step C, if Ri≤Ti, judge that position passes
Sensor works normally；If Ri ＞ Ti, judge that position sensor is faulty；If residual error RiAt a time tiIt is more than later
Threshold value TiAnd RiIt is worth color constancy, then judges that in failure moment t occurs for position sensoriDroop failure has occurred；If residual error
RiAt a time tiOccur being more than threshold value T lateriAnd RiWith TiThe increasing phenomenon of difference, then judge position sensor therefore
Moment t occurs for barrieriDrift bias failure has occurred.
3. method for diagnosing faults according to claim 1, it is characterized in that：Using algorithm Mk+1=Mk-[KTK+μI]-1KTD pairs
Wavelet neural network is trained, Mk+1For when iterations are by k+1 the entirety of the weight coefficient of wavelet neural network form
Vector；MkFor the vector that the entirety of the weight coefficient of wavelet neural network forms when iterations are by k；K is Wavelet Neural Network
The Jacobian matrix of the first derivative of the error of network weight coefficient；T is matrix transposition；μ=104For quality coefficient；I is unit square
Battle array；D is error of the wavelet neural network for weight coefficient.
4. method for diagnosing faults according to claim 3, it is characterized in that：The meter of each element in the Jacobian matrix K
Calculating formula isM, n is input variable number；
Kij(Mk+1) be k+1 moment Jacobian matrixes K function, fi(Mk) it is k moment wavelet neural network desired outputs and reality output
Difference, fi(Mk+1) be k+1 moment wavelet neural network desired output and reality output difference,For k moment wavelet neural networks
Weight coefficient the vector that is formed of entirety, Mk+1By k+1 moment wavelet neural networks weight coefficient entirety form to
Amount.
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| CN107132414B (en) * | 2017-05-26 | 2019-06-18 | 北京新能源汽车股份有限公司 | Method for detecting looseness of motor position sensor, motor controller and automobile |
| US10230321B1 (en) * | 2017-10-23 | 2019-03-12 | General Electric Company | System and method for preventing permanent magnet demagnetization in electrical machines |
| CN108880394A (en) * | 2018-06-04 | 2018-11-23 | 江苏大学 | A kind of wavelet neural network position-less sensor of switched reluctance motor forecast Control Algorithm |
| CN109781399A (en) * | 2019-02-27 | 2019-05-21 | 天津大学 | A kind of air cooling refrigeration unit sensor fault diagnosis method neural network based |
| CN110341503B (en) * | 2019-06-03 | 2020-09-01 | 中国矿业大学 | Integrated switched reluctance motor driving system of plug-in hybrid electric vehicle |
| CN110417318B (en) * | 2019-06-25 | 2021-09-10 | 苏州伟创电气科技股份有限公司 | Protection method and device for alternating current permanent magnet synchronous motor |
| CN117879446B (en) * | 2024-01-12 | 2024-06-25 | 南京航空航天大学 | Double-salient motor position sensor fault diagnosis method based on non-conducting phase current |
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