CN106647280A - HEV (hybrid electric vehicle) mode switching data driving prediction controller design method and HEV mode switching data driving prediction controller design system - Google Patents

HEV (hybrid electric vehicle) mode switching data driving prediction controller design method and HEV mode switching data driving prediction controller design system Download PDF

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CN106647280A
CN106647280A CN201710028468.5A CN201710028468A CN106647280A CN 106647280 A CN106647280 A CN 106647280A CN 201710028468 A CN201710028468 A CN 201710028468A CN 106647280 A CN106647280 A CN 106647280A
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data
hybrid vehicle
driven
mode switching
predictive
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CN106647280B (en
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孙静
邢国靖
张承慧
王殿涛
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YANTAI DONGFANG WISDOM ELECTRONIC CO Ltd
Shandong University
Shandong Technology and Business University
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YANTAI DONGFANG WISDOM ELECTRONIC CO Ltd
Shandong University
Shandong Technology and Business University
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention discloses an HEV mode switching data driving prediction controller design method. The HEV mode switching data driving prediction controller design method comprises steps that a whole hybrid electric vehicle simulation model is established in vehicle simulation software; motor torque, engine torque, and clutch torque are designed as excitation data, and the designed excitation data is used for the established hybrid electric vehicle simulation model; the input and output data block Hankel matrix of the hybrid electric vehicle is established based on the acquired open loop data, and then a prediction output equation of a hybrid electric vehicle mode switching system is established based on an identification model; an increment of an input sequence, an output sequence, and a future input sequence of a data driving prediction controller are defined, and the prediction output equation is expressed in an increment way; the data driving prediction controller is solved in an on-line manner according to a corresponding input constraint and a corresponding output constraint. Calculation amount is reduced, and a modeling error problem caused by a switching process from the data identification model to a state space model is prevented.

Description

The data-driven Design of Predictive method and system of HEV mode switching
Technical field
The invention belongs to field of hybrid electric vehicles, more particularly to a kind of data-driven PREDICTIVE CONTROL of HEV mode switching Device method for designing and its system.
Background technology
The English full name of HEV is Hybrid Electric Vehicle, and Chinese is:Hybrid vehicle.
With energy shortage and the aggravation of problem of environmental pollution, hybrid vehicle (Hybrid Electric Vehicle, HEV) the fuel consumption and emission performance of vehicle can be greatly improved, enough continual mileages are can guarantee that again, be A kind of feasible program for reducing Fossil fuel consumption and carbon emission effective in the near future, it has also become current study hotspot and main flow Direction.
Current some advanced theories, such as:Fuzzy self-adaption sliding-mode method, model matching control, model reference control, cut The methods such as control, Model Predictive Control are changed, this domain variability is applied to and is achieved many achievements in research, but these methods are all Based on mechanism model, its control effect depends critically upon the accuracy of modeling.And HEV power assembly system complex structures, and And system performance and each parameters of operating part will change (for example, the damping system of axle with the long-term ageing of HEV and the change of driving conditions Great changes have taken place for several changes meetings with ambient temperature), therefore, it is difficult to setting up accurate mechanism model to it.Enable and set up description The global mathematical models of HEV power assembly systems, surely in high-order nonlinear.High-order nonlinear model is difficult with based on mechanism The control method of model is processed.I.e. enable is processed, and the controller for also necessarily causing high-order complicated, operand is quite huge, The rapidity and requirement of real-time of HEV can not be met.In view of this, existing research has all been done at certain simplification in modeling Reason, but certainly will there is many Unmarried pregnancies, cause control effect not good.
The content of the invention
In order to solve the shortcoming of prior art, the first object of the present invention is to provide a kind of data of HEV mode switching and drives Dynamic Design of Predictive method.
A kind of data-driven Design of Predictive method of the HEV mode switching of the present invention, including:
Step 1:Whole hybrid vehicle phantom is built in automobile simulation software;
Step 2:According to the dynamic characteristic of hybrid vehicle mode switching system, design motor torque, motor torque With clutch torque as excited data, and designed excited data is acted on into built hybrid vehicle emulation mould Type;
Step 3:Data block Hankel of input and the output of hybrid vehicle is built based on the open loop data of collection Matrix, and then the prediction output equation of hybrid vehicle mode switching system is constructed based on identification model;
Step 4:Define hybrid vehicle mode switching system the increment of list entries, output sequence and future it is defeated Enter sequence, prediction output equation is stated with incremental form;
Step 5:According to corresponding input constraint and output constraint, during building the pure electronic switching to hybrid mode The cost function of optimum control, and the prediction output equation based on incremental form statement is come line solver data-driven PREDICTIVE CONTROL Device.
The hybrid vehicle phantom includes engine block, motor module, clutch module, change speed gear box mould Block, battery module, tire module, differential module and longitudinal direction of car kinetics.
The hybrid vehicle phantom of the present invention is the whole vehicle model that clutch enables single shaft parallel connection HEV, and the HEV is whole Vehicle model can well portray the Transient Dynamics characteristic of vehicle, for example, the lag characteristic of engine output torque, clutch Torsional shaking, the vibration characteristics of drive shaft and the slip rate characteristic of tire etc..The HEV includes parking, pure electronic, combined drive Dynamic, pure engine driving, braking energy feedback, charging multiple-working mode.
Wherein, the data-driven forecast Control Algorithm of data-driven predictive controller is by Subspace Identification and model prediction control Make the composition that combines.The present invention combines to realize data-driven PREDICTIVE CONTROL using Subspace Identification and Model Predictive Control Method, eliminates in the method based on mechanism model to the solution of state space equation, has both reduced amount of calculation, turn avoid from The modeling error problem introduced when data identification is to state-space model.
In the step 3, according to the prediction output equation of hybrid vehicle mode switching system, distinguished using subspace Knowledge method is estimating the following output valve of hybrid vehicle mode switching system.
The method, using simple, ensure that the robustness of numerical value without the need for advance model parameterization.
Cost function is made up of two parts, wherein, the Part I of cost function forces clutch slip speed convergence to arrive 0, so as to realizing rapidly pattern switching and reducing clutch abrasion;Cost function Part II control motor torque, from Clutch torque and the rate of change of motor torque, for the comfortableness of Assured Mode switching.
The second object of the present invention is to provide a kind of data-driven Design of Predictive system of HEV mode switching.
A kind of data-driven Design of Predictive system of the HEV mode switching of the present invention, including:
Hybrid vehicle phantom builds module, and it is used to build whole hybrid vehicle phantom;
Hybrid vehicle mode switching system builds module, and it is used for according to hybrid vehicle mode switching system Dynamic characteristic, design motor torque, motor torque and clutch torque as excited data, and by designed excited data Act on built hybrid vehicle phantom;
Prediction output equation constructing module, it is used to build the input of hybrid vehicle based on the open loop data of collection With the data block Hankel matrix of output, and then the prediction of hybrid vehicle mode switching system is constructed based on identification model Output equation;
Incremental form statement prediction output equation module, it is used to define the input of hybrid vehicle mode switching system The list entries of the increment, output sequence and future of sequence, prediction output equation is stated with incremental form;
Data-driven predictive controller solves module, and it is used for according to corresponding input constraint and output constraint, builds pure The cost function of the optimum control during the electronic switching to hybrid mode, and the prediction output side stated based on incremental form Journey carrys out line solver data-driven predictive controller.
The hybrid vehicle phantom include engine block, motor module, clutch module, battery module, Tire module, speed change tank module, differential module and longitudinal direction of car kinetics.The hybrid vehicle phantom of the present invention For the whole vehicle model that clutch enables single shaft parallel connection HEV, the HEV whole vehicle models can well portray the Transient Dynamics of vehicle Characteristic, for example, the lag characteristic of engine output torque, the torsional shaking of clutch, the vibration characteristics and tire of drive shaft Slip rate characteristic etc..The HEV includes that parking, pure electronic, combination drive, pure engine driving, braking energy feedback, charging are more Plant mode of operation.
Build in module in hybrid vehicle mode switching system, the data-driven prediction of data-driven predictive controller Control method combines what is constituted by Subspace Identification and Model Predictive Control.
The present invention combines to realize data-driven forecast Control Algorithm using Subspace Identification and Model Predictive Control, saves Go in the method based on mechanism model to the solution of state space equation, both reduced amount of calculation, turn avoid from data and distinguish Know the modeling error problem introduced during state-space model.
In the prediction output equation constructing module, according to the prediction output side of hybrid vehicle mode switching system Journey, the following output valve of hybrid vehicle mode switching system is estimated using subspace state space system identification.The method is without the need for pre- First model parameterization, using simple, ensure that the robustness of numerical value.
Cost function is made up of two parts, wherein, the Part I of cost function forces clutch slip speed convergence to arrive 0, so as to realizing rapidly pattern switching and reducing clutch abrasion;Cost function Part II control motor torque, from Clutch torque and the rate of change of motor torque, for the comfortableness of Assured Mode switching.
Beneficial effects of the present invention are:
(1) the data-driven Design of Predictive method of a kind of HEV mode switching according to the present invention, wherein, data The control method of predictive controller is driven, traditional controlling party based on mechanism model is substituted using the Forecasting Methodology based on data Method, overcomes real-time with control for the accuracy of the modelling by mechanism in the presence of complication system as HEV power assemblies Contradiction between property.Compared with traditional model predictive control method, the data that method for designing of the present invention is designed The pattern switching quality of driving predictive controller is higher, and this is favorably improved the ride comfort of HEV, while extending clutch Service life.
(2) torque coordination being used in HEV work-mode switching process using advanced data-driven forecast Control Algorithm In control, it is directly based upon the offline inputoutput data for gathering to design controller, as effectively prevent HEV power assemblies The problem of the modelling by mechanism hardly possible in the presence of complication system, eliminates in the method based on mechanism model to state space equation Solve, both reduced amount of calculation, the modeling error problem introduced when turn avoid from data identification to state-space model.
(3) control algolithm of the present invention only needs to do some off-line simulations experiments and is not required to obtaining inputoutput data Any experience and rating test are wanted, development cost is low.As time goes on, system performance and parameters of operating part may be total with power Gradually change into the aging of part, only need vehicle operation data again needed for collection algorithm, and without the need for algorithm Do any change, you can persistently ensure the excellent control performance of institute's extracting method.
Description of the drawings
Fig. 1 is whole hybrid vehicle phantom the general frame;
Fig. 2 is data-driven predictive control algorithm block diagram;
Fig. 3 (a) is the input for recognizing the 219 of forecast model sampled points and output data --- motor torque Te
Fig. 3 (b) is the input for recognizing the 219 of forecast model sampled points and output data --- clutch torque Tc
Fig. 3 (c) is the input for recognizing the 219 of forecast model sampled points and output data --- motor torque Tm
Fig. 3 (d) is the input for recognizing the 219 of forecast model sampled points and output data --- clutch both sides Speed difference Δ ω;
Fig. 3 (e) is the input for recognizing the 219 of forecast model sampled points and output data --- engine speed ωe
Fig. 3 (f) is the input for recognizing the 219 of forecast model sampled points and output data --- motor speed ωm
Fig. 4 (a) is the input data-motor torque T for checkinge
Fig. 4 (b) is input data --- the clutch torque T for checkingc
Fig. 4 (c) is input data --- the motor torque T for checkingm
Fig. 4 (d) is the identification result for exporting Δ ω;
Fig. 4 (e) is constraint output ωeIdentification result;
Fig. 4 (f) is constraint output ωmIdentification result;
Fig. 5 is to realize the Simulink block figures based on the torque coordination control strategy of data-driven forecast Control Algorithm;
Fig. 6 (a) is the speed of UDC operating modes;
Fig. 6 (b) is the vehicle impact degree controlled using the torque coordination based on data-driven forecast Control Algorithm;
Fig. 6 (c) is the speed of the clutch both sides controlled using the torque coordination based on data-driven forecast Control Algorithm Difference;
Fig. 6 (d) is the motor torque controlled using the torque coordination based on data-driven forecast Control Algorithm;
Fig. 6 (e) is the clutch torque controlled using the torque coordination based on data-driven forecast Control Algorithm;
Fig. 6 (f) is the motor torque controlled using the torque coordination based on data-driven forecast Control Algorithm;
Fig. 7 (a) is the comparison of data-driven forecast Control Algorithm and model predictive control method --- clutch both sides Speed difference;
Fig. 7 (b) is the partial enlargement of Fig. 7 (a);
Fig. 7 (c) is the comparison of data-driven forecast Control Algorithm and model predictive control method --- shock extent;
Fig. 7 (d) is the comparison of data-driven forecast Control Algorithm and model predictive control method --- motor speed and send out Motivation rotating speed;
Fig. 7 (e) is the partial enlargement of Fig. 7 (d).
Specific embodiment
Below in conjunction with the accompanying drawings the present invention will be further described with embodiment:
(1) Fig. 1 is the whole hybrid vehicle phantom the general frame of the present invention.In professional automobile simulation software Certain clutch built in Cruise enables the whole vehicle model of single shaft parallel connection HEV.Whole hybrid vehicle phantom is main Including engine block, motor module, clutch module, battery module, tire module, speed change tank module, differential module with And longitudinal direction of car kinetics, whole vehicle model parameter such as table 1.
The hybrid vehicle parameter list of table 1
Constructed HEV whole vehicle models can well portray the Transient Dynamics characteristic of vehicle, for example, electromotor output The lag characteristic of torque, the torsional shaking of clutch, the vibration characteristics of drive shaft and the slip rate characteristic of tire etc..Electromotor Power is delivered to drivetrain by the friction torque of clutch.Management level controller uses rule-based energy management plan Slightly.In order to improve fuel economy, reduce discharge, the HEV includes parking, pure electronic, combination drive, pure engine driving, system The multiple-working modes such as energy feedback, charging.
(2) the HEV mode of operation switching torque coordination control algolithms in the present invention use the pre- observing and controlling of data-driven Method processed, is combined by Subspace Identification and Model Predictive Control and is constituted, and it is as shown in Figure 2 that it constitutes schematic diagram.Specifically Embodiment is as follows:
1. open loop data collection:It is designed to reach and continues fully to encourage electromotor, clutch, motor, drive shaft and wheel The input T of the vibration characteristics of tiree、Tc、Tm, as shown in Fig. 3 (a) -3 (c) input datas, acted on built HEV car loads On kinetic model, system controlled output Δ ω and affined output ω is obtainede、ωm, such as shown in Fig. 3 (d) -3 (f).This Bright middle sampling time Ts=0.01s, i=20, j=180, therefore altogether collection electronic switches dynamic mistake from pure to hybrid mode The open loop data of 2i+j-1=219 sampling instant in journey.
Open loop data includes:The speed difference of clutch both sides, engine speed and motor speed.
2. based on the sampled data shown in Fig. 3 (a) -3 (f), the data block Hankel matrix U of u (k) and y (k) is builtp、Uf、 Yp、YfSubscript p and f of matrix is represented over respectively and future.
3. input and output Hankel matrix Us are passed throughp、Uf、Yp、YfThe prediction output equation for constructing system is:
Wherein
It is the system future output valve estimated by subspace state space system identification, using least square method, solving-optimizing Problem (3), obtains two sub-spaces matrix Ls in predictive equationw,Lu
Can try to achieve in the same manner
4. with the 220th sampling instant to the inputoutput data of the 599th sampling instant verifying that it is pre- that identification is obtained Survey equation.Constructed input data T of excitation completelye、Tc、TmAs shown in Fig. 4 (a) -4 (c), specific identification prediction effect is such as Fig. 4 (d) -4 (f).As shown in Fig. 4 (d) -4 (f), when the speed difference of clutch both sides is not 0, the output for obtaining is predicted Can coincide well with real output;It is near 0, because identification prediction that forecast error occurs in clutch both sides speed difference Sampled data during model is not including the situation that speed difference is 0.What the present invention was studied is that the cunning of clutch is rubbed the stage, in this stage It is not 0 that clutch both sides speed difference is, therefore does not affect the design of data-driven predictive controller.
5. the increment for defining the control input sequence at k moment to be optimized is:
Wherein,
Define PREDICTIVE CONTROL output sequenceWith the list entries u in futurefK () is as follows:
NpAnd NuPrediction time domain and control time domain are represented respectively.In order that prediction is exported close to given with reference to defeated Go out, generally cost function is defined as into quadric form, in order to punish the acute variation of controlled quentity controlled variable, introduce the punishment to controlled quentity controlled variable , accordingly, it is considered to input constraint and affined output constraint are arrived, the optimum control during the pure electronic switching to hybrid mode Problem processed can be described as follows:
s.t.umin(k+q)≤u(k+q)≤umax(k+q), q=0,1 ..., Nu-1 (5.2)
Δumin(k+q)≤Δu(k+q)≤Δu max(k+q), q=0,1 ..., Nu-1 (5.3)
ybmin(k+q)≤yb(k+q)≤ybmax(k+q), q=0,1 ..., Np (5.4)
Wherein
The Part I of cost function J forces clutch slip speed convergence to 0, so as to realize quick pattern switching and Reduce clutch abrasion;The rate of change of the Part II control motor torque, clutch torque and motor torque of cost function, The comfortableness of Assured Mode switching.Re(k+1) be prediction time domain in Δ ω reference sequences, wherein α ∈ (0,1) be one can The parameter of regulation.Because the speed difference of the main slave end of clutch when y (k) represents current time k, is gradually to level off to 0, institute It is less with α, then reference value r (k+i) of y (k+i), i=1,2 ..., NpIt is less, so as to realize the speed of the main slave end of clutch Difference reaches as early as possible 0, so as to complete the combination of clutch sooner.
6. in order to solve optimal problem (5.1), prediction output side is derived based on data-driven method and forecast Control Algorithm Journey.In order that system output can be input into zero steady-state error track reference, subspace predictive equation is expressed as into following increasing Amount form:
Wherein
The controlled output increment sequence that prediction is obtained is represented by:
Wherein Δ yp=[Δ y (k-i+1) Δ y (k-i+2) ... Δs y (k)]T, Δ up=[Δ u (k-i) Δ u (k-i+ 1) … Δu(k-1)]T
The optimum prediction that following output can be obtained is as follows
WhereinIt is Npm×NuL dynamic matrixs.
Y (k)=[y (k) y (k) ... y (k)]T (10)
According to (9)-(12), can be derived from the same manner withRelevant
7. the torque coordination control problem (5.1) for considering constraint is converted into into following quadratic programming problem, so as to ask online Solution data-driven predictive controller.
s.t.CuΔuf(k)≤b
Wherein
According to the roll stablized loop principle of PREDICTIVE CONTROL, only by first element interaction of Δ u (k) in HEV drivetrains System, at each moment above-mentioned calculating is repeated.Therefore, it is as follows in the control law of k-th sampling instant:
Above-mentioned data-driven predictive control algorithm is realized in Matlab/Simulink, as shown in Figure 5.
(3) in order to verify the control performance of torque coordination data-driven predictive controller, in the urban district as shown in Fig. 6 (a) Under driving cycles UDC (urban driving cycle), the pre- observing and controlling of torque coordination data-driven that will be realized in Simulink The HEV Full Vehicle Dynamics models built in device processed and Cruise, by api interface mode associative simulation is realized.In operation During Cruise phantoms, Simulink phantoms are also calculated at the same time and solved, in simulation process between the two not The disconnected exchange for carrying out data.Cruise simulation step lengths are 0.01 second.Electromotor, clutch, the peak torque such as institute of table 1 of motor Show.Np=20, Nu=5, ε=30rad/s, ωemin=720r/min, ωmmin=0r/min, α=0.8, τy,1y,2=...= τy,20=0.8,In order to improve the ride comfort of clutch engagement to improve driving Comfortableness, the excursion of controlling increment is set to:-50Nm/s≤ΔTe≤ 50Nm/s, -50Nm/s≤Δ Tc≤50Nm/ S, -100Nm/s≤Δ Tm≤100Nm/s。
First, the control performance of torque coordination data-driven predictive controller itself is demonstrated.Simulation result such as Fig. 6 (b)- Shown in 6 (f).Start within 60.44th second speed more than 27km/h, electromotor is started with the Start-up of constant torque of 60Nm when the 61.22nd second Machine rotating speed rises to close motor speed (differing 30rad/s with motor speed), and clutch initially enters the sliding friction stage, the The speed difference of clutch both sides is 0 when 61.49 seconds, according to clutch characteristics, makes clutch torque in a kind of predefined mode Increase, to ensure its secure ratcs.From simulation result, put forward the pattern switching quality is good of strategy, this from it is pure it is electronic to The mode handover procedure of hybrid power is needed altogether 1.05 seconds, can well meet the switching demand of driver, vehicle impact degree width It is worth for 9.96m/s3, it is to avoid discomfort of the passenger in mode handover procedure, the clutch slipping time is only 0.27 second, it is total from Clutch unit friction work is only 8.59J, and this contributes to extending clutch service life.
Secondly, the control performance of torque coordination data-driven predictive controller and traditional model predictive control method are entered Go and compared.Shown in simulation result such as Fig. 7 (a) -7 (e).In order to more intuitively be compared to two methods, table 2 lists energy The several important simulation result of enough representation pattern quality of handoff, it is seen then that under identical simulated conditions, is predicted based on data-driven The shock extent of the mode handover procedure of control method is less, and the pattern switching persistent period is shorter, and the clutch slipping time is shorter, from It is less that clutch slides abrasion consumption.
The data-driven forecast Control Algorithm of table 2 compares with the simulation result of model predictive control method
Data-driven forecast Control Algorithm Model predictive control method
Shock extent fluctuation range - 9.96~6.76m/s3 - 82.47~87.92m/s3
The pattern switching persistent period 1.05s 1.3s
The clutch slipping time 0.27s 0.5s
Clutch slipping is lost 8.59J 19.3J
The present invention relates to a kind of HEV mode of operations switching torque coordination control method, using the prediction side based on data Method substitutes the traditional control method based on mechanism model, overcomes for being deposited in complication system as HEV power assemblies The accuracy of modelling by mechanism and the real-time of control between contradiction.Compared with traditional model predictive control method, this The adopted method mode quality of handoff of invention is higher, and this is favorably improved the ride comfort of HEV, while extending clutch Service life.
Although the above-mentioned accompanying drawing that combines is described to the specific embodiment of the present invention, not to present invention protection model The restriction enclosed, one of ordinary skill in the art should be understood that on the basis of technical scheme those skilled in the art are not Need the various modifications made by paying creative work or deformation still within protection scope of the present invention.

Claims (10)

1. a kind of data-driven Design of Predictive method that HEV mode switches, it is characterised in that include:
Step 1:Whole hybrid vehicle phantom is built in automobile simulation software;
Step 2:According to the dynamic characteristic of hybrid vehicle mode switching system, design motor torque, motor torque and from Designed excited data is acted on built hybrid vehicle phantom by clutch torque as excited data;
Step 3:The data block Hankel matrix of input and the output of hybrid vehicle is built based on the open loop data of collection, The prediction output equation of hybrid vehicle mode switching system is further constructed based on identification model;
Step 4:Define the input sequence of the increment, output sequence and future of the list entries of hybrid vehicle mode switching system Row, prediction output equation is stated with incremental form;
Step 5:According to corresponding input constraint and output constraint, the optimum during the pure electronic switching to hybrid mode is built The cost function of control, and the prediction output equation based on incremental form statement is come line solver data-driven predictive controller.
2. the data-driven Design of Predictive method that a kind of HEV mode as claimed in claim 1 switches, its feature exists In the hybrid vehicle phantom includes engine block, motor module, clutch module, speed change tank module, battery Module, tire module, differential module and longitudinal direction of car kinetics.
3. the data-driven Design of Predictive method that a kind of HEV mode as claimed in claim 1 switches, its feature exists Combined structure by Subspace Identification and Model Predictive Control in the data-driven forecast Control Algorithm of, data-driven predictive controller Into.
4. the data-driven Design of Predictive method that a kind of HEV mode as claimed in claim 1 switches, its feature exists In in the step 3, according to the prediction output equation of hybrid vehicle mode switching system, using Subspace Identification side Method is estimating the following output valve of hybrid vehicle mode switching system.
5. the data-driven Design of Predictive method that a kind of HEV mode as claimed in claim 1 switches, its feature exists In, cost function is made up of two parts, wherein, the Part I of cost function forces clutch slip speed convergence to 0, so as to Realize rapidly pattern switching and reduce clutch abrasion;The Part II control motor torque of cost function, clutch turn The rate of change of square and motor torque, for the comfortableness of Assured Mode switching.
6. the data-driven Design of Predictive system that a kind of HEV mode switches, it is characterised in that include:
Hybrid vehicle phantom builds module, and it is used to build whole hybrid vehicle phantom;
Hybrid vehicle mode switching system builds module, and it is used for the dynamic according to hybrid vehicle mode switching system Characteristic, design motor torque, motor torque and clutch torque act on designed excited data as excited data In the hybrid vehicle phantom built;
Prediction output equation constructing module, it is used to build the input of hybrid vehicle and defeated based on the open loop data for gathering The data block Hankel matrix for going out, and then the prediction output of hybrid vehicle mode switching system is constructed based on identification model Equation;
Incremental form statement prediction output equation module, it is used to define the list entries of hybrid vehicle mode switching system Increment, output sequence and future list entries, prediction output equation is stated with incremental form;
Data-driven predictive controller solves module, and it is used for according to corresponding input constraint and output constraint, builds pure electronic To hybrid mode switch during optimum control cost function, and based on incremental form state prediction output equation come Line solver data-driven predictive controller.
7. the data-driven Design of Predictive system that a kind of HEV mode as claimed in claim 6 switches, its feature exists In the hybrid vehicle phantom includes engine block, motor module, clutch module, speed change tank module, battery Module, tire module, differential module and longitudinal direction of car kinetics.
8. the data-driven Design of Predictive system that a kind of HEV mode as claimed in claim 6 switches, its feature exists In building in module in hybrid vehicle mode switching system, the data-driven PREDICTIVE CONTROL of data-driven predictive controller Method combines what is constituted by Subspace Identification and Model Predictive Control.
9. the data-driven Design of Predictive system that a kind of HEV mode as claimed in claim 6 switches, its feature exists In in the prediction output equation constructing module, according to the prediction output equation of hybrid vehicle mode switching system, profit The following output valve of hybrid vehicle mode switching system is estimated with subspace state space system identification.
10. the data-driven Design of Predictive system that a kind of HEV mode as claimed in claim 6 switches, its feature exists In, cost function is made up of two parts, wherein, the Part I of cost function forces clutch slip speed convergence to 0, so as to Realize rapidly pattern switching and reduce clutch abrasion;The Part II control motor torque of cost function, clutch turn The rate of change of square and motor torque, for the comfortableness of Assured Mode switching.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107168319A (en) * 2017-06-01 2017-09-15 同济大学 A kind of unmanned vehicle barrier-avoiding method based on Model Predictive Control
CN108181811A (en) * 2017-12-18 2018-06-19 长春工业大学 A kind of slip rate tracking and controlling method based on linear time-varying
CN112318511A (en) * 2020-11-02 2021-02-05 北京邮电大学 Mechanical arm trajectory tracking control method based on data driving

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130268150A1 (en) * 2012-04-04 2013-10-10 Feisel Weslati Predictive powertrain control using powertrain history and gps data
CN104401232A (en) * 2014-12-21 2015-03-11 吉林大学 Electric vehicle torque optimization method based on data driven predictive control
CN105221275A (en) * 2015-08-20 2016-01-06 吉林大学 Based on the Turbocharged Gasoline Engine control system of Model Distinguish
CN106080584A (en) * 2016-06-21 2016-11-09 江苏大学 A kind of hybrid vehicle pattern based on Model Predictive Control Algorithm switching control method for coordinating

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130268150A1 (en) * 2012-04-04 2013-10-10 Feisel Weslati Predictive powertrain control using powertrain history and gps data
CN104401232A (en) * 2014-12-21 2015-03-11 吉林大学 Electric vehicle torque optimization method based on data driven predictive control
CN105221275A (en) * 2015-08-20 2016-01-06 吉林大学 Based on the Turbocharged Gasoline Engine control system of Model Distinguish
CN106080584A (en) * 2016-06-21 2016-11-09 江苏大学 A kind of hybrid vehicle pattern based on Model Predictive Control Algorithm switching control method for coordinating

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
余开江 等: "混合动力汽车模型预测控制策略研究", 《系统仿真技术》 *
王剑 等: "基于模型预测控制的混合动力汽车转矩协调控制方法研究", 《新型工业化》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107168319A (en) * 2017-06-01 2017-09-15 同济大学 A kind of unmanned vehicle barrier-avoiding method based on Model Predictive Control
CN108181811A (en) * 2017-12-18 2018-06-19 长春工业大学 A kind of slip rate tracking and controlling method based on linear time-varying
CN108181811B (en) * 2017-12-18 2020-10-13 长春工业大学 Slip rate tracking control method based on linear time variation
CN112318511A (en) * 2020-11-02 2021-02-05 北京邮电大学 Mechanical arm trajectory tracking control method based on data driving

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