CN105035079A - Power switching coordination control method of coaxial parallel hybrid electric vehicle with engine torque observer - Google Patents

Abstract

The invention discloses a power switching coordination control method of a coaxial parallel hybrid electric vehicle with an engine torque observer. Based on the method of a least square support vector machine, the testing sampledatum of the engine rack performance aretrained; the distribution estimation algorithm is adopted to optimize the parameter C and Lambda of the least square support vector machine; and through off-line training and optimization to an engine toque module and based on the optimum parameters, the engine torque observer is built. The current throttle value percentage and rotary speed are inputted to acquire a real-time torque value, thus realizing on-line observation to the engine torque. During the switching process of switching a pure electric driving model to a pure engine driving model (different power sources) of the vehicle provided herein, section coordination control is carried out aiming to different power sources, thus realizing stable transition of the whole switching process and stable torque of power system output without fluctuation, and satisfying the requirement of vehicle driving total torque.

Description

A kind of coaxial parallel-connection hybrid electric vehicle power with motor torque observer switches control method for coordinating

Technical field

The present invention relates to a kind of hybrid electric vehicle power pattern and switch control method for coordinating, particularly switch control method for coordinating about a kind of coaxial parallel-connection hybrid electric vehicle power with motor torque observer.

Background technology

The parallel hybrid power that mixed power electric car can realize under low speed pure motor driving and high speed and high load working condition travels, thus the optimization of the control making vehicle energy flow and energy ezpenditure has greater flexibility, more easily realize the target of low oil consumption and anti-emission carburetor.But the remarkable difference of the transient response characteristic due to different dynamic source; switching between vehicle power source usually can cause significantly change or the sudden change of power system Driving Torque; thus cause the not steady of Power output, larger longitudinal impact is produced to vehicle, and then reduces the driving performance of vehicle.

In current existing method for controlling hybrid power vehicle, Chinese patent CN101973267B, expects to drive resultant couple computation layer, middle level Dynamic coordinated control layer and bottom escape mechanism layer to carry out cooperation control to the power of hybrid vehicle by arranging upper strata in the scheme that name is called disclosed in " layered control method of hybrid electric vehicle traction ".According to target slippage rate, set up dynamic sliding mode control device calculation expectation and drive resultant couple, expect with engine response the low frequency part driving resultant couple, allow the motor dynamics of fast response time compensate the HFS expecting to drive resultant couple, and set up dynamic compensation mechanism.Chinese Patent Application No. 201210539336.6, proposes demand torque when predicting interval after a predetermined time based on math modeling (exponential decay model or Markov chain pattern type) in the scheme that name is called disclosed in " method for controlling hybrid power vehicle based on Model Predictive Control "; Obtain Optimal Parameters, and determine the target torque of driving engine and motor accordingly, send to engine controller and electric machine controller respectively.The representational method of this two class all well solves the intention how identifying chaufeur, determines demand total torque, and according to the feature that driving engine and electrical motor respond, distributes respective target torque, realize the cooperation control of driving engine and motor.But these two class methods all do not relate to this difficult problem of Driving Torque how obtaining driving engine reality, if the actual torque of driving engine and target torque exist larger error, that just can not ensure that the total torque needed for normal vehicle operation can be met.

At present motor torque On-line Estimation method is mainly contained: (1) driving engine mean value model method, this predicts average external variable (crankshaft rotating speed, inlet manifold pressure) and the value of built-in variable (combustion efficiency, charge efficiency) based on several circulations of driving engine, the average time is far shorter than the transformation period of engine parameter in dynamic operation condition, thus can describe the change procedure of engine parameter in dynamic operation condition preferably.But the method commonality is poor, dissimilar driving engine is needed to set up different models, and needs a large amount of test figuress.(2) based on the neural network model method of driving engine dynamic test, this is based on engine test data, utilize the strong feature of Neural Network Based Nonlinear approximation capability to carry out the dynamic torque of estimated engine, but accurate theoretical foundation is not had to neural network type and choosing of network parameter.(3) engine crankshaft transient speed method, this is based upon driving engine ignition Frequency point, exists on the basis of linear approximate relationship between instantaneous speed of crankshaft fluctuation amplitude and torque.This algorithm needs to carry out Fourler transform after each cycle crankcase transient speed sequence of acquisition, and algorithm is complicated, and calculated amount is large, and high to the hardware requirement of control system, real-time not easily ensures.

Summary of the invention

For the problems referred to above, the object of this invention is to provide a kind of coaxial parallel-connection hybrid electric vehicle power with motor torque observer and switch control method for coordinating, by carrying out off-line training and optimization to motor torque model, set up motor torque observer based on optimized parameter.Input current throttle opening and rotating speed, draw real-time torque value, realize the online observation of motor torque.At vehicle by pure motorized motions pattern in the handoff procedure of pure engine drive mode, for different propulsions source, carry out segmentation cooperation control, and then realize the smooth transition of whole handoff procedure, the torque that power system exports is stablized, ripple disable, and the demand meeting vehicular drive total torque.

For achieving the above object, the present invention takes following technical scheme: a kind of coaxial parallel-connection hybrid electric vehicle power with motor torque observer switches control method for coordinating, comprises the following steps:

Step 1, under the different solar terms door aperture of Engine Dynamic Performance experiment gained and rotating speed, the data of corresponding torque are as sample, utilize least square method supporting vector machine (LSSVM) to train;

Step 2, adopts Estimation of Distribution Algorithm to the parameter of least square method supporting vector machine: balance factor C and kernel functional parameter σ is optimized, and draws optimized parameter, sets up motor torque observer model based on optimized parameter;

Step 3, according to the operation of chaufeur to Das Gaspedal, brake pedal, identify driving intention, draw the travel condition of vehicle of expectation, at vehicle by pure motorized motions pattern in the handoff procedure of pure engine drive mode, a powershift Discrete control system is set, the foundation divided using the state of power-transfer clutch as control system, for different propulsions source, adopt different control policies, realize segmentation cooperation control;

Step 4, the vehicle start stage is in pure motorized motions pattern, now tail-off, and disengaging of clutch, only by driving motor supplies power; When the speed of a motor vehicle is greater than the vehicle velocity V preset _etime, engine starting, adopts PI type Fuzzy Hybrid mode to regulate engine speed; In the situation that power-transfer clutch two ends rotating speed difference is less, carry out power-transfer clutch joint;

Step 5, after power-transfer clutch combines completely, vehicle enters combination drive pattern; The model matching control device setting up band motor torque observer regulates the Driving Torque of drive motor, realizes the closed loop control to Power output total torque;

Step 6, works as engine work, and Driving Torque is steady, and actual vehicle speed is greater than the pure engine drive mode threshold value V preset _{eng_alone}time, then the torque progressively increasing driving engine exports, and decline is until be zero to regulate the torque of drive motor to export gradually, and only provide power by driving engine, vehicle enters pure engine drive mode.

Further, in described step 1, be spaced apart 20% with throttle opening, rotating speed is spaced apart 500r/min to obtain sampled data.

Further, in described step 1, select radial basis function as the kernel function of least square method supporting vector machine.

Further, in described step 2, the step adopting Estimation of Distribution Algorithm to be optimized least square method supporting vector machine parameter is as follows:

Step 2.1, adopts the Logistic mapping model of one dimension to carry out initialization population X=[C, σ], and the random initial value produced between a group [0,1], is expressed as X ₀=[rand (0,1) rand (0,1)], utilizes X _t+1=λ X _t(1-X _t), can chaos sequence be obtained through n iteration, and be expressed as with a matrix type:

$X=\left[\begin{array}{c}{X}_1\\ X_2\\ .\\ .\\ .\\ X_n\end{array}\right]=\left[\begin{array}{cc}{x}_{11}& x_{12}\\ x_{21}& x_{22}\\ .& .\\ .& .\\ .& .\\ x_{n1}& x_{n2}\end{array}\right]$

The span of chaos sequence is expanded to the span of parameter problem to be optimized, can be expressed as:

x _i1＝C _min+(C _max-C _min)x _i1

x _i2＝σ _min+(σ _max-σ _min)x _i2

In formula, n is population scale, and λ is controling parameters, and the span of balance factor C is [C _max, C _min], the span of kernel functional parameter σ is [σ _max, σ _min];

Step 2.2, evaluates the fitness value of each individuality in population, and a jth individual fitness value can be expressed as:

$fit\left(X_j\right)=\frac{1}{Z}\underset{i=1}{\overset{Z}{\Σ}}{(d_{ji}-y_{ji})}^2$

In formula, d _jifor jth i-th individual actual value, y _jifor jth i-th individual observed value, Z is the number of training sample;

Step 2.3, performs chaotic mutation to each individuality in population, and the variation radius of jth i-th individual parameter value can be expressed as:

$r_{ji}=2\×\frac{\underset{1\≤j\≤n}{\max }{x}_{ji}-\underset{1\≤j\≤n}{\min }{x}_{ji}}{n}\×\frac{1-fit\left(j\right)}{\underset{1\≤j^{\′}\≤n}{\max }fit\left(j^{\′}\right)}$

After mutation operation, calculate new individual fitness value, if be less than the original fitness value of old individuality, then replace old individuality with it; Otherwise, retain old individuality;

Step 2.4, sorts to fitness value individual in population, sets up the mixed Gauss model of Weight;

Step 2.5, samples by the mixed Gauss model set up, and generates n new individuality as population of future generation;

Step 2.6, judges whether to meet the condition of convergence, if do not met, goes to step 2.2 continuation and performs; If met, then the individuality that in population, fitness value is minimum is required optimized parameter.

Further, in described step 4, the step adopting PI type Fuzzy Hybrid mode to carry out speed adjustment to driving engine is as follows:

Step 4.1, according to drive motor rotational speed omega _mcalculate rotating speed of target ω _dfor: ω _d=ω _m× i _g, i in formula _gfor the transmitting ratio of change speed gear box, the gear current by vehicle determines;

Step 4.2, according to the actual engine speed ω of tachogen feedback _ewith the rotating speed of target ω determined in step 4.1 _dcalculated difference, is modified to it: Δ ω=K × (ω _d-ω _e), and as the foundation switched;

Step 4.2, as Δ ω>=ω _tHtime, adopt fuzzy controller to carry out speed adjustment to driving engine;

Step 4.4, as Δ ω < ω _tHtime, adopt PI controller to carry out speed adjustment to driving engine;

In above formula, K is that switching at runtime compensates speed-changing coefficient of correction, ω _tHfor the threshold value switched between fuzzy controller and PI controller.

Further, in described step 5, adopt first-order lag characteristic to build the response characteristic of control system, the model matching control device setting up band motor torque observer regulates the step of drive motor Driving Torque as follows:

Step 5.1, sets up motor torque observer model based on optimized parameter, input current throttle aperture α and rotational speed omega _e, export real-time torque, motor torque observer model can be expressed as: in formula, ω _efor driving engine real-time rotate speed, α is current throttle opening, f (ω _e, α) and be motor torque observer output valve, τ _efor the lag time constant of observer model;

Step 5.2, demand torque identification model, using throttle opening as input, according to the driving intention of chaufeur, carries out ratio cut partition to greatest requirements torque, draws current demand torque, and demand torque identification model can be expressed as: t in formula _dmfor greatest requirements torque, τ ₁for the lag time constant of demand torque identification model;

Step 5.3, the model of drive motor can be reduced to in formula, T _mxfor the torque rating of drive motor, τ _mfor the lag time constant of drive motor system;

Step 5.4, sets up feedforward controller, considers that Control System Design target is demand torque T _dtotal torque T is driven with reality _qbetween deviation be zero, setting up feedforward controller is:

$G_1\left(s\right)=(1+{\mathrm{\&tau;}}_{m}s)(\frac{1}{1+{\mathrm{\&tau;}}_{1}s}-\frac{1}{1+{\mathrm{\&tau;}}_{e}s});$

Step 5.5, sets up feedback controller, and feedback controller adopts PI controller:

$G_2\left(s\right)=k_p+\frac{k_i}{s}$

In formula, k _pfor proportionality coefficient, k _ifor integral coefficient.

Further, in described step 5.5, the parameter k of PI controller _pand k _ithere is many class values, and total torque T can be driven with actual by torque Td according to demand _qbetween deviation size select a suitable class value.

The invention has the beneficial effects as follows: utilize the method for least square method supporting vector machine to train the Engine Dynamic Performance test sample data obtained, and the algorithm adopting distribution to estimate is optimized the parameter C of least square method supporting vector machine and σ, motor torque observer model is set up based on optimized parameter, improve classification performance and the generalization ability of observation model, solve motor torque and estimate inaccurate, poor universality, algorithm complexity a difficult problem in real time.

Meanwhile, in parallel hybrid electric by pure motorized motions pattern in pure engine drive mode handoff procedure, hybrid electric vehicle power is set and switches Discrete control system:

(1) before power-transfer clutch combines completely, PI type Fuzzy Hybrid mode is adopted to regulate engine speed, guarantee to combine when power-transfer clutch two ends speed discrepancy is less, the combination completely of power-transfer clutch can be completed quickly, reduce the process of sliding wear, reduce power-transfer clutch by the impact to power system in the change procedure being separated to combination;

(2) after power-transfer clutch combines completely, the model matching control device setting up band motor torque observer regulates the Driving Torque of drive motor, by to motor torque online real-time monitored accurately, achieve the closed loop control to Power output total torque, improve power system output stability, reliability and robustness, effectively improve ride comfort and the handling of vehicle.The present invention can be widely used in various hybrid electric vehicle power switching controls process.

Accompanying drawing explanation

Fig. 1 is coaxial parallel-connection hybrid electric vehicle power driving system simplified structure schematic diagram of the present invention;

Fig. 2 is the coaxial parallel-connection hybrid electric vehicle power cooperation control schematic flow sheet of band motor torque observer of the present invention;

Fig. 3 is motor torque PI type Fuzzy hybrid control architecture schematic diagram of the present invention;

Fig. 4 is the diagram of curves of fuzzy Control of the present invention rule correspondence;

Fig. 5 is the model matching control structural representation of band motor torque observer of the present invention.

In figure: 1-driving engine; 2-power-transfer clutch; 3-change speed gear box; 4-drive motor; 5-main reduction gear; 6-wheel.

Detailed description of the invention

Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described.

Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Being exemplary below by the embodiment be described with reference to the drawings, only for explaining the present invention, and can not limitation of the present invention being interpreted as.

Fig. 1 is the simplified structure schematic diagram of vehicle driveline, the formation that only display is relevant with the present invention in a schematic way.

Coaxial parallel-connection hybrid vehicle as shown in Figure 1 comprises driving engine 1, power-transfer clutch 2, change speed gear box 3, drive motor 4, main reduction gear 5 and wheel 6.Wherein, the front end of drive motor 4 is connected with the power take-off shaft of change speed gear box 3, and the rear end of drive motor 4 is connected with wheel 6 by main reduction gear 5, and driving engine 1 is connected with the input shaft of change speed gear box 3 by power-transfer clutch 2.

Fig. 2 is the schematic flow sheet of the inventive method, is divided into off-line and online two parts.Off-line part is mainly about the process of motor torque observation model Establishment and optimization, and online part relates generally to vehicle by pure motorized motions pattern to pure engine drive mode handoff procedure, the step of different dynamic source cooperation control.

The inventive method comprises the following steps:

(1) off-line part

1) with Engine Dynamic Performance experiment the data obtained (under different solar terms door aperture and rotating speed, corresponding torque), as sample, wherein throttle opening is spaced apart 20%, and rotating speed is spaced apart 500r/min.

2) select radial basis function (RBF) as the kernel function of least square method supporting vector machine, utilize Estimation of Distribution Algorithm to the parameter of least square method supporting vector machine: balance factor C and kernel functional parameter σ is optimized.Optimizing process is as follows:

1. the Logistic mapping model of one dimension is adopted to carry out initialization population X=[C, σ],

Initial value between random generation one group [0,1], is expressed as:

X ₀＝[rand(0，1)rand(0，1)]

Utilize X _t+1=λ X _t(1-X _t), can chaos sequence be obtained through n iteration, and be expressed as with a matrix type:

$X=\left[\begin{array}{c}{X}_1\\ X_2\\ .\\ .\\ .\\ X_n\end{array}\right]=\left[\begin{array}{cc}{x}_{11}& x_{12}\\ x_{21}& x_{22}\\ .& .\\ .& .\\ .& .\\ x_{n1}& x_{n2}\end{array}\right]$

The span of chaos sequence is expanded to the span of parameter problem to be optimized, can be expressed as:

x _i1＝C _min+(C _max-C _min)x _i1

x _i2＝σ _min+(σ _max-σ _min)x _i2

In formula, n is population scale, and λ is the maxim C of controling parameters, balance factor C _max=500, minimum value C _min=10, the maxim σ of kernel functional parameter σ _max=10, σ _min=0.1;

2. evaluate the fitness value of each individuality in population, a jth individual fitness value can be expressed as:

$fit\left(X_j\right)=\frac{1}{Z}\underset{i=1}{\overset{Z}{\&Sigma;}}{(d_{ji}-y_{ji})}^2$

In formula, d _jifor jth i-th individual actual value, y _jifor jth i-th individual observed value, Z is the number of training sample.

3. perform chaotic mutation to each individuality in population, the variation radius of jth i-th individual parameter value can be expressed as

$r_{ji}=2\&times;\frac{\underset{1\&le;j\&le;n}{\max }{x}_{ji}-\underset{1\&le;j\&le;n}{\min }{x}_{ji}}{n}\&times;\frac{1-fit\left(j\right)}{\underset{1\&le;j^{\&prime;}\&le;n}{\max }fit\left(j^{\&prime;}\right)}$

After mutation operation, calculate new individual fitness value, if be less than the original fitness value of old individuality, then replace old individuality with it.Otherwise, retain old individuality.

4. the fitness value of individuality is sorted, set up the mixed Gauss model of Weight.Weight factor can be expressed as

In formula, q is coefficient of correction.

5. according to probability, mixed Gauss model is sampled, generate n new individuality as population of future generation.

The probability of sampling can be expressed as:

$P_j=\frac{{\mathrm{\&omega;}}_j}{\underset{i=1}{\overset{n}{\&Sigma;}}{\mathrm{\&omega;}}_i}$

6. judge whether to meet the condition of convergence, if do not met, go to and 2. continue to perform.If met, then the individuality that in population, fitness value is minimum is required optimized parameter.

Motor torque observer model is set up, with the actual engine speed ω of tachogen feedback based on optimized parameter _ewith throttle opening α as input, export the torque into driving engine.Observational error is little, highly versatile, and algorithm is simple.Owing to being off-line training, online observation, so real-time is better.

(2) online part

1) according to the operation of chaufeur to Das Gaspedal, brake pedal, identify driving intention, draw the travel condition of vehicle of expectation.At vehicle by pure motorized motions pattern in the handoff procedure of pure engine drive mode, a powershift Discrete control system is set, the foundation divided using the state of power-transfer clutch as control system.For different propulsions source, adopt different control policies, realize segmentation cooperation control.

2) the vehicle start stage is in pure motorized motions pattern, now tail-off, and disengaging of clutch, only by driving motor supplies power.When the speed of a motor vehicle is greater than the V preset _etime, engine starting, adopts PI type Fuzzy Hybrid mode to regulate engine speed, as shown in Figure 3.At power-transfer clutch two ends rotating speed difference less (| ω | < ω _t) situation, carry out power-transfer clutch joint, can realize quickly combining completely, reduce the sliding wear process of power-transfer clutch, reduce the influence of fluctuations to vehicle power transmission.Preferred parameter V _e=30, ω _t=45;

Above-mentioned PI type Fuzzy Hybrid mode regulates the method for engine speed as follows:

1. according to drive motor rotational speed omega _mcalculate rotating speed of target ω _dfor:

ω _d＝ω _m×i _g

I in formula _gfor the transmitting ratio of change speed gear box, the gear current by vehicle determines.

2. according to the actual engine speed ω of tachogen feedback _e1. the rotating speed of target ω determined in _dcalculated difference, consider the dynamic response of whole system, it is modified to:

Δω＝K×(ω _d-ω _e)

And as the foundation switched;

In formula, K is that switching at runtime compensates speed-changing coefficient of correction, preferred parameter K=1.05.

3. as Δ ω>=ω _tHtime, fuzzy controller is adopted to carry out speed adjustment to driving engine, the characteristic of engine governed speed is pressed close in the setting of its control law, avoid because current speed error is larger, increase fuel charge and suction quantity in large quantities, the situation generation causing the unexpected enriching of oil gas and burn insufficient, causes fuel utilization ratio to decline and pollution emission increases the weight of;

4. as Δ ω < ω _tHtime, adopt PI controller to carry out speed adjustment to driving engine, avoid the output vibration because limited fuzzy control rule causes, be conducive to the static error of elimination system, improve the precision of Systematical control;

In formula, ω _tHfor the threshold value switched between fuzzy controller and PI controller.Preferred parameter ω _tH=10.And the setting of fuzzy Control rule is based upon repeatedly on the basis of engine governed speed test, according to the characteristic of engine governed speed, suitable controlling quantity is selected to export.With speed error e and error rate as input, do normalized, be all mapped to domain [-6,6], controlling quantity U is then mapped to domain [-7,7], and control law as shown in Figure 4.

3) as shown in Figure 5, after power-transfer clutch combines completely, vehicle enters combination drive pattern.The model matching control device setting up band motor torque observer regulates the Driving Torque of drive motor, achieves the closed loop control to Power output total torque, has both met the requirement of driving demand torque, achieve again whole system torque and steadily export;

It is as follows that the model matching control device of above-mentioned band motor torque observer comprises model:

1. motor torque observer model is set up based on optimized parameter, input current throttle aperture α and rotational speed omega _e, export real-time torque.Motor torque observer model can be expressed as

$T_e=f({\mathrm{\&omega;}}_e,\mathrm{\&alpha;})\frac{1}{1+{\mathrm{\&tau;}}_{e}s}$

In formula, ω _efor driving engine real-time rotate speed, α is current throttle opening, τ _efor the lag time constant of observer model, preferred parameter τ _e=20.

2. demand torque identification model is using throttle opening as input, according to the driving intention of chaufeur, carries out ratio cut partition to greatest requirements torque, draws current demand torque.Demand torque identification model can be expressed as

In formula, T _dmfor greatest requirements torque, the maximum torque exported when vehicle dual power source drives should be less than, τ ₁for the lag time constant of demand torque identification model, preferred parameter τ ₁=15.

3. the model of drive motor can be reduced to

$T_m=\frac{T_{mx}}{1+{\mathrm{\&tau;}}_{m}s}$

In formula, T _mxfor the torque rating value of drive motor, τ _mfor the lag time constant of drive motor system, preferred parameter τ _m=8.

4. set up feedforward controller, realize expected value response characteristics and the pattern match of system.Consider that Control System Design target is that demand torque Td drives total torque T with actual _qbetween deviation be zero, set up feedforward controller:

$G_1\left(s\right)=(1+{\mathrm{\&tau;}}_{m}s)(\frac{1}{1+{\mathrm{\&tau;}}_{1}s}-\frac{1}{1+{\mathrm{\&tau;}}_{e}s})$

5. set up feedback controller, realize robust following feature and the stability of system.Feedback controller adopts PI controller:

$G_2\left(s\right)=k_p+\frac{k_i}{s}$

The parameter k of PI controller _pand k _ithere is many class values, and can torque T according to demand _dtotal torque T is driven with reality _qbetween deviation e size select suitable parameter, take into account the requirement of system response time and overshoot.When deviation e is larger, proportionality coefficient k _pselect higher value, integral coefficient k _jselect smaller value, to realize the object accelerating system response time; When deviation e diminishes, proportionality coefficient k _pselect smaller value, integral coefficient k _iselect higher value, with the object that the overshoot reaching system is less.

In formula, k _pfor proportionality coefficient, k _ifor integral coefficient, preferred parameter: as e=35, k _p=80, k _i=3; As e=15, k _p=45, k _i=25; As e=8, k _p=5, k _i=40.

4) work as engine work, Driving Torque is steady, and actual vehicle speed is greater than the pure engine drive mode threshold value V preset _{eng_alone}time, then the torque progressively increasing driving engine exports, and decline is until be zero to regulate the torque of drive motor to export gradually, and only provide power by driving engine, vehicle enters pure engine drive mode, achieves the switching to different dynamic source.

In the description of this specification sheets, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " illustrative examples ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, identical embodiment or example are not necessarily referred to the schematic representation of above-mentioned term.And the specific features of description, structure, material or feature can combine in an appropriate manner in any one or more embodiment or example.

Although illustrate and describe embodiments of the invention, those having ordinary skill in the art will appreciate that: can carry out multiple change, amendment, replacement and modification to these embodiments when not departing from principle of the present invention and aim, scope of the present invention is by claim and equivalents thereof.

Claims (7)

1. the coaxial parallel-connection hybrid electric vehicle power with motor torque observer switches a control method for coordinating, it is characterized in that, comprises the following steps:

Step 1, under the different solar terms door aperture of Engine Dynamic Performance experiment gained and rotating speed, the data of corresponding torque are as sample, utilize least square method supporting vector machine to train;

Step 2, adopts Estimation of Distribution Algorithm to the parameter of least square method supporting vector machine: balance factor C and kernel functional parameter σ is optimized, and draws optimized parameter, sets up motor torque observer model based on optimized parameter;

Step 3, according to the operation of chaufeur to Das Gaspedal, brake pedal, identify driving intention, draw the travel condition of vehicle of expectation, at vehicle by pure motorized motions pattern in the handoff procedure of pure engine drive mode, a powershift Discrete control system is set, the foundation divided using the state of power-transfer clutch as control system, for different propulsions source, adopt different control policies, realize segmentation cooperation control;

Step 4, the vehicle start stage is in pure motorized motions pattern, now tail-off, and disengaging of clutch, only by driving motor supplies power; When the speed of a motor vehicle is greater than the vehicle velocity V preset _etime, engine starting, adopts PI type Fuzzy Hybrid mode to regulate engine speed; In the situation that power-transfer clutch two ends rotating speed difference is less, carry out power-transfer clutch joint;

Step 5, after power-transfer clutch combines completely, vehicle enters combination drive pattern; The model matching control device setting up band motor torque observer regulates the Driving Torque of drive motor, realizes the closed loop control to Power output total torque;

Step 6, works as engine work, and Driving Torque is steady, and actual vehicle speed is greater than the pure engine drive mode threshold value V preset _{eng_alone}time, then the torque progressively increasing driving engine exports, and decline is until be zero to regulate the torque of drive motor to export gradually, and only provide power by driving engine, vehicle enters pure engine drive mode.

2. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 1 switches control method for coordinating, it is characterized in that: in described step 1, be spaced apart 20% with throttle opening, rotating speed is spaced apart 500r/min to obtain sampled data.

3. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 1 switches control method for coordinating, it is characterized in that: in described step 1, selects radial basis function as the kernel function of least square method supporting vector machine.

4. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 1 switches control method for coordinating, it is characterized in that: in described step 2, the step adopting Estimation of Distribution Algorithm to be optimized least square method supporting vector machine parameter is as follows:

Step 2.1, adopts the Logistic mapping model of one dimension to carry out initialization population X=[C, σ], and the random initial value produced between a group [0,1], is expressed as X ₀=[rand (0,1) rand (0,1)], utilizes X _t+1=λ X _t(1-X _t), can chaos sequence be obtained through n iteration, and be expressed as with a matrix type:

$X=\left[\begin{array}{c}{X}_1\\ X_2\\ .\\ .\\ .\\ X_n\end{array}\right]=\left[\begin{array}{cc}{x}_{11}& x_{12}\\ x_{21}& x_{22}\\ .& .\\ .& .\\ .& .\\ x_{n1}& x_{n2}\end{array}\right]$

The span of chaos sequence is expanded to the span of parameter problem to be optimized, can be expressed as:

x _i1＝C _min+(C _max-C _min)x _i1

x _i2＝σ _min+(σ _max-σ _min)x _i2

In formula, n is population scale, and λ is controling parameters, and the span of balance factor C is [C _max, C _min], the span of kernel functional parameter σ is [σ _max, σ _min];

Step 2.2, evaluates the fitness value of each individuality in population, and a jth individual fitness value can be expressed as:

$fit\left(X_j\right)=\frac{1}{Z}\underset{i=1}{\overset{Z}{\&Sigma;}}{(d_{ji}-y_{ji})}^2$

In formula, d _jifor jth i-th individual actual value, y _jifor jth i-th individual observed value, Z is the number of training sample;

Step 2.3, performs chaotic mutation to each individuality in population, and the variation radius of jth i-th individual parameter value can be expressed as:

$r_{ji}=2\&times;\frac{\underset{1\&le;j\&le;n}{\max }{x}_{ji}-\underset{1\&le;j\&le;n}{\min }{x}_{ji}}{n}\&times;\frac{1-fit\left(j\right)}{\underset{1\&le;j^{\&prime;}\&le;n}{\max }fit\left(j^{\&prime;}\right)}$

After mutation operation, calculate new individual fitness value, if be less than the original fitness value of old individuality, then replace old individuality with it; Otherwise, retain old individuality;

Step 2.4, sorts to fitness value individual in population, sets up the mixed Gauss model of Weight;

Step 2.5, samples by the mixed Gauss model set up, and generates n new individuality as population of future generation;

Step 2.6, judges whether to meet the condition of convergence, if do not met, goes to step 2.2 continuation and performs; If met, then the individuality that in population, fitness value is minimum is required optimized parameter.

5. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 1 switches control method for coordinating, and it is characterized in that: in described step 4, the step adopting PI type Fuzzy Hybrid mode to carry out speed adjustment to driving engine is as follows:

Step 4.1, according to drive motor rotational speed omega _mcalculate rotating speed of target ω _dfor: ω _d=ω _m× i _g, i in formula _gfor the transmitting ratio of change speed gear box, the gear current by vehicle determines;

Step 4.2, according to the actual engine speed ω of tachogen feedback _ewith the rotating speed of target ω determined in step 4.1 _dcalculated difference, is modified to it: Δ ω=K × (ω _d-ω _e), and as the foundation switched;

Step 4.3, as Δ ω>=ω _tHtime, adopt fuzzy controller to carry out speed adjustment to driving engine;

Step 4.4, as Δ ω < ω _tHtime, adopt PI controller to carry out speed adjustment to driving engine;

In formula, K is that switching at runtime compensates speed-changing coefficient of correction, ω _tHfor the threshold value switched between fuzzy controller and PI controller.

6. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 1 switches control method for coordinating, it is characterized in that: in described step 5, adopt first-order lag characteristic to build the response characteristic of control system, the model matching control device setting up band motor torque observer regulates the step of drive motor Driving Torque as follows:

Step 5.1, sets up motor torque observer model based on optimized parameter, input current throttle aperture α and rotational speed omega _e, export real-time torque, motor torque observer model can be expressed as: in formula, ω _efor driving engine real-time rotate speed, α is current throttle opening, f (ω _e, α) and be motor torque observer output valve, τ _efor the lag time constant of observer model;

Step 5.2, demand torque identification model, using throttle opening as input, according to the driving intention of chaufeur, carries out ratio cut partition to greatest requirements torque, draws current demand torque, and demand torque identification model can be expressed as: t in formula _dmfor greatest requirements torque, τ ₁for the lag time constant of demand torque identification model;

Step 5.3, the model of drive motor can be reduced to in formula, T _mxfor the torque rating of drive motor, τ _mfor the lag time constant of drive motor system;

Step 5.4, sets up feedforward controller, considers that Control System Design target is demand torque T _dtotal torque T is driven with reality _qbetween deviation be zero, design feedforward controller be:

$G_1\left(s\right)=(1+{\mathrm{\&tau;}}_{m}s)(\frac{1}{1+{\mathrm{\&tau;}}_{1}s}-\frac{1}{1+{\mathrm{\&tau;}}_{e}s});$

Step 5.5, sets up feedback controller, and feedback controller adopts PI controller:

$G_2\left(s\right)=k_p+\frac{k_i}{s}$

In formula, k _pfor proportionality coefficient, k _ifor integral coefficient.

7. the coaxial parallel-connection hybrid electric vehicle power of band motor torque observer as claimed in claim 6 switches control method for coordinating, it is characterized in that: in described step 5.5, the parameter k of PI controller _pand k _ithere is many class values, and can torque T according to demand _dtotal torque T is driven with reality _qbetween deviation size select a suitable class value.