CN106641227A - Driving intention identifying method suitable for multi-performance comprehensive optimization of gear shift schedule - Google Patents
Driving intention identifying method suitable for multi-performance comprehensive optimization of gear shift schedule Download PDFInfo
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- CN106641227A CN106641227A CN201710042971.6A CN201710042971A CN106641227A CN 106641227 A CN106641227 A CN 106641227A CN 201710042971 A CN201710042971 A CN 201710042971A CN 106641227 A CN106641227 A CN 106641227A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16H—GEARING
- F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
- F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
- F16H2061/0093—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method using models to estimate the state of the controlled object
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Abstract
The invention discloses a driving intention identifying method suitable for multi-performance comprehensive optimization of a gear shift schedule, and belongs to the technical field of step type automatic transmissions of cars. The driving intention identifying method comprises the following steps of: adopting a dynamic property and economical efficiency expectation fuzzy inferer to perform quantization on dynamic property and economical efficiency expectation of a driver separately, and determining emission performance expectation on this basis; respectively determining input parameters and output parameters of the fuzzy inferer according to the construction of the dynamic property and economical efficiency expectation fuzzy inferer, and determining subordinate functions of the input parameters and the output parameters; and finally, establishing a dynamic property and economical efficiency fuzzy inference rule base, and taking each performance expectation quantized value as a weighting coefficient to construct a gear shift performance comprehensive evaluative function which is used as a gear shift rule multi-performance index comprehensive optimization target function. On the basis of reflecting the driving intention of the driver, the driving intention identifying method enables dynamic property, economical efficiency and emission comprehensive performance of a whole vehicle to be optimal, and also enables automatic gear shift to be more personalized, so that the defect of simply sorting driving intentions in the prior art is made up.
Description
Technical field
The present invention relates to Technology of Automatic Transmission for vehicle field, more particularly to can be used for stepped automatic transmission gearshift
The driving intention recognition methods of rule multi-performance index complex optimum.
Background technology
Automobile geared automatic transmission include hydraulic automatic speed variator (AT), electric control mechanical type automatic speed variator (AMT) and
The automatic transmission with fixed qty gear such as double-clutch automatic gearbox (DCT).As geared automatic transmission core
The schedule of one of sport technique segment, defines the control parameter of automatic transmission Shift Strategy and is determined by these parameters
Gearshift opportunity (shifting points), the dynamic property, economy and discharge performance to car load have a major impact.Based on riding manipulation feature
Reasonable different drivers of embodiment are to gearshift performance with the identification of the driving intention of travel condition of vehicle information and in schedule
Expect to adapt to personalized driving style, be the inevitable requirement of automatic Transmissions Technique development.The current identification side to driving intention
Fado using classifications of patterns and Bayes' assessment, using the composite character of driver's decision behavior, by discrete model and
The behavior of continuous subsystem estimates parameter to track instantaneous discrete state, so as to realize the identification of driving intention, and adopts grain
Son filtering recurrence approaches the fuzzy logic model of probability distribution over states, and by estimating the transport condition of subsequent time driving is recognized
It is intended to, so as to predict driver's Path selection.However, existing driving intention recognition methods will drive due to lacking effective means
Intention assessment result is applied to embody the schedule of driving style and calculates, it is difficult to meet to self-shifting individual requirement.
The content of the invention
The invention aims to solve the not enough above of existing driving intention recognition methods, there is provided a set of suitable for gearshift
The driving intention recognition methods of rule multi-performance index complex optimum, it is intended to consider car load dynamic property, economy and row
Index is put, the performance expectation of driver will be recognized according to riding manipulation characteristic parameter and travel condition of vehicle information, for exchanging
The optimization of gear rule so that the automobile of equipment geared automatic transmission make on the basis of personalized gearshift is realized dynamic property,
Fuel consumption and emission performance reaches comprehensive optimum.
A kind of driving intention recognition methods suitable for schedule multi-performance index complex optimum, comprises the following steps：
Expect that fuzzy inferior and economy expect fuzzy inferior respectively to the dynamic property phase of driver using dynamic property
Hope and economy is expected to be quantified, and on the basis of the dynamic property expectation to driver and economy are expected to quantify really
Determine discharge performance expectation；The construction that fuzzy inferior is expected with economy is expected for dynamic property, fuzzy inferior is determined respectively
|input paramete and fuzzy inferior output parameter, it is then determined that each input, the membership function of output parameter, finally set up Jing
Ji property expects that fuzzy inference rule storehouse is expected in fuzzy inference rule storehouse, dynamic property；With each performance expectation quantized value as weight coefficient,
Construct the gearshift performance synthesis evaluation function as schedule multi-performance index complex optimum object function；
Described expects to determine discharge performance on the basis of quantifying in the dynamic property expectation to driver and economy
Expect, refer to that all emission performances expect that quantized value expects that quantized value and economy expect that quantized value sum is 1 with dynamic property, and respectively
The expectation quantized value of discharge performance index takes identical value；
The |input paramete of described fuzzy inferior, refers to accelerator open degree, accelerator open degree rate of change and engine speed；Institute
The output parameter of the fuzzy inferior stated, expects that fuzzy inferior is respectively dynamic property and expects and combustion for dynamic property and economy
The desired quantized value of oily economy, span is [0,1]；
Described determination is respectively input into, the membership function of output parameter, is to determine oil according to the operating experience of outstanding driver
Desired value of the driver of door aperture, accelerator open degree rate of change, engine speed and output to power performance and the economy performance
Fuzzy subset and domain, and determine the corresponding degree of membership of each fuzzy language for representing each parameter；
Described sets up fuzzy inference rule storehouse, is according to the operating experience of outstanding driver, expertise and starts
Machine characteristic, with accelerator open degree, accelerator open degree rate of change and engine speed as input, driver is to dynamic property and economy
Quantized value is expected to export, set up dynamic property and expect that fuzzy inference rule and economy expect fuzzy inference rule；
It is described with each performance expectation quantized value as weight coefficient, construct as schedule multi-performance index complex optimum
The gearshift performance synthesis evaluation function of object function, refers to dynamic property, economy and the desired quantized value of each discharge performance, point
It is not multiplied with the dynamic property subhead scalar functions after normalization, economy subhead scalar functions and each discharge performance subhead scalar functions, so
Afterwards using the cumulative summation of linear weighting method as gearshift performance synthesis evaluation function；Finally using appropriate method to gearshift rule
Rule carries out multi-performance index complex optimum.
The accelerator open degree α point is 5 fuzzy subsets, and its fuzzy language is：Very little (VS), little (S), in (M), greatly
(B) it is, very big (VB) }；
The door aperture rate of change d α/dt point is 5 fuzzy subsets, its fuzzy language for negative big (NB), bears little (NS),
Keep (0), just little (PS), honest (PB) }；
Engine speed neBe divided into 5 fuzzy subsets, its fuzzy language for very little (VS), little (S), in (M), greatly
(B) it is, very big (VB) }；
The driver is divided into 7 fuzzy subsets to the desired value of dynamic property and economy, and its fuzzy language is { very
Difference (VB), poor (B), poor (LB), in (Z), higher (LH), height (H), very high (VH) }；
The dynamic property expects that the dynamic property in fuzzy inference rule storehouse expects that inference rule is 125, see the table below
The economy expects that the economy in fuzzy inference rule storehouse expects that inference rule is 125, see the table below
By the accelerator open degree and its rate of change, engine speed in its domain decile and construct input combination：Throttle
Aperture takes respectively 20%, 40%, 60%, 80% and 100%；Accelerator open degree rate of change takes respectively -8, -4,0,4 and 8；Engine
Rotating speed takes respectively 1500,2500,3500,4500 and 5500；Totally 125 kinds of input combinations；Then for each input combination, point
Do not call dynamic property and economy to expect fuzzy inferior, calculate dynamic property and expect that quantized value and economy expect quantized value；
Driver is that emission performance expects that quantized value is calculated as follows to the desired value of emission performance index
ωco=ωhc=ωnox=(1- ωd-ωfc)/3
In formula, ωco、ωhc、ωnoxAnd ωdAnd ωfcCO emission, hydrocarbon emission are represented respectively
Amount, nitrogen oxide emission, dynamic property and economy expect quantized value (i.e. weight coefficient).
The dynamic property subhead scalar functions fd(ua) using the absolute value of the difference of adjacent two gears acceleration under same accelerator open degree
Represent, be shown below
In formula, uaFor speed,To travel acceleration, i represents gear.
The economy subhead scalar functions ffc(ua) using the difference of adjacent two gears fuel consumption under same accelerator open degree
Absolute value representation, is shown below
ffc(ua)=| bei-be(i+1)|
In formula, beFor the fuel consumption of engine, i represents gear, uaFor speed.
Each discharge performance subhead scalar functions are using under same accelerator open degree adjacent two differences for keeping off pollutant brake specific emission
Absolute value representation, CO, HC and NOx emission target function difference it is as follows
fco(ua)=| bcoi-bco(i+1)|
fhc(ua)=| bhci-bhc(i+1)|
fnox(ua)=| bnoxi-bnox(i+1)|
In formula, bco、bhc、bnoxRespectively CO, HC and NOx ratio discharge capacity, i represents gear.
Above-mentioned each partial objectives for functional value does as follows normalized, is transformed in the range of [0,1], normalization
Formula is as follows
In formula, x ' represent normalization after data, x be sample initial data, xmaxFor the maximum in sample data, xmin
For the minimum of a value in sample data；
The employing linear weighting method constructs as follows multi-performance index composite evaluation function f (ua),
In formula, fd'(ua)、ff'c(ua)、f'co(ua)、f'hc(ua)、f'nox(ua) it is respectively the dynamic property point after normalizing
Object function, economy subhead scalar functions, CO, HC and NOx emission subhead scalar functions；ωd、ωfc、ωco、ωhc、ωnoxRespectively
For dynamic property subhead scalar functions, economy subhead scalar functions, CO discharge subhead scalar functions, HC discharge subhead scalar functions and NOx
The weight coefficient of discharge subhead scalar functions；
It is described that proper method that multi-performance index complex optimum adopts is carried out to schedule for simulated annealing or something lost
Propagation algorithm, to obtain the schedule of 125 kinds of multi-performance index complex optimums.
Compared with prior art, the invention has the beneficial effects as follows：Obtained using technical solution of the present invention dynamic property,
Economy and emission performance expect the automobile geared automatic transmission schedule formulated on the basis of quantized value, can embody
It is optimal car load power, economic and discharge combination property on the basis of driver's driving intention, solves and have at present level certainly
Dynamic transmission schedule mainly considers to make dynamic property, the economy or emission performance three wherein optimum deficiency of certain single performance,
Also so that self shifter is more personalized；And can overcome the disadvantages that deficiency of the prior art to driving intention simple classification.
Description of the drawings
Fig. 1 is the overall plan of the driving intention recognition methods for schedule multi-performance index complex optimum；
Fig. 2 is that dynamic property expects fuzzy inferior schematic diagram；
Fig. 3 is that economy expects fuzzy inferior schematic diagram；
Fig. 4 is accelerator open degree membership function；
Fig. 5 is accelerator open degree rate of change membership function；
Fig. 6 is engine speed membership function；
Fig. 7 is dynamic property and economy desired value membership function；
Fig. 8 is gearshift performance synthesis evaluation function building method schematic diagram；
Fig. 9 is the comprehensive optimum schedule example one of multi-performance index；
Figure 10 is the comprehensive optimum schedule example two of multi-performance index；
Specific embodiment
Below in conjunction with the accompanying drawings, with reference to certain many performance synthesis optimization of 5 gear electric control mechanical type automatic speed variator (AMT) schedules
The utilization of middle driving intention recognition methods, the present invention is further illustrated.
Fig. 1 is for the driving intention recognition methods of geared automatic transmission schedule multi-performance index complex optimum
Overall plan, is expected the dynamic property of driver using fuzzy inferior and economy is expected to quantify, and to driver
Dynamic property expect and economy expect quantified on the basis of determine discharge performance expect.Expect for dynamic property and economical
Property expect fuzzy inferior construction, respectively determine fuzzy inferior |input paramete and fuzzy inferior output parameter, so
The membership function of each input, output parameter is determined afterwards, is finally set up economy, dynamic property and is expected fuzzy inference rule storehouse.With each
Performance expectation quantized value is weight coefficient, constructs the gearshift performance as schedule multi-performance index complex optimum object function
Composite evaluation function.
Fig. 2 is that dynamic property expects fuzzy inferior schematic diagram, accelerator open degree, accelerator open degree rate of change and engine speed Jing
As the |input paramete of fuzzy inferior after obfuscation, dynamic property expects the output parameter as fuzzy inferior, it is determined that each
Dynamic property is set up on the basis of input, the membership function of output parameter and expects fuzzy inference rule storehouse, after de-fuzzy, output is dynamic
Power expects quantized value.
Fig. 3 is that economy expects fuzzy inferior figure, and accelerator open degree, accelerator open degree rate of change and engine speed Jing are obscured
As the |input paramete of fuzzy inferior after change, economy is desired for the output parameter of fuzzy inferior, it is determined that each input, defeated
Economy is set up on the basis of the membership function for going out parameter and expects fuzzy inference rule storehouse, after de-fuzzy, export the economy phase
Hope quantized value.
Expect fuzzy inferior for dynamic property：|input paramete turns for accelerator open degree, accelerator open degree rate of change and engine
Speed；Output parameter is the desired quantized value of dynamic property, and span is [0,1].
Pin economy expects fuzzy inferior：|input paramete is accelerator open degree, accelerator open degree rate of change and engine speed；
Output parameter is the desired quantized value of economy, and span is [0,1].
And the desired quantized value of discharge performance for exporting is to expect to carry out in the dynamic property expectation to driver and economy
Determine on the basis of quantization, respectively CO emission value, hydrocarbon emission quantized value and nitrogen oxides row
Put quantized value, the expectation quantized value of each discharge performance index takes identical value, and the desired quantized value of all discharge performance with
Dynamic property quantized value and economy expect that quantized value sum is 1.
Fig. 4 is accelerator open degree membership function schematic diagram.Accelerator open degree is divided into 5 fuzzy subsets, and its fuzzy language is：{ very
Little (VS), little (S), in (M), big (B) is very big (VB) }, abscissa represents the domain model of each fuzzy language of accelerator open degree in figure
Enclose, ordinate represents the corresponding degree of membership of each fuzzy language of accelerator open degree.
Fig. 5 is accelerator open degree rate of change membership function schematic diagram.Accelerator open degree rate of change is divided into 5 fuzzy subsets, its mould
Paste language is { negative big (NB), bears little (NS), keeps (0), just little (PS), honest (PB) }, and abscissa represents accelerator open degree in figure
The domain scope of each fuzzy language of rate of change, ordinate represents the corresponding degree of membership of each fuzzy language of accelerator open degree rate of change.
Fig. 6 is engine speed membership function schematic diagram.Engine speed is divided into 5 fuzzy subsets, and its fuzzy language is
Very little (VB), little (S), in (M), big (B) is very big (VB) }, abscissa represents the opinion of each fuzzy language of engine speed in figure
Domain scope, ordinate represents the corresponding degree of membership of each fuzzy language of engine speed.
Fig. 7 is dynamic property and economy desired value membership function schematic diagram.Expectation of the driver to dynamic property and economy
Value is divided into 7 fuzzy subsets, its fuzzy language be very poor (VB), poor (B), poor (LB), in (Z), higher (LH) is high
(H) it is, very high (VH) }, abscissa represents the domain scope of dynamic property and each fuzzy language of economy desired value, ordinate table in figure
Show dynamic property and the corresponding degree of membership of each fuzzy language of economy desired value.
Following table is that dynamic property expects fuzzy inference rule table, is according to the operating experience of driver, expertise and starts
What machine characteristic was formulated.The present embodiment has formulated 125 dynamic property and has expected fuzzy inference rule,
Part inference rule is described as follows：
R1：IF α=VS AND d α/dt=NB AND ne=VS THEN ωd=LB；
R2：IF α=VS AND d α/dt=NB AND ne=S THEN ωd=LB；
……
R125：IF α=VB AND d α/dt=PB AND ne=VB THEN ωd=H.
By taking the first rule as an example, it is meant that when accelerator open degree very little, the negative big, engine speed of accelerator open degree rate of change
During very little, the reasoning results are that expectation of the driver to dynamic property is relatively low.
Following table is economy desired value fuzzy inference rule table, is according to the operating experience of driver, expertise and sends out
Characteristics of motivation is formulated.The present embodiment has formulated 125 economy and has expected fuzzy inference rule,
Part inference rule is described as follows：
R1：IF α=VS AND d α/dt=NB AND ne=VS THEN ωfc=Z；
R2：IF α=VS AND d α/dt=NB AND ne=S THEN ωfc=Z；
……
R125：IF α=VB AND d α/dt=PB AND ne=VB THEN ωfc=VB.
By taking the first rule as an example, it is meant that when accelerator open degree very little, the negative big, engine speed of accelerator open degree rate of change
During very little, the reasoning results be driver to economy be desired for it is medium.
In the present embodiment, accelerator open degree and its rate of change, engine speed decile and are constructed into input in its domain
Combination, specifically, accelerator open degree takes respectively 20%, 40%, 60%, 80% and 100%；Accelerator open degree rate of change takes respectively -8, -
4th, 0,4 and 8；Engine speed takes respectively 1500,2500,3500,4500 and 5500；Totally 125 kinds of input combinations.Then for every
One kind input combination：Call dynamic property and economy to expect fuzzy inferior respectively, calculate dynamic property and economy desired value；
Driver is calculated as follows to the desired value of emission performance index
ωco=ωhc=ωnox=(1- ωd-ωfc)/3
In formula, ωco、ωhc、ωnoxAnd ωdAnd ωfcCO emission, hydrocarbon emission are represented respectively
Amount, nitrogen oxide emission, dynamic property and economy desired value (i.e. weight coefficient).
With reference to certain many performance synthesis optimization optimization of 5 gear electric control mechanical type automatic speed variator (AMT) schedules, enter one
The utilization of step explanation driving intention recognition methods of the present invention.
Fig. 8 is gearshift performance synthesis evaluation function building method schematic diagram, constructs vehicle dynamic quality, fuel oil Jing respectively first
The evaluation function of Ji property and discharge performance, i.e. dynamic property subhead scalar functions, economy subhead scalar functions and each discharge performance subhead
Then three class subhead scalar functions are normalized by scalar functions, finally can be used for having level using linear weighting method construction
The gearshift performance synthesis evaluation function (i.e. multi-performance index composite evaluation function) of automatic transmission shift rule optimization.
In the present embodiment, dynamic property subhead scalar functions are absolute using under same accelerator open degree adjacent two differences for keeping off acceleration
Value is represented, is shown below
In formula, uaFor speed,To travel acceleration, i represents gear.
In the present embodiment, economy subhead scalar functions are using the difference of adjacent two gears fuel consumption under same accelerator open degree
Absolute value representation, is shown below
ffc(ua)=| bei-be(i+1)|
Ffc (ua)=| bei-be (i+1) |
In formula, beFor the fuel consumption of engine, i represents gear.
In the present embodiment, each discharge performance subhead scalar functions are using adjacent two gears pollutant brake specific exhaust emission under same accelerator open degree
The absolute value representation of the difference of amount, CO, HC and NOx emission target function are distinguished as follows
fco(ua)=| bcoi-bco(i+1)|
fhc(ua)=| bhci-bhc(i+1)|
fnox(ua)=| bnoxi-bnox(i+1)|
In formula, bco、bhc、bnoxRespectively CO, HC and NOx ratio discharge capacity.
In the present embodiment, nondimensionalization is done to each partial objectives for functional value using range method and is processed, be transformed into the model of [0,1]
In enclosing.Normalization formula is as follows
In formula, x ' represent normalization after data, x be sample initial data, xmaxFor the maximum in sample data, xmin
For the minimum of a value in sample data.
Multi-performance index composite evaluation function is constructed using linear weighting method, is shown below.
In formula, f'd(ua)、f'fc(ua)、f'co(ua)、f'hc(ua)、f'nox(ua) respectively normalize after properties
Corresponding subhead scalar functions；ωd、ωfc、ωco、ωhc、ωnoxRespectively dynamic property subhead scalar functions, the economy subhead offer of tender
The weight coefficient of number, CO discharge subhead scalar functions, HC discharge subhead scalar functions and NOx emission subhead scalar functions.
Each partial objectives for function pair answers the different size of weights to combine the gearshift performances different for embodying driver in above formula
Tendency, such as：ωdValue it is relatively larger, represent that driver is wished now based on dynamic property；If driver is to fuel-economy
Property expectation higher than expectation to dynamic property and discharge performance, then ωfcShould obtain relatively larger.
In the present embodiment, for aforementioned 125 kinds input combinations, can be calculated using driving intention recognition methods of the present invention
The corresponding dynamic property of various input combinations, economy and each discharge performance desired value, then using these desired values as corresponding Jing
The weight coefficient of normalized each performance indications, goes to construct multi-performance index composite evaluation function and with it as optimization aim, adopts
Offline optimization is carried out with appropriate optimized algorithm (such as simulated annealing or genetic algorithm), 125 kinds of schedules are obtained.
If accelerator open degree 75%, accelerator open degree rate of change are 7, engine speed is 4200r/min, can be calculated
ωd=0.67, ωfc=0.15, ωco=ωhc=ωnox=0.06, using the calculated many property of Simulated Anneal Algorithm Optimize
Energy index comprehensive optimum schedule is as shown in Figure 9.
If accelerator open degree 35%, accelerator open degree rate of change are 0, engine speed is 3200r/min, can be calculated
ωd=0.37, ωfc=0.45, wco=whc=wnox=0.06, referred to using the calculated many performances of Simulated Anneal Algorithm Optimize
The comprehensive optimum schedule of mark is as shown in Figure 10.
In being embodied as, it is stored in the form of gearshift table above by the calculated schedule of offline optimization
In transmission control unit (TCU), and can be made a look up according to accelerator open degree, accelerator open degree rate of change and engine speed.
In vehicle operation, TCU passes through Real-time Collection accelerator open degree, accelerator open degree rate of change and engine speed, and from storage
|input paramete is found out in gearshift table closest with the accelerator open degree of Real-time Collection, accelerator open degree rate of change and engine speed
Foundation of the gearshift table as Shift Strategy.
Finally it should be noted that above example is only to illustrate technical scheme and unrestricted.Although reference
Previous embodiment has been described in detail to the present invention, it will be understood by those within the art that：It still can be right
Technical scheme described in previous embodiment is modified, or carries out equivalent to which part technical characteristic；And these
Modification is replaced, and does not make the spirit and scope of the essence disengaging embodiment of the present invention technical scheme of appropriate technical solution.
Claims (4)
1. a kind of driving intention recognition methods suitable for many performance synthesis optimizations of schedule, is characterized in that, including following step
Suddenly：
Using dynamic property expect fuzzy inferior and economy expect fuzzy inferior the dynamic property of driver is expected respectively and
Economy is expected to be quantified, and determines row on the basis of the dynamic property expectation to driver and economy are expected to quantify
Put performance expectation；The construction that fuzzy inferior is expected with economy is expected for dynamic property, the defeated of fuzzy inferior is determined respectively
Enter the output parameter of parameter and fuzzy inferior, it is then determined that each input, the membership function of output parameter, finally set up economy
Expect that fuzzy inference rule storehouse is expected in fuzzy inference rule storehouse, dynamic property；With each performance expectation quantized value as weight coefficient, construction
As the gearshift performance synthesis evaluation function of schedule multi-performance index complex optimum object function；
Described determines that discharge performance is expected on the basis of the dynamic property expectation to driver and economy are expected to quantify,
Refer to that all emission performances expect that quantized value expects that quantized value and economy expect that quantized value sum is 1 with dynamic property, and each discharge
The expectation quantized value of performance indications takes identical value；
The |input paramete of described fuzzy inferior, refers to accelerator open degree, accelerator open degree rate of change and engine speed；Described
The output parameter of fuzzy inferior, expects that fuzzy inferior is respectively dynamic property and expects and fuel oil Jing for dynamic property and economy
The desired quantized value of Ji property, span is [0,1]；
Described determination is respectively input into, the membership function of output parameter, is to determine that throttle is opened according to the operating experience of outstanding driver
Mould of the driver of degree, accelerator open degree rate of change, engine speed and output to the desired value of power performance and the economy performance
Paste subset and domain, and determine the corresponding degree of membership of each fuzzy language for representing each parameter；
Described sets up fuzzy inference rule storehouse, is special according to the operating experience of outstanding driver, expertise and engine
Property, with accelerator open degree, accelerator open degree rate of change and engine speed as input, expectation of the driver to dynamic property and economy
Quantized value is output, sets up dynamic property and expects that fuzzy inference rule and economy expect fuzzy inference rule；
It is described with each performance expectation quantized value as weight coefficient, construct as schedule multi-performance index complex optimum target
The gearshift performance synthesis evaluation function of function, refers to dynamic property, economy and the desired quantized value of each discharge performance, respectively with
Dynamic property subhead scalar functions, economy subhead scalar functions after normalization are multiplied with each discharge performance subhead scalar functions, then adopt
With the cumulative summation of linear weighting method as gearshift performance synthesis evaluation function；Finally schedule is entered using appropriate method
Row multi-performance index complex optimum.
2. the driving intention recognition methods suitable for many performance synthesis optimizations of schedule according to claim 1, it is special
Levying is, the accelerator open degree α point is 5 fuzzy subsets, and its fuzzy language is：Very little (VS), little (S), in (M), big (B), very
Greatly (VB) }；
The door aperture rate of change d α/dt point is 5 fuzzy subsets, and its fuzzy language is for { negative big (NB), bears little (NS), keeps
(0) it is, just little (PS), honest (PB) }；
Engine speed neBe divided into 5 fuzzy subsets, its fuzzy language for very little (VS), little (S), in (M), big (B), very
Greatly (VB) }；
The driver is divided into 7 fuzzy subsets to the desired value of dynamic property and economy, and its fuzzy language is { very poor
(VB), poor (B), poor (LB), in (Z), higher (LH), high (H), very high (VH) }；
The dynamic property expects that the dynamic property in fuzzy inference rule storehouse expects that inference rule is 125, see the table below
The economy expects that the economy in fuzzy inference rule storehouse expects that inference rule is 125, see the table below
3. the driving intention recognition methods suitable for many performance synthesis optimizations of schedule according to claim 1, it is special
Levying is, by the accelerator open degree and its rate of change, engine speed in its domain decile and construct input combination：Accelerator open degree
20%, 40%, 60%, 80% and 100% is taken respectively；Accelerator open degree rate of change takes respectively -8, -4,0,4 and 8；Engine speed
1500,2500,3500,4500 and 5500 are taken respectively；Totally 125 kinds of input combinations；Then for each input combination, adjust respectively
Expect fuzzy inferior with dynamic property and economy, calculate dynamic property and expect that quantized value and economy expect quantized value；Drive
Member is that emission performance expects that quantized value is calculated as follows to the desired value of emission performance index
ωco=ωhc=ωnox=(1- ωd-ωfc)/3
In formula, ωco、ωhc、ωnoxAnd ωdAnd ωfcCO emission, hydrocarbon emission amount, nitrogen oxygen are represented respectively
Compound discharge capacity, dynamic property and economy expect quantized value (i.e. weight coefficient).
4. the driving intention recognition methods suitable for many performance synthesis optimizations of schedule according to claim 3, it is special
Levying is, the dynamic property subhead scalar functions fd(ua) using the absolute value table of the difference of adjacent two gears acceleration under same accelerator open degree
Show, be shown below
In formula, uaFor speed,To travel acceleration, i represents gear.
The economy subhead scalar functions ffc(ua) using the absolute of under same accelerator open degree adjacent two differences for keeping off fuel consumption
Value is represented, is shown below
ffc(ua)=| bei-be(i+1)|
In formula, beFor the fuel consumption of engine, i represents gear, uaFor speed.
Each discharge performance subhead scalar functions are exhausted using under same accelerator open degree adjacent two differences for keeping off pollutant brake specific emission
Value is represented, CO, HC and NOx emission target function difference are as follows
fco(ua)=| bcoi-bco(i+1)|
fhc(ua)=| bhci-bhc(i+1)|
fnox(ua)=| bnoxi-bnox(i+1)|
In formula, bco、bhc、bnoxRespectively CO, HC and NOx ratio discharge capacity, i represents gear,
Above-mentioned each partial objectives for functional value does as follows normalized, is transformed in the range of [0,1], normalizes formula
It is as follows
In formula, x ' represent normalization after data, x be sample initial data, xmaxFor the maximum in sample data, xminFor sample
Minimum of a value in notebook data；
The employing linear weighting method constructs as follows multi-performance index composite evaluation function f (ua),
In formula, f 'd(ua)、f′fc(ua)、f′co(ua)、f′hc(ua)、f′nox(ua) respectively normalize after dynamic property partial objectives for
Function, economy subhead scalar functions, CO, HC and NOx emission subhead scalar functions；ωd、ωfc、ωco、ωhc、ωnoxRespectively move
Power subhead scalar functions, economy subhead scalar functions, CO discharge subhead scalar functions, HC discharge subhead scalar functions and NOx emission
The weight coefficient of subhead scalar functions；
It is described that the proper method that multi-performance index complex optimum adopts is carried out to schedule is that simulated annealing or heredity are calculated
Method, to obtain the schedule of 125 kinds of multi-performance index complex optimums.
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| CN108361366A (en) * | 2018-01-17 | 2018-08-03 | 北京理工大学 | A kind of automatic mechanical transmission process for gear |
| CN109050534A (en) * | 2018-08-01 | 2018-12-21 | 江苏大学 | A kind of ecology driving reminding method |
| CN111027618A (en) * | 2019-12-09 | 2020-04-17 | 西华大学 | Automobile dynamic property and economic property expectation quantification method |
| CN114043989A (en) * | 2021-11-29 | 2022-02-15 | 江苏大学 | Recursive graph and convolutional neural network-based driving style recognition model, lane change decision model and decision method |
| CN115492928A (en) * | 2022-08-29 | 2022-12-20 | 西华大学 | Economic, dynamic and safety comprehensive optimal gear shifting rule optimization method |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108361366A (en) * | 2018-01-17 | 2018-08-03 | 北京理工大学 | A kind of automatic mechanical transmission process for gear |
| CN108361366B (en) * | 2018-01-17 | 2019-07-05 | 北京理工大学 | A kind of automatic mechanical transmission process for gear |
| CN109050534A (en) * | 2018-08-01 | 2018-12-21 | 江苏大学 | A kind of ecology driving reminding method |
| CN109050534B (en) * | 2018-08-01 | 2020-06-09 | 江苏大学 | Ecological driving prompting method |
| CN111027618A (en) * | 2019-12-09 | 2020-04-17 | 西华大学 | Automobile dynamic property and economic property expectation quantification method |
| CN111027618B (en) * | 2019-12-09 | 2022-05-20 | 西华大学 | Automobile dynamic property and economic expectation quantification method |
| CN114043989A (en) * | 2021-11-29 | 2022-02-15 | 江苏大学 | Recursive graph and convolutional neural network-based driving style recognition model, lane change decision model and decision method |
| CN114043989B (en) * | 2021-11-29 | 2024-06-07 | 江苏大学 | Driving style recognition model, lane change decision model and decision method based on recursion diagram and convolutional neural network |
| CN115492928A (en) * | 2022-08-29 | 2022-12-20 | 西华大学 | Economic, dynamic and safety comprehensive optimal gear shifting rule optimization method |
| CN115492928B (en) * | 2022-08-29 | 2023-08-01 | 西华大学 | Economical efficiency, dynamic performance and safety comprehensive optimal gear shifting rule optimization method |
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