CN106326581A - Determination method and device for driving range and automobile - Google Patents
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Abstract
The invention provides a method and a device for determining driving range and an automobile, and relates to the technical field of automobiles. The method comprises the following steps: acquiring first driving data within a preset time length before the current time of the automobile; determining the current driving working condition of the automobile according to the first driving data; and determining the driving range corresponding to the current residual energy according to the current driving working condition. According to the scheme, the driving range of the automobile is determined based on the driving working condition, so that higher applicability and accuracy are achieved, and the problem that the existing method for determining the driving range of the electric automobile is poor in applicability and accuracy is solved.
Description
Technical field
The present invention relates to automobile technical field, particularly relate to determination method, device and the automobile of a kind of continual mileage.
Background technology
Compared with ordinary internal combustion engine automobile, electric automobile has an enormous advantage in terms of discharge and Utilizing Energy Sources in Reason,
But the continual mileage of electric automobile is short at present, and energy supplement speed is slow, constrains popularizing of electric automobile.Therefore electronic vapour
Car should be able to provide a user with electrokinetic cell continual mileage information the most accurately, in order to user plans stroke and charging interval.
But the determination of existing electric automobile continual mileage is mainly according to electrokinetic cell output energy and running car
The equal principle of energy consumed is carried out, but, under special driving cycle, such as muddy hill path, electrokinetic cell output energy with
The energy unequal that running car consumes.Therefore, the suitability of the determination method of existing electric automobile continual mileage and standard
Really property is poor.
Summary of the invention
It is an object of the invention to provide determination method, device and the automobile of a kind of continual mileage, existing electronic to solve
The suitability of the determination method of automobile continual mileage and the poor problem of accuracy.
For reaching above-mentioned purpose, embodiments of the invention provide a kind of determination method of continual mileage, including:
Obtain the first running data in the previous predetermined time period of automobile current time；
According to described first running data, determine the current driving operating mode of described automobile；
According to described current driving operating mode, determine the continual mileage of corresponding current remaining.
The determination method of the continual mileage of the embodiment of the present invention, the previous Preset Time first obtaining automobile current time is long
The first running data in degree, as the basic data of continual mileage.Then, according to this first running data, automobile is determined
Current driving operating mode.Finally, according to the current driving operating mode determined, the continual mileage of corresponding current residual is determined.This
Sample, by determining current driving cycle based on the actual travel data of automobile, is then based on driving cycle and determines correspondence
The mode of the continual mileage of current remaining, it is contemplated that the driving cycle impact on continual mileage, adds continual mileage
Estimation accuracy, and there is the more preferably suitability.
Wherein, the determination method of described continual mileage also includes:
Extract the driving parameters in the second running data of the typical driving cycle prestored；
Described second running data is carried out principal component analysis, described driving parameters determines the first driving parameters；
According to default cluster number and described first driving parameters, described second running data is carried out cluster analysis,
Determine cluster number and the cluster centre of the running data of described typical case's driving cycle.
Wherein, according to the first running data, determine the step of the current driving operating mode of described automobile, including:
Extract the second driving parameters of corresponding described first driving parameters in described first running data；
According to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ciDistance di；Wherein, i
Represent ith cluster；
According to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
Wherein, according to described current driving operating mode, determine the step of the continual mileage of corresponding current remaining, including:
According to formulaObtain the average power consumption of each cluster of running data of described typical case's driving cycleWherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent the energy expenditure of kth operating mode fragment, fkRepresent
The degree of membership of kth operating mode fragment, FiRepresent ith cluster degree of membership sum；
Current driving operating mode according to described automobile, determines that the average energy consumption of corresponding cluster is as specific energy consumption；
According to formulaObtain the total travel power consumption E of j the operating mode fragment before of described automobilecost；Wherein,
EjRepresent the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
According to formulaObtain the mileage number l that described automobile specific energy consumption travels；Wherein, S represents that described automobile exists
Distance travelled in described predetermined time period；
According to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；Wherein, EtotalRepresent described vapour
The battery gross energy of car；
According to formula Sres=lEres, obtain the continual mileage of described automobile.
Wherein, described second running data is carried out principal component analysis, described driving parameters determines the first traveling ginseng
The step of number, including:
Determine n sample in described second running data and p variable, build first matrix of n*p；
After described first matrix is carried out standards change, obtain the correlation coefficient between each variable, obtain correlation coefficient
Second matrix；
Obtain eigenvalue and the corresponding standard feature vector of described second matrix, according to formula Yi=Xei, obtain i-th
Main constituent Yi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p；
According to described main constituent, determine the first driving parameters.
Wherein, according to described main constituent, determine the step of the first driving parameters, including:
According to formulaObtain contribution rate w of i-th main constituenti；Wherein, λiRepresent i-th main constituent
Eigenvalue；
According to formulaObtain contribution rate of accumulative total w of m main constituentm；
According to the dependency between driving parameters and main constituent and the dependency between driving parameters, to eigenvalue more than first
Predetermined threshold value and accumulation contribution rate are analyzed more than the main constituent of the second predetermined threshold value, determine the first driving parameters.
Wherein, according to default cluster number and described first driving parameters, described second running data is clustered
Analyze, determine cluster number and the step of cluster centre of the running data of described typical case's driving cycle, including:
According to default cluster number and 0, the random number between 1, initialize subordinated-degree matrix U；
According to formulaObtain the cluster centre V of l step(l)；Wherein, uikRepresent kth
Individual sample belongs to the degree of membership of the i-th class, and uikMeet
According to formulaObtain the object function J of l step(l)；
According to formulaRevise the subordinated-degree matrix U of l step(l), wherein
MeetingTime, obtain target subordinated-degree matrix and target cluster centre；Wherein, εuFor in advance
If be subordinate to termination tolerance；
The target cluster centre that described first driving parameters is corresponding with each cluster number is compared, determines state class
Distinguishing the cluster number of the running data that significantly cluster number is described typical case's driving cycle, corresponding target cluster centre is
The cluster centre of the running data of described typical case's driving cycle.
For reaching above-mentioned purpose, embodiments of the invention additionally provide the determination device of a kind of continual mileage, including:
Acquisition module, the first running data in the previous predetermined time period obtaining automobile current time；
First determines module, for according to described first running data, determines the current driving operating mode of described automobile；
Second determines module, for according to described current driving operating mode, determining the continual mileage of corresponding current remaining.
The determination device of the continual mileage of the embodiment of the present invention, acquisition module obtain automobile current time previous default time
Between the first running data in length, as the basic data of continual mileage.Then, first determines that module is according to this first traveling
Data, determine the current driving operating mode of automobile.Finally, second determines that module, according to the current driving operating mode determined, determines
Go out the continual mileage of corresponding current residual.So, by determining current traveling work based on the actual travel data of automobile
Condition, be then based on the mode that driving cycle determines the continual mileage of corresponding current remaining, it is contemplated that driving cycle is to continuous
Sail the impact of mileage, add the estimation accuracy of continual mileage, and there is the more preferably suitability.
Wherein, the determination device of described continual mileage also includes:
Extraction module, the driving parameters in the second running data extracting the typical driving cycle prestored；
First processing module, for carrying out principal component analysis to described second running data, in described driving parameters really
Fixed first driving parameters；
Second processing module, for according to the cluster number preset and described first driving parameters, travelling described second
Data carry out cluster analysis, determine cluster number and the cluster centre of the running data of described typical case's driving cycle.
Wherein, described first determines that module includes:
Extract submodule, for extracting the second traveling ginseng of corresponding described first driving parameters in described first running data
Number；
Obtain submodule, for according to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ci
Distance di；Wherein, i represents ith cluster；
First determines submodule, for according to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
Wherein, described second determines that module includes:
First processes submodule, for according to formulaObtain the traveling number of described typical case's driving cycle
Average power consumption according to each clusterWherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent kth operating mode
The energy expenditure of fragment, fkRepresent the degree of membership of kth operating mode fragment, FiRepresent ith cluster degree of membership sum；
Second determines submodule, for the current driving operating mode according to described automobile, determines the average energy consumption of corresponding cluster
As specific energy consumption；
Second processes submodule, for according to formulaObtain j the operating mode fragment before of described automobile
Total travel power consumption Ecost；Wherein, EjRepresent the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
3rd processes submodule, for according to formulaObtain the mileage number l that described automobile specific energy consumption travels；
Wherein, S represents described automobile distance travelled in described predetermined time period；
Fourth process submodule, for according to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；
Wherein, EtotalRepresent the battery gross energy of described automobile；
5th processes submodule, for according to formula Sres=lEres, obtain the continual mileage of described automobile.
Wherein, described first processing module includes:
Build submodule, for determining n sample in described second running data and p variable, build the first of n*p
Matrix；
6th processes submodule, after described first matrix is carried out standards change, obtains being correlated with between each variable
Coefficient, obtains the second matrix of correlation coefficient；
7th processes submodule, for obtaining eigenvalue and the corresponding standard feature vector of described second matrix, according to
Formula Yi=Xei, obtain i-th main constituent Yi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p；
3rd determines submodule, for according to described main constituent, determines the first driving parameters.
Wherein, the described 3rd determines that submodule includes:
First processing unit, for according to formulaObtain contribution rate w of i-th main constituenti；Wherein, λi
Represent the eigenvalue of i-th main constituent；
Second processing unit, for according to formulaObtain contribution rate of accumulative total w of m main constituentm；
3rd processing unit, relevant for according between the dependency between driving parameters to main constituent and driving parameters
Property, eigenvalue is analyzed more than the main constituent of the second predetermined threshold value more than the first predetermined threshold value and accumulation contribution rate, determines
First driving parameters.
Wherein, described second processing module includes:
Initialization submodule, for according to the cluster number and 0 preset, the random number between 1, initializing subordinated-degree matrix
U；
8th processes submodule, for according to formulaObtain the cluster centre of l step
V(l)；Wherein, uikRepresent that kth sample belongs to the degree of membership of the i-th class, and uikMeet
9th processes submodule, for according to formulaObtain the object function J of l step(l)；
Revise submodule, for according to formulaRevise the subordinated-degree matrix U of l step(l), its
In
Tenth processes submodule, for meetingTime, obtain target subordinated-degree matrix and target
Cluster centre；Wherein, εuIt is subordinate to termination tolerance for default；
4th determines submodule, for by target cluster centre corresponding with each cluster number for described first driving parameters
Compare, determine that state class distinguishes the cluster number of the running data that significantly cluster number is described typical case's driving cycle,
Corresponding target cluster centre is the cluster centre of the running data of described typical case's driving cycle.
For reaching above-mentioned purpose, embodiments of the invention additionally provide a kind of automobile, including continual mileage as above
Determination device.
The automobile of the embodiment of the present invention, it is possible to obtain the first traveling in the previous predetermined time period of automobile current time
Data, as the basic data of continual mileage.Then, according to this first running data, the current driving operating mode of automobile is determined.
Finally, according to the current driving operating mode determined, the continual mileage of corresponding current residual is determined.So, by with automobile
Determine current driving cycle based on actual travel data, be then based on driving cycle and determine the continuous of corresponding current remaining
Sail the mode of mileage, it is contemplated that the driving cycle impact on continual mileage, add the estimation accuracy of continual mileage, and tool
There is the more preferably suitability.
Accompanying drawing explanation
Fig. 1 is the steps flow chart schematic diagram one of the determination method of the continual mileage of the embodiment of the present invention；
Fig. 2 is the steps flow chart schematic diagram two of the determination method of the continual mileage of the embodiment of the present invention；
Fig. 3 is the Metro cycle UDDS operating mode fragment figure of the U.S.；
Fig. 4 is the cluster centre result schematic diagram of C=3；
Fig. 5 is the cluster centre result schematic diagram of C=4；
Fig. 6 is the cluster centre result schematic diagram of C=5；
Fig. 7 is the cluster centre result schematic diagram of C=6；
Fig. 8 is 215 fragment cluster result schematic diagrams；
Fig. 9 is driving cycle identification process block diagram in the embodiment of the present invention；
Figure 10 is the FB(flow block) of the determination method of the continual mileage of the embodiment of the present invention；
Figure 11 is the energy expenditure schematic diagram of 215 fragments；
Figure 12 is ECE15 operating mode Velocity-time change curve schematic diagram；
Figure 13 is specific energy consumption distance travelled number schematic diagram under ECE15 operating mode；
Figure 14 is that under ECE15 operating mode, energy expenditure compares schematic diagram；
Figure 15 compares schematic diagram for residue continual mileage estimation；
Figure 16 is the structural representation of the determination device of the continual mileage of the embodiment of the present invention.
Detailed description of the invention
For making the technical problem to be solved in the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing and tool
Body embodiment is described in detail.
The present invention is directed to the determination method applicability of existing electric automobile continual mileage and the problem that accuracy is poor, carry
Supply determination method, device and the automobile of a kind of continual mileage, determined automobile continual mileage based on driving cycle, reached higher
The suitability and accuracy.
As it is shown in figure 1, the determination method of a kind of continual mileage of the embodiment of the present invention, including:
Step 101, obtains the first running data in the previous predetermined time period of automobile current time；
Step 102, according to described first running data, determines the current driving operating mode of described automobile；
Step 103, according to described current driving operating mode, determines the continual mileage of corresponding current remaining.
The determination method of the continual mileage of the embodiment of the present invention, is applied to pure electric automobile, when first acquisition automobile is current
Between previous predetermined time period in the first running data, as the basic data of continual mileage.Then, according to this first row
Sail data, determine the current driving operating mode of automobile.Finally, according to the current driving operating mode determined, determine corresponding current
Remaining continual mileage.So, by determining current driving cycle based on the actual travel data of automobile, it is then based on
Driving cycle determines the mode of the continual mileage of corresponding current remaining, it is contemplated that the driving cycle shadow to continual mileage
Ring, add the estimation accuracy of continual mileage, and there is the more preferably suitability.
Wherein, in order to realize the determination to automobile running working condition, as in figure 2 it is shown, the continual mileage of the embodiment of the present invention
Determine method, also include:
Step 104, extracts the driving parameters in the second running data of the typical driving cycle prestored；
Step 105, carries out principal component analysis to described second running data, determines the first traveling in described driving parameters
Parameter；
Step 106, according to default cluster number and described first driving parameters, gathers described second running data
Alanysis, determines cluster number and the cluster centre of the running data of described typical case's driving cycle.
Such as step 104～106, in this embodiment, principal component analysis and fuzzy clustering is used to combine, the allusion quotation to pre-stored
Type driving cycle carries out feature analysis and state recognition, determines cluster number and the cluster of the running data of this typical case's driving cycle
Center.So, after getting actual travel data, it becomes possible to determine the current driving operating mode of correspondence faster, more accurately,
And then determine continual mileage accurately.
It should be appreciated that automobile running working condition, for representing the automobile driving speed-time history of specific environment, can be
The power matching of automobile relates to, emission level and energy expenditure provide reference and detection foundation.Have than more typical driving cycle:
The Metro cycle UDDS operating mode of the U.S., highway is fuel-efficient test HWFET operating mode, the new European standard driving pattern in Europe
NEDC operating mode and the 10-15 operating mode etc. of Japan.Choose representative driving cycle, by the Velocity-time number of these operating modes
Divide available multiple driving cycle fragments according to by cycle regular hour, as it is shown on figure 3, by front for UDDS operating mode 1200 seconds time
Between course carry out fragment division, be 120 seconds available 10 driving cycle fragments (hereinafter referred to as fragment) with the time cycle.
For each fragment of accurate description, it is ensured that do not have information dropout and the distortion of driving cycle, in this embodiment,
Draft 12 driving parameters for describing, as shown in table 1 below.
Table 1
As a example by front 5 fragments of UDDS operating mode, the driving parameters having calculated correspondence is as shown in table 2 below, it can be seen that 5
12 driving parameters of fragment make a big difference.
| Symbol | Fragment 1 | Fragment 2 | Fragment 3 | Fragment 4 | Fragment 5 |
| Vm | 31.89 | 39.02 | 59.33 | 34.24 | 25.81 |
| Vmax | 52.16 | 91.11 | 91.26 | 58.75 | 57.13 |
| ∑V2 | 12189 | 25064 | 42462 | 15816 | 9157 |
| Vsd | 23.13 | 91.98 | 82.95 | 41.68 | 25.04 |
| am | 0.096 | 0.141 | -0.099 | 0.019 | -0.052 |
| APA | 0.392 | 0.553 | 0.529 | 0.700 | 0.480 |
| ANA | -0.422 | -0.699 | -0.427 | -0.764 | -0.727 |
| Pa | 49.2 | 46.7 | 25 | 39.2 | 38.3 |
| Pd | 29.1 | 15.8 | 53.3 | 31.7 | 30.8 |
| Pc | 4.2 | 5 | 10 | 8.3 | 11.7 |
| Pi | 17.5 | 32.5 | 11.7 | 20.8 | 19.2 |
| L | 1.059 | 1.285 | 1.958 | 1.126 | 0.848 |
Table 2
In the manner described above, at step 104, can realize extracting the second traveling of the typical driving cycle prestored
Driving parameters in data.Such as, having prestored 20 typical driving cycles, with 120 seconds as time cycle, division obtains
215 fragments, extract driving parameters therein and are analyzed calculating, and the identification for automobile running working condition lays the foundation.
As in figure 2 it is shown, after driving parameters in step 104 extracts the second running data, next step, step 105, to institute
State the second running data and carry out principal component analysis, described driving parameters determines the first driving parameters.
Concrete, step 105 includes:
Step 1051, determines n sample in described second running data and p variable, builds the first matrix X of n*p.
In this step, determine that object of study is second running data (n > p) of n sample and p variable, be designated as x1,
x2,…xn, wherein xi=(xi1, xi2..., xip) ', (i=1,2 ..., n), the first matrix constituting a n*p is
Step 1052, after described first matrix is carried out standards change, obtains the correlation coefficient between each variable, obtains phase
Close the second matrix of coefficient.
In this step, after the first matrix step 1051 obtained carries out standards change, calculate being correlated with between each variable
Coefficient, obtains the correlation matrix between former variable, and namely the second matrix is
Step 1053, obtains eigenvalue and the corresponding standard feature vector of described second matrix, according to formula Yi=Xei,
Obtain the i-th main constituent Y of stochastic variable Xi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p.
In this step, solve the eigenvalue of the second matrix and corresponding standard feature vector ei, i=1 ... p.And with e1,
e2,…,epFor coefficient vector, obtain Y1=Xe1,Y2=Xe2,…,YP=XeP, be the first principal component of stochastic variable X, second
Main constituent ..., pth main constituent.
Step 1054, according to described main constituent, determines the first driving parameters.
In this step, the main constituent obtained according to step 1054, determine in typical case's driving cycle representative
First driving parameters.
The most concrete, step 1054 includes:
Step 10541, according to formulaObtain contribution rate w of i-th main constituenti；Wherein, λiRepresent i-th
The eigenvalue of individual main constituent.
In this step, by formulaContribution rate w of i-th main constituent can be obtainedi, the biggest representative of its value
The information that main constituent is expressed is the most.
Step 10542, according to formulaObtain contribution rate of accumulative total w of m main constituentm。
In this step, based on the contribution rate of each main constituent obtained by step 10541, by formulaObtain contribution rate of accumulative total w of m main constituentm.Generally represent when accumulation contribution rate to 80% or 85%
Front m main constituent can represent all original variables and be analyzed.
Step 10543, according to the dependency between driving parameters and main constituent and the dependency between driving parameters, to feature
Value is analyzed more than the main constituent of the second predetermined threshold value more than the first predetermined threshold value and accumulation contribution rate, determines the first traveling ginseng
Number.
In this step, based on the dependency between driving parameters and main constituent and the dependency between driving parameters, to feature
Value is analyzed more than the main constituent of the second predetermined threshold value more than the first predetermined threshold value and accumulation contribution rate, determines the first traveling ginseng
Number.
Having certain dependency between each characteristic parameter, there is overlap in the work information of expression, utilizes main constituent the most permissible
Express more work information with less variable, thus can reach dimensionality reduction purpose.
The data of 215 fragment driving parameters to obtain in above-mentioned example carry out principal component analysis, obtain 12 main one-tenth
Point.Yi(i=1,2 ..., 12) represent that each main constituent, the eigenvalue of each main constituent, contribution rate, accumulation are contributed such as table 3 below institute
Show.
| Main constituent | Eigenvalue | Contribution rate/% | Accumulation contribution rate/% |
| Y1 | 5.076 | 42.30 | 42.30 |
| Y2 | 2.022 | 16.85 | 59.15 |
| Y3 | 1.592 | 13.27 | 72.42 |
| Y4 | 1.510 | 12.59 | 85.01 |
| Y5 | 0.709 | 5.91 | 90.92 |
| Y6 | 0.521 | 4.34 | 95.26 |
| Y7 | 0.421 | 3.51 | 98.87 |
| Y8 | 0.081 | 0.68 | 99.45 |
| Y9 | 0.031 | 0.26 | 99.71 |
| Y10 | 0.022 | 0.18 | 99.89 |
| Y11 | 0.006 | 0.06 | 99.95 |
| Y12 | 0.005 | 0.05 | 100 |
Table 3
Eigenvalue reflects the index of main constituent degree of influence size in a way, and eigenvalue is typically more than the master of 1
Composition is as analyzing object.As can be seen from Table 3, front 8 main constituents almost contain all information of 12 driving parameters, its
In the accumulation contribution rate of front 4 main constituents had arrived at 85.01%, and eigenvalue is all higher than 1, so choosing front 4 main one-tenth
Divide and be analyzed.
Table 4 lists the loading matrix data of front 4 main constituents:
| Characteristic parameter | Y1 | Y2 | Y3 | Y4 |
| Vm | 0.430 | -0.003 | 0.119 | 0.038 |
| Vmax | 0.409 | -0.201 | 0.033 | 0.106 |
| ∑V2 | 0.407 | 0.017 | 0.042 | 0.178 |
| Vsd | 0.135 | -0.514 | 0.002 | 0.176 |
| am | 0.032 | 0.444 | -0.202 | 0.556 |
| APA | -0.132 | -0.304 | -0.303 | 0.500 |
| ANA | 0.122 | 0.530 | 0.109 | -0.234 |
| Pa | 0.232 | 0.242 | 0.132 | 0.069 |
| Pd | 0.207 | -0.245 | -0.338 | -0.496 |
| Pc | 0.113 | -0.023 | 0.651 | 0.179 |
| Pi | -0.369 | -0.001 | -0.523 | 0.138 |
| L | 0.428 | 0.056 | 0.086 | 0.087 |
Table 4
It is appreciated that the loading coefficient absolute value that certain parameter is in certain main constituent is the biggest, show this parameter with
The degree of correlation of this main constituent is the highest, thereby determines that the correlation coefficient of 4 main constituents and 12 driving parameters.First principal component
(Y1) average speed and distance travelled are mainly reflected；Second principal component, (Y2) reflects velocity standard difference and negative acceleration is average
Value；3rd main constituent (Y3) mainly idling ratio and at the uniform velocity ratio；4th main constituent (Y4) reflects average acceleration and subtracts
Speed ratio example.According to the dependency between characteristic parameter and main constituent and the dependency between parameter, 4 main constituents are chosen in the past and have
4 parameters of representational average speed, idling ratio, at the uniform velocity ratio and deceleration ratio are used for cluster analysis.
As in figure 2 it is shown, after driving parameters representative in step 105 determines the second running data, perform next
Step, step 106, carry out cluster analysis, determine cluster number and the cluster centre of the running data of typical case's driving cycle.
In the embodiment of the present invention, using fuzzy clustering, each sample is not the strict a certain class that is divided into, but with one
Fixed degree of membership is under the jurisdiction of a certain class.Make V={v1,v2,…,vcIt is the cluster centre of each class, uikRepresent that kth sample belongs to
In the degree of membership of the i-th class, and uikMeetObjective function is:
Wherein, U is subordinated-degree matrix, and J (U, V) is that each apoplexy due to endogenous wind sample weighted quadratic to each cluster centre is apart from it
With.The criterion of fuzzy C-means clustering just determines that U and V makes J (U, V) minimum.
Therefore, concrete, step 106 includes:
Step 1061, according to default cluster number and 0, the random number between 1, initializes subordinated-degree matrix U.
Systemic presupposition or user-defined cluster number c, in this step, determine cluster number c, and utilize 0, between 1
Random number, initializes subordinated-degree matrix U.
Step 1062, according to formulaObtain the cluster centre V of l step(l)；Wherein,
uikRepresent that kth sample belongs to the degree of membership of the i-th class, and uikMeet
In this step, according to formulaObtain the cluster centre V of l step(l)。
Step 1063, according to formulaObtain the object function J of l step(l)。
In this step, owing to having obtained V through step 1062(l), then, according to formula
The object function J of l step can be obtained(l)。
Step 1064, according to formulaRevise the subordinated-degree matrix U of l step(l), wherein
In this step, according to formulaRevise the subordinated-degree matrix U of l step(l)。
Step 1065, is meetingTime, obtain target subordinated-degree matrix and target cluster centre；Its
In, εuIt is subordinate to termination tolerance for default.
After above-mentioned steps iteration, target subordinated-degree matrix U and target cluster centre V can be tried to achieve so that target letter
NumberValue minimize, and determine the ownership of all samples according to the value of subordinated-degree matrix, enter
And arrive the purpose of cluster.And at not metTime, proceed iteration, l=l+1.
Step 1066, compares the target cluster centre that described first driving parameters is corresponding with each cluster number,
Determine that state class distinguishes the cluster number of the running data that significantly cluster number is described typical case's driving cycle, corresponding target
Cluster centre is the cluster centre of the running data of described typical case's driving cycle.
Owing to default cluster number is not unique, after step 1061～step 1065, correspondence obtains the mesh of each cluster
Mark cluster centre.In this step, for multiple target cluster centres, determining that state class distinguishes significantly cluster number is representative row
Sailing the cluster number of the running data of operating mode, corresponding target cluster centre is in the cluster of the running data of typical case's driving cycle
The heart.
In fuzzy C-means clustering, the span of C is 2≤C≤n.Consider road traffic features and practical situation, to 215
Individual fragment takes C=3, C=4, C=5 and C=6 respectively and carries out cluster analysis, and determined according to principal component analysis result before
One driving parameters, the cluster centre result of the at the uniform velocity ratio each clustered, idling ratio and deceleration ratio contrasts, and sees figure
4～Fig. 7.It can be seen that in the case of C=5 and C=6 the time scale of each state class be not it is obvious that the most at the uniform velocity than
Example, when Fig. 4 can be seen that point C=3, the Pc of cluster 1 and cluster 3 is close, and during C=4 class, each state class is distinguished obvious, institute
Relatively reasonable to sum up to determine 4 classes, therefore determine cluster number C=4.
Additionally, be the feature of same category driving cycle after further analysis clusters, with average speed Vm and idling ratio
The result after fuzzy C-means clustering is represented, as shown in Figure 8 as a example by two driving parameters of Pi.In figure, 215 fragments divide into 4 classes,
And marked the cluster centre of each class.The centre coordinate of cluster 4 is (9.382,0.449), the operating mode fragment idling of this class
Ratio is big, and speed is low, reflects this class operating mode and belongs to the city operating mode of the frequent traffic jam of start and stop.Cluster 2 centre coordinates
(99.849,0.0055), it is seen that average speed is high, and idling ratio is the lowest, and this operating mode belongs to the high-speed working condition that traffic is unobstructed.Its
He between cluster 2 and clusters between 4 two clusters, generally falls into suburbs operating mode.Reasonably will by fuzzy C-mean clustering analysis
Operating mode fragment divide into 4 kinds of different types.
Through foregoing, the second running data of the typical driving cycle prestored by principal component analysis and is obscured
C cluster analysis carries out classifying and obtaining cluster centre.Afterwards, it is necessary to go to determine automobile according to the actual travel data of automobile
Current driving operating mode.Process for the ease of data, it is preferred that the time span of running data is equal to the second of typical case's driving cycle
The time span of a fragment in running data.
Concrete, step 102 includes:
Step 1021, extracts the second driving parameters of corresponding described first driving parameters in described first running data.
In this step, with reference to the first driving parameters, corresponding the second driving parameters extracted in the first running data.Above-mentioned
In example, the first driving parameters is average speed, idling ratio, at the uniform velocity ratio and deceleration ratio, corresponding, extract the first traveling
Second driving parameters average speed, idling ratio, at the uniform velocity ratio and the deceleration ratio of data.
Step 1022, according to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ciDistance
di；Wherein, i represents ith cluster.
In this step, according to formula di=| | x-ci| |, the second driving parameters obtained in calculation procedure 1021 and each allusion quotation
Distance d of the cluster centre of the running data of type driving cyclei.Wherein x represents the second driving parameters x=of the first running data
(x1,x2,…,xn)；ciRepresent the cluster centre c of cluster ii=(ci1,ci2,…,cin)。
Step 1023, according to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
In this step, determine the classification that the first running data is corresponding, the most just according to the principle minimum to cluster centre cluster
May determine that the current driving operating mode of automobile.
215 fragments of running data of typical case's driving cycle are obtained by continuation of the previous cases by principal component analysis and cluster analysis
To 4 classes, utilize average speed, idling ratio, at the uniform velocity ratio and deceleration ratio four first to travel parameter list and show cluster centre, knot
Fruit is as follows:
C1=(62.2,0.083,0.399,0.136)
C2=(99.8,0.005,0.367,0.156)
C3=(31.5,0.149,0.351,0.075)
C4=(9.38,0.449,0.229,0.042)
Four the second driving parameters of an automobile nearest driving cycle fragment need to be extracted during operating mode's switch, and according to formula
di=| | x-ci| |, calculate this fragment distance to 4 cluster centres, what chosen distance was minimum clusters the classification as this fragment,
The driving cycle of automobile is identified.
As a example by front 5 fragments of UDDS operating mode, have identified the classification of each fragment in table 5, fragment 1,2,4,5 arrives C3's
Distance is minimum, and therefore these 4 fragments belong to cluster 3；And fragment 3 is minimum to the distance of C1, this fragment belongs to cluster 1.
Table 5
Therefore, in the embodiment of the present invention, the method using principal component analysis and fuzzy clustering to combine carries out running car
The identification process of operating mode is as it is shown in figure 9, include off-line, online and three parts of identification.The typical case that off-line part will prestore
During the running data of driving cycle carries out classifying and being clustered by above-mentioned principal component analysis and fuzzy C-mean clustering analysis
The heart；Online part and identification division i.e. real-time recognition process to automobile running working condition, obtains a nearest sheet in running car
The data of section, and extract the driving parameters of this fragment, by calculating the cluster of each cluster centre, according to cluster centre away from
The classification of this fragment is determined from minimum principle.
After determining the current driving operating mode of automobile, as it is shown in figure 1, perform next step, step 103, according to described current line
Sail operating mode, determine the continual mileage of corresponding current remaining.
Concrete, step 103 includes:
Step 1031, according to formulaObtain each cluster of running data of described typical case's driving cycle
Average power consumptionWherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent the energy of kth operating mode fragment
Consume, fkRepresent the degree of membership of kth operating mode fragment, FiRepresent ith cluster degree of membership sum；
Step 1032, according to the current driving operating mode of described automobile, determines that the average energy consumption of corresponding cluster is as unit energy
Consumption；
Step 1033, according to formulaObtain the total travel power consumption of j the operating mode fragment before of described automobile
Ecost；Wherein, EjRepresent the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
Step 1034, according to formulaObtain the mileage number l that described automobile specific energy consumption travels；Wherein, S represents
Described automobile distance travelled in described predetermined time period；
Step 1035, according to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；Wherein, EtotalTable
Show the battery gross energy of described automobile；
Step 1036, according to formula Sres=lEres, obtain the continual mileage of described automobile.
By above-mentioned steps 1031～step 1036, first each cluster of running data of acquisition typical case driving cycle is flat
All consume energy, then the average power consumption of the classification identified before is disappeared as the energy of the first running data of automobile actual travel
Consumption, the total power consumption in whole driving process of reentrying.Afterwards, it is thus achieved that the mileage number of specific energy consumption traveling and pure electric automobile
Dump energy, finally give pure electric automobile continual mileage.
Concrete, affect the multiple because have of pure electric automobile continual mileage, be broadly divided into two classes: one is automobile itself
State, such as battery pack power and whole-car parameters；One is automobile running working condition.Different automobiles because of different energy and whole-car parameters,
Continual mileage is the most different；Same automobile continual mileage under different driving cycles is the most different.The side of the embodiment of the present invention
Method is to carry out on the basis of the state of automobile itself determines substantially, travels work with the typical case of 215 above-mentioned operating mode fragments
As a example by condition, as shown in Figure 10.
First 215 operating mode fragment input whole vehicle models are calculated, obtain 4 and cluster average energy consumption.Then by upper
The recognition method stated, is identified automobile current driving operating mode, calculates work in actual travel according to the average energy consumption of cluster
The energy consumption of condition fragment, and add up, draw total energy consumption and dump energy.The mileage number that last foundation has travelled is calculated list
The mileage number that potential energy consumption travels.The mileage number travelled by specific energy consumption and dump energy draw the continual mileage of pure electric automobile
Value.
Wherein it is possible to set up the whole vehicle model of pure electric automobile under MATLAB/Simulink environment, pass through whole vehicle model
Simulation calculation goes out the energy expenditure of 215 driving cycle fragments, as shown in figure 11.
By above-mentioned analyzing and processing, 215 fragments be divide into 4 classes, and have emulated the energy expenditure having obtained each fragment,
In order to estimate the energy consumption in pure electric automobile traveling, the average energy consumption of 4 clusters need to be calculated.Cluster according to driving cycle fragment
Analyzing and understand, each fragment is to be under the jurisdiction of a certain class with certain degree of membership, calculates all kinds of average hence with degree of membership
Energy consumption.Such as step 1031, according to formulaI=(1,2,3,4) obtains the average power consumption of ith clusterPoint
It is not: 0.0473kw h, 0.1346kw h, 0.6488kw h and 0.3128kw h.
Owing to the most identifying the classification (current driving operating mode) of the nearest fragment of automobile, now, it becomes possible to according to
The average energy consumption of the category (cluster that typical case's driving cycle is corresponding) is as specific energy consumption.Then according to formula?
To automobile at the full electric total travel power consumption E so far of batterycost.Again by formulaAnd Eres=Etotal-EcostObtain this automobile
The mileage number of specific energy consumption traveling and the dump energy of pure electric automobile, eventually through formula Sres=lEres, obtain this pure electronic
The continual mileage of automobile.
It addition, the feasibility of the method in order to verify the embodiment of the present invention, circulate 15 operating mode ECE15 operating modes in European economy
Under, utilize drum dynamometer that example pure electric automobile carries out car load continual mileage test, use this with example pure electric automobile
The continual mileage that the method for inventive embodiments determines compares, and verifies.
Example pure electric automobile uses ferric phosphate lithium cell, and rated capacity is 50Ah, nominal voltage 320V；Driving motor is adopted
By permagnetic synchronous motor, rated power 11kw, peak power 27kw, car load relevant parameter is as shown in table 6 below.
| Parameter name | Parameter value |
| Kerb weight (kg) | 1200 |
| Wheelbase (mm) | 2400 |
| Length × width × height (mm) | 4155×1650×1445 |
| Front face area A (m^2) | 2.01 |
| Air resistance coefficient CD | 0.294 |
| Max. speed (km/h) | 100 |
| Coefficient of rolling resistance f | 0.015 |
Table 6
Consider that real vehicle max. speed is 100km/h, the typical driving cycle prestored and drum dynamometer measurement condition
All use ECE15 operating mode (such as Figure 12), and simulation result and test result are compared analysis.
It was a fragment with 120 seconds, is determined the energy of each fragment of ECE15 operating mode by above-mentioned operating mode's switch mode
Consume, and to identify the cluster belonging to fragment be that 3 (average energy consumption is for cluster 4 (average energy consumption is 0.0473kw h) or cluster
0.1346kw h), these fragment energy consumptions are little.Owing to ECE15 operating mode speed is low, idling ratio big, the most each fragment energy consumption
Little, the result of identification is consistent with actual.Respectively obtain in traveling in specific energy consumption travels by the method for the embodiment of the present invention
Number of passes, pure electric automobile dump energy and continual mileage.In drum dynamometer is tested, the running car time under ECE15 operating mode
Being 21360 seconds, actual total energy consumption is 14.85kw h, and actual continual mileage is 112.6km.
Figure 13 is to identify the mileage number change curve that the pure electric automobile specific energy consumption drawn travels under ECE15 operating mode.
The mileage number travelled at the starting stage specific energy consumption identified changes the most greatly, the most more stable, at 7.1km/kw h
Left and right.
Figure 14 is the comparison of pure electric automobile energy expenditure estimated value and the test value using driving cycle method of identification to obtain,
The estimated value of final total power consumption is than actual value many 0.47kw h.
The estimated value of continual mileage compares as shown in figure 15 with test value, the maximum absolute error between estimated value and test value
For 1.905km, absolute error meansigma methods is 0.742km, and mean relative percentages error is 2.92%.Maximum absolute error occurs
In the starting stage, error afterwards is gradually reduced, and this is consistent with specific energy consumption distance travelled number curve.By estimated value and test
Value comparison sheet understands that the method using the embodiment of the present invention is feasible to the estimation of pure electric automobile continual mileage, and can guarantee that
Certain accuracy.
In sum, the determination method of the continual mileage of the embodiment of the present invention, use principal component analysis and fuzzy clustering phase
In conjunction with method, to than more typical automobile running working condition carrying out feature analysis and state recognition at present, and enter on this basis
The determination of the example pure electric automobile continual mileage of based on operating mode's switch of row, it is contemplated that the driving cycle shadow to continual mileage
Ring, add the estimation accuracy of continual mileage, and there is the more preferably suitability.
As shown in figure 16, the embodiment of the present invention additionally provides the determination device of a kind of continual mileage, including:
Acquisition module 1601, the first running data in the previous predetermined time period obtaining automobile current time；
First determines module 1602, for according to described first running data, determines the current driving operating mode of described automobile；
Second determines module 1603, for according to described current driving operating mode, determining the driving of corresponding current remaining
Mileage.
Wherein, the determination device of described continual mileage also includes:
Extraction module, the driving parameters in the second running data extracting the typical driving cycle prestored；
First processing module, for carrying out principal component analysis to described second running data, in described driving parameters really
Fixed first driving parameters；
Second processing module, for according to the cluster number preset and described first driving parameters, travelling described second
Data carry out cluster analysis, determine cluster number and the cluster centre of the running data of described typical case's driving cycle.
Wherein, described first determines that module includes:
Extract submodule, for extracting the second traveling ginseng of corresponding described first driving parameters in described first running data
Number；
Obtain submodule, for according to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ci
Distance di；Wherein, i represents ith cluster；
First determines submodule, for according to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
Wherein, described second determines that module includes:
First processes submodule, for according to formulaObtain the traveling number of described typical case's driving cycle
Average power consumption according to each clusterWherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent kth operating mode
The energy expenditure of fragment, fkRepresent the degree of membership of kth operating mode fragment, FiRepresent ith cluster degree of membership sum；
Second determines submodule, for the current driving operating mode according to described automobile, determines the average energy consumption of corresponding cluster
As specific energy consumption；
Second processes submodule, for according to formulaObtain j the operating mode fragment before of described automobile
Total travel power consumption Ecost；Wherein, EjRepresent the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
3rd processes submodule, for according to formulaObtain the mileage number l that described automobile specific energy consumption travels；
Wherein, S represents described automobile distance travelled in described predetermined time period；
Fourth process submodule, for according to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；
Wherein, EtotalRepresent the battery gross energy of described automobile；
5th processes submodule, for according to formula Sres=lEres, obtain the continual mileage of described automobile.
Wherein, described first processing module includes:
Build submodule, for determining n sample in described second running data and p variable, build the first of n*p
Matrix；
6th processes submodule, after described first matrix is carried out standards change, obtains being correlated with between each variable
Coefficient, obtains the second matrix of correlation coefficient；
7th processes submodule, for obtaining eigenvalue and the corresponding standard feature vector of described second matrix, according to
Formula Yi=Xei, obtain i-th main constituent Yi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p；
3rd determines submodule, for according to described main constituent, determines the first driving parameters.
Wherein, the described 3rd determines that submodule includes:
First processing unit, for according to formulaObtain contribution rate w of i-th main constituenti；Wherein, λi
Represent the eigenvalue of i-th main constituent；
Second processing unit, for according to formulaObtain contribution rate of accumulative total w of m main constituentm；
3rd processing unit, relevant for according between the dependency between driving parameters to main constituent and driving parameters
Property, eigenvalue is analyzed more than the main constituent of the second predetermined threshold value more than the first predetermined threshold value and accumulation contribution rate, determines
First driving parameters.
Wherein, described second processing module includes:
Initialization submodule, for according to the cluster number and 0 preset, the random number between 1, initializing subordinated-degree matrix
U；
8th processes submodule, for according to formulaObtain the cluster centre of l step
V(l)；Wherein, uikRepresent that kth sample belongs to the degree of membership of the i-th class, and uikMeet
9th processes submodule, for according to formulaObtain the object function J of l step(l)；
Revise submodule, for according to formulaRevise the subordinated-degree matrix U of l step(l), its
In
Tenth processes submodule, for meetingTime, obtain target subordinated-degree matrix and target
Cluster centre；Wherein, εuIt is subordinate to termination tolerance for default；
4th determines submodule, for by target cluster centre corresponding with each cluster number for described first driving parameters
Compare, determine that state class distinguishes the cluster number of the running data that significantly cluster number is described typical case's driving cycle,
Corresponding target cluster centre is the cluster centre of the running data of described typical case's driving cycle.
The determination device of the continual mileage of the embodiment of the present invention, uses the side that principal component analysis and fuzzy clustering combine
Method, to than more typical automobile running working condition carrying out feature analysis and state recognition at present, and carry out based on work on this basis
The determination of the example pure electric automobile continual mileage of condition identification, it is contemplated that the driving cycle impact on continual mileage, adds
The estimation accuracy of continual mileage, and there is the more preferably suitability.
It should be noted that this device is the device of the determination method applying above-mentioned continual mileage, above-mentioned continual mileage
The implementation of embodiment of determination method be applicable to this device, also can reach identical technique effect.
The embodiment of the present invention additionally provides a kind of automobile, including the determination device of above-mentioned continual mileage.
This automobile is pure electric automobile, uses the method that principal component analysis and fuzzy clustering combine, to comparing allusion quotation at present
The automobile running working condition of type carries out feature analysis and state recognition, and carries out the pure electricity of example based on operating mode's switch on this basis
The determination of electrical automobile continual mileage, it is contemplated that the driving cycle impact on continual mileage, the estimation adding continual mileage is accurate
Really property, and there is the more preferably suitability.
It should be noted that this automobile is also the automobile of the determination method applying above-mentioned continual mileage, in above-mentioned driving
The implementation of the embodiment of the determination method of journey is applicable to this automobile, also can reach identical technique effect.
Needing further exist for explanation, these many functional parts described in this description are all referred to as module, in order to more
Add the independence emphasizing its implementation especially.
In the embodiment of the present invention, module can realize with software, in order to is performed by various types of processors.Citing comes
Saying, the executable code module of a mark can include one or more physics or the logical block of computer instruction, citing
For, it can be built as object, process or function.While it is true, the executable code of identified module is without physically
Be located together, but in can including being stored in not coordination on different instructions, when combining in these command logics
Time, it constitutes module and realizes the regulation purpose of this module.
It practice, executable code module can be individual instructions or many bar instructions, and even can be distributed
On multiple different code segments, it is distributed in the middle of distinct program, and crosses over the distribution of multiple memory devices.Similarly, behaviour
Make data to be identified in module, and can realize according to any suitable form and be organized in any suitable class
In the data structure of type.Described operation data can be collected as individual data collection, or can be distributed on diverse location
(being included in different storage device), and electronic signal can be only used as at least in part be present on system or network.
When module can utilize software to realize, it is contemplated that the level of existing hardware technique, it is possible to implemented in software
Module, in the case of not considering cost, those skilled in the art can build correspondence hardware circuit to realize correspondence
Function, described hardware circuit includes ultra-large integrated (VLSI) circuit or gate array and the such as logic core of routine
The existing quasiconductor of sheet, transistor etc or other discrete element.Module can also use programmable hardware device, such as
Field programmable gate array, programmable logic array, programmable logic device etc. realize.
Above-mentioned exemplary embodiment describes with reference to those accompanying drawings, many different forms and embodiment be feasible and
Without departing from present invention spirit and teaching, therefore, the present invention should not be construed the restriction become in this proposed exemplary embodiment.
More precisely, these exemplary embodiment are provided so that the present invention can be to improve again completely, and can be by the scope of the invention
Convey to those those of skill in the art.In those are graphic, size of components and relative size are perhaps based on for the sake of clear
And be exaggerated.Term used herein is based only on description particular example embodiment purpose, is not intended to become restriction and uses.As
At this made land used, unless this interior literary composition clearly refers else, otherwise this singulative " ", " one " and " being somebody's turn to do " be intended to by
Those multiple forms are also included in.Those terms be will become further apparent " comprise " and/or " including " is when being used in this specification,
Represent the existence of described feature, integer, step, operation, component and/or assembly, but be not excluded for one or more further feature, whole
The existence of number, step, operation, component, assembly and/or its group or increase.Unless otherwise indicated, narrative tense, a value scope bag
Bound containing this scope and any subrange therebetween.
The above is the preferred embodiment of the present invention, it is noted that for those skilled in the art
For, on the premise of without departing from principle of the present invention, it is also possible to make some improvements and modifications, these improvements and modifications are also
Should be regarded as protection scope of the present invention.
Claims (15)
1. the determination method of a continual mileage, it is characterised in that including:
Obtain the first running data in the previous predetermined time period of automobile current time；
According to described first running data, determine the current driving operating mode of described automobile；
According to described current driving operating mode, determine the continual mileage of corresponding current remaining.
The determination method of continual mileage the most according to claim 1, it is characterised in that also include:
Extract the driving parameters in the second running data of the typical driving cycle prestored；
Described second running data is carried out principal component analysis, described driving parameters determines the first driving parameters；
According to default cluster number and described first driving parameters, described second running data is carried out cluster analysis, determines
The cluster number of the running data of described typical case's driving cycle and cluster centre.
The determination method of continual mileage the most according to claim 2, it is characterised in that according to the first running data, determine
The step of the current driving operating mode of described automobile, including:
Extract the second driving parameters of corresponding described first driving parameters in described first running data；
According to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ciDistance di；Wherein, i represents
I cluster；
According to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
The determination method of continual mileage the most according to claim 3, it is characterised in that according to described current driving operating mode,
Determine the step of the continual mileage of corresponding current remaining, including:
According to formulaObtain the average power consumption of each cluster of running data of described typical case's driving cycle
Wherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent the energy expenditure of kth operating mode fragment, fkRepresent kth
The degree of membership of individual operating mode fragment, FiRepresent ith cluster degree of membership sum；
Current driving operating mode according to described automobile, determines that the average energy consumption of corresponding cluster is as specific energy consumption；
According to formulaObtain the total travel power consumption E of j the operating mode fragment before of described automobilecost；Wherein, EjTable
Show the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
According to formulaObtain the mileage number l that described automobile specific energy consumption travels；Wherein, S represents that described automobile is described
Distance travelled in predetermined time period；
According to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；Wherein, EtotalRepresent described automobile
Battery gross energy；
According to formula Sres=lEres, obtain the continual mileage of described automobile.
The determination method of continual mileage the most according to claim 2, it is characterised in that described second running data is carried out
Principal component analysis, determines the step of the first driving parameters in described driving parameters, including:
Determine n sample in described second running data and p variable, build first matrix of n*p；
After described first matrix is carried out standards change, obtain the correlation coefficient between each variable, obtain the second of correlation coefficient
Matrix；
Obtain eigenvalue and the corresponding standard feature vector of described second matrix, according to formula Yi=Xei, obtain the main one-tenth of i-th
Divide Yi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p；
According to described main constituent, determine the first driving parameters.
The determination method of continual mileage the most according to claim 5, it is characterised in that according to described main constituent, determines
The step of one driving parameters, including:
According to formulaObtain contribution rate w of i-th main constituenti；Wherein, λiRepresent the feature of i-th main constituent
Value；
According to formulaObtain contribution rate of accumulative total w of m main constituentm；
According to the dependency between driving parameters and main constituent and the dependency between driving parameters, eigenvalue is preset more than first
Threshold value and accumulation contribution rate are analyzed more than the main constituent of the second predetermined threshold value, determine the first driving parameters.
The determination method of continual mileage the most according to claim 2, it is characterised in that according to default cluster number and institute
State the first driving parameters, described second running data is carried out cluster analysis, determine the running data of described typical case's driving cycle
Cluster number and the step of cluster centre, including:
According to default cluster number and 0, the random number between 1, initialize subordinated-degree matrix U；
According to formulaObtain the cluster centre V of l step(l)；Wherein, uikRepresent kth sample
Product belong to the degree of membership of the i-th class, and uikMeet
According to formulaObtain the object function J of l step(l)；
According to formulaRevise the subordinated-degree matrix U of l step(l), whereinMeetingTime, obtain target subordinated-degree matrix and target cluster centre；Wherein, εuHold for the default termination that is subordinate to
Limit；
The target cluster centre that described first driving parameters is corresponding with each cluster number is compared, determines that state class is distinguished
Significantly clustering the cluster number of the running data that number is described typical case's driving cycle, corresponding target cluster centre is described
The cluster centre of the running data of typical case's driving cycle.
8. the determination device of a continual mileage, it is characterised in that including:
Acquisition module, the first running data in the previous predetermined time period obtaining automobile current time；
First determines module, for according to described first running data, determines the current driving operating mode of described automobile；
Second determines module, for according to described current driving operating mode, determining the continual mileage of corresponding current remaining.
The determination device of continual mileage the most according to claim 8, it is characterised in that also include:
Extraction module, the driving parameters in the second running data extracting the typical driving cycle prestored；
First processing module, for described second running data is carried out principal component analysis, determines the in described driving parameters
One driving parameters；
Second processing module, for according to the cluster number preset and described first driving parameters, to described second running data
Carry out cluster analysis, determine cluster number and the cluster centre of the running data of described typical case's driving cycle.
The determination device of continual mileage the most according to claim 9, it is characterised in that described first determines that module includes:
Extract submodule, for extracting the second driving parameters of corresponding described first driving parameters in described first running data；
Obtain submodule, for according to formula di=| | x-ci| |, obtain described second driving parameters x and cluster centre ciAway from
From di；Wherein, i represents ith cluster；
First determines submodule, for according to diCluster corresponding time minimum, determines the current driving operating mode of described automobile.
The determination device of 11. continual mileages according to claim 10, it is characterised in that described second determines module bag
Include:
First processes submodule, for according to formulaThe running data obtaining described typical case's driving cycle is each
The average power consumption of clusterWherein, niRepresent the operating mode fragment number belonging to ith cluster, EkRepresent kth operating mode fragment
Energy expenditure, fkRepresent the degree of membership of kth operating mode fragment, FiRepresent ith cluster degree of membership sum；
Second determines submodule, for the current driving operating mode according to described automobile, determines the average energy consumption conduct of corresponding cluster
Specific energy consumption；
Second processes submodule, for according to formulaObtain the head office of j the operating mode fragment before of described automobile
Sail power consumption Ecost；Wherein, EjRepresent the energy expenditure of described automobile jth operating mode fragment, choose EjEqual to specific energy consumption；
3rd processes submodule, for according to formulaObtain the mileage number l that described automobile specific energy consumption travels；Wherein, S
Represent described automobile distance travelled in described predetermined time period；
Fourth process submodule, for according to formula Eres=Etotal-Ecost, obtain dump energy E of described automobileres；Wherein,
EtotalRepresent the battery gross energy of described automobile；
5th processes submodule, for according to formula Sres=lEres, obtain the continual mileage of described automobile.
The determination device of 12. continual mileages according to claim 9, it is characterised in that described first processing module includes:
Build submodule, for determining n sample in described second running data and p variable, build first square of n*p
Battle array；
6th processes submodule, after described first matrix is carried out standards change, obtains the correlation coefficient between each variable,
Obtain the second matrix of correlation coefficient；
7th processes submodule, for obtaining eigenvalue and the corresponding standard feature vector of described second matrix, according to formula Yi
=Xei, obtain i-th main constituent Yi；Wherein, eiRepresent the standard feature vector of i-th main constituent, i=1 ... p；
3rd determines submodule, for according to described main constituent, determines the first driving parameters.
The determination device of 13. continual mileages according to claim 12, it is characterised in that the described 3rd determines submodule bag
Include:
First processing unit, for according to formulaObtain contribution rate w of i-th main constituenti；Wherein, λiRepresent
The eigenvalue of i-th main constituent；
Second processing unit, for according to formulaObtain contribution rate of accumulative total w of m main constituentm；
3rd processing unit, for according to the dependency between driving parameters and main constituent and the dependency between driving parameters, right
Eigenvalue is analyzed more than the main constituent of the second predetermined threshold value more than the first predetermined threshold value and accumulation contribution rate, determines the first row
Sail parameter.
The determination device of 14. continual mileages according to claim 9, it is characterised in that described second processing module includes:
Initialization submodule, for according to the cluster number and 0 preset, the random number between 1, initializing subordinated-degree matrix U；
8th processes submodule, for according to formulaObtain the cluster centre V of l step(l)；
Wherein, uikRepresent that kth sample belongs to the degree of membership of the i-th class, and uikMeet9th processes submodule, uses
According to formulaObtain the object function J of l step(l)；
Revise submodule, for according to formulaRevise the subordinated-degree matrix U of l step(l), wherein
Tenth processes submodule, for meetingTime, obtain target subordinated-degree matrix and target cluster
Center；Wherein, εuIt is subordinate to termination tolerance for default；
4th determines submodule, for being carried out by the target cluster centre that described first driving parameters is corresponding with each cluster number
Comparison, determines that state class distinguishes the cluster number of the running data that significantly cluster number is described typical case's driving cycle, corresponding
The cluster centre of running data that target cluster centre is described typical case's driving cycle.
15. 1 kinds of automobiles, it is characterised in that include the determination device of continual mileage as described in any one of claim 8 to 14.
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| CN114132321A (en) * | 2020-08-13 | 2022-03-04 | 北汽福田汽车股份有限公司 | Remaining mileage determining method and device for electric vehicle, electronic equipment and electric vehicle |
| CN114132321B (en) * | 2020-08-13 | 2023-09-08 | 北汽福田汽车股份有限公司 | Method and device for determining remaining mileage of electric vehicle, electronic equipment and electric vehicle |
| CN112721661A (en) * | 2021-01-29 | 2021-04-30 | 重庆长安新能源汽车科技有限公司 | Estimation method and device for cruising mileage of fuel cell electric vehicle and storage medium |
| CN114940132A (en) * | 2022-07-27 | 2022-08-26 | 中汽研汽车检验中心（天津）有限公司 | Electric vehicle endurance mileage prediction method, test method and system |
| CN117574694A (en) * | 2024-01-17 | 2024-02-20 | 中汽研汽车检验中心(广州)有限公司 | Method for shortening driving range and simulating and analyzing energy consumption of pure electric vehicle |
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