CN105868787A - Electric car driving range evaluation method based on working condition identification and fuzzy energy consumption - Google Patents
Electric car driving range evaluation method based on working condition identification and fuzzy energy consumption Download PDFInfo
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Abstract
The invention discloses an electric car driving range evaluation method based on working condition identification and fuzzy energy consumption. The electric car driving range evaluation method is established based on working condition identification and fuzzy energy consumption and includes the steps of dividing working conditions based on fuzzy C mean value cluster algorithm to obtain parameters for identifying working conditions, establishing a fuzzy rule base between characteristic parameters and energy consumption, optimizing driving mileage L with kilometer as the unit, and estimating residual driving range Srest. The method combines working condition identification and energy consumption modeling, obtains related information of an electric car on a real-time basis, and is rarely affected by external factors such as ambient temperature and vehicle driving states. Energy consumption is further divided in to a certain level, so that real energy consumption under certain speed can be reflected.
Description
Technical field
The present invention relates to electric vehicle engineering field, particularly relates to electric automobile driving based on operating mode's switch and fuzzy energy consumption
Mileage evaluation method.
Background technology
Along with the recoverable amount of conventional fuel oil automobile rapidly increases, fuel-engined vehicle the environmental pollution caused and whole world fuel oil
Exhausted problem is the most serious.Electric automobile is increasingly paid close attention to by people due to environmental protection, the advantage such as energy-conservation, but because of its driving
Mileage is limited, and is widely popularized.In order to improve the ease of use of electric automobile, continual mileage to be improved, also want
The status real time monitor of research electric automobile and the real-time estimation of continual mileage, thus provide vehicle letter accurately for driver
Breath.Electric automobile continual mileage is affected by many factors, such as, and speed, vehicle-state, battery status, ambient temperature etc..
The computational methods of continual mileage are the most perfect at present, need to explore further.Chinese patent CN201310151533.5 is public
Having opened a kind of electric automobile continual mileage evaluation method, the method is according to certain traffic behavior and vehicle true real-time status feelings
Carrying out continual mileage estimation under condition, although the method can accurately estimate mileage, but the information being made by is too many, and these information are also
Non-can obtain in real time.Chinese patent CN201310151290.5 discloses electric automobile electricity factor model and sets up and continual mileage
Evaluation method, the method has certain research to electricity factor model, and in reality, electric quantity consumption nevertheless suffers from several factors shadow
Ring, not can accurately set up.
Summary of the invention
For the deficiencies in the prior art, the invention provides a kind of electric automobile based on operating mode's switch and fuzzy energy consumption and continue
Sailing mileage evaluation method, the process solving estimation mileage can not obtain electric automobile relevant information, electric quantity consumption in real time by very
The problem of multifactor impact.
The present invention realizes above-mentioned technical purpose by techniques below means.
Electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption, comprises the following steps:
S1. realize the division to operating mode based on Fuzzy C-Means Cluster Algorithm and draw the parameter for identifying operating mode,
S1.1, chooses 30 standard conditions and is divided into 330 fragments, and 330 fragments take different gather respectively
Class number carries out cluster analysis；
S1.2, according to cluster result, chooses the cluster number that can substantially distinguish different operating modes, finally determines cluster number；
330 fragment operating modes are divided into the classification of respective numbers, and cluster out in every class by S1.3 according to cluster number
The heart；
S1.4, is identified emulating to operating mode according to cluster centre；
S2. the fuzzy rule base between characteristic parameter and energy consumption is set up,
S2.1, differentiates classification residing for current clip according to driving cycle, obtains the average speed meanv of current clip, adds
Speed ratio example P, deceleration ratio N, tetra-characteristic parameters of total energy consumption Ecost；
S2.2, each characteristic parameter to current clip chooses 11 groups of data successively, and to often organize data according to from little to
Big order arrangement, sets up fuzzy rule base；
S3. unit kilometer distance travelled L is optimized,
S3.1, when the fragment of sampling is within 30, in order to meet remaining mileage linearly decline trend, sets up unit energy
Consumption and the linear relationship remaining energy consumption；
S3.2, when fragment of sampling is more than 30, the continual mileage of said method estimation may be absorbed in local minimum, because of
This uses Fuzzy C-Means Cluster Algorithm, energy consumption average to unit kilometer to cluster and export；
S4. residue continual mileage Srest estimation, comprises the following steps,
S4.1, obtains current driving operating mode feature parameter in real time according to driving cycle and sampling time；
S4.2, draws current working classification according to characteristic parameter, and draws current total energy consumption Ecost；
S4.3, draws dump energy Erest according to current total energy consumption Ecost and vehicle initial total energy consumption Etotal；
S4.4, draws residue continual mileage Srest according to specific energy consumption distance travelled L and dump energy Erest.
Further, dump energy Erest=Etotal-Ecost in described S4.3.
Further, specific energy consumption distance travelled L=S/Ecost in described S4.4, wherein S is distance travelled.
Further, described S4.4 remains continual mileage
The invention has the beneficial effects as follows: operating mode's switch and energy consumption are modeled and combine by the present invention, obtain electronic in real time
Automobile relevant information, is seldom affected by other extrinsic factor of vehicle, such as ambient temperature, vehicle running state etc., simultaneously
Consumed energy is carried out a certain degree of segmentation, thus reaction speed true energy consumes more really.
Accompanying drawing explanation
Fig. 1 is electric automobile continual mileage evaluation method flow process based on operating mode's switch and fuzzy energy consumption of the present invention
Figure；
Fig. 2 is that electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention is to row
Sail operating mode and carry out cluster analysis figure；
Fig. 3 is poly-in electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention
Class is cluster centre result figure when 4；
Fig. 4 is energy in electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention
Consumption and traveling graph of a relation；
Fig. 5 is that electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention is set up
Characteristic parameter fuzzy rule energy consumption storehouse schematic diagram；
Fig. 6 is electric automobile continual mileage evaluation method unit based on operating mode's switch and fuzzy energy consumption of the present invention
The analysis result figure of energy consumption distance travelled；
Fig. 7 is that electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention travels
Working condition chart；
Fig. 8 is that electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption of the present invention is to row
Sail operating mode's switch simulation result figure；
Fig. 9 is electric automobile continual mileage evaluation method pair based on operating mode's switch and fuzzy energy consumption of the present invention
The residue continual mileage simulation result figure of ECE15 operating mode；
Figure 10 is electric automobile continual mileage evaluation method pair based on operating mode's switch and fuzzy energy consumption of the present invention
The residue continual mileage experimental result picture of ECE15 operating mode.
Detailed description of the invention
With detailed description of the invention, the present invention is described in further detail below in conjunction with the accompanying drawings, but protection scope of the present invention
It is not limited to that.
Electric automobile continual mileage evaluation method flow chart based on operating mode's switch and fuzzy energy consumption is as it is shown in figure 1, include
Step:
S1. realize the division to operating mode based on Fuzzy C-Means Cluster Algorithm and draw the parameter for identifying operating mode,
330 fragments according to 30 standard conditions of above-mentioned algorithm picks and be divided into 330 fragments, are divided by S1.1
Not taking cluster number is that C=3, C=4, C=5, C=6 carry out cluster analysis；
S1.2, is analyzed the cluster centre result of its idling ratio, at the uniform velocity ratio, deceleration ratio, chooses energy
Substantially distinguish the C value of every class operating mode, as can be seen from Figure 2 can substantially distinguish every class operating mode as C=4, the most finally select C
=4；
S1.3, obtains the cluster centre of every class operating mode when being 4 to cluster, the centre coordinate of cluster centre 2 (100.013,
0.0034), this kind of operating mode idling ratio is minimum, and speed is higher than 100km/h, reflects the most unimpeded high-speed road conditions；In cluster
The centre coordinate (11.311,0.420) of the heart 4, this kind of operating mode idling ratio is maximum, and speed is blocked up less than 15km/h, reflection
City operating mode, cluster centre 1 and cluster centre 3 are interposed between the two, and reflect suburbs, normal city operating mode, as shown in Figure 3.Its
Concrete outcome is as follows:
c1=(70.23,0.092,0.412,0.22)
c2=(102.1,0.003,0.293,0.148)
c3=(30.62,0.192,0.362,0.062)
c4=(12.62,0.411,0.25,0.0394)
S1.4, is identified emulating to operating mode according to cluster centre；
According to said method, operating mode's switch having been carried out simulating, verifying, Fig. 7 is driving cycle, after Fig. 8 is operating mode's switch
As a result, can draw from result, driving cycle is identified 4 classes, be respectively as follows: 1 and represent countryside, smooth city operating mode；2 represent freely
Logical high-speed working condition；3 represent normal city operating mode；4 represent crowded cities operating mode.
S2. the fuzzy rule base between characteristic parameter and energy consumption is set up,
Initially setting up whole pure electric vehicle energy consumption model, its input operating mode is 30 typical standard operating modes；Then according to
Mono-fragment of 60s carries out total energy consumption calculating, carries out energy consumption data acquisition according to the method shown in Fig. 4 and (arbitrarily chooses a work
Condition)；Setting up fuzzy rule base between characteristic parameter and energy consumption further according to fuzzy rule base, it specifically comprises the following steps that
S2.1, differentiates classification residing for current clip according to driving cycle, and obtain current clip average speed meanv,
Acceleration ratio P, deceleration ratio N, tetra-characteristic parameters of total energy consumption Ecost；
S2.2, each characteristic parameter to current class chooses 11 groups of data successively, and to often organize data according to from little to
Big order arrangement；Set up fuzzy storehouse, input three parameters: average speed meanv, accelerate ratio P, deceleration ratio N, output one
Individual parameter: total energy consumption Ecost；Set up fuzzy rule, according to: condition A is true and condition B is true and condition C is that true time performs bar
Part D principle, such as: when cluster is 3, its fuzzy rule is as shown in Figure 5.
S3. unit kilometer distance travelled L is optimized, as shown in Figure 6,
Specific energy consumption distance travelled number L is drawn according to current the most distance travelled and current wastage in bulk or weight energy method of being divided by,
It is multiplied with dump energy by L again and draws remaining mileage.The present invention sets up different lists respectively according to the fragment number of actual samples
Potential energy consumption algorithm, it specifically comprises the following steps that
S3.1, when the fragment of sampling is within 30, in order to meet remaining mileage linearly decline trend, sets up unit energy
Consuming the linear relationship with residue energy consumption is:
L=Lmin+k(Erest-Emin) (1)
In formula: LminIt is the specific energy consumption distance travelled minima drawn by clustering algorithm, takes 2 according to cluster result；Erest
For battery dump energy；EminFor conservative minimum electricity, take 4 according to practical situation；K is linear predictor metering.
Distance travelled S and battery electric quantity E in vehicle travel processtotalRelation is:
In formula: EtotalFor battery gross energy (J), m be vehicle mass (kg), f be coefficient of rolling resistance, CDFor windage system
Number, A is front face area (m2), v be speed (km/h), g be acceleration of gravity (m/s2)。EtotalTake 28.2J, CDTaking 0.294, f takes
0.015, m takes 1363kg, g takes 9.8m/s2, A takes 2.04m2, when v takes 40km/h, i.e. draw when vehicle at the uniform velocity travels with 40km/h
Maximum range Smax。
Vehicle maximum range SmaxElectricity E total with batterytotalRelation be:
Etotal(Lmin+k(Etotal-Emin))=Smax (3)
Finally show that linear predictor measures k=0.2066.K value mainly by the total electricity of Vehicular battery and this vehicle according to most preferably
Maximum range when speed travels determines, i.e. this value is for can demarcate according to vehicle different parameters.
S3.2, when fragment of sampling is more than 30, the continual mileage of said method estimation may be absorbed in local minimum, because of
This uses on-line talking algorithm, and it specifically comprises the following steps that
S3.2.1, obtains current clip data and calculates described operating mode classification；
S3.2.2, finds out and history fragment (fragment data travelled) identical category operating mode number according to operating mode classification
According to, and epic segment data is carried out fuzzy C-means clustering, this result is current clip specific energy consumption distance travelled L；
S3.2.3, if current working classification does not exists in history fragment, then current working L is according to the method for above-mentioned S3.1
Calculate.
S4. the last mileage L travelled according to specific energy consumption and dump energy Erest estimate the residue driving of electric automobile
Mileage Srest,
S4.1, obtains current driving operating mode feature parameter in real time according to driving cycle and sampling time；
S4.2, draws current working classification according to characteristic parameter, and draws current total energy consumption Ecost；
S4.3, draws dump energy Erest according to current total energy consumption Ecost and vehicle initial total energy consumption Etotal, the most surplus
Complementary energy Erest=Etotal-Ecost；
S4.4, draws residue continual mileage Srest, unit energy according to specific energy consumption distance travelled L and dump energy Erest
Consumption distance travelled L=S/Ecost, wherein S is distance travelled；Residue continual mileage
To residue continual mileage estimation carried out emulation and experiment, its emulation and experimental result as shown in Figure 9, Figure 10, from
Result is it can be seen that residue continual mileage is the most different along with the different downward trends of operating mode, such as when operating mode drastically changes, its
Mileage also declines comparatively fast.
Above to electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption provided by the present invention
It is described in detail, applies specific case herein and principle and the embodiment of the present invention are set forth, to be described
, the foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention.All spirit in the present invention and
Any amendment, equivalent and the improvement etc. made within principle, should be included within the scope of the present invention.
Claims (4)
1. electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption, it is characterised in that include following step
Rapid:
S1. realize the division to operating mode based on Fuzzy C-Means Cluster Algorithm and draw the parameter for identifying operating mode,
S1.1, chooses 30 standard conditions and is divided into 330 fragments, and 330 fragments take different cluster respectively
Number carries out cluster analysis；
S1.2, according to cluster result, chooses the cluster number that can substantially distinguish different operating modes, finally determines cluster number；
330 fragment operating modes are divided into the classification of respective numbers, and cluster out the center of every class by S1.3 according to cluster number；
S1.4, is identified emulating to operating mode according to cluster centre；
S2. the fuzzy rule base between characteristic parameter and energy consumption is set up,
S2.1, differentiates classification residing for current clip according to driving cycle, obtains the average speed meanv of current clip, speed-up ratio
Example P, deceleration ratio N, tetra-characteristic parameters of total energy consumption Ecost；
S2.2, each characteristic parameter to current clip chooses 11 groups of data successively, and to often organizing data according to from small to large
Order arrangement, sets up fuzzy rule base；
S3. unit kilometer distance travelled L is optimized,
S3.1, when sampling fragment within 30 time, in order to meet remaining mileage linearly decline trend, set up specific energy consumption with
The linear relationship of residue energy consumption；
S3.2, when fragment of sampling is more than 30, the continual mileage of said method estimation may be absorbed in local minimum, therefore adopt
With Fuzzy C-Means Cluster Algorithm, energy consumption average to unit kilometer clusters and exports；
S4. residue continual mileage Srest estimation, comprises the following steps,
S4.1, obtains current driving operating mode feature parameter in real time according to driving cycle and sampling time；
S4.2, draws current working classification according to characteristic parameter, and draws current total energy consumption Ecost；
S4.3, draws dump energy Erest according to current total energy consumption Ecost and vehicle initial total energy consumption Etotal；
S4.4, draws residue continual mileage Srest according to specific energy consumption distance travelled L and dump energy Erest.
Electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption the most according to claim 1, its
It is characterised by, dump energy Erest=Etotal-Ecost in described S4.3.
Electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption the most according to claim 1, its
Being characterised by, specific energy consumption distance travelled L=S/Ecost in described S4.4, wherein S is distance travelled.
Electric automobile continual mileage evaluation method based on operating mode's switch and fuzzy energy consumption the most according to claim 1, its
It is characterised by, described S4.4 remains continual mileage
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| CN106326581A (en) * | 2016-08-29 | 2017-01-11 | 北京新能源汽车股份有限公司 | Determination method and device for driving range and automobile |
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| CN107067722A (en) * | 2017-04-24 | 2017-08-18 | 中国汽车技术研究中心 | A kind of new vehicle driving-cycle construction method |
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