CN108198425A - A kind of construction method of Electric Vehicles Driving Cycle - Google Patents
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Abstract
The invention discloses a kind of construction methods of Electric Vehicles Driving Cycle, the construction method can not accurate evaluation electric vehicle property indices and the problem of energy consumption for existing driving cycle, on the basis of city road network information and the daily magnitude of traffic flow, establish the best trip mode model of driver, the computational methods and allocation rule of sample size needed for experiment are formulated, and test course has been planned according to this, it is determined that test period.A large amount of test data is obtained using GPS/IMU equipment, working characteristics and its operation characteristic in urban road for motor in electric automobile, it has formulated data prediction, the rule that data parse, has been finally based on the driving cycle that Markov static state Monte Carlo Analogue Method constructs electric vehicle.
Description
Technical field
The invention belongs to the development fields of electric vehicle real road driving cycle, and in particular to a kind of electric automobile during traveling
The construction method of operating mode.
Background technology
Automobile running working condition is that the synthesis of many factors such as country (area) road, climatic environment and driving habit is anti-
It reflects, is the basis of vehicle property indices calibration optimization, be the main reference of energy consumption of vehicles and emission test method, be guiding
With one of the key factor for restricting China Automobile Industry.
At present, existing driving cycle cannot meet the practical national conditions in China, including road traffic features, environmental condition
Deng；It can not reflect the performance of new-energy automobile strictly according to the facts, because traditional energy operating mode can not evaluate new-energy automotive air-conditioning, system
Influence of situations such as energy recycles to power consumption；Also enter China market for New Energy Sources automobile simultaneously to provide just
Profit is unfavorable for the development of the autonomous new-energy automobile in China.In addition to this, at present about the research of driving cycle mostly with internal combustion engine
Based on vehicle, and the test data parsing in later stage and driving cycle structure stage are primarily focused on, rank is planned in the experiment about early period
Section is not studied excessively, this reliability of driving cycle that will be influenced the authenticity of test data and construct.
Invention content
In order to make up above-mentioned deficiency, the present invention is directed to using electric vehicle as the new-energy automobile of representative, it is proposed that Yi Zhong electricity
The construction method of electrical automobile driving cycle.The construction method, which is set forth in detail, builds initial experiment planning rank from driving cycle
Section, to the data acquisition phase of mid-term, until the data parsing in later stage and the particular content in operating mode structure stage.By the method
With existing short stroke method, fixed step size interception method and the comparative analysis of V-A matrix-analysis methods it is found that using the method for the invention structure
The precision higher for the driving cycle built can really reflect the road traffic condition of city reality.It is in conclusion of the present invention
The construction method of driving cycle have the characteristics that theoretical driving cycle precision that is perfect, being easily achieved, build is high, be adapted to respectively
The exploitation of large- and-medium size cities driving cycle.
The present invention adopts the following technical scheme that realize：
A kind of construction method of Electric Vehicles Driving Cycle, includes the following steps：
1) traffic in city is investigated, obtains the basic road network information in city and the daily magnitude of traffic flow；
2) according to the road network information of acquisition, sample size and its allocation proportion needed for test course are calculated, and advise accordingly
Draw test course；According to the daily magnitude of traffic flow in city, peak period, flat peak phase and the ebb of one day traffic in city are analyzed
Phase thereby determines that test period；
3) according to test course and test period, road data acquisition experiment is carried out, obtains the Velocity-time of vehicle traveling
Information；
4) the Velocity-time data of vehicle are parsed, obtains and turn between the different transport condition of vehicle and state
Probability is moved, and builds driving cycle.
Further improve of the invention is, in the step 1), city is obtained by way of Literature Consult, gather material
The road network information in city；By way of reading monitor video, main through street, major trunk roads, subsidiary road and branch in city are obtained
The magnitude of traffic flow in one month.
Further improve of the invention is, in the step 2), on the basis of city road network information, is adjusted by sampling
The mode looked into chooses a sample to characterize general characteristic in overall road network, and the sample needed for experiment is calculated using formula (1)
Capacity n：
Wherein, S2For sample standard deviation, S2≈ P (1-P), Δ are maximum allowable absolute error；
For each grade road proportion in sample size of reasonable distribution, based on analytic hierarchy process (AHP), with through street, master
For the road network length of arterial highway, subsidiary road and branch as input, the range and speed limit grade of road network establish driver most as criterion
Good trip mode model according to each grade road ratio that model exports, calculates the required length of each grade road, plans according to this
Test course, it is ensured that test course joins end to end, with error calculated within 5%；
According to the magnitude of traffic flow of acquisition, the peak period of one day traffic trip, low peak period peace peak phase in city are analyzed, respectively
2 hours are therefrom chosen as test period；The big electric vehicle of ownership is as test vehicle in selection city.
Further improve of the invention is, in the step 3), according to the test course and test period of formulation, carries out
One-week road data acquisition experiment, the Velocity-time using GPS and IMU inertial navigation systems collection vehicle traveling are believed
Breath, sample frequency are set as 1Hz.
Of the invention further improve is, in the step 4), in data resolution phase, for electric vehicle in city
Traveling feature on road is carried out denoising to Velocity-time data first with formula (2), is put down later using formula (3)
Sliding processing：
Wherein vtRepresent the speed of before processing, v 'tSpeed that treated, k are smoothing parameter；
The acceleration at each moment is calculated using formula (4)：
Wherein vtIt is the speed at current time, vt-1It is the speed of previous second, unit km/h, atIt is the acceleration at current time
Degree, unit m/s2；
According to the speed of vehicle and acceleration change, speed data is divided into acceleration, deceleration, at the uniform velocity using formula (5)
With idling segment：
It selects poor maximum speed, minimum speed, average speed, velocity standard, peak acceleration, maximum deceleration, be averaged
Acceleration, acceleration standard deviation, speed is very poor and run time segment characterizations are described in totally 10 characteristic parameters；Utilize master
Componential analysis carries out dimension-reduction treatment to characteristic parameter, calculates principal component scores, the attribute variable new as segment；Utilize K-
Means clustering algorithms partition clips into 6 classes, count speed, the acceleration signature of all kinds of segments, obtained segment is defined as
Force speed, it is strong slow down, it is weak accelerate, it is weak accelerate, at a high speed at the uniform velocity with low speed at the uniform velocity 6 kinds of states, and according to formula (6) statistic behavior it
Between transition probability：
In formula, NijCurrent state is represented as i, NextState is the frequency of j；pijCurrent state is represented as i, NextState
For the probability of j, l is classification number.
Further improve of the invention is, in the step 4), builds the stage in driving cycle, randomly selects one first
Idling segment of a duration no more than 5s as start-up portion, then using MATLAB generate between [0,1] it is equally distributed with
Machine number x, if this random number meets：
Then NextState is just q, and nothing chooses segment and joins end to end with a upper segment with putting back in state q, later assigns q
The step of being worth before i, repetition chooses segment, until the time duration of driving cycle reaches 1200s, selected segment
Following principle should be met：
(1) distance of segment to such cluster centre of selection should be in preceding 15%；
(2) difference of the end speed of a starting velocity and upper segment of the segment of selection should be in 1km/h；
(3) when the segment number for meeting above-mentioned two principle is not unique, it is closest preferentially to choose cluster centre
Segment；
Later, multigroup random number is constantly regenerated, builds a plurality of alternative operating mode；
Select average speed, acceleration time ratio, deceleration time ratio, at the uniform velocity time scale, dead time ratio, 0~
10km/h velocity shootings ratio, 10~20km/h velocity shootings ratio, 20~30km/h velocity shootings ratio, 30~40km/h velocity shootings ratio
The characteristic parameter conduct that totally 11 descriptive statistics are distributed of example, 40~50km/h velocity shootings ratio, more than 50km/h velocity shootings ratio
The interpretational criteria of driving cycle calculates the average phase between alternative operating mode and original experiment data characteristic parameter according to formula (8)
To error d:
In formula, tiIt is the ith feature parameter of alternative operating mode, ziIt is the ith feature parameter of original experiment data, m is special
Levy number of parameters；
Finally, the alternative operating mode with test data average relative error minimum is chosen as the electric vehicle finally fitted
Driving cycle.
Compared with prior art, the present invention is surrounded using electric vehicle as the new-energy automobile of representative, in city road network information
On the basis of daily magnitude of traffic flow big data, the best trip mode model of driver is established, and planned test course accordingly,
Test period is determined.A large amount of car speed-time data is obtained by the acquisition experiment of real road data.With reference to electronic
Automobile has carried out denoising and smoothing processing in the operation characteristic of urban road using filtering algorithm to original experiment data.According to
Electric vehicle speed, acceleration feature, by test data be divided into several accelerate, slow down, at the uniform velocity with idling segment, and to piece
Duan Tezheng is described.The main information of characteristic parameter is extracted using Principal Component Analysis, new segment category is calculated
Property variable；The motion state of 6 vehicle travelings has been divided using K-Means clustering algorithms, and has counted turn between each state
Move probability.To solve the problems, such as that traditional Markov approach can cause small probability event to be lost, using static Monte Carlo simulation
Method constructs the driving cycle of electric vehicle, fully reflects randomness of the electric vehicle in urban road traveling with not knowing
Property.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the best trip mode model of driver that the present invention establishes.
Fig. 3 is the test course that the present invention formulates.
Fig. 4 is the experimental data processing flow chart of the present invention.
Fig. 5 is the Electric Vehicles Driving Cycle figure that the present invention is built.
Specific embodiment
The present invention will be further described with reference to the accompanying drawings and examples.
Embodiment：By taking certain domestic typical large size city as an example, Electric Vehicles Driving Cycle is built.
First, traffic investigation is planned with experiment
It is right first in order to which the driving cycle constructed is enable fully to reflect the actual traffic situation in certain city referring to Fig. 1
The road network information in certain city is investigated, as shown in table 1：
Certain the large size city road network information of table 1
In order to reduce experimentation cost, using sampling without replacement method, a sample is chosen from road network totality, passes through sample
Feature reflects overall characteristic, so as to achieving the purpose that investigation.If the road network length X Normal Distributions in certain city, i.e. X~
N(μ,σ2), (x1,x2,…,xn) be a simple random sampling from road network totality, by Principle of Statistics it is found that extract
Sample Normal DistributionI.e.
It is found by calculating, in σ2In the case of unknown, parameter Estimation is either carried out to population average or assumes inspection
It tests, can obtain an identical confidence intervalIf average of samplesEstimation
Or permitted maximum absolute error is when examining population average μKnow maximum absolute error Δ with
Under the premise of confidence level 1- α, necessary sample size n sample range at this time can be calculated, as shown in formula (1)：
As population variance σ2When unknown, σ can be used0 2Instead of S2.In the case of level of significance α≤0.05, sample is total
When body is more than 30, tα(n-1) ≈ 2 may be used approximate formula (2) and calculate sample size n sample range：
S in formula2≈ P (1-P), so sample standard deviation maximum value is 0.25.It wants to find sample in sample investigation
This amount confidence interval is 95%, a section of the limit error 16%.According to the investigation result of table 1, certain city proper road
Total length is 2562km, and the sample size that can be calculated according to formula (2) needs to choose is 39.04km.
In order to distribute different brackets actual road test size, the magnitude of traffic flow and sample with reference to each grade road are big
It is small to do overall planning.In view of certain city north and south, the otherness of thing traffic and different brackets road traffic intensity not
Together, 11 monitoring points have been selected, have been had read from early 7 points to late 20 points of monitoring videos by monitoring point, it is quick including 2
Road, 5 major trunk roads, 4 subsidiary roads and 1 branch, final statistics are obtained in one day through the average of monitoring point vehicle, such as table
Shown in 2：
Pass through the average of monitoring point vehicle in table 2 one days
| Road type | Through street | Major trunk roads | Subsidiary road | Branch |
| Mean value () | 47065 | 24414 | 14928 | 5588 |
In addition to this, in traffic trip, through street, major trunk roads or subsidiary road, branch can be selected, no matter is selected
Traveler can be sent to destination by which kind of road.When being selected, road width, speed limit grade, road are often considered
The convenience of net and many factors of traffic.Therefore, based on analytic hierarchy process (AHP), with through street, major trunk roads, subsidiary road,
As input, the range and grade of road network establish driver as shown in Figure 2 and most preferably go out the road network length of branch as criterion
Line mode model, sampling proportion of the model output result for each grade road in test course, and calculated with reference to sample size
The required length of each grade road is arrived, as shown in table 3：
The ratio and length of each grade road in 3 test sample of table
| Category of roads | Through street | Major trunk roads | Subsidiary road | Branch |
| Ratio | 0.2996 | 0.2480 | 0.2685 | 0.1839 |
| Length (km) | 11.69 | 9.68 | 10.48 | 7.18 |
With reference to the length of grade road each in table 3, planning experiments route.Test course allows for reflecting urban road
Whole synthesis feature, whole route are needed to cover main shopping centre, industrial area, culture area and living area etc., finally be planned
Test course is referring to Fig. 3.
2nd, road data acquires
In order to make the driving cycle of structure more representative, select ownership is big in city certain electric vehicle as
Test vehicle；In order to evade the influence of driving behavior, experienced driver is selected in the morning peak phase 7 in certain city:00-9:
00th, the flat peak phase 12 at noon:30-14:30th, late low peak period 19:00-21:00 recycles driving gathered data in test course.It considers
When vehicle drives to boulevard or passes through pile group, GPS possibly can not steadily receive satellite-signal, lead to sampled point
Lose, rate curve burr the problems such as, during experiment, other than GPS, be further provided with IMU Inertial Measurement Units and synchronize and adopt
Collect the Velocity-time data of vehicle traveling.In order to gathered data as much as possible, sample frequency is set as 1Hz.
3rd, test data parsing is built with driving cycle
As shown in figure 4, test data parsing is broadly divided into：Data prediction, test data parsing and driving cycle structure
Three parts are illustrated respectively with regard to this three parts below.
1. data prediction
Other than sample devices, it can all cause to test due to driver's operation error, road traffic condition mutation etc.
The distortion of data, it is therefore desirable to which denoising and smoothing processing are carried out to test data.It is removed using formula (3) different in test data
Chang Dian：
Wherein vtRepresent the speed of before processing, v 'tSpeed that treated.
Due to needing that original experiment data is divided, in order to make the segment of division excessively not mixed and disorderly, fragment length is not
It is too small, data are smoothed using formula (4)：
Wherein k is smoothing parameter.
2. test data parses
First according to state of motion of vehicle (accelerate, slow down, at the uniform velocity, idling) variation, test data is divided into several
A small traveling segment.With reference to the operation characteristic of motor in electric automobile, using the division rule as shown in formula (5)：
After data divide, 15682 traveling segments are obtained.Select max. speed, minimum speed, average speed, speed
Standard deviation, peak acceleration, maximum deceleration, average acceleration, acceleration standard deviation, whole story speed difference, run time etc. 10
The characteristic for travelling segment is described in a characteristic parameter.The characteristic parameter of segment is carried out at dimensionality reduction using principal component analysis
Reason, is classified as one kind, final 15682 kinematics segments are divided using K-Means cluster analyses by the segment with same characteristic features
For 6 classes, as shown in table 4.This 6 class segment is respectively defined as according to all kinds of speed and acceleration signature：At the uniform velocity travel at a high speed,
Low speed at the uniform velocity travels, weak Reduced Speed Now, strong Reduced Speed Now, weak give it the gun, gives it the gun by force.
4 classification results of table and each category feature
The attribute of each traveling segment can be obtained according to cluster result, the shape between 6 class segments is calculated using formula (6)
State transition probability matrix：
Wherein NijCurrent state is represented as i, NextState is the frequency of j；pijCurrent state is represented as i, NextState is
The probability of j, l are classification number.Transition probability matrix between all kinds of is as shown in table 5：
5 state transition probability matrix of table
3. driving cycle is built
The stage is built in driving cycle, can lead to asking for small probability event loss to solve traditional Markov approach
Topic carries out the structure of driving cycle using static Monte Carlo Analogue Method, is as follows shown：
Idling segment of the duration no more than 5s is randomly selected first as start-up portion, is then generated using MATLAB
Equally distributed random number x between [0,1], if this random number meets：
Then NextState is just q, and nothing chooses segment and joins end to end with a upper segment with putting back in state q, later assigns q
The step of being worth before i, repetition constantly chooses segment, until the time duration of driving cycle reaches 1200s.Selected
Segment should meet following principle：
(1) distance of segment to such cluster centre of selection should be in preceding 15%；
(2) difference of the end speed of a starting velocity and upper segment of the segment of selection should be in 1km/h；
(3) when the segment number for meeting above-mentioned two principle is not unique, it is closest preferentially to choose cluster centre
Segment.
Later, multigroup random number is constantly regenerated, builds a plurality of alternative operating mode.
Select average speed, acceleration time ratio, deceleration time ratio, at the uniform velocity time scale, dead time ratio, 0~
10km/h velocity shootings ratio, 10~20km/h velocity shootings ratio, 20~30km/h velocity shootings ratio, 30~40km/h velocity shootings ratio
The characteristic parameter conduct of 11 descriptive statistics distributions such as example, 40~50km/h velocity shootings ratio, more than 50km/h velocity shooting ratios
The interpretational criteria of driving cycle calculates the average phase between alternative operating mode and original experiment data characteristic parameter according to formula (8)
To error d:
In formula, tiIt is the ith feature parameter of alternative operating mode, ziIt is the ith feature parameter of original experiment data, m is special
Levy number of parameters.Finally, it chooses electronic as what is finally fitted with the alternative operating mode of test data average relative error minimum
Automobile running working condition.
4th, the verification of driving cycle
In order to verify the accuracy of the driving cycle of this method structure, by this method and short stroke method existing at present, V-A
The driving cycle of matrix method and fixed step size interception method structure is compared.Table 6 compared the driving cycle of 4 kinds of methods structure with
The characteristic parameter of original experiment data, table 7 are 4 kinds of driving cycles of method structure and averagely missing relatively for original experiment data
Difference.The result shows that the average error rate of the method for the invention and original experiment data only has 6.34%, and short stroke method, V-A
Matrix method and the average error rate of fixed step size interception method and original experiment data are respectively 12.62%, 9.52%, 15.02%.It can
See the driving cycle precision higher of the method for the invention structure, can really reflect the road traffic condition of city reality.
The driving cycle of 64 kinds of method structures of table and the characteristic parameter of original experiment data
The driving cycle of 74 kinds of method structures of table and the average relative error of original experiment data
| Context of methods | V-A matrix methods | Short stroke method | Fixed step size intercepts method | |
| Mean error (%) | 6.34 | 9.52 | 12.62 | 15.02 |
Claims (6)
1. a kind of construction method of Electric Vehicles Driving Cycle, which is characterized in that include the following steps：
1) traffic in city is investigated, obtains the basic road network information in city and the daily magnitude of traffic flow；
2) according to the road network information of acquisition, sample size and its allocation proportion needed for test course, and planning examination accordingly are calculated
Test route；According to the daily magnitude of traffic flow in city, peak period, flat peak phase and the low peak period of one day traffic in city are analyzed,
Thereby determine that test period；
3) according to test course and test period, road data acquisition experiment is carried out, obtains the Velocity-time letter of vehicle traveling
Breath；
4) the Velocity-time data of vehicle are parsed, the transfer obtained between the different transport condition of vehicle and state is general
Rate, and build driving cycle.
A kind of 2. construction method of Electric Vehicles Driving Cycle according to claim 1, which is characterized in that the step 1)
In, the road network information in city is obtained by way of Literature Consult, gather material；By way of reading monitor video, obtain
The magnitude of traffic flow in main through street in city, major trunk roads, subsidiary road and branch one month.
A kind of 3. construction method of Electric Vehicles Driving Cycle according to claim 2, which is characterized in that the step 2)
In, on the basis of city road network information, a sample is chosen to characterize always in overall road network by way of sample investigation
Body characteristics calculate the sample size n sample range needed for experiment using formula (1)：
Wherein, S2For sample standard deviation, S2≈ P (1-P), Δ are maximum allowable absolute error；
For each grade road proportion in sample size of reasonable distribution, based on analytic hierarchy process (AHP), with through street, trunk
For the road network length in road, subsidiary road and branch as input, it is best to establish driver as criterion for the range and speed limit grade of road network
Trip mode model according to each grade road ratio that model exports, calculates the required length of each grade road, according to this planning examination
Test route, it is ensured that test course joins end to end, with error calculated within 5%；
According to the magnitude of traffic flow of acquisition, the peak period of one day traffic trip, low peak period peace peak phase in city are analyzed, respectively therefrom
2 hours are chosen as test period；The big electric vehicle of ownership is as test vehicle in selection city.
A kind of 4. construction method of Electric Vehicles Driving Cycle according to claim 3, which is characterized in that the step 3)
In, according to the test course and test period of formulation, one-week road data acquisition experiment is carried out, is used to using GPS and IMU
Property navigation system collection vehicle traveling Velocity-time information, sample frequency is set as 1Hz.
A kind of 5. construction method of Electric Vehicles Driving Cycle according to claim 4, which is characterized in that the step 4)
In, in data resolution phase, for traveling feature of the electric vehicle on urban road, first with formula (2) to speed-when
Between data carry out denoising, be smoothed later using formula (3)：
Wherein vtRepresent the speed of before processing, vt' treated speed, k is smoothing parameter；
The acceleration at each moment is calculated using formula (4)：
Wherein vtIt is the speed at current time, vt-1It is the speed of previous second, unit km/h, atIt is the acceleration at current time,
Unit is m/s2；
According to the speed of vehicle and acceleration change, using formula (5) by speed data be divided into acceleration, deceleration, at the uniform velocity with it is idle
Fast segment：
Select poor maximum speed, minimum speed, average speed, velocity standard, peak acceleration, maximum deceleration, average acceleration
Degree, acceleration standard deviation, speed is very poor and run time segment characterizations are described in totally 10 characteristic parameters；Utilize principal component
Analytic approach carries out dimension-reduction treatment to characteristic parameter, calculates principal component scores, the attribute variable new as segment；Utilize K-Means
Clustering algorithm partitions clips into 6 classes, counts speed, the acceleration signature of all kinds of segments, obtained segment is defined as forcing
Speed, it is strong slow down, it is weak accelerate, it is weak accelerate, at a high speed at the uniform velocity with low speed at the uniform velocity 6 kinds of states, and according between formula (6) statistic behavior
Transition probability：
In formula, NijCurrent state is represented as i, NextState is the frequency of j；pijCurrent state is represented as i, NextState is j's
Probability, l are classification number.
A kind of 6. construction method of Electric Vehicles Driving Cycle according to claim 5, which is characterized in that the step 4)
In, the stage is built in driving cycle, randomly selects idling segment of the duration no more than 5s first as start-up portion, then
Equally distributed random number x between [0,1] is generated using MATLAB, if this random number meets：
Then NextState is just for q, is joined end to end with a upper segment without choosing segment with putting back in state q, later by q assignment in
I, repeat before the step of choose segment, until the time duration of driving cycle reaches 1200s, selected segment should expire
It is enough lower principle：
(1) distance of segment to such cluster centre of selection should be in preceding 15%；
(2) difference of the end speed of a starting velocity and upper segment of the segment of selection should be in 1km/h；
(3) when the segment number for meeting above-mentioned two principle is not unique, the closest segment of cluster centre is preferentially chosen；
Later, multigroup random number is constantly regenerated, builds a plurality of alternative operating mode；
Select average speed, acceleration time ratio, at the uniform velocity deceleration time ratio, time scale, dead time ratio, 0~10km/
H velocity shootings ratio, 10~20km/h velocity shootings ratio, 20~30km/h velocity shootings ratio, 30~40km/h velocity shootings ratio, 40
~50km/h velocity shootings ratio, more than 50km/h velocity shootings the ratio characteristic parameter that totally 11 descriptive statistics are distributed are as traveling work
The interpretational criteria of condition calculates the average relative error between alternative operating mode and original experiment data characteristic parameter according to formula (8)
d:
In formula, tiIt is the ith feature parameter of alternative operating mode, ziIt is the ith feature parameter of original experiment data, m is feature ginseng
Several numbers；
Finally, the alternative operating mode with test data average relative error minimum is chosen as the electric automobile during traveling finally fitted
Operating mode.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102416950A (en) * | 2011-10-31 | 2012-04-18 | 大连理工大学 | Minimum equivalent fuel consumption-based hybrid electrical vehicle control method |
| CN103942952A (en) * | 2014-03-12 | 2014-07-23 | 华南理工大学 | Assessment method for road network function gradation state grades |
| CN104792543A (en) * | 2015-04-17 | 2015-07-22 | 北京理工大学 | Constructing method of road cyclic conditions |
| US20170272905A1 (en) * | 2015-11-04 | 2017-09-21 | Martin Enriquez | In-vehicle access application |
-
2018
- 2018-02-10 CN CN201810138580.9A patent/CN108198425A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102416950A (en) * | 2011-10-31 | 2012-04-18 | 大连理工大学 | Minimum equivalent fuel consumption-based hybrid electrical vehicle control method |
| CN103942952A (en) * | 2014-03-12 | 2014-07-23 | 华南理工大学 | Assessment method for road network function gradation state grades |
| CN104792543A (en) * | 2015-04-17 | 2015-07-22 | 北京理工大学 | Constructing method of road cyclic conditions |
| US20170272905A1 (en) * | 2015-11-04 | 2017-09-21 | Martin Enriquez | In-vehicle access application |
Non-Patent Citations (2)
| Title |
|---|
| 张思远: "基于实际交通环境的电动汽车循环工况构建研究", 《汽车实用技术》 * |
| 黄良文: "《统计学 修订第三版》", 30 September 2006 * |
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