CN109960889B - Method for constructing typical speed-time running condition of track vehicle line - Google Patents
Method for constructing typical speed-time running condition of track vehicle line Download PDFInfo
- Publication number
- CN109960889B CN109960889B CN201910267408.8A CN201910267408A CN109960889B CN 109960889 B CN109960889 B CN 109960889B CN 201910267408 A CN201910267408 A CN 201910267408A CN 109960889 B CN109960889 B CN 109960889B
- Authority
- CN
- China
- Prior art keywords
- speed
- time
- daily
- train
- sub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention relates to a method for constructing a typical speed-time running condition of a rail vehicle line, which comprises the steps of collecting the daily speed-time running condition of a train of a given line by selecting proper sampling date and sampling days, and dividing a daily speed-time sequence S (j) of a jth sample collecting day (jth day for short) into K sub-segments S' (K, j) according to the stop running state of the train running; sorting K sub-segments S' (K, j) according to parking time, and deleting unreasonable data to form a daily standard speed-time sequence S of the train s (j) (ii) a The standard speed of the train per day-time sequence S s (j) Carrying out normalization processing to construct a daily typical speed-time sequence S T (j) Integrating the daily typical speed-time series S for all sampling days T (j) And selecting the typical speed-time running condition of the line. The typical line speed-time working condition obtained by the method can reflect the line running characteristics of the urban rail transit train, and provides working condition reference for the performance analysis of the train.
Description
Technical Field
The invention belongs to the technical field of working condition simplification of rail transit lines, and particularly relates to a construction method of a typical speed-time running working condition of a rail transit vehicle line.
Background
The factor influencing the train running energy consumption in the urban rail transit is mainly the running condition of the train. The speed curve of the train between the stations, the weight of the train and the like determine the traction force of the train between the stations, and further determine the running energy consumption of the urban rail train between the stations. However, at present, the construction and research of the automobile running working condition are more in China, and the construction result of the working condition suitable for the energy consumption analysis of the rail vehicle is rare. Therefore, it is necessary to develop the running data analysis of urban rail lines and construct the typical speed-time running condition of a given line.
Disclosure of Invention
The invention provides a method for constructing a typical speed-time running working condition of a rail vehicle line on the basis of the defects, by adopting the method, the complicated dynamic line speed-time working condition can be simplified to form a typical working condition of the line consisting of a plurality of constant segments, and the method can reflect the line running characteristics of the urban rail transit train and provide working condition reference for the performance analysis of the urban rail transit train of each line.
In order to achieve the aim, the invention provides a method for constructing a typical speed-time running condition of a rail vehicle line, which comprises the following steps:
selecting the daily speed-time running working condition of the given line on the jth day of the train, and dividing the daily speed-time sequence S (j) into K sub-segments S' (K, j) according to the stop running state of the train running;
sequencing K sub-segments S' (K, j) according to parking time, deleting unreasonable data, and forming a daily standard speed-time sequence S of the train on the jth day s (j)；
The daily standard speed-time sequence S of the train on the jth day s (j) Carrying out normalization processing to construct a daily typical speed-time sequence S of the j day T (j) Integrating the daily typical speed-time series S for all sampling days T (j) And selecting the typical speed-time running condition of the line.
Preferably, the method for dividing the daily speed-time sequence S (j) on the j-th day into K sub-segments S' (K, j) according to the stop driving state of train driving is as follows:
calculating the deviation of the daily speed-time sequence S (j)
Obtaining data of the acceleration a; selecting | a>0 and velocity v =0 are separation points, dividing the daily velocity-time series S (j) into K sub-segments S' (K, j).
Preferably, the K sub-segments S' (K, j) are sorted according to the parking time, unreasonable data are deleted, and a daily standard speed-time sequence S of the train is formed s (j) The specific method comprises the following steps:
arranging K sub-segments S' (K, j) according to the ascending order of the parking time, determining the parking time range of the train, and setting the abnormal parking time t x ；
Deleting the parking time T which is greater than the abnormal parking time T x The remaining sub-segments S' (k, j) form a daily standard speed-time sequence S of the train s (j)。
Preferably, the standard speed of the train per day is sequenced in time S s (j) The sub-segments in (1) are normalized to construct a daily typical speed-time sequence S T (j) Integrating the daily typical speed-time series S for all sampling days T (j) The method for selecting the typical speed-time running condition of the line comprises the following steps:
finding a daily standard speed-time series S s (j) Total characteristic value T of tot_day (i, j) and the characteristic value T (i, k, j) of each sub-segment S' (k, j) typ_fra Wherein i =1,2,3,4,5,6,7,8 corresponds to a parking time/travel time, a maximum speed, an average travel speed, a maximum acceleration, respectivelyDegree, maximum deceleration, average acceleration, and average deceleration;
according to the following steps:
calculating the characteristic value T (i, k, j) of each sub-segment S' (k, j) typ_fra With a standard speed-time series S s (j) Total eigenvalue T tot_day (ii) a normalized deviation of (i, j);
judging the periodicity of train running according to the maximum speed characteristic value and the average speed characteristic value, and selecting a daily standard speed-time sequence S s (j) Middle minimum periodic segment S M (j) Determining a daily standard speed-time sequence S s (j) The cycle period M of (A);
the minimum periodic segment S M (j) The eigenvalues and deviations of each sub-segment S' (k, j) in (a) constitute an N × M eigenvalue-deviation matrix, wherein a daily standard speed-time sequence S s (j) Comprising N minimum periodic segments S M (j)；
Selecting the sub-segments S' (k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix, sorting, and then selecting the eigenvalue deviation sigma B The sub-segments S' (k, j) of which (k, j) is smaller than the setpoint value B constitute a daily typical speed-time sequence S T (j) And calculates its characteristic value T (i, j) typ_day ；
According to the formula
Calculating a characteristic value T of the line tot_data (i) Wherein L is the total number of sampling days;
according to the normalized deviation formula
Calculating a daily typical speed-time series S T (j) Characteristic value of (i, j) typ_day And the characteristic value T of the line tot_data (i) Deviation σ of day (j)；
Selecting sigma day (j) Minimum pairTypical daily speed-time series S T (j) As a typical speed-time sequence S of a line T ', the typical speed-time travel condition of the line.
Preferably, the method for sorting the sub-segments S' (k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix comprises the following steps:
selecting the sub-segment S '(k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix, and then selecting the M sub-segments S' (k, j) according to the eigenvalue deviation sigma B (i, j) are arranged in ascending order of magnitude.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention provides a method for constructing a typical speed-time running working condition of a rail vehicle line, which is compared with the existing method for the running working condition of an electric vehicle, does not depend on the characteristics of the working condition, collects the daily speed-time running working condition of a train of a given line by selecting proper sampling date and sampling days, divides the daily speed-time sequence into a plurality of sub-segments according to the stop running state of the train running, sorts the sub-segments according to the stop time to form a standard speed-time sequence of the train per day, and constructs the typical speed-time sequence per day by normalizing 8 characteristic values of the standard speed-time sequence of the train per day; and integrating daily typical speed-time sequences of all sampling days, and selecting the typical speed-time running condition of the line. The method for constructing the typical speed-time running working condition of the line is simple, the complicated dynamic line working condition can be simplified into the typical working condition of the line consisting of a plurality of constant segments, the speed-time cumulative probability distribution of the typical working condition of the line is basically consistent with the cumulative probability distribution of the working condition of the original line, and only the abnormal parking time t needs to be determined x And a characteristic value deviation set value B and a sampling day L value, wherein each actual line working condition can obtain a unique simplified line typical working condition form. The typical line speed-time working condition obtained by the invention reflects the line running characteristics of the urban rail transit train, provides working condition reference for the performance analysis of the urban rail transit train of each line, and can be used as energy consumption simulation/measurement of the urban rail transit trainAnd (5) running conditions are tried, and on the basis of the typical speed-time working condition, reference basis is provided for energy consumption analysis of the train under different influence factors.
Drawings
Fig. 1 is a schematic diagram of a method for constructing a typical speed-time running condition of a track vehicle line according to the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be further described with reference to the accompanying drawings.
The invention provides a method for constructing a typical speed-time running condition of a rail vehicle line, which is shown by referring to fig. 1 and comprises the following steps:
(1) selecting proper sampling date and sampling days to acquire the daily speed-time running condition of the train on the given route, dividing the daily speed-time running condition of the train on the jth sample acquisition day (jth day for short) into K sub-segments S' (K, j) according to the stop running state of the train running. Namely: calculating the deviation of the daily speed-time sequence S (j)
Obtaining data of the acceleration a; selecting | a | |)>0 and velocity v =0 are separation points, dividing the daily velocity-time series S (j) into K sub-segments S' (K, j).
(2) Sequencing K sub-segments S' (K, j) according to parking time, deleting unreasonable data, and forming a standard speed-time sequence S of the train per day s (j) In that respect Namely: arranging K sub-segments S' (K, j) according to the ascending order of the parking time, determining the parking time range of the train, and setting the abnormal parking time t x (ii) a Deleting the parking time T larger than the abnormal parking time T x The remaining sub-segments S' (k, j) form a daily standard speed-time sequence S of the train s (j)。
(3) The standard speed of the train per day-time sequence S s (j) Carrying out normalization processing to construct a daily typical speed-time sequence S of the j day T (j) (ii) a Integration of daily typical speed-time series S for all sampling days T (j) Selecting the stripTypical speed-time driving conditions of the line.
Specifically, the method comprises the following steps:
finding a daily standard speed-time series S s (j) Total characteristic value T of tot_day (i, j) and the characteristic values T (i, k, j) of its individual sub-segments S' (k, j) typ_fra (ii) a Where i =1,2,3,4,5,6,7,8 corresponds to a parking time/travel time, a maximum speed, an average travel speed, a maximum acceleration, a maximum deceleration, an average acceleration, and an average deceleration, respectively.
According to the following steps:
judging the periodicity of train running according to the maximum speed characteristic value and the average speed characteristic value, and selecting a daily standard speed-time sequence S s (j) Middle smallest periodic segment S M (j) Determining a daily standard speed-time sequence S s (j) The cycle period M of (A);
the minimum periodic segment S M (j) The eigenvalues and the deviation of each sub-segment S' (k, j) in (a) form an N × M eigenvalue-deviation matrix, wherein a daily standard speed-time series S s (j) Comprising N minimum periodic segments S M (j)；
Selecting the sub-segment S '(k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix, and then selecting the M sub-segments S' (k, j) according to the eigenvalue deviation sigma B Sorting the (k, j) values in ascending order, and selecting the characteristic value deviation sigma B The sub-segments S' (k, j) of which (k, j) is smaller than the setpoint value B constitute a typical speed-time sequence S of the day T (j) And calculates its characteristic value T (i, j) typ_day ；
According to the formula
Calculating a characteristic value T of the line tot_data (i) WhereinL is the total number of sampling days;
according to the normalized deviation formula
Calculating a typical speed-time series S per day T (j) Characteristic value of (i, j) typ_day And the characteristic value T of the line tot_data (i) Deviation σ of day (j)；
Selecting sigma day (j) Minimum corresponding daily typical speed-time series S T (j) As a typical speed-time sequence S of a line T ', the typical speed-time condition of the line.
Thus, typical speed-time conditions for a single line can be obtained. Meanwhile, the method for constructing the typical speed-time running working condition of the line is not limited to the construction of the line working condition, and can expand the analysis of the typical speed-time working condition of a certain area, namely, the line typical speed-time working condition of all lines in the selected area is spliced into the initial speed-time running working condition of the line in the given area, then the characteristic value of the initial speed-time running working condition of the area and the characteristic value contained in the typical speed-time running working condition of each line are normalized, and the line segment with the deviation of the characteristic value smaller than the set value is selected from the normalized deviation to be repeatedly connected to form the typical speed-time running working condition of the area.
In summary, the method for constructing the typical speed-time running condition of the rail vehicle line according to the present invention collects the daily speed-time running condition of the train on the given line by selecting the appropriate sampling date and sampling days, divides the daily speed-time sequence into a plurality of sub-segments according to the stop running state of the train running, sorts the sub-segments according to the stop time to form the daily standard speed-time sequence of the train, normalizes the 8 characteristic values of the daily standard speed-time sequence of the train, and constructs the daily typical speed-time sequence S on the jth day according to a certain rule T (j) In that respect Integrating daily typical speed-time series S for all sampling days T (j) And selecting the typical speed-time running condition of the line. Line typical speed-time driver of the inventionThe method for constructing the condition is simple, the complicated dynamic line working condition can be simplified into the line typical working condition consisting of a plurality of constant segments, meanwhile, the speed-time cumulative probability distribution of the line typical working condition is basically consistent with the original line working condition cumulative probability distribution, and only the abnormal parking time t needs to be determined x And a characteristic value deviation set value B and a sampling day L value, wherein each actual line working condition can obtain a unique simplified line typical working condition form. The typical line speed-time working condition obtained by the invention can reflect the line running characteristics of the urban rail transit train, provides working condition reference for the performance analysis of the urban rail transit train of each line, can be used as the energy consumption simulation/test standard running working condition of the urban rail transit train, and provides reference basis for the energy consumption analysis of the train under different influence factors on the basis of the typical speed-time working condition.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention.
Claims (4)
1. A method for constructing a typical speed-time running condition of a rail vehicle line is characterized by comprising the following steps of:
selecting the daily speed-time running working condition of the given line on the jth day of the train, and dividing the daily speed-time sequence S (j) into K sub-segments S' (K, j) according to the stop running state of the train running;
sequencing K sub-segments S' (K, j) according to parking time, deleting unreasonable data, and forming a standard speed-time sequence S of the train on the jth day s (j)；
The standard speed-time sequence S of the train on the jth day s (j) Carrying out normalization processing to construct a daily typical speed-time of the j daySequence S T (j) Integrating the daily typical speed-time series S for all sampling days T (j) Selecting a typical speed-time running condition of the line; the specific method comprises the following steps:
finding a daily standard speed-time series S s (j) Total characteristic value T of tot_day (i, j) and the characteristic values T (i, k, j) of its individual sub-segments S' (k, j) typ_fra Wherein i =1,2,3,4,5,6,7,8 corresponds to a parking time/travel time, a maximum speed, an average travel speed, a maximum acceleration, a maximum deceleration, an average acceleration and an average deceleration, respectively;
according to the following steps:
calculating the characteristic value T (i, k, j) of each sub-segment S' (k, j) typ_fra With a standard speed-time sequence S s (j) Total eigenvalue T tot_day (ii) a normalized deviation of (i, j);
judging the periodicity of train running according to the maximum speed characteristic value and the average speed characteristic value, and selecting a daily standard speed-time sequence S s (j) Middle smallest periodic segment S M (j) Determining a daily standard speed-time series S s (j) The cycle period M of (A);
the minimum periodic segment S M (j) The eigenvalues and the deviation of each sub-segment S' (k, j) in (a) form an N × M eigenvalue-deviation matrix, wherein a daily standard speed-time series S s (j) Comprising N minimum periodic segments S M (j)；
Selecting the sub-segments S' (k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix, sorting, and then selecting the eigenvalue deviation sigma B The sub-segments S' (k, j) of which (k, j) is smaller than the setpoint value B constitute a daily typical speed-time sequence S T (j) And calculates its characteristic value T (i, j) typ_day ；
According to the formula
Calculating a characteristic value T of the line tot_data (i) Wherein L is the total number of sampling days;
according to the normalized deviation formula
Calculating a typical speed-time series S per day T (j) Characteristic value of (i, j) typ_day And the characteristic value T of the line tot_data (i) Deviation σ of day (j)；
Selecting sigma day (j) Minimum corresponding daily typical speed-time series S T (j) As a typical speed-time sequence S of a line T ', i.e., typical speed-time travel condition of the line.
2. The method for constructing the typical speed-time running condition of the rail vehicle line according to claim 1, wherein the method for dividing the daily speed-time sequence S (j) of the j-th day into K sub-segments S' (K, j) according to the stop running state of train running comprises the following steps:
the daily speed-time series S (j) is used for calculating the partial derivative
Obtaining data of the acceleration a; selecting | a | |)>0 and velocity v =0 are separation points, dividing the daily velocity-time series S (j) into K sub-segments S' (K, j).
3. The method for constructing the typical speed-time running condition of the rail vehicle line according to claim 1 or 2, wherein K sub-segments S' (K, j) are sorted according to the stopping time, unreasonable data are deleted, and a standard speed-time sequence S of each day of the train is formed s (j) The specific method comprises the following steps:
arranging K sub-segments S' (K, j) according to the ascending order of the parking time, determining the parking time range of the train, and setting the abnormal parking time t x ；
Deleting the parking time T larger than the abnormal parking time T x Of the partial segments S '(k, j), the remaining set of partial segments S' (k, j)Daily standard speed-time sequence S of finished train s (j)。
4. The method for constructing the typical speed-time running condition of the rail vehicle line according to claim 1, wherein the method for sorting the sub-segments S' (k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix comprises the following steps:
selecting the sub-segment S '(k, j) corresponding to the minimum deviation of each column of the eigenvalue-deviation matrix, and then selecting the M sub-segments S' (k, j) according to the eigenvalue deviation sigma B (i, j) are arranged in ascending order of magnitude.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910267408.8A CN109960889B (en) | 2019-04-03 | 2019-04-03 | Method for constructing typical speed-time running condition of track vehicle line |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910267408.8A CN109960889B (en) | 2019-04-03 | 2019-04-03 | Method for constructing typical speed-time running condition of track vehicle line |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109960889A CN109960889A (en) | 2019-07-02 |
| CN109960889B true CN109960889B (en) | 2023-02-07 |
Family
ID=67025544
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910267408.8A Active CN109960889B (en) | 2019-04-03 | 2019-04-03 | Method for constructing typical speed-time running condition of track vehicle line |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109960889B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111785024B (en) * | 2020-07-17 | 2022-03-18 | 陕西工业职业技术学院 | Urban vehicle working condition construction method based on regions and time domains |
| CN114735013B (en) * | 2022-04-26 | 2024-06-04 | 深蓝汽车科技有限公司 | Method and system for extracting vehicle speed curve of typical working condition of whole vehicle, vehicle and storage medium |
| CN116776229B (en) * | 2023-08-17 | 2023-12-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for dividing typical running conditions of automobile facing carbon emission factors |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013164865A (en) * | 2013-05-10 | 2013-08-22 | Toyota Motor Corp | Travel control apparatus |
| KR20140124937A (en) * | 2013-04-16 | 2014-10-28 | 한국철도기술연구원 | Eco-Driving Device and method for electric railway vehicles |
| CN104792543A (en) * | 2015-04-17 | 2015-07-22 | 北京理工大学 | Constructing method of road cyclic conditions |
| CN105046070A (en) * | 2015-07-07 | 2015-11-11 | 吉林大学 | Method for constructing city comprehensive working condition with turning performance |
| CN106021961A (en) * | 2016-06-20 | 2016-10-12 | 吉林大学 | Urban standard cyclic working condition constructing method based on genetic algorithm optimization |
| CN106203856A (en) * | 2016-07-18 | 2016-12-07 | 交通运输部公路科学研究所 | A kind of Combined Principal Components analysis and the vehicle driving-cycle formulating method of Fuzzy c-means Clustering |
| CN106991804A (en) * | 2017-04-26 | 2017-07-28 | 长安大学 | A kind of city bus operating mode construction method coupled based on multi-line |
| CA3013824A1 (en) * | 2016-02-09 | 2017-08-17 | Siemens Aktiengesellschaft | Detection of bearing carbonization failure in turbine systems |
| 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 |
| CN107093227A (en) * | 2017-05-05 | 2017-08-25 | 苏州海格新能源汽车电控***科技有限公司 | Vehicle operation mode recognition method, device and vehicle operation control system |
| CN107145989A (en) * | 2017-06-12 | 2017-09-08 | 南京航空航天大学 | Real-road Driving Cycle construction method based on people's car traffic |
| CN107527140A (en) * | 2017-07-28 | 2017-12-29 | 西安理工大学 | A kind of bullet train operating condition based on fuzzy membership determines method |
| CN108198425A (en) * | 2018-02-10 | 2018-06-22 | 长安大学 | A kind of construction method of Electric Vehicles Driving Cycle |
| CN108437971A (en) * | 2018-03-16 | 2018-08-24 | 重庆交通大学 | Super light mixed power automobile reversing starting operating mode division methods |
| CN108596208A (en) * | 2018-03-21 | 2018-09-28 | 上海交通大学 | A kind of vehicle drive for full working scope road recycles construction method |
| CN108663223A (en) * | 2018-06-21 | 2018-10-16 | 中车青岛四方车辆研究所有限公司 | Pulling test platform |
| CN108984970A (en) * | 2018-08-22 | 2018-12-11 | 中车青岛四方车辆研究所有限公司 | A kind of track train slide system is anti-skidding to stick together optimal control method |
| CN109409008A (en) * | 2018-11-23 | 2019-03-01 | 哈尔滨理工大学 | A kind of urban track traffic tool route typical rate-time driving cycle construction method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6301605B2 (en) * | 2013-07-31 | 2018-03-28 | 株式会社東芝 | Resistance estimation device, energy estimation device, method and program |
| DE102014006322A1 (en) * | 2014-04-30 | 2015-11-05 | Avl List Gmbh | System and method for analyzing the energy efficiency of a vehicle |
| KR102335632B1 (en) * | 2017-09-07 | 2021-12-07 | 현대자동차주식회사 | Vehicle and method for controlling the same |
-
2019
- 2019-04-03 CN CN201910267408.8A patent/CN109960889B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140124937A (en) * | 2013-04-16 | 2014-10-28 | 한국철도기술연구원 | Eco-Driving Device and method for electric railway vehicles |
| JP2013164865A (en) * | 2013-05-10 | 2013-08-22 | Toyota Motor Corp | Travel control apparatus |
| CN104792543A (en) * | 2015-04-17 | 2015-07-22 | 北京理工大学 | Constructing method of road cyclic conditions |
| CN105046070A (en) * | 2015-07-07 | 2015-11-11 | 吉林大学 | Method for constructing city comprehensive working condition with turning performance |
| CA3013824A1 (en) * | 2016-02-09 | 2017-08-17 | Siemens Aktiengesellschaft | Detection of bearing carbonization failure in turbine systems |
| CN106021961A (en) * | 2016-06-20 | 2016-10-12 | 吉林大学 | Urban standard cyclic working condition constructing method based on genetic algorithm optimization |
| CN106203856A (en) * | 2016-07-18 | 2016-12-07 | 交通运输部公路科学研究所 | A kind of Combined Principal Components analysis and the vehicle driving-cycle formulating method of Fuzzy c-means Clustering |
| 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 |
| CN106991804A (en) * | 2017-04-26 | 2017-07-28 | 长安大学 | A kind of city bus operating mode construction method coupled based on multi-line |
| CN107093227A (en) * | 2017-05-05 | 2017-08-25 | 苏州海格新能源汽车电控***科技有限公司 | Vehicle operation mode recognition method, device and vehicle operation control system |
| CN107145989A (en) * | 2017-06-12 | 2017-09-08 | 南京航空航天大学 | Real-road Driving Cycle construction method based on people's car traffic |
| CN107527140A (en) * | 2017-07-28 | 2017-12-29 | 西安理工大学 | A kind of bullet train operating condition based on fuzzy membership determines method |
| CN108198425A (en) * | 2018-02-10 | 2018-06-22 | 长安大学 | A kind of construction method of Electric Vehicles Driving Cycle |
| CN108437971A (en) * | 2018-03-16 | 2018-08-24 | 重庆交通大学 | Super light mixed power automobile reversing starting operating mode division methods |
| CN108596208A (en) * | 2018-03-21 | 2018-09-28 | 上海交通大学 | A kind of vehicle drive for full working scope road recycles construction method |
| CN108663223A (en) * | 2018-06-21 | 2018-10-16 | 中车青岛四方车辆研究所有限公司 | Pulling test platform |
| CN108984970A (en) * | 2018-08-22 | 2018-12-11 | 中车青岛四方车辆研究所有限公司 | A kind of track train slide system is anti-skidding to stick together optimal control method |
| CN109409008A (en) * | 2018-11-23 | 2019-03-01 | 哈尔滨理工大学 | A kind of urban track traffic tool route typical rate-time driving cycle construction method |
Non-Patent Citations (5)
| Title |
|---|
| 基于瞬时最优的西安市某线路公交行驶工况构建;邵攀登;《汽车实用技术》;20190130;125-126 * |
| 基于聚类分析的城市公交线路工况构建;李耀华;《重庆交通大学学报(自然科学版)》;20180315;83-88+96 * |
| 基于马尔可夫链的西安市城市公交工况构建;李耀华;《中国科技论文》;20190215;121-128 * |
| 排队车辆工况构建及经济效益分析;常云涛;《公路交通科技》;20181115;100-109 * |
| 组合主成分分析和模糊c均值聚类的车辆行驶工况制定方法;刘应吉;《公路交通科技》;20180424;79-85 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109960889A (en) | 2019-07-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110126841B (en) | Pure electric vehicle energy consumption model prediction method based on road information and driving style | |
| CN109960889B (en) | Method for constructing typical speed-time running condition of track vehicle line | |
| CN104331953B (en) | A kind of motor vehicle behavior data identification based on technology of Internet of things and management method | |
| CN108198425A (en) | A kind of construction method of Electric Vehicles Driving Cycle | |
| CN107564290B (en) | Urban road intersection saturated flow rate calculation method | |
| CN106596135B (en) | A kind of pure electric automobile actual travel energy consumption testing, evaluation and Forecasting Methodology | |
| CN103895649B (en) | A kind of driver safety driving warning method | |
| CN110395245B (en) | Hybrid electric vehicle energy management system based on fixed route driving information | |
| CN104850676B (en) | A kind of random traffic flow simulation analogy method of highway bridge | |
| CN109409008B (en) | Construction method for typical speed-time running condition of urban rail vehicle line | |
| CN109552338A (en) | A kind of pure electric automobile ecology driving behavior appraisal procedure and system | |
| CN111563555A (en) | Driver driving behavior analysis method and system | |
| CN113222385A (en) | Method for constructing and evaluating driving condition of electric automobile | |
| CN114819305B (en) | Path planning method based on carbon emission measurement scale | |
| CN103955135A (en) | Method for calculating locomotive optimized operation sequence based on double-layer mode curve | |
| CN110910531A (en) | Rapid pavement friction coefficient detection method based on vehicle-mounted OBD information | |
| CN112767684A (en) | Highway traffic jam detection method based on charging data | |
| CN111339638B (en) | Automobile driving condition construction method based on existing data | |
| CN110867075B (en) | Method for evaluating influence of road speed meter on reaction behavior of driver under rainy condition | |
| CN115935672A (en) | Fuel cell automobile energy consumption calculation method fusing working condition prediction information | |
| CN110254435B (en) | Driving style recognition method | |
| CN110796374A (en) | Construction method of urban comprehensive driving working condition with typical lane changing and turning working conditions | |
| CN114821854A (en) | Method for evaluating influence of working condition switching on vehicle oil consumption | |
| CN113095387B (en) | Road risk identification method based on networking vehicle-mounted ADAS | |
| CN113128120A (en) | Method for constructing typical running condition of automobile crane |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |