LU102194B1 - Real driving cycle database and construction method thereof - Google Patents
Real driving cycle database and construction method thereof Download PDFInfo
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- LU102194B1 LU102194B1 LU102194A LU102194A LU102194B1 LU 102194 B1 LU102194 B1 LU 102194B1 LU 102194 A LU102194 A LU 102194A LU 102194 A LU102194 A LU 102194A LU 102194 B1 LU102194 B1 LU 102194B1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M15/00—Testing of engines
- G01M15/04—Testing internal-combustion engines
- G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
- G01M15/102—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases
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Abstract
The present invention proposes a real driving cycle database and the construction method thereof, the driving cycle database includes a basic driving cycle, a aggressive driving cycle and a high emission driving cycle. When constructing the aggressive driving cycle in the driving cycle database, a reference value applied includes a RPA corresponding to the 95% quantile in the average speed-RPA statistical curve; and when constructing the high emission driving cycle, data set applied includes RDE test data, a reference value applied includes a specific emission factors calculated from the RDE test data.
Description
Description | REAL DRIVING CYCLE DATABASE AND CONSTRUCTION METHOD
TECHNICAL FIELD The present invention relates to the technical field of traffic transportation and in particular to a real driving cycle database and construction method thereof. .
BACKGROUND OF THE PRESENT INVENTION Existing vehicle driving emission tests are mostly carried out on the | revolving drum test table. Due to the limitations of the revolving drum test table in | simulating real road loads and setting of the environmental parameters, the real | 15 driving emissions of vehicles cannot be effectively controlled, and some | companies even use the defects in the revolving drum test table for emission | cheating. For this purpose, a RDE (Real Driving Emission) test is proposed in | Europe, which measures vehicle emissions on real roads. In view of the | important role of RDE test in avoiding emission cheating and reducing emissions | 20 from actual operation of vehicles, “Limits and measurement methods for | emissions from light-duty vehicles” ( CHN-VI) in China also adds the RDE testing ; | as a mandatory test. However, the existing RDE testing procedure refers to the | European standards, which is inconsistent with the real driving conditions in | China. In addition, the RDE procedure had disadvantageous such as | 25 complicate testing procedure, long testing time, vulnerable to the boundary | conditions, ordinary accuracy of the test equipment, and relatively random test | results, thus causing great difficulties for enterprises to develop and calibrate | vehicles.
Peugeot, Bosch, Honda, Toyota and other companies adopt RTS95 cycling developed based on European vehicle driving conditions to conduct RDE calibration on the revolving drum test table and powertrain bench to reduce the | test failures caused by the‘ above-mentioned weather, traffic, and drivability, thereby reducing the cost and cycle of RDE calibration. Currently, China lacks a RDE calibration cycling similar to the RTS95 cycling, and the relating enterprises are under great pressure for developing and calibrating vehicles.
SUMMARY OF THE PRESENT INVENTION To overcome the deficiencies of the prior art, the present invention provides | a real driving cycle database, and the following technical solutions are employed | in the present invention. | A real driving cycle database is used for RDE emission calibration. The | driving cycle database include a basic driving cycle, an aggressive driving cycle | 15 and a high emission driving cycle, which are formed by short trip segments. | The real driving cycle database and the construction method thereof are | further provided. Wherein, when determining the short trip segments for | constructing the aggressive driving cycle in the driving cycle database, a | reference value applied includes a relative positive acceleration (hereinafter | 20 referred to as RPA) corresponding to the 95% quantile in the average | speed-RPA statistical curve. | Wherein, when determining the short trip segments for constructing the high | emission driving cycle in the driving cycle database, a data set applied includes | Read Driving Emission (hereinafter referred to as RDE) test data, a reference | 25 value applied includes a specific emission value calculated from the RDE test | data. | | Compared with the prior art, the present invention has the following | beneficial effects: constructing the driving cycle database based on the real driving speed-acceleration simultaneous distribution, PRA distribution and RDE test data; constructing the driving cycle based on the short trip segments of the driving cycle database; and when testing vehicle performance on the revolving drum test table, the use of the real driving cycle can better simulate the real driving conditions on the actual road; and the driving cycle database have better coverage and pertinence.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow chart of the construction of the real driving cycle database according to the present invention; FIG. 2 is a curve diagram of the basic driving cycle; FIG. 3 is a curve diagram of the PRA; | FIG. 4 is a curve diagram of the aggressive driving cycle; | FIG. 5 is a curve diagram of the high emission driving cycle; i FIG. 6 is a curve diagram of the real driving cycle database; and | 15 FIG. 7 is a schematic diagram of a short trip segment. : | DETAILED DESCRIPTION OF THE PRESENT INVENTION | The present invention will be described in detail below with reference to the | accompanying drawings by embodiments. | 20 In a preferred embodiment, the test cycle is formed by short trip segments ; | in the real driving cycle database. The test cycle is suitable for light-duty vehicles, | and the data used for constructing the real driving cycle database includes real | road data and RDE test data. When collecting the real road data, select private | vehicles in a designated city, collect the vehicle operation data during driving by | 25 a data collection terminal ( such as OBD) installed on the vehicle, and the data | collection terminal sends collected data to a driving cycle data management platform through the GPRS network. | In this embodiment, three months driving data of 70 private vehicles in 5 typical cities (accumulated mileage of 250,000 kilometers) is selected as the vehicle operation data when constructing the real driving cycle database.
The steps of determining the short trip segments for constructing the basic condition comprising: a ' . S101. dividing vehicle operation data into a plurality of short trip segments and idling segments, and dividing the short trip segments into a low-speed phase, a medium-speed phase, and a high-speed phase.
As shown in FIG. 7, in this embodiment, the motion process from one stop to the next start is defined as an idling segment; the motion process from one start to the next stop is defined as a short trip.
Taking the maximum speed in the short trip segment as an index, | the short trip segment of the vehicle is divided into the low-speed phase, the | medium-speed phase, and the high-speed phase. | S102. obtaining a low-frequency dynamic traffic volume data of the ' corresponding city through the GIS(Geographic Information System) platform, | 15 that is the average speed of the road every five minutes; constructing a | speed-flow traffic flow density model to calculate the Vehicle Hours Traveled | (VHT) of the corresponding driving vehicles on the city road network, and | establishing a change trend of VHT with the average speed; setting a threshold | between different speed phases so as to segment the VHT distribution and | 20 calculate the weight of different speed phases. - | S103. calculating speed-acceleration simultaneous distribution of the | different speed sections of the. corresponding cities; weighting by using a | weighting factor matrix to obtain a uniform speed-acceleration simultaneous | distribution among different speed phases. | 25 S104. randomly selecting candidate short trip segments from each speed | phase for combination, comparing ‘the candidate short trip segments with the | uniform speed-acceleration simultaneous distribution of the corresponding speed phases by using the chi-square test, and storing the short trip segment with the optimal combination result into the real driving cycle database.
Wherein, when selecting the short trip segments for constructing the basic driving cycle, it is required to determine the duration of different speed phases through test scenarios firstly.
For:example, in this embodiment, the total duration 5 of the basic driving cycle is 1800 seconds, the proportion of the low-speed phase is 39%, the medium-speed phase is 34%, and the high-speed phase is 27% according to the weight.
Accordingly, the duration of each speed phase is 702 seconds, 612 seconds, and 486 seconds, respectively.
And then the basic driving cycle for testing is composed by extracting short trip segments of different speed phases in the real driving cycle database.
Further, the real driving cycle database also comprises an alternative | database 1 and an alternative database 2. Wherein, the alternative database 1 is | used for constructing the aggressive driving cycle, and the alternative database | 2 is used for constructing the high driving cycle. | 15 The step of determining the short trip segments for constructing the | aggressive driving cycle in the alternative database 1 comprising: | . 8201. dividing the short trip segments into a low-speed phase, a | medium-speed phase, and a high-speed phase; calculating average speed and | RPA of each speed phase, and calculating the 95% quantile of RPA | 20 corresponding to the average speed of the short trip segments in each speed | phase. ; ; | S202. based on the average speed and RPA of each speed phase, | selecting the short trip segment closest to the 95% quantile of the RPA from the | average speed-relative positive acceleration statistical curve, and storing the | 25 selected short trip segment into the alternative database 1. | Wherein, when selecting the short trip segments for constructing the | aggressive driving cycle from the alternative database 1, it is required to determine the duration of different speed phases through test scenarios firstly.
6 . For example, the total duration of the aggressive driving cycle is 1800 seconds, and according to the requirements of the test scenario selects a specified number of short trip segments from the alternative database 1 to form the aggressive driving cycle; in this embodiment, 3 low-speed phases, 2 medium-speed phases and 1 high-speed phase form an aggressive driving cycle based on the average speed of the short trip segments. The step of determining the short trip segments for constructing the high emission driving cycle in the alternative database 2 comprising:
8301. performing RDE test; | 10 8302. dividing the RDE test data into a plurality of the short trip segments, | and calculating emission factors of the short trip segments; | Wherein each emission factor is defined as the ratio of the total pollutant | emissions to the mileage of the vehicle in the short trip segment; | S303. selecting 20 short trip segments with the largest emission factors in | 15 each RDE test data, and storing the selected short trip segments into the | alternative database 2. ; | - Wherein, when selecting the short trip segments for constructing the high | emission driving cycle from the alternative database 2, it is required to determine | the duration of different speed phases through test scenarios firstly, such as the | 20 total duration of the high emission driving cycle is 1800 seconds, and according | to the requirements of the test scenario selects a specified number of short trip | segments from the alternative database 2 to form the high emission driving cycle; | in this embodiment, 3 short trip segments form the high emission driving cycle. | Wherein, the test cycle can be any of the basic driving cycle, aggressive : | 25 driving cycle or high emission driving cycle, and it can be any combination of | basic driving cycle, aggressive driving cycle and high emission driving cycle. | The forgoing description is just a preferred embodiment of the present | invention and is not intended to limit the present invention. Any modifications,
VS TEN equivalent replacements and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.
Claims (3)
1. A real driving cycle database is used for constructing RDE emission calibration, is characterized in that the driving cycle database includes a basic driving cycle, an aggressive driving cycle and a high emission driving cycle, which are formed by short trip segments.
2. A method for constructing a real driving cycle database, is characterized in that when determining the short trip segments for constructing the aggressive driving cycle in the driving cycle database, a reference value applied includes a relative positive acceleration (hereinafter referred to as RPA) corresponding to the 95% quantile in the average speed-RPA statistical curve.
3. Amethod for constructing a real driving cycle database according to claim | 2, is characterized in that when determining the short trip segments for | constructing the high emission driving cycle in the driving cycle database, a data | set applied includes Real Driving Emission (hereinafter referred to as RDE) test | 15 data, a reference value applied includes a specific emission value calculated | from the RDE test data. ee
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| CN201910199078.3A CN109932191B (en) | 2019-03-15 | 2019-03-15 | Actual road driving condition library and construction method |
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| CN109932191B (en) * | 2019-03-15 | 2021-10-15 | 中国汽车技术研究中心有限公司 | Actual road driving condition library and construction method |
| CN112562311B (en) * | 2020-10-21 | 2022-04-26 | 中国汽车技术研究中心有限公司 | Method and device for obtaining working condition weight factor based on GIS big data |
| CN112304637A (en) * | 2020-10-30 | 2021-02-02 | 广西玉柴机器股份有限公司 | WLTC and RDE test method for actual driving of light vehicle |
| CN112629880B (en) * | 2020-12-10 | 2022-02-22 | 东风汽车集团有限公司 | Vehicle test condition determining method and device and storage medium |
| CN113688558B (en) * | 2021-06-18 | 2024-03-29 | 长安大学 | Automobile driving condition construction method and system based on large database sample |
| CN113553548B (en) * | 2021-07-19 | 2023-01-10 | 中汽研汽车检验中心（天津）有限公司 | Actual operation condition VSPBin dividing method based on Internet of vehicles big data |
| CN114021617B (en) * | 2021-09-29 | 2024-09-17 | 中国科学技术大学 | Mobile source driving condition construction method and equipment based on short-range feature clustering |
| CN114048798B (en) * | 2021-10-21 | 2024-08-20 | 湖南大学 | Automobile driving condition construction method based on improved noise reduction self-encoder |
| CN114136312B (en) * | 2021-11-25 | 2024-05-07 | 中汽研汽车检验中心（天津）有限公司 | Gradient speed combined working condition development device and development method |
| CN113963462B (en) * | 2021-12-16 | 2022-02-25 | 中汽研汽车检验中心（天津）有限公司 | Electric drive assembly reliability working condition construction method and construction device based on remote transmission data |
| CN114674573B (en) * | 2022-02-24 | 2023-08-11 | 重庆长安汽车股份有限公司 | Actual road emission RDE evaluation method suitable for arbitrary test boundary |
| CN114969962B (en) * | 2022-04-22 | 2024-02-20 | 南栖仙策(南京)科技有限公司 | Method, device, equipment and storage medium for generating severe vehicle RDE emission working conditions |
| CN114965898A (en) * | 2022-06-01 | 2022-08-30 | 北京市生态环境监测中心 | Remote online monitoring method for heavy vehicle nitrogen oxide and carbon dioxide emission |
| CN115420507B (en) * | 2022-08-17 | 2023-07-11 | 武汉理工大学 | Method for establishing engine fuel economy bench test working condition |
| CN115497306A (en) * | 2022-11-22 | 2022-12-20 | 中汽研汽车检验中心（天津）有限公司 | Speed interval weight calculation method based on GIS data |
| CN115601855B (en) * | 2022-11-29 | 2023-04-07 | 天津所托瑞安汽车科技有限公司 | Vehicle driving condition construction method, electronic device and storage medium |
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| KR101592745B1 (en) * | 2014-07-29 | 2016-02-05 | 현대자동차주식회사 | Lnt system expanded operating temperature area |
| CN204895347U (en) * | 2015-04-22 | 2015-12-23 | 北京九五智驾信息技术股份有限公司 | Driving behavior analysis system |
| CN105912862B (en) * | 2016-04-12 | 2018-07-27 | 北京荣之联科技股份有限公司 | A kind of exhaust emissions quantity measuring method and air pollution analysis method and apparatus |
| AT518677B1 (en) * | 2016-05-30 | 2018-05-15 | Avl List Gmbh | Method for creating an analysis data set |
| CN106546550A (en) * | 2016-09-30 | 2017-03-29 | 张英志 | Tail gas discharge measuring method and device under the actual riving condition of vehicle |
| CN107067722B (en) * | 2017-04-24 | 2020-04-21 | 中国汽车技术研究中心 | Method for constructing vehicle running condition |
| CN106997662B (en) * | 2017-04-26 | 2020-04-10 | 长安大学 | Urban bus working condition construction method |
| CN108596208B (en) * | 2018-03-21 | 2020-12-29 | 上海交通大学 | Vehicle driving cycle construction method for full-working-condition road |
| CN109443779A (en) * | 2018-11-07 | 2019-03-08 | 中国汽车技术研究中心有限公司 | A kind of dynamic diagnosis extracts diesel vehicle actual motion NOXThe method and apparatus of maximum discharge bad working environments |
| CN109932191B (en) * | 2019-03-15 | 2021-10-15 | 中国汽车技术研究中心有限公司 | Actual road driving condition library and construction method |
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