The Effect of Driving Cycle and Shifting Pattern on Vehicle Emissions

Author(s):  
Ni Zhang ◽  
Linyun Wei ◽  
Xing Wang ◽  
Yongsheng Long
Author(s):  
Meng Lyu ◽  
Xiaofeng Bao ◽  
Yunjing Wang ◽  
Ronald Matthews

Vehicle emissions standards and regulations remain weak in high-altitude regions. In this study, vehicle emissions from both the New European Driving Cycle and the Worldwide harmonized Light-duty driving Test Cycle were analyzed by employing on-road test data collected from typical roads in a high-altitude city. On-road measurements were conducted on five light-duty vehicles using a portable emissions measurement system. The certification cycle parameters were synthesized from real-world driving data using the vehicle specific power methodology. The analysis revealed that under real-world driving conditions, all emissions were generally higher than the estimated values for both the New European Driving Cycle and Worldwide harmonized Light-duty driving Test Cycle. Concerning emissions standards, more CO, NOx, and hydrocarbons were emitted by China 3 vehicles than by China 4 vehicles, whereas the CO2 emissions exhibited interesting trends with vehicle displacement and emissions standards. These results have potential implications for policymakers in regard to vehicle emissions management and control strategies aimed at emissions reduction, fleet inspection, and maintenance programs.


2021 ◽  
pp. 101138
Author(s):  
Lihang Zhang ◽  
Zhijiong Huang ◽  
Fei Yu ◽  
Songdi Liao ◽  
Haoming Luo ◽  
...  

2012 ◽  
Vol 12 (10) ◽  
pp. 28343-28383 ◽  
Author(s):  
S. M. Platt ◽  
I. El Haddad ◽  
A. A. Zardini ◽  
M. Clairotte ◽  
C. Astorga ◽  
...  

Abstract. We present a new mobile environmental reaction chamber for the simulation of the atmospheric aging of aerosols from different emissions sources without limitation from the instruments or facilities available at any single site. The chamber can be mounted on a trailer for transport to host facilities or for mobile measurements. Photochemistry is simulated using a set of 40 UV lights (total power 4 KW). Characterisation of the emission spectrum of these lights shows that atmospheric photochemistry can be accurately simulated over a range of temperatures from −7–25 °C. A photolysis rate of NO2, JNO2, of (8.0 ± 0.7) × 10−3 molecules cm−3 s−1 was determined at 25 °C. Further, we present the first application of the mobile chamber and demonstrate its utility by quantifying primary organic aerosol (POA) emission and secondary organic aerosol (SOA) production from a Euro 5 light duty gasoline vehicle. Exhaust emissions were sampled during the New European Driving Cycle (NEDC), the standard driving cycle for European regulatory purposes, and injected into the chamber. The relative concentrations of oxides of nitrogen (NOx) and total hydrocarbon (THC) during the aging of emissions inside the chamber were controlled using an injection system developed as a part of the new mobile chamber set up. Total OA (POA + SOA) emission factors of (370 ± 18) × 10−3 g kg−1 fuel, or (14.6 ± 0.8) × 10−3 g km−1, after aging, were calculated from concentrations measured inside the smog chamber during two experiments. The average SOA/POA ratio for the two experiments was 15.1, a much larger increase than has previously been seen for diesel vehicles, where smog chamber studies have found SOA/POA ratios of 1.3–1.7. Due to this SOA formation, carbonaceous particulate matter (PM) emissions from a gasoline vehicle may approach those of a diesel vehicle of the same class. Furthermore, with the advent of emission controls requiring the use of diesel particle filters, gasoline vehicle emissions could become a far larger source of ambient PM than diesel vehicles. Therefore this large increase in the PM mass of gasoline vehicle aerosol emissions due to SOA formation has significant implications for our understanding of the contribution of on-road vehicles to ambient aerosols and merits further study.


Author(s):  
Arunkumar Subramaniam ◽  
Nurru Anida Ibrahim ◽  
Siti Norbakyah Jabar ◽  
Salisa Abdul Rahman

<span>Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing.</span>


2005 ◽  
Vol 26 (2) ◽  
pp. 145-154 ◽  
Author(s):  
I. Schifter ◽  
L. Díaz ◽  
R. Rodríguez ◽  
E. López-Salinas

2013 ◽  
Vol 8 (3) ◽  
pp. 282-290 ◽  

Vehicle emissions constitute the main source of atmospheric pollution in modern cities. The increasing number of passenger cars, especially during the last decade, resulted in composite traffic problems with serious consequences on emissions and fuel consumption. This project was carried out in the Laboratory of Fuel Technology and Lubricants at NTUA in order to examine the effects of the driving patterns on fuel consumption and exhaust emissions from cars in the Athens basin. The typical driving profile consists of a complicated series of accelerations, decelerations and frequent stops and it is simulated by driving cycles on a laboratory chassis dynamometer. The New European Driving Cycle (NEDC) is applied in laboratory test approvals in the EU and is based on traffic data from European capitals (Paris and Rome). Traffic data from Athens was not included in the development of NEDC. The FTP 75 driving cycle and the Japan 10-15 modal cycle are currently used in the United States and Japan respectively. The different than other European cities and rapidly changing traffic conditions in Athens as well as the expanding transportation network and the atmospheric pollution problems impose the need to develop the Athens Driving Cycle (ADC). In this paper, onboard electronic equipment (GPS, OBD II reader, accelerometer, etc) was used and “real world” traffic data was collected, covering almost all the Athens road network for a two year period. Dedicated software was developed for the statistical analysis of the recorded parameters and therefore the first ADC was modeled with the following characteristics: ADC duration is 1160 seconds, the overall distance is 6512 meters, the mean velocity is 20.21 km h-1 and the maximum velocity is 70.86 km h-1. For comparison purposes, three passenger cars of different classification (Citroen Xsara 1.6L, a Mitsubishi Space Runner 2.0L Turbo and a Chrysler PT Cruiser 2.4L Turbo) were tested on a laboratory chassis dynamometer, applying three distinctive Driving Cycles: the Urban Driving Cycle (ECE-15), the New European Driving Cycle (NEDC) and the newly designed Athens Driving Cycle (ADC). Results show that NOX emissions are higher in ADC than ECE and EDC, up to 2.5 times. Higher CO emissions are recorded during ADC for 1.6L and 2.0L cars while ECE-15 gave the higher CO emissions for the 2.4L vehicle. Overall HC emissions do not show any significant variation. Fuel consumption is higher for ADC mode in all cases.


ICTE 2011 ◽  
2011 ◽  
Author(s):  
Kairan Zhang ◽  
Haibo Chen ◽  
Guofang Li ◽  
Zhihui Tang

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