Real-world Emission Measurements of a High Efficient Monofuel CNG Light Duty Vehicle

2009 ◽  
Author(s):  
L. Cachón ◽  
D. Tóth ◽  
E. Pucher
Keyword(s):  
Real World ◽  
Light Duty ◽  
High Efficient ◽  
Light Duty Vehicle

Revision of Light-Duty Vehicle Emission Inventories Using Real-World Measurements-Auto/Oil Program, Phase II

1998 ◽  
Vol 48 (4) ◽  
pp. 291-305 ◽  
Author(s):  
Alison K. Pollack ◽  
Alan M. Dunker ◽  
Julie K. Fleber ◽  
Jeremy G. Heiken ◽  
Jonathan P. Cohen ◽  
...  
Keyword(s):  
Phase Ii ◽  
Real World ◽  
Vehicle Emission ◽  
Emission Inventories ◽  
Light Duty ◽  
Light Duty Vehicle

Measurement and Evaluation of Real-World Speed and Acceleration Activity Envelopes for Light-Duty Vehicles

2015 ◽  
Vol 2503 (1) ◽  
pp. 128-136 ◽  
Author(s):  
Bin Liu ◽  
H. Christopher Frey
Keyword(s):  
Real World ◽  
Test Procedure ◽  
Accurate Estimation ◽  
Vehicle Type ◽  
Light Duty ◽  
Road Grade ◽  
Light Duty Vehicle ◽  
Curb Weight ◽  
Vehicle Activity ◽  
Light Duty Vehicles

Accurate estimation of vehicle activity is critically important for the accurate estimation of emissions. To provide a benchmark for estimation of vehicle speed trajectories such as those from traffic simulation models, this paper demonstrates a method for quantifying light-duty vehicle activity envelopes based on real-world activity data for 100 light-duty vehicles, including conventional passenger cars, passenger trucks, and hybrid electric vehicles. The vehicle activity envelope was quanti-fied in the 95% frequency range of acceleration for each of 15 speed bins with intervals of 5 mph and a speed bin for greater than 75 mph. Potential factors affecting the activity envelope were evaluated; these factors included vehicle type, transmission type, road grade, engine displacement, engine horsepower, curb weight, and ratio of horsepower to curb weight. The activity envelope was wider for speeds ranging from 5 to 20 mph and narrowed as speed increased. The latter was consistent with a constraint on maximum achievable engine power demand. The envelope was weakly sensitive to factors such as type of vehicle, type of transmission, road grade, and engine horsepower. The effect of road grade on cycle average emissions rates was evaluated for selected real-word cycles. The measured activity envelope was compared with those of dynamometer driving cycles, such as the federal test procedure, highway fuel economy test, SC03, and US06 cycles. The effect of intervehicle variability on the activity envelope was minor; this factor implied that the envelope could be quantified based on a smaller vehicle sample than used for this study.

scholarly journals Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7915
Author(s):  
Isabella Yunfei Zeng ◽  
Shiqi Tan ◽  
Jianliang Xiong ◽  
Xuesong Ding ◽  
Yawen Li ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Real World ◽  
Consumption Rate ◽  
The Real ◽  
Fuel Consumption Rate ◽  
Light Gradient ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Light Duty Vehicles ◽  
Engine Power

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

scholarly journals Estimation of Real-world Fuel Consumption Rate of Light-duty Vehicles Based on Big Data

2021 ◽  
Author(s):  
Isabella Yunfei Zeng ◽  
Shiqi Tan ◽  
Jianliang Xiong ◽  
Xuesong Ding ◽  
Yawen Li ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Real World ◽  
Consumption Rate ◽  
Effective Control ◽  
Fuel Consumption Rate ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Light Duty Vehicles ◽  
The Impact ◽  
Engine Power

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

scholarly journals THE IMPACT OF DRIVING CONDITIONS ON LIGHT-DUTY VEHICLE EMISSIONS IN REAL-WORLD DRIVING

Transport ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 379-388
Author(s):  
Dong Guo ◽  
Jinbao Zhao ◽  
Yi Xu ◽  
Feng Sun ◽  
Kai Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Real World ◽  
Vehicle Emissions ◽  
Emission Rate ◽  
Vehicle Emission ◽  
Road Test ◽  
Light Duty ◽  
Emission Models ◽  
Light Duty Vehicle ◽  
Driving Conditions

To accurately estimate the effect of driving conditions on vehicle emissions, an on-road light-duty vehicle emission platform was established based on OEM-2100TM, and each second data of mass emission rate corresponding to the driving conditions were obtained through an on-road test. The mass emission rate was closely related to the velocity and acceleration in real-world driving. This study shows that a high velocity and acceleration led to high real-world emissions. The vehicle emissions were the minimum when the velocity ranged from 30 to 50 km/h and the acceleration was less than 0.5 m/s2. Microscopic emission models were established based the on-road test, and single regression models were constructed based on velocity and acceleration separately. Binary regression and neural network models were established based on the joint distribution of velocity and acceleration. Comparative analysis of the accuracy of prediction and evaluation under different emission models, total error, second-based error, related coefficient, and sum of squared error were considered as evaluation indexes to validate different models. The results show that the three established emission models can be used to make relatively accurate prediction of vehicle emission on actual roads. The velocity regression model can be easily combined with traffic simulation models because of its simple parameters. However, the application of neural network model is limited by a complex coefficient matrix.

On-Road Portable Emission Measurement Systems Test Data Analysis and Light-Duty Vehicle In-Use Emissions Development

2020 ◽  
Vol 9 (2) ◽  
pp. 111-131
Author(s):  
SoDuk Lee ◽  
◽  
Carl R Fulper ◽  
Daniel Cullen ◽  
Joseph McDonald ◽  
...  
Keyword(s):  
Test Data ◽  
Real World ◽  
Environmental Protection Agency ◽  
Emission Measurement ◽  
Mobile Source ◽  
Measurement Systems ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Inventory Development ◽  
Light Duty Vehicles

Portable emission measurement systems (PEMS) [1] are used by the US Environmental Protection Agency (EPA) to measure gaseous and particulate matter mass emissions from vehicles in normal, in-use, on-the-road, and “real-world” operations to support many of its programs. These programs include vehicle modeling, emissions compliance, regulatory development, emissions inventory development, and investigations of the effects of real, in-use driving conditions on NOx, CO2, and other regulated pollutants. This article discusses EPA’s analytical methodology for evaluating light-duty vehicle energy and EU Real Driving Emissions (RDE). A simple, data-driven model was developed and validated using measured PEMS emissions test data. The work also included application of the EU RDE procedures and comparison to the PEMS test methodologies and FTP and other chassis dynamometer test data used by EPA for characterizing in-use light- and heavy-duty vehicle emissions. This work was conducted as part of EPA’s participation in the development of UNECE Global Technical Regulations and also supports EPA mobile source emission inventory development. This article discusses the real-world emissions of light-duty vehicles with 12V Start-Stop technology and light-duty vehicles using both gasoline and diesel fuels.

scholarly journals Investigation of Driving Behavior on Performance and Fuel Consumption of Light-Duty Vehicle

2020 ◽  
Vol 13 (4) ◽  
pp. 102-113
Author(s):  
Loay M. Mubarak ◽  
Ahmed Al-Samari
Keyword(s):  
Fuel Consumption ◽  
Real World ◽  
Driving Behavior ◽  
Driving Cycle ◽  
Aggressive Driving ◽  
Data Logger ◽  
Passenger Cars ◽  
Driving Cycles ◽  
Light Duty ◽  
Light Duty Vehicle

This manuscript instrumented two light-duty passenger cars to construct real-world driving cycles for the Baghdad-Basrah highway road in Iraq using a data logger. The recorded data is conducted to obtain typical speed profiles for each vehicle. Each of the recruited vehicles is modelized using Advanced Vehicle Simulator and conducted on the associated created driving cycle to investigate fuel economy and analyze performance. Moreover, to inspect the influence of driving behavior on fuel consumption and emissions, the simulation process is re-implemented by substituting the conducted real-world driving cycle. The analyses are done for the first and second stages of simulation predictions to explore the fuel-penalty of aggressive driving behavior. The analysis for substitution predictions showed that fuel consumption could be reduced by 12.8% due to conducting vehicle under the more consistent real-world driving cycle. However, conducting vehicle under the more aggressive one would increase fuel consumption by 14.6%. The associated emissions change prediction due to the substitution is also achieved and presented.

Sign in / Sign up

Export Citation Format

Share Document