Emissions of Light Duty Vehicle Tested under Urban and Extraurban Real-World Driving Conditions with Diesel, Animal Fat Biodiesel and GTL fuels

2013 ◽  
Author(s):  
Octavio Armas ◽  
Reyes Garcia-Contreras ◽  
Angel Ramos
Keyword(s):  
Real World ◽  
Animal Fat ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Driving Conditions

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.

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.

Emissions of Light-Duty Vehicles with Respect to Cruising Speed under Real-World Driving Conditions

2015 ◽  
Vol 141 (7) ◽  
pp. 04015004 ◽  
Author(s):  
Hector E. Carrera ◽  
Jessica Portillo ◽  
Gerardo M. Mejia ◽  
Alberto Mendoza
Keyword(s):  
Real World ◽  
Light Duty ◽  
Driving Conditions ◽  
Light Duty Vehicles

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 Thermoelectric Energy Recovery in a Light-Duty Diesel Vehicle under Real-World Driving Conditions at Different Altitudes with Diesel, Biodiesel and GTL Fuels

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1105 ◽  
Author(s):  
Reyes García-Contreras ◽  
Andrés Agudelo ◽  
Arántzazu Gómez ◽  
Pablo Fernández-Yáñez ◽  
Octavio Armas ◽  
...  
Keyword(s):  
Energy Recovery ◽  
Exhaust System ◽  
Velocity Profiles ◽  
Gas Temperature ◽  
Diesel Particle Filter ◽  
Exhaust Gases ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
Driving Conditions ◽  
Different Altitudes

This work focuses on the potential for waste energy recovery from exhaust gases in a diesel light-duty vehicle tested under real driving conditions, fueled with animal fat biodiesel, Gas To Liquid (GTL) and diesel fuels. The vehicle was tested following random velocity profiles under urban driving conditions, while under extra-urban conditions, the vehicle followed previously defined velocity profiles. Tests were carried out at three different locations with different altitudes. The ambient temperature (20 ± 2 °C) and relative humidity (50 ± 2%) conditions were similar for all locations. Exergy analysis was included to determine the potential of exhaust gases to produce useful work in the exhaust system at the outlet of the Diesel Particle Filter. Results include gas temperature registered at each altitude with each fuel, as well as the exergy to energy ratio (percentage of energy that could be transformed into useful work with a recovery device), which was in the range of 20–35%, reaching its maximum value under extra-urban driving conditions at the highest altitude. To take a further step, the effects of fuels and altitude on energy recovery with a prototype of a thermoelectric generator (TEG) were evaluated.

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.

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