scholarly journals Monte Carlo Simulation Methodology to Assess the Impact of Ambient Wind on Emissions from a Light-Commercial Vehicle Running on the Worldwide-Harmonized Light-Duty Vehicles Test Cycle (WLTC)

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 661
Author(s):  
Alexandros T. Zachiotis ◽  
Evangelos G. Giakoumis
Keyword(s):  
Monte Carlo Simulation ◽  
Energy Consumption ◽  
Commercial Vehicle ◽  
Ambient Wind ◽  
Simulation Methodology ◽  
Road Load ◽  
Light Duty ◽  
Light Commercial Vehicle ◽  
Light Duty Vehicles ◽  
The Impact

A Monte Carlo simulation methodology is suggested in order to assess the impact of ambient wind on a vehicle’s performance and emissions. A large number of random wind profiles is generated by implementing the Weibull and uniform statistical distributions for wind speed and direction, respectively. Wind speed data are drawn from eight cities across Europe. The vehicle considered is a diesel-powered, turbocharged, light-commercial vehicle and the baseline trip is the worldwide harmonized light-duty vehicles WLTC cycle. A detailed engine-mapping approach is used as the basis for the results, complemented with experimentally derived correction coefficients to account for engine transients. The properties of interest are (engine-out) NO and soot emissions, as well as fuel and energy consumption and CO2 emissions. Results from this study show that there is an aggregate increase in all properties, vis-à-vis the reference case (i.e., zero wind), if ambient wind is to be accounted for in road load calculation. Mean wind speeds for the different sites examined range from 14.6 km/h to 24.2 km/h. The average increase in the properties studied, across all sites, ranges from 0.22% up to 2.52% depending on the trip and the property (CO2, soot, NO, energy consumption) examined. Based on individual trip assessment, it was found that especially at high vehicle speeds where wind drag becomes the major road load force, CO2 emissions may increase by 28%, NO emissions by 22%, and soot emissions by 13% in the presence of strong headwinds. Moreover, it is demonstrated that the adverse effect of headwinds far exceeds the positive effect of tailwinds, thus explaining the overall increase in fuel/energy consumption as well as emissions, while also highlighting the shortcomings of the current certification procedure, which neglects ambient wind effects.

Fuel Use and CO2 Emissions Under Uncertainty From Light-Duty Vehicles in the U.S. to 2050

2011 ◽  
Author(s):  
Parisa Bastani ◽  
John B. Heywood ◽  
Chris Hope
Keyword(s):  
Co2 Emissions ◽  
Alternative Fuels ◽  
Fuel Use ◽  
Policy Makers ◽  
Light Duty ◽  
Co2 Equivalent ◽  
The Future ◽  
Light Duty Vehicles ◽  
The Impact ◽  
The U.S

On-road transportation contributes 22% of the total CO2 emissions and more than 44% of oil consumption in the U.S. Technological advancements and use of alternative fuels are often suggested as ways to reduce these emissions. However, many parameters and relationships that determine the future characteristics of the light-duty vehicle fleet and how they change over time are inherently uncertain. Policy makers need to make decisions today given these uncertainties, to shape the future of light-duty vehicles. Decision makers thus need to know the impact of uncertainties on the outcome of their decisions and the associated risks. This paper explores a carefully constructed detailed pathway that results in a significant reduction in fuel use and GHG emissions in 2050. Inputs are assigned realistic uncertainty bounds, and the impact of uncertainty on this pathway is analyzed. A novel probabilistic fleet model is used here to quantify the uncertainties within advanced vehicle technology development, and life-cycle emissions of alternative fuels and renewable sources. Based on the results from this study, the expected fuel use is about 500 and 350 billion litres gasoline equivalent, with a standard deviation of about 40 and 80 billion litres in years 2030 and 2050 respectively. The expected CO2 emissions are about 1,360 and 840 Mt CO2 equivalent with a spread of about 130 and 260 Mt CO2 equivalent in 2030 and 2050 respectively. Major contributing factors in determining the future fuel consumption and emissions are also identified and include vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel consumption of naturally-aspirated engines, and percentage of gasoline displaced by cellulosic ethanol. This type of analysis allows policy makers to better understand the impact of their decisions and proposed policies given the technological and market uncertainties that we face today.

Fuel Use and CO2 Emissions Under Uncertainty From Light-Duty Vehicles in the U.S. to 2050

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Parisa Bastani ◽  
John B. Heywood ◽  
Chris Hope
Keyword(s):  
Co2 Emissions ◽  
Alternative Fuels ◽  
Fuel Use ◽  
Policy Makers ◽  
Light Duty ◽  
Co2 Equivalent ◽  
The Future ◽  
Light Duty Vehicles ◽  
The Impact ◽  
The U.S

On-road transportation contributes 22% of the total CO2 emissions and more than 44% of oil consumption in the U.S. technological advancements and use of alternative fuels are often suggested as ways to reduce these emissions. However, many parameters and relationships that determine the future characteristics of the light-duty vehicle (LDV) fleet and how they change over time are inherently uncertain. Policy makers need to make decisions today given these uncertainties, to shape the future of light-duty vehicles. Decision makers thus need to know the impact of uncertainties on the outcome of their decisions and the associated risks. This paper explores a carefully constructed detailed pathway that results in a significant reduction in fuel use and greenhouse gases (GHG) emissions in 2050. Inputs are assigned realistic uncertainty bounds, and the impact of uncertainty on this pathway is analyzed. A novel probabilistic fleet model is used here to quantify the uncertainties within advanced vehicle technology development, and life-cycle emissions of alternative fuels and renewable sources. Based on the results from this study, the expected fuel use is about 500 and 350 × 109 l gasoline equivalent, with a standard deviation of about 40 and 80 × 109 l in years 2030 and 2050, respectively. The expected CO2 emissions are about 1360 and 840 Mt CO2 equivalent with a spread of about 130 and 260 Mt CO2 equivalent in 2030 and 2050, respectively. Major contributing factors in determining the future fuel consumption and emissions are also identified and include vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel consumption of naturally aspirated engines, and percentage of gasoline displaced by cellulosic ethanol. This type of analysis allows policy makers to better understand the impact of their decisions and proposed policies given the technological and market uncertainties that we face today.

scholarly journals APPROACHES TO REGULATION OF CO2 EMISSION AND ENERGY CONSUMPTION INDICATORS OF NEW LIGHT DUTY VEHICLES IN UKRAINE

2019 ◽  
Vol 1 (43) ◽  
pp. 66-75 ◽  
Author(s):  
Alexey Klimenko ◽  
◽  
Nikolas Hill ◽  
Elisabeth Windisch ◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Co2 Emission ◽  
Light Duty ◽  
Light Duty Vehicles

Analysis of Driving Cycles for Emission Test of Light-Duty Vehicles in China

2012 ◽  
Vol 616-618 ◽  
pp. 1154-1160
Author(s):  
Jin Lin Xue
Keyword(s):  
Real World ◽  
Gasoline Engine ◽  
The Real ◽  
Driving Behaviors ◽  
Traffic Conditions ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Light Duty ◽  
Light Duty Vehicles ◽  
The Impact

The driving cycles employed to measure the emissions from automotive vehicles should adequately represent the real-world driving pattern of the vehicle to provide the most realistic estimation of emissions levels. The driving cycles used for light-duty gasoline engine vehicles in China were reviewed in this paper firstly. Then the impact of various factors, such as driving behaviors, driving conditions, road conditions, traffic conditions, on real-world emission levels were analyzed. Finally, the shortages of the existing driving cycles were pointed out. It can be concluded that the emissions levels from automotive vehicles are underestimated because of the characteristics of the existing drive cycles, so it is urgent to research and develop new driving cycles to fit the situation of China.

scholarly journals Methods and simulation to reduce fuel consumption in driving cycles for category N1 motor vehicles

2021 ◽  
Author(s):  
Alexander Kolin ◽  
S. E. Silantyev ◽  
Petr Rogov ◽  
M. E. Gnenik
Keyword(s):  
Fuel Consumption ◽  
Impact Analysis ◽  
Motor Vehicle ◽  
Commercial Vehicle ◽  
Road Traffic ◽  
Motor Vehicles ◽  
Design Parameters ◽  
Driving Cycles ◽  
Light Commercial Vehicle ◽  
The Impact

The paper presents the results of using the simulation model estimating the fuel consumption of a light commercial vehicle in road traffic cycles; virtual tests are performed. The impact analysis of the motor vehicle design parameters on fuel consumption in NEDC and WLTC cycles is conducted. Numerical values of average fuel consumption are obtained for variation of the main parameters of the structure in NEDC and WLTC cycles. Energy distribution is shown during the motion of category N1 light commercial vehicle.

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 Influence of the main parameters of the car on fuel consumption in driving cycles

2021 ◽  
Vol 18 (1) ◽  
pp. 149-156
Author(s):  
A. A. Kolin ◽  
◽  
S. E. Silantyev ◽  
P. S. Rogov ◽  
S. A. Sergievsky ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Simulation Model ◽  
Fuel Consumption ◽  
Commercial Vehicle ◽  
Road Traffic ◽  
Design Parameters ◽  
Main Design ◽  
Driving Cycles ◽  
Light Commercial Vehicle

The article presents the results of using the developed simulation model aimed at estimating the fuel consumption of a light commercial vehicle in road traffic cycles. There have been conducted virtual tests. The analysis of the influence of the main parameters of the car on fuel consumption in the NEDC and WLTC cycles is performed. There have been established numerical values of the average fuel consumption indicator through variation in the main design parameters. The distribution of energy consumption during the motion of the car is shown.

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