scholarly journals Light-Duty Vehicle Fuel Consumption Displacement Potential up to 2045

2013 ◽  
Author(s):  
A. Moawad ◽  
P. Sharer ◽  
A. Rousseau
Keyword(s):  
Fuel Consumption ◽  
Displacement Potential ◽  
Light Duty ◽  
Light Duty Vehicle

Low-Level Automated Light-Duty Vehicle Technologies Provide Opportunities to Reduce Fuel Consumption

2018 ◽  
Vol 2672 (24) ◽  
pp. 60-74 ◽  
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Constantine Samaras ◽  
Chris Hendrickson
Keyword(s):  
Fuel Consumption ◽  
Aerodynamic Force ◽  
The United States ◽  
Route Guidance ◽  
Conventional Vehicle ◽  
Automated Vehicle ◽  
Fuel Savings ◽  
Light Duty ◽  
Vehicle Systems ◽  
Light Duty Vehicle

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

scholarly journals Model-based estimation of light-duty vehicle fuel economy at high altitude

2019 ◽  
Vol 11 (11) ◽  
pp. 168781401988625 ◽  
Author(s):  
Lijun Hao ◽  
Chunjie Wang ◽  
Hang Yin ◽  
Chunxiao Hao ◽  
Haohao Wang ◽  
...  
Keyword(s):  
Internal Combustion Engine ◽  
Fuel Consumption ◽  
Fuel Economy ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Specific Fuel Consumption ◽  
Brake Specific Fuel Consumption ◽  
Engine Model ◽  
Light Duty ◽  
Light Duty Vehicle

In order to estimate the light-duty vehicle fuel economy at high-altitude areas, the coast-down tests of a passenger car on level road were conducted at different elevations, and the coast-down resistance coefficients were calculated. Furthermore, a fuel economy model for a light-duty vehicle adopting backward simulation method was developed, and it mainly consists of vehicle dynamic model, internal combustion engine model, transmission model, and differential model. The internal combustion engine model consists of the brake-specific fuel consumption maps as functions of engine torque and engine speed, and the brake-specific fuel consumption map near sea level was constructed based on engine experimental data, and the brake-specific fuel consumption maps at high altitudes were calculated by GT-Power Modeling of the internal combustion engine. The fuel consumption rate was calculated from the brake-specific fuel consumption maps and brake power and used to calculate the fuel economy of the light-duty vehicle. The model predicted fuel consumption data met well with the test results, and the model prediction errors are within 5%.

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 Analysis of fuel consumption of China light duty vehicle test cycle for passenger car(CLTC-P)

2021 ◽  
Vol 268 ◽  
pp. 01029
Author(s):  
Meng Zhou ◽  
Chongzhi Zhong ◽  
Jingyuan Li
Keyword(s):  
Fuel Consumption ◽  
Air Conditioning ◽  
Test Results ◽  
Test Cycle ◽  
Test Procedures ◽  
Light Duty ◽  
Certification Test ◽  
Light Duty Vehicle ◽  
Vehicle Test ◽  
P Cycle

Through the fuel consumption test of several listed vehicles in China, the basic fuel consumption results of cold start under CLTC-P cycle, the fuel consumption results of vehicles under the condition of air conditioning on, and the fuel consumption results of vehicles under the condition of air conditioning off are measured. At the same time, the differences between NEDC cycle and CLTC-P cycle in China's fuel consumption certification test are compared, and the results of fuel consumption test are combined The fuel consumption test results under CLTC-P cycle are higher than those under NEDC cycle, and the fuel consumption test procedures under Chinese condition are more in line with the actual driving situation in China.

scholarly journals Effects of an On-Board Safety Device on the Emissions and Fuel Consumption of a Light Duty Vehicle

2018 ◽  
Author(s):  
Cheuk Yin Ng ◽  
Yuhan Huang ◽  
Guang Hong ◽  
John Zhou ◽  
Nic Surawski ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Safety Device ◽  
Light Duty ◽  
Light Duty Vehicle

Quantifying the impact of driving style changes on light-duty vehicle fuel consumption

2021 ◽  
Vol 98 ◽  
pp. 102918
Author(s):  
Marco Miotti ◽  
Zachary A. Needell ◽  
Sankaran Ramakrishnan ◽  
John Heywood ◽  
Jessika E. Trancik
Keyword(s):  
Fuel Consumption ◽  
Driving Style ◽  
Light Duty ◽  
Light Duty Vehicle ◽  
The Impact

Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions

2003 ◽  
Vol 30 (6) ◽  
pp. 1010-1021 ◽  
Author(s):  
Hesham Rakha ◽  
Kyoungho Ahn ◽  
Antonio Trani
Keyword(s):  
Fuel Consumption ◽  
Environmental Protection Agency ◽  
Field Data ◽  
Vehicle Emissions ◽  
National Laboratory ◽  
Oxides Of Nitrogen ◽  
Micro Model ◽  
Oak Ridge ◽  
Light Duty ◽  
Light Duty Vehicle

The paper compares the MOBILE5a, MOBILE6, Virginia Tech microscopic energy and emission model (VT-Micro), and comprehensive modal emissions model (CMEM) models for estimating hot-stabilized, light-duty vehicle emissions. Specifically, Oak Ridge National Laboratory (ORNL) and Environmental Protection Agency (EPA) laboratory fuel consumption and emission databases are used for model comparisons. The comparisons demonstrate that CMEM exhibits some abnormal behaviors when compared with the ORNL data, EPA data, and the VT-Micro model estimates. Specifically, carbon monoxide (CO) emissions exhibit abrupt changes at low speeds and high acceleration levels and constant emissions at negative acceleration levels. Furthermore, oxides of nitrogen (NOx) emissions exhibit abrupt drops at high engine loads. In addition, the study demonstrates that MOBILE5a emission estimates compare poorly with EPA field data, while MOBILE6 model estimates show consistency with EPA field data and VT-Micro model estimates over various driving cycles. The VT-Micro model appears to be accurate in estimating hot-stabilized, light-duty, normal vehicle tailpipe emissions. Specifically, the emission estimates of the VT-Micro and MOBILE6 models are consistent in trends with laboratory measurements. Furthermore, the VT-Micro and MOBILE6 models accurately capture emission increases for aggressive acceleration drive cycles in comparison with other drive cycles.Key words: transportation energy, transportation environmental impacts, VT-Micro Model, CMEM, MOBILE5, MOBILE6, fuel consumption models, emission models.

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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