Modeling real-world fuel consumption and carbon dioxide emissions with high resolution for light-duty passenger vehicles in a traffic populated city

Energy ◽  
2016 ◽  
Vol 113 ◽  
pp. 461-471 ◽  
Author(s):  
Shaojun Zhang ◽  
Ye Wu ◽  
Puikei Un ◽  
Lixin Fu ◽  
Jiming Hao
Energy ◽  
2014 ◽  
Vol 69 ◽  
pp. 247-257 ◽  
Author(s):  
Shaojun Zhang ◽  
Ye Wu ◽  
Huan Liu ◽  
Ruikun Huang ◽  
Puikei Un ◽  
...  

2022 ◽  
Vol 12 (2) ◽  
pp. 803
Author(s):  
Ngo Le Huy Hien ◽  
Ah-Lian Kor

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.


Author(s):  
Xuan Zheng ◽  
Sheng Lu ◽  
Liuhanzi Yang ◽  
Min Yan ◽  
Guangyi Xu ◽  
...  

2019 ◽  
Vol 26 (3) ◽  
pp. 31-38
Author(s):  
Wojciech Gis ◽  
Maciej Gis ◽  
Piotr Wiśniowski ◽  
Mateusz Bednarski

Abstract Limiting emissions of harmful substances is a key task for vehicle manufacturers. Excessive emissions have a negative impact not only on the environment, but also on human life. A significant problem is the emission of nitrogen oxides as well as solid particles, in particular those up to a diameter of 2.5 microns. Carbon dioxide emissions are also a problem. Therefore, work is underway on the use of alternative fuels to power the vehicle engines. The importance of alternative fuels applies to spark ignition engines. The authors of the article have done simulation tests of the Renault K4M 1.6 16v traction engine for emissions for fuels with a volumetric concentration of bioethanol from 10 to 85 percent. The analysis was carried out for mixtures as substitute fuels – without doing any structural changes in the engine's crankshafts. Emission of carbon monoxide, carbon dioxide, hydrocarbons, oxygen at full throttle for selected rotational speeds as well as selected engine performance parameters such as maximum power, torque, hourly and unit fuel consumption were determined. On the basis of the simulation tests performed, the reasonableness of using the tested alternative fuels was determined on the example of the drive unit without affecting its constructions, in terms of e.g. issue. Maximum power, torque, and fuel consumption have also been examined and compared. Thus, the impact of alternative fuels will be determined not only in terms of emissions, but also in terms of impact on the parameters of the power unit.


2017 ◽  
Vol 15 (1) ◽  
pp. 58-70 ◽  
Author(s):  
Bofeng Cai ◽  
Jinnan Wang ◽  
Shuying Yang ◽  
Xianqiang Mao ◽  
Libin Cao

2005 ◽  
Vol 345 (1-3) ◽  
pp. 93-98 ◽  
Author(s):  
Z.D. Ristovski ◽  
E.R. Jayaratne ◽  
L. Morawska ◽  
G.A. Ayoko ◽  
M. Lim

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