A dual attention-based fusion network for long-and short-term multivariate vehicle exhaust emission prediction

X Fei, Z Lai, Y Fang, Q Ling - Science of The Total Environment, 2023 - Elsevier
The increasing number of vehicles is one main cause of atmospheric environment pollution
problems. Timely and accurate long-and short-term (LST) prediction of the on-road vehicle …

Prediction of cold start emissions for hybrid electric vehicles based on genetic algorithms and neural networks

D Tang, Z Zhang, L Hua, J Pan, Y Xiao - Journal of Cleaner Production, 2023 - Elsevier
The emission of hybrid electric vehicles deteriorates during cold start, and it is a cost-
effective method to reduce pollutant emissions during cold start of hybrid electric vehicles …

Investigating the impact of high-altitude on vehicle carbon emissions: A comprehensive on-road driving study

Z Jiang, L Wu, H Niu, Z Jia, Z Qi, Y Liu, Q Zhang… - Science of the Total …, 2024 - Elsevier
This study addresses the literature gap concerning accurately identifying vehicle carbon
emission characteristics in high-altitude areas. Utilizing a portable emission measurement …

Vehicle emission forecasting based on wavelet transform and long short-term memory network

Q Zhang, F Li, F Long, Q Ling - IEEE Access, 2018 - ieeexplore.ieee.org
This paper proposes a time series model based on wavelet transform and long short-term
memory (LSTM) network to forecast vehicle emission. It implements the semi-supervised …

[PDF][PDF] Evaluation of real-world fuel consumption of light-duty vehicles in China

Z Yang, L Yang - International Council on Clean Transportation (ICCT) …, 2018 - theicct.org
There is growing evidence globally and in China of the gap between laboratory test findings
and real-world carbon dioxide (CO2) emissions and fuel consumption (Tietge et al, 2017) …

A comparative assessment of CO2 emission between gasoline, electric, and hybrid vehicles: A Well-To-Wheel perspective using agent-based modeling

MM Rahman, Y Zhou, J Rogers, V Chen… - Journal of Cleaner …, 2021 - Elsevier
Road transports in the United States (US) are heavily dependent on the production and
consumption of fossil fuel. This high dependency on fossil fuels contributes significantly to …

A machine learning-based energy optimization system for electric vehicles

R Padmavathy, T Greeta, K Divya - E3S Web of Conferences, 2023 - e3s-conferences.org
The growing demand for sustainable and eco-friendly transportation has led to the
widespread adoption of electric vehicles (EVs). However, the limited driving range of EVs …

A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information

Y Sun, Y Hu, H Zhang, F Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
A parallel supervision system is built in this paper in order to accurately estimate vehicle
emissions. Only on-board diagnostics (OBD)-independent information is used, making the …

[PDF][PDF] Modelling the atmospheric concentration of carbon monoxide by using ensemble learning algorithms

A Masih - CEUR Workshop Proceedings, 2018 - researchgate.net
Air quality monitoring is among several important tasks performed in environmental science
and engineering. Photochemical reaction in troposphere is the major natural source of …

[HTML][HTML] Forecasting air transportation demand and its impacts on energy consumption and emission

ME Javanmard, Y Tang, JA Martínez-Hernández - Applied Energy, 2024 - Elsevier
With the increasing demand of passenger and freight air transportation and their key role in
energy consumptions, this study developed a hybrid framework integrating machine …