ABSTRACT
The lack of country-specific data related to the real-world fuel consumption (FC) of motor vehicles is a significant impediment to the inventory of greenhouse gas (GHG) emissions in the transport sector in Vietnam. This study aims to develop a cost-effective solution for estimating the on-road FC of motorcycles (MC) to overcome this lack. An Artificial Neural Network (ANN)-based model (R2 >0.85 and MAPE < 20%) was developed to predict the MC’s instant FC rate (FRinst) based on the experimental data that were continuously collected using the data logger installed directly on the test MC. The developed ANN-based model was utilized to estimate FRinst according to the real-world driving characteristics that were collected using the Global positioning system (GPS) device. The discrepancy between the estimated and measured FC (in terms of liters per kilometer) was only 6.8%. Consequently, the proposed approach could help improve the consumed fuel-based GHG emission inventory in the transport sector.
KEYWORDS:
Nomenclature
AF | = | Activation function |
ANN | = | Artificial neural networks |
CSEF | = | Country-specific emission factor |
EF | = | Emission factor |
FC | = | Fuel consumption |
= | Average fuel consumption for each trip | |
FRinst | = | Fuel consumption rate |
G | = | Grade |
GHG | = | Greenhouse gas |
GPS | = | Global positioning system |
GWP | = | Global warming potential |
IPCC | = | Intergovernmental Panel on Climate Change |
MAPE | = | Mean absolute percentage error |
MC | = | Motorcycles |
Qfuel | = | Lower heating value of gasoline |
R | = | Correlation coefficient |
R2 | = | Determination coefficient |
RMSPE | = | Root mean squared percentage error |
ρfuel | = | Gasoline density |
SAFDdiff | = | Speed – acceleration distribution deviation |
TA | = | Training algorithm |
TFC | = | Total consumed fuel |
Vinst | = | Instant speed |
VKT | = | Traveled kilometers |
VSP | = | Vehicle-specific power |
Acknowledgements
Khanh Nguyen Duc was funded by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.TS058.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Yen-Lien T. Nguyen
Yen-Lien T. Nguyen is a Doctor at the Faculty of Transport Safety and Environment, University of Transport and Communications. She is an expert in environmental and transportation issues.
Khanh Nguyen Duc
Khanh Nguyen Duc is a lecturer at the School of Mechanical Engineering, HUST. He is an expert in electronic control, engine upgrades, alternative fuels, and transportation.
Anh-Tuan Le
Anh-Tuan Le is a Professor at the School of Mechanical Engineering, HUST. He is an expert in alternative fuels, conventional and electric vehicles.
Hai-Yen T. Than
Hai-Yen T. Than works at the Faculty of Transport Safety and Environment, University of Transport and Communications. Her search tendency is environmental pollutants from vehicles.
Quy Cao Minh
Quy Cao Minh is a Doctor at the Faculty of Transport Safety and Environment, University of Transport and Communications. He is an expert in transportation environment management.