Multilayer Perceptron Method to Estimate Real-World Fuel Consumption Rate of Light Duty Vehicles

The actual driving condition and fuel consumption rate gaps between lab and real-world are becoming larger. In this paper, we demonstrate an approach to determine the most important factors that may influence the prediction of real-world fuel consumption rate of light-duty vehicles. A multilayer per...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.63395-63402
Main Authors: Li, Yawen, Tang, Guangcan, Du, Jiameng, Zhou, Nan, Zhao, Yue, Wu, Tian
Format: Article
Language:English
Subjects:
Artificial intelligence
Automobiles
Big Data
Drivers
Driving conditions
Fuel consumption
fuel consumption rate
Fuels
Light duty vehicles
multilayer perceptron
Multilayer perceptrons
STEM
Vehicles
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Summary:The actual driving condition and fuel consumption rate gaps between lab and real-world are becoming larger. In this paper, we demonstrate an approach to determine the most important factors that may influence the prediction of real-world fuel consumption rate of light-duty vehicles. A multilayer perceptron (MLP) method is developed for the prediction of fuel consumption since it provides accurate classification results despite the complicated properties of different types of inputs. The model considers the parameters of external environmental factors, the manipulation of vehicle companies, and the drivers' driving habits. Based on the BearOil database in China, 2,424,379 samples are used to optimize our model. We indicate that differences exist between real-world fuel consumption and standard fuel consumption under simulation conditions. This study enables the government and policy-makers to use big data and intelligent systems for energy policy assessment and better governance.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2914378