Predictability of Vehicle Fuel Consumption Using LSTM: Findings from Field Experiments
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 5
Abstract
It has been well-recognized that driving behaviors significantly impact the fuel consumption of vehicles. To explore how well deep learning methods can predict fuel consumption precisely and efficiently and then guide drivers to go in an energy-saving way, we propose a fuel consumption prediction model, namely FuelNet, based on long short-term memory (LSTM) neural networks in this study. First, we develop the proposed FuelNet model with numerous vehicle kinematics data and corresponding fuel consumption data collected in the test field and real-world scenarios. And we analyze the relationship between the prediction accuracy and different combinations of input variables, training set size, and the sampling interval of the raw data. Second, we conduct intensive field tests to demonstrate the applicability of our model to fuel consumption prediction for different speed conditions and vehicle types. Furthermore, the superior prediction performance of FuelNet is shown by comparing it with five other types of models, such as the physical model, statistical and regression model, conventional neural networks model, and other deep learning models. Finally, we apply it to three real case studies, which verify that FuelNet can precisely predict fuel consumption for different driving trajectories in many scenarios such as signalized intersection (average value of RE is 0.049), campus environments (RE is 0.030), urban roads (RE is 0.077), and highways (RE is 0.097), as well as can contribute to detecting abnormal fuel consumption.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work is supported by the National Key Research and Development Program of China (No. 2019YFB1600100), and the National Natural Science Foundation of China (No. 61973045).
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 5 • May 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 9, 2022
Accepted: Dec 12, 2022
Published online: Mar 13, 2023
Published in print: May 1, 2023
Discussion open until: Aug 13, 2023
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