FuelNet: A precise fuel consumption prediction model using long short-term memory deep network for eco-driving
Paper in proceeding, 2020

It has been well recognized that driving behaviors significantly impact fuel consumption of vehicles. In this paper, we propose a FuelNet model based on Long Short-term Memory Neural Network (LSTM NN), which can predict vehicle fuel consumption in a very accurate manner. First, we take the kinetic vehicle parameters and the corresponding fuel consumption parameters to build the FuelNet model, and analyze the correlations between the prediction accuracy and different combinations of input parameters. In addition, our model exhibits the superior capability for fuel consumption prediction (FCP) at different speed, and the comparison with different deep learning models as well as other physics model and data-driven methods suggests that FuelNet can achieve the best prediction performance in terms of both accuracy and stability. Finally, the application of FCP in distinct driving trajectories and abnormal fuel consumption detection performs well, which demonstrates the FuelNet also can provide guidance for eco-driving strategies.

Long short-term memory (LSTM)

Eco-driving strategies

Deep network

FuelNet

Fuel consumption prediction (FCP)

Author

Guanqun Wang

Changan University

Licheng Zhang

Changan University

Zhigang Xu

Changan University

Syeda Mahwish Hina

Changan University

Pengpeng Sun

Changan University

Haigen Min

Changan University

Tao Wei

Changan University

Xiaobo Qu

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Energy Proceedings

20042965 (eISSN)

Vol. 10

12th International Conference on Applied Energy, ICAE 2020
Bangkok, Thailand,

Subject Categories

Bioinformatics (Computational Biology)

Vehicle Engineering

Probability Theory and Statistics

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9/6/2024 1