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Speed forecasting has numerous applications in intelligent transport systems’ design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles’ speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles’ characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting.


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Multivariate Deep Learning Approach for Electric Vehicle Speed Forecasting

Show Author's information Youssef Nait Malek( )Mehdi NajibMohamed BakhouyaMohammed Essaaidi
LERMA Lab, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco.
TICLab, College of Engineering and Architecture, International University of Rabat, Sala Al Jadida 11100, Morocco.
LERMA Lab, College of Engineering, International University of Rabat, Sala Al Jadida 11100, Morocco.
ENSIAS, Mohamed V University, Agdal Rabat 10112, Morocco.

Abstract

Speed forecasting has numerous applications in intelligent transport systems’ design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles’ speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles’ characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting.

Keywords:

Electric Vehicle (EV), multivariate Long Short-Term Memory (LSTM), speed forecasting, deep learning
Received: 01 September 2020 Accepted: 09 October 2020 Published: 12 January 2021 Issue date: March 2021
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Publication history

Received: 01 September 2020
Accepted: 09 October 2020
Published: 12 January 2021
Issue date: March 2021

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© The author(s) 2021

Acknowledgements

This work was performed under the collaborative framework OpenLab "PSA@Morocco - Sustainable mobility for Africa", and partially supported by MIGRID project (No. 5-398, 2017-2019), which was funded by USAID under the PEER program.

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