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Title	Energy Management For Electric Vehicles In Smart Cities: A Deep Learning Approach
ID_Doc	23309
Authors	Laroui M.; Dridi A.; Afifi H.; Moungla H.; Marot M.; Cherif M.A.
Year	2019
Published	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
DOI	http://dx.doi.org/10.1109/IWCMC.2019.8766580
Abstract	We propose a solution for Electric Vehicles (EVs) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management. © 2019 IEEE.
Author Keywords	Electric vehicles; Energy control; Recurrent Deep Learning

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