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Communication Dans Un Congrès Année : 2019

Energy management for electric vehicles in smart cities: a deep learning approach

Résumé

We propose a solution for Electric Vehicle (EV) 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 trajec-tory 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
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Dates et versions

hal-02101524 , version 1 (16-04-2019)

Identifiants

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Mohammed Laroui, Aicha Dridi, Hossam Afifi, Hassine Moungla, Michel Marot, et al.. Energy management for electric vehicles in smart cities: a deep learning approach. IWCMC 2019: International Wireless Communications & Mobile Computing Conference, Jun 2019, Tanger, Morocco. pp.2080-2085, ⟨10.1109/IWCMC.2019.8766580⟩. ⟨hal-02101524⟩
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