Scipediacontent: Scipediacontent moved page Draft Content 910959500 to Marot et al 2019a

2021-01-28T18:45:29Z

Scipediacontent moved page Draft Content 910959500 to Marot et al 2019a

Scipediacontent: Created page with " == Abstract == International audience; We propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance t..."

2021-01-28T18:45:25Z

Created page with " == Abstract == International audience; We propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance t..."

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== Abstract ==

International audience; 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 managemen

== Original document ==

The different versions of the original document can be found in:

* [https://hal.archives-ouvertes.fr/hal-02101524/file/Energy_Management_For_Electric_Vehicles_in_Smart_Cities__A_Deep_Learning_Approach.pdf https://hal.archives-ouvertes.fr/hal-02101524/file/Energy_Management_For_Electric_Vehicles_in_Smart_Cities__A_Deep_Learning_Approach.pdf]

* [http://xplorestaging.ieee.org/ielx7/8761262/8766347/08766580.pdf?arnumber=8766580 http://xplorestaging.ieee.org/ielx7/8761262/8766347/08766580.pdf?arnumber=8766580],
: [http://dx.doi.org/10.1109/iwcmc.2019.8766580 http://dx.doi.org/10.1109/iwcmc.2019.8766580]

* [https://dblp.uni-trier.de/db/conf/iwcmc/iwcmc2019.html#LarouiDAMMC19 https://dblp.uni-trier.de/db/conf/iwcmc/iwcmc2019.html#LarouiDAMMC19],
: [https://academic.microsoft.com/#/detail/2963536196 https://academic.microsoft.com/#/detail/2963536196]

* [https://hal.archives-ouvertes.fr/hal-02101524 https://hal.archives-ouvertes.fr/hal-02101524],
: [https://hal.archives-ouvertes.fr/hal-02101524/document https://hal.archives-ouvertes.fr/hal-02101524/document],
: [https://hal.archives-ouvertes.fr/hal-02101524/file/Energy_Management_For_Electric_Vehicles_in_Smart_Cities__A_Deep_Learning_Approach.pdf https://hal.archives-ouvertes.fr/hal-02101524/file/Energy_Management_For_Electric_Vehicles_in_Smart_Cities__A_Deep_Learning_Approach.pdf]

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Marot et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 910959500 to Marot et al 2019a

Scipediacontent: Created page with " == Abstract == International audience; We propose a solution for Electric Vehicle (EV) energy management in smart cities, where a deep learning approach is used to enhance t..."