Federated learning for smart charging of connected electric vehicles

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Auteurs principaux: Al-Zuhairi, Yaqoob, kannan, prashanth, Aguilar Igartua, Mónica
Format: Recurso digital
Publié: Zenodo 2021
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author Al-Zuhairi, Yaqoob
kannan, prashanth
Aguilar Igartua, Mónica
author_facet Al-Zuhairi, Yaqoob
kannan, prashanth
Aguilar Igartua, Mónica
contents <p>Due to rising concerns over climate change, air<br>pollution and clean energy awareness, the demand<br>for electric vehicles (EVs) and renewable energy<br>generation has increased in recent years. The main<br>objective of this research is to design a decentralized<br>smart charging coordination framework for<br>EVs based on federated learning (FL) algorithms<br>in order to provide an acceptable collaboratively<br>learning model with privacy preservation of EVs,<br>improve charging scenarios, contribute to smart<br>grid stabilization, meet EVs energy requirements<br>wherever and whenever they request, and gain<br>welfare for EV owners. Moreover, FL is introduced<br>with the goal of bringing machine learning (ML)<br>down to the edge level in vehicular networks.<br>Ultimately, a multimetric routing protocol is also<br>used to predict the best route for transmitting<br>messages among EVs, infrastructures, charging<br>stations (CSs), and central servers.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_17315738
institution Zenodo
language
publishDate 2021
publisher Zenodo
record_format zenodo
spellingShingle Federated learning for smart charging of connected electric vehicles
Al-Zuhairi, Yaqoob
kannan, prashanth
Aguilar Igartua, Mónica
SCITEL
JITEL2021
Telecommunications
<p>Due to rising concerns over climate change, air<br>pollution and clean energy awareness, the demand<br>for electric vehicles (EVs) and renewable energy<br>generation has increased in recent years. The main<br>objective of this research is to design a decentralized<br>smart charging coordination framework for<br>EVs based on federated learning (FL) algorithms<br>in order to provide an acceptable collaboratively<br>learning model with privacy preservation of EVs,<br>improve charging scenarios, contribute to smart<br>grid stabilization, meet EVs energy requirements<br>wherever and whenever they request, and gain<br>welfare for EV owners. Moreover, FL is introduced<br>with the goal of bringing machine learning (ML)<br>down to the edge level in vehicular networks.<br>Ultimately, a multimetric routing protocol is also<br>used to predict the best route for transmitting<br>messages among EVs, infrastructures, charging<br>stations (CSs), and central servers.</p>
title Federated learning for smart charging of connected electric vehicles
topic SCITEL
JITEL2021
Telecommunications
url https://doi.org/10.5281/zenodo.17315738