https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=De_Nunzio_et_al_2016aDe Nunzio et al 2016a - Revision history2026-05-08T00:44:41ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=De_Nunzio_et_al_2016a&diff=197825&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 335196086 to De Nunzio et al 2016a2021-02-01T20:39:55Z<p>Scipediacontent moved page <a href="/public/Draft_Content_335196086" class="mw-redirect" title="Draft Content 335196086">Draft Content 335196086</a> to <a href="/public/De_Nunzio_et_al_2016a" title="De Nunzio et al 2016a">De Nunzio et al 2016a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=De_Nunzio_et_al_2016a&diff=197824&oldid=prevScipediacontent: Created page with " == Abstract == International audience; A novel eco-routing navigation strategy and energy consumption modeling approach for electric vehicles are presented in this work. Spe..."2021-02-01T20:39:50Z<p>Created page with " == Abstract == International audience; A novel eco-routing navigation strategy and energy consumption modeling approach for electric vehicles are presented in this work. Spe..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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International audience; A novel eco-routing navigation strategy and energy consumption modeling approach for electric vehicles are presented in this work. Speed fluctuations and road network infrastructure have a large impact on vehicular energy consumption. Neglecting these effects may lead to large errors in eco-routing navigation, which could trivially select the route with the lowest average speed. We propose an energy consumption model that considers both accelerations and impact of the road infrastructure, also separating the costs of all the possible turning movements in the transportation network by means of the adjoint graph. The approach makes use of only aggregated data available from existing map services. We demonstrate that the proposed strategy is more effective and reliable than the existing approaches in predicting vehicle energy consumption and in suggesting an energy-efficient route.<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [http://dx.doi.org/10.1109/itsc.2016.7795927 http://dx.doi.org/10.1109/itsc.2016.7795927]<br />
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* [https://hal-ifp.archives-ouvertes.fr/hal-01515215/file/paper_final.pdf https://hal-ifp.archives-ouvertes.fr/hal-01515215/file/paper_final.pdf]<br />
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* [http://xplorestaging.ieee.org/ielx7/7784515/7795515/07795927.pdf?arnumber=7795927 http://xplorestaging.ieee.org/ielx7/7784515/7795515/07795927.pdf?arnumber=7795927],<br />
: [http://dx.doi.org/10.1109/itsc.2016.7795927 http://dx.doi.org/10.1109/itsc.2016.7795927]<br />
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* [https://hal-ifp.archives-ouvertes.fr/hal-01515215 https://hal-ifp.archives-ouvertes.fr/hal-01515215],<br />
: [https://hal-ifp.archives-ouvertes.fr/hal-01515215/document https://hal-ifp.archives-ouvertes.fr/hal-01515215/document],<br />
: [https://hal-ifp.archives-ouvertes.fr/hal-01515215/file/paper_final.pdf https://hal-ifp.archives-ouvertes.fr/hal-01515215/file/paper_final.pdf]</div>Scipediacontent