A model-based eco-routing strategy for electric vehicles in large urban networks

Giovanni de Nunzio 1 Laurent Thibault 1 Antonio Sciarretta 1
1 IFPEN - IFP Energies nouvelles
Abstract : 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.
Keywords : eco-routing energy consumption estimation electric vehicles adjoint graph
Document type :
Conference papers
Complete list of metadatas

Cited literature [16 references]  Display  Hide  Download
https://hal-ifp.archives-ouvertes.fr/hal-01515215
Contributor : Giovanni de Nunzio <>
Submitted on : Thursday, April 27, 2017 - 10:17:37 AM
Last modification on : Thursday, February 7, 2019 - 3:48:22 PM
Long-term archiving on: Friday, July 28, 2017 - 12:44:57 PM

File

paper_final.pdf
Files produced by the author(s)

Identifiers

Collections

IFP

Citation

Giovanni de Nunzio, Laurent Thibault, Antonio Sciarretta. A model-based eco-routing strategy for electric vehicles in large urban networks. 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Nov 2016, Rio de Janeiro, Brazil. pp.2301 - 2306, ⟨10.1109/ITSC.2016.7795927⟩. ⟨hal-01515215⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

164

Files downloads

616

Contact

Informations

CCSD