Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Sanguesa, JA (Universidad de Zaragoza) ; Garrido, P (Universidad de Zaragoza) ; Martinez, FJ (Universidad de Zaragoza) ; Marquez-Barja, JM

Resumen: Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient (C-d) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead.

Idioma: Inglés

DOI: 10.1109/ACCESS.2021.3072979

Año: 2021

Publicado en: IEEE Access 9 (2021), 61475-61488
ISSN: 2169-3536

Factor impacto JCR: 3.476 (2021)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 79 / 164 = 0.482 (2021) - Q2 - T2
Categ. JCR: TELECOMMUNICATIONS rank: 43 / 93 = 0.462 (2021) - Q2 - T2
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 105 / 277 = 0.379 (2021) - Q2 - T2

Factor impacto CITESCORE: 6.7 - Engineering (Q1) - Computer Science (Q1) - Materials Science (Q1)

Factor impacto SCIMAGO: 0.927 - Computer Science (miscellaneous) (Q1) - Engineering (miscellaneous) (Q1)

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Arquit.Tecnología Comput. (Dpto. Informát.Ingenie.Sistms.)
Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)