Systems analysis of EV adoption and criteria pollutant accumulation during inversion events

Salmon, John L.; Willey, Landon C.; Vasquez, Derek; Christensen, Carsten

doi:10.1109/syscon.2018.8369583

Cited by 3 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research institutes, government agencies and industries worldwide are increasingly interested in improving urban life in cities of tomorrow smart cities, while reducing the impact on the environment. The replacement of internal combustion engine (ICE) vehicles which are responsible for a decreasing share of pollutants [1] by electric vehicles (EVs) would reduce the accumulation of pollutants on the road and the dependence on fossil fuels Also, they improve the local air quality index (AQI) by 13 points and the health of citizens [2] and many countries have provided important guidelines for adopting electric vehicles [3,4]. Therefore, electric vehicles are considered one of the main components of transport systems in smart cities.…”

Section: Introductionmentioning

confidence: 99%

System architecture to select the charging station by optimizing the travel time considering the destination of electric vehicle drivers in smart cities

El-Fedany¹,

Kiouach²,

Alaoui³

2020

Bulletin EEI

View full text Add to dashboard Cite

The main limitations of electric vehicles are the limited scope of the battery and their relatively long charging times. This may cause discomfort to drivers of electric vehicles due to a long waiting period at the service of the charging station, during their trips. In this paper, we suggest a model system based on argorithms, allowing the management of charging plans of electric vehicles to travel on the road to their destination in order to minimize the duration of the drivers' journey. The proposed system decision to select the charging station, during advance reservation of electric vehicles, take into account the time of arrival of electric vehicles at charging stations, the expected charging time at charging stations, the local status of the charging stations in real time, and the amount of energy sufficient for the electric vehicle to arrive at the selected charging station. Furthermore, the system periodically updates the electric vehicule reservations to adjust their recharge plans, when they reach their selected earlier station compared to other vehicules requesting new reservations, or they may not arrive as they were forecast, due to traffic jams on the road or certain reluctance on the part of the driver.

show abstract

Section: Introductionmentioning

confidence: 99%