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

Abstract

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.

A smart system combining real and predicted data to recommend an optimal electric vehicle charging station

“…C3S proposed system consists of three methods noted by Ag2M1, Alg2M2 and Alg2M3 is compared with the previous strategy, noted by Alg1, which based on the updating periodically of EV charging reservations at each change due to a new EV charging demand [23]- [25]. In addition, to show the performance of C3S system algorithms we adopted average trip time (ATT), the average time that EVs spend on their trip including spending time at CSs selected for charging, as a metric of evaluation.…”

“…In addition, we have also proposed a strategy to mitigate the impact of future unknown EVs charging demands caused by the dynamic change of traffic conditions on the roads in [23], [24]. Our method based on the dynamic change of the charging plans of all EVs that have a charging reservation, the updating charging plans are made at each impact in the CSs states due to a new EV charging request.…”

Smart Grid E Suas Tendências a Partir Da Evolução Do Ambiente De Contratação Livre No Brasil

“…With smart infrastructures, smart cities enjoy other benefits, such as better quality of life generated by the reduction of air pollution in cities, similarly to noise pollution, which is the excess of noise that affects the physical and mental health of the population, indirectly causing problems such as stress and sleep disturbances, due to the constant noise of cars on public roads and the noise of vehicle traffic, which is mitigated with electric cars that are quiet in nature [30] [32].…”

A smart management system of electric vehicles charging plans on the highway charging stations

“…El-Fedany et al [14], we propose an application design allowing the management of EVs charging demands, the CS selected decision based on minimizing the trip duration including waiting and charging time. Moreover, we proposed a system design update dynamically the EVs charging plans on real time [15]. A strategy to assign each EV to the most suitable CS according to a method that will minimize the waiting time [16].…”