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Machine learning-based charge scheduling of electric vehicles with minimum waiting time

机译:基于机器学习的电动汽车充电调度,最低等待时间

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摘要

In order to reduce the greenhouse gas emission and limit the rise in global temperature, the trend in automotive industry is changing rapidly and most of the manufacturers are moving towards the electrification of vehicles. Computational intelligence and machine learning play a very important role in the field of electric vehicles (EVs) due to the necessity of automatic control in battery charging and port accessibility. Due to the limited ranges of EVs, they have to be charged periodically during their travels and its charging will take more time. As the number of EVs increases, suitable charging infrastructure having many charging stations and co-ordination of scheduling the charging vehicles from charging stations are necessary. As charging stations have less number of fast charging ports, accessing these fast charging ports needs proper planning. The major challenge of an EV is to identify the charging station with a fast charging port which is on route to the destination with minimum waiting time. This article deals with the application of machine learning in selecting a charging station with available fast charging port and minimum waiting time.
机译:为了减少温室气体排放并限制全球温度的上升,汽车工业的趋势迅速变化,大部分制造商正在朝向车辆的电气化。由于电池充电和端口可访问性的必要性,计算智能和机器学习在电动车辆(EVS)领域起着非常重要的作用。由于EV的范围有限,他们必须在旅行期间定期收取,并且其充电将需要更多时间。随着EVS的数量增加,需要具有许多充电站的合适的充电基础设施以及调度充电站的充电车辆的协调。由于充电站具有较少数量的快速充电端口,访问这些快速充电端口需要适当的规划。 EV的主要挑战是使用快速充电端口识别充电站,该端口在到目的地的路线上,最小等待时间。本文涉及机器学习在选择带有可用的快速充电端口和最小等待时间的充电站。

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