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.

中文翻译：

---

基于机器学习的电动汽车充电调度最短等待时间

为了减少温室气体排放和限制全球气温上升，汽车行业的趋势正在迅速变化，大多数制造商正在向汽车电气化方向发展。由于对电池充电和端口可访问性进行自动控制的必要性，计算智能和机器学习在电动汽车 (EV) 领域发挥着非常重要的作用。由于电动汽车的续航里程有限，它们在行驶过程中必须定期充电，充电时间会更长。随着电动汽车数量的增加，需要具有许多充电站的合适的充电基础设施以及从充电站调度充电车辆的协调。由于充电站的快速充电端口数量较少，访问这些快速充电端口需要适当的规划。电动汽车的主要挑战是识别具有快速充电端口的充电站，该充电站位于以最短等待时间到达目的地的途中。本文介绍了机器学习在选择具有可用快速充电端口和最短等待时间的充电站方面的应用。