Machine learning‐based charge scheduling of electric vehicles with minimum waiting time. (10th May 2020)

• Record Type: Journal Article
• Authors: Vanitha, V.; Resmi, R.; Reddy, Karri Naga Sai Vineela
• Other Names: Srivastava Gautam guestEditor.; Hsu Ching‐Hsien (Robert) guestEditor.; Kumar Priyan Malarvizhi guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 3(2021)
• Page Start: 1047
• Page End: 1055
• Publication Date: 2020-05-10
• Subjects: charge scheduling -- electric vehicle -- fast charging port -- zigbee; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12333
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.