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Machine learning‐based charge scheduling of electric vehicles with minimum waiting time
Author(s) -
Vanitha V.,
Resmi R.,
Reddy Karri Naga Sai Vineela
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12333
Subject(s) - electrification , automotive engineering , computer science , scheduling (production processes) , charging station , automotive industry , port (circuit theory) , real time computing , electric vehicle , electrical engineering , engineering , operations management , power (physics) , electricity , physics , quantum mechanics , aerospace engineering
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.