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Title	Prediction Of User Behaviour Of Electric Vehicles Utilizing Ensembled Machine Learning Technique
ID_Doc	42854
Authors	Garg R.; Deogaonkar A.; Garia P.; Ahamad I.; Kharayat P.S.; Joshi T.
Year	2024
Published	2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024
DOI	http://dx.doi.org/10.1109/IICCCS61609.2024.10763696
Abstract	Although they help reduce greenhouse gas emissions, electric vehicles (EVs), a fundamental component of smart mobility in applications for smart cities, are becoming more and more popular. One of the main challenges, meanwhile, is the strain that a large-scale EV deployment puts on the electrical grid's infrastructure. Electric vehicles are the most significant part of transportation. An application for mobility in smart cities is built on electric automobiles. The lack of structures for charging electric vehicles is the biggest obstacle to their acceptance to solve the issue of EV charging time and usage. We are using Ml algorithms to anticipate charge analysis to solve the problem, which is advantageous for drivers. It gives comprehensive details about the energy use of an electric car and illustrates how long it takes for the battery to charge completely. Predictive analytics uses machine learning (ML) algorithms because they can be trained on even larger data sets and, with little deployment adjustments, can conduct deeper analysis on many factors. © 2024 IEEE.
Author Keywords	Electric Vehicles; Machine Learning

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