Journal : 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)

Article type : Conference Paper

Auteurs : HAFAIFA A, OUADAH A, SAÏD KHALDI B, IRATNI A

Abstract :

Although electric vehicles (EVs) have many benefits for protecting the environment and lowering emissions, their widespread adoption mainly depends on their price. With machine learning (ML) algorithms, costs can be predicted. This research aims to compare the performance of some of the most well-known ML algorithms to determine which algorithm will best predict the price of electric vehicles. To identify the key characteristics, we examined the literature to research the elements that determine the price of electric vehicles in order to estimate their cost. We theoretically compared these ML algorithms to validate our findings and then compared the output of this comparative study to the outcomes of the simulations.

Keywords:

Machine learning ; Electric Vehicles (EVs) ; EVs costs ; Supervised machine learning algorithm