Machine Learning Methodologies for Electric Vehicle Energy Management Strategies

Khaledur Rahman, Md
Saiful Islam, Md
Jakaria Talukder, Md
Nazmul Islam, Md
Shameem Ahsan, Md

BULLET : Jurnal Multidisiplin Ilmu
Vol. 4 No. 1 (2025): BULLET : Jurnal Multidisiplin Ilmu

This research study explores the usage of machine learning techniques in the improvement of energy executives’ strategies for electric vehicles (EVs), with a particular accentuation on estimating EV-related variables and classifying price ranges. The study uses machine learning such as linear regression, random forest regression, decision tree, random forest classifier, and artificial neural network (ANN). The dataset involves fundamental electric vehicle (EV) attributes, including acceleration time, maximum speed, range, efficiency, and fast charging capacity. Information readiness includes the chores of handling missing values and changing category labels into a numerical column. The evaluation measures incorporate mean squared error, R-squared, and accuracy. The outcomes exhibit the efficacy of machine learning models in estimating EV-related variables and classifying price levels. The key discoveries highlight the unique performance of regression and classification models. This examination upgrades the cognizance of machine learning applications in EV energy the executives and gives important bits of knowledge to further develop determining accuracy and decision-making processes.