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This Author published in this journals
All Journal International Journal of Multidisciplinary Sciences and Arts BULLET : Jurnal Multidisiplin Ilmu
Md Saiful Islam
Lamar University
Humanities Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing

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Integration of Electric Vehicles (EVs) with Electrical Grid and Impact on Smart Charging Md Shameem Ahsan; Faysal Amin Tanvir; Md Khaledur Rahman; Manam Ahmed; Md Saiful Islam
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 4 (2023): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i2.3322

Abstract

Integrating Electric Vehicles (EVs) with the electrical grid is a pivotal aspect of modern transportation systems. This integration poses multifaceted challenges and opportunities, influencing the grid's stability, energy management, and environmental sustainability. Smart charging solutions emerge as a crucial mechanism to address these challenges by optimizing charging patterns, balancing grid demand, and maximizing renewable energy utilization. This abstract delves into the impacts of EV-grid integration, exploring the interplay between smart charging solutions and grid dynamics while considering the implications for energy infrastructure, user behavior, and environmental impact. It also provides a concise overview of EV grid integration's key issues. It highlights the crucial role of smart charging technologies in addressing them and paving the way for a sustainable future of transportation and energy.
Machine Learning Methodologies for Electric Vehicle Energy Management Strategies Md Khaledur Rahman; Md Saiful Islam; Md Jakaria Talukder; Md Nazmul Islam; Md Shameem Ahsan
BULLET : Jurnal Multidisiplin Ilmu Vol. 4 No. 1 (2025): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

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.
Co-Authors Faysal Amin Tanvir Manam Ahmed Md Jakaria Talukder Md Khaledur Rahman Md Nazmul Islam Md Shameem Ahsan Md Shameem Ahsan