Issue |
E3S Web of Conf.
Volume 540, 2024
1st International Conference on Power and Energy Systems (ICPES 2023)
|
|
---|---|---|
Article Number | 02023 | |
Number of page(s) | 9 | |
Section | Electric Drives and Vehicles | |
DOI | https://doi.org/10.1051/e3sconf/202454002023 | |
Published online | 21 June 2024 |
Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids
1 Assistant Professor, School of Business and Management, CHRIST (Deemed to be University) Bangalore Yeshwantpur Campus, India .
2 Assistant Professor, School of Business and Management, CHRIST (Deemed to be University ) Bangalore Yeshwantpur Campus, India .
3 The Islamic university, Najaf, Iraq .
4 Department of Computing Sciences Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun-248007, India .
5 Department of Computer Science & Engineering, IES College of Technology, IES University, Madhya Pradesh 462044 India, Bhopal .
6 Researcher, Yashika Journal Publications Pvt Ltd, India Email: sharyu.ikhar@gmail.com, Wardha, Maharashtra .
1 Corresponding Author :sharon.sophia@christuniversity.in
2 david.winster@christuniversity.in
3 kassem.alattabi@iunajaf.edu.iq
4 sheela.bijlwan@gmail.com
5 research@iesbpl.ac.in
The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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