Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids

• Published in: E3S web of conferences 2024, Vol.540, p.2023
• Main Authors: J, Sharon Sophia, Winster Praveenraj, D. David, Al-Attabi, Kassem, Bijlwan, Sheela, Nagar, Mayank, Ikhar, Sharayu
• Format: Article
• Language: English
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202454002023

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.