• Atul Dwivedi
• Dr. Vijayalaxmi Biradar

The growing demand for electric vehicles (EVs) has highlighted the need for efficient energy management systems (EMS) to optimize power consumption and extend driving range. This study develops a Machine Learning-Driven EMS designed to enhance power optimization in EVs, using advanced algorithms such as regression, neural networks, and reinforcement learning. The proposed system was evaluated through simulations using MATLAB and Simulink, with performance metrics including energy efficiency, power consumption, and driving range. The results indicate a significant improvement in the overall performance of the EV compared to traditional rule-based EMS. Specifically, the proposed system achieved up to a 12% reduction in energy consumption and an 8-10% increase in driving range. Additionally, power consumption was optimized in various driving conditions, including low-load and steady-speed cycles, where traditional methods showed suboptimal performance. Sensitivity analysis revealed that key factors such as battery capacity, motor power, and driving cycles have a notable impact on the EMS performance. The findings underscore the potential of machine learning to revolutionize energy management in EVs by offering real-time adaptability and improved energy efficiency. The practical implications suggest that this EMS can contribute to more sustainable and cost-effective electric mobility solutions. Future research could further refine machine learning models and explore real-world validation and integration with smart grids.

Machine Learning, Energy Management System, Electric Vehicles, Power Optimization, Energy Efficiency, Driving Range, Regression, Neural Networks, Reinforcement Learning, MATLAB, Simulation, Sensitivity Analysis

"Development of a Machine Learning-Driven Energy Management System for Enhanced Power Optimization in Electric Vehicles", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.l55-l70, June-2023, Available :http://www.jetir.org/papers/JETIR2306B07.pdf

"Development of a Machine Learning-Driven Energy Management System for Enhanced Power Optimization in Electric Vehicles", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppl55-l70, June-2023, Available at : http://www.jetir.org/papers/JETIR2306B07.pdf