Citation:

Hari Prasad Bhupathi, Srikiran Chinta , 2021. "Integrating AI with Renewable Energy for EV Charging: Developing Systems That Optimize the Use of Solar or Wind Energy for EV Charging", ESP Journal of Engineering & Technology Advancements 1(2): 260-271.

Abstract:

The integration of Electric Vehicles (EVs) with renewable energy sources such as solar and wind presents a promising approach to achieving sustainable transportation and energy solutions. However, the intermittent nature of renewable energy poses challenges for the optimal use of these sources for EV charging. This research explores the role of Artificial Intelligence (AI) in optimizing the charging process by forecasting renewable energy availability, managing energy storage, and dynamically adjusting charging schedules to minimize costs and energy wastage. Using advanced machine learning algorithms and optimization models, the study aims to develop an intelligent system that efficiently integrates renewable energy sources with EV charging stations. The proposed AI-driven system will improve grid stability, enhance the economic viability of renewable energy integration, and contribute to a greener, more sustainable transportation ecosystem. Results from simulations will demonstrate the advantages of AI integration in reducing dependency on non-renewable energy sources and minimizing the carbon footprint of EV charging infrastructure.

References:

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Keywords:

Artificial Intelligence (AI), Renewable Energy, Electric Vehicles (EVs), Solar Energy, Wind Energy, Energy Storage, Optimization, Machine Learning Charging Infrastructure, Energy Management, Grid Stability, Sustainability.