Citation :

Hari Prasad Bhupathi, Srikiran Chinta, 2023. "Optimizing EV Ecosystems: AI and Machine Learning in Battery Charging" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 1, Issue 3: 84-96.

Abstract :

The increasing adoption of electric vehicles (EVs) presents significant challenges and opportunities for energy management and infrastructure development. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) in optimizing EV charging ecosystems. By analyzing data from charging stations, grid loads, and user behavior, AI and ML algorithms can enhance the efficiency, reliability, and sustainability of battery charging processes. This study reviews existing models and frameworks, investigates the role of predictive analytics in demand forecasting, and examines how real-time decision-making can mitigate grid strain and improve user experience. The findings highlight the potential for intelligent charging solutions to support the transition to greener transportation while ensuring economic viability and grid stability.

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Keywords :

Electric Vehicles (EVs), Artificial Intelligence (AI), Machine Learning (ML), Battery Charging Optimization, Energy Management, Predictive Analytics, Charging Infrastructure, Smart Grids, User Behavior Analysis, Sustainability.