Using Machine Learning to Optimize Electric Vehicle Charging and Grid Integration
Abstract
The widespread adoption of electric vehicles (EVs) requires intelligent charging infrastructure to optimize energy use and reduce grid stress. This paper explores machine learning techniques for optimizing EV charging, including dynamic pricing models, real-time demand forecasting, and vehicle-to-grid (V2G) energy management. AI models such as reinforcement learning, deep Q-networks, and federated learning are used to enhance smart charging strategies. Case studies highlight the role of AI in balancing grid demand, reducing charging costs, and promoting the sustainability of EV adoption worldwide.
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