EcoChargePathway: A Framework for Electric Vehicle Charging Optimization using Real-Time Analytics

The increasing adoption of electric vehicles (EVs) necessitates an efficient, sustainable, and cost-effective charging infrastructure. This study presents EcoChargePathway, a real-time analytics-driven optimization framework for EV charging, integrating machine learning, traffic-aware station selection, and blockchain-based reservations. The proposed framework leverages Long Short-Term Memory (LSTM) and Deep Reinforcement Learning models, achieving an energy demand forecasting accuracy of 94.5%, ensuring optimal grid utilization. Moreover, renewable energy integration within the charging stations resulted in a 52.8% reduction in grid dependency, lowering carbon emissions. The traffic-aware charging optimization reduced station waiting time by 51.3%, improving charging station accessibility. The blockchain-enabled secure slot reservation system eliminated double booking issues and reduced booking time by 68.8%, enhancing user satisfaction. The system demonstrated a cost reduction of up to 64% for users leveraging off-peak and renewable-powered charging schedules. Comparative analysis against conventional systems validates the effectiveness of EcoChargePathway in enhancing efficiency, sustainability, and reliability in EV charging networks. The results highlight the potential of AI and blockchain in optimizing EV infrastructure, ensuring a seamless charging experience while reducing environmental impact.