Citation:

Hari Prasad Bhupathi, Srikiran Chinta, 2022. "Predictive Algorithms for EV Charging: AI Techniques for Battery Optimization", ESP Journal of Engineering & Technology Advancements, 2(4): 165-178.

Abstract:

The rapid adoption of Electric Vehicles (EVs) has created a critical need for optimized charging solutions that not only reduce energy costs but also extend battery lifespan. This paper proposes novel predictive algorithms utilizing Artificial Intelligence (AI) techniques to optimize EV charging, focusing on battery life preservation and energy consumption efficiency. Through the integration of machine learning, deep learning, and reinforcement learning, our model forecasts optimal charging times, adapts to dynamic grid conditions, and incorporates real-time data to enhance battery management strategies. We evaluate the proposed algorithms in both simulated and real-world environments, showing significant improvements in charging efficiency, battery longevity, and cost reduction compared to traditional charging methods. Furthermore, the model integrates seamlessly with smart grid systems, enabling better load balancing and energy distribution. This work presents a promising avenue for future EV charging infrastructure, offering a sustainable and scalable solution for large-scale EV adoption.

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Keywords:

Electric Vehicles (EV), Battery Optimization, Predictive Algorithms, Machine Learning, Deep Learning, Reinforcement Learning, Charging Efficiency, Smart Grid, Battery Management System (BMS), Energy Cost Reduction, Charging Infrastructure.