Citation:

Hari Prasad Bhupathi, Srikiran Chinta, 2022. "Smart Charging Revolution: AI and ML Strategies for Efficient EV Battery Use", ESP Journal of Engineering & Technology Advancements 2(2): 154-167.

Abstract:

The rapid growth of electric vehicles (EVs) presents both an opportunity and a challenge in terms of efficient battery management and sustainable energy use. As the demand for EVs accelerates, the need for intelligent and adaptive charging systems becomes critical to ensure the longevity of batteries and optimize the integration of EVs with energy grids. This paper explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing EV charging infrastructure and battery management. By leveraging advanced algorithms, predictive models, and real-time data analytics, AI and ML can significantly enhance charging efficiency, reduce battery degradation, and optimize energy consumption. Key strategies include AI-driven charging schedules that adapt to user behavior, predictive maintenance algorithms for battery health monitoring, and intelligent integration with renewable energy sources. Furthermore, this paper delves into the use of Machine Learning for dynamic load management, demand response, and the advancement of Vehicle-to-Grid (V2G) technologies, offering a promising pathway to more sustainable, cost-effective, and energy-efficient EV charging ecosystems. The integration of AI and ML not only improves battery lifespan and performance but also contributes to the stability and optimization of the power grid, paving the way for a future of smarter, greener transportation. This paper also identifies the challenges and limitations in adopting AI-driven charging solutions, including computational demands, data privacy concerns, and infrastructure scalability, while proposing potential solutions for overcoming these barriers. In conclusion, AI and ML represent a pivotal shift in the way EVs are charged and managed, marking the dawn of the "smart charging revolution."

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Keywords:

Electric Vehicles (EVs), Smart Charging, Artificial Intelligence (AI), Machine Learning (ML), Battery Management, Predictive Algorithms, Energy Optimization, Vehicle-to-Grid (V2G).