AI Enabled Energy Storage System

The escalating demand for efficient energy storage solutions has led to the development of advanced battery systems. Effective management of these systems is crucial for optimal performance, longevity, and safety. Traditional rule-based methods often fall short in adapting to the dynamic and complex behavior of batteries. This paper presents an AI-enabled energy storage system that utilizes reinforcement learning to address these limitations. The system employs the Q-learning algorithm to learn optimal charging and discharging policies based on real-time battery data, including voltage, temperature, and state of charge. By dynamically adapting to changing conditions, the AI-enabled system aims to prevent overcharging, deep discharging, and thermal runaway, thereby enhancing the battery’s lifespan and overall performance. The results demonstrate the potential of AI to revolutionize battery management and contribute to a more sustainable energy future. Keywords: AI enabled ESS, Machine Learning, Battery Management System, Predictive control, Grid Integration