AI-Augmented Chemical Engineering Models for Predictive Battery Maintenance in IoT Applications

Abstract

This research presents a cutting-edge approach that combines chemical engineering principles with artificial intelligence (AI) to enhance battery maintenance in Internet of Things (IoT) applications. The study develops predictive maintenance models capable of accurately forecasting battery degradation, thereby optimizing usage patterns and extending battery life. By integrating electrochemical data with AI-driven predictive analytics, the project enables real-time monitoring of battery health, achieving over 90% accuracy in anticipating maintenance needs, which reduces unplanned downtime and lowers operational costs. Employing machine learning frameworks, data analysis tools, and chemical engineering simulation software, the focus is on lithium-ion and solid-state batteries, commonly used in IoT networks across sectors such as smart cities, agriculture, and healthcare. Key results include a 20−30% increase in battery lifespan and a 15% improvement in energy efficiency, significantly reducing electronic waste and environmental footprint. This interdisciplinary approach provides a foundation for sustainable battery technology in IoT technology, paving the way for future advancements in AI-augmented predictive maintenance and chemical engineering models for next-generation applications. The integration of AI with chemical engineering principles allows for the development of models that adapt to the unique electrochemical behavior of different battery types, such as lithium-ion and solid-state batteries. By analyzing lifecycle data and device usage patterns, predictive models can account for variables that influence battery performance over time, including temperature fluctuations and charging cycles. This project utilizes neural networks and reinforcement learning algorithms to train models capable of learning from historical data, enhancing their predictive capabilities. A significant aspect of this research involves the preprocessing of battery lifecycle data to ensure high data quality, which is critical for accurate model training and validation.