Physics Informed Models For Battery Prognostics And Health Monitoring Using Iot

Abstract

The advent of Internet of Things (IoT) technologies has opened up new avenues for advancing battery prognostics and health monitoring systems. Batteries are crucial components in various IoT applications, and ensuring their reliability and longevity is paramount. Physics-informed models, which integrate knowledge of underlying physical processes with data-driven approaches, have shown promise in accurately predicting battery performance and remaining useful life. the principles behind physics-informed modeling, review different modeling techniques, and explore their applications in predicting battery degradation, state of health (SoH), and remaining useful life (RUL). Furthermore, we highlight the integration of IoT technologies such as sensor networks, data analytics, and communication infrastructure in enabling real-time monitoring and analysis of battery health parameters. We also discuss challenges and future directions in this field, including the need for standardized datasets, robust validation methodologies, and scalable deployment strategies. Overall, this paper provides insights into the potential of physics-informed models combined with IoT for enhancing battery reliability and performance in diverse applications.