Abstract
Predicting accurately battery life is crucial for applications ranging from electric vehicles and energy storage to emerging technologies in the context of Industry 4.0. The Internet of Things (IoT) has witnessed significant growth, permeating diverse domains. The burgeoning electronics industry has fuelled the demand for portable devices, with IoT devices facing a considerable challenge in ensuring reliable and prolonged battery life. Rechargeable batteries, such as Li-ion systems, are preferred for their high energy density and extended cycle life, aligning with the increasing demand for miniature wireless devices. This work presents a proof-of-concept, employing an optimised Long Short-Term Memory (LSTM) recurrent neural network model to forecast battery life in IoT systems. Utilising publicly available lab datasets and controlled conditions, the results are bench-marked against state-of-the-art models, showcasing the superior performance of the proposed approach. This methodology addresses the critical concern of IoT device battery life, thereby enhancing the reliability and longevity of IoT applications.
Autori
Vanessa Verrina, Andrea Vennera, Annarita Renda
