A Web Application Framework for Battery Health Prediction in Industrial IoT Networks

This study presents a web engineering architecture for predictive battery health management in industrial IoT environments. The proposed framework leverages a scalable web-based platform that integrates data streams, web services, and machine learning modules to estimate the state of charge (SOC) of primary lithium batteries. These batteries are critical for long-term device reliability in applications such as gas advanced metering infrastructure (AMI) networks. To overcome challenges associated with flat discharge profiles and data sparsity, the framework incorporates web-enabled data processing, online augmentation techniques (e.g., CutMix), and adaptive learning models. A key contribution of this work is the design of a modular web application layer compliant with oneM2M standards and RESTful interfaces. It includes components for real-time monitoring, automated model updates, and secure service orchestration using technologies such as HTTP bindings. This architecture not only enables accurate SOC estimation without additional hardware but also demonstrates the critical role of web engineering in ensuring system scalability, security, and integration across heterogeneous IoT devices. Experimental validation in AMI systems confirms the effectiveness of the approach, which is extensible to broader domains such as smart utilities, environmental sensing, and industrial automation.