PREDICTIVE BATTERY HEALTH MANAGEMENT USING DEEP LEARNING IN A CLOUD-INTEGRATED WEB FRAMEWORK

Abstract

The increasing reliance on battery-based energy systems in electric vehicles, renewable energy storage, and portable electronics necessitates intelligent and scalable battery health management solutions. This paper presents a predictive battery health management framework using deep learning within a cloudintegrated web environment. The proposed system leverages time-series battery data, including voltage, current, temperature, and charge–discharge profiles, to accurately estimate key health indicators such as State of Health and Remaining Useful Life. Advanced deep learning models are employed to capture complex nonlinear degradation patterns and long-term temporal dependencies that conventional approaches fail to address. Cloud integration enables centralized data processing, model training, and continuous performance updates, while the web-based interface provides real-time monitoring, visualization, and decision support. Experimental evaluation demonstrates improved prediction accuracy and early detection of abnormal battery behavior, supporting proactive maintenance and enhanced operational safety. The framework offers a scalable and practical solution for intelligent battery management in modern energy and mobility applications