IoT-Based Digital Twin for Vehicle Battery Testing and Emissions Analysis

Vehicle battery failures represent a primary cause of automotive malfunctions, accounting for approximately one-third of breakdown incidents. The increasing electrification of modern vehicles, combined with advanced start-stop systems, imposes significant demands on conventional lead-acid batteries. This paper presents an IoT-based digital twin framework for real-time battery monitoring and emissions analysis. The system implements a multi-layer architecture that integrates MCU-based sensing, edge processing, mobile applications, and cloud analytics for comprehensive battery health assessment. The framework facilitates proactive failure detection while establishing correlations between battery performance and environmental impact through parametric digital twin models. Experimental results demonstrate prediction accuracy with 0.05 V mean absolute error and generate quantifiable insights into vehicle emissions using existing sensor infrastructure. Future development focuses on integrating GPS/IMU data for route-specific emission analysis and expanding the digital twin framework for advanced vehicle diagnostics.