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@article{171314,
        author = {Harsh Kumar},
        title = {Predictive Modeling and Fault Detection of Thermal Runaway in Lithium-Ion Batteries},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {3889-3892},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171314},
        abstract = {Lithium-ion batteries (LIBs) are critical for modern energy applications, such as electric vehicles (EVs) and renewable energy systems. However, their vulnerability to thermal runaway (TR)—a self-sustaining thermal failure—poses significant safety challenges. This research introduces a hybrid predictive framework combining physics-based thermal modeling and machine learning (ML) techniques. The Bernardi equation simulated heat dynamics, while Random Forest and XGBoost classified multi-sensor data to detect TR risks. The XGBoost model achieved 95.1% accuracy with a time-to-fault prediction error of ±5 seconds. Multi-sensor fusion of temperature, voltage, and state of charge (SOC) data enhanced detection accuracy by 10%. These findings underscore the potential of integrating predictive models into battery management systems (BMS) to improve LIB safety and reliability.},
        keywords = {Lithium-Ion Batteries, Fault Detection, State of Health (SOH), Remaining Useful Life (RUL), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Hybrid Models, Probabilistic Models, Battery Management Systems (BMS), Thermal Runaway, State of Charge (SOC), Anomaly Detection, Data-Driven Techniques, Physics-Based Modeling, Predictive Maintenance, Energy Storage Systems, Electric Vehicles (EVs).},
        month = {January},
        }