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@article{171826,
        author = {HARSH KUMAR},
        title = {Advancing Fault Detection in Lithium-Ion Battery},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1046-1049},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171826},
        abstract = {Lithium-ion batteries have become indispensable in numerous applications, including electric vehicles, renewable energy systems, and portable electronics, due to their high energy density, long cycle life, and lightweight construction. However, their widespread adoption has introduced challenges related to safety, reliability, and operational efficiency. Advanced fault detection techniques leveraging artificial intelligence (AI), machine learning (ML), and hybrid approaches are emerging as transformative tools for addressing these issues. This paper reviews the state-of-the-art in fault detection and health monitoring systems for lithium-ion batteries, with an emphasis on AI-driven innovations, key methodologies, major findings, and research gaps. Future directions for advancing this critical field are also discussed.},
        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},
        }