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@article{174369,
        author = {Rishitha Sagar and Raman Garg and Tanmayee Etla and R.Kundan and Galla Venkateswara  Rao and Radhika Patthi},
        title = {Lithium Batteries RUL And SOH Prediction – Literature Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4060-4068},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174369},
        abstract = {Lithium batteries have established themselves as a fundamental element of modern energy storage solutions, driving advancements in both personal electronics and electric mobility. To ensure the battery’s reliability, safety, and cost-effectiveness it is vital to accurately predict the Remaining Useful Life (RUL) and State of Health (SOH). This study provides an in-depth analysis of current literature on lithium battery RUL and SOH prediction methodologies, factors used. It explores various approaches such as data-driven models, physics-based techniques, and hybrid methods, highlighting their applications, strengths, and drawbacks. This review also highlights recent achievements in machine learning and artificial intelligence regarding battery life prediction methods. Issues face such as data accessibility, computational demands, and model generalizability, alongside recommendations for future research opportunity are provided in this paper.},
        keywords = {Lithium Battery, RUL, SOH, Lifespan prediction,},
        month = {March},
        }