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@article{186770,
        author = {Lalit Ravindra and Gorde V. S. and Abhale B.A, and Mr. Ansari Tanveer Ahmed Nisar Ahmed and Mr. Desale and Mr. Bramhane Sanket Sanjay and Mr. Koli Diptesh Rajkumar},
        title = {Survey on ML-based Battery Management System for Solar Batteries},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {1992-1998},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186770},
        abstract = {A Battery Management System (BMS) is essential for maintaining the safety, reliability, and performance of rechargeable batteries by tracking key operational factors like the State of Charge (SOC) and State of Health (SOH). Traditional estimation techniques often struggle with nonlinear battery behaviour, sensor noise, and environmental variations. Machine learning (ML) techniques have recently enhanced SOC and SOH prediction by identifying patterns in both historical and real-time battery data, leading to more precise and adaptable estimation models. This enhancement is particularly impactful in solar battery storage systems, where precise energy management is essential for optimizing charge/discharge cycles, extending battery lifespan, and ensuring operational safety. Integrating ML into BMS platforms enhances reliability and enables predictive maintenance, while also supporting monitoring across systems of various sizes and capacities. Applications span from residential solar systems and electric vehicles to grid-scale energy storage and second- life battery integration. By leveraging data-driven models, ML-enhanced BMS technologies represent a transformative step toward more intelligent, sustainable, and efficient energy storage solutions},
        keywords = {Safe, Efficient, Rechargeable Batteries, Machine Learning, Sensor Noise, Solar Battery Storage, Battery Management System (BMS), State of Charge (SOC), Charge/Discharge Cycles, Battery Lifespan, State of Health (SOH).},
        month = {November},
        }