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@article{205214,
        author = {Pruthvi Nayaka S and S G Srivani},
        title = {Implementation of AI-Based Intelligent Battery Energy Management Electric Vehicles},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5799-5806},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205214},
        abstract = {Electric Vehicles (EVs) are becoming increasingly important due to growing environmental concerns and the demand for sustainable transportation. Conventional Battery Management Systems (BMS) often struggle with nonlinear battery characteristics, temperature variations, and dynamic operating conditions. This paper presents an AI-based Battery Energy Management System (BEMS) using three machine learning algorithms—Linear Regression, Random Forest, and XGBoost—trained on a publicly available 5-year EV battery dataset covering voltage, current, temperature, State of Charge (SOC), and State of Health (SOH) parameters. After rigorous preprocessing (data cleaning, feature selection, normalization, 80/20 train-test split), all three models were evaluated using RMSE, MAE, and R² metrics. The trained model was deployed in a Python-based GUI testbench for real-time battery parameter prediction with live SOC and SOH. The results confirm that XGBoost-based ML significantly improves intelligent battery monitoring and operational efficiency in EV Battery Management Systems.},
        keywords = {Battery energy management system, electric vehicles, machine learning, random forest, state of charge, state of health, XGBoost, GUI-based testbench, lithium-ion battery},
        month = {June},
        }