Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{163173,
        author = {SAMBOU KONE and SAGAR MISHRA and KUMAR RAHUL PRASAD and FAISAL SAIFI and Prof. SUNIL M P},
        title = {SMART BATTERY MANAGEMENT SYSTEM FOR PORTABLE DEVICES TO  ENHANCE BATTERY LIFE BASED ON MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2464-2469},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163173},
        abstract = {In order to enhance battery performance, this study introduces a unique smart battery management system (BMS) for portable devices that makes use of machine learning. The system makes use of a trained machine learning model to predict patterns in energy use and a data collection setup to get accurate sensor readings. By keeping an eye on user activity, battery health, and dynamically altering power allocations, the BMS attempt to prolong battery life. Temperature control prevents overheating, and an adaptive charging method prevents overcharging. With the help of customization and insights from the user interface, a complete solution to prolong the life of portable device batteries and enhance user satisfaction has been developed.},
        keywords = {SOC, ANN, ML, BATTERY MANAGEMENT SYSTEM.},
        month = {},
        }