Copyright © 2026 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{206215,
        author = {Aditya Appa Devkule and Prof. H. M. Mallad},
        title = {AI Based Battery Management System For Predictive Maintenance And Operational Efficiency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {610-612},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206215},
        abstract = {The Battery Management System (BMS) is a core part of electric vehicles (EVs) because it keeps the battery running safely and reliably. In this paper, we present a monitoring and fire prevention system that tracks key parameters like state of charge (SOC), voltage, current, and temperature. Our setup aims to stop overcharging, deep discharging, and overheating—issues that shorten battery life and cause dangerous hazards. By connecting the system to the Internet of Things (IoT), we can log battery data in real time and catch faults early. We also included a predictive maintenance model using basic machine learning to estimate SOC and battery health, helping us spot potential failures before they happen. If the battery gets too hot, the hardware instantly cuts off the power and triggers an alarm. As more people switch to EVs, building smarter, connected BMS solutions is becoming incredibly important for everyday safety.},
        keywords = {Artificial intelligence, Battery management system, Electric vehicles, Internet of things, State of charge, Temperature monitoring.},
        month = {July},
        }