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@article{197113,
        author = {Nithya R and Priya K and Srimathi G and Subiksha M U},
        title = {Design of EV Battery Monitoring along with Accident Detection System using IoT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5253-5260},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197113},
        abstract = {Electric vehicles (EVs) have become one of the key solutions of green transportation and a greenhouse gas mitigation measure. Nonetheless, it is difficult to guarantee the safety of the batteries and fast response in case of accidents. In this paper, the article gives a complex IoT-based solution, which involves real-time monitoring of EV batteries alongside automated detection of accidents and emergency responses. The system supports critical battery parameters (voltage, current, and temperature, state of charge) of 95 and 98 accuracies in addition to vehicle accidents with 98- and 11-seconds response time respectively. The hardware system is accessible through an ESP32 microcontroller that is placed with a variety of sensors such as DS18B20 temperature, voltage/current sensors, 3 axis accelerator, gadgets, GPS, and flame sensors. The system can automatically send emergency alerts like SMS and SOS calls to the emergency services and other hospitals nearby when an accident is detected. Machine learning neural nets are capable of 90-95% battery health prognostication power, which facilitates proactive maintenance and allows the threat of possible catastrophe like thermal runaway to be avoided. The experimental evidence indicates that the system is effective in improving EV-level safety, lowering the costs of maintenance, and coordinating the emergency response to the situation, which will help to eventually expand the use of a widespread solution based on sustainable transportation.},
        keywords = {Electric Vehicles, Battery Monitoring, Accident Detection, IoT, Machine Learning, Predictive Maintenance},
        month = {April},
        }