International Journal of Science, Engineering and TechnologyAuthors: Bhagyashree Bhausaheb Rajput, Mrs. Pragati Patil, Dr. Mayur Barman
Abstract: The increasing adoption of Electric Vehicles (EVs) has intensified the demand for advanced Battery Thermal Management Systems (BTMS) capable of ensuring high performance, operational safety, and extended battery lifespan under diverse driving and environmental conditions. This paper presents a novel EV battery cooling framework integrated with intelligent vehicle health monitoring and IoT-based real-time data analytics for next-generation smart mobility applications. The proposed architecture combines adaptive liquid cooling via a silicone pump, embedded multi-parameter sensing, cloud connectivity through MQTT/HTTP protocols, and machine learning-assisted predictive diagnostics. The system continuously acquires cell temperature, voltage imbalance, current flow, State of Charge (SoC), State of Health (SoH), coolant flow rate, and motor operating conditions via distributed sensors interfaced to an ESP32 microcontroller. A dynamic control algorithm regulates pump speed and fan operation proportional to thermal load, minimizing energy consumption while sustaining optimal battery temperature. Experimental evaluation demonstrates significantly reduced thermal stress, prevention of overheating events, and mitigation of thermal runaway risk. Cloud analytics generate predictive maintenance recommendations, efficiency reports, and real-time alerts accessible through mobile and web dashboards, collectively contributing to safer, smarter, and more energy-efficient electric transportation.
DOI: https://doi.org/10.5281/zenodo.20089046
