Fault Detection in Electric Vehicles Using Machine Learning Techniques

The system integrates an IoT-based Battery Management System (IoT-BMS) and Firebase and Simulinkbased machine learning (ML) models to improve the reliability and efficiency of electric vehicles (EVs). A microcontroller ESP8266 records real-time battery data (voltage, current, temperature, state of charge) and stores it on Firebase for secure cloud storage. Simulink-based machine learning algorithms (AdaBoost, Gradient Boosting and Random Forest) perform processing on the data to detect and classify electrical and mechanical faults, e.g., gear faults or circuit faults. A simple web-based interface provides vital parameters like battery health and fault status in real-time. Essentially, the system enables predictive maintenance, reduces downtime, and improves EV safety and performance.