Predictive Maintenance of Electric Vehicle Components Using IoT Sensors

The increasing global adoption of electric vehicles (EVs) has placed significant emphasis on ensuring their long-term reliability, safety, and performance. Traditional maintenance approaches, such as time-based and reactive methods, often result in unnecessary costs, unplanned downtimes, and safety risks. With the rapid advancement of the Internet of Things (IoT), predictive maintenance has emerged as a transformative strategy to proactively monitor and optimize EV component health. This paper presents an IoT-enabled predictive maintenance framework for critical EV subsystems, including batteries, motors, braking units, and power electronics. By integrating real-time sensor data streams, such as temperature, vibration, voltage, and current, with cloud-based analytics and machine learning models, the proposed system enables the early detection of anomalies and the prediction of component failures before they occur. The study emphasizes data acquisition, feature extraction, and predictive modeling, while also addressing challenges related to sensor accuracy, data integration, cybersecurity, and scalability in large-scale EV fleets. A simulation-driven evaluation demonstrates the potential for reducing operational costs, improving safety, and extending component lifespans. This work highlights how IoT-driven predictive maintenance can enhance the resilience of EVs, contributing to sustainable transportation systems and supporting global transitions toward electrified mobility.