Data Analytics for IoT-Driven EV Battery Health Monitoring

The health and longevity of batteries are critical to the performance of electric vehicles (EVs). As the adoption of EVs increases globally, it is essential to develop efficient systems for monitoring and maintaining the health of EV batteries. Traditional methods for battery monitoring are often static and lack real-time capabilities, making them less effective in detecting potential failures early. This paper explores the use of Internet of Things (IoT) technology combined with data analytics to create a dynamic, real-time system for monitoring EV battery health. By leveraging sensor data from EVs, such as voltage, current, temperature, and state of charge (SOC), and applying advanced data analytics techniques, this system provides a comprehensive solution for early failure detection and performance optimization. We propose a framework that integrates IoT-enabled battery monitoring systems with machine learning algorithms to predict battery degradation and remaining useful life (RUL). Through experimental results, we demonstrate that the IoT-based system improves battery health prediction accuracy, enhances operational efficiency, and extends the lifespan of EV batteries. Furthermore, we discuss the scalability of this system and its potential for integration with existing EV infrastructures.