Enhancing IoT-Enabled Electric Vehicle Battery Performance Monitoring and Prediction using AI Techniques

Strong battery management systems (BMS) are required to guarantee the best possible performance, safety, and longevity of battery packs due to the quick development of electric vehicles (EVs). This study introduces an Internet of Things (IoT)-enabled battery performance monitoring and control system that uses artificial intelligence (AI) methods to improve EV batteries' dependability and efficiency. In order to gather data on vital battery parameters like voltage, current, temperature, and state of charge (SoC) in real time, the suggested system combines a number of sensors. Through the use of a Wi-Fi communication module, this data is sent to a cloud-based platform for analysis, where artificial intelligence (AI) algorithms more precisely, a machine learning model are used to accurately forecast possible faults and monitor battery health. The technology maximizes battery life and energy efficiency by optimizing charging and discharging cycles through the use of predictive analytics. In order to prevent critical failures and guarantee safe operation, the system is also built to deliver alerts when battery characteristics surpass safe thresholds. The usefulness of the suggested strategy and its potential for use in smart EV technologies in the future are validated by simulation results showing notable increases in battery performance.