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

The rapid adoption of Electric Vehicles (EVs) has intensified the need for efficient battery monitoring and predictive maintenance to ensure safety, longevity, and optimal performance. This study presents an advanced framework integrating Artificial Intelligence (AI) with Internet of Things (IoT) technology to monitor and predict EV battery health in real time. The IoT-based system employs state-of-the-art sensors to collect key battery parameters, including temperature, voltage, current, and charge cycles. Data is analyzed using AI models such as Machine Learning and deep learning algorithms to detect anomalies, forecast degradation, and optimize energy management. The proposed approach achieves high predictive accuracy, with metrics Mean Absolute Error (MAE: 0.0790), Root Mean Square Error (RMSE: 0.1040), and $\mathbf{R}^{2}$ score (0.9900). This enables early fault detection, reduces maintenance costs, and extends battery life, while improving thermal management and charging efficiency. The results demonstrate that combining IoT data acquisition with AI analytics offers a robust, data-driven solution for proactive EV battery management, enhancing efficiency, reliability, and sustainability in modern transport systems.