RFID Secured Intelligent Battery Management System with Predictive Analytics and Cloud Connectivity

The accelerating adoption of electric vehicles (EVs), renewable energy infrastructure, and smart-grid technologies has created a heightened demand for advanced Battery Management Systems (BMS) capable of delivering precise, secure, and intelligent control. Although traditional estimation techniques—such as Coulomb Counting and Extended Kalman Filters (EKF)—continue to be widely used due to their simplicity and real-time performance, they suffer from notable limitations. Sensor noise, model uncertainties, and progressive battery aging can significantly diminish the accuracy of State of Charge (SoC) and State of Health (SoH) assessments, ultimately impacting reliability in real-world applications like EV range prediction and energy storage diagnostics. To mitigate these challenges, an Intelligent Battery Management System (IBMS) is designed within a MATLAB/Simulink simulation framework. This system integrates machine learning (ML)-driven SoC/SoH estimators trained on synthetic battery datasets, yielding superior predictive accuracy compared to conventional methods. Enhanced security is achieved through RFID-based access control, restricting system interaction to authorized users. Furthermore, API-enabled cloud connectivity provides real-time telemetry, remote diagnostics, and predictive analytics. System operation—covering charging, discharging, idle, and fault states—is managed through Stateflow logic, ensuring robust, adaptive, and fault-tolerant behavior.

Keywords: Intelligent Battery Management System (IBMS), Battery Management System (BMS), State of Charge (SoC), State of Health (SoH), Machine Learning (ML), Electric Vehicles (EVs).