Smart grid integrated wireless EV charging system using machine learning

The rapid growth of electric vehicles (EVs) demands intelligent, efficient, and sustainable charging infrastructure integrated with smart grid technologies. This project presents a Smart Grid Integrated EV Charging System with Machine Learning and Power Flow Analysis, designed to enable wireless EV charging, real-time power monitoring, and intelligent decision-making. The proposed system uses wireless power transmission coils for contactless charging, where IR sensors detect vehicle presence and activate relays to initiate charging. Electrical parameters such as voltage and current are continuously monitored using sensors and transmitted to a Python-based platform for analysis. A Random Forest machine learning algorithm is employed to analyze sensor data and predict charging behavior, efficiency, and abnormal conditions. The system also integrates IoT functionality using NodeMCU, allowing real-time data upload to cloud platforms for remote monitoring. A solar-powered charging station charges a battery to support green energy utilization, while a robotic vehicle chassis represents the EV, controlled via Bluetooth. LED indicators display charging status and system states. The proposed solution demonstrates an intelligent, scalable, and eco-friendly EV charging system suitable for future smart grid applications.