An Intelligent EV Charging Station Recommendation System Using Machine Learning and Real-Time Data Analytics

Abstract

The rapid adoption of electric vehicles (EVs) has significantly transformed the transportation sector, offering a sustainable alternative to conventional fuel-based vehicles. However, the limited availability of charging infrastructure and inefficient utilization of existing charging stations pose significant challenges for EV users. This research proposes an intelligent EV charging station recommendation system that leverages machine learning and real-time data analytics to optimize charging station selection.The proposed system aims to assist EV users in identifying the most suitable charging station based on multiple parameters such as distance, waiting time, charging cost, battery level, and station availability. Traditional navigation systems primarily focus on distance-based routing and fail to incorporate dynamic factors such as congestion at charging stations and energy demand fluctuations.The system is developed using Python and deployed through a Django-based web application. It integrates real-time data from charging stations and user inputs to provide personalized recommendations. The system also ensures compatibility with modern machine learning libraries by addressing version conflicts, particularly between NumPy and TensorFlow.Machine learning models are used to predict charging station availability and waiting times based on historical usage patterns. These predictions are combined with optimization algorithms to recommend the best charging station. The system dynamically updates recommendations as conditions change, ensuring accurate and efficient decision-making.The proposed framework improves user convenience by reducing waiting time and optimizing energy consumption. It also contributes to efficient utilization of charging infrastructure, thereby supporting the scalability of EV ecosystems.Performance evaluation demonstrates that the system effectively reduces charging delays and enhances route efficiency compared to traditional methods. The integration of real-time data and predictive analytics ensures robust performance under varying conditions.This research contributes to the development of smart transportation systems by providing an intelligent and adaptive solution for EV charging station recommendation. Future work may involve integrating advanced technologies such as IoT sensors, edge computing, and reinforcement learning to further enhance system performance.