Publication

Abstract : Electric vehicles (EVs) are rising as one of the favourable strategies to overcome the increasing environmental concerns due to over-dependence on fossil fuels, emissions, and energy insufficiency. This work aims to design and develop a software framework to contribute to the EV charging infrastructure. The proposed work includes (1) creating front-end software for charging station operators and owners for managing and operating charging points, (2) developing cloud-based intelligent models for region prediction, price prediction, station filtering, and slot booking, (3) designing slot scheduling algorithms for optimal utilization of the charging station. Python and software tools like Streamlit, SQL, and Flask facilitate the implementation of the front-end software, database, and computational algorithms. The web application, built using the Streamlit Python framework, acts as the user interface for all stakeholders. The work utilizes the Linear Search Algorithm and the Binary Search Algorithm to filter stations based on the user requirements, such as distance and price range, which users indicate through the web application. The Gradient Boosting approach gives good results for region prediction for the chosen dataset of EV charging stations. In this work, a priority scheduling approach considering the time-of-use price achieves EV charging slot allocation. The software framework provides a comprehensive electric vehicle charging infrastructure where users can easily find the best charging station and reserve a spot for charging. The work's future extension can include the addition of functionalities for Vehicle-to-vehicle and Vehicle-to-grid.