Streamlining EV Travel with a Smart, Integrated Charging Network GRID UNITY

Abstract

India’s electric vehicle (EV) market is expanding quickly, but drivers still struggle with a fragmented charging landscape. Because each operator runs its own app, users often juggle multiple applications just to locate and book charging slots—an especially painful problem on long trips. This project proposes a single, unified web and mobile platform that connects charging stations from numerous providers. It will show live slot availability, enable quick booking, and support route planning by surfacing suitable charge points along the journey. Using provider APIs, the system aggregates real-time data, and can apply machine learning to forecast station demand from historical patterns—improving planning and reducing queues. The overarching aim is to make EV travel in India simpler and more dependable through one smart, integrated charging experience.

Introduction

India’s rapid EV adoption—driven by environmental goals, supportive policies, and battery advances—is hindered by fragmented charging infrastructure. Multiple charging providers operate in isolated ecosystems, forcing drivers to use several apps to locate, access, and reserve stations. This fragmentation creates uncertainty, delays, and poor user experience, especially for intercity travel, ultimately slowing broader EV uptake.

The proposed work addresses this gap through a unified web and mobile platform that integrates multiple charging networks via APIs. The platform offers real-time station availability, seamless booking, route planning with charging stops, and predictive demand forecasting to reduce waiting times and improve reliability. Built using Python, Django, and Django REST Framework, it leverages machine learning, deep learning, clustering, optimization, and neuro-fuzzy logic to forecast demand, optimize routing and pricing, balance grid loads, and support sustainable energy use.

Grounded in prior research on smart grids, AI-driven demand forecasting, and user-centered platform design, the solution responds directly to limitations identified in existing studies—such as static data, computational complexity, limited real-world deployment, and lack of interoperability. While challenges remain around data availability, scalability, integration standards, cybersecurity, and user behavior modeling, the work highlights unified digital platforms and predictive analytics as essential to creating a convenient, efficient, and sustainable EV charging ecosystem in India.

Conclusion

A robust charging infrastructure is central to accelerating EV adoption and achieving sustainability goals. Unified, smart platforms improve convenience and make long journeys practical. Advances in ML and optimization sharpen demand forecasting, pricing, and load management, while renewable integration supports grid health and cuts emissions. Nonetheless, fragmentation, compute constraints, security, and regulatory complexity persist. Addressing data breadth, real-world validation, and interoperability—supported by strong public-private collaboration—will be critical to scaling reliable, user-centric charging systems. In short, integrating multiple networks into one coherent platform is essential to elevate the EV user experience and speed the transition to clean transport.

References

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Copyright

Copyright © 2026 Sindhu M P, Abhishek M S, Anandhu Suresh, Arjun Suresh, Anand Raj V A. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.