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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65585
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Computer Applications
Nehru Institute of Information Technology and Management, Coimbatore, Tamil Nadu, India

Department of Computer Applications
Nehru Institute of Information Technology and Management, Coimbatore, Tamil Nadu, India
Electric vehicles are becoming more common in India, but many users still find it hard to know how far their vehicle can go before the battery runs out, and they often struggle to find a working charging station nearby. VoltIQ is a web application built to help with these problems. It predicts how far an EV can travel by taking into account the battery level, battery health, weather, temperature, speed, terrain, traffic, and the weight of passengers and luggage. It also helps users find the best charging station near them by ranking stations based on distance, waiting time, available slots, charging speed, and current load. In addition, the application supports trip planning, slot booking, and keeps a record of past charging sessions. An admin panel is provided for managing stations and users. The backend was built using FastAPI and the frontend using Next.js 15 with React 19.
KEYWORDS
This paper described the design and implementation of VoltIQ, a web-based electric vehicle range prediction and charging station management application developed for EV users in India. The application combines a multi-factor range prediction model with station discovery, trip planning, and booking features to support users throughout their journey. The system estimates the available driving range based on battery level, battery health, weather, temperature, speed, terrain, traffic, and passenger load. Additional features such as charging station ranking, trip feasibility checking, slot booking, charging history, and an admin panel for station management provide complete support for everyday EV use. The implementation results show that the application can help users make better decisions about when and where to charge, reducing dependence on fixed manufacturer range values and manual station searches. The developed platform demonstrates how a combination of practical prediction methods and integrated features can improve the EV charging experience for users in growing markets like Tamil Nadu.
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Publication Date
6/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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