A smart mobility web application that helps Electric Vehicle (EV) users discover nearby charging stations, check availability, and select the most efficient station using AI-assisted logic.
Built as an MCA Final Year Project using Python, AI concepts, and Web Technologies.
- Project Overview
- Problem Statement
- Key Features
- Tech Stack
- System Workflow
- AI Recommendation Logic
- Screenshots
- Project Structure
- Demo Credentials
- Future Enhancements
- Academic Relevance
- License
- Author
EVision is an AI-powered EV charging station tracker designed to solve common problems faced by EV users, such as long waiting times, unclear station availability, and inefficient charger selection.
The system combines interactive maps, smart filters, and an AI-based recommendation engine to assist users in making fast and informed charging decisions.
EV users often face:
- Uncertainty about charging station availability
- Long waiting times at busy stations
- Difficulty choosing between AC and DC chargers
- Lack of intelligent station recommendations
EVision addresses these issues by providing a smart, data-driven charging station selection system.
- 🔍 Nearby EV charging station discovery
- 🗺️ Live interactive map (OpenStreetMap)
- 🟢 Real-time availability indicators
- ⚡ AC / DC charger identification
- 🤖 AI-assisted smart station recommendation (prototype)
- 🎛️ Distance, charger type, and demand filters
- 🌙 Modern dark-themed UI
- 🔐 Demo login system
- HTML5
- CSS3
- JavaScript (ES6)
- Leaflet.js
- OpenStreetMap
- Python
- Flask / Django
- Pandas
- NumPy
- Scikit-learn
- User Location ↓
- Nearby EV Station Data ↓
- AI Scoring Logic ↓
- Best Station Recommendation ↓
- Map Visualization & Station Highlight
Each charging station is assigned a score based on:
- Distance from the user
- Estimated waiting time
- Charger speed (DC preferred over AC)
- Predicted demand level
A smart and user-friendly EV Charging Station Tracker built to help users locate nearby charging stations, apply filters, and get AI-based recommendations for the best charging option.
- 🔐 Secure Login System
- 🗺️ Interactive Dashboard with station overview
- 🤖 AI-based Charging Station Recommendation
- 🎛️ Filters by charger type, availability, and demand
- 🌗 Clean UI (Light/Dark friendly design)
- 📍 Location-based results (sample dataset)
- Frontend: HTML, CSS, JavaScript
- Backend / Logic: Python
- AI Logic: Rule-based recommendation system
- Data: Sample EV station dataset
- User logs in securely
- Dashboard displays nearby EV charging stations
- Filters help narrow down the best option
- AI logic scores stations based on availability & demand
- Best station is highlighted for the user
- Python backend integration
- Real EV station API integration
- Machine learning-based waiting time prediction
- Google Maps routing and navigation
- User analytics and history
- Mobile application support
This project demonstrates:
- Artificial Intelligence decision logic
- Python-ready backend architecture
- Smart mobility system design
- Real-world problem-solving approach
Suitable for:
- MCA Final Year Project
- AI / ML Academic Demonstration
- Smart City & EV Research
This project is developed for educational and research purposes only.
SKY
MCA Final Year Student
AI • Python • Smart Mobility