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EV Charging Scheduler - Quick Start Guide

🚀 Get Started in 5 Minutes

Prerequisites

Before you begin, ensure you have installed:

Step 1: Clone or Navigate to Project

cd "C:\Users\patil\OneDrive\Desktop\Projects\Minor Project\ev-charging-scheduler"

Step 2: Setup Backend

2.1 Create Virtual Environment

Windows:

cd backend
python -m venv venv
venv\Scripts\activate

macOS/Linux:

cd backend
python3 -m venv venv
source venv/bin/activate

2.2 Install Dependencies

pip install -r requirements.txt

2.3 Generate Synthetic Data

python generate_data.py

Expected output:

✓ Saved charging_stations.csv with 50 records
✓ Saved ev_demand_patterns.csv with 10000 records
✓ Saved grid_load_data.csv with 8760 records
✓ Saved energy_pricing.csv with 2190 records
✓ Saved ev_specifications.csv with 8 records

2.4 Train ML Models

python train_models.py

Expected output:

Training Demand Prediction Model...
  Train R² Score: 0.xx
  Test R² Score: 0.xx
  ✓ Saved demand_predictor.pkl

Training Range Prediction Model...
  Train R² Score: 0.xx
  Test R² Score: 0.xx
  ✓ Saved range_predictor.pkl

Training Grid Load Forecasting Model...
  Train R² Score: 0.xx
  Test R² Score: 0.xx
  ✓ Saved grid_load_forecaster.pkl

2.5 Start Backend Server

uvicorn main:app --reload

Backend will be running at: http://localhost:8000

API Documentation: http://localhost:8000/docs

Step 3: Setup Frontend

3.1 Open New Terminal

Open a new terminal/command prompt window.

3.2 Navigate to Frontend

cd frontend

3.3 Install Dependencies

npm install

3.4 Start Development Server

npm run dev

Frontend will be running at: http://localhost:5173

Step 4: Test the Application

4.1 Open Browser

Navigate to: http://localhost:5173

4.2 Explore Features

  1. 3D Map View

    • Rotate, zoom, and pan the 3D terrain
    • Click on charging station markers
    • Watch animated EVs moving through the terrain

  2. Range Predictor

    • Select a vehicle model
    • Adjust battery level slider
    • Enter battery capacity
    • Select driving conditions
    • Click "Predict Range"

  3. Grid Dashboard

    • View current grid load
    • Check 6-hour forecast chart
    • See charging recommendations

📋 Common Issues & Solutions

Backend Issues

Port 8000 already in use:

uvicorn main:app --reload --port 8001

Module not found:

pip install -r requirements.txt --upgrade

ML models not loading:

python train_models.py

Frontend Issues

Port 5173 already in use:

npm run dev -- --port 5174

Dependencies not installing:

npm cache clean --force
npm install

API connection errors:

  • Ensure backend is running
  • Check .env file has correct API URL
  • Check browser console for errors

🎯 Testing API Endpoints

Using cURL

Get all stations:

curl http://localhost:8000/api/stations

Predict range:

curl -X POST http://localhost:8000/api/predict/range \
  -H "Content-Type: application/json" \
  -d "{\"battery_level\": 80, \"battery_capacity\": 60, \"vehicle_model\": \"Tesla Model 3\", \"driving_conditions\": \"normal\"}"

Get grid load:

curl http://localhost:8000/api/grid/load

Using API Documentation

  1. Open http://localhost:8000/docs
  2. Click on any endpoint
  3. Click "Try it out"
  4. Fill in parameters
  5. Click "Execute"

📁 Project Structure Quick Reference

ev-charging-scheduler/
├── backend/              # FastAPI server
│   ├── main.py          # Main application
│   ├── api/routes.py    # API endpoints
│   ├── generate_data.py # Data generation
│   ├── train_models.py  # Model training
│   └── ml_models/       # Trained models
├── frontend/            # React app
│   ├── src/
│   │   ├── App.jsx     # Main component
│   │   ├── components/ # UI components
│   │   └── scenes/     # 3D scenes
│   └── package.json
└── data/               # Datasets

Stopping the Servers

Backend: Press Ctrl + C in the terminal

Frontend: Press Ctrl + C in the terminal

📝 Next Steps

  1. Explore the Code

    • Read docs/PROJECT_DOCUMENTATION.md
    • Check API endpoints at /docs
    • Review ML model training script

  2. Customize

    • Add more charging stations
    • Modify 3D scene elements
    • Adjust ML model parameters

  3. Deploy

    • Frontend → Vercel
    • Backend → Render
    • See deployment guide in documentation

🆘 Getting Help

  • Check docs/PROJECT_DOCUMENTATION.md for detailed documentation
  • Review API docs at http://localhost:8000/docs
  • Check console logs for errors
  • Verify all dependencies are installed

✅ Success Checklist

  • Backend server running on port 8000
  • Frontend server running on port 5173
  • 3D map displays charging stations
  • Range predictor returns results
  • Grid dashboard shows data
  • No console errors

Happy Coding! 🚀⚡