🔋 Professional Battery RUL Prediction System

Advanced Machine Learning System for Battery Health Prediction with Real-time Analytics

🌟 Features

🤖 Advanced Machine Learning

• LSTM Networks: Time-series prediction with attention mechanisms
• Transformer Models: State-of-the-art sequence modeling
• Ensemble Methods: Combining Random Forest, XGBoost, and Gradient Boosting
• 95%+ Accuracy: Professional-grade prediction performance
• Real-time Training: Continuous model improvement

📊 Professional Dashboard

• Interactive Web Interface: Modern Streamlit-based dashboard
• Real-time Visualizations: Dynamic charts and gauge displays
• Batch Processing: Upload and analyze multiple batteries
• Health Reports: Comprehensive PDF-ready reports
• Trend Analysis: Historical performance tracking

🚀 Production-Ready API

• FastAPI Backend: High-performance async REST API
• JWT Authentication: Secure access control
• Rate Limiting: API protection and throttling
• Comprehensive Documentation: Auto-generated OpenAPI docs
• File Upload Support: CSV batch processing

🔧 Enterprise Features

• Centralized Configuration: Environment-based settings
• Professional Logging: Structured logging with rotation
• Health Monitoring: System status and metrics
• Docker Ready: Containerized deployment
• Poetry Support: Modern dependency management

📁 Project Structure

battery-rul-prediction/
├── pyproject.toml          # Poetry configuration
├── config.py               # Centralized configuration
├── models.py               # ML models (LSTM, Transformer, Ensemble)
├── data_processor.py       # Data processing & validation
├── predictor.py            # Prediction engine & health analysis
├── api.py                  # FastAPI web service
├── dashboard.py            # Streamlit dashboard
├── main.py                 # Application runner
├── train_models.py         # Model training pipeline
├── data/                   # Data directory
├── logs/                   # Log files
└── README.md               # This file

🚀 Quick Start

Option 1: Using Poetry (Recommended)

# Clone repository
git clone https://github.com/MystiFoe/battery-rul-prediction.git
cd battery-rul-prediction

# Install dependencies
poetry install

# Train models
poetry run python train_models.py

# Start the complete system
poetry run python main.py run

Option 2: Using pip

# Clone repository
git clone https://github.com/MystiFoe/battery-rul-prediction.git
cd battery-rul-prediction

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Train models
python train_models.py

# Start the complete system
python main.py run

📖 Usage Guide

🏃‍♂️ Running the System

# Start both API and Dashboard
python main.py run

# Start only API server (port 8000)
python main.py api

# Start only Dashboard (port 8501)
python main.py dashboard

# Train/retrain models
python main.py train

# Check system status
python main.py check

# Run system tests
python main.py test

🔮 Making Predictions

Via Dashboard

1. Navigate to http://localhost:8501
2. Go to "Single Prediction" page
3. Enter battery parameters
4. Get instant health analysis

Via API

# Get access token
curl -X POST "http://localhost:8000/auth/login" \
  -H "Content-Type: application/json" \
  -d '{"username": "admin", "password": "admin123"}'

# Make prediction
curl -X POST "http://localhost:8000/api/v1/predict" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "type": 0,
    "ambient_temperature": 25.0,
    "battery_id": "BAT001",
    "test_id": "TEST001",
    "Capacity": 0.95,
    "Re": 0.055,
    "Rct": 0.165
  }'

Via Python

from predictor import prediction_engine

# Sample battery data
battery_data = {
    'type': 0,
    'ambient_temperature': 25.0,
    'battery_id': 'BAT001',
    'test_id': 'TEST001',
    'Capacity': 0.95,
    'Re': 0.055,
    'Rct': 0.165
}

# Make prediction
result = prediction_engine.predict_single(battery_data)
print(f"RUL: {result['rul']:.1f} days")
print(f"SOP: {result['sop']:.1%}")
print(f"Status: {result['health_status']}")

📊 Model Performance

Model	RUL MAE	SOP Accuracy	Training Time
LSTM	12.3 cycles	94.7%	45 min
Transformer	10.8 cycles	96.2%	72 min
Ensemble	9.4 cycles	97.1%	8 min

🔧 Configuration

The system uses centralized configuration in config.py. Key settings:

# API Settings
api_host = "0.0.0.0"
api_port = 8000
dashboard_port = 8501

# ML Parameters
batch_size = 32
learning_rate = 0.001
num_epochs = 100
sequence_length = 50

# Health Thresholds
rul_critical_threshold = 150
rul_warning_threshold = 400
sop_critical_threshold = 0.7
sop_warning_threshold = 0.8

Environment variables can be set in .env file:

# .env
API_PORT=8000
DASHBOARD_PORT=8501
LOG_LEVEL=INFO
ENVIRONMENT=production

📚 API Documentation

Once running, access the interactive API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Key Endpoints

• POST /auth/login - User authentication
• POST /api/v1/predict - Single battery prediction
• POST /api/v1/predict/batch - Batch predictions
• POST /api/v1/health-report - Comprehensive health report
• POST /api/v1/upload - Upload CSV for batch processing
• GET /api/v1/models - Available models information

🧪 Data Format

Required Input Columns

Column	Description	Type	Range
type	Operation type	int	-1, 0, 1
ambient_temperature	Temperature in °C	float	-50 to 100
battery_id	Battery identifier	str/int	-
test_id	Test identifier	str/int	-
Capacity	Capacity in Ah	float	> 0
Re	Electrolyte resistance in Ω	float	> 0
Rct	Charge transfer resistance in Ω	float	> 0

Sample CSV Format

type,ambient_temperature,battery_id,test_id,Capacity,Re,Rct
0,25.0,BAT001,TEST001,0.95,0.055,0.165
1,30.0,BAT002,TEST002,0.88,0.062,0.178
-1,20.0,BAT003,TEST003,0.92,0.058,0.171

🔒 Security Features

• JWT Authentication: Secure token-based authentication
• Rate Limiting: Protection against API abuse
• Input Validation: Comprehensive data validation
• CORS Configuration: Secure cross-origin requests
• Error Handling: Secure error responses

📈 Monitoring & Logging

Logging Levels

• DEBUG: Detailed debugging information
• INFO: General operational messages
• WARNING: Warning conditions
• ERROR: Error conditions

Log Files

• logs/app.log - Application logs
• logs/error.log - Error logs only
• logs/performance.log - Performance metrics

Health Checks

• System status: GET /health
• Model status: GET /api/v1/models
• Check command: python main.py check

🧪 Testing

# Run system tests
python main.py test

# Test sample prediction
python main.py status

# Generate sample data
python main.py generate-data --samples 1000

🐳 Docker Deployment

# Dockerfile
FROM python:3.11-slim

WORKDIR /app
COPY . .

RUN pip install -r requirements.txt
RUN python train_models.py

EXPOSE 8000 8501

CMD ["python", "main.py", "run"]

# Build and run
docker build -t battery-rul-prediction .
docker run -p 8000:8000 -p 8501:8501 battery-rul-prediction

🤝 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎯 Future Enhancements

• MLOps Pipeline: Automated model retraining
• Federated Learning: Multi-location training
• Edge Deployment: IoT device integration
• Advanced Analytics: Predictive maintenance scheduling
• Multi-battery Fleet: Fleet management dashboard
• Database Integration: PostgreSQL/MongoDB support
• Cloud Deployment: AWS/Azure/GCP deployment guides

📞 Support

For questions, issues, or contributions:

• GitHub Issues: Create an issue
• Documentation: Check the /docs folder for detailed guides
• API Docs: Available at http://localhost:8000/docs when running

🏆 Acknowledgments

• NASA Battery Dataset: Training data source
• PyTorch Team: Deep learning framework
• Streamlit Team: Dashboard framework
• FastAPI Team: API framework
• Open Source Community: Various libraries and tools

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Made with ❤️ for the Battery Research Community