Battery Digital Shadow is an AI-powered battery monitoring and analytics platform that creates a virtual representation of battery systems using real-time and historical data. It predicts key battery metrics such as State of Charge (SOC), State of Health (SOH), and Remaining Useful Life (RUL), while providing interactive dashboards for battery performance monitoring, anomaly detection, and predictive maintenance.
- Real-time battery KPI monitoring
- State of Charge (SOC) prediction
- State of Health (SOH) estimation
- Remaining Useful Life (RUL) forecasting
- Interactive dashboard with charts and analytics
- Battery degradation trend analysis
- Predictive maintenance insights
- Scalable architecture for future Digital Twin integration
- Python – Core development
- Streamlit – Interactive dashboard UI
- Plotly – Data visualization
- Pandas / NumPy – Data processing
- Scikit-learn / ML Models – Predictive analytics
battery-digital-shadow/
│── app.py
│── data/
│── models/
│── notebooks/
│── assets/
│── requirements.txt
│── README.mdgit clone https://github.com/NanSha06/battery-digital-shadow.git
cd battery-digital-shadowpython -m venv venv
source venv/bin/activate # Linux / Mac
venv\Scripts\activate # Windowspip install -r requirements.txtstreamlit run app.pyThe dashboard provides:
- Battery health scorecards
- SOC, SOH, RUL metrics
- Interactive time-series graphs
- Degradation trends
- Performance monitoring KPIs
- Predictive alerts
- Electric Vehicles (EVs)
- Energy Storage Systems
- Telecom Backup Batteries
- Industrial Power Systems
- IoT Devices
- Smart Battery Monitoring
- Real-time IoT sensor integration
- Advanced deep learning forecasting
- Battery anomaly root cause analysis
- Multi-battery fleet monitoring
- Full Digital Twin simulation engine
To transform raw battery data into intelligent insights for better battery lifespan, safety, reliability, and operational efficiency.
Nandini Sharma Data Science & AI Enthusiast