A high-fidelity monitoring and analytics dashboard for battery health, built with React (Vite) and FastAPI. This portal provides deep engineering insights into battery performance across its entire lifecycle.
- Voltage Profile: Real-time telemetry visualization with automated limit violation detection (V_MAX/V_MIN).
- Current & C-Rate: Dynamic current tracking and automatic C-Rate calculation based on nominal capacity.
- Internal Resistance (DCIR): Advanced trend analysis using a robust DCIR fallback algorithm (ΔV/ΔI) to track aging even when raw IR data is missing.
- State of Health (SOH): Industry-standard health scoring based on discharge capacity normalization.
- EOL Prediction: Automatic detection of End-of-Life (80%) thresholds with explicit cycle marking.
- Health Status Classification: Real-time binning into Healthy, Warning, and Critical states based on degradation trends.
- Frontend: React, Vite, Recharts (Data Visualization), CSS3 (Modern Glassmorphism Design).
- Backend: FastAPI (Python), NumPy (Data Processing), Pickle (Data Interchange).
- Data Source: High-frequency
.pklbattery telemetry files.
- Navigate to the backend directory:
cd backend - Create and activate a virtual environment:
python -m venv venv .\venv\Scripts\activate
- Install dependencies:
pip install fastapi uvicorn numpy pandas
- Start the server:
uvicorn app.main:app --reload
- Navigate to the root directory:
cd .. - Install dependencies:
npm install
- Start the development server:
npm run dev
The portal uses advanced data processing techniques:
- Extrema-Preserving Downsampling: Ensures peaks and spikes are never lost in visualization while maintaining UI performance.
- Natural Numeric Sorting: Correctly maps cycle indices across disparate data formats (List vs Dict).
- Medial Filtering: Used in DCIR calculations to filter out sensor noise and physical outliers.
Developed for advanced battery lifecycle monitoring and diagnostics.