🌐 Live Demo: https://sdg-hackthon.vercel.app/
🔗 Backend API: https://sdg-hackathon.onrender.com/
📦 Repository: https://github.com/pulkittaneja09/sdg-hackthon
🌍 Problem Statement
With the rapid growth of EVs and energy storage systems, millions of lithium-ion batteries are approaching end-of-life.
However, “end-of-life” for EV use does not mean unusable.
Most retired batteries:
Still retain usable capacity
Can be repurposed for lower-load applications
Can reduce environmental waste significantly
The challenge is:
❌ No intelligent system exists to evaluate battery reuse potential from raw telemetry data.
💡 Our Solution
SCRAP 2 SPARK (S2S) is an AI-powered battery evaluation platform that:
📊 Analyzes battery telemetry (CSV upload)
🔮 Predicts Remaining Useful Life (RUL)
♻️ Recommends reuse vs recycling
🌱 Calculates sustainability impact
📈 Visualizes degradation trends
All in one unified dashboard.
🏗 System Architecture Frontend (Vite + React + TypeScript) ↓ FastAPI Backend (Python) ↓ ML Model (Random Forest Regressor) ↓ Deployment Engine + Sustainability Engine 🔬 Core Features 1️⃣ CSV Telemetry Upload
Upload structured battery telemetry including:
Cycle count
Capacity
Voltage
Current
Temperature
Time
2️⃣ Remaining Useful Life (RUL) Prediction
Machine learning model predicts:
Expected remaining cycles
Confidence score
Degradation rate
3️⃣ Deployment Recommendation Engine
Based on predicted RUL:
Grade Recommendation A High-load reuse B Medium-load storage C Low-load backup D Recycling recommended 4️⃣ Risk Assessment
Evaluates:
Thermal stress
Degradation speed
Voltage instability
Outputs:
Low / Moderate / High Risk
5️⃣ Sustainability Impact Analysis
Calculates:
♻️ Usable energy saved (kWh)
🌍 CO₂ emissions reduced (kg)
🔋 Lithium preserved (kg)
🌳 Tree equivalent impact
6️⃣ Advanced Data Visualizations
Capacity degradation trend
State-of-health curve
Voltage curve per cycle
Temperature stress trend
RUL Gauge
Risk Gauge
🧠 Machine Learning Model
Algorithm: Random Forest Regressor
Feature Engineering:
Capacity fade slope
Voltage decay pattern
Temperature variance
Current stability
Output:
Predicted RUL
Confidence score
⚙️ Local Setup Backend cd backend pip install -r requirements.txt python -m uvicorn main:app --reload
Runs at:
http://localhost:8000 Frontend cd frontend npm install npm run dev
Runs at:
http://localhost:5173 🌐 Production Deployment Frontend:
Hosted on Vercel
Backend:
Hosted on Render
Environment Variable (Vercel):
VITE_API_URL=https://sdg-hackathon.onrender.com
🎯 Intended Users
This platform is built for:
EV manufacturers
Battery recycling companies
Energy storage providers
Sustainability analytics firms
Circular economy startups
Not for individual consumers — but for industrial battery evaluation pipelines.
📊 Sample Use Case
EV battery reaches end-of-life.
Manufacturer uploads telemetry CSV.
S2S analyzes degradation pattern.
System recommends:
Reuse for grid storage
OR recycle responsibly
Sustainability metrics calculated.
🚀 Why This Matters
Reduces lithium mining demand
Cuts carbon emissions
Extends battery lifecycle
Enables circular energy economy
Supports UN SDG Goals
🔥 Innovation Highlights
End-to-end AI + Deployment engine
Production-grade full-stack architecture
Real-time visualization dashboard
Sustainability scoring layer
Risk-aware decision system
👨💻 Tech Stack
Frontend
React
TypeScript
Vite
Tailwind CSS
Axios
Backend
FastAPI
Pandas
Scikit-learn
Uvicorn
Deployment
Vercel
Render
🏆 Future Improvements
Multi-battery batch analysis
API authentication layer
Battery passport integration
Live IoT telemetry ingestion
Explainable AI visualization
Dashboard for enterprise users