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Year
2026
Tech & Technique
Python, FastAPI, SimPy, AWS Fargate, TimescaleDB, Docker, React
Description
A comprehensive Full-Stack Digital Twin platform designed to simulate and optimize city-scale battery swapping networks for the Indian EV market.
⚠️ Note on Live Demo: The frontend is live, but I have spun down the AWS Backend (Fargate/RDS) to save costs after the hackathon/demo period. The architecture and code are fully viewable on GitHub.
Key Features:
Technical Highlights:
⚠️ Note on Live Demo: The frontend is live, but I have spun down the AWS Backend (Fargate/RDS) to save costs after the hackathon/demo period. The architecture and code are fully viewable on GitHub.
Key Features:
- 🏙️ City-Scale Simulation: Event-driven SimPy engine modeling entire battery ecosystems.
- 🧠 AI SOC Estimation: Ensemble ML (LightGBM, GRU) for precise State of Charge prediction.
- 🌡️ Physics Engine: Models thermal dynamics and cooling constraints of batteries.
- 💰 Greenfield Planner: AI-driven economic optimization for new station deployments.
Technical Highlights:
- Architecture: Chose AWS Fargate over EKS for 60% lower ops overhead and 40% cost reduction.
- Database: Implemented TimescaleDB for high-frequency time-series telemetry.
- Optimization: Built a custom "Out-of-the-Box" discrete event simulation using SimPy that models Indian traffic and festival patterns.
My Role
Backend Lead & Cloud Architect
I architected the entire simulation engine and cloud infrastructure:
I architected the entire simulation engine and cloud infrastructure:
- ✅ Simulation Core: Built the SimPy-based discrete event engine handling thousands of swap events.
- ☁️ AWS Architecture: Designed a serverless-first architecture using Fargate, reducing cold start times and costs.
- 🔒 Security: Implemented multi-layer security with JWT, VPC isolation, and WAF.
- 📊 Data Engineering: Managed the flow from raw telemetry to actionable ML inference.