Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI-Driven Digital Twins for Battery Life Cycle Assessment combines two ideas — the digital twin and life-cycle assessment — for batteries. You learn to build a digital twin that mirrors a battery through manufacture, use, second life and recycling, and to feed it real data so it continuously estimates environmental impact and remaining value. The course connects twin-based simulation to greener design, circular reuse and end-of-life decisions. You finish able to reason about a digital-twin LCA for battery systems. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI-driven digital twins for battery life-cycle assessment — modelling a battery’s environmental and performance footprint across its whole life with a live digital twin.
📋 Course Objectives
1. Build a digital twin of a battery’s life cycle.
2. Feed the twin real usage and process data.
3. Estimate environmental impact across life stages.
4. Model second-life and recycling value.
5. Turn twin insight into circular decisions.
👥 Who Should Enroll?
• Battery and sustainability engineers
• LCA and circular-economy analysts
• Energy-storage professionals
• Students of sustainable energy
🚀 Key Learning Outcomes
• An understanding of digital-twin battery LCA.
• A whole-life battery perspective.
• A circular-battery project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Day 1 – Data Preparation & Setup
Clean and preprocess real‑world battery cycling data • Map time‑series data to dynamic LCA parameters • Configure Python environment and essential libraries
Day 2 – AI Model Training & Evaluation
Build predictive models with XGBoost and Random Forest • Tune hyper‑parameters and evaluate model performance • Forecast remaining useful life and carbon‑footprint impact
Day 3 – Interactive Dashboard Deployment & LCA Visualization
Deploy a Streamlit dashboard integrating the AI models • Create dynamic visualizations with Plotly for real‑time scenario analysis • Generate actionable LCA impact reports for research or grant proposals
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | XGBoost |
| Covered Tool / Platform | Random Forest |
| Covered Tool / Platform | Streamlit |
| Covered Tool / Platform | Plotly |
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