| Attribute | Detail |
|---|---|
| Format | Recorded Lectures |
| Level | Intermediate |
| Duration | 3 Days (60-90 minutes each day) |
| Certification | e-Certification + e-Marksheet |
| Fee | Free |
| Tools | Google Colab Python pandas NumPy scikit-learn Matplotlib Seaborn |
About the Battery Genome Project: Advanced Feature Engineering for Accurate Degradation Modeling Course
Explore cutting‑edge techniques in battery performance optimization and degradation analysis through machine learning.
Gain hands‑on experience predicting battery lifetime and enhancing reliability of energy‑storage systems.
Program Highlights
• Comprehensive coverage of Battery Genome Project from fundamentals to advanced applications
• Hands-on projects and real-world case studies in battery analytics
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Google Colab, Python, pandas, NumPy
• Career-oriented training for academic and professional growth in battery analytics
Course Curriculum
Module 1: Day 1 – Battery Fundamentals and Degradation Data
- Understand battery types, working principles, and lifecycle behavior
- Analyze capacity fade, cycle aging, and failure mechanisms
- Import, clean, and visualize cycle data in Google Colab
Module 2: Day 2 – Machine Learning for Battery Lifetime Prediction
- Explore AI’s role in health monitoring and lifetime estimation
- Engineer predictive features and build regression models
- Develop a simple ML model to forecast capacity fade in Google Colab
Module 3: Day 3 – Interpretation, Optimization, and Research Insights
- Interpret feature importance and degradation drivers
- Tune, validate, and compare model performance
- Generate research‑ready analysis and visualizations
Tools, Techniques, or Platforms Covered
Google Colab Python pandas NumPy scikit-learn Matplotlib Seaborn
Real-World Applications
- Apply Battery Genome Project skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical battery analytics competencies
- Solve industry-relevant problems using Battery Genome Project methodologies and tools
- Contribute to open-source projects and collaborative research in battery analytics
- Prepare for competitive examinations, interviews, and professional certifications in battery analytics
Who Should Attend & Prerequisites
- Industry‑recognized e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
