📚 Syllabus & Course Curriculum
Nanotechnology & Materials Science
Module-by-module breakdown of AI for Degradation Modeling in Energy Storage Systems, from foundations to a certified capstone project.
Outline
Explore core energy‑storage technologies and degradation mechanisms • Identify key health indicators such as SOC, SOH, and RUL • Contrast data‑driven and physics‑based modeling approaches • Analyze real battery datasets in Google Colab
Outline
Extract informative features from voltage, current, temperature, and cycle data • Build regression and ensemble models (Linear Regression, Random Forest, SVM) • Validate models using MAE, RMSE and robust cross‑validation • Implement a full SOH prediction workflow with Scikit‑learn
Outline
Design LSTM‑based time‑series models for RUL forecasting • Integrate physics‑informed AI for accurate health estimation • Explore AI‑enabled Battery Management Systems and predictive maintenance • Experiment with digital‑twin concepts for grid‑scale storage analytics
Earn government-registered certification in AI for Degradation Modeling in Energy Storage Systems
e-Certificate and e-Marksheet issued on successful completion.
