
Machine Learning for Battery Lifetime and Degradation Analysis
Predict Battery Health, Lifetime, and Performance Using Data-Driven Models
Skills you will gain:
About Program:
Explore cutting-edge techniques in battery performance optimization and degradation analysis through machine learning, and gain hands-on experience in predicting battery lifetime and enhancing reliability in energy storage systems
Aim: The aim of this workshop is to equip participants with the knowledge and practical skills to leverage machine learning techniques for analyzing battery lifetime, predicting performance degradation, and optimizing the reliability of energy storage systems.
Program Objectives:
What you will learn?
🔋 Day 1: Battery Data & Degradation Basics
Understand battery behavior
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- Battery lifecycle (simple explanation)
- Degradation mechanisms
- Types of battery data:
- Cycles
- Voltage
- Capacity
- Hands-on:
- Load battery dataset
- Visualization of degradation trends
- Output:
- Battery degradation curves
⚡ Day 2: ML Models for Lifetime Prediction
Predict battery life
-
- Regression models
- Feature extraction
- Hands-on:
- Train ML model to predict:
- Model evaluation
- Output:
- Battery lifetime prediction model
📊 Day 3: Insights + Optimization + Research Output
Make results usable
-
- Feature importance (what affects degradation)
- Model improvement
- Interpretation
- Hands-on:
- Plot results
- Compare predictions vs actual
- Final Output:
- ML model + graphs + insights
- Research-ready case study
🧰 Tools
- Python (Colab)
- Pandas, Scikit-learn
- Excel
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
Career Supporting Skills
Program Outcomes
