Battery Genome Project: Advanced Feature Engineering for Accurate Degradation Modeling

Predict Battery Health, Lifetime, and Performance Using Data-Driven Models

Mode: Virtual / Online
Type: Mentor Based
Level: Moderate
Duration: 3 Days (60-90 minutes each day)
Starts: 16 April 2026
Time: 05: 30 PM IST

About This Course

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.

Workshop Objectives

Introduce machine learning for battery lifetime and degradation analysis.
Explore data collection, feature extraction, and model development.
Provide hands-on experience with battery performance prediction.
Optimize battery performance and enhance storage system reliability.

Workshop Structure

📅 Day 1 — Battery Fundamentals and Degradation Data

Battery systems overview: battery types, working principles, and lifecycle behavior
Battery degradation basics: capacity fade, cycle aging, and failure mechanisms
Battery data analysis: cycle, voltage, capacity data cleaning and visualization

🛠️ Hands-on:
Import and visualize battery cycle data, clean raw dataset, and plot degradation trends in Google Colab

📅 Day 2 — Machine Learning for Battery Lifetime Prediction

ML in battery analytics: role of AI in health monitoring and lifetime estimation
Feature engineering and modeling: extracting useful parameters for prediction
Lifetime prediction: remaining useful life and capacity loss using regression models

🛠️ Hands-on:
Build a simple ML model to predict battery capacity fade and lifetime using Google Colab

📅 Day 3 — Interpretation, Optimization, and Research Insights

Model interpretation: feature importance and degradation-driving factors
Model improvement: tuning, validation, and prediction comparison
Research applications: graphs, insights, and research-ready battery case study

🛠️ Hands-on:
Interpret model results, compare prediction performance, and generate research-ready battery analysis outputs in Google Colab

Who Should Enrol?

Researchers and academicians in battery technology and machine learning.
Ph.D. scholars and postdocs in energy systems.
Industry professionals in energy, battery tech, and data science.
Data scientists and engineers focused on predictive modeling.
Students with an interest in battery performance optimization and ML.

Important Dates

Registration Ends

April 16, 2026
IST

Workshop Dates

April 16, 2026 – April 18, 2026
IST 05: 30 PM

Workshop Outcomes

Understanding machine learning techniques for battery lifetime and degradation analysis.
Proficiency in data collection, feature extraction, and model development for battery performance.
Ability to predict battery lifetime and optimize performance using machine learning models.
Enhanced skills in improving the reliability of energy storage systems through data-driven insights.

Fee Structure

Student

₹2499 | $75

Ph.D. Scholar / Researcher

₹3499 | $85

Academician / Faculty

₹4499 | $95

Industry Professional

₹6499 | $115

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Need Help?

We’re here for you!

(+91) 120-4781-217

★★★★★

Cancer Drug Discovery: Creating Cancer Therapies

Undoubtedly, the professor's expertise was evident, and their ability to cover a vast amount of material within the given timeframe was impressive. However, the pace at which the content was presented made it challenging for some attendees, including myself, to fully grasp and absorb the information.

Mario Rigo • November 30, 2023 at 5:18 pm