New Year Offer End Date: 30th April 2024
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Program

Battery Genome Project: Advanced Feature Engineering for Accurate Degradation Modeling

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:

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

What you will learn?

📅 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

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

  • 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.

Career Supporting Skills

Program Outcomes

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