New Year Offer End Date: 30th April 2024
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Program

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:

  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 Data & Degradation Basics

        Understand battery behavior

    • 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

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.