AI-Driven Digital Twins for Battery Life Cycle Assessment
Empowering Battery Innovation: Predictive Models for Sustainable Life Cycle Assessmen
Skills you will gain:
About Program:
Join our 3-day workshop on AI-Driven Digital Twins for Battery Life Cycle Assessment. Learn to build predictive models using NASA’s battery dataset, apply XGBoost and Random Forest to forecast battery degradation, and deploy an interactive dashboard for LCA visualization. Gain practical skills with tools like Python, XGBoost, Streamlit, and Plotly to advance sustainability assessments in battery technologies.
Aim: The aim of this workshop is to teach participants how to build AI-driven predictive models for battery life cycle assessments, focusing on forecasting degradation, carbon impact, and enhancing sustainability in next-gen battery technologies.
Program Objectives:
What you will learn?
📅 Day 1: Data Preparation & Setup
- Introduction to Data Preprocessing: Begin by cleaning and preparing real-world battery cycling data, focusing on time-series voltage and current data.
- Mapping to Dynamic LCA Parameters: Hands-on work mapping the processed data to dynamic Life Cycle Assessment (LCA) parameters, establishing a foundation for predictive analysis.
- Environment Setup: Participants will set up their Python environment and tools to streamline data manipulation and model building.
📅 Day 2: AI Model Training & Evaluation
- Building Predictive Models: Dive into training AI models using XGBoost and Random Forest algorithms to forecast battery degradation and predict remaining useful life (RUL).
- Model Performance Evaluation: Hands-on exercises in assessing model accuracy, tuning hyperparameters, and evaluating predictive performance. Participants will interpret the model outcomes to assess the environmental impact (e.g., carbon footprint).
- Practical Application: Use the trained models to forecast battery RUL and carbon impact, providing real-world insights into sustainability metrics.
📅 Day 3: Interactive Dashboard Deployment & LCA Visualization
- Dashboard Development: Participants will deploy an interactive dashboard using Streamlit, integrating the predictive models to visualize battery degradation and the LCA impact trajectories.
- Dynamic Visualization: Hands-on experience in building dynamic visualizations where users can adjust battery usage parameters in real-time, and instantly generate downloadable impact trajectory charts.
- Research Application: Attendees will finalize their projects by generating actionable LCA impact reports and preparing them for academic research or grant proposal submissions.
Tools and Technologies Covered: Python, XGBoost, Streamlit, Plotly.
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Researchers, academicians, and professionals in clean energy, sustainability, battery technology, or AI applications in environmental science.
- Basic understanding of data science, machine learning, and Python programming.
- Interest in Life Cycle Assessment (LCA) and AI-driven sustainability applications. Prior experience with tools like XGBoost, Streamlit, or Plotly is beneficial but not required.
Career Supporting Skills
Program Outcomes
- Develop predictive models for battery life cycle assessment using real-world data.
- Apply machine learning techniques to predict battery degradation and remaining useful life (RUL).
- Create interactive tools for visualizing LCA impacts.
- Integrate AI solutions into sustainable battery research.
- Interpret LCA impact trajectories for better decision-making.