Attribute

Detail

Format

Recorded Lectures (Self-Paced)

Level

Intermediate

Duration

3 Days (60-90 minutes each day)

Certification

e-Certification + e-Marksheet

Tools

Python, Google Colab, Jupyter Notebook, Scikit-learn, TensorFlow, Keras

About the Ai Degradation Modeling Course

The course explores how artificial intelligence and machine learning can be leveraged to understand, predict, and model degradation in batteries and other energy‑storage devices.

Participants will dive into battery aging, state‑of‑health (SOH), remaining useful life (RUL), predictive analytics, and AI‑driven prognostics,{nbsp}all Hands‑on labs in Google Colab/Jupyter Notebook let you apply intelligent modeling techniques to real degradation datasets.

Program Highlights

• Comprehensive coverage of AI for Degradation Modeling in Energy Storage Systems from fundamentals to advanced applications

• Hands-on projects and real-world case studies in energy storage AI

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: Python, Google Colab, Jupyter Notebook, Scikit-learn

• Career-oriented training for academic and professional growth in energy storage AI

Course Curriculum

Module 1: Day 1 – Foundations of Energy Storage Degradation

Explore core energy‑storage technologies and degradation mechanisms
Identify key health indicators such as SOC, SOH, and RUL
Contrast data‑driven and physics‑based modeling approaches
Analyze real battery datasets in Google Colab

Module 2: Day 2 – Machine Learning for Degradation Prediction

Extract informative features from voltage, current, temperature, and cycle data
Build regression and ensemble models (Linear Regression, Random Forest, SVM)
Validate models using MAE, RMSE and robust cross‑validation
Implement a full SOH prediction workflow with Scikit‑learn

Module 3: Day 3 – Advanced AI for Prognostics & Smart Battery Systems

Design LSTM‑based time‑series models for RUL forecasting
Integrate physics‑informed AI for accurate health estimation
Explore AI‑enabled Battery Management Systems and predictive maintenance
Experiment with digital‑twin concepts for grid‑scale storage analytics

Tools, Techniques, or Platforms Covered

Python
Google Colab
Jupyter Notebook
Scikit-learn
TensorFlow
Keras

Real-World Applications

Apply AI for Degradation Modeling in Energy Storage Systems skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical energy storage AI competencies
Solve industry-relevant problems using AI for Degradation Modeling in Energy Storage Systems methodologies and tools
Contribute to open-source projects and collaborative research in energy storage AI
Prepare for competitive examinations, interviews, and professional certifications in energy storage AI

Who Should Attend & Prerequisites

Industry‑recognized e‑Certification + e‑Marksheet from NSTC
Hands‑on training with practical projects and industrial datasets
Dedicated expert mentorship and doubt resolution

Prerequisites:

Frequently Asked Questions

1. What is the format of this AI for Degradation Modeling in Energy Storage Systems course?

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of energy storage AI concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Days (60-90 minutes each day). The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to energy storage AI. Our mentors are industry experts and experienced professionals.

Enroll in AI for Degradation Modeling in Energy Storage Systems today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering energy storage AI skills that matter.

Brand	NSTC
Format	Recorded Lectures
Duration	3 Days (60-90 minutes each day)
Level	Intermediate
Domain	energy storage AI
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	Python, Google Colab, Jupyter Notebook, Scikit-learn, TensorFlow, Keras

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