Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Battery Circularity: Recycling, Second-Life Integration teaches how to keep batteries and their materials in use rather than in landfill. You learn the pathways of a circular battery economy: assessing retired batteries for second-life uses like grid storage, the recycling processes that recover critical materials, and designing for recyclability from the start. The course connects these to the sustainability and material-security case for battery circularity. You finish able to reason about a circular approach to battery end-of-life. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers battery circularity — recycling, second-life integration and material recovery to make batteries part of a circular economy.
📋 Course Objectives
1. Assess retired batteries for second life.
2. Integrate second-life batteries into storage.
3. Understand battery-recycling processes.
4. Recover critical materials from cells.
5. Design batteries for circularity.
👥 Who Should Enroll?
• Battery and energy-storage engineers
• Recycling and circular-economy professionals
• Sustainability and materials teams
• Students of sustainable energy
🚀 Key Learning Outcomes
• An understanding of battery circularity.
• A second-life-and-recycling perspective.
• A circular-economy energy project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Battery Circularity Foundations
Apply mathematical modeling techniques to simulate battery behavior and predict recycling outcomes • Develop a comprehensive understanding of AI fundamentals, including machine learning and deep learning concepts, to inform battery circularity strategies • Evaluate the role of data quality and preprocessing in ensuring accurate predictions and decision-making for battery recycling and second-life integration
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines to extract, transform, and load battery-related data from various sources, including IoT devices and sensor networks • Configure data preprocessing techniques, such as data normalization and feature scaling, to prepare datasets for machine learning model training • Develop and deploy feature engineering pipelines to extract relevant features from battery data, including charging cycles, state of charge, and temperature
Model Architecture, Algorithm Design, and Battery Circularity Methods
Develop and train machine learning models, including regression, classification, and clustering algorithms, to predict battery health, state of charge, and remaining useful life • Design and evaluate model architectures, including convolutional neural networks and recurrent neural networks, to analyze battery data and inform recycling and second-life integration decisions • Implement optimization techniques, such as hyperparameter tuning and model selection, to improve model performance and accuracy for battery circularity applications
Training, Hyperparameter Optimization, and Evaluation
Train and evaluate machine learning models using various metrics, including accuracy, precision, recall, and F1-score, to assess performance and identify areas for improvement • Implement hyperparameter optimization techniques, such as grid search, random search, and Bayesian optimization, to optimize model performance and improve battery circularity outcomes • Develop and deploy model evaluation pipelines to assess model performance, identify biases, and ensure fairness and transparency in battery recycling and second-life integration decisions
Deployment, MLOps, and Production Workflows
Design and deploy machine learning models in production environments, including cloud-based and edge-based deployments, to support real-time battery monitoring and decision-making • Develop and implement MLOps pipelines to automate model training, deployment, and monitoring, and ensure continuous integration and delivery of battery circularity solutions • Configure and manage production workflows, including data ingestion, model serving, and monitoring, to ensure reliable and scalable battery circularity operations
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and mitigate biases in machine learning models, including data biases, algorithmic biases, and human biases, to ensure fairness and transparency in battery circularity decisions • Develop and implement responsible AI practices, including explainability, interpretability, and transparency, to ensure accountability and trust in battery recycling and second-life integration applications • Evaluate and address ethical concerns, including environmental impact, social responsibility, and human rights, to ensure that battery circularity solutions align with organizational values and principles
Industry Integration, Business Applications, and Case Studies
Develop and deploy battery circularity solutions in various industries, including automotive, energy, and consumer electronics, to support sustainable and responsible business practices • Analyze and evaluate business applications, including cost-benefit analysis, return on investment, and total cost of ownership, to assess the economic viability of battery recycling and second-life integration solutions • Design and implement case studies to demonstrate the effectiveness and impact of battery circularity solutions, including reduced waste, improved resource efficiency, and enhanced environmental sustainability
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
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