Predicting Battery Lifetimes with the Power of Artificial Intelligence
To equip participants with the knowledge and practical skills to use AI and machine learning for analyzing, modeling, and predicting degradation in energy storage systems, helping improve battery health assessment, reliability, and lifetime prediction.
To introduce the fundamentals of degradation mechanisms in energy storage systems.
To understand key battery health indicators such as SOC, SOH, and RUL.
To explore AI and machine learning techniques for degradation prediction.
To develop skills in analyzing battery datasets and building predictive models.
To provide hands-on experience using Python in Google Colab/Jupyter Notebook for energy storage data analysis.
Understand key degradation mechanisms in energy storage systems.
Analyze battery health indicators such as SOC, SOH, and RUL.
Apply AI and machine learning techniques for degradation prediction.
Build basic predictive models using Python in Google Colab/Jupyter Notebook.
Interpret degradation data to support reliability and lifetime assessment of energy storage systems.
Students interested in renewable energy, sustainability, and energy analytics
PhD scholars, researchers, and academicians in energy, environment, and climate studies
Industry professionals in renewables, utilities, grid systems, and decarbonization
Policy analysts and government professionals working on energy transition strategies
Data scientists and AI/ML practitioners applying analytics in the energy sector
Entrepreneurs and climate-tech innovators building energy-focused solutions
