BatteryML
Machine Learning for Battery Degradation Rolston Lab – Arizona State University
BatteryML is an early-stage research project focused on applying machine learning methods to battery data in order to better understand degradation mechanisms and predict battery health.
The project is being developed in the Rolston Lab at Arizona State University, which studies reliability and materials science challenges in energy technologies such as photovoltaics and batteries.
Goals
• Explore machine learning approaches for battery diagnostics • Analyze battery cycling and electrochemical data • Develop models for battery degradation and health prediction • Build reproducible pipelines for battery data analysis
Scope
BatteryML aims to provide tools and workflows for:
• Battery dataset processing • Feature extraction from cycling and impedance data • Machine learning model experimentation • Visualization and benchmarking of battery health metrics
Status
🚧 This repository is currently in early development. Code, datasets, and documentation will be added as the project evolves.
Lab
Rolston Lab Arizona State University