FURI | Fall 2024, Summer 2024

Simulating Battery Degradation with PyBaMM for Efficient Data Generation

Battery degradation is a problem affecting usage and deployment, and it is an issue that takes weeks or months to appear as a problem when charging and discharging experimentally. This project uses PyBaMM, a Python library useful to simulate the behavior of batteries under an environment set by a researcher. It is focused on generating data from this artificial environment that is close to the real-world data. This can save lots of resources like time and money. In the future, this reliable data can be used to generate massive datasets that can be used to train machine learning models at very little expense to understand battery failure and design more stable devices.