Ari Everett
Computer science
Hometown: Princeton, New Jersey, United States
Graduation date: Spring 2027
FURI | Fall 2025
Machine Learning-enabled Prediction of Lithium-ion Battery Degradation
Forecasting lithium-ion battery degradation remains a key challenge for modern energy storage. Building on Gaussian Process Regression studies that identified low-frequency Electrochemical Impedance Spectroscopy (EIS) features as aging indicators, this research evaluates an alternative eXtreme Gradient Boosting regression along with frequency weighting for improved predictions. This research aims to leverage EIS as a rapid state-of-health assessment tool which can be used to predict capacity fade and degradation processes in batteries. This will contribute to the predictions of battery health/lifespan and identify protocols to longer-lasting batteries guided by machine learning design.
Mentor: Nick Rolston
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