Topic: Reliability Study of Battery Lives: A Functional Degradation Analysis Approach

Speaker: Youngjin Cho

Date: September 25, 2026, 10:00 AM EST/ 7:00 AM PST/ 4:00 PM CEST

Abstract

Renewable energy is critical for combating climate change, whose first step is the storage of electricity generated from renewable energy sources. Li-ion batteries are a popular kind of storage units. Their continuous usage through charge-discharge cycles eventually leads to degradation. This can be visualized by plotting voltage discharge curves (VDCs) over discharge cycles. Studies of battery degradation have mostly concentrated on modeling degradation through one scalar measurement summarizing each VDC. Such simplification of curves can lead to inaccurate predictive models. Here we analyze the degradation of rechargeable Li-ion batteries from a NASA data set through modeling and predicting their full VDCs. With techniques from longitudinal and functional data analysis, we propose a new two-step predictive modeling procedure for functional responses residing on heterogeneous domains. We first predict the shapes and domain end points of VDCs using functional regression models. Then we integrate these predictions to perform a degradation analysis. Our functional approach allows the incorporation of usage information, produces predictions in a curve form and thus provides flexibility in the assessment of battery degradation. Through extensive simulation studies and cross-validated data analysis, our approach demonstrates better prediction than the existing approach of modeling degradation directly with aggregated data. 

Speaker Bio

Youngjin Cho is an Assistant Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas. He received his Ph.D. in Statistics from Virginia Tech. His research focuses on the development of statistical methodology, with primary interests in smoothing splines, functional data analysis, survival analysis, and high-dimensional statistics. His recent research has focused on developing nonparametric testing and inference procedures in the smoothing spline framework.

Register Now