Reliability Study of Battery Lives: A Functional Degradation Analysis Approach

TL;DR

This paper introduces a novel functional data analysis method for predicting Li-ion battery degradation by modeling entire voltage discharge curves, leading to more accurate assessments than traditional scalar-based models.

Contribution

It presents a new two-step functional regression approach that predicts full voltage discharge curves and their end points, improving battery degradation modeling accuracy.

Findings

Outperforms existing scalar-based degradation models in prediction accuracy.

Provides flexible, curve-based predictions incorporating usage data.

Demonstrates effectiveness through extensive simulations and real data analysis.

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 in 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 approach is fully functional, 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.