A0377
Title: Functional degradation modeling of battery lives
Authors: Quyen Do - Corning Inc (United States)
Pang Du - Virginia Tech (United States) [presenting]
Yili Hong - Virginia Tech (United States)
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 unit. 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 modelling degradation through one scalar measurement summarizing each VDC. Such simplification of curves can lead to inaccurate predictive models. The degradation of rechargeable Li-ion batteries from a NASA data set through modelling and predicting their full VDCs are analyzed. With techniques from longitudinal and functional data analysis, a new two-step predictive modelling procedure is proposed for functional responses residing on heterogeneous domains. The shapes are first predicted, and the domain end points of VDCs using functional regression models. Then these predictions are integrated to perform a degradation analysis. The approach is fully functional, allows the incorporation of usage information, produces predictions in a curve form, and thus provides flexibility in assessing battery degradation. Through extensive simulation studies and cross-validated data analysis, the approach demonstrates better prediction than the existing approach of modelling degradation directly with aggregated data.
