A Convolutional Neural Network Model for Battery Capacity Fade Curve Prediction using Early Life Data

Saxena, Saurabh; Ward, Logan; Kubal, Joseph; Lu, Wenquan; Babinec, Susan; Paulson, Noah

doi:10.1016/j.jpowsour.2022.231736

A Convolutional Neural Network Model for Battery Capacity Fade Curve Prediction using Early Life Data

Journal Article · Wed Jun 29 00:00:00 EDT 2022 · Journal of Power Sources

DOI:https://doi.org/10.1016/j.jpowsour.2022.231736· OSTI ID:1910034

^[1]; Ward, Logan ^[1]; Kubal, Joseph ^[1]; Lu, Wenquan ^[1]; Babinec, Susan ^[2]; Paulson, Noah ^[1]

Argonne National Lab. (ANL), Argonne, IL (United States)
Argonne National Lab. (ANL), Argonne, IL (United States). Argonne Collaborative Center for Energy Storage Science (ACCESS)

Herein, early prediction of battery performance degradation trends can facilitate research of new materials and cell designs, rapid deployment of batteries in real-world applications, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we design a convolutional neural network model to predict the entire battery capacity fade curve - a critical indicator of battery performance degradation - using first 100 cycles of data (~ three weeks of testing). We use the discharge voltage-capacity curves as input to the model and automate the feature extraction process through the convolutional layers of the network. Our approach can predict the per cycle capacity fade rate and rollover cycle (knee point) in the capacity fade curve, which indicate the onset of rapid capacity decay. On the publicly available graphite/LiFePO₄ battery dataset, optimized networks predict the capacity fade curves, rollover cycle, and end of life with 3.7% (worst-case), 19%, and 17% mean absolute percentage errors, respectively.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1910034

Alternate ID(s):: OSTI ID: 1962240

Journal Information:: Journal of Power Sources, Journal Name: Journal of Power Sources Vol. 542; ISSN 0378-7753

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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A Convolutional Neural Network Model for Battery Capacity Fade Curve Prediction using Early Life Data

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