De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
DOI
10.1109/BigData59044.2023.10386134
First Page
2221
Last Page
2228
Abstract
The usage of Lithium-ion (Li-ion) batteries has gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems. A central challenge in Li-ion battery reliability lies in accurately predicting their Remaining Useful Life (RUL), which is a critical measure for proactive maintenance and predictive analytics. This study presents a novel approach that harnesses the power of multiple denoising modules, each trained to address specific types of noise commonly encountered in battery data. Specifically, a denoising auto-encoder and a wavelet denoiser are used to generate encoded/decomposed representations, which are subsequently processed through dedicated self-attention transformer encoders. After extensive experimentation on NASA and CALCE data, a broad spectrum of health indicator values are estimated under a set of diverse noise patterns. The reported error metrics on these data are on par with or better than the state-of-the-art reported in recent literature.
Funding Number
23-UGA-08-044
Keywords
Battery Health, Denoising Auto-Encoders, Lithium-ion Batteries, Prognostics and Health Management, Remaining Useful Life, Transformer
Department
Computer Science
Recommended Citation
Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, and Saptarshi Sengupta. "De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics" Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (2023): 2221-2228. https://doi.org/10.1109/BigData59044.2023.10386134
