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arXiv · 2310.00023

De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics

Gaurav Shinde·Rohan Mohapatra·Pooja Krishan·Saptarshi Sengupta
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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.

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Gaurav Shinde, Rohan Mohapatra, Pooja Krishan, Saptarshi Sengupta. 2023-09-28. De-SaTE: Denoising Self-attention Transformer Encoders for Li-ion Battery Health Prognostics. https://arxiv.org/abs/2310.00023

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