  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.
