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

Lithium Ion (Li-ion) batteries have gained widespread popularity across various industries, from powering portable electronic devices to propelling electric vehicles and supporting energy storage systems. A central challenge in managing Li-ion batteries effectively is 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 we use a denoising auto-encoder and a wavelet denoiser to generate encoded/decomposed representations, which are subsequently processed through dedicated self-attention transformer encoders. After extensive experimentation on the NASA and CALCE datasets, we are able to characterize a broad spectrum of health indicator estimations under a set of diverse noise patterns. We find that our reported error metrics on these datasets are on par or better with the best reported in recent literature.