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.

利用多个去噪模块的力量，本研究提出了一种新方法，用于准确预测锂离子电池的剩余寿命(RUL)，并通过去噪自编码器和小波去噪器生成编码/分解表示，然后通过专用的自注意力变换编码器进行处理，通过NASA和CALCE数据集上的大量实验证明，我们能够在多种噪声模式下表征广谱的健康指标估计，而且我们报告的误差指标与最近文献中的最佳结果持平或更好。

De-SaTE: 去噪自注意力变压器编码器用于锂离子电池健康预测