An HI Extraction Framework for Lithium-Ion Battery Prognostics Based on SAE-VMD

Abstract

The signals of lithium-ion battery degradation are non-stationary and nonlinear. To adaptively extract the health indicator(HI) that can accurately represent the battery degradation characters and improve the prediction precision of battery remaining useful life (RUL), a stacked auto encoder-variational mode decomposition(SAE-VMD) based HI construction framework is proposed. Firstly, the stacked auto encoder(SAE) is used to reduce the noises of battery parameters and lower the data dimensionality and construct a syncretic HI that contains the battery degradation characters. Then the variational mode decomposition(VMD) is employed for effectively separating the syncretic HI into three modalities: the global attenuation, the local regeneration and the noises. The three modalities are selected as HIs to eliminate the HI noises and improve the RUL prediction precision. The RUL prediction results of lithium-ion battery indicate that the HI extracted by using the present method can obtain a better RUL prediction precision and verify the high quality of the extracted HI.