Accurate prediction of battery performance under various ageing conditions is
necessary for reliable and stable battery operations. Due to complex battery
degradation mechanisms, estimating the accurate ageing level and
ageing-dependent battery dynamics is difficult. This work presents a
health-aware battery model that is capable of separating fast dynamics from
slowly varying states of degradation and state of charge (SOC). The method is
based on a sequence-to-sequence learning-based encoder-decoder model, where the
encoder infers the slowly varying states as the latent space variables in an
unsupervised way, and the decoder provides health-aware multi-step ahead
prediction conditioned on slowly varying states from the encoder. The proposed
approach is verified on a Lithium-ion battery ageing dataset based on real
driving profiles of electric vehicles.Comment: 6 pages, 10 figures, IEEE Vehicle Power and Propulsion confernce(IEEE
VPPC 2023