86 research outputs found
Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach
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
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