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.
@article{arxiv.2310.14289,
title = {Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach},
author = {Tushar Desai and Riccardo M. G. Ferrari},
journal= {arXiv preprint arXiv:2310.14289},
year = {2023}
}
Comments
6 pages, 10 figures, IEEE Vehicle Power and Propulsion confernce(IEEE VPPC 2023)