Online state estimation for a physics-based Lithium-Sulfur battery model

Chu Xu, Timothy Cleary, Daiwei Wang, Guoxing Li, Christopher Rahn, Donghai Wang, Rajesh Rajamani, Hosam K. Fathy

Research output: Contribution to journalArticlepeer-review

Abstract

This article examines the problem of Lithium-Sulfur (Li-S) battery state estimation. Such estimation is important for the online management of this energy-dense chemistry. The literature uses equivalent circuit models (ECMs) for Li-S state estimation. This article's main goal is to perform estimation using a physics-based model instead. This approach is attractive because it furnishes online estimates of the masses of individual species in a given Li-S cell. The estimation is performed using an experimentally-validated, computationally tractable zero-dimensional model. Reformulation converts this model from differential algebraic equations (DAEs) to ordinary differential equations (ODEs), simplifying the estimation problem. The article's first contribution is to show that this model has poor observability, especially in the low plateau region, where the low sensitivity of cell voltage to precipitated sulfur mass complicates the estimation of this mass. The second contribution is to exploit mass conservation to derive a reduced-order model with attractive observability properties in both high and low plateau regions. The final contribution is to use an unscented Kalman filter (UKF) for estimating internal Li-S battery states in simulation-based studies, while taking constraints on species masses into account. Consistent with the article's observability analysis, the UKF achieves better low-plateau estimation accuracy when the reduced-order model is used.

Original languageEnglish (US)
Article number229495
JournalJournal of Power Sources
Volume489
DOIs
StatePublished - Mar 31 2021

Bibliographical note

Funding Information:
This work is funded by National Science Foundation, USA Grant 1351146 . The authors gratefully acknowledge this support. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Li-S battery
  • Observability
  • State estimation
  • Unscented Kalman filter

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