IEEE_SoC_V0.5distilled.pdf (1.59 MB)

Likelihood Partitioned Bayesian Filtering for Lithium Sulfur Battery State Estimation

preprint

posted on 2021-05-24, 18:39 authored by Srinivasan MunisamySrinivasan Munisamy, Daniel AugerDaniel Auger, Abbas FotouhiAbbas Fotouhi, Bob Hawkes, Euthymios Kappos

The link for data can be found in the link at acknowledgement of paper.

Funding

European commission grant 814471

Innovate UK TS/P003818/1

Innovate UK TS/R013780/1

History

Email Address of Submitting Author

srinivasan.munisamy@cranfield.ac.uk

ORCID of Submitting Author

https://orcid.org/0000-0003-3497-8149

Submitting Author's Institution

Cranfield University

Submitting Author's Country

United Kingdom

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Keywords

battery management systems Nonlinear filters lithium sulfur battery system Extended Kalman Filter nonlinear equations PARAMETERIZATION state estimation State of charge (SoC)state of energy

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