Kalman-variant estimators for state of charge in lithium-sulfur batteries

Date published

2022-05-01 01:10

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Cranfield University

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Citation

Propp, Karsten; Auger, Daniel; Fotouhi, Abbas; Longo, Stefano; Knap, Vaclav (2021). Kalman-variant estimators for state of charge in lithium-sulfur batteries. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.3834057

Abstract

This fileset is a set of MATLAB/Simulink R2016a models implementing state-of-charge estimators for lithium-sulfur batteries as described in the associated publications. The associated experimental data is also included. Instructions are included in a 'readme.txt' file in the root directory.


This fileset is a set of MATLAB/Simulink R2016a models implementing state-of-charge estimators for lithium-sulfur batteries as described in the associated publications. The associated experimental data is also included. Instructions are included in a 'readme.txt' file in the root directory.

Description

Software Description

Software Language

Github

Keywords

lithium-sulfur battery', 'state of charge', 'extended kalman filter', 'unscented kalman filter', 'particle filter', 'Engineering not elsewhere classified'

DOI

10.17862/cranfield.rd.3834057

Rights

CC BY-NC-ND 4.0

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Funder/s

EP L505286/1