File(s) under embargo

Reason: Confidentiality required by consortium agreement

7

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3

month(s)

25

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Research data for 'State of Charge Estimation in Lithium-Sulfur Cells using LSTM Recurrent Neural Networks'

dataset

posted on 2020-07-22, 11:10 authored by Zhihuo Wang, Abbas FotouhiAbbas Fotouhi, Daniel AugerDaniel Auger

Full data set including time histories of voltage and current and MATLAB programs to perform the operations described in the research paper.

This dataset is embargoed for contractual reasons and we are not in a position to release it ahead of the specified embargo date.

Funding

ALISE Project, European Commission, H2020-NMP-GV-2014, Grant Agreement No. 666157

LiS:FAB Project, Innovate UK/Faraday Battery Challenge, TS/R013780/1

History

Authoriser (e.g. PI/supervisor)

d.j.auger@cranfield.ac.uk

Usage metrics

Transport Systems

Categories

Energy generation, conversion and storage (excl. chemical and electrical)

Keywords

lithium-sulfur battery LSTM RNN state of charge estimation Energy Generation, Conversion and Storage Engineering

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