Feed-forward State of Charge estimation of LiFePO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" display="inline" id="d1e897"><mml:msub><mml:mrow/><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math> batteries using time-series machine learning prediction with autoregressive models-Reference-Cited by-同舟云学术

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Feed-forward State of Charge estimation of LiFePO4 batteries using time-series machine learning prediction with autoregressive models

Published:2024-10 Issue: Volume:100 Page:113516
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Sadykov Marat^ORCID,Haines Sam,Walker Geoffrey^ORCID,Holmes David William^ORCID

Funder

Department of Industry and Science, Australian Government

Australian Government Department of Industry Innovation and Science

Publisher

Elsevier BV

Reference30 articles.

1. Battery research group, center for advanced life cycle engineering;CALCE,2017

2. Global EV Outlook 2023: Catching up with climate ambitions;IEA,2023

3. Making electric vehicles profitable;Baik,2019

4. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures;Xing;Appl. Energy,2014

5. A neural network approach to absolute state-of-health estimation in electric vehicles battery degradation study based on fleet data;Johansson,2018

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