Yuansheng Gao
Changlin Li
Lei Huang
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A Corrigendum on
An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery
by Gao Y, Li C and Huang L (2022). Front. Energy Res. 10:1032660. doi: 10.3389/fenrg.2022.1032660
In the published article, there was an error in the Funding statement. It did not include the funding that supported this work. The correct Funding statement appears below.
Funding
This work was supported in part by the 2022 Liaoning College Students’ Innovative Entrepreneurial Training Plan Program (Project Number: S202210147033).
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: lithium-ion battery, remaining useful life, data-driven forecasting method, deep extreme learning machine, grey wolf optimization algorithm based on the adaptive normal cloud model
Citation: Gao Y, Li C and Huang L (2023) Corrigendum: An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery. Front. Energy Res. 11:1228014. doi: 10.3389/fenrg.2023.1228014
Received: 24 May 2023; Accepted: 25 May 2023;
Published: 31 May 2023.
Approved by:
Frontiers Editorial Office, Frontiers Media SA, SwitzerlandCopyright © 2023 Gao, Li and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yuansheng Gao, Z2FveXVhbnNoZW5nMjAyMUAxNjMuY29t