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CORRECTION article

Front. Energy Res., 31 May 2023
Sec. Energy Storage
Volume 11 - 2023 | https://doi.org/10.3389/fenrg.2023.1228014
This article is part of the Research Topic New energy and energy storage system control of lithium-ion battery View all 5 articles

Corrigendum: An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

This article is a correction to:

  1. An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

    1. Read original article
Yuansheng Gao Yuansheng Gao*Changlin LiChanglin LiLei HuangLei Huang
  • College of Science, Liaoning Technical University, Fuxin, China

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, Switzerland

Copyright © 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

Disclaimer: 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.