Indirect prediction of remaining useful life for lithium-ion batteries based on improved multiple kernel extreme learning machine-Reference-Cited by-同舟云学术

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Indirect prediction of remaining useful life for lithium-ion batteries based on improved multiple kernel extreme learning machine

Published:2023-08 Issue: Volume:64 Page:107181
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Zhang Yingda,Ma Hongyan,Wang Shuai,Li Shengyan,Guo Rong

Funder

Beijing University of Civil Engineering and Architecture

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference46 articles.

1. State of health estimation and remaining useful life prediction for lithium-ion batteries by improved particle swarm optimization-back propagation neural network[J];Ma;J.Energy Storage,2022

2. Image-and health indicator-based transfer learning hybridization for battery RUL prediction[J];Couture;Eng. Appl. Artif. Intell.,2022

3. Review on the selection of health indicator for lithium ion batteries[J];Zhou;Machines,2022

4. Remaining useful life prediction of lithium-ion batteries based on stacked autoencoder and gaussian mixture regression[J];Wei;J.Energy Storage,2022

5. Research on the remaining useful life prediction method of lithium-ion batteries based on aging feature extraction and multi-kernel relevance vector machine optimization model[J];Qiu;Int. J. Energy Res.,2022

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