A nonlinear spatiotemporal modeling method combined with t-distributed stochastic neighbor embedding and broad learning system for the lithium-ion battery thermal process-Reference-Cited by-同舟云学术

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A nonlinear spatiotemporal modeling method combined with t-distributed stochastic neighbor embedding and broad learning system for the lithium-ion battery thermal process

Published:2024-09 Issue: Volume:135 Page:108433
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Zhu Chengjiu^ORCID,Xie Yuyang,Yang Haidong,Li Zhan,Hu Luoke,Xu Kangkang

Publisher

Elsevier BV

Reference49 articles.

1. Distributed thermal-electrochemical modeling of a lithium-ion battery to study the effect of high charging rates;Anwar;IFAC Proc. Vol.,2014

2. Index t-SNE: tracking dynamics of high-dimensional datasets with coherent embeddings;Candel;arXiv preprint,2021

3. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture;Chen;IEEE Transact. Neural Networks Learn. Syst.,2018

4. Universal approximation capability of broad learning system and its structural variations;Chen;IEEE Transact. Neural Networks Learn. Syst.,2019

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

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