State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network-Reference-Cited by-同舟云学术

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State of charge estimation of lithium-ion batteries based on PSO-TCN-Attention neural network

Published:2024-04 Issue: Volume:84 Page:110806
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Li Feng,Zuo Wei,Zhou Kun,Li Qingqing,Huang Yuhan

Funder

Natural Science Foundation of Hubei Province

China Scholarship Council

National Natural Science Foundation of China-Henan Joint Fund

Australian Research Council

Publisher

Elsevier BV

Reference58 articles.

1. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries;Luo;J. Energy Chem.,2022

2. Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer;Sun;Renew. Sustain. Energy Rev.,2020

3. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries;Ren;Energy Rep.,2023

4. Machine learning and data-driven techniques for the control of smart power generation systems: An uncertainty handling perspective;Sun;Engineering,2021

5. Numerical investigation on the performance enhancement of PEMFC with gradient sinusoidal-wave fins in cathode channel;Chen;Energy,2024

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2. State of charge estimation of lithium batteries in wide temperature range based on MSIABC-AEKF algorithm;Alexandria Engineering Journal;2024-12

3. A multi-dimensional machine learning framework for accurate and efficient battery state of charge estimation;Journal of Power Sources;2024-12

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5. State of charge estimation for the lithium-ion battery based on fractional-order multi-dimensional Taylor network;Journal of Energy Storage;2024-10

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