Lithium-ion Battery Health Estimation Using DCNN Paralleled LSTM-Self Attention Networks-Reference-Cited by-同舟云学术

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Lithium-ion Battery Health Estimation Using DCNN Paralleled LSTM-Self Attention Networks

Published:2024-05-17 Issue: Volume: Page:3025-3030
ISSN:
Container-title:2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)
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Author:

Zhang Longhan¹,Huang Xinrong¹,Li Yuanyuan²,Meng Jinhao³,Liu Wenjie⁴,Zhang Yipu¹

Affiliation:

1. Chang’an University,School of Energy and Electrical Engineering,Xi’an,China,710018

2. Southwest Minzu University,College of Electrical Engineering,Chengdu,China,610041

3. Xi’an Jiaotong University,School of Electrical Engineering,Xi’an,China,710049

4. Northwestern Polytechnical University,School of Automation,Xi’an,China,710072

Funder

Research and Development

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10567049/10567050/10567442.pdf?arnumber=10567442

Reference20 articles.

1. Online estimation of SOH for lithium-ion battery based on SSA-Elman neural network

2. Data-Driven ICA-Bi-LSTM-Combined Lithium Battery SOH Estimation

3. Battery life estimation based on cloud data for electric vehicles

4. SOH prediction of lithium battery based on IC curve feature and BP neural network

5. Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy

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