An end-cloud collaboration approach for state-of-health estimation of lithium-ion batteries based on bi-LSTM with collaboration of multi-feature and attention mechanism-Reference-Cited by-同舟云学术

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An end-cloud collaboration approach for state-of-health estimation of lithium-ion batteries based on bi-LSTM with collaboration of multi-feature and attention mechanism

Published:2024-01-08 Issue:10 Volume:21 Page:2205-2217
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Jiang Pengchang¹,Zhang Tianyi²,Huang Guangjie²,Hua Wei¹,Zhang Yong³,Wang Wentao²,Zhu Tao⁴

Affiliation:

1. School of Electrical Engineering, Southeast University, Nanjing, China

2. School of Transportation Science and Engineering, Beihang University, Beijing, China

3. College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing, China

4. Warwick Manufacturing Group, University of Warwick, Coventry, UK

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2023.2299402

Reference29 articles.

1. Impact of battery cell imbalance on electric vehicle range

2. Battery state-of-charge estimation using machine learning analysis of ultrasonic signatures

3. HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery

4. Multiscale observation of Li plating for lithium-ion batteries

5. Prognostics in Battery Health Management

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A coarse-to-fine ensemble method for capacity prediction of lithium-ion batteries in production;International Journal of Green Energy;2024-07-30

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