State-of-health estimation of lithium-ion battery based on convolutional-gated recurrent neural network with self-attention mechanism-Reference-Cited by-同舟云学术

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State-of-health estimation of lithium-ion battery based on convolutional-gated recurrent neural network with self-attention mechanism

Published:2024-04-03 Issue:12 Volume:21 Page:2898-2911
ISSN:1543-5075
Container-title:International Journal of Green Energy
language:en
Short-container-title:International Journal of Green Energy

Author:

Chen Zewang¹,Xu Zhaofan¹,Wang Hanrui¹,Shi Na¹,Yang Lin¹

Affiliation:

1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Non-destructive Testing and Monitoring Technology for High-speed Transportation Facilities, Key Laboratory of the Ministry of Industry and Information Technology, Nanjing, China

Funder

the Fundamental Research Funds for the Central Universities, China

the Aeronautical Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/15435075.2024.2335510

Reference34 articles.

1. Interpretation of intelligence in CNN-pooling processes: a methodological survey

2. Understanding of a convolutional neural network

3. Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation

4. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

5. Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use

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