Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment-Reference-Cited by-同舟云学术

Adaptive Kalman filter and self-designed early stopping strategy optimized convolutional neural network for state of energy estimation of lithium-ion battery in complex temperature environment

Published:2024-04 Issue: Volume:83 Page:110750
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Li Jin,Wang Shunli,Chen Lei,Wang Yangtao,Zhou Heng,Guerrero Josep M.

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference45 articles.

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3. A novel non-experiment-based reconstruction method for the relationship between open-circuit-voltage and state-of-charge/state-of-energy of lithium-ion battery;Zhang;Electrochim. Acta,2022

4. Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons;Xi;Appl. Energy,2022

5. Wavelet attention-powered neural network framework with hierarchical dynamic frequency learning for lithium-ion battery state of health prediction;Wang;J. Energy Storage,2023

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