Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features

Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features

Lithium-ion batteries play a vital role in human society. Therefore, it is of critical significance to reliably predict the evolution of State of Health (SOH) degradation patterns in order to improve the high accuracy and stability of lithium-ion battery SOH prediction. This paper proposes a novel S...

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Bibliographic Details
Main Authors: Guoju Liu, Zhihui Deng, Yonghong Xu, Lianfeng Lai, Guoqing Gong, Liang Tong, Hongguang Zhang, Yiyang Li, Minghui Gong, Mengxiang Yan, Zheng Ye
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
state of health
lithium-ion battery
health factor
correlation coefficient
deep learning
Online Access:https://www.mdpi.com/2076-3417/15/13/7555
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