State-of-Health Estimation of Lithium-ion Batteries Based on WOA-CNN-LSTM-Attention

被引：1

作者：

Li, Zhiwei ^{[1
,2
]}

Li, Yong ^{[3
,4
]}

Liao, Chenglin ^{[1
,2
]}

Zhang, Chengzhong ^{[1
,2
]}

Wang, Liye ^{[1
,2
]}

Wang, Lifang ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Inst Elect Engn, Beijing, Peoples R China

[2] Univ Chinese Acad Sci, Beijing, Peoples R China

[3] Univ Sci & Technol Beijing, Shunde Innovat Sch, Foshan, Peoples R China

[4] Univ Sci & Technol Beijing, Sch Mech Engn, Beijing, Peoples R China

来源：

2023 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYTICS, ICCCBDA | 2023年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

SOH; LSTM; CNN-LSTM-attention; CAPACITY ESTIMATION;

D O I：

10.1109/ICCCBDA56900.2023.10154659

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, five health characteristics are extracted from battery charging and discharging data, and combined with the whale optimization algorithm, the performance of the two models in battery health state estimation is compared, namely, the long-short term memory neural network and the long-short term memory neural network with attention mechanism. On this basis, the convolution neural network module is added to extract hidden features from the data, so that the model can obtain more information, thus improving the performance of the model. Finally, the relative error of the improved model for battery health state estimation is controlled within 1.6%.

引用

页码：572 / 578

页数：7

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