An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery

2022 ◽  
Vol 46 ◽  
pp. 103804
Author(s):  
Qingrui Gong ◽  
Ping Wang ◽  
Ze Cheng
Keyword(s):  
Deep Learning ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Health ◽  
Model Based

scholarly journals Sequence-in-Sequence Learning for SOH Estimation of Lithium-Ion Battery

2021 ◽  
Author(s):  
Thien Pham ◽  
Loi Truong ◽  
Mao Nguyen ◽  
Akhil Garg ◽  
Liang Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Lithium Ion Battery ◽  
Sequence Learning ◽  
Lithium Ion ◽  
Learning Approaches ◽  
State Of Health ◽  
Real Dataset ◽  
External Conditions ◽  
High Level

State-of-Health (SOH) prediction of a Lithium-ion battery is essential for preventing malfunction and maintaining efficient working behaviors for the battery. In practice, this task is difficult due to the high level of noise and complexity. There are many machine learning methods, especially deep learning approaches, that have been proposed to address this problem recently. However, there is much room for improvement because the nature of the battery data is highly non-linear and exhibits higher dependence on multidisciplinary parameters such as resistance, voltage and external conditions the battery is subjected to. In this paper, we propose an approach known as bidirectional sequence-in-sequence, which exploits the dependency of nested cycle-wise and channel-wise battery data. Experimented with real dataset acquired from NASA, our method results in significant reduction of error of approximately up to 32.5%.

A novel deep learning framework for state of health estimation of lithium-ion battery

2020 ◽  
Vol 32 ◽  
pp. 101741
Author(s):  
Yaxiang Fan ◽  
Fei Xiao ◽  
Chaoran Li ◽  
Guorun Yang ◽  
Xin Tang
Keyword(s):  
Deep Learning ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Health ◽  
Learning Framework
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