Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries

2020 ◽  
Vol 67 (10) ◽  
pp. 8723-8731 ◽  
Author(s):  
Yandan Tan ◽  
Guangcai Zhao
Keyword(s):  
Lithium Ion Batteries ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Health ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals State of Health Estimation for Lithium-Ion Batteries Based on Healthy Features and Long Short-Term Memory

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 28533-28547 ◽  
Author(s):  
Yitao Wu ◽  
Qiao Xue ◽  
Jiangwei Shen ◽  
Zhenzhen Lei ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Health ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve

2021 ◽  
pp. 014233122110409
Author(s):  
Mingqiang Lin ◽  
Denggao Wu ◽  
Gengfeng Zheng ◽  
Ji Wu
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Battery Degradation ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Input Gate

Lithium-ion batteries are widely used as the power source in electric vehicles. The state of health (SOH) diagnosis is very important for the safety and storage capacity of lithium-ion batteries. In order to accurately and robustly estimate lithium-ion battery SOH, a novel long short-term memory network (LSTM) based on the charging curve is proposed for SOH estimation in this work. Firstly, aging features that reflect the battery degradation phenomenon are extracted from the charging curves. Then, considering capture the long-term tendency of battery degradation, some improvements are made in the proposed LSTM model. The connection between the input gate and the output gate is added to better control output information of the memory cell. Meanwhile, the forget gate and input gate are coupled into a single update gate for selectively forgetting before the accumulation of information. To achieve more reliability and robustness of the SOH estimation method, the improved LSTM network is adaptively trained online by using a particle filter. Furthermore, to verify the effectiveness of the proposed method, we compare the proposed method with two data-driven methods on the public battery data set and the commercial battery data set. Experimental results demonstrate the proposed method can obtain the highest SOH accuracy.

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