End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation

2022 ◽  
Vol 520 ◽  
pp. 230823
Author(s):  
Te Han ◽  
Zhe Wang ◽  
Huixing Meng
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Domain Adaptation ◽  
Lithium Ion ◽  
Capacity Estimation ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
End To End

scholarly journals Online capacity estimation of lithium-ion batteries with deep long short-term memory networks

2021 ◽  
Vol 482 ◽  
pp. 228863
Author(s):  
Weihan Li ◽  
Neil Sengupta ◽  
Philipp Dechent ◽  
David Howey ◽  
Anuradha Annaswamy ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Capacity Estimation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals State-of-Charge Estimation of Lithium-Ion Batteries via Long Short-Term Memory Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 53792-53799 ◽  
Author(s):  
Fangfang Yang ◽  
Xiangbao Song ◽  
Fan Xu ◽  
Kwok-Leung Tsui
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Charge ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
State Of Charge Estimation

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.

A Hybrid CNN-LSTM for Discharge Capacity Estimation of Lithium-ion Batteries

2021 ◽  
pp. 1-13
Author(s):  
Yongsheng Li ◽  
Akhil Garg ◽  
Shruti Shevya ◽  
Wei Li ◽  
Liang Gao ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Discharge Capacity ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Battery Management System ◽  
Capacity Estimation ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Charge Discharge

Abstract Predicting discharge capacities of Lithium-ion batteries (LIBs) is essential for safe operation of the battery in Electric Vehicles (EVs). In this paper, a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) based deep learning is proposed to estimate the discharge capacity of LIBs. The parameters such as the voltage, current, temperature and charge/discharge capacity are recorded from a Battery Management System (BMS) at various stages of the charge-discharge cycles. Data was recorded keeping the stress constant because this parameter couldn't be controlled. Two different sets of data were obtained at two magnitudes of stress values. The experiments conducted to collect the data was recorded in cycles, where each cycle was divided into 7 steps. Each testing cycle comprises of charging, discharging, rest and cross validation test. The initial layers are convolutional layers that helps in feature extraction followed by a Long Short Term Memory (LSTM) layer. The evaluation model was done using multiple train test split method. The lower values of weighted mean squared error (MSE) obtained suggests that discharge capacity estimation using CNN-LSTM is a reliable method when compared to the conventional voltage-based method. The CNN-LSTM program can further be compiled in BMS in EVs to obtain real time status for State of Charge (SOC) and State of Health (SOH) values.

scholarly journals State of charge prediction framework for lithium-ion batteries incorporating long short-term memory network and transfer learning

2021 ◽  
Vol 37 ◽  
pp. 102494
Author(s):  
Yu Liu ◽  
Xing Shu ◽  
Hanzhengnan Yu ◽  
Jiangwei Shen ◽  
Yuanjian Zhang ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Lithium Ion ◽  
State Of Charge ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory
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