scholarly journals A Recurrent Neural Networks Approach for Estimating the Core Temperature in Lithium-Ion Batteries

2020 ◽  
Author(s):  
Olaoluwa Ojo ◽  
Xianke Lin ◽  
Haoxiang Lang ◽  
Youngki Kim
Keyword(s):  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Core Temperature ◽  
Recurrent Neural Networks ◽  
Lithium Ion ◽  
The Core

State of health estimation for lithium-ion batteries using recurrent neural networks with gated recurrent unit

2021 ◽  
Vol 50 (2) ◽  
pp. 20200339-20200339
Author(s):  
张少宇 Shaoyu Zhang ◽  
伍春晖 Chunhui Wu ◽  
熊文渊 Wenyuan Xiong
Keyword(s):  
Neural Networks ◽  
Lithium Ion Batteries ◽  
Recurrent Neural Networks ◽  
Lithium Ion ◽  
State Of Health ◽  
Gated Recurrent Unit

scholarly journals Online Capacity Estimation for Lithium-Ion Batteries Based on Semi-Supervised Convolutional Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 256
Author(s):  
Yi Wu ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Capacity Estimation ◽  
Practical Applications

Accurate capacity estimation can ensure the safe and reliable operation of lithium-ion batteries in practical applications. Recently, deep learning-based capacity estimation methods have demonstrated impressive advances. However, such methods suffer from limited labeled data for training, i.e., the capacity ground-truth of lithium-ion batteries. A capacity estimation method is proposed based on a semi-supervised convolutional neural network (SS-CNN). This method can automatically extract features from battery partial-charge information for capacity estimation. Furthermore, a semi-supervised training strategy is developed to take advantage of the extra unlabeled sample, which can improve the generalization of the model and the accuracy of capacity estimation even in the presence of limited labeled data. Compared with artificial neural networks and convolutional neural networks, the proposed method is demonstrated to improve capacity estimation accuracy.

Damaged Cell Location on Lithium-Ion Batteries Using Artificial Neural Networks

2020 ◽  
pp. 477-486
Author(s):  
Mateus Moro Lumertz ◽  
Felipe Gozzi da Cruz ◽  
Rubisson Duarte Lamperti ◽  
Leandro Antonio Pasa ◽  
Diogo Marujo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Artificial Neural
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