A deep belief network approach to remaining capacity estimation for lithium-ion batteries based on charging process features

2022 ◽  
Vol 48 ◽  
pp. 103825
Author(s):  
Mengda Cao ◽  
Tao Zhang ◽  
Jia Wang ◽  
Yajie Liu
Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.


PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0200169 ◽  
Author(s):  
Zengkai Wang ◽  
Shengkui Zeng ◽  
Jianbin Guo ◽  
Taichun Qin

2021 ◽  
Vol 482 ◽  
pp. 228863
Author(s):  
Weihan Li ◽  
Neil Sengupta ◽  
Philipp Dechent ◽  
David Howey ◽  
Anuradha Annaswamy ◽  
...  

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