State-of-health estimation of Lithium-ion battery based on back-propagation neural network with adaptive hidden layer

被引：0
作者：
Liping Chen
Changcheng Xu
Xinyuan Bao
António Lopes
Penghua Li
Chaolong Zhang
机构：
[1] Hefei University of Technology,School of Electrical Engineering and Automation
[2] University of Porto,LAETA/INEGI, Faculty of Engineering
[3] Chongqing University of Posts and Telecommunications,Automotive Electronics Engineering Research Center, College of Automation
[4] Jinling Institute of Technology,College of Intelligent Science and Control Engineering
来源：
Neural Computing and Applications | 2023年 / 35卷
关键词：
Back-propagation neural network; Adaptive hidden layer; Lithium-ion battery; State-of-health;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The reliability and safety of lithium-ion batteries (LIBs) are key issues in battery applications. Accurate prediction of the state-of-health (SOH) of LIBs can reduce or even avoid battery-related accidents. In this paper, a new back-propagation neural network (BPNN) is proposed to predict the SOH of LIBs. The BPNN uses as input the LIB voltage, current and temperature, as well as the charging time, since it is strongly correlated with the SOH. The number of hidden layer nodes is adaptively set based on the training data in order to improve the generalization capability of the BPNN. The effectiveness and robustness of the proposed scheme is verified using four distinct battery datasets and different training data. Experimental results show that the new BPNN is able to accurately predict the SOH of LIBs, revealing superiority when compared to other alternatives.
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页码：14169 / 14182
页数：13
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