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

• Published in: Neural computing & applications 2023-07, Vol.35 (19), p.14169-14182
• Main Authors: Chen, Liping, Xu, Changcheng, Bao, Xinyuan, Lopes, António, Li, Penghua, Zhang, Chaolong
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Automation; Back propagation; Back propagation networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Emission standards; Engineering; Image Processing and Computer Vision; Lithium; Lithium-ion batteries; Methods; Neural networks; Original Article; Probability and Statistics in Computer Science; Propagation; Rechargeable batteries; Saturn; Training
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08471-7

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.