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

• Type: Article in International Scientific Journal
• Date: 2023

Authors

Chen, LP (Author) Other; Xu, CC (Author) Other; Bao, XY (Author) Other; António Mendes Lopes (Author) FEUP; Li, PH (Author) Other; Zhang, CL (Author) Other

Journal

Title: Neural Computing and Applications

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-00Y-5MC

DOI: 10.1007/s00521-023-08471-7

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14