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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

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

Revista

Título: Neural Computing and Applications

ISSN: 0941-0643

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14