State-of-health estimation of Lithium-ion battery based on back-propagation neural network with adaptive hidden layer
Liping Chen, Changcheng Xu, Xinyuan Bao, António M. Lopes, Penghua Li, Chaolong Zhang
Abstract
Abstract 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.