文章基本信息

标题：Comparison of Intelligent Methods of SOC Estimation for Battery of Photovoltaic System
作者：Tae-Hyun Cho ; Hye-Rin Hwang ; Jong-Hyun Lee 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：9
DOI：10.14569/IJACSA.2018.090907
出版社：Science and Information Society (SAI)
摘要：It is essential to estimate the state of charge (SOC) of lead-acid batteries to improve the stability and reliability of photovoltaic systems. In this paper, we propose SOC estimation methods for a lead-acid battery using a feed-forward neural network (FFNN) and a recurrent neural network (RNN) with a gradient descent (GD), a levenberg–marquardt (LM), and a scaled conjugate gradient (SCG). Additionally, an adaptive neuro-fuzzy inference system (ANFIS) with a hybrid method was proposed. The voltage and current are used as input data of neural networks to estimate the battery SOC. Experimental results show that the RNN with LM has the best performance for the mean squared error, but the ANFIS has the highest convergence speed.
关键词：Lead-acid battery; SOC; FFNN; RNN; ANFIS; gradient descent; levenberg-marquardt; scaled conjugate gradient