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DOI: 10.14569/IJACSA.2018.090907
PDF

Comparison of Intelligent Methods of SOC Estimation for Battery of Photovoltaic System

Author 1: Tae-Hyun Cho
Author 2: Hye-Rin Hwang
Author 3: Jong-Hyun Lee
Author 4: In-Soo Lee

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 9, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: 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.

Keywords: Lead-acid battery; SOC; FFNN; RNN; ANFIS; gradient descent; levenberg-marquardt; scaled conjugate gradient

Tae-Hyun Cho, Hye-Rin Hwang, Jong-Hyun Lee and In-Soo Lee, “Comparison of Intelligent Methods of SOC Estimation for Battery of Photovoltaic System” International Journal of Advanced Computer Science and Applications(IJACSA), 9(9), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090907

@article{Cho2018,
title = {Comparison of Intelligent Methods of SOC Estimation for Battery of Photovoltaic System},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090907},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090907},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {9},
author = {Tae-Hyun Cho and Hye-Rin Hwang and Jong-Hyun Lee and In-Soo Lee}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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