<span class="fontstyle0">In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of the<br />SoC.</span>
<span>In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.</span>
This paper presents new physical properties of nanocrystalline SnFe 2 O 4 ferrites, synthesized by co-precipitation method. Magnetization measurement indicates superparamagnetic behavior; the blocking temperature is about 300 K. We performed X-ray diffraction, infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), Energy Dispersive Spectroscopy (EDS), UV-visible measurement, superconducting quantum interference device (SQUID) and zero-field-cooled (ZFC)/field-cooled (FC) measurements. TEM images show the high crystallinity and grain size of ferrite nanocrystals. The refinement result showed that the type of the cationic distribution over the tetrahedral and octahedral sites in the nanocrystalline lattice is a partially inverse spinel. The obtained UV-vis data were used to calculate the energy band gap (3.82 eV) of nanocrystalline SnFe 2 O 4 . On the other hand the magnetic properties of the samples; saturation magnetization (M s ) and coercive field (H c ) were determined using a superconducting quantum interference device (SQUID).
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