Development of advanced energy storage system using fuzzy control

Muntaser, Akram; Elwarfalli, Hamed; Kumar, J. Siva; Subramanyam, Guru

doi:10.1109/naecon.2016.7856795

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…Lithium-ion battery (LIB) is the most common type of batteries commercially used these days and that is due to its features such as high energy density, lack of memory effect, and high charge and discharge rate capabilities [15,16]. The equivalent circuit of the battery is shown below in Fig.…”

Section: Lithium-ion Batterymentioning

confidence: 99%

DC Microgrid Based on Battery, Photovoltaic, and Fuel Cells; Design and Control

Muntaser¹,

Ragb²,

Elwrfalli³

2022

Preprint

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Microgrids offer flexibility in power generation in a way of using multiple renewable energy sources. In the past few years, microgrids become a very active research area in terms of design and control strategies. Most of the microgrids use DC/DC converters to connect renewable energy sources to the load. In this paper, the simulation model of a DC microgrid with three different energy sources (Lithium-ion battery (LIB), photovoltaic (PV) array, and fuel cell) and external variant power load is built with MATLAB/Simulink and the simulative results show that the stability of DC microgrid can be guaranteed by the proposed maximum power point controller MPPT. The three energy sources are connected to the load through DC/DC converters, one for each. This type of topology ensures protection for each energy source as well as optimum stability at the load.

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Section: Lithium-ion Batterymentioning

confidence: 99%

DC Microgrid Based on Battery, Photovoltaic, and Fuel Cells; Design and Control

Muntaser¹,

Ragb²,

Elwrfalli³

2022

Preprint

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“…Many batteries are available for these applications, but the most promising proves to be lithium-ion batteries (LIBs), due mostly to their ability to provide high voltages and large energy storage densities [5]. LIBs have found uses in a very wide range of applications as environmentally friendly electric vehicles [6,7] and rechargeable phone batteries [8][9][10]. The main advantage of Lithium-ion batteries (Li-ion) is their high energy density.…”

Section: Introductionmentioning

confidence: 99%

Design and Fabrication of Lithium-ion Batteries with Different Anodes

Muntaser¹

2023

Preprint

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Lithium-ion (Li-ion) batteries are becoming more common in portable electronic devices due to their high energy density, lack of memory effect, and high charge and discharge rate capabilities. Li-ion batteries are a relatively new technology, first marketed in the early 1990s, and research and development work are ongoing to improve safety and increase capacity, charge/discharge rate, and lifetime [1]. In this paper, we review the types and characteristics of Li-ion batteries and summarize charging methods and safety considerations. Following, we design and fabricate Li-ion battery and highlight the steps of fabrication that ensure fast charge/discharge rates that give the battery more reliability and high energy density

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“…All these algorithms are regression algorithms that are necessary and important to be utilized for dealing with the computations of prediction and for help in identifying the most accurate to reach of it. The house of prediction is capable of introducing all these algorithms so that there is a proper function of the prediction method with the absolute rate of accuracy [4], [7], [8], [10], [11], [13], [14], [15]. Some of the other concepts that are related to house prediction using machine learning is elaborating the price prediction with the context of estimation.…”

Section: Introductionmentioning

confidence: 99%

Hybrid GRU-LSTM Recurrent Neural Network-Based Model for Real Estate Price Prediction

Ragb¹,

Muntaser²,

Jera³

et al. 2023

Preprint

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<p>Real estate prices are an important reflection of the economy and their prices are great interest to both buyers and sellers. Hundreds of houses are sold every day and the buyer asks himself what is the reasonable price that this house deserves. In this paper, a new regression model is proposed for the accurate prediction of house prices. This model is based on the hybrid recurrent neural network where the Gated Recurrent Unit (GRU) is fused with the Long Short-Term Memory (LSTM) and applied to a particular dataset that characterizes houses in Boston. Massachusetts dataset from Scikit-learn is used in this research to train and evaluate this regression model using the data on Boston housing. Several experiments were conducted on the proposed algorithm and evaluated with the commonly used metrics. The results of these experiments showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually. It also has better accuracy and lower Root Mean Square Error when compared to several states of art methodologies.</p>

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Development of advanced energy storage system using fuzzy control

Cited by 5 publications

References 8 publications

DC Microgrid Based on Battery, Photovoltaic, and Fuel Cells; Design and Control

DC Microgrid Based on Battery, Photovoltaic, and Fuel Cells; Design and Control

Design and Fabrication of Lithium-ion Batteries with Different Anodes

Hybrid GRU-LSTM Recurrent Neural Network-Based Model for Real Estate Price Prediction

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