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Open AccessJournal ArticleDOI

Reliable and Robust Observer for Simultaneously Estimating State-of-Charge and State-of-Health of LiFePO4 Batteries

TLDR
A reliable and robust observer is proposed which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates and the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters.
Abstract
Batteries are everywhere, in all forms of transportation, electronics, and constitute a method to store clean energy. Among the diverse types available, the lithium-iron-phosphate (LiFePO4) battery stands out for its common usage in many applications. For the battery’s safe operation, the state of charge (SOC) and state of health (SOH) estimations are essential. Therefore, a reliable and robust observer is proposed in this paper which could estimate the SOC and SOH of LiFePO4 batteries simultaneously with high accuracy rates. For this purpose, a battery model was developed by establishing an equivalent-circuit model with the ambient temperature and the current as inputs, while the measured output was adopted to be the voltage where current and terminal voltage sensors are utilized. Another vital contribution is formulating a comprehensive model that combines three parts: a thermal model, an electrical model, and an aging model. To ensure high accuracy rates of the proposed observer, we adopt the use of the dual extend Kalman filter (DEKF) for the SOC and SOH estimation of LiFePO4 batteries. To test the effectiveness of the proposed observer, various simulations and test cases were performed where the construction of the battery system and the simulation were done using MATLAB. The findings confirm that the best observer was a voltage-temperature (VT) observer, which could observe SOC accurately with great robustness, while an open-loop observer was used to observe the SOH. Furthermore, the robustness of the designed observer was proved by simulating ill-conditions that involve wrong initial estimates and wrong model parameters. The results demonstrate the reliability and robustness of the proposed observer for simultaneously estimating the SOC and SOH of LiFePO4 batteries.

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Novel Control Strategy for Enhancing Microgrid Operation Connected to Photovoltaic Generation and Energy Storage Systems

TL;DR: The enhanced operation and control of DC microgrid systems, which are based on photovoltaic modules, battery storage systems, and DC load, are presented and it is illustrated that the grid-tied mode of operation regulated by voltage source converter control offers more stability than the islanded mode.
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Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

- 01 Jan 2022 - 
TL;DR: In this paper , a new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online monitoring of computer numerical control (CNC) machines is introduced.
Journal ArticleDOI

Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

TL;DR: A new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online monitoring of computer numerical control (CNC) machines is introduced, indicating that deep learning can outperform other traditional machine learning methods for vibration control.
Journal ArticleDOI

Comprehensive Review on Renewable Energy Sources in Egypt—Current Status, Grid Codes and Future Vision

- 01 Jan 2022 - 
TL;DR: In this paper , the authors present a recent review supported by a statistical analysis about the current situation in Egypt according to the last data carried out from local/global reports, and discuss specifications of technical design standards, terms, and equipment parameters for connecting small, medium, and large-scale solar plants, respectively to the Egyptian grid in accordance with the Electricity Distribution Code (EDC), Solar Energy Grid Connection Code (SEGCC), and the Grid Code (GC).
Journal ArticleDOI

Comprehensive Review on Renewable Energy Sources in Egypt—Current Status, Grid Codes and Future Vision

TL;DR: In this article , the authors present a recent review supported by a statistical analysis about the current situation in Egypt according to the last data carried out from local/global reports, and discuss specifications of technical design standards, terms, and equipment parameters for connecting small, medium, and large-scale solar plants, respectively to the Egyptian grid in accordance with the Electricity Distribution Code (EDC), Solar Energy Grid Connection Code (SEGCC), and the Grid Code (GC).
References
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Journal ArticleDOI

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters.

TL;DR: In this paper, a new infrastructure based on machine learning is introduced to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake, and the proposed infrastructure validates the amount of data loss via communication channels and the internet connection.
Journal ArticleDOI

Electrochemical-thermal modeling and experimental validation of commercial graphite/LiFePO4 pouch lithium-ion batteries

TL;DR: In this paper, a high-fidelity fully coupled electrochemical-thermal model for a commercial 20 Ah battery is developed to simulate the distribution of electrochemical and thermal variables through 48 electrode layers in the pouch cell.
Journal ArticleDOI

The sequential algorithm for combined state of charge and state of health estimation of lithium-ion battery based on active current injection

TL;DR: A sequential algorithm, which uses the frequency-scale separation and estimates the parameters/states sequentially by injecting currents with different frequencies by incorporating a high-pass filter, the ohmic resistance and the RC pair can be independently characterized by injecting high-frequency and medium-frequency currents, respectively.
Journal ArticleDOI

Enhancing Diagnostic Accuracy of Transformer Faults Using Teaching-Learning-Based Optimization

TL;DR: In this paper, a novel approach is proposed to enhance the diagnostic accuracy of the transformer faults based on introducing new gas concentration percentages limits and gases' ratios that help to separate the conflict between the diverse transformer faults.
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