Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

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 theSoC

Intelligent control of battery energy storage for microgrid energy management using ANN

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

Fuzzy logic-based controller of the bidirectional direct current to direct current converter in microgrid

Microgrids are small-scale power networks that include renewable energy sources, load, energy storage systems, and energy management systems (EMS). Lithium-ion batteries are the most used battery for energy storage in microgrids due to their advantages over other types of batteries. However, to protect the battery from the explosion and to manage to charge and discharge based on state-of-charge (SoC) value, this type of battery requires the use of an energy management system. The main objective of this paper is to propose an intelligent control strategy for energy management in the microgrid to control the charge and discharge of Li-ion batteries to stabilize the system and reduce the cost of electricity due to the high cost of grid electricity. The proposed technique is based on a fuzzy logic controller (FLC) for voltage control. The FLC is based on the measured voltage of the direct current (DC) bus and the fixed reference voltage to generate buck/boost converter signal control. The proposed technique has been simulated and tested using MATLAB/Simulink software which illustrates the tracking of desired power and DC bus voltage regulation. The simulation results confirm that the proposed systems can diminish the deviations of the system's voltage

Microgrid energy management and monitoring systems: A comprehensive review

Microgrid (MG) technologies offer users attractive characteristics such as enhanced power quality, stability, sustainability, and environmentally friendly energy through a control and Energy Management System (EMS). Microgrids are enabled by integrating such distributed energy sources into the utility grid. The microgrid concept is proposed to create a self-contained system composed of distributed energy resources capable of operating in an isolated mode during grid disruptions. With the Internet of Things (IoT) daily technological advancements and updates, intelligent microgrids, the critical components of the future smart grid, are integrating an increasing number of IoT architectures and technologies for applications aimed at developing, controlling, monitoring, and protecting microgrids. Microgrids are composed of various distributed generators (DG), which may include renewable and non-renewable energy sources. As a result, a proper control strategy and monitoring system must guarantee that MG power is transferred efficiently to sensitive loads and the primary grid. This paper evaluates MG control strategies in detail and classifies them according to their level of protection, energy conversion, integration, benefits, and drawbacks. This paper also shows the role of the IoT and monitoring systems for energy management and data analysis in the microgrid. Additionally, this analysis highlights numerous elements, obstacles, and issues regarding the long-term development of MG control technologies in next-generation intelligent grid applications. This paper can be used as a reference for all new microgrid energy management and monitoring research

Hybrid Sliding Mode Control of Full-Car Semi-Active Suspension Systems

With the advance in technology in driving vehicles, there is currently more emphasis on developing advanced control systems for better road handling stability and ride comfort. However, one of the challenging problems in the design and implementation of intelligent suspension systems is that there is currently no solution supporting the export of generic suspension models and control components for integration into embedded Electronic Control Units (ECUs). This significantly limits the usage of embedded suspension components in automotive production code software as it requires very high efforts in implementation, manual testing, and fulfilling coding requirements. This paper introduces a new dynamic model of full-car suspension system with semi-active suspension behavior and provides a hybrid sliding mode approach for control of full-car suspension dynamics such that the road handling stability and ride comfort characteristics are ensured. The semi-active suspension model and the hybrid sliding mode controller are implemented as Functional Mock-Up Units (FMUs) conforming to the Functional Mock-Up Interface for embedded systems (eFMI) and are calibrated with a set experimental tests using a 1/5 Soben-car test bench. The methods and prototype implementation proposed in this paper allow both model and controller re-usability and provide a generic way of integrating models and control software into embedded ECUs

Multi-Agent-Based Fault Location and Cyber-Attack Detection in Distribution System

Accurate fault location is challenging due to the distribution network’s various branches, complicated topology, and the increasing penetration of distributed energy resources (DERs). The diagnostics for power system faults are based on fault localization, isolation, and smart power restoration. Adaptive multi-agent systems (MAS) can improve the reliability, speed, selectivity, and robustness of power system protection. This paper proposes a MAS-based adaptive protection mechanism for fault location in smart grid applications. This study developed a novel distributed intelligent-based multi-agent prevention and mitigation technique for power systems against electrical faults and cyber-attacks. Simulation studies are performed on a platform constructed by interconnecting the power distribution system of Kenitra city developed in MATLAB/SIMULINK and the multi-agent system implemented in the JADE platform. The simulation results demonstrate the effectiveness of the proposed technique