Search

Distributed generation (DG) is increasing in day to day life to meet the global energy consumption demand. One of the major problems with such DG is islanding. The islanding is caused in the grid connected system when a part of DG is disconnected from the main grid and continues to feed the load connected. As per the IEEE 1547 DG interconnection standards, the islanding should be detected within 2 s after islanding. This paper presents, a new rate of change of frequency (ROCOF) based hybrid islanding detection approach. It combines the passive and active ROCOF methods. The simulation results indicate this method is very efficiently classifying the islanding and non islanding events. It detects the zero power balanced islanding within 200 ms and unbalanced islanding within 100 ms. The simulation results are carried on Matlab/Simulink 2018B environment.

In this paper, a dual stator induction machine is studied. This nonlinear system is built on the Matlab/Simulink tool to test the efficiency of the direct torque control loop for controlling this complicated system. It makes the speed and torque tracking more comfortable. On the other side, it is mandatory to assure the independence of the electrical alimentation of the dual stator. All these goals are guaranteed with this direct torque control technique. The conventional direct torque control loop is based on the switches’ controllers. This method makes many fluctuations in the speed performances, the flux evolution, and the electromagnet torque progress. The conventional direct torque control drawbacks are resolved by improving this control loop using the fuzzy control topology. Then, an improved direct torque control loop using the intelligent controllers is presented to control the torque and speed of a dual stator induction machine and supervise the two stator fluxes and currents. Matlab/Simulink tool was used for implanting this innovation and showing the behavior of the results.

The transportation systems around the world are drastically going electrified as a trend because of inevitable heed being paid towards global warming caused by carbon dioxide emissions. A variety of equipment is contained inside these modern versions of the electrified vehicles, and many appear to be similar in types. The internal electric motor and battery power used in these designs are largely responsible for their robustness and durability in transportation systems. The energy conservation problem, on the other hand, is a vital factor that must be handled in order to ensure overall performance and cost-effectiveness. This paper provides a thorough examination of the internal equipment needed to facilitate and optimize the transportation system’s energetic global success with these critical parameters. In the present study, a specific investigation is emphasized which mainly aims to recognize a new electric motor within the propulsion system. The same type of machine is discussed by elaborating internal designing through the implementation of the corresponding mathematical model and along with the corresponding electronic inverter. The associated control system is also established and implemented. Therefore, a significant theoretical contribution, made in this paper, can be used to examine the performance of a particular electrical machine for EV applications. A robust FOC-based control application confirmed this analysis. When a vehicle’s power needs to be increased rapidly, this one-of-a-kind electrical machine proves its worth. Using the MATLAB simulation method, the robustness of this system’s control has been demonstrated in this special mode.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.

An intelligent control strategy is proposed in this paper which suggests the Optimum Power Quality Enhancement (OPQE) of grid-connected hybrid power systems with solar photovoltaic, wind turbines, and battery storage. Unified Power Quality Conditioner with Active and Reactive power (UPQC-PQ) is designed with Atom Search Optimization (ASO) based Fractional-order Proportional Integral Derivative (FOPID) controller in the proposed Hybrid Renewable Energy Sources (HRES) system. The main aim is to regulate voltage while reducing power loss and reducing Total Harmonic Distortion (THD). UPQC-PQ is used to mitigate the Power Quality (PQ) problems such as sag, swell, interruptions, real power, reactive power and THD reductions related to voltage/current by using ASO based FOPID controller. The developed technique is demonstrated in various modes: simultaneous to improve PQ reinforcement and RES power injection, PRES > 0, PRES = 0. The results are then compared to those obtained using previous literature methods such as PI controller, GSA, BBO, GWO, ESA, RFA, and GA and found the proposed approach is efficient. The MATLAB/Simulink work framework is used to create the model.

This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.

Electric vehicle seems largely based on electrical machines. Finding the best motor type seems be important for having more performances and a transport system robustness. In this work, we present an analytical model of the synchronous machine with variable reluctances in linear and saturated modes. The angular position of the rotor (θ) and the phase current (i) will beused as parameters. The analytical model of this machine will allow us to determinate its magnetic characteristics such inductors, magnetic flux and electromagnetic torque. The results obtained by the analytical model are compared with those obtained by the finite element method. So, basing on Matlab/Simulink tool and by working with finite element method, these results are depicted and the paper objective is illustrated.

This article belongs to the Special Issue Future Perspectives of Safety and Reliability Assessment for Electric-Powered Vehicles. Copyright © 2021 by the authors. This paper presents a straightforward power management algorithm that supervises the contribution of more than one energy source for charging a vehicle, even if the car is in motion. The system is composed of a wireless charging system, photovoltaic (PV) generator, fuel cell (FC), and a battery system. It also contains a group of power converters associated with each energy resource to make the necessary adaptation between the input and output electrical signals. The boost converter relates to the PV/FC, and the boost–buck converter is connected with the battery pack. In this work, the wireless charging, FC, and PV systems are connected in parallel via a DC/DC converter for feeding the battery bank when the given energy is in excess. Therefore, for each of these elements, the mathematical model is formulated, then the corresponding power management loop is built, which presents the significant contribution of this paper. The efficient power management methodology proposed in this work was verified on Matlab/Simulink platforms. The battery state of charge and the hydrogen consumption obtained results were compared to show the effectiveness of this multi-source system.

This chapter deals with the problem of energy storage inside an electric vehicle. The main source of energy is based on a wireless system. This recharge tool regroups inside several components as the storage system, which consists of an ensemble of batteries and serving as the main power source, a special electronic converter that is based on the buck-boost principle and a coil receiver placed undo the vehicle. From the other side, one or more than coil transmitters are placed on the road, where the vehicle is. Modeling all of these components and expressing their mathematical models seems interesting for defining the possible control method that can guarantee a high autonomy when the vehicle is moving. So, taking into account if the care is driving or stopped, this recharge system is studied for verifying the effectiveness of this recharge system and showing the relationship between the vehicle situation and the quantity of received power. Using the platform Matlab/Simulink the results were shown and discussed.

The transportation systems have become more electrified, and the major countries of the world program using electric scooters, electric bicycles, electric trains, electric buses, and electric vehicles for their transport. The traditional energy resource stocks are still decreasing rapidly, which makes the world afraid about the future of the transport sector. Therefore, several international restrictions and laws have limited using this kind of energy in relation to the transport sector by encouraging public transport and making a high taxes for the highly energy-consuming cars. The robustness and the efficiency of transportation systems designs are related especially to the internal electric motor and to the battery capacity used. From the other side, the energy management problem presents a serious factor that must be optimized in order to guarantee the overall efficiency and rentability. This chapter explores the modeling and control of hybrid electric vehicles.

This paper overviews a theoretical and practical design issue related to the wireless recharge system. It verifies a mathematical method for identifying the transfer time for the wireless transfer system using a functional prototype and concerning the speed movement of the receiver face the transmitter. The accurate analysis tries to define the static and dynamic model relation and parameters. The influence of the speed value in this system is depicted inside the given model. The main objective of this work is to express the dynamic model of the wireless charger system (wr) and defines the related parameters. The results try to prove the relationship between the outputted power and the car's speed.

In this paper, we expose and Control the charging system of a solar vehicle. The main energy source is the photovoltaic system. The main objective, in this work, is to study the influence of the PV (Photovoltaic) cell number and the vehicle surface cover in relation to the outputted power. Basing on these variables and taken account the vehicle situation and position, the battery SOC (State of charge) is controlled and supervised in order to define the best way and the high yield factor in relation to this source of energy. Matlab Simulink is the used software for doing the needed simulations.

In automotive application, electric vehicles and electrical busses are based essentially on electrical machine. Controlling the motor electric current is the only possible solution for varying it speed. This kind of application needs a high torque/speed relation factor for moving this transportation system (load). Brushless direct current (BLDC) machine is one of the most motors who can be used for this system, however the BLDC motors performances are highly affected by the disturbance of the load. Controlling its speed needs generally a high precision control method and needs knowing the motor parameters for finding the optimal regulator parameters. Basing on various solutions, it is possible to calculate these parameters without necessity finding the motor parameters. Particle swarm optimization (PSO) method is one of the optimization solutions who can resolve this problem. Therefore, this paper deals with the implantation of this optimization tool for help finding the optimal proportional-integral (PI) parameters for controlling the BLDC speed.

Recently, most transportation systems have used an integrated electrical machine in their traction scheme, resulting in a hybrid electrified vehicle. As a result, an energy source is required to provide the necessary electric power to this traction portion. However, this cannot be efficient without a reliable recharging method and a practical solution. This study discusses the wireless recharge solutions and tests the system’s effectiveness under various external and internal conditions. Moreover, the Maxwell tool is used in this research to provide a complete examination of the coils’ position, size, number, and magnetic flux evolution when the coils are translated. In addition, the mutual inductance for each of these positions is computed to determine the ideal conditions for employing the wireless recharge tool for every charging application. A thorough mathematical analysis is also presented, and the findings clearly demonstrate the relationship between the magnet flux and the various external conditions employed in this investigation.

Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the city&rsquo, host&rsquo, s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles and buildings communication for sharing some special information related to the vehicle status and to the road condition. EVs can share their own information related to the energy experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on bi-communication between vehicles and buildings. The principle of this method is established on two sections, the first one is related to vehicles&rsquo, classification and the second one is attached to the buildings&rsquo, recommendation, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and the neural network was employed, for giving the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle&rsquo, s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations were discussed in this approach. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, for showing the efficiency of this approach.

Smart cities and smart technologies have been incorporated into several axes to increase the comfort of life. The connected building's concept was introduced for this reason. However, it was utilized in power management for better organizing, greater buildings management, and monetary savings. Cars technologies and the number of vehicles are also involved; Nowadays, each house has at least one car. Technological evolution helped to make those cars intelligent and connected. In the latest versions, the majority of those cars were equipped with several sensors, several communication protocols and a principal electrical control unit (ECU), especially for the electric vehicle model. This type of architecture was an essential element in a smart city, thus, it helps to manage power and decide when a vehicle needs to be charged. Based on the smart city concept and using possible network communication between buildings and vehicles, EVs can share their own information related to the powerful experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, we propose in this paper a new approach for power management inside an electric vehicle based on bi-communication between vehicles and buildings. The proposed approach is founded on two essential parts; the first is related to vehicles’ classification and buildings’ recommendation according to different car positions. Two algorithms, related to the SVC and neural network was employed in this work for implementing the final process. Different possibilities and situations were discussed for this approach. The proposed method was tested and validated using Simulink/Matlab application. The state of charge of the used battery was compared at the end of this work, for two specified cases, for showing the contribution of this approach.

In this paper, we expose an energy optimization method for an ensemble of electrical vehicles. The idea is based on vehicles information sharing in a cloud database. Installed sensors in a vehicle will allow us to know the needed vehicle information. The regrouped data will be used then for building an optimal energy experience which will be shared with the new vehicles. The neural network technique is used here for the learning phases. The proposed application for this work was validated for five connected cars. Matlab Simulink was used for simulating the results and given the present statistics.

In this paper, we expose and discuss the importance of application of recharge systems to an electrical vehicle. In addition to its vital role in supplying vehicle with the required power recharge system has many type the most important of then is wireless charge system that transmits power from transmitter to receiver without any contact. It is obvious that this power is variable in relation to the speed and has a main function which is loading the battery. In this work our main objective is to focus on the change of SOC when we vary the car's speed by applying Matlab Simulink in order to obtain the needed results.

DC-DC boost converters which provide dc output voltage are widely used in several applications such as photovoltaic power systems, hybrid vehicle, cellphones… Maintaining a constant output voltage of the DC-DC boost converter under variable input voltage and load changes have always attracted the attention of many researchers and many studies have been published on this issue. This paper, present an averaged model of DC-DC boost converter in continuous conduction mode (CCM). A single loop control is generally used to regulate the output voltage of the boost converter. In this paper, a cascade loop control includes two cascaded control loops; internal current control, and external voltage control is used to improve control performance. Subsequently, Proportional-Integral (PI) controller is used to tracks the input desired reference signal and eliminates the load disturbances. The mathematical model and the controller are implemented in the MATLAB/SIMULINK environment. Simulations results are presented and discussed to evaluate the performance of the controller.

Electrical cars and hybrid electric vehicles are usually based on an electrical machine. It is widely used in the traction phase and in some particular applications during the recharging phase. Kno...

This paper presents an improved speed estimator for a permanent magnet synchronous motor (PMSM). It focuses on hybrid electric vehicles (HEVs). The speed estimator is based on reactive power model reference adaptive system (Q-MRAS). The MRAS parameters are tuned using particle swarm optimization (PSO) algorithms. The proposed method has been experimentally verified with a 100 kW, 5000 rpm PMSM, and a good agreement between the measured speed and the estimated speed is found. It is shown that the proposed method is able to handle the transition into the flux weakening mode without any problem.

As world population continues to grow and the limited amount of fossil fuels begin to diminish, it may not be possible to afford the needed amount of energy demanded by the world by only using fossil fuels. Meanwhile, the abundant nature of renewable energy sources brings new beginning for next generations. Greater penetration of electric vehicles will play an important role in building green and healthy world. The main remaining issue to make the switch from conventional to electric vehicle is performance cost; Efficient EVs that can drive for long distances, on single charge, are still expensive for ordinary consumer. To address this range problem, many attempts have been made during last decade. The goal was to conceive a power efficient electric vehicle, capable of managing its energy and reach longer distances. It depends on the electrical architectures and used algorithms.This paper adds new perspective for power Management in EVs; The proposed methodology introduces a new power management architecture based on communication and car learning. The conventional software level in EV has been replaced with self readjustable software. EVs are connected through a database, and can upload or download adjustment parameters while software is running.To take advantage of the new architecture, a new learning technique concept is introduced too, based on Cloud experience exchange between Electric Vehicles. This enhancement aims to build a better EV experience in power management through Cloud sharing and definitely cut with conventional architecture that may have reached its boundaries.

The objective of this paper is to define correctly the overall components of the wireless recharge system and study its efficiency in relation to the vehicle situation under various speeds values. This study required a detailed mathematical analysis and needs to define the correct relationship between more than parameters as the vehicle speed, the battery SOC, the distance between the two coils, the injected electrical power and the transmitter/receiver dimension and number. Also, the recharge time factor was taken in consideration in this phase. The analysis study was elaborated using Matlab/Simulink software and the obtained results were showed that the efficiency of this recharge system depends, numerous and essential factors, especially the vehicle speed. So, a selective recommendation was given at the end of this study.

In this chapter, two computational algorithms are proposed and applied on an estimation algorithm, in order to improve the global performance of the estimation phase. The proposed system is studied based on the Model Reference Adaptive System (MRAS). The importance of the estimation phase in a large applications number is basically observed on the applications applied on electrical motors, where a lot number of parameters are measured with real measurement equipments, as Tesla Meter, speed shaft, and others. The idea is based generally on the software applications, where it is possible to guarantee the desired estimation phase using a software algorithm. In this chapter the MRAS technique is proposed as the software algorithm, for replacing the measurement materials for online estimate the overall characteristic PMSM parameters. Our approach aims to ameliorate the MRAS technique with intelligent optimization methods called BFO and PSO.

In this paper, an improved robust vector control strategy is designed to drive the Permanent magnet synchronous motor in a wide speed range mode. The designed control method guarantees the precision and robustness of speed regulation performance by using recurrent neural network architecture. The stator current controller parameter tuning problems, which characterize this control strategy, are resolved using a bacterial foraging optimization algorithm to find the optimal parameters of the current controllers used. A field weakening control algorithm generates an adaptive magnetizing current command to achieve the desired high speed mode. The robustness and effectiveness of the global control scheme are verified through computer simulations established under a Matlab-Simulink environment.

A robust high speed algorithm, RHSA, applied in the field oriented vector control strategy, is presented in this paper for the PMSM sensorless control loop. The present algorithm, RHSA, is formed for online generating the direct and quadrature reference stator current. Based on the fuzzy logic controller and the field weakening principle, the RHSA is constructing. The sliding mode, based on Lyapunouv stability criterion, is used for the online speed estimation. The Robustness of the global control scheme is verified through computer simulation using matlab/Simulink software.