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Among the different clean energy conversion devices, fuel cells systems are largely employed due to their high-power densities and efficiencies. Several models are available in literature to characterize their functioning, both for stationary and dynamic behaviors. Vehicular application of a fuel cell requires a model that is accurate enough, and simple enough for quick computations for real-time reporting. To meet these constraints, the model of the different sources must be simple and efficient as they are used in the energy management strategy block to estimate the power references to be requested from the sources. The objective of this paper is then to develop a fuel cell model that is both efficient at representing the dynamic and static behavior of the fuel cell, and low consuming in terms of storage space and computation time. The aim is to have a suitable model for use in online simulations for vehicle energy management applications. The proposed model is compared to a representative dynamic model, and the results approve the performance of the model. An experimental test bench is performed to evaluate and validate the proposed model. The model shows good agreement with the experimental data.

This study is a contribution to development of material based on transition metal oxides with application in electrochemical devices. A pyrochlore-like solid solution with general chemical formula ...

Photocatalytic production of H2 from the decomposition of water has attracted increased attention, as the environmental damages caused by the rapid evolution of industry are threatening the development of human society. This energy production is considered a green and eco-friendly resource. It has the potential to replace the carbon component of fuelling the society; on the other hand allows for the limitation of greenhouse gas emissions, thereby mitigating the worsening of climate change. While titanium dioxide is widely used in the photocatalytic field, its yield is still low due to the fast recombination of the photo-generated charge carriers. Graphitic carbon nitride (g-C3N4) possessing high thermal and chemical stability, non-toxicity and low band gap energy is a promising candidate for photocatalytic applications. In this study the exfoliation of the bulk g-C3N4 made with melamine was synthesised via a chemical approach using nitric acid at room temperature, in order to get prolonged carrier lifetime. Moreover the surface of bulk g-C3N4 made with melamine and urea and the exfoliated g-C3N4 made with melamine was modified with graphene (0.5 wt% and 1 wt%). Hydrogen generation from methanol/water mix proved that only hydrogen was produced in the unmodified bulk and exfoliated g-C3N4 , while H2, CH4 and CO have been generated in the modified specimens with graphene. This was assigned to the increased spatial charge carrier separation conducted by graphene.

The new type of coronavirus COVID-19 has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Many mechanisms to detect the coronavirus disease COVID-19 are used like clinical analysis of chest CT scan images and blood test results. Several methods can be used to detect the presence of Covid-19 such as medical detection Kits. Though, such devices require huge costs and it takes time to install and use. In addition to that, the negatively diagnosed patients consume many of the needed resources and space in hospitals that could be used by other patients having higher chance of being infected. In this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Nowadays, almost every household possesses at least one smartphone with powerful processors and advanced sensors. By combining the data collected by the various sensors, such as temperature data, coughing and breathing recording with questionnaires about the background of the phone user, artificial intelligence (AI) and advanced signal processing tools may analyze the recorded data in order to produce viable diagnosis of Covid-19 infection, and hence alleviate the ongoing pressure on the health system (hospitals and stuff).

Fuel cell (FC) vehicles seem to be the solution for clean and efficient transportation. However, the coupling of the fuel cell with another power source remains essential to overcome its low power density and preserve its lifetime as long as possible. Thanks to its fast response and high power density, the ultra-capacitor (UC) can meet a fast and short-duration engine power demand. This paper presents a power management strategy between the FC and the UC, based on a neural network (NN). The proposed NN model is trained with wavelet decomposition outputs. To ensure that both sources operate optimally, the fuel cell power limits as well as the state of charge (SOC) of the UC are considered.

This study proposes a fuzzy-based MPPT controller used to extract the maximum power point (MPPT) from the WECS using doubly fed induction generator (DFIG). The MPPT was accomplished by applying control to the generator speed and blade pitch angle. The parameters of the FLC are optimized by using a genetic algorithm (GA). Initial simulation results prove that the proposed MPPT algorithm possesses good dynamic and steady state performances under different operating conditions.

Battery storage system is an essential component for the depletion of the microgrid, and of the smart grid on a larger scale. It acts as an interface between the highly intermittent renewable energy resource such as solar photovoltaic or wind energy system, the load, such as residential homes and businesses, electric vehicle fleets and the electric power grid. In principle, battery storage is intended to store excess electricity production from the renewable resource. The energy stored is then typically discharged in the evening when demand is higher, or sold to the grid when the available electricity is higher than the demand. However, the implementation of such a system becomes more complicated when taking into account the stochastic nature of the renewables, the variations of the load demand and the electric vehicle availability. In this project, a fuzzy logic management system, optimized through genetic algorithm process, is proposed. It seeks to obtain a fully functional microgrid that minimizes electricity consumption from the utility grid, while offering ancillary services to the grid such as peak shaving. These objectives are accomplished by maximizing the energy input from the renewables and optimizing the utilization of the energy storage system. Finally, the microgrid’s control main objective is to maintain a constant voltage at the DC bus under all operating conditions. Initial simulations and experimental data show some promising results.

Fuel cell vehicles have been considered as the next generation of automobiles since they could be the solution for a clean, safe and high efficiency transportation. However, their commercialization and mass production are restrained by the durability and the short lifetime of fuel cells compared to internal combustion engine vehicles. The hybridization with a high power density sources such as ultra-capacitors or batteries relieves the fuel cell from sudden power demands and therefore increase its lifetime. This paper presents dynamic models of fuel cell and supercapacitors. Then give a brief overview of the most used power management strategies in hybrid fuel cell vehicles. Then it proposes a smart energy management strategy based on wavelet transform. This strategy is suitable for analyzing transient signals in order to properly select the power source to drive the vehicle according to the power demand and to the environment. Initial investigation show promising results.

Electrochemical batteries, especially of lithium-ion type, have been primarily adopted to feed electric vehicles thanks to their superior overall performance. However, they suffer from some limitations such as lower efficiency at peaking power demand. In some applications, they are combined with supercapacitors, who can deliver high power at the expense of lower energy aptitudes. This combination of power sources constitutes a very attractive hybrid energy storage system for electric vehicles. There are numerous topologies and control schemes for such systems and standard drive cycles are usually used to validate such systems. In this paper, a machine learning method is proposed to manage the energy demand from the Li-ion battery and the supercapacitor with the objective of maximizing the efficiency of these devices. Initial simulations and experimental testing show promising results.

This paper is about the design and implementation of a thermal management of an energy storage system (ESS) for smart grid. It uses refurbished lithium-ion (li-ion) batteries that are disposed from electric vehicles (EVs) as they can hold up to 80% of their initial rated capacity. This system is aimed at prolonging the usable life of li-ion EV batteries by reusing them for energy storage. However, the high value of the internal resistance of refurbished batteries generates more heat and consequently accelerates further their aging process as their operating temperature plays a vital role on their reliability, lifespan, safety and performance. Battery Thermal Management System (BTMS) must keep the operating temperature of the battery pack between −10 °C and 40 °C in order to achieve good performances and long lifespan. Active BTMS consumes energy from the pack and reduces its available capacity. In order to reduce this adverse impact, this paper presents a novel approach that takes advantage of the non-uniform surface distribution of li-ion battery cell, which results from complex reactions inside the cell. First, li-ion hotspots were identified and found next to the positive and negative tabs. Then, thermoelectric coolers (TECs) are mounted next to the tabs and in the center of the li-ion battery. A control circuit is designed to turn on and off TECs in order to reduce the parasitic power feeding the proposed system, referred to as modular battery thermal management system (MBTMS). Experimental results show the performance of the MBTMS under normal conditions, such as an ambient temperature of 25 °C and a discharge rate of C/1 to 2C, and under extreme conditions, such as 40 °C and 3C discharge rate. Finally, its performance is compared against a compatible BTMS based on forced air flow. The experimental results show the feasibility of the proposed system.

The electrical vehicle (EV) has revolutionized the transportation sector as it uses electrochemical batteries as power sources instead of fossil fuel based gasoline, which helps reduce greenhouse gas emissions. Due to technological advances in electronics and bidirectional power inverters, EV is no longer viewed as just transportation tools, but in addition, could serve as electric storage units for the grid by supplying ancillary services. The concept of vehicle to grid (V2G) no longer a fiction, but rather a reality. Numerous scientific papers and research activities are addressing the V2G via plug-in cable energy transfer. However, the tedious and inconvenient aspects of cable based power transfer hinder some users and may hinder the development of the EVs, thus wireless charging is seen as a key enabling technology to increase the adoption of electric vehicles. This paper outlines the state of the arte in the wireless V2G concept, presenting its potential convenient electricity markets and the existing bidirectional wireless power transfer technology for EVs. Furthermore, it presents an outlook on some challenges to overcome for high efficiency, and faster deployment of this technology.

The problem of load variation and the form quality in the output voltage of the standalone inverter is solved by using the back-stepping controller which allows to have good sinusoidal form of the voltage output and maintain it at 220V RMS. This work proposes to model the H-bridge signal phase inverter and to design a controller to precisely enslave the reference signal of the output inverter. In case of standalone mode, the electrical loads operate under a stable voltage of 220V. The general objective of the research project presented in this article is to design a non-linear back-stepping controller to settle down the error between the output voltage of inverter and the desired value even if there is a disturbance in inverter output. To do this, a mathematical modeling of the inverter with filter LC was performed. Subsequently, the design of the back-stepping controller was made from the model. Its stability was then validated using Lyapunov's stability theory. As result, this proposed back-stepping controller has good sinusoidal form of output voltage which mains a low total harmonic distortion THD in standalone mode. This work is simulated and tested in Matlab/Simulink platform to insure a good performance of this controller applied to inverter in standalone mode.

We propose a high-performance and robust control of a transformerless, single-phase PV inverter in the standalone mode. First, modeling and design of a DC-DC boost converter using a nonlinear back-stepping control was presented. The proposed converter uses a reference voltage that is generated by the Perturb and Observe (P&O) algorithm in order to extract the maximum power point (MPP) by responding accurately to varying atmospheric conditions. Another goal for using the boost converter is to raise the voltage at the input of the inverter without using a transformer in this system, thus making the system more compact and less expensive. Secondly, the single-phase H-bridge inverter was controlled by using back-stepping control in order to eliminate the error between the output voltage of the inverter and the desired value, even if there is acute load variation at the output of the inverter. The stability of the boost converter and H-bridge inverter was validated by using Lyapunov’s stability theory. Simulation results show that the proposed PV system with back-stepping controllers has a good extraction of the MPP with an efficiency of 99.93% and 1 ms of response time. In addition, the sinusoidal form of the output voltage of the inverter is fixed to 220 V and the total harmonic distortion of the output voltage was found to be less than 1%.

Thanks to the technological advances in electronic devices and bidirectional power inverters, electric vehicles (EVs) are no longer viewed as just transportation tools. Rather, they could also serve as electric storage units for the grid and supply ancillary services within the framework of vehicle to grid (V2G) technology. Following the successful implementation of this emerging technology in several countries such as Germany and Spain, numerous scientific papers and research activities are addressing the use of V2G via plug-in cable energy transfer or inductive wireless charging. This paper investigates the suitability of such technology for the Moroccan national grid. It outlines the state of art of the V2G technology together with the current status of the country's power grid. Furthermore, the paper takes into consideration the evolution of the EV market in the country as well as the EV charging infrastructure deployed within its territories. Moreover, a survey on the services that the V2G could provide is presented. Finally, the authors conclude that the V2G would be very beneficial for the national power grid, providing up to 7.7 GW of controllable, and mobile loads by 2030, as well as for the EV owners once the recommended incentives are put in place.

Nowadays, bidirectional power transfer is a necessity for Vehicle-to-Grid technology. Making accurate and fast SOC estimation more crucial than ever by battery management systems. Striving to establish the optimal strategy for real-time SOC estimation on-board of an electric vehicle for the newly established V2G technology, this paper presents a detailed and critical comparative study of the unscented Kalman filter and artificial neural network. Unlike most conventional comparative studies in literature, this paper presents a critical view of the design and implementation processes of each strategy. All the design aspects are addressed and critically compared including predesign and design requirements. Their accuracy and sensitivity to the erroneous initial SOC values, alongside their tolerance to unpredicted operational conditions, are investigated using different load scenarios typically encountered by the V2G environment. Furthermore, a hybrid nonlinear least square algorithm is presented for battery internal parameter identification, needed for UKF. The experimental results indicate that UKF presents the best overall performance and compromise between accuracy, robustness, computational power and required resources; 91% fewer data were required than the ANN, robustness against erroneous initial SOC for up to 100% deviation, and more accurate under fast changing current and unpredictable conditions; 1.5% compared to 3%.

Lithium-ion (Ii-ion) batteries are considered as the best choice available for energy storage system (EES) for portable devices, electric and hybrid vehicles and smart grid, thanks to their high energy and power densities, lack of memory effect and life cycle. However, the heat generated by these batteries remains a challenge. Without an appropriate battery thermal management system (BTMS), the Ii-ion battery surface temperature can increase very quickly, and thus creating a hazard for the users and the equipment. This paper presents a solid-state hybrid active/passive BTMS system. It includes Peltier effect heat pumps, heat sinks, heat spreaders and fans. It is designed with the objective of regulating the operating temperature of the battery cells while minimizing the parasitic power used to operate the BTMS. After its construction, C/1, 3C and 5C continuous discharge current tests were applied under ambient temperature values of −20°C, 0°C., 25°C and 40°C. Experimental results show that the proposed hybrid BTMS works in passive mode (hence does not consume any power) in an ambient temperature ranging from 0°C to 25°C., and consumes very little power in extreme values (less than 5% of available stored energy in Ii-ion battery pack).

This paper presents an energy storage system (ESS) suitable for the Smart Grids. Nowadays, smart grids suffer from peak demands of electricity, which pushes the utility companies to resort to fossil fuels to satisfy the peak demands. Fossil fuels, such as natural gas, are the main cause of the global warming. The project presented in this paper is about the design and implementation of an ESS made from disposed electric vehicle (EV) li-ion batteries. These batteries can hold up to 80% of their initial rated capacity. The proposed ESS uses load demand forecasting using exponential smoothing method. The forecasts produced by the Holt-Winters Taylor exponential smoothing are fed to a microcontroller that controls the charging and the discharging of the proposed ESS through a bidirectional AC-to-DC converter. The charging of the ESS happens at low demand of electricity, or when the renewable resources have an output. The discharge happens when there is a peak demand.

Open source and closed software security has been debated for decades, vulnerabilities reported for both types of software has been under scrutiny for years. In this study, a descriptive and correlation study for two selected systems is conducted using SPSS 19. The results show that the severity score average of Linux kernel vulnerabilities is lower by %30 than the severity score average of vulnerabilities of windows server 2008, and the time required to patch published vulnerabilities is relatively faster for commercial than open source software. However, a positive correlation between Windows server 2008's variables reveal the irony that severity scores and patch delays are moving in the same direction. The contribution of this article is to shed light on the perception about the security divide between open source and closed software under study that is, although the commercial companies respond faster, however, not according to the severity of vulnerabilities disclosed.

The lithium-iron-phosphate (LiFePO 4 ) battery is becoming the favored choice of electrochemical energy storage for electric vehicles (EV) and hybrid vehicles (HV) due to its high energy density and low self-discharge. However, battery operating temperature plays a vital role on the reliability, lifespan, safety and performance of EV and HV. Battery Thermal Management System (BTMS) must keep the operating temperature of the battery pack between 20 °C and 40 °C in order to achieve good performances and long lifespan. To this day, this task remains a challenging subject for the electric vehicle (EV) development. BTMS consumes energy from the on-board battery pack, thus reducing the range of the vehicle. In order to reduce this adverse impact, this paper presents a novel approach that takes advantage of the non-uniform surface distribution of li-ion battery cell, which results from complex reactions inside the cell. First, li-ion hotspots were identified and found next to the positive and negative tabs. Then, thermoelectric coolers (TEC) are mounted next to the tabs and in the center of the li-ion battery. The control circuit is designed to turn on and off TECs in order to reduce the parasitic power feeding the BTMS. Experimental results show the feasibility of this system.

Electrical Vehicle Batteries (EVB) study has gained a lot of interest in recent years, with the aim of better managing their use, due to the high changes in the electric vehicle dynamics and operational modes, which could cause severe damages to the battery if not properly managed. Recently lithium-ion (Li-ion) batteries have become the most suitable technology for electric vehicles, because of their interesting features such as a relatively long cycle life, lighter weight and high energy density. However, there is a lot of work that is still needed to be done in order to ensure safe operating lithium-ion batteries, starting with their internal status monitoring, cell balancing within a battery pack and thermal management. In this paper, a comparative study of two different methods for state of charge estimation techniques are presented: Kalman filtering observers and artificial neural network based observers. The respective results are compared in terms of accuracy, implementation requirement, and overall performances.

Nowadays, electric mobility is starting to define society and is becoming more and more irreplaceable and essential to daily activities. Safe and durable battery is of a great significance for this type of mobility, hence the increasing interest of research activity oriented to battery studies, in order to assure safe operating mode and to control the battery in case of any abnormal functioning conditions that could damage the battery if not properly managed. Lithium-ion technology is considered the most suitable existing technology for electrical storage, because of their interesting features such as their relatively long cycle life, lighter weight, their high energy density, However, there is a lot of work that is still needed to be done in order to assure safe operating lithium-ion batteries, starting with their internal status monitoring, cell balancing within a battery pack, and thermal management. Tasks that are accomplished by the battery management system (BMS) which uses the state of charge (SOC) as an indicator of the internal charge level of the battery, in order to avoid unpredicted system interruption. Since the state of charge is an inner state of a the battery which cannot be directly measured, a powerful estimation technique is inevitable, in this paper we investigate the performances of tow estimation strategies; kalman filtering based observers and sliding mode observers, both strategies are compared in terms of accuracy, design requirement, and overall performances.

This paper quantifies the performance degradation of video streaming over WLAN network (IEEE 802.11g) due to distance, obstacles and motion. Wireless networks are generally marked by the presence of noise and channel interferences which present an overhead to video quality. First, we present the research done on the metrics used for performance assessment which are: PSNR, transmission delay, jitter, and packet loss; the way they affect the quality of the video, and why they are considered in this study. Thereafter, we describe the tools used in this research and the way they were deployed. From the results, we find that distance and obstacles do have a negative effect on video performance. Moreover, background traffic does create a sort of bottleneck to the bandwidth making it difficult for the video to be well streamed. Additionally, both the delay and jitter increase with distance and background traffic. Finally, the effects of motion on video are mainly due to packet loss. Results have shown that even in small distances between the access point and the receiver, packet loss seems to be the major impact of motion on video performance over WLANs.

This paper describes the design, fabrication and application of an electric vehicle diagnostic and rejuvenation system (EVDRS). This system is based on mosfet controlled thyristors (MCTs). In case when an electric vehicle (EV) (or a hybrid vehicle) starts losing quickly its power, this system will identify any failing battery(s) within the EV pack and will rejuvenate the whole battery pack without dismantling them nor unloading them from the EV. A novel algorithm to rejuvenate electric vehicle sealed lead-acid batteries is described. This rejuvenation extends the useful life of the batteries and makes the EV user-friendlier.

A Thermal Management System for Electric Vehicles (EV) based on the Peltier-Effect Heat Pumps was designed, fabricated, installed into an EV and its performance was tested. A mathematical model was derived from this system and was simulated using a SPICE program. The results show the applicability of the Peltier-effect heat pumps for the temperature regulation of an EV.

Several experiments were conducted at the University of Massachusetts Lowell battery evaluation laboratory in order to measure the battery's charge acceptance ability and the time necessary for it to be charged to reach 80% of its capacity. A high charging current source was connected to the batteries through a data acquisition system. The profiles of the charging current, capacity, temperature and battery impedance were recorded during the test. The time needed for each battery to be charged to 80% and full charge was also recorded. The results show that the charging acceptance varies depending on the type of the battery and its internal impedance. The time necessary to reach 80% of it capacity, as well as full capacity depends on the type of the battery too. Also, the results can be used to size a fast battery charger and the charging algorithm needed.

The purpose of this project is to design, fabricate and evaluate a portable solid state heater cooler (SSHC). Thermoregulation of inner recording chamber is achieved using the Peltier effect modules and a feedback control unit. The operating temperature range is from -5/spl deg/C to +100/spl deg/C, and the temperature can be maintained within +/- 1/spl deg/C of the command value; it can also be rapidly and reliably changed. The temperature can be controlled by regulating the current to the thermoelectric modules, from either a computer or a designed analog circuit. This project was also aimed to maximize the efficiency and the power used by the Peltier modules by using high-density fins heat sinks. The SSHC is being used in the power laboratory for small batteries test, it can also be used as a vaccine refrigerator, an organ transfer coolant, or for temperature control for the electric vehicle.