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Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive.

Bioelectrochemical systems (BESs), such as microbial fuel cells (MFCs) and microbial electrolysis cells (MECs), are generally regarded as a promising future technology for the production of energy from organic material present in wastewaters. The current densities that can be generated with laboratory BESs now approach levels that come close to the requirements for practical applications. However, full-scale implementation of bioelectrochemical wastewater treatment is not straightforward because certain microbiological, technological and economic challenges need to be resolved that have not previously been encountered in any other wastewater treatment system. Here, we identify these challenges, provide an overview of their implications for the feasibility of bioelectrochemical wastewater treatment and explore the opportunities for future BESs.

The global impact of hybrid electric vehicles (HEVs) is exponentially rising as it is an emission-free and reliable alternative to fossil fuel-based vehicles that cause enormous negative impacts on the socioeconomic and environmental sectors. Fuel cell hybrid electric vehicles (FCHEV) have been widely considered in the latest research as an energy-efficient, environmentally friendly, and longer-range green transportation alternative. The performance of these FCHEVs, however, is primarily dependent upon the optimal selection of Energy Management Strategies (EMSs) adopted for optimum power split and energy resource management. This research reviews the latest EMS techniques presented in the literature and highlights their working principle, operation, and impact on the FCHEV performance and reliability. This research also highlights the challenges associated with the globalization of FCHEVs and recommends future work and research directions essential for optimal FCHEV performance and commercialization.

All-electric ships are now a standard offering for energy/propulsion systems in boats. In this context, integrating fuel cells (FCs) as power sources in hybrid energy systems can be an interesting solution because of their high efficiency and low emission. The energy management strategy for different power sources has a great influence on the fuel consumption, dynamic performance and service life of these power sources. This paper presents a hybrid FC/battery power system for a low power boat. The hybrid system consists of the association of a proton exchange membrane fuel cell (PEMFC) and battery bank. The mathematical models for the components of the hybrid system are presented. These models are implemented in Matlab/Simulink environment. Simulations allow analyzing the dynamic performance and power allocation according to a typical driving cycle. In this system, an efficient energy management system (EMS) based on operation states is proposed. This EMS strategy determines the operating point of each component of the system in order to maximize the system efficiency. Simulation results validate the adequacy of the hybrid power system and the proposed EMS for real ship driving cycles.

Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods.

Microgrids (MGs) are essential in the distribution system by utilizing widely dispersed generation sources. Due to their economical and environmentally friendly attributes, Islanded AC MGs are commonly used to supply electricity to isolated locations independent of the primary grid. This study focuses on optimizing the configuration of an islanded AC MG to meet the electrical requirements of an international school in the New Administrative Capital, New Cairo, Egypt. Hybrid Optimization of Multiple Energy Resources (HOMER) software is employed to obtain the optimal size of the sources in the MG by minimizing the Levelized Cost of Energy (LCOE) and Total Net Present Cost (TNPC). According to the HOMER simulation results, a 200 kW PV system, a 180-kW wind turbine, a 50 kW FC, a 50 kW electrolyzer, a 50 kg hydrogen tank, a 180 kW DG, and a 686-kWh lead-acid battery form the optimal configuration of the islanded AC MG. The results reveal the contribution of each energy component to meeting the electricity demand, yielding an LCOE of $0.153/kWh and a TNPC of $1,775,300.00. The dynamic performance of the islanded microgrid is examined, introducing a Model Reference Adaptive Control based PI controller (MRAC-PI) to enhance transient response across all operational conditions. A comparative analysis is performed against traditional PI-PSO and PI-WOA controllers under load variations and changing weather conditions. The results indicate that the proposed control strategy effectively maintains system frequency and voltage amid various disturbances, improves dynamic performance, and achieves a balanced power generation and load demand. Additionally, the proposed controller demonstrates superior dynamic response, featuring reduced overshoot, undershoot, ITAE, and settling time compared to the others.

The generation of energy by conventional systems leads to several environmental issues. Fuel Cell (FC), being a new renewable energy source, has emerged as one of the promising alternatives to obtain clean and efficient energy generation. This paper highlights the power quality enhancement of the grid connected FC through a boost converter and 25 level Cascaded H-Bridge (CHB) Multi-Level Inverter (MLI) using the classical PID controller. To drive the MLI connected to the grid for governing the Point of Common Coupling (PCC) voltage between the FC and the grid, two PID controllers have been utilized. The conventional evolutionary techniques such as Particle Swarm Optimization (PSO) and Squirrel Search Algorithm (SSA) are implemented to tune the PID controllers for dynamic operations. To further enhance the convergence speed of computation and precision of the classical techniques used, an Improved Squirrel Search Algorithm (ISSA) has been proposed in this work. The grid connected power network considered for study here is designed using MATLAB/Simulink environment. Moreover, the system is led to various rigorous voltage sag and swell conditions to test the effectiveness of the proposed controller. A detailed comparison between the conventional PID, PSO, SSA, and proposed ISSA techniques in voltage profile improvement, power quality enhancement, and reduced execution time has been featured. The results obtained highlight the proposed technique’s superiority over the classical methods in terms of improved dynamic voltage response, enhanced power quality, and reduced harmonics. The power quality indices are found out using Total Harmonic Distortion (THD) analysis. The values found out are well within the IEEE-547 indices for the proposed controller, thus justifying its real-time implementation.

The majority of rail vehicles worldwide use diesel as a primary fuel source. Diesel engine carbon emissions harm the environment and human health. Although railway electrification can reduce emissions, it is not always the most economical option, especially on routes with low vehicle demand. As a result, interest in hydrogen-powered trains as a way to reduce greenhouse gas (GHG) emissions has steadily grown in recent years. In this paper, we discuss advancements made in hydrogen-powered freight and commuter trains, as well as the technology used in some aspects of hydrogen-powered vehicles. It was observed that hydrogen-powered trains are already in use in Europe and Asia, unlike most developing countries in Africa. Commuter trains have received most of the research and development (R&D) attention, but interest in hydrogen-powered freight trains has recently picked up momentum. Despite the availability and use of gray and blue hydrogen, green hydrogen is still the preferred fuel for decarbonizing the rail transport sector.

The majority of urban CO2 emissions come from the transportation sector. To be able to reduce them, it is definitely necessary to replace Internal Combustion Engine (ICE) vehicles with electric ones. In this article, a public transport system is proposed, consisting of a supercapacitor (SC)-powered electric vehicle (EV) charged through a fuel cell-powered (FC) Inductive Power Transfer (IPT) system. The bus runs the usual route and it is charged each time it reaches the terminal, where the charging system is placed. The main advantages of the proposed system are related to the long-term cost of the EV, compared to a classic battery-powered system, to the aspects of ease of use and safety for charging operations and to the possibility of realizing a net-zero-energy transport system thanks to the use of green hydrogen. In addition, the proposed charging methodology allows for better energy utilization avoiding major changes to the main power grid. In this article, the system is presented considering a real case study; it is simulated at system and hardware level, and then validated through the realization of a scaled-down prototype.

This paper deals with the determination of the optimal values to be given for the seven unknown parameters of the proton exchange membrane fuel cell (PEMFC). To this end, the weighted mean of vectors optimizer (INFO) metaheuristic algorithm is applied to estimate these parameters by minimizing the sum of squared errors (SSEs) between the measured and calculated voltages of the PEMFC. Three commercial types of PEMFCs are investigated: (i) BCS 500 W Stack, (ii) NedStack PS6 Stack, and (iii) Horizon 500 W Stack. The accuracy of the applied INFO algorithm is verified by comparing the estimated voltage–current ( I - V ) characteristics with the measured data. Furthermore, the estimated parameters of electrical PEMFCs, the minimum reached SSE, and the standard deviation Std values achieved by INFO are compared with the results obtained using other competitive metaheuristic optimization algorithms such as Honey badger algorithm, Gradient-based optimizer, Harris hawks optimization, and others. From the obtained results, the convergence curves show that the unknown parameters of the three PEMFCs are better estimated using the proposed INFO than other algorithms.