Search Results (38)

There is a growing trend towards vehicles powered by alternative energy sources due to the environmental pollution caused by fossil fuel vehicles. Electric vehicles (EVs) are thought to make a significant contribution to reducing environmental pollution. This study presents a performance comparison of field-oriented control (FOC) and finite control set-based model predictive current control (FCS-MPCC) methods for controlling PMSM motors, which are commonly preferred for EV applications. A multilevel ANPC inverter topology, which has a higher-quality power flow than classical two-level inverters, was preferred to power the PMSM. While the classical FOC method has a fixed switching frequency by including cascaded PI controllers and a pulse width modulation (PWM) modulator, the FCS-MPCC method determines a variable frequency-switching signal that minimizes the cost function by predicting the future current behavior of the PMSM using the mathematical model of the system. The performance comparison of FOC and FCS-MPCC methods was carried out by conducting real-time experimental studies. Both control algorithms were analyzed under variable speed and load conditions using the same motor and drive structure. Performance analysis of FOC and FCS-MPCC control algorithms was carried out in terms of speed tracking, torque, current, and harmonics. According to the results obtained, the total harmonic distortion (THD) value of the stator current was 7.03% in the FOC method, while it was 22.19% in the FCS-MPCC method. Furthermore, a comparative analysis was conducted on the dynamic performance of the two methods in different scenarios using the mean absolute error (MAE), root mean square error (RMSE), integral absolute error (IAE), integrated time absolute error (ITAE), and integral squared error (ISE) criteria. The FCS-MPCC method was observed to be superior in different speed scenarios according to these criteria. In terms of processor load, it was calculated as 17.09% in the FOC method and 63.75% in the FCS-MPCC method. This study is important for determining the control strategy of PMSMs used in EV drives. Full article

Proton exchange membrane fuel cell–battery hybrid power systems provide an effective solution to overcome the limited endurance of battery-powered multirotor unmanned aerial vehicles. However, the highly transient power demands of quadcopter platforms, combined with balance-of-plant losses and operational constraints, create significant challenges for reliable energy management. This study proposes a degradation-aware stress-mitigation model predictive control-based energy management framework to maximize mission endurance under realistic conditions. A control-oriented, physics-consistent model is developed using manufacturer polarization data from a 500 W Aerostak proton exchange membrane fuel cell. The model captures polarization behavior, balance-of-plant loads, battery dynamics, and direct current-bus power balance. The model predictive control strategy optimally allocates power by maintaining direct current-bus stability, regulating battery state-of-charge within safe limits, and constraining fuel cell power ramp rates to mitigate degradation. High-fidelity simulations are conducted under stochastic wind disturbances and mission-dependent load profiles, including takeoff, climb, cruise, and maneuvering phases. The results show continuous power delivery without unmet load demand. The hybrid system achieves a flight endurance of 220–224 min, consuming a total of 89.99 g of hydrogen at an average rate of 0.398–0.412 g/min, indicating a notable reduction under the considered operating conditions. Additionally, long-term analysis indicates that over 97% of initial endurance is preserved after 100 cycles, demonstrating robustness against fuel cell aging. An analytical real-time feasibility assessment further indicates that the control-oriented formulation is compatible with the computational resources of typical unmanned aerial vehicle-class onboard processors, while the integration of adaptive and robust predictive control techniques is identified as a direction for future work. Full article

This paper proposes a unified energy management, fault detection, and fault-tolerant control (EMS–FDI–FTC) framework for Hybrid Hydrogen Electric Vehicles (HHEVs) integrating a fuel cell (FC), battery (Bat), and supercapacitor (SC). While such multi-source architectures enable high-efficiency propulsion under dynamic driving conditions, actuator and state faults such as FC voltage sag, Bat internal resistance increase, and SC capacitance degradation can compromise safety, availability, and component lifetime. The proposed framework converts real-world GPS-recorded vehicle speed profiles into route-aware traction power demand and combines interpretable model-based indicators with an AI-based fault detection and classification module. Based on the diagnosis outcome, a fault-tolerant supervisory strategy performs online power reallocation among the FC, Bat, and SC while enforcing operational constraints. Validation is conducted in a MATLAB-based software-in-the-loop (SIL) environment using three urban driving routes collected from on-road measurements in Tunisia with injected ground-truth faults. The results demonstrate reliable fault classification performance and effective service continuity during fault intervals, supplying over 94% of the demanded energy across all routes, with energy-not-served remaining below 0.02 kWh. In addition, processor-in-the-loop (PIL) implementation on an STM32F407VG controller confirms real-time feasibility with a 10 Hz supervisory sampling rate and execution time margins compatible with embedded automotive deployment. Overall, the proposed closed-loop framework provides a practical route-aware diagnosis-to-control solution for robust and fault-resilient HHEV operation under realistic driving variability. All energy and efficiency indicators reported in this study are derived from control-oriented component models and are intended for consistent comparative evaluation across routes and operating scenarios, rather than absolute representation of a specific commercial vehicle. Full article

Rice processing generates substantial residual biomass globally—about 170 million tons of husk, 62–71 million tons of bran and 23–39 million tons of broken rice annually—which remains largely underutilized and creates environmental burdens and lost economic opportunities. This study was conducted to address the necessity for integrated sustainability assessments of rice mill biorefineries. The focus of this study is on transitioning from a global context of residual biomass generation to a local-scale application in small and medium mills (100–300 tons/day). We apply a resource-centric framework, combining process simulation, techno-economic analysis, and Life Cycle Assessment (LCA—selected for its capacity to quantify trade-offs and avoid burden-shifting across multiple impact categories) with Social-LCA. Five valorization scenarios are assessed. Results demonstrate that biorefinery pathways fundamentally alter supply provision: husk cogeneration boosts energy self-sufficiency (SGI = 12.54), displacing fossil fuels, while silica and nutrient recovery create new, local material flows, substituting for virgin resources. However, chemically intensive routes increase human toxicity impacts (up to 4.0 × 10⁻¹ kg 1,4-DB eq/kg) despite product diversification. Social analysis reveals a tension between worker preferences for advanced technology and community priorities for low-chemical, employment-generating options. Probabilistic sensitivity analysis identifies a diversified configuration (oil, flour, feed, cogeneration) as most robust, optimizing overall resource productivity and circularity. This work transitions the conceptual model of a rice mill from a linear processor to a multi-output bio-resource hub, offering actionable pathways to enhance regional energy, mineral, and nutrient security through circular economy implementation. Full article

Establishing secure vehicular communication is essential for the development of autonomous driving capabilities and to make new functionalities possible, such as smart traffic management, or systems aimed at avoiding or mitigating traffic accidents. In this scenario, where the deployment of a Public Key Infrastructure (PKI) may be difficult, the use of identity-based cryptography is proposed as a good alternative, because this approach simplifies encryption by enabling dynamic key generation and secure communication, without requiring prior key exchanges, making it highly scalable and efficient. In this way, this paper proposes a communication protocol applicable to V2V (vehicle-to-vehicle) and V2X (vehicle-to-all) communications, replacing schemes based on PKI with identity-based cryptographic schemes using the VIN (Vehicle Identification Number) as an unequivocal vehicle identifier. This paper also describes a prototype implementation in a conventional vehicle and the performance metrics of the system. Through a defined proof of concept, we obtained various quantitative results, demonstrating the importance of the processors used for the encryption and decryption operations required. The proposed system provides secure and flexible vehicle identification, with multiple practical applications. It can enables digital authentication, support toll payments without extra hardware, and facilitate V2X communication via the VIN for improved traffic management and safety. Additionally, it can streamline processes in repair workshops and optimize fuel payment and tracking at service stations. Full article

This paper addresses a common challenge in space systems: how to effectively manage multiple actuators under demanding mission conditions. We introduce a flexible, optimization-based algorithm designed to dispatch control commands among a set of available actuators while focusing on minimizing resource usage, such as power and fuel. The method guarantees feasible solutions even in the presence of actuator failures, making it highly suitable for space applications. To illustrate its versatility, we show how the algorithm can be tailored to different mission scenarios with minimal effort. Several benchmark problems were implemented and tested on space-graded hardware for processor-in-the-loop verification. For this purpose, a customized solver was developed, ensuring high numerical efficiency. This paper highlights key results that demonstrate the algorithm’s practical value and mission readiness. Full article

Due to their improved performance and advantages for the environment, fuel cell hybrid electric cars, or FCEVs, have garnered a lot of attention. Establishing an energy management strategy (EMS) for fuel cell electric vehicles (FCEVs) is essential for optimizing power distribution among various energy sources. This method addresses concerns regarding hydrogen utilization and efficiency. The Arithmetic Optimization Algorithm is employed in the proposed energy management system to enhance the strategy of maximizing external energy, leading to decreased hydrogen consumption and increased system efficiency. The performance of the proposed EMS is evaluated using the Federal Test Procedure (FTP-75) to replicate city driving situations and is compared with existing algorithms through a comparison co-simulation. The co-simulation findings indicate that the suggested EMS surpasses current approaches in reducing fuel consumption, potentially decreasing it by 59.28%. The proposed energy management strategy demonstrates an 8.43% improvement in system efficiency. This enhancement may reduce dependence on fossil fuels and mitigate the adverse environmental effects associated with automobile emissions. To assess the feasibility and effectiveness of the proposed EMS, the system is tested within a Processor-in-the-Loop (PIL) co-simulation environment using the C2000 launchxl-f28379d Digital Signal Processing (DSP) board. Full article

This study investigates the effect of potassium (K) promotion on Pt/m-ZrO₂ catalysts in methanol steam reforming (MSR), revealing critical insights into reaction pathways and catalyst performance. While increasing K loading reduces catalytic activity, it selectively enhances the hydrogen-producing formate dehydrogenation and de-carboxylation pathway. Structural analyses using HR-TEM and DRIFTS show that higher K concentrations block Pt sites and promote agglomeration, reshaping catalytic behavior. Notably, the 3.1% K-promoted catalyst achieves high stability at 358 °C, with a CO₂ selectivity exceeding 80% and minimal methane formation, outperforming the unpromoted catalyst in terms of CO and CH₄ selectivity. Temperature studies further demonstrate reduced CO selectivity at higher temperatures, highlighting distinct advantages of K-doped catalysts. These findings underscore the role of K in enhancing surface basicity and its impact on formate interaction, offering valuable insights for optimizing MSR catalysts and advancing hydrogen production technologies. Full article

A step-up DC/DC converter is required to match the fuel cell’s stack voltage with the DC-link capacitor of the propulsion system in fuel cell-based electric vehicles (FCEVs). Typically, the nominal voltage of a single fuel cell ranges from 0.5 V to 1 V, and the DC-link voltage usually lies between 400 V and 800 V. This article proposes a new modular step-up DC/DC converter capable of providing a wide voltage-boosting range from the input to the output side using series-connected isolated boosting submodules (SMs). Modified versions of boost and Cuk converters are designed and used as the SMs to deliver a flexible output voltage, combining the voltage-boosting capability with the ability to embed a medium/high-frequency transformer, which provides both galvanic isolation and an additional degree of voltage boosting while drawing a continuous input current from the fuel cell with minimal ripple, enhancing performance. The proposed modular converter offers the advantages of improved controllability, scalability, and greater reliability, particularly during partial faults. The feasibility of the proposed converter is demonstrated through computer simulations conducted using MATLAB/SIMULINK^® R2024a software where a DC link of 400 V is created from 50 V input sources. Additionally, a 1 kW small-scale prototype is designed and controlled using a TMS320F28335 digital signal processor to validate the mathematical analysis and simulation results, where the SMs are controlled to create a DC link of 100 V from four 25 V input sources with an electrical efficiency of approximately 95%. Full article

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options. Full article

Electric vehicles (EVs) are becoming more and more popular as they provide significant environmental benefits compared to fossil-fuel vehicles. However, they represent substantial loads on the power grid, and the scheduling of EV charging can be a challenge, especially in large parking lots. This paper presents a metaheuristic-based approach parallelized on multicore processors (CPU) and graphics processing units (GPU) to optimize the scheduling of EV charging in a single smart parking lot. The proposed method uses a particle swarm optimization algorithm that takes as input the arrival time, the departure time, and the power demand of the vehicles and produces an optimized charging schedule for all vehicles in the parking lot, which minimizes the overall charging cost while respecting the chargers’ capacity and the parking lot feeder capacity. The algorithm exploits task-level parallelism for the multicore CPU implementation and data-level parallelism for the GPU implementation. The proposed algorithm is tested in simulation on parking lots containing 20 to 500 EVs. The parallel implementation on CPUs provides a speedup of 7.1x, while the implementation on a GPU provides a speedup of up to 247.6x. The parallel implementation on a GPU is able to optimize the charging schedule for a 20-EV parking lot in 0.87 s and a 500-EV lot in just under 30 s. These runtimes allow for real-time computation when a vehicle arrives at the parking lot or when the electricity cost profile changes. Full article

This study investigates the potential of using gold nanoparticles (Au NPs) synthesized from e-waste as electrocatalysts for the methanol oxidation reaction (MOR), with the aim of applying them as an anode in alkaline direct methanol fuel cells (ADMFCs). The research addresses the pressing environmental challenge of e-waste disposal and explores the recycling of e-waste to obtain valuable materials for sustainable applications. Vulcan-supported gold nanoparticles (Au_e-w/C NPs) are synthesized from gold coatings recovered from Intel Pentium 4 processor pins, demonstrating the feasibility of e-waste as electrocatalyst precursors. Comprehensive characterization techniques such as UV-Vis spectroscopy, high-resolution transmission and transmission electron microscopy (HR-TEM, TEM), selected area electron diffraction (SAED), scanning electron microscopy (SEM), and X-ray diffraction (XRD) are employed to evaluate the structural properties of the electrocatalyst. Electrochemical evaluation in 0.5 M KOH electrolyte by cyclic voltammetry reveals that the synthesized Au_e-w/C NPs exhibit electrocatalytic activity (25.5 mA·mg⁻¹_Au) comparable to their commercially synthesized counterparts (30.1 mA·mg⁻¹_Au). This study highlights the potential for sustainable approaches in the production of electrocatalysts by utilizing e-waste as a source of valuable catalyst materials. It represents a pioneering effort in harnessing e-waste as a sustainable resource, offering new avenues for sustainable energy technologies while addressing environmental concerns and technological challenges in the field of ADMFCs. Full article

This paper presents a method based on particle swarm optimization (PSO) for optimizing the power settings of unmanned aerial vehicle (UAVs) along a given trajectory in order to minimize fuel consumption and maximize autonomy during surveillance missions. UAVs are widely used in surveillance missions and their autonomy is a key characteristic that contributes to their success. Providing a way to reduce fuel consumption and increase autonomy provides a significant advantage during the mission. The method proposed in this paper included path smoothing techniques in 3D for fixed-wing UAVs based on circular arcs that overfly the waypoints, an essential feature in a surveillance mission. It used the equations of motions and the decomposition of Newton’s equation to compute the fuel consumption based on a given power setting. The proposed method used PSO to compute optimized power settings while respecting the absolute physical constraints, such as the load factor, the lift coefficient, the maximum speed and the maximum amount of fuel onboard. Finally, the method was parallelized on a multicore processor to accelerate the computation and provide fast optimization of the power settings in case the trajectory was changed in flight by the operator. Our results showed that the proposed PSO was able to reduce fuel consumption by up to 25% in the trajectories tested and the parallel implementation provided a speedup of 21.67× compared to a sequential implementation on the CPU. Full article

To increase the efficiency of a fuel processor and HT-PEMFC (high temperature-proton exchange membrane fuel cell) combined system, it is essential to improve the efficiency of the fuel processor. In this research, the fuel processor was simulated by the Aspen Hysys^® simulator, and the effect of the various operating conditions on the total efficiency was investigated. The thermal efficiency of the fuel processor increased as the temperature and S/C (steam-to-carbon) ratio increased, and the efficiency was higher at an S/C ratio of 3 than at an S/C of 4 with a reformer temperature of 700 °C and higher. Under the selected operating conditions of the fuel processor, the recycling of unreacted hydrogen from the anode off-gas (AOG) of the HT-PEMFC improved the overall efficiency of the combined fuel processor and HT-PEMFC by a factor of 1.28. The operating conditions where the AOG supplied more heat than was required for fuel processor operation were excluded. The high-efficiency operating conditions of the fuel cell system were proposed with the target of 5 kW of output as the capacity of the household HT-PEMFC. Full article

This article presents a microgrid that uses sustainable energy sources. It has a fuel cell (FC), wind energy production devices, and a superconducting magnetic energy storage (SMES) device. The performance of the suggested microgrid is improved by adapting an optimal control method using an artificial bee colony (ABC) algorithm. The ABC algorithm has many advantages, including simplicity, adaptability and resilience to handle difficult optimization issues. Under usual circumstances, wind and FC energies are typically appropriate for meeting load demands. The SMES, however, makes up the extra capacity requirement during transient circumstances. Using the ABC optimum controller, the load frequency and voltage are controlled. Measurements of the microgrid’s behavior using the newly developed optimal controller were made in response to step variations in wind power and load demand. To assess the performance of the suggested system, simulations in Matlab were run. The outcomes of the simulations demonstrated that the suggested microgrid supplied the load with AC power of steady amplitude and frequency for all disruptions. Additionally, the necessary load demand was precisely mitigated. Furthermore, even in the presence of variable wind speeds and SMES, the microgrid performed superbly. The outcomes under the same circumstances with and without the optimal ABC processor were compared. It was discovered that the microgrid delivered superior responses using the optimal ABC controller with SMES compared to the microgrid without SMES. The performance was also compared to the optimally controlled microgrid using particle swarm (PS) optimization. Full article