Ahmed Abdelhamid Zaki Diab

Battery capacity and resistance affect different vehicle responses, and their apparent importance depends on the range criterion. This study compares these effects in a fixed 2011 Nissan LEAF ZE0 model using measurements linked to the same vehicle identification number (VIN). Beginning-of-test (BOT) and end-of-test (EOT) pack measurements define controlled capacity-only, resistance-only, and coupled states. Under repeated Worldwide Harmonized Light Vehicles Test Cycle (WLTC) operation from 100% to 20% state of charge (SOC), replacing the rated pack capacity of 66.2 Ah with the measured BOT capacity of 57.6 Ah reduced the simulated distance from 113.59 to 100.48 km (−11.54%). BOT resistance scaling alone yielded 113.42 km (−0.15%); the coupled state yielded 100.02 km (−11.95%). The 66.2- Ah rating is not a measured beginning-of-life capacity, so these differences are nominal reference simulation contrasts, not measured lifetime range losses. Capacity dominance is expected under the SOC stopping rule. Resistance scaling was more visible in the diagnostic integral of current squared times effective resistance (I²R): BOT increases were 12.34% under WLTC and 13.01% under the US06 supplemental driving schedule, while terminal energy changes were −0.10% and <0.01%, respectively. Under the severe EOT resistance sensitivity, these diagnostic increases reached 197.27% and 231.12%, and the US06 operational minimum battery terminal voltage fell from 360.84 to 277.55 V. The contribution is a controlled comparison of range, terminal energy, voltage, and resistance-sensitive diagnostics. Numerical effect sizes apply to the tested model and boundaries; voltage-, power-, and temperature-limited operation require separate evaluation.

The increasing demand for sustainable and reliable electricity in isolated and off-grid areas has accelerated the deployment of standalone Hybrid Renewable Energy Systems (HRESs). These systems typically integrate Photovoltaic (PV) panels, Wind Turbines (WT), Diesel Generators (DG), and energy storage technologies to enhance system reliability, operational flexibility, and resilience against the intermittent nature of renewable generation. This study presents a systematic, comprehensive, and critical review of optimization techniques and energy management strategies for standalone HRESs, with particular emphasis on the role of energy storage under uncertainty. Existing studies are analyzed using a structured framework covering system configurations, storage technologies, optimization objectives, solution methods, uncertainty modeling, and energy management approaches. Various energy storage technologies, including electrochemical, mechanical, electrical, thermal, and chemical storage, are compared in terms of performance, cost, and suitability for hybrid systems. Classical optimization methods and recent metaheuristic algorithms are critically evaluated for optimal system sizing and operational planning under uncertain conditions. The review also highlights the growing adoption of Hybrid Energy Storage Systems (HESSs), where complementary storage technologies improve overall system performance. Current research gaps include the lack of integrated optimization frameworks for multi-storage systems and limited real-time energy management under uncertainty. A standalone PV–WT–FC–DG–Battery (FC indicates fuel cell) microgrid in Abu Minqar, Egypt, is presented as a case study. The proposed Gray Langurs Optimizer (GLO) achieved the best performance, yielding a cost of energy of 0.2066 USD/kWh, a net present cost of USD 4.99 million, a renewable fraction of 90.0%, and 194.01 t CO2/year emissions, outperforming GWO and WOA in optimization accuracy and robustness. Future research directions toward intelligent and flexible HRESs with integrated energy storage are also discussed.

Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as promising clean-energy-conversion devices for various applications, including transportation and stationary power generation. The accurate modeling and parameter estimation of PEMFCs are fundamental to optimizing their performance, extending their operational lifespan, and facilitating their widespread commercialization. However, their inherent nonlinear characteristics and complex electrochemical processes present significant challenges in developing accurate mathematical models and reliable parameter-estimation methods. This review systematically examines the state of the art in PEMFC parameter estimation and modeling, with particular emphasis on the critical challenges posed by nonlinearities, the evolution of analytical and computational methods, and the role of sensitivity analysis in model refinement. The paper synthesizes findings from recent studies employing evolutionary algorithms, metaheuristic techniques, reinforcement learning, and hybrid approaches, evaluating their relative merits in terms of accuracy, convergence speed, and robustness. Furthermore, it explores how sensitivity analysis provides valuable insights into parameter influence under varying operational conditions, guiding model optimization efforts. The review identifies persistent gaps in current methodologies, particularly regarding generalizability across different fuel cell types and real-time applicability, while highlighting promising directions for future research, including hybrid algorithms and advanced data-preprocessing techniques.

Texturing is a critical process for enhancing monocrystalline silicon solar cell efficiency by reducing surface reflectance and improving light absorption. This study investigates the effect of introducing potassium silicate (K₂SiO₃) as an additive during alkaline texturing while simultaneously reducing the concentration of isopropyl alcohol (IPA)—a volatile organic solvent commonly employed to control anisotropic etching, ensuring uniform pyramid formation. The analysis examines surface morphology, particularly the pyramid structures formed on silicon wafers, and evaluates changes in photovoltaic performance with and without K₂SiO₃ addition. Wafers were weighed before and after texturing to quantify material removal. Weight loss measurements showed that texturing with 500 mL IPA alone (without K₂SiO₃) resulted in excessive etching (1.057 g, exceeding the optimal range of 0.6–1.0 g), while addition of 5 g K₂SiO₃ with reduced IPA (250 mL) yielded controlled weight loss (0.942 g) within the acceptable range. This demonstrates that potassium silicate regulates the etching process, preventing over-texturing and improving surface quality. Following plasma-enhanced chemical vapor deposition (PECVD), the thickness and refractive index of the deposited SiNₓ anti-reflection coating were measured using ellipsometry, yielding values of 87.1 nm and 1.99 for wafers textured without K₂SiO₃ (500 mL IPA), and 81.6 nm and 1.93 for those processed with K₂SiO₃ (250 mL IPA + 5 g K₂SiO₃). Completed solar cells were characterized using current–voltage (I–V) measurements to determine electrical parameters and efficiency. All experiments were conducted at the Egyptian-Chinese Laboratory for Renewable Energy Applications. Pyramid homogeneity was further analyzed using scanning electron microscopy (SEM). Statistical analysis including ANOVA and Tukey HSD post-hoc testing was performed to validate the significance of observed efficiency improvements.

The integration of distributed generation (DG) into electrical networks offers technical and environmental benefits, including reduced power losses, improved voltage stability, and enhanced reliability. Improper DG placement or sizing, however, can degrade network performance. This study addresses optimal DG allocation under uncertainty, focusing on voltage profiles and stability indices in distribution networks. Variations in photovoltaic (PV) and wind turbine (WT) generation due to solar irradiance and wind speed are considered. Seven optimization algorithms-FVIM, SBO, SCSO, PSO, WOA, ALO, and Harmony Optimization-are applied and compared. Two DG types (active power only and active-reactive power) and a hybrid scenario with capacitor banks are analyzed. The IEEE-33 bus network is used as a test system. Results show reductions in total annual cost ($7.654 M → $2.614 M), voltage deviations (38.376 p.u. → 10.826 p.u.), and power losses (4043.462 kW → 2500.466 kW). Minimum voltage improved from 0.9065 to 0.9538 p.u. WOA achieves the lowest overall cost ($2,614,363), followed by PSO ($2,648,914) and FVIM ($2,699,308). FVIM demonstrates consistent technical performance, with total VDDT of 11.3144 p.u., VSIT of 730.6071, minimum bus voltage of 0.9527 p.u., and total active/reactive losses of 2789.703 kW and 2029.58 kVAr, maintaining a strong balance between economic and operational objectives.

The increasing integration of intermittent renewable energy sources (RESs) into islanded Hybrid Power Systems (HPSs) is a critical step towards global energy sustainability; however, it poses significant challenges to frequency stability owing to low system inertia and stochastic power fluctuations. To address these challenges and enable higher penetration of green energy, this study proposes a novel and robust Load Frequency Control (LFC) strategy based on the Crested Porcupine Optimizer (CPO). A customized Mode-Dependent Adaptive Balloon (MDAB) controller is developed, wherein the virtual control gain is dynamically tuned based on the real-time operating modes and disturbance severity. Furthermore, to optimize communication resources and mitigate actuator wear in networked microgrids, an intelligent event-triggered (ET) mechanism is seamlessly integrated into the adaptive logic. The proposed control framework is rigorously validated through comprehensive nonlinear simulations and comparative analyses with state-of-the-art metaheuristic algorithms (GTO, GWO, JAYA, and GO). The evaluation encompasses step load disturbances, severe parametric uncertainties (+25%), realistic 24-h diurnal cycles with solar cloud shading and wind turbulence, and extended practical constraints, including Battery Energy Storage System (BESS) integration and Internet of Things (IoT) communication delays. The results demonstrate the superiority of the CPO-tuned framework, which achieved the fastest transient recovery (settling time of 3.4367 s) and the lowest absolute Integral Absolute Error (IAE). Additionally, the proposed ET-based strategy not only reduced the communication burden but also improved the overall control performance by 37% in terms of IAE compared with continuous approaches. By inherently filtering measurement noise, mitigating control signal chattering, and maintaining resilience under nonideal latency, the proposed architecture offers a highly robust and resource-efficient solution that directly guarantees the operational sustainability and reliability of modern smart microgrids.

Robotics is increasingly used in higher education laboratories, but most commercial robots are costly and designed for industrial use. This paper presents the design, modeling, and experimental evaluation of a low-cost four-degree-of-freedom (DOF) SCARA robot for educational and research purposes. The robot supports pick-and-place and laser engraving tasks. Direct and inverse kinematics were developed using Denavit–Hartenberg parameters, and the mechanical structure was validated through the dynamic analyses. A new machine learning (ML) framework integrating Support Vector Machine (SVM) and Random Forest (RF) models was implemented to enhance motion precision, predict task success, and compensate positioning errors in real time. Experimental tests over 360 cyles under varying speeds, payloads, and object types show that the SVM predicts grasp success with 94.4% accuracy, while the RF model estimates XY positioning error with an RMSE of 1.84 mm and cycle time error with an RMSE of 0.41 s. Moreover, a novel approach in this work that combines it with a laser engraving machine has been suggested. Repeatability experiments report 0.97 mm ISO-standard repeatability, and laser engraving trials yield mean positional errors of 0.45 mm, with maximum deviation of 0.90 mm. Compared to a baseline PID controller, the ML-enhanced strategy reduces RMS positioning error from 3.30 mm to 1.83 mm and improves repeatability by 36.5%, while slightly decreasing cycle time. These results demonstrate that the proposed SCARA robot achieves high-precision, consistent, and flexible operation suitable for both academic and light-duty practical applications.