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A robust and efficient multi-port converter design for electric vehicle applications: optimized via artificial gorilla troops algorithm

Research Abstract

This paper presents a multiport converter (MPC) designed for electric vehicle (EV) applications, with
potential use in renewable energy systems (RES). The proposed MPC interfaces the AC grid, enabling
seamless energy transfer from input sources to multiple output ports. These output ports facilitate the
simultaneous management of two distinct voltage levels through a single DC link, enhancing system
flexibility and efficiency. The Control scheme, employing Proportional-Resonant (PR) and Propor-
tional-Integral (PI) controllers, is implemented to regulate power flow between input sources and
loads. The system operates in three modes: Mode 1 employs power factor correction (PFC) to
synchronize voltage and current while reducing harmonics, the Total Harmonic Distortion (THD) of
the grid current is 3.65%; Mode 2 allows grid-to-battery charging while supplying a low-voltage load;
and Mode 3 functions as a single-input dual-output converter to power both the motor and auxiliary
loads. Additionally, Artificial Gorilla Troops Optimizer (AGTO) is used to rapidly and precisely tune
controller parameters. The proposed MPC features a modular structure and achieves a high efficiency
of 98.64%, surpassing the reported efficiencies in previous studies, while consisting of 12 components
only, making it a promising solution for sustainable energy management in EVs and RES applications.

Research Authors
Alaa A Mahmoud, Mahmoud Aymen Ahmed, and Ahmed A Hafez
Research Date
Research Department
Research Member
Research Year
2025

A Model for Mitigating Causes of Waste Effect Using Lean Management Techniques in Green Building Projects

Research Abstract
Lean construction is considered a new methodology for minimizing the causes of waste that hinder the achievement of green building (GB) goals. The main aim of this study is to develop a lean model using fuzzy logic technique to mitigate causes of waste effect in GB projects and to determine the most appropriate lean tools affecting these causes. The inputs of this model include GB waste and four lean tools, comprising Quality Function Deployment (QFD), Last Planner System (LPS), Value Stream Mapping (VSM), and 5S, while the outputs include four improvement level indices based on the lean tools. The model uses various logical rules to achieve several relations among the inputs and outputs, and it is applied and verified using data related to several causes of waste categorized under five groups. The strongest correlation is found between VSM and 5S indices, while an adverse relationship is observed between QFD and 5S indices. The results indicate that a cause of waste that refers to poor assessment of site conditions is considered the most substantial one due to its high improvement level indices across all lean tools. The most significant waste group is related to GB stakeholders, which contains 38% of key causes of waste. The improvement using QFD increases by 10% compared to VSM and 28.20% compared to 5S. QFD and LPS are measured as the most suitable lean tools to mitigate the causes of waste effects due to their high impact and high improvement level indices.
Research Authors
Ahmed Gamal AbdelHaffez, Usama Hamed Issa, Alaa Atif Abdel-Hafez, and Kamal Abbas Assaf
Research Date
Research Department
Research Journal
Buildings
Research Pages
3538
Research Publisher
MDPI
Research Rank
Q1
Research Vol
15
Research Website
https://doi.org/10.3390/buildings15193538
Research Year
2025

Identifying and evaluating causes of waste effect in green building projects

Research Abstract

Green building (GB) projects in the Middle East face several causes of waste that occur during design and construction stages. These causes affect the objectives of GB projects (economic, environmental, and social). Therefore, this research aims to define causes of waste in GB projects and evaluate the effect of these causes on the objectives of GB projects. Forty-five causes of waste are determined and classified into five main groups as follows: (G01) green materials, (G02) green building design, (G03) sustainable site, (G04) green building technologies, and (G05) green building stakeholders. Through field surveys, including semi-structured interviews and brainstorming sessions, the probability of occurrence for each cause of waste and impact on the economic, environmental, and social objectives are evaluated, as well as the waste severity is determined based on a combined effect of probability and impacts. The correlations among the waste indices are assessed, and the highest correlation is observed between probability and economic followed by economic and social objective. The results show that the most significant cause of waste that has the highest value for economic, environmental, and social objectives is “Poor assessment of site conditions before design, such as topography, hydrology, climate, vegetation, and soil.” Group 05 has the maximum number of critical causes of waste, which is considered the most significant group, due to its high values related to all objectives. Results indicate that the economic objective is classified as the most affected one by the causes of waste, followed by the environmental objective.

Research Authors
Usama Hamed Issa, Ahmed Gamal AbdelHaffez, Alaa Atif Abdel-Hafez, and Kamal Abbas Assaf
Research Date
Research Department
Research Journal
Journal of Engineering and Applied Science
Research Pages
25
Research Publisher
Springer Nature
Research Vol
72
Research Website
https://doi.org/10.1186/s44147-025-00580-5
Research Year
2025

Temperature Estimation of Thin Shape Memory Alloy Springs in a Small-Scale Hip Exoskeleton with System Identification and Adaptive Control

Research Abstract

This study presents a small-scale hip exoskeleton incorporating bi-directional artificial muscles constructed with springs of Shape Memory Alloy (SMA). The prototype can effectively support hip motion in both extension and flexion, spanning an angular range of  to . Experiments for thermo-mechanical characterization were executed to assess the performance of the SMA muscles throughout the entire motion range. The outcomes not only confirmed the suitability of the SMA muscles for the designed exoskeleton but also provided valuable insights into their behavior and capabilities. System identification experiments were carried out to establish an accurate transfer function, guiding the tuning of Proportional-Integral-Derivative (PID) controllers for enhanced motion-control effectiveness. The safety of the SMA system was addressed with a focus on preventing overheating. Challenges in accurately measuring the temperature of a thin spring were overcome by utilizing two thermocouples for each SMA springs group. Additionally, conventional SMA temperature measurement methods, such as infrared and resistance-based techniques, are limited by high cost, nonlinearity, and small range. This study presents a model-based temperature estimation algorithm that integrates a heat transfer model, electrical input data, and thermocouple data to enable accurate and real-time SMA temperature estimation without additional sensors, offering a cost-effective and reliable alternative. To evaluate the small hip prototype and its controller capabilities, control experiments were executed for both stepping and sinusoidal trajectories. The exoskeleton successfully tracked the desired trajectories, showing its precision. Moreover, system performance under adaptive control was further investigated, revealing an RMSE of  940 in sinusoidal trajectory experiments, indicating reliable disturbance rejection in the angle measurements.

Research Authors
Ali, Hussein F. M., Youngshik Kim, Ejaz Ahmad, and Shuaiby Mohamed.
Research Date
Research Journal
Actuators
Research Publisher
MDPI
Research Vol
15
Research Year
2026

Bi-objective motion trajectory generation and online modification of a large rotary crane considering load-sway suppression and collision avoidance

Research Abstract

The main challenge in the automation of the large rotary crane with tower-torsion is the accurate positioning and vibration suppression of the load-sway. The start-of-the-art optimal trajectory generation approaches need to consider several state and input constraints to increase the accuracy; therefore, it requires a large amount of computation time and is not applicable for the practical environment. This study presents an efficient method for optimal trajectory generation considering load-sway suppression and collision avoidance in a fast computation time, which includes two control strategies: the offline bi-objective trajectory generation between the contradictory objectives of total motion time and the collision avoidance fitting function, and the online modification of the optimal trajectory, which is formulated as one-degree-of-freedom optimization to reduce the total motion time and satisfy the entire constraints. The experimental validation with a lab-scale three-dimensional rotary crane is provided to show the effectiveness of the proposed method for practical applications.

Research Authors
Abdallah Farrage, Min Set Paing, Nur Azizah Amir, Hideki Takahashi, Shintaro Sasai, Hitoshi Sakurai, Masaki Okubo, Naoki Uchiyama
Research Date
Research Journal
Mechanical Systems and Signal Processing
Research Pages
1-25
Research Publisher
Mechanical Systems and Signal Processing
Research Rank
Q1
Research Vol
234
Research Website
https://www.sciencedirect.com/science/article/pii/S0888327025004534
Research Year
2025

Slow and safe motion trajectory generation for final positioning support of a load considering load-sway and obstacle avoidance in large rotary crane operation

Research Abstract

Rotary crane systems are essential for transporting heavy loads and hazardous materials. Manual operation can be challenging for new or unskilled operators. This study addresses the challenge of precise final load positioning in construction sites by proposing a trajectory generation system that integrates obstacle avoidance and load-sway suppression. A load monitor camera (LMC) captures the load environment, and the result is displayed on a user-friendly interface designed with error prevention, simplicity, and ergonomic considerations. A usability evaluation has confirmed that the interface reduces task completion time and is well accepted by novice users. The operator selects the final load position from the LMC image, after which a slow-motion trajectory is automatically generated using a cycloidal velocity profile to suppress load-sway. The A* algorithm is used for obstacle avoidance, and its efficiency has been validated through comparison with the Dijkstra algorithm. A benchmark comparison with an S-curve trajectory using trapezoidal trajectory profile has demonstrated the proposed method’s superiority in minimizing sway. Additionally, a disturbance sensitivity analysis under wind conditions has evaluated system robustness and highlights potential improvements. Simulations and lab-scale experiments have confirmed that the proposed method enables safe, smooth, and precise final positioning while avoiding obstacles.

Research Authors
Nur Azizah Amir, Abdallah Farrage, Hideki Takahashi, Shintaro Sasai, Hitoshi Sakurai, Masaki Okubo, Ryosuke Horio, Naoki Uchiyam
Research Date
Research Journal
Mechanical Systems and Signal Processing
Research Pages
1-26
Research Publisher
Elsevier
Research Rank
Q1
Research Vol
242
Research Website
https://www.sciencedirect.com/science/article/pii/S0888327025013895
Research Year
2025

Enhancement of the operating time of the overcurrent relay of the distribution network with high-level penetration of renewable energy sources

Research Abstract

The calculation of the output current of distribution generation (DG) units interfaced with an inverter during the fault is a major issue for isolated and grid-connected distributed networks. The droop control inverter interfaced DG has controlled output current within 2 pu. during the fault. Furthermore, the current output of DG during the fault depends on solar irradiation and wind speed, increasing the uncertainty due to the intermittent nature of renewable energy sources. The installation of DG modifies the fault current direction and strength, which makes relay coordination more difficult. Overcurrent relays are used to defend isolated and grid-connected microgrids. This paper uses different techniques to study the fault current's probability distribution function (PDF) for isolated and grid-connected MGs. We use the droop control and virtual impedance techniques to calculate the probability of the short circuit current that the inverter-interfaced DG contributes. Wind and PV system output power generation samples are tested on MGs using the Monte Carlo Simulation (MCS) approach. A coordination time probability for relays on each line has been calculated to find the mean and standard deviation values of a setting time for overcurrent relays on a faulted bus. The proposed probabilistic model has been tested on the isolated and grid-connected IEEE 33-bus with 5 DGs and MGs using MATLAB code. We found that the droop control method gives a much longer overcurrent relay operating time than the virtual impedance method. This is true for DG buses for both modes of isolated and grid-connected MGs, as well as buses that connect branches. Additionally, for two modes—isolated and grid-connected MGs—the standard deviation of the relay operating time calculated by droop control is higher than its value calculated by the virtual impedance on the same bus.

Research Authors
Mahmoud Aref, Mahmoud A Mossa, Emad Abdelkarim, Khairy Sayed, Mishari Metab Almalki, Alaa FM Al
Research Date
Research Department
Research Journal
Results in Engineering
Research Member
Research Pages
104859
Research Publisher
Elsevier
Research Vol
26
Research Website
https://doi.org/10.1016/j.rineng.2025.104859
Research Year
2025

Optimizing fast charging protocols for lithium-ion batteries using reinforcement learning: Balancing speed, efficiency, and longevity

Research Abstract
Although lithium-ion batteries are essential for contemporary energy storage applications, maintaining battery longevity, safety, and health frequently clashes with the requirement for quick charging. The problem of developing rapid charging protocols to strike a balance between battery protection and charging speed is addressed in this work. We create an adaptive charging strategy that dynamically modifies charging rates in response to battery conditions while respecting safety limitations including voltage and temperature limits using Reinforcement Learning (RL). In order to maximize performance metrics and avoid degradation, the RL agent is trained in a simulated environment.
To examine their effects on charging time, capacity, temperature, deterioration, energy efficiency, and State of Health (SoH), five charging profiles—constant, decreasing, and alternating current techniques—are assessed. The findings show that quicker charging profiles speed up deterioration, raise temperature, and hasten the drop of SoH even though they shorten charging times. Slower profiles, on the other hand, improve long-term battery health and efficiency by controlling temperature and minimizing deterioration, even though they require longer charging times.
The RL-based approach balances quick charging with battery preservation by implementing a reward system that penalizes dangerous conditions like high voltage or temperature in order to lessen these trade-offs. These results highlight the necessity of sophisticated charging processes to maximize efficiency in battery-dependent systems, such as electric cars and portable devices.
Research Authors
Khairy Sayed, Mahmoud Aref, Mishari Metab Almalki, Mahmoud A Mossa
Research Date
Research Department
Research Journal
Results in Engineering
Research Member
Research Pages
104302
Research Publisher
Elsevier
Research Vol
25
Research Website
https://doi.org/10.1016/j.rineng.2025.104302
Research Year
2025

Feasibility study and economic analysis of PV/wind-powered hydrogen production plant

Research Abstract

In Egypt, the production of power and the associated environmental problems are starting to take the stage. One environmentally responsible way to lessen the power crisis is to employ renewable energy sources effectively and efficiently. This paper proposes to develop a hydrogen energy storage-based green (or environmentally friendly) power plant on many Egyptian cities such as Sohag city. To produce green hydrogen, the proposed power station uses energy storage, solar, and wind power. Energy storage systems are used to store extra energy produced by wind turbines and solar panels and to supply energy when the output of renewable energy is low. An optimized design of the proposed power plant uses hydrogen energy to satisfy peak load requirements and reduce GHG (greenhouse gas) emissions. Electrolysis is the method used in the proposed solar/wind power plant to create hydrogen. Water can be split into hydrogen and oxygen via electrolysis, a process that uses electricity. Renewable energy sources can be used to power this procedure, ensuring that the hydrogen produced is “green” and does not contribute to greenhouse gas emissions. The design of the power plant incorporates advanced electrolysis technology, such as proton exchange membrane (PEM) electrolyzers, which are efficient and well-suited for integrating with renewable energy sources.

Research Authors
Khairy Sayed, Mohamed Khamies, Ahmed G Abokhalil, Mahmoud Aref, Mahmoud A Mossa, Mishari Metab Almalki, Thamer AH Alghamdi
Research Date
Research Department
Research Journal
IEEE Access
Research Member
Research Pages
76304-76318
Research Publisher
IEEE
Research Vol
12
Research Website
https://ieeexplore.ieee.org/abstract/document/10540433
Research Year
2024

A distributed architecture of parallel buck-boost converters and cascaded control of DC microgrids-real time implementation

Research Abstract

To enhance the stability and reliability of the system, the converters’ parallel operation can be cascaded to address the constraints posed by the substantial integration of renewable resources. Buck-boost DC-DC converters are often controlled via a cascaded control approach to allow parallel operation. The converter’s output current and its voltage will be controlled by nested loop control. This study proposes adaptive droop control parameters that are updated and verified online using the principal current sharing loops to minimize the fluctuation in load current sharing. When the converters in the microgrid are paralleled, load sharing will be accomplished using the droop control approach in addition to nested proportional-integral-based voltage and current control loops. To restore the correct voltage across the DC microgrid, an outer addition voltage secondary loop will be used, rectifying any voltage disparities caused by the droop management strategy. Several common load resistances and input voltage variations are used to test the suggested method. Using a linearized model, this work assesses the stability and performance of the proposed method. It then confirms the findings with an adequate model created in MATLAB/SIMULINK, Real-Time Simulation Fundamentals, and hardware-based experiments.

Research Authors
Mohamed A Mesbah, Khairy Sayed, Adel Ahmed, Mahmoud Aref, Mahmoud A Gaafar, Mahmoud A Mossa, Mishari Metab Almalki, Thamer AH Alghamdi
Research Date
Research Department
Research Journal
IEEE Access
Research Member
Research Pages
47483-47493
Research Publisher
IEEE
Research Vol
12
Research Website
https://ieeexplore.ieee.org/abstract/document/10480682
Research Year
2024
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