Dr. Muhammad Majid Gulzar is currently an assistant professor in the Department of Control & Instrumentation Engineering (CIE), King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. Prior joining to KFUPM, he served the University of Central Punjab, Pakistan from 2008 to 2022 as a Lecturer\Assistant Professor\Associate Professor. He did his Post-Doctorate certification from Nanjing University of Aeronautics and Astronautics, Nanjing, China in 2019. He received his Ph.D. and M.S degrees with specialization in Electrical Engineering (Control Science and Engineering) from the University of Science and Technology of China (USTC) in 2016 and the University of Engineering and Technology (UET), Lahore, Pakistan in 2012 respectively. He is a member of the Pakistan Engineering Council (PEC) and IEEEP (P). His areas of interest are Operation and Control of Renewable Energy Systems, Optimization Techniques and Applications, Multi-agent Networks, Analysis and Design of Linear/Nonli
RESEARCH INTERESTS
Operation and Control of Renewable Energy Systems, Optimization Techniques and Applications, Multi-agent Networks, Analysis and Design of Linear/Nonlinear Systems, Economic Energy Dispatch
Comprehensive energy audit and conservation strategy for public buildings: enhancing energy efficiency and grid sustainability Salman Habib, Muhammad Tamoor, Muhammad Majid Gulzar, Ali Faisal Murtaza, Md Shafiullah, Abdulrahman Alsafrani Scientific Reports, 2026 Limited access to energy presents a substantial barrier to attaining the Sustainable Development Goals (SDGs), particularly SDG-7. This impacts around 110 million people living in Pakistan, which accounts for approximately 18.3% of the global population dealing with similar problems. The primary contributing factor is suboptimal use of available capacity. This research study promotes a transformative approach incorporating comprehensive energy audits and efficiency measures to address these energy-relevant issues. These initiatives include optimized operational procedures and sophisticated electrical systems. The selected hospital building has been considered representative of public buildings since it reflects the diversity demonstrated by the architectural styles and energy consumption patterns prevalent in commercial areas throughout the country. The research findings indicate that energy consumption is highest in AC units, chiller, and cooling towers (approximately 64%), followed by lighting and fan load (23%), biomedical equipment, and laundry (8%). The maximum energy consumption was in July at 1,315,280 kWh, while the minimum in March at 122,080 kWh. The power factor of system has been varied within the range of 0.66 to 0.99. Implementing light retrofits, including the replacement of incandescent bulbs and tube lights with energy-efficient lamps, the installation of sensor lights, and reducing number of lamps, would result in an annual energy savings of 161,441 kWh (PKR 3,002,807) for the building. Retrofitting the fan results in estimated 40.1% reduction in electricity consumption, leading to annual savings of 223.64 kWh (PKR 13,976,605), with a payback period of 1 year. Furthermore, replacing the AC unit from a non-inverter to an inverter type would yield 39.85% saving in electricity consumption, leading to estimated annual savings of 385,459.2 kWh (PKR 7,169,541), with a payback period of 1.81 years. The insulation of AC pipelines would yield an estimated annual savings of PKR 343,746. Replacing air conditioners with more efficient models diminishes greenhouse gas emissions, fostering a more environmentally sustainable strategy. The recommended modifications have to collectively reduce the hospital's current consumption, indicating substantial improvements in energy efficiency and conservation. Scaling this research initiative to offer significant potential for energy savings in similar public-sector hospitals and substantially improve electricity accessibility in Pakistan and worldwide, in accordance with the concepts of SDGs.
EV charging station selection and routing flask application with ACO and NSGA-II including photovoltaic energy constraints Meriem Belaid, Said El Beid, Salman Habib, Muhammad Majid Gulzar, Jamal Aldahmashi, Ahmed Emara, Said Doubabi Scientific Reports, 2026 This paper presents an intelligent electric vehicle routing and charging optimization framework designed to minimize travel distance, energy consumption, and charging costs while maximizing the integration of photovoltaic energy. The proposed hybrid model combines Ant Colony Optimization for efficient route planning with the Non-dominated Sorting Genetic Algorithm II for multi-objective optimization under realistic conditions, including State of Charge, vehicle speed, charger occupancy, dynamic pricing, and renewable energy availability. Implemented as a Flask application with real-time communication via MQTT, the framework was validated across five realistic driving scenarios in Morocco, encompassing urban, suburban, and highway routes. Results demonstrate that the proposed algorithm significantly outperforms traditional A*, Ant Colony Optimization , and Non-dominated Sorting Genetic Algorithm II approaches, reducing travel distance by 7-10% and energy consumption by 10-15% compared to A*, and achieving up to 15-20% faster travel times than Non-dominated Sorting Genetic Algorithm II. In scenarios with photovoltaic availability, it lowers charging costs by 30-40%, increases renewable energy utilization remains high (≈70-98%) achieving ≈3-4 kg CO₂ reduction per trip relative to grid-only charging. Overall, the hybrid PV-aware optimization framework surpasses all benchmark algorithms by jointly enhancing efficiency, economic performance, and environmental sustainability, establishing a scalable foundation for next-generation electric vehicle routing and charging management in smart cities.
Optimized control strategy for frequency and voltage stabilization in renewable maritime microgrids Amil Daraz, Muhammad Majid Gulzar, Salman Habib, Md Shafiullah, Aymen Flah Results in Engineering, 2026 • A cascaded PIDD2–PI controller is developed for coordinated voltage and frequency control in maritime microgrids. • Osprey Optimization Algorithm is employed for optimal tuning under multi-objective performance indices. • Renewable maritime microgrid integrates wind, solar, sea-wave, biodiesel, and energy storage systems. • Significant reductions in settling time, overshoot, and undershoot are achieved under real data scenarios. • Robust performance is validated under ±40% parametric uncertainties and renewable intermittency. . Global climate change poses one of the greatest threats to the planet today, and the reduction of greenhouse gas (GHG) emissions concerns all industries contributing to the problem. Marine power grids are reducing GHG emissions by reducing the usage of traditional energy, and renewable energy sources (RESs) are rapidly expanding. These factors prompted the amalgamation of RESs into maritime microgrid system (MMGS) and the investigation of the resulting predominant control mechanism. Frequency and voltage stability are key components to the reliable and efficient operation of a marine microgrid system. Thus, this study explores the first attempt to incorporate frequency and voltage regulation into the MMGS. The maritime microgrid consists of a biodiesel generator, wind turbine, solar, an energy storage system (flywheel and battery energy storage), and sea wave energy. To address the issue of voltage and frequency stabilization in MMGS with renewable energy sources and energy storage, a unique cascade structure combining a proportional integral double derivative (PIDD2) controller with a proportional integral (PI) controller has been developed. The cascade controller coefficients are enhanced employing the strong and recent metaheuristic-based algorithm known as Osprey optimization algorithm (OOA). A maritime microgrid's performance is evaluated using real time data from sea waves, wind power fluctuations, and solar radiation. To authenticate the supremacy of the proposed PIDD2–PI controller over existing PIDD2, PID, and PI controllers, comprehensive results are provided. The proposed approach achieves improvements in settling time by 39.28%, 63.72%, and 31.92%, while reducing peak overshoot by 72.12%, 56.83%, and 66.45%, and minimizing undershoot by 89.23%, 78.45%, and 82.34%respectively. Demonstrating the efficacy of OOA, its performance is compared with recent optimization algorithms, including the Fox Optimizer Algorithm (FOA) and Whale Optimization Algorithm (WOA).
Bridging mobility and clean energy: A machine learning perspective on plug-in electric vehicles and smart energy networks Muhammad Habibul Ilmi Nasution, Salman Habib, Farheen Ehsan, Muhammad Majid Gulzar, Ali T. Al-Awami, Bilal Khan Results in Engineering, 2026 • Review cutting-edge machine learning applications in PHEV energy management and smart charging • Compare optimization techniques including reinforcement learning, neural networks, and MPC • Explore ML’s role in predictive maintenance and battery health enhancement • Assess ML-enabled solutions for Shared Autonomous Electric Vehicles (SAEVs) and dynamic mobility • Evaluate integration of ML-based charging models with renewable energy systems • Identify challenges and propose adaptive frameworks for real-world deployment of ML in PHEVs . As the urgency to combat climate change intensifies, the convergence of clean energy, electric transportation and artificial intelligence emerges as key to a sustainable future. Plug-in Hybrid Electric Vehicles (PHEVs), empowered by Machine Learning (ML), hold immense promise in redefining the transportation market. This paper reviews cutting-edge advancements in ML-integrated energy management strategies (EMS), predictive maintenance, and smart charging infrastructure for PHEVs. By inferring insights from over 200 peer-reviewed sources, it categorizes and compares optimization techniques, including reinforcement learning, neural networks, and model predictive control, highlighting their role in improving vehicle efficiency, battery longevity, and environmental impact. The review further delves into the transformative effects of Shared Autonomous Electric Vehicles (SAEVs) services, highlighting ML’s capability to analyse real-time data streams for intelligent mobility. Charging optimization models, dynamic pricing mechanisms, and integration with renewable energy systems are explored to underscore how ML bridges the gap between energy efficiency and grid resilience. The paper also investigates the challenges of implementing such an optimization model and proposes adaptive algorithms and collaborative frameworks to overcome real-world limitations. Hence, our work provides a unified, cross-domain exploration of PHEV charging infrastructure, EMS optimization, SAEV systems, and challenges of ML integration, into a comprehensive, well-structured framework that extends the focus of PHEV research. Thus, this review synthesizes current breakthroughs and charts a visionary path for future innovation by blending technology, infrastructure, and sustainability toward ML-enabled PHEVs to shift global attention toward cleaner transportation.
Power electronics in electric vehicles Hasnain Ahmad, Muhammad Majid Gulzar, M.M.R. Ahmed, Adel M. Sharaf, Ijaz Ahmed, Muhammad Khalid Handbook of Power Electronics in Smart Grids and Intelligent Energy, 2026
Robust Load Frequency Control of Hybrid Power System Muhammad Majid Gulzar, Malik Muhammad Umar, Mujahed Mohammad Al-Dhaifallah 2023 International Conference on Control Automation and Diagnosis Iccad 2023, 2023
Particle Swarm Optimization Tuned PID Control of Hybrid Renewable Energy Based Multi-Area Power System Muhammad Majid Gulzar, Muhammad Shahab, Adnan Shakoor 2023 IEEE International Conference on Dependable Autonomic and Secure Computing International Conference on Pervasive Intelligence and Computing International Conference on Cloud and Big Data Computing International Conference on Cyber Science and Technology Congress Dasc Picom Cbdcom Cyberscitech 2023, 2023
Consensus and Phase Synchronization of Multi-agent Systems using Adaptive Models Muhammad Majid Gulzar, Muhammad Munaward, Adnan Shakoor, Huma Tahreem 2023 IEEE International Conference on Dependable Autonomic and Secure Computing International Conference on Pervasive Intelligence and Computing International Conference on Cloud and Big Data Computing International Conference on Cyber Science and Technology Congress Dasc Picom Cbdcom Cyberscitech 2023, 2023
Studying semigroups using the properties of their prime m-ideals Government Postgraduate College, M. Munir, N. Kausar, Agriculture University, B. Davvaz, Yazd University, M. Gulistan, Hazara University, M. Gulzar, Government College University Faisalabad Bulletin of Irkutsk State University Series Mathematics, 2021
œ-Boids Consensus Algorithm using Adaptive Flocking Model Muhammad Majid Gulzar, Muhammad Munawar, Javaria Khalil, Daud Sibtain, Adeel Ahmed Proceedings 2021 IEEE 4th International Conference on Computing and Information Sciences Iccis 2021, 2021
ANTI FUZZY BI-IDEALS ON ORDERED AG-GROUPOIDS Nasreen Kausar, Mohammad Munir, Muhammad Gulzar, Gezahagne Mulat Addis, Rukhshanda Anjum Journal of the Indonesian Mathematical Society, 2020
A note on complex fuzzy subfield Muhammad Gulzar, Fareeha Dilawar, Dilshad Alghazzawi, M. Haris Mateen Indonesian Journal of Electrical Engineering and Computer Science, 2020
Pythagorean fuzzy N-soft groups MUHAMMAD Haris Mateen Indonesian Journal of Electrical Engineering and Computer Science, 2020
Algebraic properties of ω-Q-fuzzy subgroups International Journal of Mathematics and Computer Science, 2020
Certain properties of-fuzzy subrings Dilshad Alghazzawi, Wafaa H. Hanoon, Muhammad Gulzar, Ghazanfar Abbas, Nasreen Kausar Indonesian Journal of Electrical Engineering and Computer Science, 2020
Ordered LA-groups and ideals in ordered LA-semigroups Italian Journal of Pure and Applied Mathematics, 2020
Ideals in LA-rings Italian Journal of Pure and Applied Mathematics, 2020
Fuzzy bi-ideals in LA-rings Italian Journal of Pure and Applied Mathematics, 2020
Implementation of Windows Interface for Disabled Persons Muhammad Majid Gulzar, Muhammad Yaqoob Javed, Syed Tahir Hussain Rizvi, Muhammad Hassan Rahman, Muhammad Saeed Tahir 3rd International Conference on Innovative Computing Icic 2019, 2019
Active pixel digital sun sensor for satellites Muhammad Awais Arshad, Ali Nasir, Muhammad Majid Gulzar, Mansoor Fayyaz, Jawad Khalid Qureshi, Amer Saeed 2018 International Conference on Electrical Engineering Icee 2018, 2018
Six degrees of freedom robotic testbed for control systems laboratory Muhammad Awais Arshad, Muhammad Majid Gulzar, Jawad Khalid Qureshi, Aamir Hayat, Mohammad Shamir, Fawad Ahmed, Sadia Rasheed 2017 International Symposium on Recent Advances in Electrical Engineering Raee 2017, 2017
Towards MOOCs and their role in engineering education Sajid Iqbal, Xizhe Zang, Yanhe Zhu, Danish Hussain, Jie Zhao, Muhammad Majid Gulzar, Shahid Rasheed Proceedings 2015 7th International Conference on Information Technology in Medicine and Education Itme 2015, 2016
Realization of an improved path planning strategy Muhammad Majid Gulzar, Qiang Ling, Muhammad Yaqoob, Sajid Iqbal Iccais 2015 4th International Conference on Control Automation and Information Sciences, 2015