Dragan Randelovic | Mathematical Modeling | Mathematical Modeling Breakthrough Award

Prof. Dr. Dragan Randelovic | Mathematical Modeling | Mathematical Modeling Breakthrough Award

Full Professor at University Union Nikola Tesla, Faculty for diplomacy and security, Serbia

Prof. Dr. Dragan Ranđelović, Full Professor at Union University – Nikola Tesla, Faculty of Diplomacy and Security, is a renowned expert in Information Technology, Informatics, and Mathematics with over 45 years of academic and research experience. Earning his Ph.D. from the University of Pristina, he has led and contributed to numerous national and international projects, applying mathematical modeling to biological, economic, and sociological systems. Author of over 300 publications, including 40 Web of Science-indexed papers, 15 university textbooks, and co-editor of international volumes, he has over 400 citations with an h-index above 10. A mentor to more than 50 postgraduate candidates, he serves on editorial boards of multiple international journals and continues to influence research, education, and interdisciplinary innovation on a global scale.

Professional Profile

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Education

Prof. Dr. Dragan Ranđelović earned his degree in Electronics from the Faculty of Electronics, Niš, Graduating first in his class ahead of schedule. He completed his master’s degree at the same faculty and obtained his Ph.D. from the Faculty of Science and Mathematics, University of Pristina. His academic journey reflects a deep integration of mathematics, informatics, and technology, forming the foundation for his multidisciplinary expertise. Over the years, he has also pursued continuous academic engagement through teaching, research, and scholarly collaborations. His educational achievements, rooted in rigorous technical disciplines, have underpinned a career dedicated to advancing both theoretical and applied aspects of information technology, with a strong emphasis on the role of mathematical modeling in solving complex, real-world challenges.

Experience

With over 45 years of professional experience, Prof. Dr. Ranđelović has seamlessly bridged academia, industry, and research institutions. He began as a high school teacher before joining the Institute of Electronic Industry in Niš as a development engineer, later managing one of its key research laboratories. He held senior leadership roles in various factories under Elektronska Industrija Niš, followed by significant academic appointments. His tenure includes assistant, associate, and full professorships across universities in Serbia, including the University of Pristina, University of Belgrade, University of Novi Sad, and the University of Criminalistics and Police Studies. Currently, he is Full Professor and Head of the Department of Software Engineering and IT at Union University – Nikola Tesla, Faculty of Diplomacy and Security.

Research Interest

Prof. Dr. Ranđelović’s research interests span information technology, informatics, applied mathematics, and advanced computational methods. He has focused on mathematical modeling for decision-making processes across diverse domains, including biological, economic, and sociological systems. His work integrates statistical analysis, experimental design, and simulation models to develop innovative solutions to complex interdisciplinary problems. He is particularly engaged in research on analytical decision-making frameworks that leverage modern information technologies for real-world applications. His interests also include artificial intelligence applications, data-driven methodologies, and the integration of mathematical models in policy-making and industrial optimization. Through his extensive projects and editorial work, he continuously advances the application of mathematical principles in emerging technology-driven environments, influencing both academic research and industry practices.

Award and Honor

Prof. Dr. Ranđelović’s distinguished career has been marked by significant academic and professional recognition. He has been invited to serve on the editorial boards of multiple international journals, such as Artificial Intelligence Advances and Mathematics and Computer Science, reflecting his scholarly authority. His role as Guest Editor for a special issue of the prestigious Symmetry journal highlights his continued leadership in the academic community. Additionally, his contributions as a mentor to over 50 master’s and specialist candidates, as well as doctoral students, demonstrate his commitment to developing future scholars. His long-standing involvement in high-impact national and international projects further attests to his professional standing and the respect he commands in the fields of information technology and mathematical modeling.

Research Skill

Prof. Dr. Ranđelović possesses an exceptional combination of theoretical expertise and applied research skills. His proficiency includes advanced mathematical modeling, algorithm design, data analytics, simulation techniques, and the development of decision-support systems. He has significant experience in managing interdisciplinary research projects, coordinating teams, and integrating complex datasets into actionable insights. His skill set extends to academic writing, peer reviewing, and editorial leadership, supported by over 300 publications and 150 review assignments. His ability to translate mathematical frameworks into practical applications across varied fields—from economics to security—has positioned him as a versatile researcher. These competencies, coupled with his leadership in both academic and industrial settings, underscore his capacity to address emerging challenges through innovative and methodical approaches.

Publication Top Notes

  • Title: Triple Modular Redundancy Optimization for Threshold Determination in Intrusion Detection Systems
    Authors: I. Babić, A. Miljković, M. Čabarkapa, V. Nikolić, A. Đorđević, M. Ranđelović, …
    Year: 2021
    Citations: 14

  • Title: Determining VLSI Array Size for One Class of Nested Loop Algorithms
    Authors: E. I. M. Titokic, I. Z. Milovanović, D. M. Randjelović
    Year: 1998
    Citations: 13*

  • Title: Challenging Ergonomics Risks with Smart Wearable Extension Sensors
    Authors: N. Maksimović, M. Čabarkapa, M. Tanasković, D. Randjelović
    Year: 2022
    Citations: 12

  • Title: Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach
    Authors: A. Aleksić, S. Nedeljković, M. Jovanović, M. Ranđelović, M. Vuković, …
    Year: 2020
    Citations: 12

  • Title: Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool
    Authors: M. Ranđelović, S. Nedeljković, M. Jovanović, M. Čabarkapa, V. Stojanović, …
    Year: 2020
    Citations: 12

  • Title: SOSerbia: Android-Based Software Platform for Sending Emergency Messages
    Authors: M. Jovanović, I. Babić, M. Čabarkapa, J. Mišić, S. Mijalković, V. Nikolić, …
    Year: 2018
    Citations: 12

  • Title: A Multicriteria Decision Aid-Based Model for Measuring the Efficiency of Business-Friendly Cities
    Authors: M. Jovanović, S. Nedeljković, M. Ranđelović, G. Savić, V. Stojanović, …
    Year: 2020
    Citations: 11

  • Title: An Advanced Quick-Answering System Intended for the e-Government Service in the Republic of Serbia
    Authors: S. Nedeljković, V. Nikolić, M. Čabarkapa, J. Mišić, D. Ranđelović
    Year: 2019
    Citations: 11

  • Title: The Design of the Personal Enemy-MIMLebot as an Intelligent Agent in a Game-Based Learning Environment
    Authors: K. Kuk, I. Z. Milentijević, D. Ranđelović, B. M. Popović, P. Čisar
    Year: 2017
    Citations: 11

  • Title: Intelligent Agents and Game-Based Learning Modules in a Learning Management System
    Authors: K. Kuk, D. Rančić, O. Pronić-Rančić, D. Ranđelović
    Year: 2016
    Citations: 11

  • Title: Study Program Selection by Aggregated DEA-AHP Measure
    Authors: G. Savić, D. Makajić-Nikolić, D. Ranđelović, M. Ranđelović
    Year: 2013
    Citations: 11

  • Title: Determination of Invariant Measures: An Approach Based on Homotopy Perturbations
    Authors: V. Stojanović, T. Kevkić, G. Jelić, D. Ranđelović
    Year: 2018
    Citations: 8

  • Title: Different Methods for Fingerprint Image Orientation Estimation
    Authors: B. M. Popović, M. V. Bandur, A. M. Raičević, D. Ranđelović
    Year: 2012
    Citations: 8

Conclusion

Prof. Dr. Dragan Ranđelović’s illustrious career stands as a testament to his dedication to advancing information technology, mathematics, and interdisciplinary research. His educational foundation, vast professional experience, and prolific scholarly output have left a lasting impact on both academia and industry. Through decades of research, teaching, and leadership, he has fostered innovation in mathematical modeling and its real-world applications. His mentorship of future professionals and active engagement in editorial and collaborative roles exemplify his commitment to the global academic community. Looking forward, his expertise and vision position him to continue contributing groundbreaking work, further solidifying his reputation as a leader in integrating mathematical and technological solutions to address complex societal and scientific challenges.

Shunli Wang | Mathematical Modeling | Lifetime Achievement Award

Prof. Dr. Shunli Wang | Mathematical Modeling | Lifetime Achievement Award

Academic Dean at Inner Mongolia University of Technology, China

Professor Dr. Shunli Wang is an internationally esteemed leader in New Energy and Energy Storage Systems 🔋, serving as Executive Vice President of the Smart Energy Storage Research Institute and Academic Dean at Inner Mongolia University of Technology 🎓. A Fellow of the Institution of Engineering and Technology (IET) and an academician of the Russian Academy of Natural Sciences 🌍, he ranks among the world’s top 2% scientists according to Stanford University 📊. Dr. Wang has authored over 258 SCI-indexed papers 📚, secured 63 patents and standards ⚙️, and directed 56 significant national and international research projects 🧪. His pioneering work in battery modeling, fault diagnosis, and intelligent control has shaped the future of smart grid applications and energy storage technologies 🚀. Renowned for bridging academia and industry, he has led transformative collaborations and cultivated top-tier talent 👨‍🏫. Dr. Wang’s contributions are driving global progress toward sustainable, intelligent energy solutions 🌱🌐.

Professional Profile

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Scopus Profile
ORCID Profile

Education 🎓📘

Professor Dr. Shunli Wang laid a strong academic foundation with a Ph.D. in Control Theory and Control Engineering from Northeastern University, China, where he honed his expertise in systems modeling and intelligent energy control. His early education was marked by academic excellence, leading to prestigious scholarships and research opportunities 🌟. Dr. Wang’s academic journey included postdoctoral research and collaborative projects with globally renowned institutions, further enriching his interdisciplinary knowledge base 🧠. His education not only equipped him with advanced technical skills but also ignited his passion for sustainable energy systems and automation ⚙️. With a commitment to lifelong learning, he continues to evolve through global academic exchanges and cutting-edge workshops, fostering innovative solutions in new energy technologies 🔬. Dr. Wang’s educational pathway is a model of intellectual rigor, strategic focus, and forward-thinking vision, laying the groundwork for his profound contributions to academia and industry alike 🌐.

Professional Experience 🏢🧑‍🏫

Dr. Shunli Wang brings over two decades of impactful leadership in academia and industry. Currently, he is the Executive Vice President of the Smart Energy Storage Research Institute and Academic Dean at the Inner Mongolia University of Technology 🏫. Previously, he held pivotal roles in major research institutions and high-tech enterprises, leading teams on energy storage, intelligent control, and fault diagnosis projects 🔋. His career highlights include directing 56 national and international research projects and mentoring hundreds of graduate students 👨‍🎓👩‍🎓. As a respected academician of the Russian Academy of Natural Sciences and IET Fellow 🌍, Dr. Wang bridges the gap between theoretical innovation and real-world application. He actively consults on policy and industrial strategy for smart grids and battery management systems ⚡. His professional journey exemplifies versatility, vision, and dedication, placing him at the forefront of global advancements in energy and automation technologies 🚀.

Research Interests 🔬⚡

Professor Dr. Shunli Wang’s research spans cutting-edge domains in new energy systems and advanced control engineering. His primary focus lies in battery modeling, intelligent energy storage systems, smart grid applications, fault diagnosis, and predictive control technologies 🔋🧠. He has developed innovative algorithms for real-time system optimization, helping to improve the reliability, safety, and efficiency of large-scale energy infrastructures 🌐. Dr. Wang’s work often integrates artificial intelligence, machine learning, and digital twin technologies, creating adaptive and intelligent control systems that meet the demands of future energy needs 🤖📡. With over 258 SCI-indexed publications and dozens of patents, his research has significantly influenced policy, industry standards, and academic curricula. Passionate about bridging fundamental science and applied technology, he continuously fosters interdisciplinary collaborations that advance energy sustainability, automation, and environmental resilience 🌱. His research is a powerful catalyst for a cleaner, smarter, and more connected energy future 💡.

Awards and Honors 🏅🎖

Dr. Shunli Wang’s outstanding contributions have earned him numerous prestigious awards and honors worldwide. He is ranked among the top 2% of global scientists by Stanford University, recognizing his influential research output and academic impact 📈. As a Fellow of the Institution of Engineering and Technology (IET) and a member of the Russian Academy of Natural Sciences, his work is celebrated for its global reach and transformative outcomes 🌍. Dr. Wang has received multiple national science and technology awards, innovation prizes, and academic leadership honors 🏆. His patents and publications have been widely cited, further solidifying his status as a thought leader in smart energy and control systems ⚙️📘. He is frequently invited to serve on editorial boards, keynote panels, and international think tanks, reinforcing his role as a visionary in sustainable innovation. These accolades underscore not only his academic excellence but also his enduring commitment to technological progress and societal betterment 🌐✨.

Conclusion 🌟📌

Professor Dr. Shunli Wang stands as a beacon of excellence in the realm of intelligent energy systems, blending deep academic insight with practical innovation. His multifaceted contributions—from education and groundbreaking research to international collaborations and mentorship—have profoundly shaped the global energy landscape 🔋🌍. With a visionary approach and a relentless pursuit of excellence, Dr. Wang continues to influence emerging trends in energy sustainability, smart grid design, and AI-powered control systems ⚡🤖. His legacy is built on innovation, impact, and integrity, serving as an inspiration to scholars, engineers, and policymakers worldwide 🧑‍🎓🌱. As the world navigates the complexities of energy transition and climate resilience, thought leaders like Dr. Wang are lighting the path forward—empowering new generations to innovate boldly and act wisely 🌟🚀. His story is not only one of personal achievement but also of global significance in shaping a smarter, greener future for all 💡🌐.

Publications Top Notes

Online state of charge estimation for lithium-ion batteries using improved fuzzy C-means sparrow backpropagation algorithm

  • Authors: Hai Nan, Wang Shunli, Cao Wen, Blaabjerg Frede, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage ⚡🔋

    • Innovative fuzzy-based methods for battery state estimation.


A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries

  • Authors: Zou Yuanru, Shi Haotian, Cao Wen, Wang Shunli, Nie Shiliang, Chen Dan

  • Year: 2025

  • Source: Energy 🚗🔋

    • Advancements in multi-temperature battery state of energy estimation.


Improved particle swarm optimization-adaptive dual extended Kalman filtering for accurate battery state of charge and state of energy joint estimation with efficient core factor feedback correction

  • Authors: Wang Shunli, Zhou Heng, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Energy 💡🔋

    • Optimized algorithms for accurate battery performance estimation.


Joint state of charge and state of energy estimation of special aircraft lithium-ion batteries by optimized genetic marginalization-extended particle filtering

  • Authors: Wang Shunli, Luo Tao, Hai Nan, Blaabjerg Frede, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage ✈️🔋

    • Enhancing battery estimation for aviation applications.


Improved volumetric noise-adaptive H-infinity filtering for accurate state of power estimation of lithium-ion batteries with multi-parameter constraint considering low-temperature influence

  • Authors: Wang Shunli, Hu Bohan, Zhou Lei, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Journal of Energy Storage ❄️🔋

    • State-of-power estimation under extreme conditions.


Battery pack capacity estimation based on improved cooperative co-evolutionary strategy and LightGBM hybrid models using indirect health features

  • Authors: Zhou Yifei, Wang Shunli, Li Zhehao, Feng Renjun, Fernandez Carlos

  • Year: 2025

  • Source: Journal of Energy Storage 🔋💡

    • Capacity estimation with advanced hybrid modeling techniques.


Enhanced transformer encoder long short-term memory hybrid neural network for multiple temperature state of charge estimation of lithium-ion batteries

  • Authors: Zou Yuanru, Wang Shunli, Cao Wen, Hai Nan, Fernandez Carlos

  • Year: 2025

  • Citations: 1

  • Source: Journal of Power Sources 🧠🔋

    • A hybrid approach for temperature-aware battery state estimation.


A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification

  • Authors: Chen Lei, Wang Shunli, Chen Lu, Fernandez Carlos, Blaabjerg Frede

  • Year: 2025

  • Source: Journal of Energy Storage 📊🔋

    • A multi-scale estimator for energy and battery performance.


Multiple measurement health factors extraction and transfer learning with convolutional-BiLSTM algorithm for state-of-health evaluation of energy storage batteries

  • Authors: Shi Zinan, Zhu Chenyu, Liang Huishi, Wang Shunli, Yu Chunmei

  • Year: 2025

  • Citations: 1

  • Source: Ionics 🔋💡

    • Health evaluation using advanced neural networks for energy storage.


Battery lumped fractional-order hysteresis thermoelectric coupling model for state of charge estimation adaptive to time-varying core temperature conditions

  • Authors: Zeng Jiawei, Wang Shunli, Takyi-Aninakwa Paul, Fernandez Carlos, Guerrero Josep Manuel Ramos

  • Year: 2025

  • Citations: 1

  • Source: International Journal of Circuit Theory and Applications ⚡❄️

    • State-of-charge estimation with adaptive temperature modeling.


Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries

  • Authors: Wang Shunli, Fan Y., Jin S., Takyi-Aninakwa P., Fernandez C.

  • Year: 2023

  • Citations: 371

  • Source: Reliability Engineering & System Safety 🔋🔮

    • Improved life prediction with noise-adaptive neural networks.


An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage influence

  • Authors: Wang S., Takyi-Aninakwa P., Jin S., Yu C., Fernandez C., Stroe D.I.

  • Year: 2022

  • Citations: 309

  • Source: Energy ⚡🔋

    • A more accurate prediction of battery state-of-charge over its lifecycle.


Transforming knowledge systems for life on Earth: Visions of future systems and how to get there

  • Authors: Fazey I., Schäpke N., Caniglia G., Hodgson A., Kendrick I., Lyon C., Page G.

  • Year: 2020

  • Citations: 306

  • Source: Energy Research & Social Science 🌍🌱

    • Future sustainability through knowledge system transformations.