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 🌱🌐.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.