| Brief Introduction: Shunli Wang is a Professor, Doctoral Supervisor at Sichuan University, Academic Dean at Inner Mongolia University of Technology, Executive Vice President at Smart Energy Storage Institute, Fellow of the Royal Society of Arts and Crafts (RSA Fellow), Fellow of the Institute of Engineering and Technology (IET Fellow), IEEE Senior Member, PCIM Asia Committee Member, IEEE PES Committee Member, Top 2% Worldwide Scientist, Global Highly Cited Researcher. Focusing on the major national strategic needs of new energy and energy storage systems, the research of green and low-carbon energy storage is conducted in smart grids, undertaken 56 projects such as the National Natural Science Foundation and National Key Research & Development, with a Research Interest Score value of 14995, and 258 articles published on SCI-indexed famous journals with 53 articles in the First Area / TOP journals in Chinese Academy of Sciences, 42 high-cited ones, 23 international and domestic invention patents, 20 software Copyrights and standard formulation and 9 books have been published by first-class international and domestic publishing houses. 10 awards at or above the provincial or ministerial level have been achieved, including 3 international gold medals. 17 chairmanships of international conferences have been participated in, with 5 editorial boards of international periodicals. The core technical achievements have reached an international advanced level and have been reported by the People's Daily. |
| Abstract: Against the backdrop of global energy transition and the accelerated development of new power systems, large-scale energy storage has become a key technological infrastructure for supporting high-penetration renewable energy integration and ensuring the safe and stable operation of power systems. As a critical flexible regulation resource in the coordinated source-grid-load-storage framework, the accuracy of energy storage modeling and the effectiveness of safety management directly determine the operational reliability and regulation efficiency of power systems. Focusing on the complex characteristics of 100 MW-level large-scale energy storage stations, including multi-string parallel operation and multi-level coupling, this presentation systematically investigates mechanical, electrical, and material multi-physics coupled modeling methods. A multi-feature-based characterization method for internal battery properties is proposed, effectively addressing the limitations of traditional electrical models in accurately describing nonlinear dynamic battery responses. Furthermore, an online estimation framework for key battery state parameters based on multidimensional information fusion is developed, overcoming the bottleneck of coordinated prediction of multi-timescale state parameters. This enables real-time perception and intelligent early warning of safety states throughout the full lifecycle of energy storage systems. The research outcomes significantly enhance the intelligent management capability and operational safety of large-scale energy storage stations, providing theoretical support and engineering solutions for the large-scale and industrial application of energy storage technologies in new power systems. |
