Application of Artificial Intelligence in Flow Battery Energy Storage

Lecture Topic: Application of Artificial Intelligence in Flow Battery Energy Storage
Expert: Chu Fengming
Date: November 27, 2025
Time: 13:30
Location: Lecture Hall 1517, Energy Research Institute
Organizer: Energy Research Institute

Speaker Profile:
Chu Fengming is a Professor and Doctoral Supervisor at the School of Mechanical and Energy Engineering, Beijing University of Technology, and a key member of the Beijing Key Laboratory of Heat Transfer and Energy Utilization at the university. He earned his Ph.D. from North China Electric Power University in 2018. From July 2018 to March 2025, he worked at the School of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, and has been with the School of Mechanical and Energy Engineering at Beijing University of Technology since April 2025. His main research areas include large-scale energy storage and hydrogen energy technologies targeting the dual-carbon goals, with a particular focus on multi-component transport and multi-physics coupling processes involving chemical reactions in micro-porous media within flow batteries, fuel cells, and water electrolysis for hydrogen production. In recent years, he has led over 10 projects, including those funded by the National Natural Science Foundation, the Beijing Natural Science Foundation, postdoctoral programs, and enterprise commissions. He has published more than 30 high-quality SCI papers, including 4 ESI Highly Cited Papers. He serves as a Young Editorial Board Member for the journals Clean Energy Science and Technology and Industrial Minerals & Processing, a member of the Test and Measurement Technology Committee of the Beijing Society of Thermal Energy, a member of the IEEE Subcommittee on Hydrogen Energy and Energy Storage, and an expert member of the Standards Working Committee of the Chinese Electrotechnical Society.
Research Expertise: Application of artificial intelligence in flow battery energy storage.

Lecture Summary:
Addressing the challenges of global climate change and achieving the goals of "carbon peaking and carbon neutrality" are major strategic objectives for China's sustainable development. Increasing the proportion of solar and wind energy in the energy mix is a crucial pathway to realizing these dual-carbon goals. However, solar and wind energy suffer from intermittency, decentralization, and instability. Vigorously developing energy storage technologies is the most effective approach to overcoming these limitations of renewable energy. Long-duration energy storage technologies that use flowing media as the energy storage medium, such as flow batteries, overcome the capacity limitations imposed by the structure of traditional batteries, enabling the decoupling of power and capacity. They offer advantages such as intrinsic safety, long cycle life, a wide selection of energy carriers, independent charging and discharging, and suitability for medium-to-long-duration energy storage. The energy and mass transport processes within porous electrodes represent a multi-scale, multi-phase complex phenomenon that couples electrochemical reactions and mass transport, ion transport, and electron transport from the microscopic to the macroscopic level. The rates of multi-component transport are influenced by the surface structure, morphology, configuration of the electrodes, and the characteristics of the flow channels. By reconstructing the flow channels and porous electrode morphology in combination with artificial intelligence technologies, novel methods for actively enhancing multi-component transport in porous electrodes are proposed, providing theoretical guidance and scientific basis for the design of high-efficiency flow batteries.

Faculty and students are welcome to attend!