Lecture Information

【Speakers】

Professor I-Yun Lisa Hsieh

【Moderator】

Professor Shiqi Shawn Ou

【Presentation Title】

Battery-Swapping Networks for V2X Coordination and Forecast-Free Energy Management in Integrated Transport–Energy Systems

【Date and Venue】

Date: April 20, 2026, 10:00-11:30 a.m.

Venue: Online (Microsoft Teams meeting: Meeting ID: 933 090 462 157 7, Passcode: eh74if)

Expert Introduction

Dr. I-Yun Lisa Hsieh is an Associate Professor in the Computer-Aided Engineering Division of the Department of Civil Engineering at National Taiwan University (NTU), with a joint appointment in the Department of Chemical Engineering. She also serves as Director of the Energy, Economics and Environment Research Center (NTU E3 Center). She received her bachelor’s degrees in Chemical Engineering and Finance from NTU and her Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology (MIT). Her research focuses on AI-driven energy system analytics, low-carbon transportation, vehicle-to-grid integration (V2X), renewable energy forecasting, and energy policy assessment. She develops interdisciplinary data-driven approaches to support decision-making for net-zero transitions. In recent years, her work has integrated real-world operational data with optimization models to advance smart energy management, green logistics systems, and urban-scale transport–energy system coupling analysis. Dr. Hsieh currently serves on the editorial board of Communications Earth & Environment in the Energy & Resources research area, and actively collaborates with government agencies and industry partners on electric mobility deployment, carbon mitigation strategies, and integrated transport–energy system planning.

Abstract of the Presentations

Presentation Title: Battery-Swapping Networks for V2X Coordination and Forecast-Free Energy Management in Integrated Transport–Energy Systems

Abstract：With the accelerating electrification of transportation, large-scale battery infrastructures are gradually evolving from single-purpose charging facilities into important distributed energy resources. In particular, densely deployed electric scooter battery-swapping networks create new system-level opportunities for vehicle-to-grid (V2G) interaction and vehicle-to-building (V2B) energy coordination. This presentation first introduces the development of Taiwan’s electric scooter battery-swapping network and discusses its potential role as a distributed storage resource participating in coordinated grid dispatch. Results show that by jointly considering grid load structures and variations in carbon intensity, battery-swapping-based V2G scheduling strategies can be designed to simultaneously achieve peak-load reduction, operational cost savings, and carbon-emission optimization, thereby enhancing the resilience of urban energy systems. Furthermore, this presentation proposes a forecast-free energy management framework in which a mixed-integer linear programming model serves as a teacher model to generate optimal dispatch strategies, while a supervised deep learning model is trained as a neural controller for real-time deployment without requiring future load or photovoltaic generation forecasts. The proposed approach significantly reduces computational complexity while maintaining near-optimal dispatch performance and improving system robustness under stochastic demand conditions. Overall, the study highlights the critical role of battery-swapping networks in enabling deep coupling between transportation and energy systems and provides a new pathway toward scalable V2X coordinated dispatch architectures.