Zhaocai Liu

Zhaocai Liu serves as a Researcher IV in Data Science at the National Renewable Energy Laboratory's (NREL) Computational Science Center within the Center for Integrated Mobility Sciences. His work focuses on leveraging high-performance computing for large-scale transportation system analysis, with expertise spanning electrified and automated transportation systems.

His educational background includes a PhD in Civil and Environmental Engineering from Utah State University (2019), where he was named an Outstanding Engineering PhD Scholar, alongside a Master's and Bachelor's degree in Transportation Engineering from Southeast University in China.

Liu's research specializes in transportation network modeling, sustainable systems with electric/autonomous vehicles, and optimization techniques for coupled transportation-power systems. He develops scalable algorithms for large-scale transportation planning and implements simulation-based optimization frameworks for infrastructure development.

His publication portfolio shows consistent output since 2021, with recent work emphasizing electric vehicle charging demand analysis, school bus electrification, and grid integration challenges. The research demonstrates increasing focus on practical implementation of electrified transportation systems and their interaction with energy infrastructure.

• ITE Intermountain Section Ellis L. Mathes Scholarship (2019)
• Utah State University College of Engineering Outstanding PhD Scholar (2019)
• USUSA Graduate Enhancement Award (2019)

Liu leads multiple DOE-funded initiatives including Valuing V2X for Grid Resilience, Colorado Residential Resiliency and Managed Charging, and EVs@Scale FUSE. His collaborative approach connects NREL with university researchers and industry partners to develop innovative transportation solutions. Current projects focus on electric bus energy prediction, heavy-duty truck routing optimization, and airport shuttle system analysis.

His work occurs within NREL's Computational Science Center, where he utilizes high-performance computing resources to analyze massive transportation datasets and model complex system interactions for national transportation electrification efforts.