Wan Li | Oak Ridge National Laboratory

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Presentation Description

In this study, we extend the scope of the established Market Acceptance of Advanced Automotive Technologies (MA3T) model, originally developed for light-duty vehicles (LDVs), to the realm of Medium and Heavy-Duty Vehicles (MHDVs) by creating the Truck Choice model. This model is designed to simulate the selection of advanced vehicle technologies by various segments of MHDV fleets. It takes into account future projections in vehicle powertrain technology advancements, energy prices, fleet operation characteristics, policy impacts, and expansion of refueling infrastructure. The Truck Choice model is unique in its capacity to assess market acceptance for a range of fuel types, including diesel, electricity, and hydrogen. Moreover, the model is designed with the flexibility to incorporate other emerging fuel technologies as they gain relevance in the MHDV sector. The numerical experiments suggest that from 2021 to 2050, DVs show a declining market share while FCEVs and BEVs grow, particularly under high technological progress. The incentives for BEVs and their charging infrastructure reduce the Total Cost of Ownership (TCO) and boost their adoption. The Truck Choice model provides a robust framework to capture the complex decision-making process of MDHV fleet operators and owners. It can provide strategic planning and decisions on the development of MHDV technologies and related refueling infrastructure to improve vehicle decarbonization in the broader vehicle industry.

Speaker Biography

Dr. Wan Li is a Research Associate Staff Member in the Mobility and Energy Transitions Analysis (META) group at the Oak Ridge National Laboratory (ORNL). She received her PhD in Civil Engineering at the University of Washington in 2019, and MS in Civil Engineering from the Louisiana State University in 2014. Her research interests mainly focus on traffic system modeling and simulation, urban transportation network operation and control, data-driven spatiotemporal forecasting, and transportation big data analytics. In particular, she is interested in integrating data driven methods, optimization algorithms, and big data analytics tools into intelligent transportation systems to explore how they could support the research and benefit the development of smart cities.

Co-presenters

Shiqi Ou

Ruixiao Sun
Oak Ridge National Laboratory

Boyu Wang
Beijing University of Civil Engineering and Architecture

Presentation File