Short Abstract: Electric vehicle (EV) fleet operators face specific challenges in urban settings, including higher land costs, complex regulatory hurdles, and limited space for charging depots - which are essential for achieving cost-efficiency scale and density. This presentation investigates the potential of co-locating fleet-owned chargers with/alongside public charging stations, with cost-sharing during installation and various cord-sharing strategies, to address the charging demand from increasing household EV users and shared automatic electric vehicle (SAEV) fleets in Austin area using an agent-based model POLARIS.

Short Abstract: This paper examines how advanced powertrain technologies can enhance fuel economy in highway transportation, aligning with the U.S. DOE-VTO's goal of reducing petroleum dependence. Using Autonomie simulation models, the study evaluates targets for engine efficiency, battery energy density, and lightweighting across various timeframes. By focusing on small SUVs, the research provides a detailed analysis of the transition to vehicle electrification, including cost implications, through systematic simulations over standard driving cycles.

Short Abstract: This paper discusses how the automotive industry faces challenges with addressing Corporate Average Fuel Economy (CAFE) standards, with a focus on using large-scale simulation processes like Autonomie for compliance. By developing advanced vehicle powertrain sizing algorithms and simulating diverse driving scenarios, researchers and regulatory bodies collaborate to accelerate the transition to fuel-efficient vehicles. Autonomie's integration enables automakers to expedite sustainable mobility solutions development, fostering innovation towards a future where efficiency, sustainability, and performance converge, while emphasizing the importance of collaborative efforts in shaping a more sustainable transportation ecosystem.

Short Abstract: This research explores the interaction between advanced vehicle automation and connectivity technologies for light-, medium- and heavy-duty vehicles at a regional level. We utilize the POLARIS modeling workflow, which combines the POLARIS activity-based travel demand model and mesoscopic traffic simulator with the Autonomie vehicle energy consumption model. The workflow was used to explore how the adoption of three different levels of automation and connectivity would impact regional land use, mobility characteristics, energy consumption, and emissions.

Short Abstract: This paper focuses on the collaboration between the National Highway Traffic Safety Administration (NHTSA) and the Argonne National Laboratory (ANL) in advancing sustainable transportation through Autonomie, a full-vehicle simulation tool. ANL's Autonomie tool has become a standard for evaluating vehicle performance, energy consumption, and new technologies, supporting NHTSA's CAFE rulemaking efforts. The study explores the performance metrics and energy consumption evolution of electrified vehicles, providing insights into the impact of NHTSA's regulations and shaping the future of automotive sustainability.

Short Abstract: Medium and heavy-duty vehicles (MHDVs), primarily responsible for U.S. freight movement and expenses, pose significant environmental challenges. This study focuses on battery electric vehicles (BEVs) within the MHDV category and scrutinizes hourly loading profiles under different charging speed scenarios. We conducted simulations using a methodological framework that integrates advanced software tools developed by Argonne National Laboratory. Our research aims to provide valuable insights into optimizing charging practices for MHDVs and the resulting hourly loading profiles across various charging speed scenarios for a specific local region, which can also be scaled up to the U.S.

Short Abstract: This study explores medium-sized agricultural tractor electrification using Autonomie, focusing on energy consumption and performance. It develops and validates a detailed tractor model, comparing energy consumption and component operation based on fieldwork. Alternative powertrain models, including hybrids and battery electric configurations, are developed to match conventional performance levels, showing promising energy and emission reduction potential but also highlighting challenges for future research.

Short Abstract: Electric vehicles (EVs) offer great potential to the ride-sourcing service, due to environmental and cost benefits. This study contributes by designing a stable and fair matching algorithm (SFMA) to pair riders and drivers in an EV ride-sourcing market in a large-scale network simulation using POLARIS. The performance of the SFMA on system level metrics with varying emerging technologies is demonstrated through vehicle-rider matching rate, the fairness property, obtained benefits for the two sides, and empty vehicle miles traveled (eVMT).