T2: Autonomie AI

Co-organizer: Ayman Moawad, Argonne National Laboratory, USA
Co-organizer: Bokai Xu, Argonne National Laboratory, USA

Abstract: AutonomieAI, developed by the Vehicle and Mobility Systems Group at Argonne National Laboratory, is a novel toolkit for efficient energy estimation of a wide variety of vehicles under various trip scenarios, routes and drive cycles. Based on Autonomie studies, it leverages state-of-the-art Machine Learning techniques to deliver fast energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. AutonomieAI is the result of large neural network-based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics-based simulation softwares. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. Autonomie AI is now fully integrated into AMBER, Argonne’s next-generation model-based systems engineering (MBSE) platform.

Co-organizer Bios:

Ayman Moawad

Bio: Ayman Moawad is a principal research engineer in the Vehicle and Mobility Simulation group at Argonne National Laboratory. He received a master’s degree in Mechatronics, Robotics, and Computer Science from the Ecole des Mines, France and a master’s degree in Statistics from the University of Chicago, USA. His research interests include engineering applications of artificial intelligence for energy consumption and cost prediction of advanced vehicles, machine learning, large scale data analysis, and high-performance computing.

 

Bokai Xu

Bio: Bokai Xu is a predoctoral researcher in the Vehicle and Mobility Simulation group at Argonne National Laboratory. He received his Master of Science in Data Science degree from University of Michigan in 2023. His research interests include data Science, data driven modeling, AI based modeling for vehicles and transportation systems.