Ayman Moawad

Ayman Moawad is a research engineer specializing in data science, machine learning and modeling to optimize energy in transportation systems.

Biography

Highlights

Ayman Moawad is a research engineer in the Intelligent Vehicle Control Team at Argonne National Laboratory, where he develops advanced modeling and simulation tools to improve energy efficiency in transportation. His work focuses on integrating AI and data science to accelerate vehicle simulation, optimize automated vehicle controls, and analyze large-scale real-world driving data. Moawad led the development of Autonomie AI, a machine learning-based toolkit that enables rapid energy prediction for diverse vehicle technologies. With expertise spanning surrogate modeling, driver behavior analysis, and transportation system optimization, he is driving innovation at the intersection of artificial intelligence and mobility research.

“Now, we can accomplish what used to take days or even months in hours or minutes. AI and data-driven models allow us to ask bigger questions and explore broader solutions.” — Argonne Research Engineer Ayman Moawad

Research Focus

Moawad’s research centers on using data-driven modeling and machine learning to improve energy efficiency in transportation. He develops AI-powered tools that accelerate vehicle simulations, optimize connected and automated vehicle controls, and analyze real-world driving data to refine vehicle and driver behavior models.

Moawad’s work includes the creation of surrogate models that enhance simulation speed and accuracy, allowing large-scale optimization and decision-making for transportation systems. He is advancing the development of more efficient, data-driven solutions for mobility and vehicle technologies through integrations of AI with engineering and statistical modeling.

Impact

Moawad’s work is helping transform how transportation systems are designed, evaluated, and optimized. His development of AI-driven simulation tools has significantly reduced the time required for complex vehicle energy assessments, enabling rapid analysis that once took days or months to be completed in minutes. His research on adaptive cruise control has provided critical insights into automation’s impact on energy efficiency, influencing future vehicle technology strategies.

Moawad helps industry partners and policymakers identify the most effective vehicle technologies and control strategies to maximize energy efficiency and operational performance by leveraging machine learning, large-scale driving datasets, and AI-based decision tools.

Ayman Moawad’s passion for mathematics, computational methods, and data-driven problem-solving has shaped his career from the beginning. Trained as an engineer, he pursued additional expertise in statistics and data science, combining rigorous analytical skills with real-world applications. His multidisciplinary background allows him to integrate engineering, AI, and statistical modeling to tackle complex challenges in transportation and mobility.

At Argonne, Moawad has been instrumental in developing Autonomie AI, a machine learning-based toolkit that revolutionizes energy estimation for vehicles under varying conditions. This technology has enabled large-scale energy optimization and co-simulation with transportation system models, allowing researchers and policymakers to make faster, more informed decisions about vehicle efficiency. His work on surrogate modeling has drastically accelerated vehicle simulations, making large-scale optimizations more feasible and accessible.

Beyond simulation advancements, Moawad has conducted groundbreaking research in driver behavior and automation. He uses real-world driving data to assess the impact of adaptive cruise control on fuel consumption. His studies have provided valuable insights into how automation influences energy efficiency across diverse driving scenarios. Through collaborations with major industry partners, he has helped build extensive datasets that bridge the gap between simulation models and real-world transportation systems.

Moawad’s expertise extends to cost estimation methodologies using Explainable AI, where he has developed models that enhance transparency and accuracy in vehicle component cost analysis. His approach, leveraging machine learning and game theory, has contributed to more reliable techno-economic assessments for advanced vehicle technologies.

With a career dedicated to advancing AI-driven solutions in mobility, Moawad continues to push the boundaries of what’s possible in transportation modeling and energy efficiency. His work accelerates critical research and ensures that AI and data-driven insights are meaningfully shaping the future of vehicle technology.

Read the latest Argonne news about Ayman Moawad:

Argonne’s machine learning model estimates technology contributions towards MSRP

Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed an explainable machine learning model to better estimate the manufacturer’s suggested retail price (MSRP).

Education

M.S. in Data Science - University of California Berkeley (2023)
M.S. in Statistics - University of Chicago (2019)
M.S. in Engineering - Ecole des Mines — France (2009)
B.S. in Mathematics & Computer Science — Class Preparatoire Grandes Ecoles — France (2006)

Honors and Awards

Impact Argonne Award (2023, 2022, 2020)
Argonne Commercialization Excellence Award: Autonomie software (2020)
Best paper award at IEEE Vehicle Power and Propulsion Conference (2017)
Argonne Pacesetter Award (2013)
U.S. Department of Energy, Vehicle Technologies Program R&D Award (2012)

“I became fascinated by how data could explain real-world phenomena and how machine learning helps translate complex systems into simpler, more actionable insights.” — Argonne Research Engineer Ayman Moawad

Select Publications

Moawad, A., Zebiak, M., Pierre, M.S. et al. “Insights into adaptive cruise control and energy efficiency in electric vehicles.” npj. Sustain. Mobil. Transp. 2, 49 (2025). https://doi.org/10.1038/s44333-025-00066-0
A. Moawad et al., “Effect of adaptive cruise control on fuel consumption in real-world driving conditions,” Nature Communications, vol. 15, no. 1, p. 10016, Nov. 2024, doi: https://doi.org/10.1038/s41467-024-54066-8.
A. Moawad et al., “A real-time energy and cost efficient vehicle route assignment neural recommender system,” Expert Systems with Applications, vol. 263, p. 125634, 2025, doi: https://doi.org/10.1016/j.eswa.2024.125634.
A. Moawad et al., “A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles,” arXiv preprint arXiv:2111.12861, 2021.
A. Moawad et al., “Explainable AI for a no-teardown vehicle component cost estimation: A top-down approach,” IEEE Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 185–199, 2021, doi: https://doi.org/10.48550/arXiv.2006.08828.
J. Yao and A. Moawad, “Vehicle energy consumption estimation using large scale simulations and machine learning methods,” Transportation Research Part C: Emerging Technologies, vol. 101, pp. 276–296, 2019.
A. Moawad et al., “Assessment of vehicle sizing, energy consumption and cost through large scale simulation of advanced vehicle technologies”, 2016.
A. Moawad et al., “Novel large scale simulation process to support DOT’s CAFE modeling system,” International Journal of Automotive Technology, vol. 17, pp. 1067–1077, 2016
A. Moawad et al., “AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit,” Transportation Research Part D: Transport and Environment, vol. 142, p. 104686, May 2025, doi: 10.1016/j.trd.2025.104686.