Bin Xu (PI), binxu@ou.edu, CV, Google Scholar, Research Gate
Research Interests
Theory:
Reinforcement Learning (Warm start to reduce learning time; Adaptability study for continuous learning)
Supervised Learning (Black-box/ Grey-box modeling, rule-extraction from large data to assist real-time control and system development)
Data-Driven Modeling and Control
Optimal Control (Model Predictive Control, Dynamic Programming, Learning based control)
Optimization
Application:
Propulsion Electrification for Greenhouse Gas (GHG) emission reduction and Energy Saving
Connected and Automated Vehicles (CAV) and Advanced Driver Assistance Systems (ADAS) for Vehicle Safety and Energy Saving (Automated Emergency Braking - AEB, Adaptive Cruise Control - ACC, Lane Keeping, Lane Changing, Point to Point Navigation)
Grants
DOE Vehicle Technology Office - WIDE: Improving Accessibility and Efficiency of Public Transportation via Optimal Integration with Shared Autonomous Electric Vehicles in Tribal Communities (Co-PI, Research Credit 35%, $3.1 million) (9/2024-8/2027) (Featured in abc 10 news)
DOE Office of Fossil Energy and Carbon Management: A Regional-Scale Showcase of Hybrid Methane Sensing Networks in the Anadarko Basin (Co-PI, Research Credit 10%, $8.49 million) (9/2023-3/2028)
Association of Central Oklahoma Goverment (ACOG) Public Fleet Grant: Boosting Electric Vehicle Charging Capability on University of Oklahoma Norman Campus (Co-PI, Research Credit 40%, $187,333) (2/2024-1/2026)
OU DISC Faculty Seed Funding Award: Integrated Off-Road Long-Range Navigation, Local Path Planning and Following via Fully Automated Cooperation of Vehicle Embedded Autonomous Drones and Ground Vehicles (PI, Research Credit 40%, $12k) (7/2025-6/2026)
OU DISC Faculty Seed Funding Award: Nanocrystalline Transition Metal Oxides for a New Generation of Lithium-Ion Batteries (Co-PI, Research Credit 10%, $10k) (8/2022-7/2023)
Teaching
AME 5970: Dynamics and Control of Autonomous Driving (Fall 2024);
AME 4442: IC Engine Lab (Spring 2022-present)
AME 2420: Engineering Computing (Fall 2022, 2023);
Acknowledgements
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