Bin Xu

Assistant Professor

Email: binxu@ou.edu
Phone: (405) 325-1728
Office: Felgar Hall 207
Office Hours: Monday/Wednesday 12:00pm-1:00pm
Lab Website: MiLa

Education
Ph.D., Automotive Engineering (2017)
Clemson University
B.S., Automotive Engineering (2013)
Hunan University, China

Research Focus

Application:
- Autonomous Vehicle Full Stack In-house Software Development (perception, path planning, path tracking)
- Autonomous Vehicle Modification, Instrumentation and Drive-by-Wire (DBW) Realization
- Autonomous Vehicle Road Testing (ADAS, Robotaxi, SAE L2/L3/L4)
- Connected Vehicles Testing (V2X, V2I, V2V, V2C)
- Vehicle Electrification (electric vehicle & hybrid electric vehicle)
- Dynamics and Control
Theory:
- Reinforcement Learning
- Supervised Learning
- Optimal Control
- Physics-based Modeling

Courses Taught

AME 5970-Dynamics and Control for Autonomous Driving
AME 4442-IC Engine Lab
AME 2402-Engineering Computing

Professional Activities and Organizations

Associate Editor for SAE International Journal of Electrified Vehicles
SAE Sustainable Mobility Committee

Recent Publications

Z. Arjmandzadeh, M. H. Abbasi, H. Wang, J. Zhang, and B. Xu, “A Comparative Study on Autonomous Vehicle Local Path Planning Through Model Predictive Control and Frenet Frame Mehtod”, SAE International Journal of Connected and Automated Vehicles, 2024 (accepted).
H. Wang, Z. Arjmandzadeh, Y. Ye, J. Zhang, B. Xu, “FlexNet: A Warm Start Method for Deep Reinforcement Learning in Hybrid Electric Vehicle Energy Management Applications”, Energy 288, 129773, 2024.
H. Wang, Z. Arjmandzadeh, J. Zhang, B. Xu, “Automated Expert Knowledge-based Deep Reinforcement Learning Warm Start via Decision Tree for Hybrid Electric Vehicle Energy Management”, SAE International Journal of Electrified Vehicles, 2023.
Y. Ye, H. Wang, B. Xu, J. Zhang, “An imitation learning-based energy management strategy for electric vehicles considering battery aging,” Energy, P. 128537, 2023.
J. Shi, J. Wu, B. Xu, Z. Song, “Cybersecurity of Hybrid Electric City Bus with V2C Connectivity,” IEEE Transactions on Intelligent Vehicles, P. 1-16, 2023.
B. Xu and H. Wang, “A comparative analysis of adaptive energy management for a hybrid electric vehicle via five driving condition recognition methods,” Energy, p. 126732, 2023.
H. Wang, Y. Ye, J. Zhang, and B. Xu, “A comparative study of 13 deep reinforcement learning based energy management methods for a hybrid electric vehicle,” Energy, vol. 266, p. 126497, 2023.
B. Xu and Z. Arjmandzadeh, “Parametric study on thermal management system for the range of full (Tesla Model S)/compact-size (Tesla Model 3) electric vehicles”, Energy Conversion and Management, Vol. 278, p. 116753, 2023.
B. Xu, J. Shi, S. Li, and H. Li, “A Study of Vehicle Driving Condition Recognition Using Supervised Learning Methods,” IEEE Trans. Transp. Electrification, pp. 1–1, 2021, doi: 10.1109/TTE.2021.3127194.
J. Shi, B. Xu, X. Zhou, and J. Hou, “A cloud-based energy management strategy for hybrid electric city bus considering real-time passenger load prediction,” Journal of Energy Storage, vol. 45, p. 103749, 2022.