Mobility Intelligence Lab

Autonomous Vehicle Testing 09/2021- present

Research topics:

Automatic Emergency Braking (AEB)
Adaptive Cruise Control (ACC)
GPS-based Path Following

Featured Publication:

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 Method,” SAE International Journal of Connected and Automated Vehicles, vol. 7, no. 12-07-04–0029, 2024, Accessed: Dec. 16, 2024. [Online]. Available: https://www.sae.org/publications/technical-papers/content/12-07-04-0029/. (PDF)

Automatic Emergency Braking (AEB) test video: Dashcam detect pedestrain and estimate distance, automatically brake when collision is close.

Combined_text.mp4

Drive-by-Wire (DBW) test video: top left: computer controlled steering; top right: PS5 game controller controlled steering; bottom left: PS5 game pad controllered precise steering.

Computer_steer

gamepad_steer

proportional_steer.mp4

Vehicle modification highlights:

Pizza Braking Test

Reinforcement Learning (RL) and Optimal Control in Autonomous Driving 01/2021- present

Research topics:

Inverse Reinforcement Learning (IRL) based path planning
Lyapunov optimization based path planning
Model Predictive Control (MPC) based path planning
Geometry based mono-camera depth estimation
Vehicle Drive-by-Wire (DBW) modification including steering, braking, and accelerating actuators control by wire
Environment perception sensor instrumentation including lidar, radar, camera, acoustic sensor, GNSS, IMU, LTE.
Autonomous Vehicle testing including Automated Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Centering Assist (LCA), and Point to Point Navigation.

Featured Publication:

Xu, B., Rathod, D., Zhang, D.R., Yebi, A., Zhang X.Y., Li, X.Y., Filipi, Z., “Parametric Study on Reinforcement Learning Optimized Energy Management Strategy for a Hybrid Electric Vehicle”, Applied Energy, vol.259, p.114200, 2020. (PDF)

Reinforcement Learning (RL) in Propulsion Systems 01/2014- present

Research topics:

RL function mechanism interpretation based on hybrid propulsion system energy management application
Learning time reduction of RL via warm start (e.g., value/ policy functions initialization with expert knowledge or conventional control)
Adaptiveness investigation of RL over various environmental and vehicular conditions (e.g., activation and deactivation mechanism of adaptiveness process)
Ensemble RL (e.g., combination of various RL algorithms and conventional controls to improve robustness and optimality of the results)
RL Convergence study via recent Basal Ganglia Neuroscience studies (e.g., new computational model for RL learning end process, which will replace existing learning rate)
Unifying theories of tonic (action) and phasic (learning) Dopamine release considering neuroscience experimental study.
Investigation of sensory, learning, and motoring circuit in Basal Ganglia by combining neuroscience experimental data and computational models.

Featured Publication:

Xu, B., Rathod, D., Zhang, D.R., Yebi, A., Zhang X.Y., Li, X.Y., Filipi, Z., “Parametric Study on Reinforcement Learning Optimized Energy Management Strategy for a Hybrid Electric Vehicle”, Applied Energy, vol.259, p.114200, 2020. (PDF)
Xu, B., Hu, X.S., Lin, X.K., Li, H.Y., Rathod, D., Filipi, Z., “Ensemble Reinforcement Learning as a Hybrid Electric Vehicle Supervisory Control for Fuel Economy Improvement”, IEEE Transactions on Transportation Electrification, 2020, doi: 10.1109/TTE.2020.2991079. (PDF)
Xu, B., Hou, J., Shi, J., Li, H., Wang, Z., Rathod, D., and Filipi, Z., “Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control Strategy in Hybrid Electric Vehicle Applications”, IEEE Transactions on Transportation Electrification, 2020, doi: 10.1109/TTE.2020.3019009. (PDF)
Xu, B., J. Shi, S. Li, H. Li, and Z. Wang, “Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle,” Energy, vol. 229, p. 120705, 2021. (PDF)
Xu, B., Tang, X.L., Hu, X.S., Lin, X.K., Li, X.Y., Rathod, D., and Wang, Z., “Q-learning Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles, IEEE Transactions on Intelligent Transportation Systems, 2021, doi: 10.1109/TITS.2021.3062179. (PDF)
Xu, B., and Li, X.Y., “A Q-learning Based Transient Power Optimization Method for Organic Rankine Cycle Waste Heat Recovery System in Heavy Duty Diesel Engine Applications”, Applied Energy, 2021, doi: https://doi.org/10.1016/j.apenergy.2021.116532. (PDF)

Supervised Learning in Propulsion Systems 09/2017-04/2020

Research topics:

Development of Data-Driven Lithium-ion battery design assistant tool by building machine learning models connecting vehicle specification and battery design parameters
Real-time realization of offline Dynamic Programming optimization results by utilizing machine learning models to extract optimal rules from Dynamic Programming results
Multi-layer hierarchical ensemble machine learning to improve the prediction accuracy

Featured Publication:

Xu, B., Rathod, D., Yebi, A., Onori, S., Filipi, Z., “Real-Time Realization of Dynamic Programming Using Machine Learning Methods for IC Engine Waste Heat Recovery System Power Optimization”, Applied Energy, vol.262, p.114514, 2020. (PDF)
Xu, B., Rizzo, D., and Onori, S., “Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems", SAE 2020 World Congress & Exhibition, 2020. (PDF)
Xu, B., Zhang, D., and Tang, S., “Malware Classification Utilizing Supervised Learning in Autonomous Driving Applications”, SAE - 19th Asian Pacific Automotive Engineering Conference (APAC), Shanghai, China, 2017. (PDF)

Exhaust Waste Heat Recovery on Heavy-Duty Diesel Engine Project (BorgWarner) 09/2013- 11/2020

Research topics:

Plant modeling and experimental validation using a 13L heavy-duty diesel engine and ORC test rig.
Control-oriented model and nonlinear model predictive control development.
Power optimization utilizing reinforcement learning for the benefits of model-free Q-learning.
Extracting rules from offline dynamic programming results using machine learning models and implementing the trained models in real-time.

Featured Publication:

Xu, B., and Li, X.Y., “A Q-learning Based Transient Power Optimization Method for Organic Rankine Cycle Waste Heat Recovery System in Heavy Duty Diesel Engine Applications”, Applied Energy, 2021, doi: https://doi.org/10.1016/j.apenergy.2021.116532. (PDF)
Xu, B., Rathod, D., Yebi, A., Onori, S., Filipi, Z., “Real-Time Realization of Dynamic Programming Using Machine Learning Methods for IC Engine Waste Heat Recovery System Power Optimization”, Applied Energy, vol.262, p.114514, 2020. (PDF)
Xu, B., Rathod, D., Kulkarni, S., Yebi, A., Filipi, Z., Onori, S., Hoffman, H., “Transient Dynamic Modeling and Validation of an Organic Rankine Cycle Waste Heat Recovery System for Heavy Duty Diesel Engine Applications,” Applied Energy, 205: pp. 260-279, 2017. (PDF).
Xu, B., Yebi, A., Hoffman, M., and Onori, S., “A Rigorous Model Order Reduction Framework for Waste Heat Recovery Systems Based on Proper Orthogonal Decomposition and Galerkin Projection", IEEE Transactions on Control Systems Technology, 2018, doi: 10.1109/TCST.2018.2878810. (PDF)
Xu, B., Rathod, D., Yebi, A., Filipi, Z., "A Comparative Analysis of Real-time Power Optimization for Organic Rankine Cycle Waste Heat Recovery Systems," Applied Thermal Engineering, vol. 164, 114442, 2020. (PDF)
Xu, B., Rathod, D., Yebi, A., Onori, S., Filipi, Z. and Hoffman, M., “A Comparative Analysis of Dynamic Evaporator Models for Organic Rankine Cycle Waste Heat Recovery Systems”, Applied Thermal Engineering. p. 114576, 2019. (PDF)
Xu, B., Yebi, A., Rathod, D., Onori, S., Filipi, Z., Hoffman, H., “Experimental Validation of Nonlinear Model Predictive Control for a Heavy-Duty Diesel Engine Waste Heat Recovery System", ASME Journal of dynamic systems measurement and control. vol. 142, Issue 5, 2020. (PDF)
Xu, B., Rathod, D., Yebi, A., Onori, S., Filipi, Z., Hoffman, H., “A Comprehensive Review of Organic Rankine Cycle Waste Heat Recovery for Heavy Duty Diesel Engine Applications", Renewable & Sustainable Energy Reviews. vol. 207, pp. 145-170, 2019. (PDF)

Hybrid Electric Vehicle Energy Management 12/2017 - 05/2019

Research topics:

Implementing real-time reinforcement learning based supervisory control to split the torque between engine and electrical motor.
Battery aging modeling using severity factor empirical method.
Proposing battery aging ECMS method by integrating fuel economy and battery life in the cost function.
Vehicle topology analysis with different propulsion architectures.
Battery, electrical motor and battery cooling fan size optimization using Particle Swarm Optimization and Latin Hyper Cube Sampling methods.

Featured Publication:

Xu, B., Malmir, F., Rathod, D., and Filipi, Z., “Real-time Reinforcement Learning Optimized Energy Management for a 48V Mild Hybrid Electric Vehicle", 2019-01-1208, SAE 2019 World Congress & Exhibition, 2019. (PDF)
Falmir, F., Xu, B., and Filipi, Z., “A heuristic supervisory controller for a 48V hybrid electric vehicle considering fuel economy and battery aging”, SAE International Powertrains, Fuels & Lubricants Meeting. 2019. (PDF)

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