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)
Supervised Learning in Smart Manufacturing 01/2017 - 05/2020
Research topics:
Real-time products failure prediction based on 1000+ sensor signals collected in production line to reduce test cost and improve product quality using unsupervised and supervised learning
Real-time product quality monitoring in injection molding process to improve products quality using supervised learning
Raw data cleaning, data reduction, key feature extraction and data visualization.
Multilayer machine learning model construction and model selection. Hyper-parameter optimization.
Trained model real-time prediction implementation.
Cloud computing for the periodic model training and real-time optimization.
API in Linux Python Flask environment.
Featured Publication:
Farahani, S., Xu, B., Filipi, Z., Pilla, S., “A Machine Learning Approach to Quality Monitoring of Injection Molding Process Using Regression Models”, The International Journal of Computer Integrated Manufacturing, 2021, doi: https://doi.org/10.1080/0951192X.2021.1963485.
Zhang, D., Xu, B., and Wood, J. “Predict failures in production lines: A two-stage approach with clustering and supervised learning”, 2016 IEEE International Conference on Big Data, 2070-2074, Washington DC, 2016. (PDF)