Journal papers
[J.36] 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. (PDF)
[J.35] 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.34] 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. (PDF)
[J.33] 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. (PDF)
[J.32] 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. (PDF)
[J.21] 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. (PDF)
[J.30] Y. Ye, J. Zhang, S. Pilla, A. M. Rao, and B. Xu, “Application of a new type of lithium-sulfur battery and reinforcement learning in plug-in hybrid electric vehicle energy management,” Journal of Energy Storage, vol. 59, p. 106546, 2023. (PDF)
[J.29] B. Xu, Q. Zhou, J. Shi, and S. Li, “Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle,” Journal of Energy Storage, vol. 46, p. 103925, 2022. (PDF)
[J.28] 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. (PDF)
[J.27] 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. (PDF)
[J.26] J. Shi, B. Xu, Y. Shen, and J. Wu, "Energy Management Strategy for Battery/ Ultracapacitor Hybrid Electric City Bus based on Driving Patter Recognition", Energy, 2021, https://doi.org/10.1016/j.energy.2021.122752. (PDF)
[J.25] A. H. Ganesh and B. Xu, “A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution,” Renew. Sustain. Energy Rev., vol. 154, p. 111833, 2022. (PDF)
[J.24] X. Li, B. Xu, H. Tian, and G. Shu, “Towards a novel holistic design of organic Rankine cycle (ORC) systems operating under heat source fluctuations and intermittency,” Renewable and Sustainable Energy Reviews, vol. 147, p. 111207, 2021. (PDF)
[J.23] B. Xu, 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)
[J.22] P. Girade, H. Shah, K. Kaushik, A. Patheria, and B. Xu, “Comparative Analysis of State of Charge Based Adaptive Supervisory Control Strategies of Plug-In Hybrid Electric Vehicles,” Energy, p. 120856, 2021, doi: 10.1016/j.energy.2021.120856. (PDF)
[J.21] S. Sarvaiya, S. Ganesh, and B. Xu, “Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life,” Energy, vol. 228, p. 120604, 2021. (PDF)
[J.20] 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)
[J.19] 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)
[J.18] 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)
[J.17] 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)
[J.16] 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)
[J.15] 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)
[J.14] 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)
[J.13] 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)
[J.12] 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)
[J.11] 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)
[J.10] 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).
[J.9] 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)
[J.8] Xu, B., Yebi, A., Liu, X., Shutty, J., Anschel, P., Onori, S., Filipi, Z. and Hoffman, M., “Transient Power Optimization of an Organic Rankine Cycle Waste Heat Recovery System for Heavy-Duty Diesel Engine Applications", SAE International Journal of Alternative Powertrains. 6(1):2017, doi:10.4271/2017-01-0133. (PDF).
[J.7] Yebi, A., Xu, B., Liu, X., Shutty, J., Anschel, P., Onori, S., Filipi, Z., Hoffman, H., “Estimation and Predictive Control of a Parallel Evaporator Diesel Engine Waste Heat Recovery System", IEEE Transactions on Control Systems Technology, vol. PP, pp. 1-14, 2017. (PDF).
[J.6] 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.
[J.5] Du, Z., Xu, B., Pisu, P., “Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads", SAE International Journal of Connected and Automated Vehicles, 2020. (PDF)
[J.4] Rathod, D., Belwariar, U., Xu, B., and Hoffman, H., “An Enhanced Evaporator Model for Working Fluid Phase Length Prediction, Validated with Experimental Thermal Imaging Data”, International Journal of Mass and Heat Transfer, vol. 132, pp. 194-208, 2019. (PDF)
[J.3] Rathod, D., Xu, B., Filipi, Z., and Hoffman, M., “Experimental Evaluation of Evaporator Thermal Inertia for an Optimal Control Strategy of an Organic Rankine Cycle Waste Heat Recovery System”, SAE International Journal of Engines, 2020. (PDF)
[J.2] Rathod, D., Xu, B., Filipi, Z., and Hoffman, H., “An Experimentally Validated, Energy Focused, Optimal Control Strategy for an Organic Rankine Cycle Waste Heat Recovery System”, Applied Energy, 2019. (PDF)
[J.1] Liu, X., Yebi, A., Anschel, P., Shutty, J., Xu, B., Hoffman, H., Onori, S., “Model Predictive Control of an Organic Rankine Cycle System”, Energy Procedia, 2017. (PDF).
Book chapters
[B.1] Xu, B., Yebi, A., and Filipi, Z., Modeling for Organic Rankine Cycle Waste Heat Recovery System Development, Organic Rankine Cycle Technology for Heat Recovery, Enhua Wang, IntechOpen, DOI: 10.5772/intechopen.78997, 2018. (PDF).
Conference papers
[C.12] Y. Ye, J. Zhang, and B. Xu, “A Fast Q-learning Energy Management Strategy for Battery/Supercapacitor Electric Vehicles Considering Energy Saving and Battery Aging,” in 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), Dec. 2021, pp. 1–6. doi: 10.1109/ICECET52533.2021.9698682.
[C.11] 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)
[C.10] 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)
[C.9] 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)
[C.8] Xu, B., Yebi, A., Onori, S., Filipi, Z., Liu, X., Shutty, J., Anschel, P., Hoffman, H.,” Power Maximization of a Heavy Duty Diesel Organic Rankine Cycle Waste Heat Recovery System Utilizing Mechanically Coupled and Fully Electrified Turbine Expanders,” Proceedings of the ASME 2016 Internal Combustion Fall Technical Conference, Greenville, SC, USA, Oct 8-12, 2016. (PDF)
[C.7] Xu, B., Liu, X., Shutty, J., Anschel, P., Onori, S., Filipi, Z. and Hoffman, M., “Physics-Based Modeling and Transient Validation of an Organic Rankine Cycle Waste Heat Recovery System for a Heavy-Duty Diesel Engine", 2016-01-0199, SAE 2016 World Congress & Exhibition, Detroit, MI, USA, April 12-14, 2016
[C.6] Farahani, S., Losftis, J., Xu, B., Pilla, S., “Towards Multi-tiered Quality Control in Manufacturing of Plastics and Composites Using Industry 4.0", The Annual Technical Conference for Plastics Professionals, 2020. (PDF)
[C.5] Rathod, D., Xu, B., Yebi, A., Vahidi, A., Filipi, Z. and Hoffman, M., “A Look-ahead Model Predictive Control Strategy for an Organic Rankine Cycle - Waste Heat Recovery System in a Heavy Duty Diesel Engine Application", 2019-01-1130, SAE 2019 World Congress & Exhibition, 2019. (PDF)
[C.4] 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)
[C.3] Malmir, 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)
[C.2] Yebi, A., Xu, B., Liu, X., Shutty, J., Anschel, P., Onori, S., Filipi, Z., Hoffman, H., " Nonlinear Model Predictive Control Strategies for a Parallel Evaporator Diesel Engine Waste Heat Recovery System ", Proceedings of ASME Dynamic System and Control Conference, Minneapolis, Minnesota, October 12-14, 2016. (PDF)
[C.1] Mittal, N., Bhide, S., Bhagat, A.P., Xu, B., Acharya, B., “Real-Time Optimization of Control Strategy for a Range-Extended Electric Vehicle using Reinforcement Learning Algorithm", SAE 2020 World Congress & Exhibition, 2020. (PDF)