Journal
D. Egan, Q. Zhu, and R. Prucka, “A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation”, Energies, vol. 16, no. 8, 3450, 2023, https://doi.org/10.3390/en16083450,
Q. Zhu, A. Kumar, A. Sundar, D. Egan, et al., "Development of a Series Hybrid Electrified Powertrain for a High Speed Tracked Vehicle Based on Driving Cycle Simulation," SAE Int. J. Adv. & Curr. Prac. in Mobility 4(4):1403-1412, 2022, https://doi-org.libproxy.clemson.edu/10.4271/2022-01-0367.
R. Koli, D. Egan, Q. Zhu, and R. Prucka, “Nonlinear model predictive control of a DISI turbocharged engine with virtual engine co-simulation and real-time experimental validation”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 237, no. 14, pp. 3380 – 3396, Dec. 2023, doi: 10.1177/09544070221146586.
Q. Zhu, R. Prucka, “Transient Hybrid Electric Vehicle Powertrain Control Based on Iterative Dynamic Programming” ASME Journal of Dynamic Systems, Measurement, and Control. vol. 144, no. 2, doi: https://doi.org/10.1115/1.4052230.
A. Mamun, Q. Zhu, M. Hoffman, and S. Onori, “Physics-based Linear Model Predictive Control strategy for Three-Way Catalyst Air/Fuel Ratio Control”, IMechE, Part D: Journal of Automobile Engineering, 235(14), 2021: 09544070211021207.
Q. Zhu, S. Wang, R. Prucka, M. Prucka and H. Dourra, “Online spark timing optimization with complex high-fidelity combustion phasing knock and COV of IMEP models”, ASME Journal of Dynamic Systems, Measurement, and Control, vol. 143, no. 6,: 061007., June 2021,
Z. Wang, Q. Zhu, and R. Prucka, “Air-to-fuel ratio error source determination for a spark-ignition engine using a disturbance observer”, International Journal of Powertrains, vol. 10, no. 1, pp. 54 – 78, Apr. 2021.
Q. Zhu, R. Prucka, “Integrated engine state estimation using Extended Kalman Filter and disturbance observer”, SAE International Journal of Advances and Current Practices in Mobility, vol. 2, issue 2019-01-2603, pp. 929-938, 2019.
Q. Zhu, S. Onori and R. Prucka, “An economic nonlinear model predictive control strategy for SI engines: model-based design and real-time experimental validation”, IEEE Transactions on Control Systems Technology, vol. 27, no. 1, pp. 296-310, 2019.
Q. Zhu, R. Prucka, M. Prucka and H. Dourra, “A Nonlinear Model Predictive Control Strategy with a Disturbance Observer for Spark Ignition Engines with External EGR”, SAE International Journal of Commercial Vehicles, vol.10, no.1, pp. 360-372, 2017.
Q. Zhu, R. Prucka, M. Prucka and H. Dourra, “Model predictive engine speed control for transmissions with dog clutches”, Journal of Engineering for Gas Turbines and Power, vol.139, no.11, Jun., 2017.
Z. Wang, Q. Zhu, R. Prucka, M. Prucka and H. Dourra, “Observer based cylinder air charge estimation for spark ignition engines”, Journal of Engineering for Gas Turbines and Power, vol.139, no.10, May, 2017.
Q. Zhu, R. Prucka, S. Wang, M. Prucka, and H. Dourra, “Model-based optimal combustion phasing control strategy for spark ignition engines”, SAE Int. J. Engines, vol. 9, no. 2, pp.1170-1179, 2016
S. Wang, R. Prucka, Q. Zhu, M. Prucka and H. Dourra, “A real-time model for spark ignition engine combustion phasing prediction”, SAE Int. J. Engines, vol. 9, no. 2, pp. 1180-1190, 2016.
Q. Zhu, S. Wang, R. Prucka, M. Prucka and H. Dourra, “Model based control-oriented combustion phasing feedback for fast CA50 estimation”, SAE Int. J. Engines, vol. 8, no. 3, pp. 997-1004, 2015.
S. Wang, Q. Zhu, R. Prucka, M. Prucka and H. Dourra, “Input adaptation for control oriented physics-based SI engine combustion models based on cylinder pressure feedback”, SAE Int. J. Engines, vol. 8, no. 4, pp. 1463-1471, 2015.
Q. Zhu, L. Feng, A. Mayyas, MA. Omar, and etc., “Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing”, Journal for Cleaner Production, vol. 105, pp. 178-183, 2015.
M. Omar, Q. Zhu, L. Feng, M. Khraisheh and et al. “A hybrid simulation approach for predicting energy flows in production lines”, Int. J. of Sustainable Engineering, vol. 9, no. 1, pp. 25-34, 2015.
Conference
Z. Feng, H. Zhan, Z. Chen, Q. Yan, X. Xu, C. Cai, B. Li, Q. Zhu, Y. Xu, “Naruto: Neural active reconstruction from uncertain target observations”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 21572 - 21583
A. Sundar, A. Ghate, Q. Zhu, R. Prucka, “Energy – Aware Predictive Control for the Battery Thermal Management System of an Autonomous Off-Road Vehicle”, SAE Technical Paper, no. 2024-01-2665, Apr., 2024
A. Sundar, A. Vannarath, R. Prucka, Q.Zhu, et al., “The Influence of Cooling Air-Path Restrictions on Fuel Consumption of a Series Hybrid Electric Off-Road Tracked Vehicle”, SAE Technical Paper, no. 2023-01-1611, Oct., 2023
D. Egan, A. Sundar, A. Kumar, Q. Zhu, and et al., “Synthesis of Statistically Representative Driving Cycle for Tracked Vehicles”, SAE Technical Paper, no. 2023-01-0115, April, 2023
H. Mirzaei, Q. Zhu and R. Prucka, "Parametric Solution for Efficient Li-ion Battery Charging with Current and SOC Constraints," 2022 American Control Conference (ACC), Atlanta, GA, USA, 2022, pp. 1100-1107, doi: 10.23919/ACC53348.2022.9867169.
D. Egan, B. Xu, Q. Zhu, et al. “Reinforcement Learning Based Control of an Organic Rankine Cycle Waste Heat Recovery System Over a Drive Cycle for Heavy-Duty Diesel Engines”, Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2022, 86540: V001T04A008.
D. Egan, A. Sundar, A. Kumar, Q. Zhu, and et al. “Synthesis of Statistically Representative Driving Cycles for Tracked Vehicles”, SAE Technical Paper., no. 2023-01-0115, 2023
H. Mirzaei, Q. Zhu, and R. Prucka, “Constrained optimization of Li-ion charging current via Pontryagin’s Minimum Principle and Quadratic Programming”, accepted by 2022 American Control Conference, 2022
G. Ozkan, P. Hoang, P. Badr, L. Timilsina, C. Edrington, Q. Zhu, et al. “Model-based active thermal management for neutral-point clamped power converter with adaptive weight”, SAE Technical Paper, no. 2022-01-0727, Mar. 2022
Q. Zhu, A. Kumar, A. Sundar, D. Egan, et al. “Development of a series hybrid electrified powertrain for a high speed tracked vehicle based on driving cycle simulation”, SAE Technical Paper, no. 2022-01-0367, Mar. 2022
Q. Zhu, M. Schmid, et al., “Control Informed Design of the IAC Autonomous Racecar for Operation at the Dynamic Envelope”, 2021 ICRA Workshop: Opportunities and Challenges with Autonomous Racing, May 31, 2021,
Q. Zhu, G. Ozkan, P. Badr, et al. “Fast engine torque variation compensation for HEVs using permanent magnet synchronous motor and explicit MPC”, SAE Technical Paper, 2021-01-0718, 2021
R. Koli, D. Egan, Q. Zhu and R. Prucka, “Quantification of Linear Approximation Error for Model Predictive Control of Spark Ignited Turbocharged Engines”, SAE Technical Paper. 2019-24-0014, 2019
D.Egan, R. Koli, Q. Zhu, R. Prucka, “Use of Machine Learning for Real-Time Non-Linear Model Predictive Engine Control”, SAE Technical Paper 2019-01-1289, 2019.
R. Koli, H. Arunachalam, Q. Zhu, S. Onori and etc, “Nonlinear model predictive control of Dual Loop – Exhaust Gas Recirculation in a turbocharged spark ignited engine”, in Proc. of 2018 ACC, Jun., 2018, Milwaukee, WI.
Q. Zhu, R. Prucka and etc, “Control Optimization of a Charge Sustaining Hybrid Powertrain for Motorsports”, SAE Technical Paper 2018-01-0416, 2018.
Q. Zhu, R. Koli, L. Feng, S. Onori and R. Prucka, “Nonlinear Model Predictive Air Path Control for Turbocharged SI Engines with Low Pressure EGR and a Continuous Surge Valve”, in Proc. of 2017 American Control Conference, Seattle, WA, USA, May, 24-26, 2017.
Q. Zhu, R. Prucka, S. Wang, M. Prucka and H. Dourra, “Control oriented modelling of engine IMEP variation”, in Proc. of 2016 ICEF, Oct., 2016, Greenville, South Carolina.
Q. Zhu, S. Onori, R. Prucka, “Nonlinear economic model predictive control for SI engines based on sequential quadratic programming”, in Proc. of 2016 ACC, Jul., 2016, Boston, Massachusetts.
Z. Wang, Q. Zhu and R. Prucka, “A review of spark-ignition engine air charge estimation methods”, SAE Technical Paper, no. 2016-01-0620.
Q. Zhu, S. Onori and R. Prucka, “Pattern recognition technique based active set QP strategy applied to MPC for a driving cycle test”, in Proc. of 2015 ACC, Jul., 2015, Chicago, Illinois.
L. Feng, L. Mears, Q. Zhu, C. Beaufort and et al. “Plant level energy supply analysis and optimization in energy economy and environment”, in Proc. of ASME 2014 Int. MSEC, Jun., 2014, Detroit, Michigan.
Q. Zhu and B. Ayalew, “Predictive roll handling and ride control of vehicles via active suspensions”, in Proc. of 2014 ACC, Jun., 2014, Portland, Oregon.
Others
(Presentation) Prucka, R., Zhu, Q., “Real-time Non-linear Model Predictive Control Algorithms for Spark Ignition Engines,” Presented at Ford Motor Company, Dearborn, Michigan, February 25, 2019.
(Presentation) Lucas, J., Brooks, J., Schwambach, B., Mims, L., Jenkins, C., Prucka, R., Zhu, Q., Anderson, M., & Trippedo, N.. The impact of concussion on three reaction time tasks using a driving simulator: A pilot study. Presentation at the 2018 American Medical Society for Sports Medicine (AMSSM), Orlando, FL (2018, April).
(Patent)“Engine Operation Control,” USA, 10012204, with S. Wang, R. Prucka, H. Dourra.
