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Model-Based Control Development for an Advanced Thermal Management System for Automotive Powertrains

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Model-Based Control Development for an Advanced Thermal Management System for Automotive Powertrains Model-Based Control Development for an Advanced Thermal Management System for Automotive Powertrains A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Kyle Ian Merical, Graduate Program in Mechanical Engineering The Ohio State University 2013 Master's Examination Committee: Professor Marcello Canova, Advisor Professor Giorgio Rizzoni, Committee Member Professor Shawn Midlam-Mohler, Committee Member c Copyright by Kyle Ian Merical 2013 Abstract Rising fuel prices and tightening vehicle emission regulations have led to a large demand for fuel efficient passenger vehicles. Among several design improvements and technical solutions, advanced Thermal Management Systems (TMS) have been recently developed to more efficiently manage the thermal loads produced by internal combustion engines and thereby reduce fuel consumption. Advanced TMS include complex networks of coolant, oil and transmission fluid lines, heat exchangers, recuperators, variable speed pumps and fans, as well as active fluid flow control devices that allows for a greatly improved freedom to manage the heat rejection and thermal management of the engine and transmission components. This control authority can be exploited, for instance, to rapidly warm the powertrain fluids during vehicle cold-starts, and then maintain them at elevated temperatures. Increasing the temperatures of the engine oil and transmission fluid decreases their viscosity, ultimately leading to a reduction of the engine and transmission frictional losses, and improved fuel economy. On the other hand, robust and accurate TMS controllers must be developed in order to take full advantage of the additional degrees of freedom provided by the available actuators and system hardware configuration. To this extent, this work focuses on developing model-based TMS controls for a pro- totype light-duty automotive powertrain during fully warmed-up vehicle operation. ii The design of the models and control algorithms is conducted in parallel with the development of a prototype TMS, hence realizing a co-design of the TMS hardware and control system. In order to achieve this goal, first-principle models are created to characterize the thermal dynamics of the TMS components, and calibrated on specific components' data. The submodels are then integrated into a complete TMS model predicting the temperature dynamics of the powertrain fluids in response to commands to the available system actuators as well as operating and boundary conditions. The developed model is then used as a tool for model-based system analysis, optimiza- tion and control design. Specifically, a proof-of-concept control design is conducted to verify the feasibility of the TMS in maintaining the temperatures of the power- train fluids within the recommended range. In particular, a model-based optimization is conducted to define the open-loop actuator positions for various engine operating conditions that maintain the coolant temperature at the desired set-points. The open- loop strategy is then combined with a feedback control loop that combines rule-based and PI controllers to regulate the actuator position based on coolant temperature tracking error, compensating for disturbances and modeling errors. The prototype TMS controller developed in this work is shown to be effective in re- ducing the fluctuations in the coolant temperatures during the FTP driving cycle, compared to a baseline rule-based controller. Based on the preliminary results ob- tained, indications on the design of a state-space multi-variable feedback controller are made. This will further reduce the coolant temperature tracking error and allow all TMS actuators to work together in unison. iii This Thesis is dedicated to my parents. They have always been supportive of me in all my endeavors. This Thesis is another addition to the long list of things I could not have done without them. iv Acknowledgments My sincere thanks go to Professor Shawn Midlam-Mohler. Without several long conversations with him, I would probably not have made the decision to pursue my Master's Degree at all. His advice in that decision making process ultimately led me down this path, which is undoubtedly a great one for my future career. I would also like to thank my advisor, Professor Marcello Canova, for inviting me to join this research project, as well as all of the help he has extended to me. I could not have gotten through my work without his support and advice in the model and control development process. Additionally, his guidance in steering the direction of my work helped keep me on track and moving forward. My heartfelt thanks go to Dr. Fabio Chiara, as well. His vast technical knowledge on building and troubleshooting complex models was truly invaluable to my work. His attention to detail and willingness to drop everything to help me was also greatly appreciated. I thank Dr. Lisa Fiorentini for her help with the system analysis and control de- velopment associated with my work. Her extensive knowledge of system dynamics and control design helped shape my work. Also, her optimistic and helpful attitude helped keep me motivated when things got rough. I would also like to thank my fellow students working on the Chrysler/DoE project at OSU. Their collaborative help troubleshooting models and answering my general v questions is greatly appreciated. The continued work of Sabarish Gurusubramanian's efforts to implement the controls developed in this work on the production vehicle are also appreciated. My roommate and colleague, Jeremy Couch, was a great partner to have for this project. His help in my day-to-day struggles with this project as well as class work is greatly appreciated. More importantly, working alongside a friend always helps improve morale during Engineering analysis and Theis writing marathons. The past efforts of those having already completed their graduate work on this project were not forgotten either. I thank Neeraj Agarwal and Ben Grimm for the work they put into this project to get the full model of the production vehicle working, so that I could start my work where they left off. I also thank Chrysler Group LLC for their funding on this project. Additionally, the support and information I received from several Chrysler employees helped to improve the accuracy and significance of my work. vi Vita 2007 . .Bellbrook High School 2011 . .B.S.M.E., The Ohio State University 2011-present . .Graduate Research Associate, Depart- ment of Mechanical and Aerospace En- gineering, Center for Automotive Re- search, The Ohio State University Fields of Study Major Field: Mechanical Engineering vii Table of Contents Page Abstract . ii Dedication . iv Acknowledgments . v Vita . vii List of Tables . x List of Figures . xii 1. Introduction . 1 1.1 Scope . 2 1.2 Document Layout . 3 2. State of the Art . 5 2.1 Overview of Advancements in Engine and Powertrain Technology . 7 2.2 Current Thermal Mangement System (TMS) Technology . 11 2.2.1 Overview of Thermal Management Systems . 22 2.3 Thermal Management System Control . 25 2.3.1 TMS Control During Rapid Warm-up Phase . 26 2.3.2 TMS Control During Fluid Conditioning Phase . 34 2.4 TMS Control Design Methodology . 44 3. Modeling of a Thermal Management System . 45 3.1 Overview of Thermal Management System . 45 3.2 Thermal System Modeling Approaches . 48 viii 3.2.1 Lumped Parameter Modeling . 48 3.2.2 Lumped Thermal Capacitance Method . 49 3.2.3 Receivers . 49 3.3 Models Previously Developed . 50 3.3.1 Engine Thermal Model . 51 3.3.2 Slow Response Heat Exchangers . 56 3.3.3 Fast Response Heat Exchangers . 57 3.3.4 Transmission Thermal Model . 59 3.4 Updated Models . 61 3.4.1 Coolant Flow Network . 62 3.4.2 Electronic Thermostat . 66 3.4.3 Radiator Fan . 68 3.5 Modeling of Exhaust Gas Recirculation Cooler Thermodynamics . 70 3.5.1 EGRC Model Calibration and Validation . 72 3.6 Complete TMS Model Results . 79 3.6.1 FTP Engine Operating Range . 82 3.6.2 TMS Inputs . 84 3.6.3 TMS Outputs . 86 3.6.4 TMS Actuator Positions . 88 3.6.5 Fuel Consumption . 91 4. Thermal Management System Control Development . 92 4.1 Control Objectives and Methodology . 93 4.2 System Analysis . 97 4.3 Baseline TMS Controller . 102 4.4 Open-Loop Control Design . 108 4.4.1 Open-Loop Controller Performance . 117 4.5 Closed-Loop Control Design . 123 4.5.1 City Driving Feedback Control . 124 4.5.2 Highway Driving Feedback Control . 130 4.5.3 Closed Loop Controller Structure . 136 4.5.4 Closed-Loop Controller Performance . 138 5. Conclusions and Future Work . 144 5.1 Conclusions . 144 5.2 Future Work . 146 5.2.1 Future Control Development Methodology . 147 Bibliography . 163 ix List of Tables Table Page 2.1 DOE of TMS Actuator Positions . 33 3.1 EGRC Calibration Coefficients . 73 4.1 Baseline Coolant Temp. Tracking Error . 105 4.2 Baseline Actuator Energy Consumption . 107 4.3 DoE Operating Points . 110 4.4 Coolant Pump Power Consumption . 111 4.5 Radiator Fan Power Consumption . 111 4.6 Open-Loop Coolant Temp. Tracking Error . 118 4.7 Open-Loop Actuator Energy Consumption . 123 4.8 PI Controller Step Response Metrics City Driving . 127 4.9 PI Controller Gains for City Driving . 130 4.10 PI Controller Step Response Metrics for Highway Driving . 132 4.11 PI Controller Gains for Highway Driving . 136 4.12 Coolant Temp. Tracking Error . 142 4.13 Actuator Energy Consumption . 143 x 5.1 EGRC Thermal Model State Variables . 152 5.2 EGRC Thermal Model Control Inputs . 153 5.3 Equilibrium EGRC Thermal Model Control Inputs . 154 5.4 Equilibrium EGRC Thermal Model State Variables . 154 5.5 Summary of TMS Model Order Reduction . 160 xi List of Figures Figure Page 1.1 Prototype TMS Architecture .
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