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Model Predictive Control of Organic Rankine Cycle for Waste Heat Recovery in Heavy-Duty Diesel Powertrain

[+] Author Affiliations
David Luong, Tsu-Chin Tsao

University of California, Los Angeles, CA

Paper No. DSCC2014-5881, pp. V002T21A001; 7 pages
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4619-3
  • Copyright © 2014 by ASME


This paper continues the development of a previous conference paper that introduces advanced controllers for an Organic Rankine Cycle (ORC) in a heavy-duty diesel powertrain. The ORC’s heat exchangers are modeled as control-oriented, nonlinear Moving Boundary models. The pump and expander, which are coupled to the engine crankshaft, have relatively faster dynamics than the heat exchangers and are modeled as static components. The driving cycle produces transient heat source and engine conditions for the ORC whose goal is to maximize waste heat recovery under specified operating constraints. This paper describes a Model Predictive Controller (MPC) and compares it to Proportional Integral (PI) and Linear Quadratic Integral (LQI) controllers. The three controllers attempts to regulate heat exchanger pressures while satisfying specified operating constraints. Extended Kalman Filters (EKFs) are designed and implemented for state estimation feedback for MPC and LQI requiring full state feedback. The simulation results show the advantage of the advanced controllers in reducing pressure regulation error, with MPC having the lowest pressure regulation error among the three controllers and able to incorporate the operating constraints into its control law.

Copyright © 2014 by ASME



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