This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Model Predictive Control of Turbocharged Gasoline Engines for Mass Production
Technical Paper
2018-01-0875
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
This paper describes the design of a multivariable, constrained Model Predictive Control (MPC) system for torque tracking in turbocharged gasoline engines scheduled for production by General Motors starting in calendar year 2018. The control system has been conceived and co-developed by General Motors and ODYS. The control approach consists of a set of linear MPC controllers scheduled in real time based on engine operating conditions. For each MPC controller, a linear model is obtained by system identification with data collected from engines. The control system coordinates throttle, wastegate, intake and exhaust cams in real time to track a desired engine torque profile, based on measurements and estimates of engine torque and intake manifold pressure. The MPC optimizes torque tracking during both transient and steady-state operations, minimizing specific fuel consumption and taking into account predefined fuel-efficient steady-state actuators positions, as well as constraints on input and output variables. Actuator commands are computed by solving an optimization problem at each sampling instant. Each linear MPC controller is equipped with a Kalman filter to reconstruct the system state from available measurements. Compared to more classical controls, the presented MPC approach achieves better coordination of multiple actuators for improved fuel economy and drivability, while maintaining robustness with respect to measurement noise, ambient conditions, and part-to-part variations. Moreover, the systematic, model-based framework developed for production enables an immediate adaptation of the design to different engine hardware architectures. ODYS developed the MPC core software that could be run in an embedded controller and was configurable for different problem formulations. General Motors and ODYS worked together to integrate the MPC core software to meet the requirements of various projects, improve the run-time performance and to deploy and validate in production engine control units (ECUs).
Recommended Content
Authors
Topic
Citation
Bemporad, A., Bernardini, D., Long, R., and Verdejo, J., "Model Predictive Control of Turbocharged Gasoline Engines for Mass Production," SAE Technical Paper 2018-01-0875, 2018, https://doi.org/10.4271/2018-01-0875.Also In
References
- Streib , H.M. and Leonhard , R. Hierarchical Control Strategy for Powertrain Functions SAE Technical Paper 925052 1992
- Gerhardt , J. , Hönninger , H. , and Bischof , H. A New Approach to Functional and Software Structure for Engine Management Systems -BOSCH ME7 SAE Technical Paper 980801 1998
- Mencher , B. , Jessen , H. , Kaiser , L. , and Gerhardt , J. Preparing for CARTRONIC - Interface and New Strategies for Torque Coordination and Conversion in a Spark Ignition Engine-Management System SAE Technical Paper 2001-01-0268 2001
- Heintz , N. , Mews , M. , Stier , G. , Beaumont , A. et al. An Approach to Torque-Based Engine Management Systems SAE Techical Paper 2001-01-0269 2001
- Livshiz , M. , Kao , M. , and Will , A. Engine Torque Control Variation Analysis SAE Technical Paper 2008-01-1016 2008
- Rafal , M.D. and Stevens , W.F. Discrete Dynamic Optimization Applied to on-Line Optimal Control AICHE Journal 14 1 85 91 1968
- Qin , S.J. and Badgwell , T.A. A Survey of Industrial Model Predictive Control Technology Control Engineering Practice 11 7 733 764 2003
- Bauer , M. and Craig , I.K. Economic Assessment of Advanced Process Control - A Survey and Framework Journal of Process Control 18 1 2 18 2008
- Samad , T. A Survey on Industry Impact and Challenges Thereof [Technical Activities] IEEE Control Systems 37 1 17 18 2017
- Mayne , D.Q. Model Predictive Control: Recent Developments and Future Promise Automatica 50 12 2967 2986 2014
- Beale , E.M.L. Journal of the Royal Statistical Society. Series B (Methodological) 173 184 1955
- Bemporad , A. , Morari , M. , Dua , V. , and Pistikopoulos , E.N. The Explicit Linear Quadratic Regulator for Constrained Systems Automatica 38 1 3 20 2002
- Bemporad , A. A Multiparametric Quadratic Programming Algorithm with Polyhedral Computations Based on Nonnegative Least Squares IEEE Transactions on Automatic Control 60 11 2892 2903 2015
- Cimini , G. and Bemporad , A. IEEE Transactions on Automatic Control 2017
- Hrovat , D. , Di Cairano , S. , Tseng , H. E. , and Kolmanovsky , I. V. The Development of Model Predictive Control in Automotive Industry: A Survey IEEE International Conference on Control Applications , 2012 , 295 302
- Falcone , P. , Borrelli , F. , Asgari , J. , Tseng , H.E. , and Hrovat , D. Predictive Active Steering Control for Autonomous Vehicle Systems IEEE Transactions on Control Systems Technology 15 3 566 580 2007
- Bernardini , D. , Di Cairano , S. , Bemporad , A. and Tseng , H.E. Drive-by-wire vehicle stabilization and yaw regulation: A hybrid model predictive control design Proc. 48th IEEE Conf. on Decision and Control Shanghai, China 2009 7621 7626
- Graf Plessen , M. , Bernardini , D. , Esen , H. , and Bemporad , A. IEEE Trans. Contr. Systems Technology 2017
- Di Cairano , S. , Yanakiev , D. , Bemporad , A. , Kolmanovsky , I.V. , and Hrovat , D. Model Predictive Idle Speed Control: Design, Analysis, and Experimental Evaluation IEEE Trans. Contr. Systems Technology 20 1 84 97 2012
- Esen , H. , Tashiro , T. , Bernardini , D. , and Bemporad , A. Cabin heat thermal management in hybrid vehicles using model predictive control 22nd Mediterranean Control Conference Palermo, Italy 2014
- Ripaccioli , G. , Bernardini , D. , Di Cairano , S. , Bemporad , A. , and Kolmanovsky , I.V. A Stochastic Model Predictive Control Approach for Series Hybrid Electric Vehicle Power Management Proc. American Contr. Conf Baltimore, MD 2010 5844 5849
- Zhou , X. , Li , Y. and Hu , Y. Torque tracking control of turbocharged gasoline engine using nonlinear MPC 2015 European Control Conference 2015
- Santillo , M. and Karnik , A. Model Predictive Controller Design for Throttle and Wastegate Control of a Turbocharged Engine American Control Conference 2013
- Ortner , P. and del Re , L. Predictive Control of a Diesel Engine Air Path IEEE Trans. on Control Systems Technology 15 3 449 456 2007
- Stewart , G. , Borrelli , F. , Pekar , J. , Germann , D. et al. Toward a Systematic Design for Turbocharged Engine Control Automotive Model Predictive Control, LNCIS 402 211 230 2010
- Vermillion C , Butts K , and Reidy K Model Predictive Engine Torque Control with Real-Time Driver-in-the-Loop Simulation Results American Control Conference 2010
- Lombaerts , T.J.J. , Joosten , D. A. , Breeman , J. , Smaili , H. , van den Boom , T.J.J. , et al. “Assessment Criteria as Specifications for Reconfiguring Control,” AIAA 2006-6331 Guidance, Navigation, and Control Conference Aug 2006 Keystone CO
- Esfahani , N.R. and Khorasani , K. A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites Intl Journal of Control Vol. 89 5 2006
- Kufoalor , D. K. , Johansen , T. A. Reconfigurable Fault Tolerant Flight Control Based on Nonlinear Model Predictive Control American Control Conference (ACC) 2013
- Bemporad , A. , Ricker , N.L. and Owen , J.G. Model predictive control - New tools for design and evaluation Proc. American Contr. Conf Boston, MA 2004 5622 5627
- Pannocchia , G. and Rawlings , J.B. Disturbance Models for Offset-Free Model-Predictive Control AICHE Journal 49 2 426 437 2003
- Moraal , P. and Kolmanovsky , I. Turbocharger Modeling for Automotive Control Applications SAE Technical Paper 1999-01-0908 1999
- Bemporad , A. , Bernardini , D. , Livshiz , M. , and Pattipati , B. Supervisory Model Predictive Control of Powertrain with Continuously Variable Transmission SAE World Congress Experience 2018
- Guzzella , L. and Onder , C. Introduction to Modeling and Control of Internal Combustion Engine Systems Springer 2009