One effective possibility to reduce pollutant emissions and fuel consumption of an internal combustion engine is the use of improved process control. It is made viable by the implementation of additional actuators and sensors which allow to operate the process more flexible. For full exploitation of the setup an appropriate control algorithm is necessary. Classical engine control structures rely on the use of many calibration parameters which result in high demands on the calibration time. Model predictive control (MPC) is an advanced control algorithm which is able to overcome this drawback. It allows to use a mathematical plant model for control synthesis which reduces calibration time and makes reusability possible. The present paper introduces the MPC algorithm and discusses the benefits of MPC for engine control. A special emphasis is put on the application for gasoline airpath control. For clarification, the benefits are demonstrated by numerical simulation studies. For the example of gasoline two stage turbocharging, the advantage concerning control performance are shown as well as the possibility to easily adapt to changed specifications for the closed-loop control dynamics.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Payri F, Luján J, Guardiola C, et al. A challenging future for the IC engine: new technologies and the control role. Oil Gas Sci Technol - Rev IFP Energies Nouvelles, 2015, 70: 15–30
Isermann R. Engine Modeling and Control. Berlin: Springer, 2017
Zweigel R, Thelen F, Abel D, et al. Iterative learning approach for diesel combustion control using injection rate shaping. In: Proceedings of the European Control Conference, Linz, 2015. 3168–3173
Del Re L, Allgöwer F, Glielmo L, et al. Automotive model predictive control. In: Lecture Notes in Control and Information Sciences. London: Springer, 2010. 402
Qin S J, Badgwell T A. A survey of industrial model predictive control technology. Control Eng Practice, 2003, 11: 733–764
Di Cairano S. An industry perspective on MPC in large volumes applications: potential benefits and open challenges. IFAC Proc Volumes, 2012, 45: 52–59
Diehl M, Ferreau H J, Haverbeke N. Efficient numerical methods for nonlinear MPC and moving horizon estimation. In: Nonlinear Model Predictive Control. Berlin: Springer, 2009. 384
Kirches C, Wirsching L, Sager S, et al. Efficient numerics for nonlinear model predictive control. In: Recent Advances in Optimization and Its Applications in Engineering. Berlin: Springer, 2010
Hrovat D, Di Cairano S, Tseng H E, et al. The development of model predictive control in automotive industry: a survey. In: Proceedings of the IEEE International Conference on Control Applications, Dubrovnik, 2012. 295–302
Wei H, Zhu T, Shu G, et al. Gasoline engine exhaust gas recirculation-a review. Appl Energy, 2012, 99: 534–544
Colin G, Chamaillard Y, Bloch G, et al. Exact and linearized neural predictive control: a turbocharged SI engine example. J Dyn Sys Meas Control, 2007, 129: 527–533
Santillo M, Karnik A. Model predictive controller design for throttle and wastegate control of a turbocharged engine. In: Proceedings of the American Control Conference, Washington, 2013. 2183–2188
Hadef J E, Olaru S, Rodriguez-Ayerbe P, et al. Nonlinear model predictive control of the air path of a turbocharged gasoline engine using laguerre functions. In: Proceedings of the 17th International Conference on System Theory, Control and Computing, Sinaia, 2013
Hadef J E, Olaru S, Rodriguez-Ayerbe P, et al. Explicit nonlinear model predictive control of the air path of a turbocharged spark-ignited engine. In: Proceedings of the IEEE International Conference on Control Applications, Hyderabad, 2013
Hadef J E. Approche quasi-systématique du contrôle de la chaîne d’air des moteurs suralimentés, basée sur la commande prédictive non linéaire explicite. Dissertation for Ph.D. Degree. Universit d’Orléans, Français. 2014
Albin T, Ritter D, Liberda N, et al. Two-stage turbocharged gasoline engines: experimental validation of model-based control. IFAC-PapersOnLine, 2015, 48: 124–131
Albin T, Ritter D, Liberda N, et al. In-vehicle realization of nonlinear MPC for gasoline two-stage turbocharging airpath control. IEEE Trans Contr Syst Technol, 2017. Doi: 10.1109/TCST.2017.2724020
Albin T, Ritter D, Abel D, et al. Nonlinear MPC for a two-stage turbocharged gasoline engine airpath. In: Proceedings of the 54th IEEE Conference on Decision and Control, Osaka, 2015. 15–18
This research was performed as part of the Research Unit (Forschergruppe) FOR 2401 “Optimization based Multiscale Control for Low Temperature Combustion Engines” which is funded by the German Research Association (Deutsche Forschungsgemeinschaft, DFG). The support is gratefully acknowledged.
About this article
Cite this article
Albin, T. Benefits of model predictive control for gasoline airpath control. Sci. China Inf. Sci. 61, 70204 (2018). https://doi.org/10.1007/s11432-017-9342-7
- model predictive control
- engine control
- airpath control