Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Armin Norouzi, Hamed Heidarifar, Mahdi Shahbakhti, Charles Robert Koch and Hoseinali Borhan
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Armin Norouzi: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Hamed Heidarifar: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Mahdi Shahbakhti: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Charles Robert Koch: Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 2R3, Canada
Hoseinali Borhan: Cummins Technical Center, Research and Technology, Cummins Inc., Columbus, IN 47201, USA

Energies, 2021, vol. 14, issue 19, 1-40

Abstract: An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

Keywords: internal combustion engines; combustion control; optimization; predictive control; artificial intelligence; machine learning; emissions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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