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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Armin Norouzi & Hamed Heidarifar & Mahdi Shahbakhti & Charles Robert Koch & Hoseinali Borhan, 2021. "Model Predictive Control of Internal Combustion Engines: A Review and Future Directions," Energies, MDPI, vol. 14(19), pages 1-40, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6251-:d:648088
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    References listed on IDEAS

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    Cited by:

    1. Nasim Samadi & Mahdi Shahbakhti, 2023. "Energy Efficiency and Optimization Strategies in a Building to Minimize Airborne Infection Risks," Energies, MDPI, vol. 16(13), pages 1-28, June.
    2. Saeid Shahpouri & Armin Norouzi & Christopher Hayduk & Reza Rezaei & Mahdi Shahbakhti & Charles Robert Koch, 2021. "Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines," Energies, MDPI, vol. 14(23), pages 1-25, November.
    3. Zongyu Yue & Haifeng Liu, 2023. "Advanced Research on Internal Combustion Engines and Engine Fuels," Energies, MDPI, vol. 16(16), pages 1-8, August.
    4. Loris Ventura & Roberto Finesso & Stefano A. Malan, 2023. "Development of a Model-Based Coordinated Air-Fuel Controller for a 3.0 dm 3 Diesel Engine and Its Assessment through Model-in-the-Loop," Energies, MDPI, vol. 16(2), pages 1-23, January.
    5. David C. Gordon & Armin Norouzi & Alexander Winkler & Jakub McNally & Eugen Nuss & Dirk Abel & Mahdi Shahbakhti & Jakob Andert & Charles R. Koch, 2022. "End-to-End Deep Neural Network Based Nonlinear Model Predictive Control: Experimental Implementation on Diesel Engine Emission Control," Energies, MDPI, vol. 15(24), pages 1-23, December.
    6. Maxime Jean & Pascal Granier & Thomas Leroy, 2022. "Combustion Stability Control Based on Cylinder Pressure for High Efficiency Gasoline Engines," Energies, MDPI, vol. 15(7), pages 1-10, March.
    7. Roberto Finesso & Omar Marello, 2022. "Calculation of Intake Oxygen Concentration through Intake CO 2 Measurement and Evaluation of Its Effect on Nitrogen Oxide Prediction Accuracy in a Heavy-Duty Diesel Engine," Energies, MDPI, vol. 15(1), pages 1-26, January.

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