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A Robust Model Predictive Control for efficient thermal management of internal combustion engines

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  • Pizzonia, Francesco
  • Castiglione, Teresa
  • Bova, Sergio

Abstract

Optimal thermal management of modern internal combustion engines (ICE) is one of the key factors for reducing fuel consumption and CO2 emissions. These are measured by using standardized driving cycles, like the New European Driving Cycle (NEDC), during which the engine does not reach thermal steady state; engine efficiency and emissions are therefore penalized. Several techniques for improving ICE thermal efficiency were proposed, which range from the use of empirical look-up tables to pulsed pump operation. A systematic approach to the problem is however still missing and this paper aims to bridge this gap.

Suggested Citation

  • Pizzonia, Francesco & Castiglione, Teresa & Bova, Sergio, 2016. "A Robust Model Predictive Control for efficient thermal management of internal combustion engines," Applied Energy, Elsevier, vol. 169(C), pages 555-566.
    • Handle: RePEc:eee:appene:v:169:y:2016:i:c:p:555-566
    DOI: 10.1016/j.apenergy.2016.02.063
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    References listed on IDEAS

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    1. van Staden, Adam Jacobus & Zhang, Jiangfeng & Xia, Xiaohua, 2011. "A model predictive control strategy for load shifting in a water pumping scheme with maximum demand charges," Applied Energy, Elsevier, vol. 88(12), pages 4785-4794.
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    3. Castiglione, Teresa & Pizzonia, Francesco & Piccione, Rocco & Bova, Sergio, 2016. "Detecting the onset of nucleate boiling in internal combustion engines," Applied Energy, Elsevier, vol. 164(C), pages 332-340.
    4. Bova, Sergio & Castiglione, Teresa & Piccione, Rocco & Pizzonia, Francesco, 2015. "A dynamic nucleate-boiling model for CO2 reduction in internal combustion engines," Applied Energy, Elsevier, vol. 143(C), pages 271-282.
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    Cited by:

    1. Teresa Castiglione & Pietropaolo Morrone & Luigi Falbo & Diego Perrone & Sergio Bova, 2020. "Application of a Model-Based Controller for Improving Internal Combustion Engines Fuel Economy," Energies, MDPI, vol. 13(5), pages 1-22, March.
    2. Junhong Zhang & Zhexuan Xu & Jiewei Lin & Zefeng Lin & Jingchao Wang & Tianshu Xu, 2018. "Thermal Characteristics Investigation of the Internal Combustion Engine Cooling-Combustion System Using Thermal Boundary Dynamic Coupling Method and Experimental Verification," Energies, MDPI, vol. 11(8), pages 1-20, August.
    3. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
    4. Jinguan Yin & Tiexiong Su & Zhuowei Guan & Quanhong Chu & Changjiang Meng & Li Jia & Jun Wang & Yangang Zhang, 2017. "Modeling and Validation of a Diesel Engine with Turbocharger for Hardware-in-the-Loop Applications," Energies, MDPI, vol. 10(5), pages 1-17, May.
    5. Fatigati, Fabio & Di Bartolomeo, Marco & Cipollone, Roberto, 2022. "Development and experimental assessment of a Low Speed Sliding Rotary Vane Pump for heavy duty engine cooling systems," Applied Energy, Elsevier, vol. 327(C).

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