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Minimization of the fuel consumption of a gasoline engine using dynamic optimization

Author

Listed:
  • Saerens, B.
  • Vandersteen, J.
  • Persoons, T.
  • Swevers, J.
  • Diehl, M.
  • Van den Bulck, E.

Abstract

This paper discusses the minimization of the fuel consumption of a gasoline engine through dynamic optimization. The minimization uses a mean value model of the powertrain and vehicle. This model has two state variables: the pressure in the engine intake manifold and the engine speed. The control input is the throttle valve angle. The model is identified on a universal engine dynamometer. Optimal state and control trajectories are calculated using Bock's direct multiple shooting method, implemented in the software MUSCOD-II. The developed approach is illustrated both in simulation and experimentally for a generic test case where a vehicle accelerates from to in . The optimized trajectories yield minimal fuel consumption. The experiments show that a linear engine speed trajectory yields an extra fuel consumption of 13% when compared to the optimal trajectory. It is shown that, with a simple model, a significant amount of fuel can be saved without loss of the fun-to-drive.

Suggested Citation

  • Saerens, B. & Vandersteen, J. & Persoons, T. & Swevers, J. & Diehl, M. & Van den Bulck, E., 2009. "Minimization of the fuel consumption of a gasoline engine using dynamic optimization," Applied Energy, Elsevier, vol. 86(9), pages 1582-1588, September.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:9:p:1582-1588
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    Citations

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    Cited by:

    1. Saxena, Vishal & Kumar, Niraj & Saxena, Vinod Kumar, 2019. "Multi-objective optimization of modified nanofluid fuel blends at different TiO2 nanoparticle concentration in diesel engine: Experimental assessment and modeling," Applied Energy, Elsevier, vol. 248(C), pages 330-353.
    2. Salvi, B.L. & Subramanian, K.A., 2015. "Experimental investigation and phenomenological model development of flame kernel growth rate in a gasoline fuelled spark ignition engine," Applied Energy, Elsevier, vol. 139(C), pages 93-103.
    3. Togun, Necla & Baysec, Sedat, 2010. "Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine," Applied Energy, Elsevier, vol. 87(11), pages 3401-3408, November.
    4. D'Errico, G. & Cerri, T. & Pertusi, G., 2011. "Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models," Applied Energy, Elsevier, vol. 88(3), pages 767-777, March.
    5. Achour, H. & Carton, J.G. & Olabi, A.G., 2011. "Estimating vehicle emissions from road transport, case study: Dublin City," Applied Energy, Elsevier, vol. 88(5), pages 1957-1964, May.
    6. Irene Carvalho & Ricardo Simoes & Arlindo Silva, 2018. "Applying the Theory of Inventive Problem Solving (TRIZ) to identify design opportunities for improved passenger car eco-effectiveness," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(6), pages 907-932, August.
    7. Molina, S. & Guardiola, C. & Martín, J. & García-Sarmiento, D., 2014. "Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine," Applied Energy, Elsevier, vol. 119(C), pages 405-416.
    8. Paúl Andrés Molina Campoverde, 2023. "Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    9. Carvalho, Irene & Baier, Thomas & Simoes, Ricardo & Silva, Arlindo, 2012. "Reducing fuel consumption through modular vehicle architectures," Applied Energy, Elsevier, vol. 93(C), pages 556-563.

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