High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines.
Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels
Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 16, 2014; final manuscript received February 23, 2014; published online May 2, 2014. Editor: David Wisler.
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Bidarvatan, M., and Shahbakhti, M. (May 2, 2014). "Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels." ASME. J. Eng. Gas Turbines Power. October 2014; 136(10): 101510. https://doi.org/10.1115/1.4027278
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