| description abstract | High fidelity models that balance accuracy and computation load are essential for realtime modelbased control of homogeneous charge compression ignition (HCCI) engines. Graybox modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feedforward artificial neural network models to form a serial architecture graybox 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 graybox model is trained and validated with the steadystate and transient experimental data for a large range of HCCI operating conditions. The results indicate that the graybox model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the graybox model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 آ°C, respectively. The graybox model is computationally efficient and it can be used for realtime control application of HCCI engines. | |
